{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Macro Level Analysis: The evolution of the System with Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first part of the analysis we will focus on how the global capabilities change with time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "\n",
    "- [1.Characterisation of Years](#one)\n",
    "    - [1.1.Years in the database](#one-one)\n",
    "    - [1.2.Capability Matrixes of years](#one-two)\n",
    "        - [1.2.1.Getting the labels](#one-two-one)\n",
    "        - [1.2.2.Function](#one-two-two)\n",
    "    - [1.3.Year profiles](#one-three)\n",
    "- [2.Year Correlation Matrix](#two)\n",
    "    - [2.1.Considerations](#two-one)\n",
    "    - [2.2.Final Year Correlation Matrix](#two-two)\n",
    "    - [2.3.Year correlation matrix clustering](#two-three)\n",
    "- [3.Correlation of years over time](#three)\n",
    "- [4.Research terms over time](#four)\n",
    "    - [4.1.Evolution of output terms](#four-one)\n",
    "    - [4.2.Evolution of processing technology terms](#four-two)\n",
    "    - [4.3.Evolution of feedstock terms](#four-three)\n",
    "- [5.Contextual relationships](#five)\n",
    "    - [5.1.Oil](#five-one)\n",
    "    - [5.2.Sugar](#five-two)\n",
    "- [6. Comparing Years](#six)\n",
    "    - [6.1.Visualizing the differences](#six-one)\n",
    "    - [6.2.Understanding the differences](#six-two)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by importing all of the external libraries that will be useful during the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# python libraries\n",
    "from py2neo import Graph\n",
    "import numpy as np \n",
    "from pandas import DataFrame\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json\n",
    "import math\n",
    "import pandas as pd\n",
    "import plotly \n",
    "import plotly.graph_objs as go\n",
    "import qgrid\n",
    "from scipy import stats, spatial\n",
    "from sklearn.cluster.bicluster import SpectralBiclustering\n",
    "import operator\n",
    "from IPython.display import display, HTML\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "\n",
    "# connection to Neo4j\n",
    "local_connection_url = \"http://localhost:7474\"\n",
    "connection_to_graph = Graph(local_connection_url)\n",
    "\n",
    "# plotly credentials\n",
    "#plotly_config = json.load(open('plotly_config.json'))\n",
    "#plotly.tools.set_credentials_file(username=plotly_config['username'], api_key=plotly_config['key'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total database matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by geeting all the feedstock, processing technology and output terms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of terms: 352\n",
      "[u'vegetable oil', u'recycled ethanol', u'woodwaste/bagasse', u'natural gas', u'various grasses', u'nonedible oils', u'animal fats', u'paper', u'ponderosa pine', u'grass seed', u'milo', u'osb', u'paper waste', u'corn cob', u'logging residues', u'willow', u'crop waste', u'firewood', u'jatropha', u'sugarcane bagasse', u'sugar', u'durum', u'wood', u'municipal solid waste', u'banna grass', u'waste oil', u'cassava pulp', u'barley', u'mixed oilseeds', u'wood fuel', u'starch', u'white grease', u'coniferous wood', u'corn oil', u'cellulosic sugars', u'vegetable waste', u'corn stalks', u'yellow grease', u'molasses', u'hybrid poplar', u'food waste', u'citrus residues', u'wood chips', u'spent sulphite liquor feedstock', u'palm, rapeseed oil, waste fat', u'cotton residue', u'industrial waste', u'deciduous forests', u'corn stover', u'cereals/sugar', u'waste fat', u'waste vegetable oil', u'tallow', u'sewage', u'poultry fat', u'grain', u'poplar/energy woods', u'biogas from municipal wastewater treatment facility digesters', u'loblolly pine', u'stump material', u'husk', u'palm', u'sulfite spent liquor from spruce wood pulping', u'switchgrass', u'bark', u'fall rye', u'agriculture', u'textiles', u'animal waste', u'cobs', u'forest residues', u'grass clippings', u'fog', u'plywood', u'dry biomass', u'southern yellow pine', u'trap grease', u'beef tallow', u'syngas from gasifier', u'animal manure', u'wheat', u'organic waste', u'oil plants', u'corn', u'cellulose', u'dung', u'agricultural waste', u'elephant grass', u'rice straw', u'biogas from landfills', u'mixed grass', u'waste water sludge', u'poppy seed', u'algae', u'soft white spring wheat', u'barley straw', u'Giant reed', u'rice hulls', u'canola oil', u'msw', u'digester gas', u'seeds', u'mixed prairie grass', u'fat products', u'distillers grain', u'woody and agricultural by-products', u'sugar beet', u'mixed biomass', u'corn stover, cobs', u'brewery waste', u'camelina', u'cellulosic biomass', u'multi feedstock', u'plastics', u'palm stearin', u'white pine', u'straw', u'water hyacinth', u'mixedwood', u'hogs', u'beverage waste', u'corn sugar', u'sugar base', u'soy', u'woody biomass', u'maple', u'wheat straw', u'jathropa', u'medium density fiberboard', u'biodegradable waste', u'macroalgae', u'hardwood', u'feed wheat', u'waste', u'soy oil', u'hog fuel', u'pal waste', u'grass straw and corn stalks', u'eucalyptus', u'agricultural biomass', u'glycerin', u'used cooking oil', u'hydrous ethanol', u'seed oil', u'shrub willow', u'juncea oil', u'grains', u'syngas', u'barley starch', u'particle board', u'energy crops', u'slash pine', u'lumber', u'oilseeds', u'Decommissioned electricity poles and railway ties', u'miscanthus', u'agricultural residues', u'grass', u'sorghum', u'virgin oil', u'cps wheat', u'rapeseed oil', u'cooking oil', u'natural fats', u'red pine', u'radiate pine', u'cheese whey', u'manure', u'corn/barley', u'free fatty acid', u'animal residue', u'yard trimmings', u'stover', u'biogenic waste oils', u'triticale', u'bagasse/cane trash', u'mixed cellulose', u'crude glycerine', u'brown grease', u'waste water', u'garden waste', u'wood processing residues', u'yeast', u'oak', u'pine', u'douglas fir', u'forestry', u'bagasse', u'rice straw, corn stalks', u'steel waste gas (co)', u'energy grasses', u'whey', u'lignocellulosics', u'wood waste', u'juniper', u'mixed biomass, natural gas', u'sugarcane', u'urban wood', u'flaxseed oil', u'residues', u'cotton gin trash', u'rapeseed', u'arundo donax', u'dedicated energy crops', u'black liquor', u'non-food grade canola oil', u'corn/milo', u'beets', u'lemna', u'sugarcane trash', u'refined vegetable oil', u'poplar', u'palm oil', u'sawdust', u'blend', u'non-cellulosic portions of sfw', u'woodchips', u'algae transesterification', u'hydrochloric acid', u'landfill gas', u'consolidated bioprocessing', u'methane recovery', u'hydroprocessing', u'solar conversion', u'biomass combustion', u'gasification', u'enzymatic hydrolysis', u'biogas productions', u'chemprocessing', u'oil extraction', u'bioenergy generation', u'coking', u'orc process', u'scrubbers', u'cbp', u'microwave', u'btl - syngas', u'bioforming', u'algal oil extraction', u'blg', u'methanol synthesis, mtg catalysis', u'biomass gasification', u'black liquor gasification', u'grate furnaces', u'algae burning/transesterifcation', u'steam reform/ft', u'biochemical conversions', u'catalysis', u'alcoholic fermentation', u'fixed bed gasifiers', u'fischer-tropsch', u'hydrotreating', u'transesterification', u'algae oil extraction', u'gas cleaning', u'pyrolysis oil', u'algae fermentation', u'fast pyrolysis', u'charring', u'tbd', u'advanced fermentation', u'acid hydrolysis', u'ft microchannel reactor', u'plant cell culture', u' aerobic digestion', u'pyrolysis', u'liquefaction', u'gasification-fermentation', u'hydrolysis', u'algae oil hydrotreating', u'solvents', u'pressing', u'metathesis', u'thermochemical conversions', u'fluidized bed furnaces', u' anaerobic digestion', u'ti gasification', u'fermentation', u'renewable liquefied natural gas', u'furanics', u'biodme', u'chems', u'cellulosic heating oil', u'chems, fibers', u'succinic acid', u'syng', u'succinic acid, polylutylene succinate', u'nutriceuticals', u'ethanol blending', u'succinic acid/ bdo', u'pellets', u'cellulosic naphtha', u'drop-in fuels', u'bioresin', u'adipic acid', u'bioplastic', u'biobutanol', u'ester renewable diesel', u'eth from bagasse', u'renewable compressed natural gas', u'renewable jet', u'methanol/then ethanol', u'renewable electricity', u'renewable diesel', u'renewable gasoline', u'celleth from bagasse', u'methanol', u'biogas', u'lpg', u'cellulosic ethanol', u'pla from cassava', u'biodieseliesel', u'gasoline', u'butanol', u'renewable heating oil', u'biopolymers', u'advanced biofuel', u'biofene', u'bio-oil', u'naphtha', u'biofuels from algae', u'biogasoline', u'cellulosic renewable gasoline', u'electricity from biomass', u'biodiesel', u'cellulosic biofuel', u'cellulosic renewable gasoline blendstock', u'sng', u'celleth from wheat straw', u'bioplymers from corn, potatoes, tapioca', u'ethanolanol', u'renewable chemicals', u'ethanol', u'cellulosic diesel', u'biodiesel bioethanol', u'papaya to fatty acids', u'rdif', u'polyethylene', u'bioethanol', u'biofuels for transport', u'mixed alcohol fuels', u'renewable jet fuel', u'biodiesel blending', u'renewable fuel', u'pdo', u'biobdo', u'bdo', u'renewable diesel from animal fats, veggie oils', u'renewable oils', u'fatty acid ethyl ester', u'bioieselethanolanol', u'biomass-based diesel']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:1: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:2: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:3: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f_terms = list(set(DataFrame(connection_to_graph.data('MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock) RETURN  fs.term, count(a)')).as_matrix()[:, 1]))\n",
    "o_terms = list(set(DataFrame(connection_to_graph.data('MATCH (a:Asset)-[:CONTAINS]->(fs:Output) RETURN  fs.term, count(a)')).as_matrix()[:, 1]))\n",
    "pt_terms = list(set(DataFrame(connection_to_graph.data('MATCH (a:Asset)-[:CONTAINS]->(fs:ProcessingTech) RETURN  fs.term, count(a)')).as_matrix()[:, 1]))\n",
    "bbo = list(f_terms + pt_terms + o_terms)\n",
    "print 'Number of terms:', len(bbo)\n",
    "axis_names = bbo\n",
    "print axis_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a function that return the capability matrix of the whole database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_total_matrix(normalization):\n",
    "    \n",
    "    # define queries\n",
    "    # non intersecting part\n",
    "    q1 =         \"\"\"    \n",
    "                MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock)\n",
    "                MATCH (a:Asset)-[:CONTAINS]->(out:Output)\n",
    "                MATCH (a:Asset)-[:CONTAINS]->(pt:ProcessingTech)\n",
    "                RETURN fs.term, pt.term, out.term, count(a)\n",
    "                \"\"\"\n",
    "    \n",
    "    process_variables = ['Feedstock', 'Output', 'ProcessingTech']\n",
    "    \n",
    "    # interesecting part\n",
    "    q2 =        \"\"\"    \n",
    "                MATCH (a:Asset)-[:CONTAINS]->(fs:{})\n",
    "                MATCH (a:Asset)-[:CONTAINS]->(t:{})\n",
    "                WHERE fs<>t\n",
    "                RETURN fs.term, t.term, count(a)\n",
    "                \"\"\"\n",
    "    # total assets of year\n",
    "    q3 =        \"\"\"\n",
    "                MATCH (n:Asset)\n",
    "                RETURN count(n)\n",
    "                \"\"\"\n",
    "    \n",
    "    # treat incoming data\n",
    "    total_documents = DataFrame(connection_to_graph.data(q3)).as_matrix()[0][0]\n",
    "    \n",
    "    # get data\n",
    "    data_q1 = DataFrame(connection_to_graph.data(q1)).as_matrix()\n",
    "    \n",
    "    # create matrix\n",
    "    total_matrix = np.zeros([len(axis_names), len(axis_names)])\n",
    "    \n",
    "    # for no intersections data\n",
    "    for row in data_q1:\n",
    "        # the last column is the frequency (count)\n",
    "        frequency = row[0]\n",
    "        indexes = [axis_names.index(element) for element in row[1::]]\n",
    "        # add frequency value to matrix position not inter\n",
    "        for pair in itertools.combinations(indexes, 2):\n",
    "            total_matrix[pair[0], pair[1]] += frequency\n",
    "            total_matrix[pair[1], pair[0]] += frequency\n",
    "    \n",
    "    # for intersecting data\n",
    "    for category in process_variables:\n",
    "        process_data = DataFrame(connection_to_graph.data(q2.format(category, category))).as_matrix()\n",
    "        for row in process_data:\n",
    "            frequency = row[0]\n",
    "            indexes = [axis_names.index(element) for element in row[1::]]\n",
    "            # add frequency value to matrix position inter\n",
    "            for pair in itertools.combinations(indexes, 2):\n",
    "                total_matrix[pair[0], pair[1]] += frequency / 2 # Divided by two because query not optimized\n",
    "                total_matrix[pair[1], pair[0]] += frequency / 2 # Divided by two because query not optimized\n",
    "    \n",
    "    # normalize\n",
    "    norm_total_matrix = total_matrix  / total_documents\n",
    "    \n",
    "    # dynamic return \n",
    "    if normalization == True:\n",
    "        return norm_total_matrix\n",
    "    else: \n",
    "        return total_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us visualize the normalized and non normalized versions.\n",
    "\n",
    "We create a function that gives borders to our graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:28: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:48: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def borders(width, color, size=get_total_matrix(normalization=False).shape[1]):\n",
    "    plt.axhline(y=0, color='k',linewidth=width)\n",
    "    plt.axhline(y=size, color=color,linewidth=width)\n",
    "    plt.axvline(x=0, color='k',linewidth=width)\n",
    "    plt.axvline(x=size, color=color,linewidth=width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:28: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:48: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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8cU6Cz5FNcQZZRk4KS8de3PjccaSKMqlq3nShsTaVmHG+Kn0ohVi9gBOru1D8RN7cwN9dXV0NvhOSud/vR83329vbhdoJD8fjc8Ntb2+P/N+HVC5F26q1Ft1uF1tbW2Px+HQTX32SDu97xxdfSJdT4qjkHtaDBw8mhyHbcPecaBl89uHS8zxbcvqdv1fG3p7CSufk3GdV2fVTPLF5iLWvv/XWW5NliT1bE/tekTqNjSfvvZTzw27YvPbO64fOmvre8ckeKkP+m3QOWZJD4tChQ2NxSmdD6T3+votUJkX7AKW/b9++LF4eF53TcfHVKZdNeufo0aNjzyTZU9uMojSZIvN6aI6cVcqce6SwfLwIzdVVnLGsat50kc7sxRAzzk/ynCNRpExSzg+n6BP83SNHjgTfCckdKkf+m+8scl6Z8HC+Ns3f86VDSOVStK0aY7Bnz54sTh6PTzfx1SeXS3rHF58k+zTadpPR0lIURVEURVEURVFmkko+WFO26UPv5sUTMjOIJXYLvi6TpFk2AYg1W/SZcZPZRdV0Oh3vbylmOLOA264mbfom1Q+ZyZSlzrZdp3lWiFDbqzIdRWkiVfT5Ksxqy8aVeiykSmbZ/Dl2fAuZcUtHRcoSkmuWdaxZRSrPvLkvhllq21W1i7w+oe2vPgp9sPIzqz5Xzb1eLzurySuYztc98cQTAAZum+n8A8XDz79wm33aPqc0qWFQx3I7GCni3Kac4qBn0hm0Xq+X2ddvbm5mSv61a9fQ7XbR7/fHzqH6sNZicXExi+PKlSt45JFHsjxLdvwXL17MwvL/UzlKHxjSh8jVq1cBjJbntWvXvHJvbW1l5xykwYrS+J3f+R20Wq3sHV4WZC7B0+h0OmKd8olse3sb29vbuHjx4lj+JLMJKjdukrG+vi7mi78PjH5wLy4uesPxgYe/Q/VD5eF+qLsDVqfTycqA+k7MWZLLly+PnA2SBkJ38KR4O52OmAbvC9vb21lbpvipntbW1kbCu38/99xzmWzPPvssgEF5uO35ypUrAIDHHntMjAuQz5W6+aLrLnyTgXs+Z2NjA6dPnwYwOi6sr69ndcnz/uSTT4rxXr58OXhm+KGHHhr5t7UW165dG3sv77yOojSdmPP51JfcxWdrLc6ePQsAeP7558fmAD6+cv1DuuLDnS/dsYTGA94nKR565s4J/X4/C8fnj5WVlWzsSVkMX11dzeS6fv16NoZSvqX5l3xHuHoBpeuWv6RYk77Dy9N3rp/kcMtTSuNTn/rUyNU1JBfViTSH93o9b73yeWxxcXGsPCSdk97haYXGXR4nfXCT7Gtra96wfB7g71D98HbA/TtwSD+W5szQIszS0tJI30j1QeHTW7kMVOeu7G6fdOO5dOlS9g7pSL1eb6zurl+/DgB47rnnRBkJ6Syob6Mkxn/N9vY2Ll26BGC0/W9tbWX55/343LlzYpyLi4vBM8MnT54Uw0n9rIpFgd2ESVkBOX78uFUX9oqiKNVz/PhxnDhxQu9cUxqD6gSKoij1oDrBKHqGNYdZMmnYDag5haIoijKrqE4wWbS8FUUB9IM1lyo86HH0zFuYpntN08lVZqcuRGh9K8ruomqdYKeOjVVRdXlPGp0jZHZquezUfM0Czf46aCAprs+V5lHl5NqEgS9WxiYrHU2oB0VRmknTF2mVME2e++qkyeWiOsF0qGSkLLJrKF0wXBfTblyuw5oil1LXIUtR6twlrtJjZF3hJlV3TRjQXRl9ZRPKy7T7Zx5Nll1RpkGZeV37VPOYJQ/xys5gluu9CbrZTmRqS3uuJzlO3uAX4xFMSktCSos/88mS5xHRF9/m5uZInkOyhTyR+d71pRt6RnBZQu+V/cDLc38fM/lJcRT5kOaeYmPTKzo5Fwk3q4M2X3Th3gR9A3nIG15dg3/Io7BL0XKu4xonRdmN+Dyq+p7FUkQvCM1ZvvhSxgI+BpG3dh53zPxbpExi9QTptzJXEhal7AJ/0cX1IlfxTGp+n1WdgBPyrE/k1U0dekHqt0MR9NhffeypIpIiJi10/Umr1RprmHnx+Rqy7zmlJSGlxZ/5ZNm3b19IxBG4GfChQ4e8v7lQfmI6rvSOJHsoPf5bqA5C5RkjW6j+Yk2mpThS5aJ4YsLx9IoOpEX6yayu5HG5qM5Csu7du7d2mVyk8rbWVmqClzIOKMpuoUgfC40jZcZBX9iYuVf6dxE9w4WXz/79+8fiDpVfil7gC+uTJfRbKL26jjoZY0rVfVG5iqRbVX3UEWbSULuZRpsJkfrtUIQieqgSRyWaW5GVJdpxke4UzVvt8K1+pt5JFQpDz/mF2EV31viKS6fTGVmJLbKaHBtGkje008V3N0Np0O5yXrmE7nt1oXuwtra2knc8iZT64buDMeH4O6mDG+V3FlZTY+PLe4+XR2hngfejIhTNv2S5Afjrrmg6kqWFOlFRdjtF+gCN+1KfqmOHteiOYeyuYwj3rs5J7cqkWibl7W6GxvfUcvfpJvze2xBld+Z5Xn3zRyj+FL2A8tqE3dJYUq3QJr0TmbpTX7XV1U6q62lRyVJAkZUS2nFxdxyB/I7vW/30hYtZrfQ956slRXdmePm4O01FVpNjw0jyhna68lZT6RntKuWtBvvyceDAgbFntMos/RYbd0r9SPUbInalWYLyNAurqbHx5b3HyyNUhqnlnCqHj9S+WrRvSzus6kRF2e2U2WHlO45EHTusoThDv5WZC6Q4JrnTJMmbstPs+10a31PL3aebxFqxlN2Z5+/Gtt+i9U95bcJuaSyp5TfpHdZJ7JgDfl1nJ9X1tEiaVUI7aak7KBRXkV2xSSCt9pU5D0HErtzlpbW2thb8vcgKd+y53Nj3Ynbe+N/8/7GrdUXahBtfbL1KMseWc8yZjtg4QnLFsLm5ORZ2eXk5KV7JSiDUtuvsuynnzFPPny0uLgbTJsuAELqyquxU8vwRFI1vVvuMNFeUtaiIzWteOpLlUtG0ODG6Xaz+FzuXx+yWVnmmNtWPSiidIrt4dVgQpCDNlRsbG8nx8P7h0+cmQRXnzAF/Owjp3yErRmJWx7dZJ+mD1bdqYq1N3kG5evVqFlaKT6LoCkXqh6a1NlsB47LwFaGipgXWWqyvrwPwf8RYazE3NxdM4/Dhw8E0W62WmO9QWfAVzio6lK9NuM55jDEjJrr0jIjdIY+tZwpDabZaLVy+fDkY5vLly1m4TqcTddaI02q10O/3k9sw/yCKWbGOMSc/ePDgWNijR48mxcvzHRq8qY1Tm68D31lVWpnnk5QxRpyEfPV47NixYNqrq6vic55fXVlVdiqhMSm13V+/fj3rn5PoM6kfmr55uchusqsTxBwbyUsnxjpJGv/yPiKrPJMXO5e7c4/UJmJ2yIsuJrRaLVy/fj33Pb7QK+kusRQxJy4S1oe0iy1ZP7q47Zh05M3NzUyvc8cCChOzwFKUmHPm0sc1x+fvwlob1L/5hgAnRpdTwhSyXXM/DE6dOjX2Dt915R2s3+/j8uXLeMlLXgIAePzxx8c6IFeAz549O/IbxUkDEXndBcY7ADWca9eujclH8SwsLIj5IxmuXr2axb+4uJitFnW73bEdJd85jnPnzmVl9thjj+GNb3wjgBtKrSv3xYsXszzylWqpo9MzWgDgfPKTnwQwunp24cIFr/fghYUF9Ho9WGvHVtf6/X4Wz4c//OGxPIdW0drt9kib4Uo+ybK8vIzNzU2cOnUKFy5cEOMhrLVj52gB4MqVK2PPCN7GPve5z6Hb7WJrawvWWqysrGTtgJc5l/l7v/d7s7/Pnz+fxSmtkLofZ2tra9nZZXdHjsK5ixgU7oMf/KCYB2J1dXXsg8xXBpQOvc/PDVPalD7PG6UD3Oj7i4uLmcw/9EM/BACYn58fm+SXlpYAAEeOHBHlIZncFV7XY2i/30en0xnrK9xSg5fdysoKnn32WQDI/g8M2tnTTz+dpbG2tgZrLZaXl7M2zPP93HPPiWMEcccdd4hy02RPMvnGBl1pVZpMyBLm3LlzY+9T/3CVxevXr+OWW24BAJw5c2ZMx+BKoDvXuWOFOze44yb9vrKy4pXPXYjq9/vZeEljBb1H/ZjmTik+F65XPPfcc3jnO98J4MZYLO148TmK58+XT5KVc+rUKbRarZHxaGFhwauwr66uZulJcxfF89hjj2XP6f2Q12cAmQ5FcJ2Dz4vtdhvnz58X9Tg3PheajyQZeN2cPXs203FI/+Htg5c1l/mXfumXsr/n5+ezeKX0+NxFvjp6vd5Yvbl6Ho9ra2sLjzzySDDPXCd28dUF9x1C8vBztu6GAjCoL74Asrq6mrWRX/mVXwEwaH/ugjb9+9WvfrUoCzBoa1LepLbf6/XG+otvd3djYyPTr+njemNjI9M5+/1+Vu7r6+tieV2+fFkcO4gv+ZIvEWX+yEc+MtZnfeOD6gUyJqVgjh8/bk+cOFGfNIqiKLuU48eP48SJE7r0qjQG1QkURVHqQXWCUQrtsLqrGSdPnhx7p9/vZytotHJG/+71eiMrcu5uHsXf6XTGVp/cs698l/OJJ54YeZdWaCSTRVoJ4qso9P9ut5ulu7a2lsm3traWpUe7PXlnEPr9PlZXV7OVlKtXr+JP/uRPsnxLq6L0jFbq3BVLafVMWqkhM1e+UnXu3LngKtvFixdhrRXP5tFq7fvf/34AN/LsO8fHV5H46iKterqrq1tbWzhz5kyUqYiUps88k2TgYTudTrbKLZ3f7Pf7I7uFXKbPfe5zY/KHFn6uX7+OXq+HpaWlTG76v9T2iDNnzmQ7lD5850bd9kDv8J2K7e3tkRVIvqvslom7svnUU09lZUI7mFtbW2N1R33505/+dDAP7ool9dm888a+ct/c3MzKjsfN+zPP78mTJ8XV23a7nZWZlNaHPvShMXmkc6+6kqrsREIWRs8///zYuzRe0fjHLVr4++74TnFLHnWlOZiPa66VFoWX5hmae2ls5GF4OG6dwndYY/w7WGtHdsGWl5fx1FNPjeQ7dJzHd2erNJa44yfNaTz++fn5oHUUWZZJugqNpWTNReUA5J/l41ZbwA0rIy4LzdUx5Sql5zPPpPR52F6vl+2eX7x4UTzGYq0dec7TPHPmzJj8IUunfr+fWV9J8ku7cFeuXMn1XxKy3Inx9+BaV7m7yq58xPnz57N4aBez3W6P5Yv6Mrd8kuKWztD6TOdjTb9JPt4ueH/meX3hhRfEcux2u6KFH/Hoo4+O5QWQv0P0RoE0Cu2w9vv9sTNskk032a9LdDod7N27FxsbG2O28jxcKA6i3W5j3759Y++SnK68wGDAuOmmm8TfqEy4DT6PL0VGbmY4NzeXXaPCz8i6YSkd97elpaXcM3WcXq+Hubm5EbnpmU/WmDw9+uij+Iqv+IpoOSj/JEO32x07F0PmRXQvb955HanepGcET9PXTmLSAG60nVRoMHTL39euaGDPO0MU00ektELP8uLk7WhzcxMHDx4UwywuLuLWW2/1jhEhGcpARw/uvvvukbjddEhmX7/Y3t4WvZYSq6urY+bORfOiq6lK0+A7rCnjkAT1QanPFYmbxnw3LP1bipPSln6TwhXNM58T6aPdnaulMFJaeWMrR9ItiqTpcvr0abz85S8HcEO3y4PrWSE5er1ephfkxeerM4mQLhRK0xdn3lzhk5nrRnlp0AdO1Z7oU8tOgtefTycHbrTXra2t4LnrsuNJKM6YPuxrj3ntm/QhX9opqE4wSqHT9G4l+gbLUOXEXmsTU8HuNSuunFKjow8O6Tdf+j539nmH//nvc3NzI4Nk6HoW97eUj1VKy5U71oV9KE8pH6sUF4/P5wI/ZpIjpHoLDeI8TV87iY2vyMcqpSuVv69dxTq7SBkEY8stL06eDxqcpTC33norgDgnYVXSarVw9913j8XtpkMy+/pFngIinc3Va22U3UhZ5bLqa21o/PQ565HipLRDV6Sk6icSfE4MjU9S+i6xH6s8/iJODUPQxyoQvjovFLdPjtjrT0J1lhpvkat+Uj9WKa4UueuaW1LLToLL5tPJgRvtNc9JWB2OiVL6sK+s89q39LEaSkeJp3Trb5pJW54J724mtkwkE9qU8KlUca1NlbKViWva7S7Vc3QK084bpypZ8tws5uroAAAgAElEQVTgl7n+QFGU2UL7aTGKXIFShlA9aR2m43PEqYyTpy9p+6uP0h+sTVs1CK2u7nZiy0S6AiUlfCpVrABXKVuZuKbd7qSV46ou8J523jhVyZK3y133BfOKokwO7afFiLkCpUpirrRR4km1WNvN5OlL2v7qo5IW6TsQH7pnlBzkkBtxHzGX8PJ4pX+HVkQkxwv80Lp7JQ+RsivF3fCvra2NXPMSioc7puDEOl2S3gsd2OdOi0I7S76rWXy4sklykUOu9fX1qNXakKOhPHm4k4xerxd1mbTrlACIX4F0r2JKoardz6rhfYQ7QnDzSO+EHGJJ9VXFKqXPSVlsX4mRQ8pXypilKDsR35gasnAiRyjdbjfY76oaE0PjseQAR9InuJwp4zu3Utra2hqbI2Jk8+k7IaR3Qk4OuXOalPKKwTdXcHq9Hra3t72OHWPTiC0b3zVpPng7j9U/UuXyhZ01fHqzL48hh1h14Sv3GKdSsfjyNau6XJOo5EZo6dJhwH8W0xiTnf3yhSVSzjX6zqqEVkQkO3oeD0/fdxY0z8nKnXfemf3tnjcJyUZnImJWuvLORvrS5/DzeKGdJfesRsp5R1cuKjtKL/Z8qJQmpZMnT0y7cOHtlNpE7Aok5a3IimVVu58hijgJ4mVMfUg6j0P/Dt3DWsX5GQnpDDd/zinqdETKV8qYpSg7Ed/8EbJworNfeVYNVY2JoTHP7cOuHwrpueTMzQe3UuI6SMjvhitbkTFSChM6S8jP46WUVww+fwLAjfJzfX4UTSOmrHznikPwthqrf6TK5QtbN6lOgnx6sy8O31nPOvGVe4ofj7xy8eVrErrcTmfqe/47YTdi0qYTZdKrYueqSht9NTuZPkXqYKed0/DlR9unokyWnbATMQ2zwJ1kiriT8tJUtA5ktFymRyXaWMjczwe/G81VFqv6iA2ZH5EJgHtHm/s7DxtjOipB96ECA1Og+fn54KTsuxeUkMrbd1+U+5t0R6T7fh7unZkSIfNpKe9kTiLd2xVrrhEyleG/cfOi2A8vLlPsXXv8b8lUJk9OustY+q0okgkbz4+vz7j/5ncVX7lyJXvHbUP0zjPPPBMlU1V0u13RpJ7f/cfrxTcJ8XuaJR5//PGxZ6E+xtlpH/2KQhQx9+NHZ1zq+IgNHUWYn5/3/lbUDJjD79fe2NjI5vxQfNJRGS5LTJlL5RjS32LzJ91ZKhEqO19a3W43+v7tUHqh33x3oObB5SqqH6Y48aGjTNJvZXDTANLzw+8q5nfY+/S/CxcuBOWpY36UzOklvSUE3b3sg+7jdSnynaSMUugeVkVRFKVa9M41pWmoTqAoilIPqhOMovZuiqIoiqIoiqIoykxS+oM11SRm2mZwsaYGdXliK+oBdxLlFmuK7Zqqcuq453babaYKZiUPKR5ym0xV5a13rimKAtTX14t6wAXqH39iTbElD/Hu7/z/ZdkJ4+4s5aFKL7mzSlX5ydOXdlq5zRKlP1iLehctc9VHGSRPphJ1OVs5dOhQboOuy2tqHrGe/rhXX5c67rndCYfcZyUPKR5yq2TSg3hV5a2e/RRlMqRcCzIN6hrD9+/fXzjPdc8rseOfMSbqvvSq5J2V+bQMs5SHFC+5VdFUnUAdMU6PqZV8mas+XIo0/GlOirM0UBWhqHMBZWcS49hiGm1+EothTe/LijIrpFwLstNoep53opWOUg/T1Akmofc3vS/PMpV8sPoaQYyX3ioGOmogeXJIYfJIiTMmjk6nI3pkqwPfRdwx74fkokWGOmSPbQ9Vmk2nhqv7/arCTooYc69pKjR1luFOuIJDUaqmzCJyE8a8svA8TnsMqVIHq4NptYfd0A7rIlR2efp609HFm/oo/cEaukQ3ZAYSc0F2KnlyECkdJTbOUPzuhcr833UO9FLcIRMfLndILmMM+v2++E7Zziq1BynOFLPpIibY03y/qrB10u/3s3INXYKeehF7lZS5nJ3IazuxJnM7dXJWFIkifa6O4ySzCs/jtI8dhMbmogvzZeJxkdpD2fE0JvxuaIdVEnNNHGen3lWcouuoXpBGaS2SPl5SKXJPWyq+xsAbbVUDsi9+KY68eKbViGM7Wrfb9b5bh4l32TjzFixi2i93jKEraIM6oXLl5eFru9MwI/fVU5X9y3ensIsqP8puokgfi7nfetbY6Qpn7LjlW8BOjSeWsvHFhI+pW+6ocqe3hTy4f5iYspiGZUEVdZQXR4reoXpBGpVsexT5oDhw4ACAepX/qld5JtW4Zr0RxzpnKkJox74qePz9fj+q/e7fvz/7O7W978Qzv77deF/dTXOHleNrX6HFrRjzeDdMbPyKshMpMobv27cPQLP6yqzP1ZNi2rvEVROrh3BdKKUt7PRF72k6Ng2RUkcxG14pv+tYUZ5KWkzedSih84bGmNpWVkODwtbWVjBs1ZMm/2jh5pQpZzGLDnJVn5k9c+ZMZXEVJfUjkK/m8V3uIoNmaj34PCrnkXdVQFWUTSN0LcPq6iqA8HVOdbQjay2WlpbGnqceD8gb26S8S21TJytlN1Fk94T3xToX+UJ9PvZqtyrg4647BqfoBWWdTlYx/l65cqV0HGVJncdC7xcZr1PKsczH2qT0rjLphPoRWVeWuc6pKGtra9Hv+tpA3tjmG7umfVZ9J1BMk3bI23ELnWE1xmQrq1UTGhRoh9dH1QomfbQYY0ZWI1POYhYd5Ko+M/uyl72ssrhcYuNM/QjMK/MUJrUyGHsFU1mK5IfLFepLR44cAQAcPnw4Kq6qMMbg2LFjUe+FZMgb26S8F12gUJSdQpEdN94X6+xDofGmTushFz7uumNwil5Q5rxw0fAud911V+k4ypI6j1U9j+80C7wy6YT60cGDBwHk6+B1ENJDYskb23x532lWCNOgUI91V158qym+XURrbbbD+cQTT4i/p6SfR+icWd4ZNL4bWlQGHken0xnxFFzEUUFs2tIKouul2I2XwoTyTLtlsXK453Z9l1Rba9HpdKJWSlM9IEth+/0+ut2ud0XMtwqdugpPYXk6Mbvo29vbhS+pLrrqHuupk+cldI7HbTMSUr2l1qUElZ3PuoET0wYkaAeZoyupym7H1wdC4wuNI2fPnp3ILlJobA3tePr8UBT1h9Hr9SoZM2LST5038+YEelZkt8wtR1/8vV6vcHtI3XXlukFeG3b/TqnDkP4ZSi+kv9VNXro8L65FmxRPXpuR6q6sNRivX598RNE+6fPPs9PNwCdBoWVMd+XFt6Lg270wxmSrK6985Stz40/93UVa2aA4Qr8B/lW4FBl4HG5ZFXFUEJu2JHto5Yvv6IVWeA8dOpQkh/te6Nxf7Ap3qgdkKWyr1Yq67Nz9O3UVnsLyXYOYXXR+bjZGxpjnsXHlhed5CZ3jcduMhFRvqXUpQWXJZfXVt29HJ68caAeZoyupym7H1wdC8wuNI/fdd199gjFCY39ox7OKMbcOL8FFfXaE0s/LNz2LmavyZPHFX6Z8UndRY7za+8okRc6Q/hlKb5IWACE5JHheQhZtsW1GqoOqHHDyeHz5KtruaAfZZRpndncau6oE6/AIvBspupJbV1rToq6zl4qiKMpkSLEUUmQmWYazXg9Vyzfr+VWUSbGrPlhn0SNwE6na+/Ik4qmDaZ7hVRRFUcpT1FJIucEky3DW66Fq+WY9v4oyKSr5YM3zuCtBdt7dbjf3vIgPyR49FvIW5js3IZ1pK+PVjOKj87sUl5S+L53Y867umZO886Mx8XMefvhhAGlnOWPqNGUlkTxLx4bhZyi53NKZRqnc+N9FPExTOil573a7uWWcetco98idcv5IOrdOaa+srACQz3zQbwsLC9646zjz2e12Rc+V/X5fLDOfp/Ll5eVgOvPz8yP/ttZifX09QVJFaS6+caaIt13qg2XOLHKKxBEa230+IYpC4561Nst7KL66PBgX8aPhcurUqezvouN52XPBqZ6l+bznyhwrC3+WettFkfYZOlsbE7fvuVR2qe2t2+1m8ZN+L/UZ0sUk/w+cqs989no9XL9+fey5Ty/ztaeQPw7ArzMU+U5SRjEpneb48eP2xIkT9UmjKIqySzl+/DhOnDihy+lKY1CdQFEUpR5UJxilEi/B0ooD36V0V0p6vV62+7G2tib+7kvL98wNl0dod4nLs7q6Knp5Tdlt5eXz0EMP4dy5c1n4kJdiksP13BsqEx7f4uLi2HuhVS3ugU7ydEZyUN3l1ZMbjpBWIre3t9FutzE/Px+1sietvj300EPe97l829vbI3Xq8+rGw/C6d8sw1nseX4H04d7Nl7fDl0qs9YK7uui2ve3t7SxfFy5cyOR1obo8ffp0YTlDz0N5oHrlO57tdntETsqDb/Wz3W4HV0affvrpsWfSDqueQ1J2E775ke8qcvr9frb7sbW1Jf5OpPalIjs17hzE0+TzBZ8DU3akePmcPHkys9SgOHye9KX/SzJyXH3GxTf/AaP5k+ZtSpPvXMWWtyuvtKPV7XaxvLxceFfx5MmTUbJQuXPdK2bHlMsVKkcXV7cL4c7ZeTt8RYjRC/LqgFtdXbt2zRsX1fOlS5cKySoROyZQv+NzuquXUR58/TnP8o30IV/aHNUL0tAdVkVRlBlAV1OVpqE6gaIoSj2oTjDKjnS6tBs80U2TMmcoUsmLZ5r1pF6CFUVRmkHZsVXHZj9Fd1WLMOv1oF6CFaUeSn+wVnkwuqqPk1n2RDfLg09sXa6vr+c6+6nSS/CsllkTvQRL9VaH0yOXSddhVenllY1eBq7sZprU/suOrbvRW2vsOCqZcUvxVKWbzapOADTTS7DPUeROoqo2k1cus9w2m07pD9YqL8NN6Zh1r9SVjb9qj36xZw+rSiPE4cOHvXU16V3HsoN5Uz+kilLVZdzcu3JMniat7M2acjnteleUOqhy/t/p1L0wOE2Ln4MHD87cmKvEI9Vdat8O3YIwC8xi+5zl8ppFanO6lAcdvN7e3s6clOSt8vAGx/+mA9Ru+NDVNRRHFW6+pTj27t2byW+tHXHzvbGxETzQf/XqVVFWmvBiO57rrCmUB2B0gAo5dHjwwQdhjBGdDPgGOelQu/SONKlLcdJ1KbEf8mfOnMlNP0QZ9/WUpu9KFV865AxAqkcebwqS7EXc8fd6vayuyOEGd7pAkNMLcsIgEXIwUpRut4sPfehDYlqS4xdfGeS53pecYklxTXKBR1GmTZEr4Khfdjqd4LVvsX3GN9cVvV5Cmpt8H6GSjHNzc9lv1t64yqbf72f5DV3N4nPAR+NnjF4gLTKGPqRjdY2PfvSj2d9Fx78Yp1Gh+EgPjW0fXM9KcebnI/VaHWoHKXM4XflU9VGpKq614ddRke4tOZp8/vnnAdzQ4XyU6fs++R599FExTqkOfPWZ51zLd62dFN8sfkTPMrvS6ZK1tvaGMok0qiZF5ibmT1FmGXWwoDSNnaITKIqizBqqE4xSiT1P3sqf9FG8sLAAQD4PyVdAz549603Xjdd3+bO72pt3BoKbO/KwPnf2EvQxZ60d2d27ePEi1tfXx9yoS/ng5cfTo7IDbqzaSNeQSNcKXbx40SszP4cSyt/S0lLuxypPk67BIXiZcvfu/X4fa2trYzv2sSvbVD95izDutSYxplr8nbyVXLc98zymrMKm7sgWRUojL12+ik79lfcbgto5XdsgUZepnLST4u5Y57X3vFVmaec4daVdUZqKb/zx7TKEjhGQNQO/dozg/TA0lkj4xrKUHSSflRIfB1N2yvicuLi4mI1Vvmt/ePw+ayVuDdLr9YL+Cngc0vV3RGz+aEctD54uvwoHGC1X3k62trbGdLjYOSvWesgt75i5100zxSqA5y/1OsZJWeWk7jrzeY/KPaTf5u2w1nGG1tcepJ1+X/p587svX5PwFbLTmfoOq+7UKTudom283+/vmDNiTc5LSPZer5eZ+pVFV1OVpqE6gaKkU7SN76S+0eS8hGSvUtdRnWCUqWuQTW2wihJL0Tbe1A88iSbnJSR7VR+riqIMUJ1A2ekUbeM7qW80OS8h2Zus68w6Uy/ZWXc6stNce886ky7vWa/fWe4fKV6Cp5mPOtOe5fpRFKV6tM9PlkmWd+gY26wwy7IRTZCxLnZz3utm6h+ss77Koqslk2XS5T3r9TvL/YNki5FxmvmoM+1Zrh9FUapH+/xkmWR5kz4wy3U8y7IRTZCxLnZz3uumEm3dd5g4dH0JdxZUhUtxKVze4ek8Gd04Y65myUuj2+2OOEQIxVN2pSbFVbf7fijtIlcWcHxxk0Ocojt2Ra+riTkM7143U4RU+bib+KoIXSETe0UQMOogI7QqTc9CDk7quNaGnFO4Tip8TitinDFISPma9V17RambvHlVek5hpPG4zHgQSjM1jPtbypjpi4PrAylWK0UIOcHJez8kU9X3zvtkmFTY2OtmiuqEUvgYYvWjOuTIe4e3o5h2nKd3VX2tTShO6blPz0sph5RwSj6l72G11noHK1qtcj3sGWO8d6e68EEgbxLknnmBGw3HlYPLLK2G8APV7sTi5k2SI/Sc37nmi4e8LnPZUj+YfANbaEeRe+cNrRKRZ1R3UOL1U+RuT2PMmNw+b4f8dyKUN15m3Atl7AF5aTCOvc/PWotHHnkErVbL60HYjRsY5CdPYUhVxriXaMrTn/3ZnwFAVv78N5+cGxsbWR8i75Srq6ve/Pk8iftw25/bp93n7t8LCwvo9XowxozE1ev1RtoChVldXRXLjO6R9UF3ynFSJm79uFWajG8u8j2X5hXqp3zOKPLB6MOdy917S6U5PuV6Nzcd3+++55IOJcUleV726Se+NFN1Aq6rhMqEe0Z1x7xY3SU0Rro6VKje+ZgakpmXV6fTGflIDTnV8aVFMsXoPtZanD59WryxIvRvt66KLMZI8JsnKE8vvvhitFzAoK1QmdKNDfzmCTd+nydxKW4i5OgoL/zKyorXu7TUd9zbKogXXnhBfE5cvnw5SsYQ+nErM3UvwYqiKIp6BFSah+oEiqIo9aA6wSild1gB//2XtKIg3RFK95Ctra2NhZPu4wql76bnwu8nc5HuBHXNQaQdtVizJWvtyErShQsXsLi4GGVG6TNXjb1fTbovNGTOS6th1lpxJZTSpTvXYsyaKX0ug7Tbtri4iF6vhz/8wz8Mrry5snDy7vXiYSk83ymU5CZ43dP7FE+s2WiqOY+1NnmnOg+pXRcxh+Fl5t7/J6UXuuuvLpNgksXdUZV2QH3l3O12g+1buodVzYSV3Y7vHkbJGoee072c0q6M1Gdj8b3P71V3ce9J9+1OSkcjYuTh8/C1a9eyOTU0JvPfYsZM6R1p/g+Z89LOIxDWefj8GGMOKv0u7Wj1+3185jOfiTqGJKXn2yWTwvK5ge8WhuBlx3UwX3uR3k3Bp5uVJUaOvPbN5eJ9y7f7mXd3b9W7jLzfuONJyvG5vKNaKfew6k5qGnuKBHK35ffu3Ss+J/MF9+oHYwzuuOOO7B1ffFKcvmeh54cPHxafA8D+/fvH5OVmhNw05MCBA9m7c3NzY2YjPllvuumm7N/33HNPrrkJd2YjXZshmfDQezzegwcPjj3bs8df5fv374e1Fq1WS3yP4qEyIznyTGrdvB49enTsnWPHjsEYg9e85jVRV4VI5XfkyJHccMCovAcPHowyP5HaSazDJirLfr8fLH8J3heqgMsccprkqwN6l+eD9y+prwOD+o2RqUpIFi6r26dIPl9+8+pLypdUZ7Pu3EtRqoT6jTu20L/d/mCMyeYF14wfgNhny0Lzo4Sr0/jS5H09to8bY0bmk9tvvz2bg0Lp8d9i9CJJHp4uEZpv9+7dm8kmvUdpSjpbXj25vx86dGjsnVarhQceeCCqbKX0pDhDYSmdffv2RYUrkm+eTqzpMSdVh4ghRWYfvH1wXVnq60BYL4+VKQXeb9zxRErLl988/dSXr1D/UeKopOXndaBQY4gdUGJI/ZAtE2fReN3OMWkPpqHO5uu4bnxVf0TxuGPvtUxZyMiLJ9XLberdm9JHXkq4aZCibMQuvlSZftXhi96nqvewKso4RRZo3AXRqmiCUjhNGVMXnCXqHAcnudhXdL4C0uT0LdzEhptFJqXXToqi7U4Xp+ujUMm62+eSWZzvfdoWJxPBtbW1oCOaIof0CTIjkeIgUw7JLIibXUiOdWJNXoitra0sveXlZSwtLWVmA1IcZBoVis9FMruQHFuFTGva7bbX6Q6P44knnhh7FjqQ75rCcvNQSqfT6aDdbuPq1avBtAnJnCTUVvhvPP75+fmxfEjpcZNRkl/ycC3JSyYnPhNvH+12e0TuFE+MMWbzIVMvyYyfw9sgmV1JJs/Uzufn572y1mEua63NTM/d8URKz2fCuLm5GZRPcrAgtU31HKjsRHxzQN7xDHd+AG4cLZBMgqX3Y+KWoHFU6pP0zD0iwM0GQ+NwbH9ut9tZWhsbG9mYIR3jIfKOykgyu7imzvyZBJ/jQuV+7tw5Md1QPbpzLR83+XGy5eXlqDlC0otC8y3/zT2itby8HFWXfJ6k9htjrs3NhlPmP7plQkq/DFKduXLl6S68HZGeGTLTznPEWIdJsNS/fCa+VLbub9xpp8TS0pL4PEZvV8JU5nQp1tsqQaYQRUwipkUVsnLTn1B8qeVZBbH5297ernwVPJVpt5tJpD/LaUjhXLM2zjTaM+CXs6pyrTJf6mBBaRohnaBIP2uiXlAWntdQvme5TDqdTi2WV8o4s9wOUmWbpbxUJUuVeVKdYJTK7mGVlLbQ3Yahe1hT78XMI7SrmOfUiP9etBHyVbCNjY2olSt+BUrKfWDSqqFPFhdfviXZ3HRDxLgcp5XGTqdTeNUp9poZSb68lUp3RS51UKZwKUgu76umzMAq5csXXx2OIvKgiaPOVcyqnWIpyk4hdeyicVUaX8v0YZ8cRe+GrmI84WPm9vb22FU7Uhr8o5b/P4bQHeuheSnmWkGgmBlkaPfVfV70TvKi98PGWkOl7PxL7LQdtlD7del2u1PfdOB/VyXLNHSd3UIlH6y+wcp3cJ07PpEmp6JOd3yEdgPznBqRcyWguIkCP7vIHTDlndtMce7jO0weksWF5zVUB5LjnhCx8rdaLezdu7ewgwV+0D8G/sGVcg47z7udL2zqOZ/UD+MqyWvrfIDnzsp88eSVb50f5u4CS0jOVGJ3FdT0V9ltpH7E0Pu+BdyixDhMiklPOndYtF/7HNSE0nflSJkbQnN27FwfI1MKPodc0ntzc3OF0ii669vv95P10JDXaR+p7brf78/MjqQL7wsx+ZqW/wfJEqxKnSAlX6oXpFHJB2uZwWrapqUxlDmILzEp08gqJhGJsgNNKI1JduCiTqQkL8d1ME2HPnntIHbRIta5RB2TcIpjrqLpl3ESpijKDaiPxHponQWaphPURd3yT1qxLzL38s2IupjldjJpx4pFiU23qHxFnG8pcUyt9fOPQGkwqttUYporG2Ub6bTNSKpM342ryJknpRx1mrVOs36qSDuvbLT9KcpkmERfa3J/bvpYG4pPFfvJU5dpa5P7GKAmv9Nk6ss1PhO9uleSmjwATnuVrcr0y8bV5HqcFerc0Zjlq3liyCsbbX+KMhkm0dea3J+bPtbWGZ+STh33vQLNr9u6ykXJZ2pfPrRKMauNt+y51RB5KzTcUUCZ9OtayUp1HuTic7pEcdchd8jVfpnwda8WzvKuAoXzuX/nhByf1UVKH/ZZDeT1VSlfRR19KMpupuy8Ujd1jsUxVkuzXD7TtvoqS9k5cJI0YYcypq3O+jzpK+e8tu7L1yz336YwtaWCWV+lqPrcKicv7/xMZZn061oMqOMMa1HHREXSLHu2d5IXZM/yrgKFo/YcimcaZ9VT+rBvpz+vr0r50usdFCWdaZ7Zj6HOsXiWndTEMG2rr7KUnQMnyaxu8nBi2uqsz5O+cs5r6758zXL/bQqNHWWasMqk1E9MO9C2oiiKoiiKoijNpLEfrGVWmWjLvkkfMpOQNdasx1obvDOtakJxxrSDJqxINo1+v9+o/qMoihJCTfbGqWqMr9spkzJ9tE6Uupltu9yaoC37Jn3ITELWWLOemGtMqqRJ9bRbaLoJmKIoCkdN9sapau5Vp0w7H60TpW4q0TrLOLGZ1k4N7SbmpV2FbNZab3ohB0R1EBt36L2y16CEdmdjd3m3trZKyVCG3b6SGGrD7m+0azENpxwxOyYx/T/0zm5qC8aYtxljrhpjnmDPbjXGfMQY8+zw/8eGz40x5j8bY04bYx43xvxFFub1w/efNca8fhp5UeplFp0FVpV21buO0x5Dqki/iqs+ylptTduJz7TrsSnE6t51pl0GrecbGGO+wRjzzHCe/7fC73/DGPMZY0zXGPP3nN+S9YBKPliXlpbGnllrM4WRK470UXL27FkAwPr6+lgDuHr1avb35cuXxbglfI1xY2Nj7BmtBpEcbjwkMw+7tbU15hk1D7q2h3akXnzxRVy7dg2bm5sA5EGWpyF97FJYjvsBZ63N3uPl9alPfcorK9WjtRZnzpwZ+53kuHbtGoAb9Zqn1LsfuJIX5M3NTXQ6HVy9ehXLy8veuAjJGc76+vqInBwuHy2SdDodtNtt78cvD0NxA8Dq6mqWDynf7ocStZvQBxRfwOFyrq2tifLUTahOjTFot9uZrNRfpQUHqutz584F04p5lkK/38/6Lu8v/X5/xKsxjQO+/uy7dot4/vnnx54tLCyI8ewAfg3ANzjP/i2Aj1prvxDAR4f/BoBvBPCFw//eAOC/AoMPXAA/DuArAbwKwI/TR66yc+DjFofGBz5O0Fhz5coVAKPzLMHnBEnfoHhSnoc8l5MsPA6Smc9n/O9Uk2IaVxYWFrCysjISnzQecU/6/N+SLBLWWvGdZ555xhuG16NbJlwGkh+I/yBx8+jqicAgT8vLy6IO5yLtkNPcHrswSTJ0u92oD2CuO1/m3ZEAACAASURBVNA8I7UDaV7MW6CXFjSstVNbrI+pT3rn+vXrWRg3j1Q+8/PzhdMqgrU26/O87/vqoegRgUuXLonPSW/cKRhj5gD8MgZz/ZcC+A5jzJc6r50D8D0Aft0JW0gPKPTB6lbuzTffPPaOMSYbQPhAQh9vL33pSwEAhw4dGmucd955Z/b3S17yEjFuju+KHHp+6NChsTio07/sZS8b+63VamUy33TTTdnzubm5Mc+oebh5u/fee3Hrrbdm9ztK9zxSmD179ox87BIHDx4cC3PgwIGRfxtjxPe+8iu/0ivrsWPHsrD333//2O8kBykP9G9jTFCp37dv30ibcdsDMMjTvn37cNddd+Ho0aPeuAhpgKG6ksxVeT2QvHv37sXc3NxY2bmyAaNtiN7fu3evmG934jxw4EDWH0Ifga7s7XZ7RLYqTG5cRRG40U/4b3l12mq1svBUX7zPEzSJ33bbbd64YvMVmkCk9kBKDu8HrVZrxHsw5cEnw/b2dnDy5GMVceTIkbFnO8Fcylr7MQCLzuNvAfDg8O8HAXwre/4OO+BTAG4xxnwegK8H8BFr7aK1dgnARzD+Eaw0EN5PpDkXuDG+8XGOxpo77rgDwMDzttvn+JxA85SL28doTHCf0zgiefimj5S77rprLG6SmYfj412sSbGbt9tvvz0bM0ivkPQLCkfpUL7oecyd0fQOl+GLv/iLvWEOHz6c/e2WCZeBL+jGemffs2fPiBxumwAGeTp69Ki3PXGkOYDmT0kWSSegsm21WlFebPn8TGUrtQNXJyHdgc+jLlI5djqdWrzrhhbe8+ZIgs+nVF+S/kofi9I8KcWVR8xHv5s278NuvvLym7eQccstt4jPJX284bwKwGlr7fPW2jaA92Aw72dYa89Yax8H4FZSIT2g0BlWtwEWuaJG+pgtiu9ajZBcRRpPkYFC+njKO//nK5MqXK9XcfbwFa94RbI8PN1QncfGmTc5h9LnacS2Px4mNW1fPHn4PqTLIJUD9ZOUtsH7VkhOUi5Dk5NEzEIAx5W91WqJCpYbD6XjiztvnJDyVaZ91IExJmbm/ycY7IQSb7XWvjUi3F3WWlpSvgyACv0eAOfZexeGz3zPlYZTZFzlSB+zZfDFE5Itte8Wyac0tkkLli6+34roBTxMFYtptAGRGl+MbhIbX6oemrcgm0rRqxpTyquuuSU038bKx/tCSE5aBMmbW2PTDdWV2859H5OSXuSLN68OfPmatas8K9ALpLncvyM2SiE9oNDMwE05+v3+yBY4X5WhHQ6+IkHmto888giAgTmhu3PCzVC4KZAbH5lx0IqIu/JBzx966KGRZ9z04+LFi2L+yHTmwoULWTwki7V2ZEUxhLUWFy5cGHn22c9+NgsvmSVROr1eLzNX4PnkJiHcdIbC0HOqF77K5DNXAIAzZ85k6Uj5o7gff/xxdLvdEdNK16SVr3ptb2+P/JvXLz1fWVnB9vY2Pve5z2XmJBS3tIJG+eV5+/CHP+zNG29jy8vL6Pf7aLfb2NjY8Jrd8r+5GRqZwHY6nbEz2JK8m5ubWXq+XUJfuXGzmRTTWd9z3keoDKU26JqOueZJa2trWVyPP/54FrfbB8lU7Md+7MdEeQB5hdRn1u2Ty6XX62X1xM3Vtra2RvoxtWGfCePi4qJogk/86I/+6NgzySR4Gmd4U7DWvtVa+5fYfzEfq24cFsCOsH1W4nCPWhB8vHTHNEA+JvTss88CuDE+c/icx81D+djlG1vd5yTzyZMnx96ldBcXRw0Jer1eNlbQkRguS4qpprV2JA4AeOGFFwDcyKe0i8OPvLi6jy8MzxMweuzHfSZx9epVUe9w4z5z5szYHMblk8xb3SM1kl6zsbGBM2fOjOkj0rgvmVE/9thj3rzxctnY2BiZD7a3t6PqU9JVSW8Lydtut7P0fHODr8z4fFaV6SzvI1QGbntyy9eVb2trK4uHjpR1u92x/kfz6Tvf+U6vPLwupDRdYo7o9fv9rI74GEJ6IMVP8vrqf3V1NXic4B3veIf4nNcbUYfpc5VUoRdUSaEPVndV7MUXX8z+zVdZaNvdXeXY3NzEu971LgDA6dOnxzosX8HgCiqZD7srFb4OT43qi77oi0bSN8Zkv0nmp8aYEXNi/jFGjfnAgQNjA5PUaYwxOHDgQCbjZz/7WXz5l3959m6oo3GTEG4iwfPv7hDxVbGPfvSjAEYHo/X1de/HwN13341utyuaY/Czf0899RT27NmTrUBxk1K+Ss5NmKQBUcI1xZFMSnieeNsi0yb349ktg/n5+Sw/nU5HPDPb7/fHTHEImuB5W+dmJO7KoDEmS983CdAgzsNubm7i4YcfDpqo+D46fauTa2trY3XL25OkbAA3BniSd2FhIRvQf+RHfgTAQJFzlQ469/Rbv/Vbojw8To5bh6TUhlZdubzLy8uZsnL+/I2FvM3NzUwZ5Ysuly5dEhcN2u22+AFK7/3Mz/zMSPrWWrzvfe8bK+NpOgRptVq5/5XgytDUF8P/kwOCFwG8lL137/CZ77myg+AfZLx9SVZKdCb+xIkTAAaLyKExivclbj7s7nb69AIKf8894wv69JtrgsrH8AMHDoyMTzQuuUdffDLQuxTHCy+8kB3BkcYgKbx0rMHXj/nzz372s2PxS2eGiVtvvTX4QUVlcv78+ZEjQpQGySeZt7ZaLe8c7crv6nzSPCDJee+9947JLL2/vLw8kp9utzvykSx9PLoy0xlFXt6hDxJK3803/ZvmdJ7XdruNU6dOBXWCTqfj/ej0sbm5OSarq99IH908HysrK9mzBx8cnBJZXV0dW2Qm/enhhx/2ysMXiDjSAoikF0jlvr6+jueeew7A6PjUbrezuqP+DAwWraR673a73vOo1lq8+93vFp9zXY7HNS0q0AvKzOWFwpqUL/zjx49bmlSaDh9ElTRmoexmQQYln2nWU6/Xa9RVFcePH8eJEycqL6w9e/bkDvLdbjcqXWPMywD8vrX2lcN//zyABWvtz5qBl8BbrbX/xhjzzQB+EMA3YWAm9J+tta8yA2cLjwIgr8GfAfAV1lr3bKzSAHaSTqAUR+djJZZpthW+qdIE6tIJgPJ6gTFmD4A/A/C1GHxsfhrAa621Twrv/hoGesNvDv9dSA8oVHPuR65kTucz5QQGjYZWKDY2NoLe4lKupnDlkjyCEbTyJK1A8VUWbvJJW/rSalOIzc3NLB/r6+sjO3rSCotr7hoytyUkebjpNBHytsd3jEPeiz/xiU9k78cQMl3ieSNTizzzSWutmLeQCY/bpkgmbv7tyuSTmSwKUtoA9Ye8vPHf+Q5gVfByoLYteWfM2ynY2NjInlFflvo81YlrYsdJKcdYBwvdbjdbNXZ3TKX25zPxySt/7tGckEyIp2n6Qzsyof8i4/kNAJ8E8MXGmAvGmO8F8LMAvs4Y8yyA1wz/DQAfBPA8gNMA/juAHwCA4YT0kxhMbp8G8Cb9WN0ZxIzFvn5g7Q2v9tIc4HpPL0No7qf+LpkRS/MCNyVMgXtZ39raGpuXpTyGPJnHyiDN2SHzxrwdUErz1KlTYpgQod1K/o7PG78LP6JEhHYYQ21K2uGTypnLTJY4sW2B2lTe+256dRwvkbx2u+m4ckr6Nj3jc6DP2qhKr7mhMuQ6DZliu7qC1PZ8bSdP7+VH2jiSh+4m6wXW2i4Gi9J/COApAO+11j5pjHmTMeZvD9P4y8aYCwD+PoD/Zox5chi2kB6wa3dYldlEV2qV3Updq6n79+/PHeS3t7e10ynJqE6g1I3qBMpupc4d1ibqBZXsjftW6Hz3hfHVVGn1LG8Fw7fS43sec/8lR1ptct9N2V3kKyvb29tYX1+PPiTuk1FKx0VaIXIdQHG4TKEdVn7HVgx5K3P0jM4Ix6wgSuUfWyfcaUW/348658FlSj2PSGFTd0tpB3gWkOqQ8kW7A6FdgZi7dWNIWV2W5HJ3lPlzibw2Lq2mTvsCe5eqdlgVJZYYB0AuruPA2HA+8vpuynUY/N++v1NkdOda0qFi4kjZUXWRytZ1AMWJOWMKINoJZRFcx4ah92KeSbi7nb55QgpHpNzb6bNkiqHo/aB1w8uP2rNUbyR/zN26KWnH4JMr1moxBl9fmBVdjmiiXlDIz3K32x05BC/dZwb43WIbc+OOUMkJQ965Mym+0PPUa1R8V6AUcdtPDhaI/fv3e8vLJ0dMw5HekcrWdULA4XXqc44B3LhnKsGUMFdW6iCx5wuk8o+tE7d+Y9LMcxwSE7aI2/1ZcYUu1SE9IwclUjmS/DF368aQcv5EksuY8bti6blEXhuX3OTXcU9eGZp0ZkdpLvx8mK8PhNpiyj2WMeT13djrMNx/+/4uei0YL6uYOHz6TgxS2d5+++3e92Ovo+P31VdNkXk99ExCqu+YdIvohDxckXY9q34ZeFlIDlcJkj/mbt0iaYfwySXVQ9EPNl9fmBVdjmiiXlCoBGet4BVFURSZWVwpVXYeTVSAFEVRdiNN1Av0y1NRFGUH08SJSVEURVGUemiiXqAfrIqiKDsY3flSFEVRFIVool5QSGL3gPLFixeD77sH06212dUgnU4nePCcO2uJdbZEhA5+03UUvgPslC4PSw5W8i4kd2UkZ0IU7/LysujiOpZYpzGUf37YO8bphQS5bAeAt7zlLQDGrzOSrguhv7kMvjInB1U+pxYc7rSLCOXtmWeeyf5eXV0dub4nz327mw4dqo912kEOHKy1Iw7K1tfXx9Li10GsrKyM/Pvy5ctjafA6iGlTV65cGUmr1+vhoYceyp6Rgws3b27bfuGFF7I+fObMGQCDccB1LPDss88CAD7+8Y97ZQo5+EpxVMDb1Ysvvoinn34awGg9ra+vj1xFQa71T58+nbVTXidvfvObg84X3vOe94zJTOXCkeqm0+lkeXfTrZImOldQmoFvrA5dY+VC/ZPmGH69mkSssxZfHNKVaATN+9LYTuM+Dyc944TmOkpjfX09i6eMY5aUecx1ROcjJA9d8wcAf/AHf5D97V5pFCoD95o1nwwxY6PktCsUjjugJH2CO+pMvSaI8h3rJJPyy+c/6TooPndsbGyMvL+0tCTGz+OJaVPceSDpRCdPnhyTmePW65UrV7I+TPr14uLi2Hv0vfDkk2PXdY4gyR3jSNWFjy9U5zwv29vbI3oZtYVLly6Jzrfe+973Buv4Yx/7mCgz16EJKY+9Xs/rsLZKmqgX6LU2iqIoM0BdLuyPHTuWO8gvLS3N3uykzDyqEyiKotRDndfaNFEvqGRPOPVKCL5qIa1khS6yLiJHaCcsz511kThd3N1F7qY9xY19KqFdVwm+khRalUt1qx57rQ3fiSxC0asPUusgNZ0y1xDUcUl41UgXthP0rIxFQVF810JVuWop5WvWrh1o4kqq0myKXBNF/VUa8+q4KiokS5HfUsYVd2exzjEqj1DZ8rpI1aPySNmJrFIHig2XGrZIWpMKM2li5sBpXPNSRheLxZevWdPlmqgXVPLBmnolBL9KpNVqjb0Xe+1LbHohW+08d9a+RiaF83UC7oac8pvi1rxow5HChcqWyxLyBJ3qVj1G/n6/D2NMqU5d9OqD1HD8/RRFqkg9NuGcAb++ytcH+NVOkybmWqWiFLmWa9I0cWJSmk2Ra6K4TuBSx1VRIVlCv/nGuJR+xMcIqQ9Osk+GypbXRaoelYebR6lcQ3NKkTRSwqWG5e/HLloWka8J4zVv3776m+ZtI3WWoW/+nzVdrol6wVR2WPlv0jtVr0SkXBDu4mt8Upy+CpZ2bGNWeupYBUo5d1kkjrx4gfDdt2WU/Tp3WLnMPD8pilQTVkZToPxQ2VhrvYrXNFYXZ22CmBatViv3P0Wpkqp1gjrGzqJx+vpLSnx5O6qTnCuq0EGqkDe04VBUeS4jV5kd1lg9ZqfpBBIxevGkmMWPsGnRRL1gKjusQHg1lT+LdQ4Qwk0jxrwzL628yuTh+bvu6kWRVd5UUwqex5DcXOY8ufLKjf8eM0jwHWd3hzvWRHxSO6ypH2G0SmyMKWzuXhVS2ylqruvWa6g8Jj34VWVulFc2s2b+K9HElVSlGaTuNsbMd9I7vgXDunbf8o4KpcaX924VO6wpi4Kxc31s+cbuhBapL2knNtayqczYVnSHNSWPxphazN1TkeaxKubQUJuc9LxT1VydVy6zZvrro4l6QSVaZJGGwL19+TydAeMfexJ5H0/SDh/J7FsJK1tZ7gTrfiSXadSpphS+yd4lxowDAK5du4ZWq+U14QHCiwQpK7qtVivaRLzq3WpfGGo7KR9hVC55prE8Te4trqoPMN52qA2WNdeljzqpL5G3wtCHeh0rrXv27Mm8WPK+5o5V0jlX/v7evXuD8kkeKWdtwmrixKQ0g9Q5OQTvP64Hel+aVXxscUjuvKNCVcAtVKo4W1dkPspLMzbO5eXlpIXpmLRDccRaNk1yF4/aTmo7SdHnuO5Y5Twj6X5lTHZJX5HaD32gT/pDfW5ubuSGACLGuzZ/tmfPnkIWiLO2m95EvaASI/IiZpxUGHNzczh27Fip9H2DamjFdpLnzNwJdtINIXaC94VxueOOO5LiSpGhrhXRKs+KlGk7eXLw33k6dZz3qGrXM/TBe+DAAQDhs9N19Yebb74ZwGg+3bqj37gMMYtkBM+Xb7Fm2syaPMrOp8yZxlarhcOHD5eWoci4Msm+Is2J01ASq0jz6NGjU0t7mvFziradFBljzxSXoYoyC+krtNhQx7n0PA4dOgTAP9+7v/mehcrIl69Z+wBsol5QicTS3VEcvrJAK6cURlqN4KtIdE9iDLHnZvi/pbuRgBsrRFw+Xz7zVrroXieKo9vtBsMUMc9w4/OtxHFZQnKGoDvjXHi58rjc/PC8UPmTJ8B2uz22axXKe6wXQw6Pv8h9aySPrz1I7Y2epawqdjqdsR1Xl9RVVl4vFDe/mzU23rW1tUwe2j116w64UVa+NgPE3yss4Xuv3W6L5rw+j5O+ux2fe+65YPp035ybtivfNFdXm7iSqjQDn3VVyjEDGkdD96PyMTR2nuJhY57zf0t6h3Q3oi+fef2dh+t0OlH3Lvp0JR+heSgmD3m/cfJ0QCk+SWchePlL+lIVvjhC8cV6Jpasd2LLjFsZxuLqK1Xt5klzFr+bNSbOra2tsbtlpbpz9QYfZXaQJVm73a73blfpfZ98ly5dCqZNdzlL6c8STdQL9B5WRVGUGaCuO9fuvffe3EH+woULszc7KTOP6gSKoij1UOc9rE3UC6bnV1pRFEWpnVlcKVUURVEUZTo0US+YmhHzrBxAnvQ2/azkuwypeWiCJ1VFKUIT+nMTTX8UZZronFU/s+acTlF2E03UC6a2wzorhTHpy4tnJd9lSM3DJB1cKcokaUJ/bqJzBUWZJjpn1Y+OS4oyPZrY/yqR2LdLGXI4Qs5KpJVM6aqIIsQ4PMnbYeWyFL0TljskIMc0ZS5Jj12ZlOIJheUOgUIy0AH9onUjyUDl3Ol0Crs7L+qoJ6Y8eTvJcyvvOhiq2nnArEFlIzkvoHr1OTWqE187LXpfoYTkbGTW6qyJK6lKsymyQ0nOSvKcC02if4XGiCpk4XNcnhPG2LSK3oMae21XiCos1ULXyNW94y3pY6lOl0I6nTtPVKHXzjIhHZ7KbBp30ud9q1RB0XvtJ00T9YJKthd9u5QhV+133nknAHklkz+r4pqTUBx5O6xclpTrLjh0tQcwfgVIjAttl9iVESmeUFjujjskA+WhaN1IMlA5l3F1XvTKnpjy5O2E3veFu+mmm5Ljl7DWFirjXq/n3SEI/VYUKpvQ9VG8D9QJlZm1NmtLXK5+v598X2GoHqQrfWZtoG/iSqrSbIqMMXQ1itRei869RQn1mSpk4fNcrJVXypVoKe+EwsXWYxWWanVcI5eaduoVgFJbkMK581/RdlNUJwDCc1/qvJhHSIendMre/55C6G7ZMmUqMY3reorQRL2geRIXoMzqXGjVb9Ky7AbKrHTttLItOoiGJvhpmbpNanDkSkPqgk1enBJNGPSbuJKqKHVT9RnKovHpWc762GllW2asjl2EmRSTnHdC81zVcjRlPm2iXrCjvQTTykkZJb3qVb+dcDam6hUpTpl4d0LZKtVRZzttEloGijJO1Up60fiasOiVx6yOtTuhbBWlDmaxv+Yx8d48yRWvmArx7ehJchbd/StykXWIusowdoeyTLm6vzXhPIaPJstO+C7NjgknMekV7ZDsVQzIZXftZ6GNtFqt3P8UZVrMQh+JoUo568hzHXHGjudNVH6VMKntyfd+U/p3LDtl176JekEhidwKIwdKvvf4+61WC71eD5/4xCcAAJcvXx5TCvn70iHpWGWZ3ltcXPS+Kymk/X4/C7uxsZH9zQ+J83fy5OIH7i9fvox3vetdmdMFycEQpZPS0WPfXV5eDr5L5SIdHKeyunDhwkiaUgd2JzBezjzP9N729jY6nQ7m5+ejnC5JeVhYWIh6f3V1daRONzc3c9PjdU/pkOx5Zd9ut2GtRbfb9R76J0dNPK7NzU1cvHgxGLfvgyqmLXa7XVhrs/rjH69bW1viwgL9f2lpKQv/qU99aiSfUnrve9/7vHmQnC/4JoXQB6RbdktLSwBuOHMhefh4QPWxsLAgprm+vo61tbWx+Im3v/3tY8/OnTtXytFT1TTR9EdpHrzN8z4nvcPfNcag3+/j1KlTAIDr169753IgfRHJNxaurq5633XT4GMjH69iHRW68HBLS0s4ceLESLpSHlOdEcbKk+cQj+KR5i6qJz73xji7dMP74u/1elheXo6qcyk9qY4lNjc3szkaGJR1jPMcXiexaQGjc68vb9K8uL29Leqzbty++cZXJzyv5BTUfdctD7ee19bWsnSfeeaZTBZfPPQN4JNTau+pur+bLs3l3DFmp9MZqTuqj5WVFa8DLcnZIvHQQw+Jz+fn58fKbJof803UC0xKgR0/ftzSwKooys5gVs25ZoFJls3x48dx4sSJyhN7xStekTvIP/XUU9oAlGRUJ1AURamHunQCoJl6QaEd1ryVlxgoTJ5L96Ku2vPChnYHfXHw3agU+GoVreKFZPOtZMbKnCdLWVZWVpLDxF4FIP1Wxe6UL/3Y8iizws/Jc//P02m325VcF+DG77MMSCkXfjUT9WV3V5beA8JtJtaKIrSqKa2wSivUvV5v5BqeUHpA/q6GtDsxayZDTTT9UZpNkXGLwvCxpQxljjqkzAtljwpZa6PmlLxjSlUcWSpK0WvLiup3VbePWGu5EJNwuhV7BVIqoTKOLQuuo/Kdauk9INxmYvsEEP7+cOvYJ0+KTpc3tvmu65k10+gm6gWFnC65Ow5F3FNTmDx36EVdteeFpd/yKsV1c15kt8WNIy/P0u9czjINqYrdoptvvjk5TOxVAFV5do1Nv8hVOGWcO6W09zrcvofKN6VceBmQnNLVNfReqM1IZRJzNQDHbSPGGOzfv98rj5tGyrU1nEOHDuXKMm1091yZNEWuOaEwVTnPKzNXF70irWhaMXmWxpUic1kojqJI42BVaadexVMkbTe+IvFPwulWFdcHSYTKOLYseD5ITumKF3ov1GZSnKWG5mi3jkPyxJJXB5Le4coyC8yaPDHsaC/BiqIou51Z+4BWFEVRFGV6NFEvqMTp0rVr18be4Vv67jZ8r9fD2bNnAQwclLjb8dzJieRIR3KIIKVDckqH4eldyakNN7vY3t7O0uNb/bEmT9ZafPKTn8ziu3TpEk6fPp2ZQ5ADI0luSo87xQEgOgmSzETI8UWsuQs5HgCAF154Yex3KodHH30UAEZMK0PmDq6JFzfh4Hnd3NzE/Px8lIm5VAZkdppnQsTrdHNz0+ssgZep5HQptg1QfjY2NrzOASRz742NDVy5ciVompNqZsJldtPkafv6GLG5uZn1oaeeeiqL232P6oTajIRkjuNzhuYzh3Kfd7vdrG/x+u12u1l9cFl95sbb29tBE6yPf/zjY89obOPsBOcKxph/YYx50hjzhDHmN4wxB4wx9xtj/tQYc9oY8z+MMfuG7+4f/vv08PeX1ZhFZQbgbdx3BMDn7LDf72N+fh7AwDmJ2+d4/4wZr7k8vrFMmkPoXdexDZ/DOp1OlhbXOWLNCq21ePrpp0cc2F26dAnAjXnGp1PxNN15wecUh0N6R6xOwOO8cuXK2O9UDs8999zYszy9wHVcKc1N7XYby8vLUXOtpDf48utCdUp12G63vc4YeVy8/qnNx5jsUn62t7dz68LVXa5fv175fCK13ZBuIsH1qvPnz2fxSroDAJw+fdobl+/4nc8sPuZYYK/Xy/oVr1vfMSHfcaBOpxMs/yeffFJ8TuPbrFCFXmCM+QZjzDPDef7fCr+LeoAxZq8x5kFjzOeMMU8ZY34kSmZ1uqQoijJ96nKw8MADD+QO8idPngyma4y5B8AnAHyptXbTGPNeAB8E8E0Aftta+x5jzK8A+Ky19r8aY34AwJ+31n6/MebbAfwda+0/KJ8bZZZQnUBRFKUe6nS6VFYvMMbMAfgzAF8H4AKATwP4DmvtKfaOqAcYY14L4G9ba7/dGHMIwCkAx621Z0LyNG9PWFEURYmmQucKewAcNMbsAXAIwCUAXwPgN4e/PwjgW4d/f8vw3xj+/rWmiYdmFEVRFGWHUYFe8CoAp621z1tr2wDeg8G8z/HpARbATUNd4iCANoBcb676waokU8ZLrqIokyXG9McY8wZjzCPsvzfwOKy1LwL4jwDOYfChugzgUQDXrbVkr3cBwD3Dv+8BcH4Ytjt8/7b6c6soyqSZNc/oiqKEqUAvyOb4IXz+H3vH0QN+E8A6BrrEOQD/0VobvmAY6nRJKUBVXhwVRamfmB1Ua+1bAbzV97sx5hgGq6X3A7gO4H0AvqEiERVFaTBNdOCiKLuZKvSCErwKQA/A3QCOAfi4MeYha+3zoUBTG2V2ky6SRwAAIABJREFU84rcrN3HpCjKzqUip0uvAfCCtXbeWtsB8NsA/iqAW4ZmPQBwL4AXh3+/COClw/T3ADgKYNyDnqIM0XlRURRlMlSgF2Rz/BA+/4+94+gBrwXwP621HWvtVQAPA/hLeQkW+mB1PbuRV18XMh3lH6ftdhvW2swL39bW1pg3MterJ8G9duVdos3/7YYjz2XWWq/HXcoTl497F80zi+XyLS0tZX+fOXNmRB7J+x2lQ3K6FxtLMkuemCUZfZ7vONZaUS4qh4WFhRGPgdyLHPeyR7h1xWWlv8lb3vLy8kh76na7QVl4Hn1e3fj7JE/Ii7UEv+SaPDmur69Hee7tdDpot9vo9XpJCzWSJ02JFEWPe6WmcE888URynO12e8ybdbvdHqsrisc3RtA7VV96f+3atTEvlIC/X/jK9vHHHw+mL3nT5u1pFpTwij5YzwH4KmPMoeEZlK/FwFHC/wLw94bvvB7A7w7//sDw3xj+/kd2FgpDqRTfPOh77nq8B254F6d5n3QEDp+3UryZS8+kuYPm2ZDHXfJU7Ju/88Z2LgcfD69evZrFI3lsd9PiZejqVi48jz4v6zFe+X0ycZ3t+vXrYjgup+QJWJKVz8287Om3kCyh9uGD5iBe/jFh+e0B1C58Huc55JlWmqNC+Dxhu6QOtdwrNYWVvN2H4LcEUFnwGzfc+GPKqSi+/K+srIz95usXvjjOnDkTTFvypg3E3yoxKSrQCz4N4AuHNwXsA/DtGMz7HJ8ecA4D/xcwxtwE4KsAPJ2XYGmT4D179ngvAJZMR+mS32PHjgEYuI+/++67Ady4yPbIkSMj8RP80l9pO9taK14GzeNwwx04cGAsnlarhcOHDwMYXAJM8VI8ZnipsZueGwdBeQWA++67b6ThSpcQUxlR+blpHDx4cCyMeyHy3Nyc2Aml/LrwvLrPAeDmm28eSY+e8zzzy5zdMuftguLZt28fjDE4fPjwyO95lzTHvsvLkOShZ9Jl0i68jVO4m266KTctSi+vvUjQJCqVbyi9EPfcc+OIAYV75StfKabN43X/zQ/lUx3s3bt3TBYK5ysrn/yxZeV75/bbb0e/34cxBv1+f0xWl16vJ5bvl33Zl6HX63nD3XvvvWNyS31jmlRhrmet/VNjzG8C+AyALoDHMDAV+gMA7zHG/NTw2a8Og/wqgHcaY04DWMRgMlN2GL5+4XsutUUat2nOXVtbw6233jryDp/zeBxuOiljBg/rKmh8/qLfae7cs2dPNj7xOPL6GY+f8goAd9xxRzZXUxyhcuLzV0hmN4+tVktUwqVwEpJMlP6hQ4e8dc7l5HO0NE+6cu/Zs2ek7PlvPllC7cOHK0uv14vSC/bv3z8WR6yOVfRoFZ8bfe09dd657bYb7gUo7H333edNV/p3q9Ua61shnYyXXSxl9YIjR47AGBOlV/V6PVH+++67b0SncLn99tvF53m67KQpqxdYa7vGmB8E8IcA5gC8zVr7pDHmTQAesdZ+AH494JcBvN0Y8yQAA+Dt1trw7gAKfrC6jSGvAUkFQ88+7/M+r4gI0ekAYfliGr9vEokdFNxOFjMQVkFogpHIGwzo97Lyh+SK7USSnKkfg0U/JmIneMK38JBHHQNcrAx5bZ3LFsqfuzhQtZwhQsqfi69N5ykWbrhZ+EB1qUoma+2PA/hx5/HzGJxHcd/dAvD3K0lY2XGExgq+wFtXWlXFV0XcZT5eiqRVB1XIX3ZOr5IiOk7KfF30Y6Gus8JFPgJDCw4hOfM+tlNkKBo+Jh5ffebtPjbFx0sVfcta+0EMrrfjz/49+1vUA6y1a9LzPCpp/b6tbsn8h/5NJpaS+U/e1nmqGYRkBiuZBUnxSGYqvnA+mbgJwcbGBtbW1jKTiZA5qnuZccjMQ/pNMvWRTBiJ2DwtLy8DiD+HHGPG0uv10Ol0sL29HRWv9E6MuTOlT6ts/NLo2PRi0yGoXGPqj6fT7/dHzGYmaVGZlxYvMzLlk8LQewsLaccXq8grlR3vB64ZHVHU3JqbUhGS+fM0rWErMP1RlCR8c0moH5B5pTQel/F54UszZAobmrNcM1tfmBDcbHBraysbqyju0FxcpiyksvWZMMakReWyvr4+9iwPn6m2mz6ZBafGB8SbO0tmojH6EA8XmxbFL6Wbl4a1NuoI0zTg5cWPUPne40f/JgU3VebEHCWIZWVFvp2lThPoIjRRL6hkC8e3GuXbXeEmgpJZQN5KVaoZhGRCK5kFSfFIO0m+cD6Z7r///uzfrmlkaCWPmyC7coVkJqSdQC6LL728NI4ePQogfrUvZmd6bm4Oc3Nz0SubUtpSPUuk7ua678amQ8TssEoytVqtEfOiSQ4geWnxtnLzzTd7w9B73OSoivRjoLLj/cA3EBc1t5bMf7i5X2w8daIePJVJk2e2KUG6gDQHlGnDvjRDljKhOctnBpwi41133ZX9LZm7hvSLMmUhzfFcltS0qFy4XhM71sVY6qXcEx2rA4XClrH0SrG8SrE8cnf0J2Whlwpvs75jgvw9fvRvUlDZuf2gyt190odcYkzFJ0kT9YLZMqpWFEVRKmUWV0oVRVEURZkOTdQL9INVSSZ04FypjjLnbOtmlmUjmiDjJNC+qihKnehYqyjNool6wa69h7WIfXpV59Dy8p563m/SxJ51LUJdZ/2qjHdS5xF95y3LELouIQV+tUAoPJ3rST33WxZr7ciVA3n4zgXlnUuSzuqknGWaBE08q6LsPnbzrUcxefedjZsFpq3PFaHq9jaJ9lvmCri8eF1S9Ty6hjEvLOkLefNkHflMmZt9ecjTl3x6xw681mbi1Op0iQg5YyEHPlXga+DSYBpbGfyerKIV6N4Lxp0ISDKnnvfzIXU4Pqi4cJlCgwWtzEiOgopCafK7vFIJDZJu3fF712Lk5zKl5jfGaZZEzApYFXFK55ry4p2bmxuLK3RlwqRX84y5cRVCyLkH/e07M7Zv376ovsCZtTNGTZyYlGaTp+yGFOSQw5YipDppLBpf0Tj6/f6Yg0UJ39m4FFIczrnyhGSTxpCqyimks+QRyltI5iIfhinvF81PXWO1z68IJ09mfq1NCNc3S4pMZSF9JKb8fToLXWuVGm7WvAc3US+oxCS4iJMkajjkwKcKfAVcRlmuQtF27wVz73+rC6mDhDpNrMtv14lQFWVEcZVR9lMGhFTX6j6nG3WkxUm5269KUiaTmAmoyJ1rVRFy7hHThkP5khwpzNpA30TTH6XZ5I2RIQdtIYctRUh10lg0vqJxcMdCdY8doSsGJaSxMjbeqsqpjKKfOvbVOVdL6RRhWvNLik4QU2fTXNhNdXaV8lvoirxZool6QSGJFxcXs7/b7Tbe+973jr3DrwuhbXhrLdrtNtrtNn7/938fAPD+978fjz76KIAbu63/P3tvHmNZVt4J/s57kXtWrrVkrarChT0Gte3BeBnZZlJgj+32GDwatwwaDVhCwi1Dy4M9smlb8qZ2C5AM3S1vwmCgp9sGhMc2GsziKpymWFyCgqKKpFyQmVVk5R4ZGZGRkbG95cwfEd/N7533ne3ec9+7N+L8pFS+uPcs39m+891zvoWu1LXWIzewdFpGt56kxkdhY+hklk4/Pv/5zwO45bZ9MBhgcXERw+EQ73//+wEA8/PzY7Svra3h/PnzADASgubrX/86BoMB1tbW0Ov1ihNhooufKhMN6+vruHDhAp555hkAwFNPPYUPf/jDeOyxxwAACwsLY/X/0R/90Uh51N7Z2VkAsgt5M3QHAJw7dw7AqCrRE088YQ3PMzc3V7TV7HfeXnp25syZ4vdwOMTNmzeLNPzWfX19fUTN+cqVK8VvSre6uorZ2VmcOXNmxP33cDgcmW/Uvq997WtjdF68eBEStNZ4//vfX/TLxYsXixA6ly5dKmjjKq5a65Gx4b9pvhOdpuquebvQ7/extLSEGzduFHXx2+l+v1/0IVcneeyxx3Dq1KmibBpj6jN++2/C5rb9C1/4wkgogn6/j8997nNF+VSXGZqFq8xqrfHYY48VIV3+8A//EMDGfDPV1n79138dAPDKV75SpJPaYYL6gYcEWl9ft6rbmOGJvvGNb+BlL3sZtNYjbXnhhReKeULaDgDw2c9+Vgw18Pa3v92pWvwDP/ADI3/3ej187GMfE9tjC9tQN9p4kprRDvC1wdcZ8RQOHiqE3yj2+330+3186UtfAgB88YtfxKlTpwDc2tM5/5H2P+KFdCMH3Nr3uTwBbPAG4BZPHw6HWF5ehtYajzzyyFhbqH7i3aurq0UdZ8+eHdkfTR5iM8G4du0aXnjhBQAb8stjjz0GrTVOnjw51kbCxz/+8ZEyqQ7aA11hMygttYHfYp8+fdoanufGjRtFW81+5+3ldVy6dKn4m0wzJBmp3++PhDbhezml6/V6uHTp0ph8Y46P1roI2cfbJsl3lP6RRx4paJ6fnx/ZDxYWFsbCtUn9wH/TnDf3SmB8HgwGA6yurmJlZWVsz+S3ymYYm5MnT+LixYtFuVwWoLxaa6uWgk3zgeRTYGO9DAaDYp2QPGTOL7OskydPFm3527/9WwAbYd9McyCSvX/zN39TpIXaweVEAvUF789+v2816eE85+zZs/iVX/mVsbZcvXq1mCc8/de//nVxz/7oRz/qDC301re+dezZYDDA448/bm2PiUmol7dRLlAxHXP8+HF94sSJkWeTdMDT7/eLU9iy9Q6Hw2Iw6nQUYCubtyEGk+jny5cvO13ch9BC71LRS4ypiadB2flURkocP34cJ06cSM6QfvInf9LL5D/1qU81b3fKaDwkmWCSMHlw2T19mk6DquwjddO9sLCAQ4cOVaaD3qegt6qJVt3IDqgyUqEumQBop1xQWSW4qsAes7j5h17Zenm+OpmKrewyH6vAZD7YQj5WATct9C4VvU3+IGwybRkZhCw8ZbQJMTJBSIzvEExzjdQRYzYVQj5WQ+ioomobW9e00XT6MjKAds7TJBK363occDtdIhXbGNicqJjPiS5JDcLntVRS5Qh1QGCCOw0g9U+u6mjCVHcJqVN65+p3XxmSKhO9J1XgGPj6LtbpkkSfy9GHpGJF9drUMm00x3rPK+t0qYo35pg+tM1xF0z1cECeW6TK5fLOV4fXaUmFCRhV+QmBS80OsHsEbBLaqPqT0W6UWdOcH/lkiiowTXk4XHxK4uNVnAGZ6sw2mgg+XhSCKmYJrraSGnAVemxyQRX6yvZnqNMln8yUGlXriGlT7NyW5AhXVAKfs9Y6+lNa3zFzzFYGR528KyXaKBckcbrkM552OViYmZmJvnW0OVExnxNdkgH4nj17guqw3cjGDCav32yrVM7+/fudNIW+c/W7rwyXE4UXvehF1jJCynbRGjoXJPpchv779u2z0hXilGMaTpeqOJsIqUu6DQ+lkdNG+aWxI+dq9L+vrFRQSokOkWJvM6QyOKbpTCoUWRMgY9Ios6ZpnpqOCVODypbWhYtPSXy8rFDH67b9NuHjRSGQ6A3lD662Hjt2rDI9NrmgjGNEQtn+LFPnJPhs1TpinA3Fzm1JjnBFJfDJenX0p7S+Yz/OXDwCaF6UABvaKBckodh3miqdXtAphHRDMQmDYx9CTjxjy6LfkzqBcd2SSvD1u3QjVwaueugGuk5w5xwux0UcZW4hpbyx9aS+fUxxmm6WEzKfU8VhDe1728m97eS8bD9Lp/RSf0yTp7XxJDWj3fDxGdcabOoNRdmbJ1dZ9LvO2Oa2el3PYsurk7+F7tFVYMp7ZW5Yy9RXJm0d8lHqOR0ynyetneSSCSSU7WfbDeyk1ngo2igXJLlh9X2pu05ZpBuK1B1VxgheOiFKZUxf1o41FmVduqcqr0w93W63dB+Hjg+liTnFn1RoJHO+pb5pSHGabpbjOlGkMXFpNMSsqzLpbBoZHGX7WTqll/pjmsy/iRtPxtZGqE0jB63Bpt5QSDdPZWUCM8+kYjSm5gWT4C2TuAky5b3U+1GVfOZ8q6M/UtsWh8znSWsnxWpGlO1n2w1sE+Owtg2VZ37Kk6+6TtHKDIx0upKCOSmlvO0sYxNchhYToSdKIbdlZWkM7WPTrbstb0q7oyqgOs1TNhctdTAUaW7x0AKhkPrTZUPkslcJaWfsmPV6vSQn0b6+aYMNK8V5dP3LyCgD27osw7uaoFnlgkRfKkE/pO113dC42hA6Jj67vtjyqiCVFtGkYNI7aZkAKOfnxUTo7X0qTb1YcF8yHLF96uuXpmqImGijXFCZosFgkOSE0faMI4WBe6iBdQrvxxJCXNhLJ8y8vLqYVigD3717t7cPU9JI8WSl8n231Sntjqrkt93qxtolV4XUXzb7Xhek/nTdmlTVKojti5mZmSTB7G325ASfPUsT0EbVn4x2IOXcSfGRW0VF05e3altt5Yfe0k7jhia0P3fs2BGUNtV84XHdTaTSIpoUUnm6TkkDEG8zHSrTp44kEYoq2nscvn6ZlAZlVbRRLqjcs5McnBQG7pMaCFsdVeiuG6Eb4qQ3zvvuu2+i9W1luDaQrYRU66WMamPTsBXHNyMDCFP3L1NOHbDV0WQeEso7Js1jjh49OtH6tjrKXCBtV2wFmQBop1yQhOKYU02fK2sTZZzduE4yXeVz2NxfV0XI7a6PzrpUckPVemyOkcrU6euPa9euOcstc9OeShXIdcprwhVKgcOlupzCYca1a9eK30TL5cuXo8ow66W2uRxmTUL9itdx48YN0VmHzYGHjT5f3ywuLgaXNS208SQ1ox1IoaYay8PLmnfEyAXSM0l2Se2spkre0P07BqHymk3dEijXPlceW9i/uuqLRaiJDXfw5MIk9hPep1TfwsJC6fKoba55MWmsrKxY13oMjb5+WV5ejqZtGmijXJDkejREldemwulThykTciPmJNN2ymBzf10VnU7H22Yfnb6QPLYyfAhVb7TdqsfYn5pODmw4cuSI832ZU6JUCzHmlNcVSoHDpbqc4hSU9yfRctddd0WVYdYbovZbts/LOmS67bbbxDSx88XXNwcOHHDS0QQ0jZ6MrYOy2jYxe4CJsuYdMXKB9Ezib6lsWFPkrcM8IVSDzjUPUpmMEXxmGqnri4Vt7zER6uBpEvyb9ynVd+jQodLl8RBVTYFNbo7tX1+/7N27N6q8aaGNcsFE9HknbafXdGzHNnNs9/ZnhGNSc2Urz8k2qv5kbG1s5fWWkZGR0XS0US5IQrHNK5brmp3UVySVAa5mFKNyVCaekk/lz+boKUaNiavqrK2tod/vOz2rmu9S9Y8vfWj7qqoe2TzMaq3R7/eD2uSK4xdbf6yadlk1uDpUY6albsP7wNV/9G4aajI2tfUY0wBf/0pxWJuiAkVoo+pPRrtRhkeS+qlNlV/6HYIyqqoutb/YeI4SuKptr9cr+stVhktmAMr3kSttaPtiTb18dfFnw+EwiSlVLC2xfHzSXm+bhlDzPXo3DQ/7dZkQcNjk4ywXVEepD9bQ0ByuBpOqQK/XG8vP1QiqqBRQudJJAr2LUYcs6/Rp586dxWI2velJfUT9S+/of8oX2j+S3aSLbv7OlY5sNMouQEmlixZIp9MpPeaufC5aQ8ZSGqcywopPkOPlPPHEE+KBifnxLNV98+ZNsXzOTEkoo3LX19eL9z7X7GfPni2EFEo7GAzG2kdprly5Yi3rwx/+8NgzKjOmj/k8X11dLWy9+fO1tbWRPiB6FxcXRZthl72U1lpsVwrhLSXa6L4+o30o42+Cg+ahtH5C96YYuHi6S+0vhfOkmZmZoi6udusqg/rXTCPJObY+kvaQUA04VzruVyOFXMCfkVxQJn/TnXOGHpgTTp06NZaf/0+wyRnSASswuuZIdqAy6Z1PdpmdnR3xZwFAPGygNNevX3eW99hjj409K3MQRu1YX18vPpJ5f/V6vRF5x3XIrrW29iHB1q6mHWi0US4opRJsfhiUsZ2gMmJdZ7sQ4zGwjF1cFQbGXXn7JkJVG1GCZDeZggmT7WYdDL0ul/S2cSzTBurX2HKUUt6PcV7O93//94+8k2xebHPFFqqGr1USyqhc/k4KrcTx0EMPFb8pALjUNirzwQcftJb1C7/wC2PPqP6YPuZzh/MV23NOsySgKqWc9khKKTzwwANW2puCJp6UZmw98HVWJnqAxIdSIIWda6r0Ut6QfQGwH8jG0CDtISn4A+eTqflNW/hXWX8aMe17+OGHx/Lz/wm2uWKTt/l6JdnB9Dvjm6P88of2QKlPqLw777zTWd6P/diPjT0rc5lB7eB8hfeXuV8TzZIMpZTy+pC54447xOdNsucF2rOuONoRMCgjIyMjoxTauDFlZGRkZGRk1IM2ygVJ7nxtbrzp2p1foZNtAIWDWFhYGFMB4lfnMSoA5pU7VwWwpZX06Ln9Ar/e5yFBJBsHmw3G+vp60Y6lpSUsLCyIKo8Ebs8iqURKKlNS26ndoa7ve71ekKt1UoP02dT43vN3169fR7/fx9mzZ4PC9kgqq1J7zXrM3wsLC0G2P7w+mruk7mKOh1k/5bWFVOHg9fd6veShCqT20bNQWyZKS/OUVIik/LS+fOo/IfWG2MpySHTZbJNsfIbbm0u4evWqWG8Ib5gU2qj6k9Fu+Hg456e0NkgF7+bNm9a9HKhuw+raR+mdtL+YdAJyOJAQengIsNXV1YJXhfqYCLHXlNJIbXaZMHCTE1f7OH+P6Qffu8FggNnZ2aB9UOq7suYZJg+30crrpHlBewx/Z1PZDekr0wSoDpOTEF8qIWXwOW3LS2srlV+LkHlEkOgKsQnn8H2TSKHugA1ZqEl2rG2UC5LcsNrU5ugLnqsh0DMKByGp4vGOirlGNztYUgUw05I6o5QPAA4ePFj85iFBQl3hK6VG6jddskt5JJVT/rekbiW1neoNdX3PVSNcpy+kymFTRzHLCFHNpn6WVCx9tBKk9pr1mL/N+WdbpLw+mrtczdtWF88bwgB4mjpUSyUabO3gMNvE05LqjJSf1hdfRzEItRuX3kl02eaijc/4VBtvv/12a70c0zzNbONJaka74VObk/YaCgfhWz9VwuDwv11haiTea9IJyOFAQujhdXP5yCXv+PiYbx8C5Da7eBynx9U+zt/LmFvZ3nW7XauKpQmp78qopgPjczBk36B5wWm35bfJeBJ4mrrUSl0yU8x4mjK/lJfWVqrwLzGmfxJdtvw2PuMbAynUHSB/a0wTbZQLknywah0eK9HEcDhs5Jd8RkZGxlZAGzemjO2LKvJERkZGRoYfbeSxSb4Uy7iKput2nze7uhFTj00VwFeGqc4UohrqwtzcnDdNrJoD4PcMSwhRSXGpL8WocNShQlHGu6+UJ7W6romq80QCHztJZT8GXG2L/y2hbCigskgV+sLlJRiQTQqyl+CM7YKyoR+ktPS/TZCapDpdaF1lw2GZ+0mKttlMs3xIwbfLhJSLeVc3UtTtUjVPhVRzxYQ0flXkm5D9d9Jec2P7zZbeJy/Z5OhJy0A+tFEuSEJRTMNoM/LlmVRnxdRjUwXwlWGqM3U6nUqnG+SlN7RODhetoSqoIWo2LvWlGBWOOk6BynhIlPKk9mZpouo8kSCFUCjrqdtUfXLROmkPeVXVBgmmCr8JSc2nrBpaXSBVLde/wHIOKaU+qpT6F6XUM0qp/0kpdUQp9Q9KqW9t/n94M61SSv0XpdQppdRTSqmX1drIjKnAZw4SU4YvzyRvBELrsvGZmLbEeoq1weXRPJQWE6F8OzRdzN4/SaSo26Vqngqp5ooJl4f/Mog1f5oEYvvNlt4nL9nk6CZ6Ca4qFyilfkop9ezmPv824f0updSHN98/rpR6kL37HqXUF5VSJ5VSTyulvIJokhnjCwDM39MJEcWAXF5eHjvJkG6BQmBL66LPd0J67do10RGDz7EPB3dAsbi4iMXFxSDnRmS4Lzm8MSGd3kjtdhl+83dSeeaNXFnnCjYHPFprnD59OsgYX6o7xFmTifX19aD5xk/VKL1tfExIsb9CkfoUsurNAYG3m5/EmvlMJwyTAjk2od8STRyh2gUmpHi3W/gk9T8D+KTW+n8A8L0AngHwNgCPaq1fDODRzb8B4KcBvHjz35sA/GnKNmU0G771JL2ntSTtW1XWlI3nuWh07e9c66IsXZxnLi8vjzjsCYHEw8ryOVc/9Hq9Ma04qU6fbBQCiZdqrXHx4kWvjGlD7C0hvyV1OU2SyueaRj7tKJfTzVAaJ40Qh5HSLbNNa67snlsFtvUa6qAsBDZZZ6vFYVVKdQH8MTb2+pcAeJ1S6iVGsjcCmNdaPwzg3QDesZl3BsB/A/BvtdYvBXAcgHdC1HrEQZOA30KYt1OdTmdsYqQ6eXGpGPk+FCnPwYMHi98+VVJbPfxkZdeuXdi1a9dYcGYbbd1uN+jWUTq9kfpxOBxaT0527txZvHMtrrKqRwTJqQb1+QMPPFD6xi/0BIur1YTe6PLTxkuXLhVppfExwed/zGYT8jEcC06ra+752sTnES/H5vhjGpsszQc+l20encvynJiDq2kh0UnqQQCvAPA+ANBar2utFwC8BsAHN5N9EMDPbf5+DYD/qjfwzwAOKaXuTt22jHaB1oZ5C6G1Lm6mOp3OmGDJ12fV9RXK40xQeu4spox5CTDuXI/6I+QwWGst8iuzPS4He2Z5NszMzDh5OL0r4/HVLE/a95VSuPPOO4Nu+yT6Qvm6OSdMvmibK3we0yWMUsqrHVXWoWIdZkImbOX71guXm3kZNhmrKTKBTdZNfQO8BeWCHwRwSmt9Rmu9DuBD2Nj3Obh88FEAr1IbBf8vAJ7SWn8NALTWc1pr7+lfqRExNxOyqTTtFqXbFXp++vRpABvCvzkx+E2ZdApjC1NhCqPU4dKNHpXBGS2/9aSyHnnkkaK9xJAA2W7PdivJb2m/+c1v4tSpU9i5c+dIH3HwMChSqBnXKSmnh+jlz5aWlqwLZ3FxsfhIMtvC+4RUBdKjAAAgAElEQVQ+WCmNL/THYDAYaeeFCxdGygWAM2fOoNfrYceOHSPhg2yQTlwpn+8jgtO7uLg4Mj9tJ8V8DpEXuMFggPX1de/JGYVp4KfVNvrMuc1PnWM+jmzPpTkq3fbbTj/5mqbfs7OzRdnmGqR+e+6558TybHURLWWZvNYa58+fL+giSAdkgN0VPQ9JJYH4GIdPa2TSCNmYlFJvUkp9mf17k1HMQwBmAbxfKfVVpdR7lVL7ANyltb64meYSAIoefy+AF1j+c5vPMrYoOO+iPcL8oJO0MugdHQReu3ZtTFiSbrJM2HiFyZ+pbOlGzwx3x/kQ1fvkk08W6XgZEh+13Uryffj8+fO4eHFjCc3MzFjlAp9mj8ljpH6i22Ge16WZtLKy4gx7RnXwMlw3shx8PgAQ9/3BYIButxt0SC7xWJ8PAgJ9CNI8u3nzpnXOcZr5+JNnbAp159q7SJvNdSgt7YFa61q0lfhY2T4obXsYX9P0m/Z9Hv6OQH12+fJlJ00uLb8y0FoXYejM9kp12Q5heEgqCbSWTUjajdPUxkogF4Ts8UUarXUfwHUARwF8JwCtlPqUUuorSqlfD6G5lLK9eZNF4V5Mu0Wa+OZpUqfTKcKJHDt2zHnLJX3lUz38REeiiyCFzqG0nGb6TSdkAPCjP/qjRVpuOyrZ7dlONPfs2VOku//++4sTVV4PBw+DItmCum4SOT3Ubv7MFV6k2+0WZZsnmkrdcsV/zz33jNDhO00028nd1FO+u+++G91uF1evXhVDhZiQbAd52CGJBgK/FT106NDIB5MttA9/zt3X0zhymPNy586d6HQ6mJmZ8do88fczMzMjt9FSH9v63fZccrXP5z2nWQKl5fZSNJ7dbneMAdN8u/de+7eKK4RE7C0Iz3/33XePld/pdMQ+OHz4sJU2Fw0PPfTQ2LO9e/eO5ZmmXWvISbHW+j0A3uNIMgPgZQD+ndb6caXUf8Yt9V8qQyulmnWMnDEx8HlG/MG2l/P1QDeGxOsOHz7s3U9intvmv2SfbvJC/j+9e+lLXyryQYmP2m5CuTbTHXfcUfQH3WhK+bjMId2AhKxzajPPK2k88TKpXFc4Pb73Sv1gg0826XQ6WFxctIYK4ZD2EWqvuSdL9XAZZ//+/SMfaLYQNbxPSCZRguaVWT+Ns0tDS9oDu91uaQ00F0Ju7G17GKXjoaxoLDudztjHHclPPl8skpxbVh4g0Dw1x1Nqv81/hW8vP3bsmPh89+7dY/RP0641kVxQFjMAfhTADwBYBvCoUuoJrfWjrkxJ7rxtC4g6xJwcSincf//9RV6z47jKjUt1IpRhu8qQYkHxCcyZOW9HyAIncEZ86NAh7Nu3zxn7kjMqaWOSJrnURlusUhudfIG66DJjWfkmvkmvFKNv79696HQ6QR+rvG4OVxxWDv6BHboB8L7hqmshH5ExcVhNNM1QXwI/PDDppb4oG4e1LEgY4DQQYvrUN5ckQWqLOl06B+Cc1vrxzb8/io0P2MtqU9V38/8rm+/PA7if5b9v81nGNoBtz5X2DHpGvF/ao1w8xoeQwzsTrvjs/F1IjGcJXO7Yt2/fyMeOrSxTLjBh7i9S+2JjlfK90UWTdMAZsg9z2MoI+Vi1gdrmo8Wcl51OJ0gNWYqhK42PbQ8q8wHWRO+tJsxDYg5qc6o4rKHgcn2Zw55Q2GJQN23cEsgFIXt8kUZt2K0eBDCHDXnis1rrq1rrZQB/jw15wolm9WBGRkZGRlKkcLqktb4E4AWl1HdtPnoVgG8A+BiAN2w+ewOAv9v8/TEAr1cb+GEA15nqcEZGRkZGRsaUkEAu+BKAFyulHlJK7QTwWmzs+xxcPvh5AJ/RG3rRnwLwr5RSezc/ZP9nbMgTbpoj2tcaxNr1SahLtzyEBimNK65pKoTa2VEsWRdC7FCB5hmixyC0jQCcdkAcdfcHt38hWsp4V+aoam9aB1z2wjEoG3OtSUh0wwoA/w7Af1dKPQXg+wD8RwBvB/ATSqlvAfjxzb+BjRPTMwBOAfhzAL+csk0ZzUCT1rwPVeWCpnn5BOrv/5j4qiG0hNqUthmhbQzdNyexxiTfHVXisDZRJvDZFYfCt+c3LQ67DVXlgk2b1Ldg4+PzGQAf0VqfVEr9vlLq1ZvJ3gfgqFLqFIBfxaYZkdZ6HsC7sPHR+ySAr2itP+6jubLuWq/XG7MLc2E4HFq/3KV3/Bm3A3CV47IJIMZKecmoP6QMnx1ESNqQMqT3vV6vUBsaDAZRaoeuviqDhYUF0S4YAM6dO4f77rvPahNooqpNgm38XIgZRxdcNrMmqP/5OEqIpSu2LZyZdjqdwtGETY0lpN7hcIhut4t+vy96/0zR17GwecO2rQUbnUtLS06V8cFgENTmafUDUH2NEbTWTwJ4ufDqVUJaDeDNSSrOaCxsH3Exe45rbfjWkvneVZZPLuBpJPqrrqMqMoENnOem6nOOULpu3rzptIWdm5vD0aNHvXGtUyC1vBOL0DZS30r7ppSuTvB9jNZDv9+PisUqrUuSDWzpJolU629lZcU5Xk082JKQYgy01n+PjcNp/uy32e9VAP/Gkve/YSO0TTAqf7DGuud2MRKfG3bewWUZknlyYPvYibFRtdUTWm4I+EdOrI1caJ2h5bo+1O677z4Ak2GyQDkbz2l9OACybVQVxLbF3EyVUqXsS6U15HKeNGnY+jn2kMtnU20LxRBa/iTQNNuZjK2DmP3TBtfa8K0ln61gWRqq7v8hdaQo12UrWIYWE6Fl+j7SfA52UqJt/K5siJuUMPcxpVS0jakkn9fhOKksfHb1ofDZVMd85E8TbVsnQIIP1oyMjIyM5mKaH8sZGRkZGRkZzUIb5YL8wZqRkZGxhdHGk9SMjIyMjIyMetBGuWBqFDfJGHsaaHP720h7ExxubWe0xa4jFm1oV0KnSxkZGRljaKNMEIs28Po2YavOmba0q41ywdRuWJvYGZNEm9vfRtpjaG5D7NO2oY2neSFoQ7vauF4zMjLag+3AY9rA69uErTpn2tKuttDJUXkFxt5Gudxdpz6ZMMvjrtd9dcW0S6onJK0tnc9tdl0nfaFtvn79ujdNShqr3nhqrScSFshWt/R/TF7b377nEp577rmxZ2fOnAnOTxgOh0W95Hl4bW3NSsukXb3Pzs4CGO8bTjeHjW5f31y8KIcWDR2TSdzmp4jDmpEhweUlOBSh++UkMO3bEVf9Pl5RB+2h47i8vBxURioaq8oXZhieaYx7itAvqei+fPly0LNQWmiuusLLTUOTTZJdbSGZbHRfunTJWcf8/Hw54hgmcZvfRrmg8g1r7G0UfdVPwpOmy4Ogr66YdsV4Kgyhwec1rq6JFNrmEK+yKWmseuNpqjdM8mTJnO8Bsa2saVN4mXzooYfGnr3oRS8Kzk/g40vepV0ekGM9W1fFHXfcASDM+6eUjuDrm7vvvjuqPBOTuM1v40lqRjtgW091egmuE9NeK676fbyiDtpDx9HlUdYW6aEKqsoXqbxLp6ChSt2p6L7rrruCnoXS4oocYKaZJCTZNVauOnbsmLOO0JCOLkziY3HavK4MstOljIwGYZJMZJqxQTMmhzzGGRkZGRkZGYQ2ygWlPuPNq/KbN2+KaUgN0LzeHgwGuHDhAgDgxo0b4ntCzNW4TcXgkUceEekDNoIAm+B1Li4ujqk+AsDq6mowXbx/Pv3pT+Pb3/62k2aq3/yf6JD6hN7x8ubm5sbS8faY4OobUr9Q2ZIaZIwq0/r6+lialZUVrK+v49KlS+J7E7EqE5w+Ul2lMmzqTDwPV9NeXFy0ppNA86bf73vp5u+Hw2GQ+nUMQlWITBVeU31pbW2tGNcXXngBgKxuS/32rW99K4qmUPVnV3toXPn6W19fH5mP9Nu2ntfX151r/etf//rYMxs/nBbaqPqTsTVB601S1b927RqAjb1Aek+IXUs2nvvkk09a85h7EK+T7xecl/hMeTg4T/nqV79amDBQGa493ve/CV7WjRs3xt671Hl5+6R9mcrmapChe7OZTjIbWV9fR7/fDzIpqcJjqd+5HLW2tubNx/vH1Y8muGzno9tUXZb2l6pIoR7d6/WKdnGTHLM8Gkv6BkiBWJr5+jPlMvptW8/9ft+51s+ePeutk5DlgjiomA47fvy4PnHiRH3UZGRkZGxTHD9+HCdOnEh+7Plbv/VbXib/B3/wB+07bs2YOrJMkJGRkVEP6pIJgHbKBVNTCXapI05SVXESdQ2Hw5HTimmqYjZBDdQ39kA71RUyxkFjba6BSdZdZzlSu5qwxjiaeFKakWGiKTLBJNCk9jSJFhvaQGNGHKYxppOo01ZH0+ZwG+WCRoa1mYZTnDphToxpTtomLJimjH1G/aDxnAZzTDWXXOVI7WraHG4aPRkZErbTvtCk9jSJFhvaQGNGHKbp6GoadTRtDjeNnhC07xN7CyAHoM5IjRB7mypoUuiJUEi2XSG0uuznXGmb2kdtDBCekbGd0FQemtFuxNhVx6Kp+90ksBXa10a5IHsJngLaeBUfgqapPGwnuELKpEAbbz+ksAEhtIaGwokpe5p9tFX5TUbGVkFTeWhGu+ELkVgFTd3vJoGt0L42ygWt/WBt8sdRk2lLAVv7ptHmrd7XqZH7a/shj3fGdkHmb9NBk/q9SbRkZDQVbVwjST6xy6gjkqrCYDAo3FxL1+w2dTze2bZwNuRmXCr3+eefd9InuVHv9/tBahDmROAhVAaDwVhoDRPPPvuss/xQSGFwyqpx8Hcf+MAHAIyH7wjNX1WVRGtduNgv446dhwmKCalCCHGxbyszpu183oTQFQJJRSgkjJCJwWBQzGEK87O+vj5GI7l4v3LlSnQdEkL7YDAY4Jvf/CaA8VBB0tqzjSmF2rBBel+nGlYZtNF9fUa7UWYN0Lq0rVGCiwfwvdcmO7jkFRefkmhy0ekD359tIQA5zp07V7ouW92pynj00UeL32b4jkmpT5bZlwllTEVMlA21V1auq5qGwybvxmA4HI6FCpTKoJA3qUP2heD8+fMAxuVGaexsa3tpaclZhxQ6ylXetNBGuSDJDWsZdURSVeh2u8Uz6Ys/pNN4GRx79+61lvvggw86y5yZGe8a6RmHy9ia3nW7XSu9hO/6ru+KKt9HD+/Dsmoc/N0v/uIvAgD27dtXKn9VVRKlFHbu3AkgvG0clKescbxvHrjKjGm7aUeQ4kSM+s33zAc+hw8cOGAt54EHHgAA3HnnndF1SAjtg263i+/8zu8EMDpHbPzENqZHjhxx1iO9r1MNqwzaeJKa0W6UWQPEU3x7fiyfN+GSV1x8Stq3fXu5C3x/Jlpdbb/vvvtK12WrO1UZr3rVq4rfu3fvTl5XCIiHl6mvjKmIiVgBP0QmKENHmTLLyLsmePtJ9pbKuOOOOwAABw8ejCo/Be69914A4/Ko1F+2tb1//35nHbfddpv4vAqvqANtlAtaqxKcMT20caJnZGxX5PWakZFRJzKPychoF9q4ZpPc+caoFA6HQ2it8dRTTwHYUM+RVCEJLvUdU6XCLIfUkqQySAXApzbE3/uu9G1qGNRmYEMN4tSpUyPqTyZsKgUuOqS6qZzQ/gxVP7148aJYt00911S9df3u9XpjfeJS2y3TNrMMU31Nqo+rtkhzw5Wft9/WNkmFTmtdqLDY6IpVvVtaWhqbP6YKlwRzjXNV/kceeQTAhmqwWRbl+7Vf+zVr2SGqMrEq1Wtra1hZWQEwqno0GAyKv7kqkI2HnT171jmv3vrWt449++xnP2tN76K5LrRR9SejHbDN3xh1QuKPZKbj42lVVe6J39h4Lv9fqpe/86mB2vqHP19eXi72VG6uYoL4mQ0hKqlUBi/f1Z+hJjfz8/POvCFl2NINBoMoNdgybbPV60vP+1yaHyF12dpm2xfn5ubE5758Lqyuro7NH986M9c4V+V/8sknAWzMN3NvpXzve9/7rGVzmTkEIXJBr9cr9nJuzjYcDgsVZp7exsNmZ2edffPnf/7n4vOTJ09a80wDbZQLVMykOH78uD5x4sTY8+Fw6GycZATf7/cxMzODXq83pj4UazTvS1/GCJ/y+NoWSx/p+Xe7XSilRNpSOQ2QyhkMBlbVBEpvq5+em2OWkl4qy1dein5z9UVs3XXlK0tjVfho5euC1rIL0jqvEzZ7chvK9vPa2tqYimFIf0g4fvw4Tpw4kfzY8x3veIeXyf/Gb/xG+45bM6YOm0xQZk8mniKtxVR7TBX49scyZdFvrfWInFFXe139HppeQp371CTHvoq8Nyk6U8ikZRCzpkPmw6Rlm9jxKdvPNlmnTHvrkgmAdsoFpWa9+ZEbe8pIN2mAfDIXa7zOPwalekNPt/jJFHeS5Lsd84Gf1PT7fe+NdBlj/NDbN1ffcqN5X7rQMl30me+VUlhbWysdpzb09k1rXTAP6WRRAk8T64yA5lCv14s6/bQ5A6gC100z4L594OA3tdyBmm1uxDp2CrmZcIGf9ppjx8eAypufn7eeoLvqlG6nbYc900Ib461ltBtl+BbxVcmpSZX1U4WXSFpHnCeUpcvU3Aq5NU7BQ0K1s/g7/s+WVxrvFHuXTesqJn9oOv6B0uv1om51gbj2Utp+v1/aWVNK+G6FfY7QgI3bVC43Uz5J/gf8clTqdnJ5ypR5pDGwOVfy0WVby03bZ9soF5SyYTUb4rtNME8plFKFYf7tt98+lr7sqYtUj/Scv+Nt4fXSb35SYivfB17Gzp07sWPHDqfBvc8pkAQpreQEx3XL5XOARc/MG6WQ8fK1hd7v2bPHW5atvJh4mUSz6SDCBl527E0h1RXr3EgplfxW0jWuQDitt912W5GP+lCaB9Rv5IRBQszJZ4zTJZMGYJxXUXlHjx4t5fSNHE7Z6jbrmQaaqNqTsbXh2xOk9UC8TlpTVeZwWed6Zr3027dPhoCXMTMzY+VLvmexkGQ111iFOsCS9qkUfIc7WCybPzQdTxu679rkRx+ob8po4oTUE3ub6JtvISqie/bsKfJITjHNcss4a61yi21z0GnrT5vzJF/9Nhm2aftw0+gJQZIbVlvoBzpd4ScpdNtEoTBOnz49diIh3YDY4Lr54qdYJv1UrnSrSjdwwIZ9CaUld9yUNsT2EcBIGJvl5WUsLi463dibthBmuaG3dKSvz/O77GD6/X5xYyTVQbReunRppNyQG3ZOw8LCwsg7YKOPer0e5ubmomwaTfpj0lNbQ063OU2XL1+2lilhZWWlmFO200apnpWVlVIho1yQ5jufi6Fjyuc02YNIp6mUxuXCXupHaVOIufUfDAZF3/E22265bTfA/AZeopPzBJ4nhva60caT1Iz2ga8P3+2EGWoKuLU3Xbx4cWyNxob3ct18uXic+Y7vwfSM+93gfI3LDT7wm7W1tbWCh1J+qY2ud7b2mJDsLF3aL1zOcfWXtKeHaFVxmGHygI1+WlxcLK3FFWvPyWWBkLycLuqDkPlJIeB8Gk9mHSQn+VDG9Mis35xvvnbxOU17oHQzT2mk8fYhVnvJ5APSXLbdctvkSd/tO33bmJDWWda8ikOST2zppIQWo1Jq5Eu+2+3i8uXLRUyxO++8U7RXIbgmqE/HnPKaC9w2GFQWL3fHjh1Fffw2eDgcjp3C2QaYbFbp99LS0pjqBAeltTEKqc0ulRxOlyuWrNa6uC1zLSRqN6WJtTflJ+j8FlwphV27dpVWj3JtvLx+Kn/nzp1O20peB3d+ROF8Qhf0rl27oJQK2gB5mWZfxAoA0jvzlFFrXZzy0hiYdADj85SfuBKDdqnPumKXhZ70dToda/lSGbSO+MYzMzMjbs62sdy5c6dTG0L6YJUOGaZ9w9o25woZ7YaPp5q3NwsLC7h69SqAjXAXodoyUtnmfmPyDHonCaRm2dzOlH7zWzG+l/F93getb9msaq1HBHwqy4TZJ7Z2+eo10/pi3pr7vYQUtoiSFg7dsJaNsRqaj/qAxjbU3pA7QCLZKWQcSMtOGmfbHKR8dcTzdO2ftv3PpJ3LD+TEyHXQ4HMilkLrynxHbeJ92O12g8aB4LsVtx3OS3JtlgvikEQl2IzHSWm4IMxx7NgxHDt2DIAciyk03qlZrk2txqVm6outxjde3u4YVQ6udrp///6ROE4uVRpbbLZQ1UWp3TY1B5MWqX1Ux9GjR0f+9i06kzaJVqrPF+PKVYZL7ZSD2ulblLxdFE80hkYC1SGpNpl9Z6qg8bpC1apd78rG5DXXCZ8r99xzDwC3ulmqGIJlVIL5gZqpYmaqMJnwMe2XvvSlY88knjbNjamJJ6UZWw/mYZsE255x6NAhHDp0CIAsT4QITzbB2vbh4VI1de1ZNpXgmI82zj93795dyAjEQ11mBbZ2hqxzqc2h8pGLJi5XuA74pLy2v6nO0H6V8oeqnZofZ6F1UkxRINy8iNcTIkfydimlouqJpcf87YK5RnifUXxyl5mQZA5YJ2xmXfxbhcM2Nr7+4bIih8TXpok2ygU5DmtGNKblpW67YVoegutAFduTacM139uwFtra7xkZGe1Am/l7m9CG/WY7wDXf27IW2kCjial9sLZx4aWYiKFqtE1G1XGTxp76dtLhT0IxDSbUxI/Vsv2wVed7G3hYG2jMyGiLoFcH2t72FLRLfUCmZU3cC4HJj1vm5c1AjHZaU9HGuTQ1iicxqNM0aLaB7GdDDcWbiLL2JASX57iQj9XQQOIcPH2Z/m0LE6obsf1AtiEhdsmpkSqsggvSWmja+m2jc4WM7YdJzcOmrU9g1Fa2jUhhVymNv1Iq6GO1DK+vKhMAWS4oA8nh6aTrrhPTaFcZtFEumNoHa6g30ipI3eGpyiNnVHXXUxemfdrZ7Xaj+6iMjca00WThxeWFk8Nmhz0JcKcmHDH9GmufHVv+JJDSuYJSqquU+qpS6v/b/PshpdTjSqlTSqkPK6V2bj7ftfn3qc33D9bSuIwtg1BvpFXRRP7fds2racsEZfaXNsoEbYFrDYfaONeBSdRpm4tbUS5QSv2UUurZzX3+bcJ7pxyglHpAKbWklPq/g2gObJsTVU63Ql2Wx5Rp/l1loqSYZOZJnsvbad20pOhr8oJWxwIMLVNKV5ae2HxV+jCmrjI3ydPAJG8HYuqISVt2TEO8eU4biU9SfwXAM+zvdwB4t9b6YQDzAN64+fyNAOY3n797M13GNkGV/Y2HkpsWLZPGNG9YU9RZJkRJKNowfsBk5A+XJ/5pI6VMVhdcETJC05ato2mHIlXlAqVUF8AfA/hpAC8B8Dql1EuMZD454F0APhFKcxLJqoyARidyhw4dSibg2bzOVZkoKSaZdJI3LTvAFH1N3hzrWIChZdrUh+qsk1BHEHsJMzMzjWNyhNg5neoEPqY/bHRJZZQd0zKB3yeNVB+sSqn7APwMgPdu/q0AvBLARzeTfBDAz23+fs3m39h8/yrV1MmckRxlhprW4L59+5LyvTZNu7beOtXpAbUt4zcJ+YOHjmkaYmWCaRzs2uhJKU827cDahgRywQ8COKW1PqO1XgfwIWzs+xxWOUAp9XMAngNwMpTmdvRsRkZGMFw2FG2xrygLn1ZF6hPfpp0gSwhR/VFKvUkp9WX2701CUf8JwK8DoGvlowAWtNZkyHsOwL2bv+8F8AIAbL6/vpk+IyMjI2PCcGkR1Wma1wRorbNMYCCBXFDs8Zvg+/9YGi4HKKX2A/gNAL8XRXNsIzcrHvnbpg4i2alqrXHp0iX8zM/8DAAUAbM51tfXi98uZy1ra2tOuuhvl2MUSYDXWhd5VldXi7ScrhgVgps3bxbv5ufnceXKlaJdEqOgd7ZFJtEstZH6h5dh9hkHBXu20UXlLCwsjPztY3ZmG/iY0rvV1VUMh0PMz89jaWnJWZ6tzlDHPryvBoMBzp49a6VVAs1Zm8quSVu/38dwOIz+WBwMBiNrK7SfXbeZijn8iqHHrJu3/fLly8Uzcx7SmHzsYx8LrssFnz0qnQoOh8ORNSzRROn4/yZ8DsY+9KEPjT2bnZ210jcNhJykaq3fo7V+Ofv3HqOM/xXAFa31E1NqRkaL4NpngHEzmYWFBfze723ILhIfN3m2C2Z+G89wlWPyA611kZ6Xz+mKkQk4T1paWirMbKiO0H3fRXPoHhkqY7l4Gd+nyppi2WSxpaUlUU6U0oaUKYH6iss0sXyc5EOX7MTrIw/IMRgOh8FjYiI05nwoTVKbCCQjSnIPrZnHH388qJ4UILlHWsNKqREaffPXN6cee+wx8Tmt8aYghVxQAb+LDVMiv7DPUEqfzbwqtqmDUDq+UJRSOHbsGD7+8Y8DkIMt8+DWLq+xZlBom+qfpLZH72zBsCkPp4/TFaNCwPvn8OHDI+8kJkJttpUn0Sy1kfqHl+MKpL13714nXVSOqRLsU4Ew22EGbQZu9bPZPza4+s0H3lfdbnck0HOIGgjRavswNGkrqzba7XZH5k5sP/toi1HTdQUJv+uuu4o0trH+2Z/92eC6XAhR7zXp42tYKTUyT3xOoXxj97rXvW7sGQ8m3wT1rUQ0/AiAVyul/jWA3QAOAPjPAA4ppWY2T0/vA3B+M/15APcDOKeUmgFwEMBcCkIymg/XPgOMqw8eOnQIv/M7vwNA5uMmz3bBzG/jGa5yTH6g1C1vtbx8TleMTMB50v79+4vfVIeUz9duk+bQPTJUxnLxEb5PlTV7sslivH9cKNNnBOorLtPE8nGSD12yk62+UHQ6neAxiQEvJ1Sl1dYm4JaMKJVFa+aHfuiHoumsAk6LOef5PPHNX9+cesUrXiE+P3jwYBCdk0KCuUN7PIHv/2YaUw74IQA/r5R6J4BDAIZKqVWt9R+5KswqwRkZGUngYoBN+HDbrkjhDVBr/e+11vdprR8E8FoAn9Fa/x8A/hHAz28mewOAv9v8/bHNv7H5/jO6CdfNGRkZGRkZ2xwJ5IIvAXix2ogUsBMbcoGpSifKAVrrH9NaP7gpT7tHxqcAACAASURBVPwnAP/R97EKlPxgNdXkLly4MJaGrt+5VzNSce31ejhz5gwA4Omnny7USSgdv67nari8bGBcnc9UbSE1khs3boyVQeVyNVjePiqr1+sVKgDXrl0r2jAYDMau+G0qQYuLi0V9V65cwbPPPovz58+LNAO31GtMNRWiQ1JTlNQ45ubmRvLxZxLW1tYKOldWVsbaQeV85CMfGSmX+sSlQsFpWFxcHCmX2nTu3DksLi4GxXmVVM54uSZ4mWtrayPqrDYVZN6nkkoSlelTUyW1mH6/P9Y2SfWMMDs7OzLHQlTO+NqQ0vN+I7VeGnOu5mvOS3MuXr16tch3+vRpABtrysx39epVAMCXv/zlMVokmjhtJlweEs1+XFlZwXPPPVf8Jty8eXNEVZ7yXb16VZzDc3NzTrUrSf2H1raLvkkigXMFF34DwK8qpU5hw0b1fZvP34cNW5VTAH4VwJjL+4ytBb5mr127JqaxreHBYIBLly4BAJ5//vkxnsDL9u0PpkqfyUtsexwv26yfqzaSXANsyBac54Z4y9VaY3l5uajr+vXrOHfuHAB5zybQHmS2j2gx80j9TLIQ50eufbPX6xV0mrIY55+f+9zniuehvG44HI6k5bIY0T43N4fl5eUg1V5JlpLkO4nOXq83IuMsLCxY1ZB5v0ryaUj7qe/4XJLKN3H9+nWxn1w0hjw3zWS4WVzo/OKyG63lfr8/lo/m26lTp0RaJJo4bSZcsqc5VmS+xMdtbW1NNFNbXFwUy7U9J5w8KfsQkmTvaZ7hVpULNrWq3gLgU9iIHPARrfVJpdTvK6VevZksqRygYjrs+PHj+sSJE1Xqy8jIyMgQcPz4cZw4cSL5VfQHP/hBL5N/wxvekK/AM6KRZYKMjIyMelCXTAC0Uy5IohIcciNGMG9DpROTqrcRMadLPqcInJaYdnLw05ter+eNpeXrE+nkL/SUztWG0Fhq5CDJBfM0idPD89JJFz8N5+9tJ/VVTqZMJ2AhKHPCz0F5fCfFPK/ZFylu6fjcIZq4BkKow4z19fUxZ2TSSafk5MBE2XXlo0+6Abedxtr6lm6IbZAcKYQ6/5oUar5hzdjGsK2bGF5l3oTG7NMxqCoXmHsVUJ4n832Aa7a42umjU7oBDYGrDSHOjoBbN5Q+SNpuBJ6f7wmmvBTilDEWPi0pG0L3Z9eeE+O00uyLVDd0fO4QPZKWnQt8zPjeK2mcAX5ZKHVEA3J+GQpbe10aCYD9Vr9pERraKBck+WCNcSpDetGUR9KTrhrHyNbRZZ0iEHzG1rYJLjmMcE0G3n4SfvkziTbJSYSPFhPc6ZILu3fv9o7R0aOjESxs9JCjAs7EeNojR46I+aosJtMJWGwe33w3y9RaF3l8c4jn7Xa7I/WmiO/F5w7RdNttt43V7+uXnTt3FmlcTsKIZpdjjzpimXL6TAcvLjpN3H777c56eN8RQp1/TQpt3Jgy2gHbuonhVabzvph9OgZV5QLudJFQlifzfSB0P+LvSPjlz0z6yuxtJiRZQ8KOHTuC6pP4pQS+J5gfGaFOmGIQ6szPROj+LMkEPod/Ul7TsWEq3s3nDtGzZ88eKx0S+JjRb9fa8slCqWK3mzSFwtbeAwcOOPOZ/UZI3Z6qaKNckERS1FpHN26SnVGGPkIMcwjZEGPpqCL8xrY7NG2V/gQ2NiCbR8OZmZnSZVelywVebl392la45vc02z6JDaINQcLbQGNGxlbnkzaUaXfThN8UGA6HYru63e6WmxtbrT0uNEkmmMRe2JaxbaNckITiMlfdoaqHKVBlAqWefJOczHXVVWbMeB5XmKEqi2hSfTupesqoyvLT6Bj141jEqsVxWqSYrqlhOvTgiFGp8r1vmvqvhDaepGa0G1XW9DQdkYSgjvXS9Db7kIJ+20d45k+3UHZdpVBjL1ufDT7TrNR0hjpnqoqmqf7a0Ea5IMkNaxmVvlDVw62Itre5zEdl29s8DZRZVzFxVlMd5ITMB5sKnPkuFULVs8pqTRCapv4roY0nqRntRpU1vR33ira3ue30twVl11Xsfl0VIfPBpwqfms5QdfuqaIv2QxvlguTGY3WqZW4F5P7J2I7YqvO+De1qOn0ZWxttWCPTRO6fjO2IrTrv29KuNtBoIsknNr+6D+2EUI+0VRCrlpCiPAlcRcClllCFjhT9WcZjbh311K0eVaZ8SX1lEmpcbVAVc9HIYxdOGpNQ151mfNVQtFH1J6Pd4DwhdH6FeqSdBEL5bln+HBN7sywm2Z8p+GCsh+SUKFt+XR6tY+tsGkKiYEyjHZOQQ9owPkA75YIkN6xlrpb37dsXlC6VwyQbyngzjIVLHVKCr83Su9D+dCG0fVVVHqqqYVZFmfIl9ZVJLGipjiac4HEaXLTQXKnDE7APMeq6Zfu0Deo/bVT9yWg3yqylUI+0k0Ao/WX5cBlP9bGYZH+m4DGTUtlMWb6Ur6m01o1QmSDUO3IdyI4Yb6EtdHJMXoqMRFMXZ53Yjm3OCEcT5kcVr8lNRBM9U6dC0+nLyMjIyGg38j7TLrRxvBr/wZqR4UIbPhi2OrZz/7eh7W2gMSMjIyMjI2MyaKNcUOpO2NTR9tmKSbYUS0tLAICFhYWxdzych89GwJcWcOutD4dDZx29Xk+0WYzRU+ftX1lZGflbsv3g9Uk2r6EuwNfX14OeEXq9ntPmkOqlPqe/Y+1XpPmytrYGYMMWN8T2kOrmiy40DAzvv5WVlSD6edk+d+wh9YZgMBhMzR7GVy/vAxovrbV1DGi9TxLSurWtdxePcM2rGzduBKWfpl1Lp9Px/svIqAo+x322YhKPp31R8scQu8eE8GhXmS6ZgPPlsuuat399fX1sX05hGymll3iTi78NBoOin1yyikteCIVtzqytrZW2PSw7b9bX1yuFZqnLBtq2f00CMb5HXGHsCE2xWbf5lXHNHdd8XFlZEZ83zd9FG+WCUhTFfplzWwoatF27dgEAbrvttrH0Nns36SOFpzXporokvXV61+l0xPY88cQTADbs4Oh9GUcSwK220u9ut1uUJU0Koo0bP/uYhVSOZMO3c+dOaxla66KvpD6jNhOjMWOnhgosEl3UR3v27Bl5b1vk0pi47CRt/bd79+6ghcnT8A/3mHkgjaNrXEPtnWOeX7lypfg9GAyglMKFCxe8+c2/V1dXC0GHxrrf74/NG8rnsieN2YBDmf7169dFfqG1FuemzWnZ+fPnnfNK+mCNsWuaVBzqtjlXyGgH+FqKmUecH5g8Ys+ePWPpbbzQtn5cNqKuvVfiGYTTp08D2Ngbq64ZzlN27NhR8E1X/bHOCqUypH3dt2+6bA6pDukAQqrfxb9t9oU7d+4ceRfTD649VPrApPRc7nPB3FuAjTbWZQNtk1er4vr168VvGqNr164585jjsL6+PnbhQTKGlM9nT5r6MmB5edl6ECTNS7pEMTE3N+ek3fbB2jS0US5I8gntO8E0GQg/JVpcXBzLb/M67OtA86SQ0rtOEKWJOhwO8fKXv7z4Tfl5/TEnfryOfr8/cqIj0WZ+AJpqr6ETSaJxeXnZmp5/SLtOeG1pOF3mBmPeckll0w0df+8TVEJvWM3+45uL69bZrI//DnUk5DroqOqAyrb2bPmOHj1a/KYxuueee8bK8wk/nKHRYYPrkMPVV9I8tQk2oad+Bw4cEJ93u12RFqWUWCfvGwm33377yN/80CcEk9gU2niSmtEO2Oa67yDN5Il8j5AEy1g+x2HyF8oTc8OqtcbDDz9c/LbJDaEwD3ddMhBBkgs4QvpC6kebYM7rtOUNeWcrz8xnkznM/vEd/vH3rjGRZCquPRaiseX66A3NVwdiy+cXR0T/kSNHnGW6ZAI6fHIdcpT5YK3CB/bu3Ss+73Q6UXyMy1ASbLJH0/bZNsoFpSjiAr5rYVODzVOoTqdTTIS9e/eOfdjyjooJTWETiqVbH/7BItHNbzmpXJ42RijlH4LdbnekHIlm6l/ucW1tba0oIzZcBx8fl+dAznBct0QmfQSbGoj5MeAqe2ZmZmT8h8Ohc0Mtc5rKT0A7nY719o/nkcY79NAi5IZcqrPX64kq7zGbtwt0cBQSskcSqKhuUvddXV215pudnbXSIdFqu2FwtYu3YzAYFOPDn6+srIzxL2B0HfI6JJMFDn4yTXRL6mSTuEm1oY0nqRntgHkYS7DxRml/oQNZWiO7du1yCsex6qG2vdp1w2rKBZw+pZSoVRQj4HEZiP92lUX7Pu9DbrIU0i9S+1xaV6EH5S6ZxKWe7dvLlFJjN9paa2d9Ifujmc6UPTudjlfTj6fndYUcXJTluf1+v1JolpB9iPZZ3x7mkglIC0+6DKB85t5ZhtaQtHx9SJdo6+vropxlu9X3mTfZtLXosiqU7rrRRrmglNMlzuCUUtaTCwnmrQxXl5U6KCY0ha0u6STIF25D+tiu4hKbnyr5yuF9Ij0L7RNqG2+ja1MN3XBtJ0yu8D38nW0hSIuk0+mI/SGNrYt+WzrXorS9o/6s4yaNpzMFCanNrr6UII2R1DfmujDL42GU6HRW4gOU79ixYyI9gDyfXYcaNvBxtc15U92QyuTPeT2HDx921nnHHXeMPZMEwGky/yZuPBlbA7Y1J/FsG8xbGd/+VmdoCmqPS1tESl8G/NBU4sUmpH7hz0L6hdJU3QNN2G6VfHWF9J8kv7n2jNCPbCldGXoIkwjXEqrVFSsXhMwHc36Z6fhlCO2nLtnNt7eGrkHXc/7OJoPa5CzzO4ewf/9+F9k4ePCg+Nym2TUttFEuqFUlOASu27NJ0pERjtzPk8E0+tmlEs4Re8ufQp2sLELqLkuDT6WtCWij6k/G9kUsb8nIyJg+qtz8NglVZQJbeU1DG+WCyhRVFfRc6iixSHliUKdHr1jbiJRIUe40Pz5SQFLLiKG7zNwo0y+TOgGTVKh8dUuOsWJsTjhs/Wk+j9kQ19bWRtT6OT2Serqtvb61yh0sVFFbrBNtVP3JaD9ieR6lr6JVVTfq2t+mzS+qtqvtMoGJMjS3sZ0pEeMYy5fGJhOE2Hq7wFXozXJNG2jbvuhbq+YlXIyq+CTRRrmgchzWso3ik2I4HDbua37aah11TZYU5brGq4mT3ISkYhSDMnOjyf1SlTaXOlvIB3CojVmMKuCuXbvQ6/XGyjAZMdnP2UD2ZbY0NnOIOtUWY9E03pqxPRDLV2LW5bRQF03T5hdV2zUcDq1taOI4+lCG5ja2sy6E9IVv3w3JE7u37dixA4PBwKve7IJPJrCZQzRtH24aPSGYGsVkmExOmOpAU068TIc/NqPs1JDqKeP8x0SdE30SY7aVVM6a0JaQWMgpVf9DQQdDppMlfkIaMt9ct6xnz54de0a3rk3hP208Sc3YfiBHLU39WE0J05RgUjEp6+LDdQu/dfPSad9wp0QT2hJymzgt2aXb7Y7JLCFxmzlc7bM5mCQ5JMsF5VH5hrUsyGlLnZ3SlA43ndpwhzV1QqqnjPOfSaKuerkQ1GSVs1g0oS2uWMiEGCcsqTAYDDAzMzPmPMHlHMyEz5bj/vvvH3tGDieayH8yMpoKctqyHear6fDH5b0/JabBh1Og7jkx7RvulGhCW0IOMKYpu5gyS6ijLnrv6mMz1B2B5JCm8Lem0BGDUsdi5ulCGd1syiO5ei5bts2WTnruO+VIeQrC28Bj0NpgO20taxNk3vCGwJXOPBmzxVW1/e0q23wXYwsRGnPNV2dofVvBzsU8jQ0NkeBbt7GwjYEvRi6fi8vLyyOhbGhT4nTeuHFjJD+ttZWVlSDb8lC6CUSP1I4y9ZVBG50rZLQPqewgfbwnxT7oep6q3tCyQuWSkHjhVWkpm07iZbE+DeoYh7JjFiKnpa6zSSgjh6W+2Y2dbzZauD8LqUzuhwK4JVOsra2ValPMDa3UjkndkLdRLih1w2o2pEzDbOEzfHWFpLXpuceeoqQCb0NIe2ynrWVtgswb3hC40pmntK7g0NLfMaF1Ymwhyi6w2HyhjolceZsCmy2H75Q21L1+KGxj4HPKxk9puT2pzU0/D5AO3FprZribUPj6id5L7UjdhzY0bc5lbE1UnWehvCfFPuh6nqre0LJC5ZKUDipttJRNlyI0WR3jUHbMUoQrajPKyGGpb3Zddfr2Tk6LL3SmuffTXC6rjRAyd1zfP5O6IW/jPJ2aSnBGRoYb28GWq0400ZnbNJDnUEZGRkbGdkeWqW6hjf1QSpqbm5sb+fvSpUsjf9uuxPnzpaUlAMDzzz/vvN4PUdekck2HAlSuVAZ/J4XP4OpC9Ns0mg69uh8Oh4Xq4draGhYWFsZo51hcXBTLofpsxupmWaTqwFWJlpeXrXQOBgOxz0yj9Mcee2yEHnrvUm164oknit+cfl4ur9+HWHVniTatNVZWVop8pjMeTgt/R0b1/JmLbppPWutgw3tirGVVqK5du+ac98AtdVhqx+rqalGmbxzOnj2L9fX1og9t9FAd8/PzxbOQNU19zMuU1JD5WuTvLly4gKeffhr9fr/gNUQ3d+5C7Zybmxtb18PhEKdPny7yS6r6f/EXfzH27OrVq2P0udZd3Wij6k9GO+BSt4/NT+vr8uXLwbw8BDYTFqkcvqfZ9hb+jvhHiFkKB98Ler1e4SDRJVPYeIi0f/F6OEhG4vzd5YiJy0cuM5+TJ0+O0SPlMXHq1Knit80ZXhUVybLqubS3AW4TOP77+vXrY89stPM0saYhVULG3LhxQ3zH6aF5Sc+oL3zjMDs7W7TFJefQO74vS+n+6Z/+aewZycbm/LC11xy7559/HoPBYGQvn52dHeER1E7eV3wNXLp0qcgvyeKPPPKISIsk10/DGSWhjXJBKYqOHDky8vf+/ftH/qYvd/Ojgp6vra3hH/7hHwAABw8eHCufL4wY+0hTLYWeSxup5HiF0pPbamB0QnJ31lrrsat726JZXl4urv4XFxdx4sSJYmFLTIBUf2ybn6RGIJ0c0UcCn3guoaLb7RZt4h+5VC7Rc/fddxfp6Xmn03GqNr3sZS8bodVWNrdDDAGvx9U282OGPo47nU4hCPDxNA3rOYOTjOf5mJjzgKuku0LAmO3ynQa63h05csSrqk3qsJRu9+7dxXtzjpmC2f3334+ZmRkopfBXf/VXAOSNg8r+5je/OfbMLJuDHBdweiW7Iq7Wx9PecccdOHjwIGZmZvDkk08Wzw8fPlx8YA+HwyLP/v37x1QSO50ODhw4UGwqkqr+G9/4xrFnf/ZnfzbG8+pS5wtBG70BZrQDNnX7UD8MlL/X6+GrX/0qANlZoO0jwQWqy+Rl9Ny0XQPGTQn4OuYHWqY6Mb03eZtNJlhbWyv4zMrKCp5++umR8qQ9kOQbW5khpjn0YczTSv3Ay6QyXHaqXCaksjl/teHhhx8eK8ssn9sgxsJ1UMjrm5ubw3A4LPpdKVXwfZd6LJcPaXz4expj27w3f/tQ9Ybwtttu8+Y35ZudO3eKjob44Q6wsWdTGvrYNA9+gFv9eeHCheKZSdNgMMArXvGKMdoOHDgwlt71AW+Wu2/fPnS7XZw5c6Z4tn///mKs+Zzl8hCX2/bu3VuMu6QK/853vlOk5ROf+MQYrZMyC5LQRrlAxZxAHT9+XJ84caI+aiaIaaoLTlMtIUXdTVC1bAINGRtwzSkp5tmksLi4WGxwdUFqe9k1dvz4cZw4cSI5Y/j85z/vZfI/8iM/0rzdKaPx2EoyATC9vbntMkETVC2bQEPGBlxjMU3ZbXl52Ro7PRVsbS8zP+uSCYB2ygXb1oZ1mh87ZZlqv9+vfCKTgqE34UOxCTRkbMA1p/gJ86SFCdPBkkRH1Q/OWMci00DT6MnIaCqmtVaq1Fv1AyBFm5vAY5pAQ8YGXGMxTdmt7o9VwN72ps3PptETgiQzJzb0DLffSFl2DEJsCE2UDXTssu2UYNoEE+hjtS6bjlDbk5CxM/PHqnGFIjS9awxCypD6pszcLGtTE4IQW5hU81pSz3XZUMW221RBjgXZu5oqSSaTpjHkdmA8zfPPP++sh+yWOCQVO2muVAmdEIM2qv5ktAO2+Rszr0Pt5suUnRJSvSlCU4W2h/sB4OAquKkRSlvZfqhjLCc5PyS5Ylrz04YQedFlzx0KSf23ii16Cppi6wTGbdzNNly5csWZn9TuTUhys00NPssFMpJ8sMaGnlFKBdt01XUaE2JDaKJsoONYe4Vjx44531dRsQw9+XKlCxk7M3/oOMYukjI3YWaekDKkvikzN+tkAiG376nmtWmzDMjzsmxfUVvK9tfMzExBY8gNMNFntuHBBx901iPZ4EshcqT2S31YB1I4V1BK3a+U+kel1DeUUieVUr+y+fyIUuoflFLf2vz/8OZzpZT6L0qpU0qpp5RSL3PXkNFGpLhNoLSh2kNNugVNYYMW2p7Dhw8739chK4XSVrYf6hjLSc4PSa5omqAfIi9KNMfKmdJe65qTZWTwquZFMbKeTXa58847nfklG3xAlpttGlotkgt+Sin17OY+/zbh/S6l1Ic33z+ulHpw8/lPKKWeUEo9vfn/K4NojmwjgPpuPTPagTKnqU07dawbKW4emoym84DYW3Dbya1vvELLn2Z/JTpJ7QP4Na31SwD8MIA3K6VeAuBtAB7VWr8YwKObfwPATwN48ea/NwH409TtymgWtgpvy4hHWf6W58zWQVvGMpRO25wuczOcIm1qVJULlFJdAH+Mjb3+JQBetykTcLwRwLzW+mEA7wbwjs3nVwH8rNb6XwF4A4D/J4TmUh+s2X5we6PMaWrTTh3rRlvsGMqi6Twg9mbXdnLrG6/Q8qfZXylOUrXWF7XWX9n8fQPAMwDuBfAaAB/cTPZBAD+3+fs1AP6r3sA/AziklLo7ddsymoOtwtsy4lGWv+U5s3XQlrEMpdM2p8vcDKdImxoJ5IIfBHBKa31Ga70O4EPY2Pc5uHzwUQCvUkoprfVXtdbkJvokgD1KqV1emoNbx9CWk5S2oun92/Tbta2Cps+DOpGi7SnK2ApzPeQkVSn1JqXUl9m/NznKexDA/wjgcQB3aa0vbr66BOCuzd/3AniBZTu3+Sxji2I786vtjjz2GW1AU+SKJiCBXBCyxxdptNZ9ANcBHDXS/O8AvqK19galLfXBmuJUgGJmphQIyzjTmTQorpgLqU5dyjqJssGMLTlNVBnb1P2SAhJNTejnEISs4dg+T9F2miO8bq11VNB2pZSTdu50icql9E3hPyEbk9b6PVrrl7N/77GUtR/AXwP4v7TWI5HQ9UaDm9HojImj6polZ2VNWTeTxKT2pBROorYiqjrzqQNNpCkUIWt4mu3jdZtOjkLkGRftZuxfKtuMWzttpJQLKtDwUmyoCf9SSPokemoxE48GixyTbHbKyDspfQjMDTPEI+40bA07nY6z/KWlJWf+0I986UrfVa/vHfXv4uKiNZ1Ulta6kgdI6RnRUsYegas0lxnnsnlcXmElx0dm+hRzUvJgKx2g+OpaWloasxOVxpieuZwlSN7zqgpWvV6vOBRzOZWiuXDu3Dnx/fz8fLBTKlrXxNM4P5rmJpVCJRgAlFI7sPGx+t+11v/v5uPLpOq7+T+5UDwP4H6W/b7NZxlbCLZ5XeYgetcur0ZYElTZA+tCyMf+6uqq830IfbGqu6FtlvaVEEzzo4W3je9PZce5rFxgyyftmXV5kJX2YPMQxVcvn5+m930OGnPfXJRkgCrzZTAYFPOU963NUdTc3JxYzs2bN6OcP5nfOFtILgjZ44s0SqkZAAcBzG3+fR+AvwHweq316SCaQxL5EDt4wKgXUHpm85hVFq4ONz2BpazXBaUUduzY4Sx///79zjKq2OW56g19J3lGdaVXSgXPkdg5UMYewaQtFmXzxHqFNdOnmJOSB1tJUPTVtX///rG1LNk20zPXnJW851X1vrljxw5xntI4mLjvvvvEco4cOeKsx6xDKYWZmZmx/pvmbXlV5wqbZSgA7wPwjNb6XezVx7DhNAGb//8de/56tYEfBnCdqQ5nbBHY5k4Zm0bfnpwKKfbA1Ajhd7t373a+D6EvdlxC21w2vmVVr69VkFr2KysXxOSLTR8KaQ82D2p99fL5aXrf56B3vvKkNVFlvnS7Xes8lWg5etTUXN2ATz6X6iAZeIvJBV8C8GKl1ENKqZ0AXouNfZ+Dywc/D+AzWmutlDoE4OMA3qa1/nwozc32nLJF4VMJ3oqYxsLcjv281VDlBNIW+zQ2XwpPwFvAS/CPAPg/AbxSKfXk5r9/DeDtAH5CKfUtAD+++TcA/D2AMwBOAfhzAL+cvGEZGVsETTRT2YrI/by9UVaLs4q54bS0NnyoKhds2qS+BcCnsOGE8SNa65NKqd9XSr16M9n7ABxVSp0C8Ku4FUXgLQAeBvDbTJ5wxwsCUD2IWAk0yRZyGvCpPq2vrwfHqc2wI1TFzFTTKJumbvT7/SRx/7YLbLFPOYbD4dgz398cfF5QOmmuSGVIddeBFHVorT8HwLYAXiWk1wDeXLnijIxtgBCzg8z/q6NMzPEmYzAYTPWWum0I0eCT9u8qN6OxWhuTkjUTyQV/j43Daf7st9nvVQD/Rsj3HwD8h9j6knC/2A6etuDfdOSP1ckiUCWy+C3N90l8fGwVYSWGX5TlFaF11MGLyqip14nMbzMy2o+twv+3E+r++Mgfq+kx7f1yUvVPu51lUIoDmqc6bWz4VkQ+basHIfO96XFJm4Q6+QUdHNgEBfNgoSotTbh59yHPzYxJYFIaAxnhyGNSH0L6tul7w3aBS6sz9R7eBpkAaKdckCSsTRm7ANPVcwqkDGtT1WNxTLmpYH6s1lUXeW+LKf/mzZtB6Zri8pvD7Ncy9oguT9g2XLt2LbqeGJjtiFmLsX3gavfCwsLYs1i+QOVzJkw0hpRlo4/nlcqRvGM2LXZrIhvWjAwn+Dwqs6+bXsfrQhP3mLpgCqV1td0MExIKn+fjLVM3XwAAIABJREFUJiNV38bkI8/3dcKkJ3Q9xrbfl16SGcvyBuJNKXgMzyuVI3lc5nU3BW2UC0rdsJoLtYyqCnVGyhvBlB64QnTdU5VbF+qqi3t4DsW+ffuC0jVxkZiQaPTdblOemPYdOnQonrgImOs4Zi1W8TZpnkxL7YzlC661z8uy0W0bF55XoknyCNi0k8s2rKmM9oPPszJrwLQFrwvbeT3U1fayGnc+z8dtQohGT2g+G3wealMgxP9CSL6Y9NKtpCQzVuENNv4USzfPK9Fj853SNL7TNHpCkMQooo0Nz8goC2m+16GK3bQPn1SYRLuyHcgtbNV5lNFctGFdZGTUjdS8d6uuq63ariajjXJBtuLPyMjI2MLIwkBGRkZGRkYGoY1ywdQ/WNtioJxxC1ttzLZae7YSYsamjIOR7eCUZKu3LyMjIyMjQ0KsfLdd5ME2ygVT/2DdDhNjq2GrjdlWa89WQszYlGHAbWTascjzOyMjIyNjO6KKbe1WRhvbWUpa4x7h+v0+FhcXnem5d6yVlRVorbG0tFT87fLmyfPGeh4kb128DKrL5smL0pw7d24kvfk7FpS31+thfn7e2ZbLly+PPSPPvIDsmRQY7x/ywMfbz8sxce7cOasnM972L3zhC+K7Xq9X5Dc93PL+5mWZHoelNtBc4aD0zz333NgzKS2vn8rTWqPf73vHdTAYjKRZW1sraAvFlStXMBgMouZwv9/3el+0zYUQUHlPP/108Yzaac4Ts+7Z2dmiT69fvw5gw9uv2ZeUz0WntBYlb37D4dA6VvSc/v/TP/1TrK6uFmNMuHTp0oj3QUr/la98RSz79a9/vdML+lvf+taRv/v9Pubn5630mXCtx1RoozfAjHaA7/18ji8vLweXQeuf+On6+rrTm6e0n8dA8nJPv23rUWuNq1evWvOVAd/zaE9ytcfnTV3io2Z5kkzk2pOuXr3q3FcJzzzzjPX9YDAofpt7Oe9vG01mG3q9nnPv5fKTa3x43aasxGm2YTgcjqShfcIlW3Jcv37duafZ6uTpbTSG0iCByvz2t7898twnu1y/fr3oU1r/N2/etEbv8NEorUUp2oLW2jtHtdb4xCc+UciovC3z8/OFTMfznD59Wiz33e9+t7Mv3vve944942tcos/EJCINtFEuUDEM9/jx4/rEiRNJKu73+9s2EPakVQ62a5wpG8rEq217m6eNacQItqn7xoylL22v18OOHTuC6vXh+PHjOHHiRPJJdvbsWS+Tf+CBB/LkzohGSplgO8cRz/vLdLEdTEOahmn0eYp15ivD9m1Tpu66ZAKgnXLB1L4Yt+vHKjD5q/jU9bV9Yy0jFLW9zdPGNATR2BA2ZdKaH6uueqeFPHcz2oDt+rEK5DU6bTSNZ28HTKPPJxGa0vZt07Q13jR6QlBqxpgBffl1ug90o0t5fHmrXI1L6gOhoCt/Sf2nTHlcDWJ9fd3ZLpu6C+UJrd+lUuGi0Qcaf1cbzHeSOpNZp6TaERO0usy4mGo9NnAVEPptU2dy5Y+Zz1WCqtvqsann0O+YuWWuB1deV7tJ1c5Fpw02tSJSTxoOhyNjd+nSpRGew9e5VOe1a9ectJw/f36MZj5uNEdCx6MOtFH1J6Md4POXryuXGr0NlMeXN9WaiS3Hp55cFv1+X1RTNmHrlxi5JNasKrR9kkplSHmmLBlSf0yflx2f0L1QMhkL3eNj5Tmgmqqvq64U89hnvhRbp8/U0AVJ9r1582Yh7/ExWlhYGFlbvnG5ceOGs+65ubmRvyUVaOn7QspTJ9ooF5S65ty7d+/I39Itgw3UCRRcd+fOnc70KQIFl+l4Ou3leauUR3mova4JaetP6ouQ+rXWYt+58oa2i8bONTbmu0OHDnnrlOrvdDpBqhRlFxeV7wM//ae28ZM0V/1aa3E+SeBqcVWCqofcLpr9qpTCcDiEUsqrnsffh6wL17vbb7/dmt6nNsRp5O05ePCgOG8OHTo0Etib8q+trYkBv/fv3++k/e677x6jmY+bb81OYlPItwcZdYHPX76Xl9Ggon3PlzfVmoktJ3Y/DUVoX9n4cYxcEssLQtsXuhea7/bt2xddv1IqWL2y7PiE5uP9SXlC+zg0Hd8DffKyD1X2Id9eTO9DP3Z8aQ4cOGB95xt/Tiel5XON5927d++IzE15SbXXrGfPnj1Ouo8cOTLytyn78zqyXBCHUhTzzlxdXY1y/EIOWvgthOR0SXKO5HO4YkK6vSVjenpnOzV99tlni99mXhOSIwMTdOKztrY25sTHxD/+4z9a6wBkp0wS6JQw1HHU17/+des7fmL1z//8z2JZN27cKJ6ZNNpoMG8RzTJXVlbEkzZKd+bMGSvNHLz/zBuwWOZAp3G+kzaCUgoXLlwAMH4jYc4ZLpSY8006YY/RbgBGHSzZhAGTDgn0cQvc6s/5+fmx9tCadp0MuxwPEVM157H53nz3zne+U+xfrfWIgw1+O8HbRJA+Yjne/OY3jz3j84L603azQWNap5OFNp6kZrQDV65cKX7zeRTDl8hBC60F+iABRteF5BipjMaQxEe540BbXq5NYeYzEeJMjXhCr9cLum176qmnnPXYtJgIJr/jz20wHe9wcJ5G8pJUHjnalGi00cH3C7O89fV1UeakdKHyEe87c9xjb6J5nlB52HRMyeng4B8WpkaYb18JhWucJTok8HVL47e0tGTVmnPRaNvveV2UTpq//D3hr//6r0ccuvG6JBmeZAIz/dGjR610ARsOHyVI88I2fmXmXyzaKBdMzelSRkZGRsYt1OVg4fLly14mf9dddzVvd8poPLJMkJGRkVEP6nS61Ea5IMmdcJnbgVh9/xBMy7YlBJNwU21COk1NQUcZO4qQm3JgMrr7HGX6YxKnX0A1G9YQGqv0dWjeEPsqyY5JGpcYm2kgrv9CbmOkNFL4jjL2e3WijSepGe1GFT8Pk94DpoVptjP0hjUUZcNzxfiBmCTK+sOoG1VtWOukMbbPfOklLY1Ym2bzXczebCvXZ6tr0y6ZlNwYijbKBUk+WKvEEvRN2mkwMUk1sCpcah0mUtfNJ14KvXWyVyk7NjYapjXWseBqqjHgai4hxvY+dVQXbCq95tyyzUXfxnjx4sWg9ofYV0ntlITXGJvpwWBQ2JKaatiSmqFNnU6yUXLVC8g26NMUxtq4MWW0G1WEsyZ9uHCkpsv0JzCJ+qkMn++IWKTw8NwEPkR7QxW5IBYxMnCMvxgJIbKXTc3WJ+dL8cdd8PWxLTQM/99XDn83HA6L/jNNraT22hyC+daOjZ6meUFvo1yQJLZMGUNwWjghtnKhSNnBdRokh9gCpEBdEy7G+RMhZI5MY4GUqTPGyYWZz5c3tSBhwvwAs9XhG6977rknGU3SxhTqpMoGzld4GebHMb07fPiwtxwJoY6xpsn82+hcIaPdKON0KdZpzaRR5xqu04FQ6jJSltu08IZNDLVSt0wQWodvrExnQ1Uh7b1lZE8zr5nfdgiwf/9+bzkSqjrGmhSaymddKEWxeUuztLTkTC+pn9DV/PXr10s5TohJ6zoZsjldojw8fIdZTqxaJLBxc2Xe8rjSh9IsgTuXIbhOvn03gPTMHO+QfvCpYtBNXxWVl5ibfu7UI/bk2uWYw1YXhVfxhS/g4xPSnljazTLN+RDa/1/96lfHHKNIzgwIrjnrUv2JOX0215nU16ajLGr/e9/7XrGu1dVV55oxXdjzMnndsc6xUqKNJ6kZ7YBt/ZUxZ6B14wt1EosQXuvLw/dGzsvK3iTz8vv9ftCekkIbTeLvPnkihKay5ish/VdFgy9mfLjcV+Y22xeqxKyLywUulJlvsfSb5ZbR8uMOMEM0yHw0SjJjGbmA0O/3xTEynSFR2z/96U+LZfvCUtqccUqaXdM0H2qjXJDE6RJ504pBmTxthdnWSbV9O/VxxtaCz4X+JOtzvUu5xupysLCwsOBl8ocOHcqMIiMa2elSRkbGpDBJmdZV16ToqNPpUhvlgiT6GFXUKrcDzLZOqu3bqY8zthYmra4yiQ/SaaHt9GdkZGRkZExyLwu1jW0r2tiGZhkQZGRkbElM+sY0FWLsi5qKNtCYkZGRkbF9sBUOg9uMNvZ9EgmyjLfUxcVFANXsJ0LpSuWiPJWXvps3bzptRVPptce6AA95D1Snry4vkC4bSg7TjifELoTPhzL2HWXnYhX7HRcdKcshD7uu+ZbKhjPWJgkYH2/ep/TO1s8rKyvO+X769OmxZzdu3GiUp9NOp+P9l5GREmVkAgoR1aS1w1HnvkX80VVHirAYZdoQkqdpITtsiPFzErLPx9hk2vJWtTdtCng7QuzQU9pvxspkLl8e9M7Wz+vr684xuHTpkvh8ZWWlUbytjXLBVFSClVI4cOAAgHo95pb15grIdKXy0rdv3z5nmVVdl7vKTnFjVJW+SXkqDPWAG+JunM+HMgu57FxM7UWxDg/Uhw4dspZNz/bs2ZOk3hjX8FS3Od68T+mdrZ99dH/Hd3zH2LPbbrstmMZJoI0nqRntRhmZYO/evaXyTgp17lvkvdxVx7RCx4TkaVrIDhtiPOCG7PNVPPiWlQma2te8HVzGtSGlN91Ymcwcb96n9M7Wzz66jx07Jj5PJQOlQlP5rAtT+4SO8ahWFSluaVNBaz0xz2BNOs0JRZUYtCnj1NVdTyjaMIZ1eDNOiRCPha68rvUqeQSUPHFOs/1t9AaYsf2QOv54W6C1ntitWRv2ExNtpLlOtKE/Ym+n2wbXejW9DhNSa8tVRRvlgiTXN2V00avGWYyBVEcozVXpM+tRSiW7QfWhrr6t0/Yg9KRMomFSceqaYvjfFITcAk+6HXx+mHXTu1CaXOtVitUmncBOcxybqNqTkWFiu85TpdTEbs3asJ+YaCPNdaIN/RF7O902uNar7SY1xxyujlIUk60Jgcdf4jDjNA6HQ/T7fQyHQ1y9ehUA8Mwzz4ydVnDdftvtBrcBpfTmCQblNe3niA5Avg3p9Xq4fv168duMNcZjaNlA75RSIzEhl5aW8O1vf9tpr/Pcc89Zy6UyfNBa49q1awBGT4Nspz/AaOxK3pcUn4xo/dznPjdSrmSPy38PBoORNkn9NhgM0Ov1xBsrqY+kmHguO0neB4PBoIi/ura2Zo33ZbaBQHOXzx1fDNLl5WWsrKwUth22ec37/ezZs2IsU95m21ywzc3Z2dmRutbW1op2aK1x4cIFABtxVm3tIVBb6H8+RygPle2as1/+8pfHnvH156LBnKeEK1eu4MyZMyM0ABv8htbecDgs1nSn0xkbE601nnzySWuMwcFggL/5m78Z+Zv3IdUByPFabW1KjTaepGa0A5wvcv578eJFZz6+x9O6Ib8WL7zwwhj/svFiW7k2GoFbPENa79L+R2UQ3+B8zoyf6lrP/B2PCbm6uoorV64AgNOW9fLly9ayqRwXtNbF/hq6b5K8Boz3I40bAJw8ebJ4bsoDtj4ZDocjbbKNnSSzuGQC/s6lHWP6pSA7apI7bfNM2pMBFPPXdpNm7o2099K42erjz2dnZ0U5y+wP21ywjQXtt1Rfr9cbi0lryvlSWVQvzSkzzj1fY775eurUqbFnJGtIsc45pPcLCwu4fPnyCA3ABr/hsj3JC69+9avFMTlz5ozzu+SLX/zi2DMuj3OabDFbs1wgo9Qnv3mCcNdddznT05c8N+SlMh566CHraUWIW2mexqaXbj7ng8FvQ+jmZceOHYU9Db9d2b17d5Ffaz12QmG71ZmZmSkm4K5du3DkyJGiLOmm0LyhMdOE6MLb7IRdpzwzMzNFWp5OKYVOp1PQ8N3f/d0AbvUv5bHZc3S7Xdx///0j7THb1ul00O12xbkgzQOpztATLK31yNwIsVfmaahf+dxw3fZ2Oh3s3LlzZN7Zbu14+48dOybOKU6LzVbEdnp28ODBkbq63e7I4crdd98NAPi+7/s+a3uAjY2D6qZ+7/f7Y/OG/nad5pl1cTp5edJaof4aDocYDodF+oMHD2L37t1jGg0PPPBAQTe3HXvsscfQ7XYLYYXG6/bbb3eO1cMPPzzyt9a6WNtUB2+PiTq1FQhtPEnNaAf43OL71uHDh535OE+jNUxr8a677ko6Z809hf6W9hpp/6O0RB9/R7whxB7R3BNpH9yxY0dh9+6yj/Ptbz7bOqXUCO8LKbfb7RZpzf7iMgHf38292dYnnU4Ht99+e/G3jb9zfsrbYntm9nMISJ6jPYO321U3n6ckM9r605SPZmZmRmQCG638uW1dmbRKfSalI3A5otvtotPpFPOT8rzoRS9ylrW6ulrUS/0yGAzG5KRQ7UqzPk6njz/Qe7pY6na72LdvX7Gv8z698847R2zIid63v/3tRTlctjlw4IB1rDqdTiFDmbRI65PmzDTQRrmgFMXm6aftNJXSmbcgN27cwJvf/GYAG6cb5gkaP/HjpxK8DH57q7UuTkj4yUen08Hi4qJIH51sSKc8dCNG6aguOn3j9XLw0xhajFprzM7OFnQ988wzeO1rX4tOp4MbN26g0+mM3Vjfe++9eP755wGEezSVntHNFe+TU6dOWU9v6FSNn3bzPqE6PvjBD44853XYTsL54uB9zvOvra3hwoULI+Nvs++RTiX5zZZJC/dad+LECfT7faysrKDT6eDmzZtjeYk2k2kDG3OWaDa1CHi9/BS+3+8XdZl1ALfGj9czGAzwgQ98QKSB1xVjG2GuNaXUyPzg85aDPAHzumiciNmvr6+PaSz8y7/8CwDgd3/3d600DQYD8QZ2fX19rM3mWrEJD3Nzc3jLW94CAPjLv/zL4vn8/Dw++clPFvQTH3jxi19cCA98g3300UedGiTf+73fO0bP4cOHcfvtt48IYDMzM1hdXXXexNeFVCepSqmfUko9q5Q6pZR6W81kZ7QANq2a3bt3izwVkDVFVlZW8Cd/8icANjRYXJ69XbcSdJgM3NqTzP2D9g5TtlBKFfxF0p7hezX3+Mlvk0zYbs2uX79etOmFF17AO9/5zhHaJM0vKst2YyzVZT7rdDp49tlnoZQq8nW73WJPM8FvWE3eOxwOi/I/85nPFM+pXyR6zH2S/83bzPfFfr+Pq1evjmnbmP1NfIzXa2q28DnK5ZCnn34ag8FgRPNufn5+jH5T84r2ReCW5pVN68elIcT3ZUmmIAyHQzzyyCNemSDWJtqc73x+mGVzkDwzHA6xe/fuQmON9lB+U0s4d+4cgNF92USv1xPXgCkn0Y0ovyHm74FbcsGOHTuwsrKCd73rXVBK4cSJEwA25Je5uTl85StfKepeWVnBvffeKx4mfO1rX7N6AgY2LuEk/NIv/dLYuNk04doiF/hkAqXULqXUhzffP66UepC9+/ebz59VSv1kEM0xHXP8+HFNg5yRkZGRkQ7Hjx/HiRMnkl+3DgYDL5PvdrvOepVSXQDfBPATAM4B+BKA12mtv5GEyIxWIssEGRkZGfWgLpkAqC4XhMgESqlfBvA9Wut/q5R6LYD/TWv9C0qplwD4KwA/COAeAI8A+E6ttfOkpdQNq6nDL52EmOl4em67wG8dJH182wd1yIe2aWPJQad1tnir5g0jpaUTNqn+EFp7vV5x42tLTyfTpp0PIdQLKaXjJ4mu27j19XWn3Qk9oxMyszzTRoeD0y/dRkrp6L3LDoXfxrpsinmf0djy+eE7TeT2OtzmMwTUBn5zL9Uh5eN02U5NY25Y+Ykw1W07SXb93e/3i3nF14qZjsbEPAHlWFpa8nrOppN+s622G/0zZ84UPIbPkcXFRZw9e3Ys/bVr18ST7U9+8pPO0+r3vOc9Y8/W1tbE/vPZ2tSFRPHWfhDAKa31Ga31OoAPAXhNrYRnNB4uO7+QGxp6prUueBPfh+qAKwa6abfHabTZUpah1dwrTR4slSlpm3FeFbIPSPa7Lh4UurfQ7aJJE+C2P3bJAq50Lpp9fg8IvG00tnxuxNqw2uaODSRTxMQoN2+WbTJPrMdtKW5qiK8Us07qey7n2GQJV6zW1dXVoL2R+jDEnwywYQe+sLAwMkdWVlYK3x6mfxBJHn7iiSec9X3qU58ae0YyYOjcmITH9ARyQYhM8BoApJL5UQCvUhtXt68B8CGt9ZrW+jkApzbLc9Mc0b4CvNO5rruZxmZnoJQa0ec2bQ9cDI7n8QnVpo2l9M5GI3n/lNR8XflMSIuJyrZN3jvvvLMoj/qRqySE2GWQ3j5wS2XTl5fbpLjaRzacwLitKy/LVY+Zx7ZRmCqaBOo7aR5J4GVQHt5Wye6El8fns/nch8FgUNirmLYlNhVcehcy7jHe5/jYUd0UR1Wiy/Y3ty1y2WNzlVgbyG6IwzyUIQYq2aPzNIR77rkHt912G7TWI3n27ds3osJM8473AS/ne77ne5xzWbKzWV1dHWvPcDgU5+ck7EhUGN6klPoy+/cmo5h7AXC9wXObzzK2MThP4jxM2p/puQTOgyV1tBCZwAaTDsn/AcEmF3DezffzELU5H028bJeqJ7eD52rKBImXmIf/XD3SlS/kHQe3gTTzhMoFNvmJ/0/pXHJM6H7I00njbqPVtv/HyAXD4dBp22iDuTZsNMbuK5ItZUgsVZM2mlfUJtt3AOCWRW1+I8wDFJJZQ+fwkSNHcOjQoZG6d+3aVdTHHarx9vM2PPTQQ876JJ8+atP0SpILfDTXhQRyQYhMUKTRWvcBXAdwNDDvGEo5XTI7k38QEVwbE3DLeFwyDg91EuQTql1wbVr8PZ/YZTYnk7nu2LGjWBy2SWn7CCSEOCaSPgpt5Un1usA/emww2+b7yLY5u7CB2hHaNv7OdAwQMq4uRxsxeX3rIvR5WYQ4mAoBz2MeAEjpXJufNObmJh7rJp/zFfOjnzYU7rzMVv4999zjrPPHf/zHx55JDpbKOJabJLTW7wEwfl2ckREIcy6Hhm+jfHSQK+XjZceGgIlZYyF7iOuDLBS8jG63W/AiV1k+WcTH26WDAF+doYJziCNIqS5f+ZQ+ZsxDZQIO6RA7BNKBckjeKh8kdewZsfPCVwY/fLKlszmGAsIP52NplGTvzv/f3rnHWFJV/36d093DoxkYwPEPBOIvBoMJiY9E/zMSNMaYcP+6Mb9Ew41/mEju1YRrMAT8wxhjDImYGIPxCWp84INEIgI+rrwyBmYYRoZhgGGYHobpmeme7unu093nVXXq/tGu3at27b1rV52qOnX6fD//9Ol67L32e+1de6/VbKoFa58PIFdddZUzDpMRSSLzokDdDR/VTS+od24BAACoA6eJ6Drx/7X/uQYAAACAycJHJ1DPNBqNaSK6goiWPN9NgAnrCKjCAhgAw5BWR3daHZZnlIGR/UR0Q6PR+K9Go7GLiP6biB4ZsUwAAAAqYNJ0girOkY45PjrBI0T0v/7z+38S0f+LtirKI0T0340tK8L/RUQ3ENHzaRHmmrDqhmPSHFrr5xAGg4EyN97r9RLGBHQjRbbwGDZzr19nYzCmMA4dOuQMXxrk4XB1Q0wuA0VMGIbU6XSUUYW1tbWYaxmTUYNjx46pcGXYbOTG9I6v6xeTMSmZHpMrIqL4mcKvfvWriWsuIwW6YS6TkQHO55WVldSzyURb7oF0TK51GDa0I+Vms/wuQxB62ETbBiZskxxTPtjqiqvudDod6vV6KrwiBgSXvLbzOKb32IgUEdErr2wZhTO5emJeeumlTHL6tHvXPekSRzdYJtPLv4MgMG4Bevrpp51GWh5++OGEHOySSiLPxowj/zl/8n+I6AkiOkpEv4ui6MhopQJ1IovxGEb2izyOB0HgNPji6l8lbEhHv8f6i2kcZRdWLpcsMjxdF3LJI8Pp9/sqje12m+bn5ymKIjU2meK3uQmyue8xIcc+xmRMipE6iM2dDhHRAw88YLyux6WHLZ+19fkbGxte46bJNY/LaBQb2pHPSR3VZTzKFD7rVqZx0Gbc0xaujV6vF3M1ZGPYc95ESZnTtt9KfYjLwmWUzDROSkz9iW/bt93jNOjGsmTb5t+2PDxy5Iiz7Pbt22d8b2FhIXGtCqOLZWHTCRqNxjcajcb/+M9jPyWiqxuNxhtE9H+J6K7/vHuEiH5HRK8Q0eNE9L+jFAvBRHBrAwAAtaBME/YAlAF0AgAAKAfoBHEK+cL64osvqt9ydYlXzHTXJ+vr62qlY//+/YnVVOkY/LXXXovd078CDgYD9b4uF3/FfeONN2LvE21/JVtcXEysnPb7fXVfurtYXFxUcUoLo4zty+Hc3JxK0z333EM33nij+qpscvfxzDPPEFFyNZTDN5kD39zcTMjz+9//nojiX1VfeOEF64qUNOOtO9xut9sq/ltuuSUmH6/Q2lzcbG5uxlbTZJp5hanT6VC73aZXXnnF6QKFKP6FXsbJX6ZNq49ydesrX/kK9ft92tjYoJWVFeWmR5a//uX1kUceUfcPHDigntPToa8aE23la7fbpTAMlcNpfYXOVHcuXLhAzz33nHou7cuvxFbG7XY7sXuA45bv6Kbz9a8V6+vrtLm5SVEUqS/uq6uribrp82X1+PHjifj1cIIgoFarRa+++mrsOsspndgTET3//PP0/ve/n4iI7r33XnX98OHDdPvtt6u8OH/+PEVRpP7q4dx5551qN4ZOGIaJVWe2BMgry5ymlZUVY/llcUkEQN2QdVr2YdymicwWfvUvC51Ohz74wQ8S0VY/Ll2xEcW/Akq3ajJ8+SUwiiIVht638ljM7uUkPFbqO5HCMFTXpL6yurqq4jPtorB9NVxYWFBp+uUvf0lf/OIXY7KZvtweObK1oUH/GshxmMYQXZ5nn302Eb7c8aUj3Q5KvYxoa/znuO+++251neVjeWxfA7vdbmxcljLxO/1+n06dOmX9ks1EUZRwV0eUdBEokXrGT3/6UwrDMKZjSVc9HK7+5ZXHZ6JtPdPkwk/Pg06no3Yp2VwamerO+vo6vf7664lndTlt5Wm7LvVZPX6pUI0rAAAgAElEQVTT10S5G4CvdzodlX/8xX1jYyPRltO+rBJttU3TjgI9LNZNXH2CzPsvfelLRBTfGTU3N0f3338/EW3VybW1NVWfeAyXYTzwwAN04sQJo9w2bwBhGNJ1112X+OJs2j3A4YAk+MIKABhLgiBwWpVOu19UPEWB1VQwbkAnAADUCekySCcMw8xWxssMJw3oBHFgdAkAMJakTSKLmmRWMVkFAAAAwHC4XMUUNcmsYrIKkhQyYR1nYyIAFEEZWzjyhGnb1gvMoO8CAABQBkWPL2UZXgRgHChkwlqGI2MAxokyHEDnCVO2xbo7pa4D6LsAAACUQdHjSxHhYcwD4wo0WgAAAAAAAAAAtQQTVgAAAAAAAAAAtWToCWve/fDstsLlUHmY8G3hDfvOsGGxS5yiMcklnWC7nvO5J3E5Ah8Wl6Npn3d9qJOz5jRZqnB7Mmy5sSn79fV1q7zSzUUesp7HjaLImrcm9w954zW5W8AZIQCyY3KvZWJU7avIeHUdpyr3VjYXJUWFO67Uyd5DmixV6S/DlCm7xOl0OlZ5TW6lspJFRptrQCK7e8A8cdr0i3FvI3Ug14RVTrgajUbCZxlReuHMzMwQ0ZaSq1ci3mPvO3mR/tck/L9pzz7HaWpM0pes7o9ShuVbARuNRiy+Xq/nfDfNt6ZJZlMadT+u+u8syPdOnTqVkCOKIufZiIceesgrniAIvM5YmPLP9Z58ns926j7VfOEOSSoZaYMIKyW6bzPd2pyU59e//nXsHKpJ2bCxsrJivC7bLvuZY9k3NjYSPvRsHDhwQPkk5jRddNFFied4Qpd1kNXLJet5XJZND6vf7xvz1JbeNLl1v3B6fABMCrI/zLOQw31hu90uVLnT26OrfZrGfXnd9TsL7KuZaKuP8Zmwpj3j0+9wGL6L2C7ke4uLi5nkYNjnvIsqJmi6zpknT3gcNPlhNWHy+02UHOukLE899ZRx/NKx1RXdv7npedbn9bExbVL3xhtvKN2Z08R6vsR3YcqE/k6W87hSr9d9Q5v0C1t60+p3mv4O8gM/rAAAUAPgcw2MG9AJAACgHKATxMEZVgBA6WB1EQAAAAAA5AETVgBA6cCUPgAAAAAAyAMmrAAAAAAAAAAAakmuCat+6DjPoXh+Z319PXEv7/ZBmxwuC7ppzw9jtdYUhumQfRbZfO4zXE76AXOfsF1xZLGyyvhaYfNNW9YyHSYunbxGIOShf3nNxuLiYux53ZCCS36ftmCShd9zWdQjIlpdXVW/pSEn3QI2l7vN2APHWzRSfj2dWepOWlmb2gKMLoFJRLeGnxV+x2TIbBiyyFKkd4AscfrIWJY137S4feLNYmVV4jOWVn2cJG98eeq8Sb90xa8bOLXln+16Hg8PUid0PSfHeKmr6HoLy5bWzosu96y6vO1ZGF0aHdN5XtItaumWTn3gdy677LLEvbzbB21ymMJzxSHvFbGVUYbhY+00LU5fmTgu+XxaWfGzrjhM1mDTMFmLc8Wf57msZVV0PUvDVPau+rB3797Y/9PT8ebqkt+nLejvS1kajYYz/CuuuEL93rVrl/E30Xa5z87OWsPKagHYBym/no4sdSetrE1toYz0AFB3so5zOvzOxRdfXJhMWWWx9QNlHmlI62uLlMEURlr++MTrO77r+IylVR8nyRtfnjqfVY+5/PLLY//b8s923Sajjz6cli9yjJe6iq63sGxp7bzoci9KP0wrZ1tbwLGo4alEs/L1EToppKW5Kp9sefH9wpjHFcCkr0LJL5RZVl7TVg9t7c3mviGtHPr9/liWVdFfUMYxDwCoI1ncdu00fNJcJ//hOpOox1WF1AfLahvD7EJiwjCcyLargzwoj1xfWLPiWkWaxK8RaWnWV6Tqhu8XRtvXPN+v25OI/ELp82XQ5568r9c9/X/f1dS8K+qjpugvKJNeXwEoCt++Zyfik+a8O3uqYBL1uKqQ+mBZbWOYXUhMnetnlUxi/1UVQ/cyVe73r5Kyz6yMM6M451MkdZKzDqvmw+YHt+UgCKztehS7Bqqop3UoPwB2AnXql6tmktMO4tRFNx6mTvK7rq+urnSOy7llE3Upv53I0BPWRqORq7DrrugVvUria8ymDOTh9iLith0qd+XZsAY5ikTKOYxxkDzobcW1KhkEQcKI0bCYZM+zPV/WY65fzWYzsdLOYbfb7VzyZpEn7zUXaf2UKc9GXb8BGDV5dIJJazcyvVUr6K6jJ3koYkGyTpP2PLIUJb/ra3UYhqUs/hZxTEseS+L61Ww2E3ohP+My3lnWV8oiyiivkbI61e9xpZB9HMNMWKuYuNZhQs0Tk36/T4PBQFX6MgdpTkPRRiyynK1khjXI4UMWy89SFp8ORv7O05nmKeepqalKtodzeaQNlJJGo6GeZ+uApjRyXp0+fboQWW3YBkWdKIpofn5e/W9afNEXV1xlZ7JyntdiJgCTDPcxVU1cs+oFRcvF/WcQBInzu2Upt5wG3TjesBQhbxmTlLxeB6rY1pmnrJvNZmVbb9O26OvtQRoP40VsU9r4maWlpcJk9cGlEywvL6v/fTxpuMrMtjhfd9s040Ah2nCeCQhPoqpofHk6n7Lk0geKMs9+mNJQRHy6ZdS67NnPa/nZ51zjsJajTRab0/C1HplHjqy42gNbM3ZNrm+88cZc8Wah1+up9mXLu2azSe9617vU/2lnddLO6l555ZWJa3msaMNgBdhJ5Om3uO1WdR4yq4xlyWU6o1jWmGpKQxFx1dWmwTBeB8qKS38+q05QBsPoNCbYi4DrmWuvvTZznHkIgoCmp6edet7VV18d+99F2tzA5PmEqL5tZJzASXkAwI4gz1eDuhg4m5qaqs3CDwAAALATyDrGw4BYfUHJAAAqoeotMUVtzcUWXwAAAKBYqj63XtRRv7rb4NmpVD5h1c9uFrkNrsjDzmX6uxo2bJ/388Qz7lsSd4JlZzk5KjvOXq9n3YqaFrc8d+XzzmAwKP1rph5/UVtwhgkH51YAcDMu/lfrPr746gVFhwnKperJEVv7z2qrJOuxliiKKvmaKWUq6qjfsOFgwpuPyvfD6QZeitwGV6SPxTqdFcgTRp3SXBVlyl9V3sjJUdlxurbQpsVtmny63qliYKpj/a3LlmMA6sq4+F+t+/hShl5Q9zKZBKr2b2obs4r2w1pV3apjHYbP2nwUokVW7RpkHBllemXcRcgx7OpQncq+znU3DMPccWXxfZbXYrXPe3lc5qSF5UsVcRbhOgeAnUaddjXVlVGm12YBPy+j6N/LpK711/Tlswhc41jWr6Zp74xyR0UVcdapHu80Cpmw1nEFY1iKrnTyC1Padt2itgtwHDLuIr505dl6JAe0UR9q193aZGUY+dMG9s3NzVg8eePS2yTHa/KTys/qddSFyZ+waUsQh12k2wNfH4Kmfom3XPN70kexibT7afhO2E1brAEYV3aiTlA0pjxKO1ZRRtxFlFXevmtYd3FFUYRbm7zyp+Wd9Fdq8ms6DBy3yy2cb3wmncDl6q7o8U4ev7GFbUqLrm+n2ayQ+sew5FkQn2RyacNFNO5RT1rSKHv7jyv8vNsFqvIl5rPNUY+7TuWdNV+K7DTS8kG6RMlbfqZBguN1+U7Tr7toNBqqHnDYLku3RW6BkVuZfV0Z6XLwvTQfxZdccklm+UzKYFq5u8zuAzAODNtPov4Xf6yiKoU3b/9elzLPI0dReZsWd1G2GFw+UU26Y57t4/r46qqzReuEJvdQPuhypOV3mjeCIt0U1aV91IVcNQaZWE9QLuVQZb4WMbGrcpAYN4pOP9ocAFugLdQPlEl5VJW3RY1ZWRd3J4VJTvu4kaslyE/iYRhSq9VyPi+/6vR6PYqiiFZXV4mIqN1uOy0G+3zmt103WejkZ13WO6MoovPnz6vfcs99nlW1wWCg0hiGIS0vLzvjP336dOKafN62JUGXrd1uJ6674uU0p4X9yiuvxK7x336/r37rdcK2tVKWPW8pkXH1+/3Ylhj9vcXFRavMErnto9vtqjjCMEzdgh1FUewZzldZDmnbtVZXVymKokT+62mT4czPz8e2p/C7ev7osnJ9M9XVH/zgBzGZiIjuvfdedY3T6dr2EkURnTt3TpUp19c333wzUc4czvPPP28Nz7QFpwiL31zO8p1Wq0ULCwuJZ7lf0uN49NFHnXHeddddxrB0bPUDW37AOMN9IVF8bJHXTZjGJD4OYWuLjGxLedqPqb+X44GNtbW1RJzDbIWVxwV4vHSNI0tLS4lrUl6XvsNw3ySv+6Q5jbfeessat2ns1OWxvcu/9friGjN4XEtD5jXrLtIuQ9qYrm+B5bT46KxE2/U9bUuqDEPXHW0y2uq4TZ7HHntM/d7Y2CAioj/+8Y+xZ9Is3q+srCjZua6eO3cukR4O59ixY9awdJ1LXrc974N+JIhoq06a6ow8piOf379/vzO+Bx980Hjdp32mXZ90hp6wTk1N0blz54zPcWOSKxi7du2iRqOhlMZOp0Nnz56NPScrqvzN93VjLysrK7H/GW40spPkMP72t7+p+E3s2bNHhcnvrK2tUaPRUA1fr8ymShaGIW1sbKiJ1dLSEu3fv1/FK88sMnLw4jRNT087Bw/TuT6OQ947e/astTFcddVVKj6ZL1EUxSZWn/vc54hoe7LF+SO3hM7OzqrnB4NBrBy5QyTaXj3k+/Pz87FynJ6ejm2TZfbv309ERHv37lXXOC9N5XH8+PGYPIzp7KC+gMJlznB9bTabxk7VNODs3r3bOGDMzMyowTIIgtjW3cXFRTp9+jStra1RFEUJy9pRFKk0y7bmWpH9/Oc/r56dnZ2lKIroy1/+MhFtlTl/4TWddWUajQZtbGyo8udJ2+zsbKINctl94QtfyOTiRc8n22IGo9/rdrt00UUXGVdPL7vsMhWmbBtczrK/eN/73mdso8xnPvOZhMymdooBCOxE5JZ62b55TNYxtQPeysfv9Ho9unDhAhElx3wis05gGo/X19eNcfL4Ksc0DufQoUNKBl1uHtN0ZTctfTqDwYA6nY5SklutFh07dowGg4GK17RIKxegOZ6pqSlr/xQEQaL/M02qOK9N8LhFlBzXZB595zvfSVw3bQuV9UWf8EmdQ8q9tLQUy9epqSnjts3XX3+diIiuuOIKdY3z0VQuZ86cUb+lnkfkXtSw6QWcj81m02jfQeeSSy6xtgd+PgzDWB1fXV2l5eVlVeams6dSX9PHbRuf+MQn1LN8DObWW28lou1yT/sS2e121bM///nPiWhr/Nfzgsvue9/7nnWxJMt4qT8rw9T1BY5bT4utHzPZzbjuuuuM7ZP56Ec/arxuaqe2dOKrr5lGlopx8803R08++WR50tSEKIpKrzBVxJEVX5l8nqsqfXXMx6IIgiB2LoPTOhgMcm8TkvnF4eTJQ9s7dSkPmxzD5F2WePJw880305NPPjn6zAPAk0nRCaqiLv0nU6ROAIYnDEPjsaFh81++z7+zhjmudaBouaETlEcuzU1fMUmzqqUTBAEdPXqUiLa+Rrgsi5m2PNgm2bYvOPKLngtTuLbtF1nkkquwa2trdOTIkdh2IB3bFmkf9yES0zYH1/ZtngwRmbfqcBx6fpritlmp1X/LMMIwjG0H80HG45JLxinzJQxD42qZ/jVUrv6atlq7MMnhs1Wr0+nQs88+mwjPZ8JlW7k0rT7KPLTtFtDlk9u/f/WrXxHR1gqrbWVZbjvWydKWsmAKQ8pNRPTUU085wxgMBs6dDR//+McT10xtbN++fc54ABh39G2bWQjDkE6dOkVEW/2raYeF6bcPtnEzrwVw2xecLHLJMWdzczOxpdYUlq1vzhKvSRdybd+WXxFdWxptY2gaafk3GAyGsqDuKmMZJueL1Mt89Fr5TF7rsVn02X6/r45kSdK+DjO2tmDaZu+yHGyST3oJ4EWsfr9v1e8ffvhhq5ym+IbFN59ffvnl1HBcu66+9rWvGa+b2hnPg4Af+MIKAAA1AKupYNyATgAAAOUAnSDOZJsMBQAAAAAAAABQW3JNWPUtcq4tt7btK7wtc3193bnV1cdSm+1Z1/ZWl2ViNoCj39e3pvh+ndYNC1y4cMG4BZSx5Sdv9fDZ1sFxyfeI3Fu1bFuddHjrlm7h11YWURQljDiZCILAuUXadM1kpc8Uj81oh75F1ISeNn37UBrS6JTNSAf/ldtJ33rrLfW/LS7T1nKXhUM2GMW/O51OTCbe2pVm+fvo0aMqH95++20i2uoX9O1IXCaHDx+2huXaeqW3N1d/ILcftVotarVaNBgMEtvVZPvi9tBut5XscrvP3NycUz651ZfriWkrvstIAwDjimyfst3k2W7L73c6HWffmnWroE0HyWq1k/sH+UyahXkfmXq9njIQ5RqfbX1Imi4j4b7MNh7awk5DehiQY1ra9mXZt9q2BPukS4+XSfMGIeMh2s4Ln23I+v0sfbwcy2x6C/+V4/Pi4mImC9z6b1uapOGtlZUV6vV6ifSkxXvq1Cn1jjR4qtchLpO5uTlneFnalquseKtyu91Wxw30MV32V7Jc+LfMi4WFBadspm2+URQZ9fqsxyknnVwT1ssvv9zrOd3yGtH2OUl2hdFsNhNn8nwnWPrhZttBZ2m4Rr4r/9ruy8bWbDZjVoJ9D1bLCUu326WVlRWj9UM9HbpsulNmPQ49H3Wrubb4TLgGBM5P+YzuaNpU7i5Y/iAIEp2B6cwmhyfD1Y0RmM5mEm1bj+SwbSby5VkOWQ9d9cmEPAeky6jXs927d6t773znO1W8tvzj92UemdoUI60+XnnllXTxxRfHwmYrelIOoqSp/uuvv15ZzeQzHysrKwnXSJze+++/3yiPDf0cDf8vLTvqzMzMqHS3222ampqiZrMZc/S9sbERs2DN6dq1a5eSla0kEm1ZPnadGX7ttdfUb24DJguWNmfjRZ/TAWBUXHrppep3Wn9lus5Krj6WEPlPsEzYZLEZr7HJabrHfYNrMmBCTliCIFDKrEsvsGHrn0w6CivmeRbeXc/JvJR9dxbjM6ZnG40GhWGYyA/bszquvlumh/NFWjZOs2DLsrni8ll4cZUfUXw82rNnj1f9N51DdZWHbLuzs7PKm4dEykGUbId79+5VXgFOnjxJRFu6lv6Bi9P7l7/8xSq/70SO293y8rL1GfZe0ev1lA4vx+hOpxOzEszpmp6eVrLK8dvmfYAxuaVsNBpGvdF0jdMFkuAMKwBgR5BlEWmYd8oC51XAuAGdAAAAygE6QZxCzrBmWQ1k0ra3MmWtNKR9YS0jLv6dtnpUpnW0IsLOuw2qLgybB1WtfsltaGXHk/VZuVVLbj23bXlO205UBGlWDV3v2J5N65/y9H0A7HTy9JGura07mar6eVf8w4J+sDqqyuthtt7zNmfpW1h/blyPyWTdKg6Ko5AJa54CMjnBLit+02Dg40fUJ+w0+AsOhxOGYcz8twm5ZVWStaMybScd1ldXHjmI/Ca5WeqC6TnfQT/tXI0Jk9n3rGHI52Ra5fYweZZCP1Oclj7beR8pf9rWOllPJbozeDkI8e+pqSm1JUiPV57fyJP/TL/ftx4T0M/N8cKQdF0TBIHxjJxeHlJ+28AaRVFsW5PLSb3pTJ/NZRYA406ecdNl16EMsrqJKxo5nvq0f9uiXxZZ8+hCPuTNr7L7vWGOQGV9b9gwbOOi/MChl5+PzQ+fbcmmY2OubflEyeN60h4Iy9lsNhNHZMqYsJq2jUu5dJml6xqbCyOXLuX66HTgwAFj/KbwTOGM+wehMsk1YdUr6quvvmp8jpVAuXLKhnW4oA4fPmw1lpRlux5XBikbD4CmSiFXgEz3eE98v99XFchl1McWFp9/lBOSU6dOUaPRsCq4u3fvpjfeeMMYFhE5fUBJ2PiL6ayGCVkuJl+7HM6//vWvxLuuyc9gMIgZ8TEZd2KDOSsrK9ZwTNfkPVcnIusFn5M9f/48RVFEvV7P6kPNVKZcN+Q919e9MAxpbW2NWq1WLP/lOVx5FopZXV2NLTrw83pes9EjPp9hkoHDP3ToUOxap9OJnWvi/H/ggQcS57cll156qTI4xO/r50jCMDROLk3nm11+/GQ6ZmZmrOc+5DkUWZ4f+9jH1O9+v08zMzNqEYnT2Gw2jed+er1eYhIu+chHPqJ+83PSn7GUTc+L6enp1HNMANQZ2Rfp5/l036I2eKGN28fJkyczKd0m9P5XwvGYxiyTsUV5j8/j8Ziut2nfxTi5aN3v92lxcVH9tr1nGhu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      "text/plain": [
       "<Figure size 1224x1224 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## call functions\n",
    "colors = 'binary'\n",
    "\n",
    "year_in_focus = 2016\n",
    "# create a subplot\n",
    "plt.subplots(2,1,figsize=(17,17))\n",
    "\n",
    "# first heatmap\n",
    "plt.subplot(121)\n",
    "vmax = 1000\n",
    "sns.heatmap(get_total_matrix(normalization=False) , cmap=colors,  cbar=True,cbar_kws={\"shrink\": .2}, square=True, xticklabels=False, yticklabels=False, vmax=vmax)\n",
    "borders(1.5, 'k')\n",
    "plt.title('Capability Matrix Absolute')\n",
    "\n",
    "# second heatmap\n",
    "plt.subplot(122)\n",
    "vmax = 0.1\n",
    "sns.heatmap(get_total_matrix(normalization=True) , cmap=colors,  cbar=True,cbar_kws={\"shrink\": .2}, square=True, xticklabels=False, yticklabels=False, vmax=vmax)\n",
    "borders(1.5, 'k')\n",
    "plt.title('Capability Matrix Normalized')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total database matrix: clustered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:28: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:48: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/seaborn/matrix.py:603: ClusterWarning:\n",
      "\n",
      "scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "whole_database = get_total_matrix(normalization=True)\n",
    "a = sns.clustermap(whole_database,  figsize=(12, 12), xticklabels = False, yticklabels=False, cmap='binary', square=True)\n",
    "borders(1.5, 'k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract of cluster order:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[u'sugarcane bagasse',\n",
       " u'wood chips',\n",
       " u'pine',\n",
       " u'agricultural residues',\n",
       " u'rice straw',\n",
       " u'wheat straw',\n",
       " u'blend',\n",
       " u'succinic acid',\n",
       " u'biobutanol',\n",
       " u'oil extraction',\n",
       " u'microwave',\n",
       " u'natural gas',\n",
       " u'pellets',\n",
       " u'barley',\n",
       " u'poplar',\n",
       " u'sugar beet',\n",
       " u'grains',\n",
       " u'acid hydrolysis',\n",
       " u'consolidated bioprocessing',\n",
       " u'cbp']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_order = []\n",
    "for i in a.dendrogram_row.reordered_ind:\n",
    "    cluster_order.append(axis_names[i])\n",
    "\n",
    "print 'Extract of cluster order:'\n",
    "cluster_order[50:70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Characterisation of Years <a class=\"anchor\" id=\"one\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Years in the database <a class=\"anchor\" id=\"one-one\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not all years in the Neo4j database contain technological assets. For this reason, two lists will be created. A completely chronological one and a database one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The database list starts in 1938, ends in 2019 and contains 38 years.\n",
      "The real list starts in 1938, ends in 2019 and contains 82 years.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:8: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# query years\n",
    "years_available_q = \"\"\"     MATCH (n:Asset)\n",
    "                            WITH n.year as YEAR\n",
    "                            RETURN YEAR, count(YEAR)\n",
    "                            ORDER BY YEAR ASC \"\"\"\n",
    "\n",
    "# create a list with the years where records exist\n",
    "years_available = DataFrame(connection_to_graph.data(years_available_q)).as_matrix()[:, 0][:-1]\n",
    "years_available = [int(year) for year in years_available]\n",
    "\n",
    "# create a pure range list\n",
    "first_year = int(years_available[0])\n",
    "last_year = int(years_available[-1])\n",
    "real_years = range(first_year, last_year + 1, 1)\n",
    "\n",
    "# give information \n",
    "print 'The database list starts in {}, ends in {} and contains {} years.'.format(years_available[0], years_available[-1], len(years_available))\n",
    "print 'The real list starts in {}, ends in {} and contains {} years.'.format(real_years[0], real_years[-1], len(real_years))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all of the years available, we can start building the technological capability matrixes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Capability Matrixes of years <a class=\"anchor\" id=\"one-two\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.1. Getting the labels <a class=\"anchor\" id=\"one-two-one\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final list of terms has 352 terms. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.2. Function <a class=\"anchor\" id=\"one-two-two\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by creating a function that given a certain year, returns the year's capability matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_year_matrix(year, normalization=True):\n",
    "    \n",
    "    # define queries\n",
    "    # non intersecting part\n",
    "    q1 =         \"\"\"    \n",
    "                MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock)\n",
    "                MATCH (a:Asset)-[:CONTAINS]->(out:Output)\n",
    "                MATCH (a:Asset)-[:CONTAINS]->(pt:ProcessingTech)\n",
    "                WHERE a.year = \"{}\"\n",
    "                RETURN fs.term, pt.term, out.term, count(a)\n",
    "                \"\"\".format(year)\n",
    "    \n",
    "    process_variables = ['Feedstock', 'Output', 'ProcessingTech']\n",
    "    \n",
    "    # interesecting part\n",
    "    q2 =        \"\"\"    \n",
    "                MATCH (a:Asset)-[:CONTAINS]->(fs:{})\n",
    "                MATCH (a:Asset)-[:CONTAINS]->(t:{})\n",
    "                WHERE fs<>t AND a.year = \"{}\"\n",
    "                RETURN fs.term, t.term, count(a)\n",
    "                \"\"\"\n",
    "    # total assets of year\n",
    "    q3 =        \"\"\"\n",
    "                MATCH (n:Asset)\n",
    "                WITH n.year as YEAR\n",
    "                RETURN YEAR, count(YEAR)\n",
    "                ORDER BY YEAR ASC\n",
    "                \"\"\"\n",
    "    \n",
    "    # treat incoming data\n",
    "    raw_data_q3 = DataFrame(connection_to_graph.data(q3)).as_matrix()\n",
    "    index_of_year = list(raw_data_q3[:, 0]).index('{}'.format(year))\n",
    "    total_documents = raw_data_q3[index_of_year, 1]\n",
    "\n",
    "    # get data\n",
    "    data_q1 = DataFrame(connection_to_graph.data(q1)).as_matrix()\n",
    "    \n",
    "    # create matrix\n",
    "    year_matrix = np.zeros([len(axis_names), len(axis_names)])\n",
    "    \n",
    "    # for no intersections data\n",
    "    for row in data_q1:\n",
    "        # the last column is the frequency (count)\n",
    "        frequency = row[0]\n",
    "        indexes = [axis_names.index(element) for element in row[1::]]\n",
    "        # add frequency value to matrix position not inter\n",
    "        for pair in itertools.combinations(indexes, 2):\n",
    "            year_matrix[pair[0], pair[1]] += frequency\n",
    "            year_matrix[pair[1], pair[0]] += frequency\n",
    "    \n",
    "    # for intersecting data\n",
    "    for category in process_variables:\n",
    "        process_data = DataFrame(connection_to_graph.data(q2.format(category, category, year))).as_matrix()\n",
    "        for row in process_data:\n",
    "            frequency = row[0]\n",
    "            indexes = [axis_names.index(element) for element in row[1::]]\n",
    "            # add frequency value to matrix position inter\n",
    "            for pair in itertools.combinations(indexes, 2):\n",
    "                year_matrix[pair[0], pair[1]] += frequency / 2 # Divided by two because query not optimized\n",
    "                year_matrix[pair[1], pair[0]] += frequency / 2 # Divided by two because query not optimized\n",
    "    \n",
    "    # normalize\n",
    "    norm_year_matrix = year_matrix  / total_documents\n",
    "    \n",
    "    # dynamic return \n",
    "    if normalization == True:\n",
    "        return norm_year_matrix\n",
    "    else: \n",
    "        return year_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We finally test our function with the year 2016. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:36: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:53: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The matrix from 2017 has shape (352, 352) a max value of 0.229850746269, a min value of 0.0 and a mean of 0.000211213110583.\n"
     ]
    }
   ],
   "source": [
    "year = 2017\n",
    "print 'The matrix from {} has shape {} a max value of {}, a min value of {} and a mean of {}.'.format(year, get_year_matrix(year).shape, np.amax(get_year_matrix(year)), np.amin(get_year_matrix(year)), np.mean(get_year_matrix(year)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us print the capability matrices of 2016 normalized and absolute versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:36: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:53: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1224x1224 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## call functions\n",
    "colors = 'binary'\n",
    "vmin = 0.0000\n",
    "vmax = 0.05\n",
    "\n",
    "year_in_focus = 2016\n",
    "# create a subplot\n",
    "plt.subplots(2,1,figsize=(17,17))\n",
    "\n",
    "# first heatmap\n",
    "plt.subplot(121)\n",
    "sns.heatmap(get_year_matrix(year_in_focus, normalization=False) , cmap=colors,  cbar=True,cbar_kws={\"shrink\": .2}, square=True, xticklabels=False, yticklabels=False)\n",
    "borders(1.5, 'k')\n",
    "plt.title('Capability Matrix Absolute: {}'.format(year_in_focus))\n",
    "\n",
    "# second heatmap\n",
    "plt.subplot(122)\n",
    "sns.heatmap(get_year_matrix(year_in_focus, normalization=True) , cmap=colors,  cbar=True,cbar_kws={\"shrink\": .2}, square=True, xticklabels=False, yticklabels=False, vmin=vmin, vmax=vmax)\n",
    "borders(1.5, 'k')\n",
    "plt.title('Capability Matrix Normalized: {}'.format(year_in_focus))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:36: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:53: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## call functions\n",
    "color1 = 'Blues'\n",
    "color3 = 'Reds'\n",
    "rwhite = ListedColormap(['white', 'red'])\n",
    "gwhite = ListedColormap(['white', 'green'])\n",
    "blwhite = ListedColormap(['white', 'blue'])\n",
    "bwhite = ListedColormap(['white', 'grey'])\n",
    "year_in_focus = 2017\n",
    "graph_holder = 0.001\n",
    "\n",
    "original = get_year_matrix(year_in_focus, normalization=False)\n",
    "\n",
    "threshold = len(f_terms)\n",
    "f_mask = np.ones(original.shape)\n",
    "f_mask[0:threshold, 0:threshold] = 0\n",
    "\n",
    "threshold = len(f_terms) + len(pt_terms)\n",
    "pt_mask = np.ones(original.shape)\n",
    "pt_mask[len(f_terms):threshold , len(f_terms):threshold] = 0\n",
    "o_mask = np.ones(original.shape)\n",
    "o_mask[threshold:: , threshold::] = 0\n",
    "\n",
    "plt.subplots(1,1,figsize=(9, 9))\n",
    "plt.subplot(111)\n",
    "sns.heatmap(original, cmap=bwhite, center=0.001, cbar=None, square=True, xticklabels=False, yticklabels=False)\n",
    "sns.heatmap(original, mask = f_mask, cmap=rwhite, center=graph_holder, cbar=None, square=True, xticklabels=False, yticklabels=False)\n",
    "sns.heatmap(original, mask = pt_mask, cmap=gwhite, center=graph_holder, cbar=None, square=True, xticklabels=False, yticklabels=False)\n",
    "sns.heatmap(original, mask = o_mask, cmap=blwhite, center=graph_holder, cbar=None, square=True, xticklabels=False, yticklabels=False)\n",
    "borders(1.5, 'k')\n",
    "\n",
    "plt.title('Capability Matrix Absolute: {}'.format(year_in_focus))\n",
    "plt.savefig(\"Capability_Matrix.png\", dpi=300, quality=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:36: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:53: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## call functions\n",
    "colors = 'binary'\n",
    "year_in_focus = 2017\n",
    "# create a subplot\n",
    "\n",
    "plt.subplots(1,1,figsize=(9, 9))\n",
    "plt.subplot(111)\n",
    "sns.heatmap(get_year_matrix(year_in_focus, normalization=True) , cmap=colors,  cbar=True,cbar_kws={\"shrink\": .2}, square=True, xticklabels=False, yticklabels=False, vmin=0.00, vmax=0.05)\n",
    "borders(1.5, 'k')\n",
    "\n",
    "plt.title('Capability Matrix Normalized: {}'.format(year_in_focus))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Year profiles <a class=\"anchor\" id=\"one-three\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to analyse the correlation of the years between themselves, we will need to transform each year matrix into a list. Since the matrix is symmetrical, we will only need the upper triangle. For control purposes, we have designed our own upper triangulization matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_list_from(matrix):\n",
    "    only_valuable = []\n",
    "    extension = 1\n",
    "    for row_number in range(matrix.shape[0]):\n",
    "        only_valuable.append(matrix[row_number, extension:matrix.shape[0]].tolist()) # numpy functions keep 0s so I hard coded it. \n",
    "        extension += 1 \n",
    "    return [element for column in only_valuable for element in column ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us print the capability lists of two example years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:36: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:53: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 61776)\n",
      "(2, 3700)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# apply functions to both countries\n",
    "a_list = get_list_from(get_year_matrix(2012, normalization=True))\n",
    "b_list = get_list_from(get_year_matrix(2013, normalization=True))\n",
    "\n",
    "# create a matrix where each row is a list of a country\n",
    "corelation = np.vstack((a_list, b_list))\n",
    "print corelation.shape\n",
    "good_cols = [i for i in range(corelation.shape[1]) if np.sum(corelation[:, i]) != 0]\n",
    "        \n",
    "good_corelation = corelation[:, good_cols]\n",
    "print good_corelation.shape\n",
    "\n",
    "# plot the matrix \n",
    "plt.subplots(1,1,figsize=(20, 5))\n",
    "plt.subplot(111)\n",
    "sns.heatmap(good_corelation,cmap=ListedColormap(['white', 'black']), center=0.00000001, cbar=None, square=False, yticklabels=['2012', '2013'], xticklabels=False)\n",
    "plt.yticks(rotation=0)\n",
    "plt.title('Year Capability List Visualization', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is already apparent that these two consecutive years are highly correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Year Correlation Matrix <a class=\"anchor\" id=\"two\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Considerations <a class=\"anchor\" id=\"two-one\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As previously done with countries, a year correlation matrix will be built. \n",
    "\n",
    "We first define the scope of the matrix, by defining which years will be analyzed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1938, 1975, 1980, 1981, 1983, 1985, 1986, 1988, 1989, 1990, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]\n"
     ]
    }
   ],
   "source": [
    "number_of_years = len(years_available)\n",
    "years_in_matrix = years_available\n",
    "years_correlation = np.zeros([number_of_years, number_of_years])\n",
    "print years_in_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looping over each year and calculating its capability list, we create a correlation matrix. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a ductionnary where every key is a year, and its value the capability list of that same year. We do this to reduce memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:36: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:53: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "year_capability_dictionnary = {}\n",
    "\n",
    "for year in years_in_matrix: \n",
    "    year_capability_dictionnary[year] = get_list_from(get_year_matrix(year, normalization=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/scipy/stats/stats.py:3038: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in double_scalars\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# for every year A\n",
    "for row in range(number_of_years):\n",
    "    year_1_list = year_capability_dictionnary[years_in_matrix[row]] # get its capability list\n",
    "    \n",
    "    # for every year B\n",
    "    for column in range(number_of_years):\n",
    "        year_2_list = year_capability_dictionnary[years_in_matrix[column]] # get its capability list\n",
    "        years_correlation[row, column] = stats.pearsonr(year_1_list, year_2_list)[0] # calculate the correlation between the two and place it in the matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now print the correlation matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,1,figsize=(9, 9))\n",
    "plt.subplot(111)\n",
    "sns.heatmap(years_correlation,square=True, cbar=True,cbar_kws={\"shrink\": .2}, yticklabels=years_in_matrix, xticklabels=years_in_matrix)\n",
    "plt.title('Years Correlation Matrix: Unordered', size=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a lot of data missing. \n",
    "\n",
    "Let's plot the amount of records in our databse over time to get a better sense on how to approach the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:2: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get all of the data\n",
    "data = DataFrame(connection_to_graph.data(years_available_q)).as_matrix()\n",
    "raw = [int(a) for a in data[:-1, 0]]\n",
    "timeline = range(min(raw), max(raw))\n",
    "qtties = []\n",
    "\n",
    "# build a timeline and number of records. \n",
    "for year in timeline:\n",
    "    if year not in raw:\n",
    "        qtties.append(0)\n",
    "    else: \n",
    "        idx = list(data[:, 0]).index(str(year))\n",
    "        qtties.append(data[idx, 1])\n",
    "\n",
    "# re arrange it\n",
    "amountOfRecords = np.column_stack((timeline, qtties))\n",
    "\n",
    "# plot the graph\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.subplots(1,1,figsize=(16, 5))\n",
    "plt.subplot(111)\n",
    "plt.title(\"Number of assets over time\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Available assets\")\n",
    "plt.plot(timeline, qtties)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Final Year Correlation Matrix <a class=\"anchor\" id=\"two-two\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To counteract the fact that our dataset is not uniformily distributed across the years, we will only consider the last 15 years. [2004-2018]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "number_of_years = 22\n",
    "years_in_matrix = years_available[:-1][-number_of_years:]\n",
    "years_correlation = np.zeros([number_of_years, number_of_years])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now rebuild and plot the heatmap of correlations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the matrix again. \n",
    "for row in range(number_of_years):\n",
    "    year_1_list = year_capability_dictionnary[years_in_matrix[row]]\n",
    "    for column in range(number_of_years):        \n",
    "        year_2_list = year_capability_dictionnary[years_in_matrix[column]]\n",
    "        years_correlation[row, column] = stats.pearsonr(year_1_list, year_2_list)[0]\n",
    "\n",
    "# print it\n",
    "plt.subplots(1,1,figsize=(8, 8))\n",
    "plt.subplot(111)\n",
    "sns.heatmap(years_correlation, cbar=True, cbar_kws={\"shrink\": .5},square=True, yticklabels=years_in_matrix, xticklabels=years_in_matrix)\n",
    "plt.title('Years Correlation Matrix: Chronologically Ordered, last {} years'.format(number_of_years), size=13)\n",
    "plt.savefig(\"Year_Correlation.png\", dpi=300, quality=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Year correlation matrix clustering <a class=\"anchor\" id=\"two-three\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us reorder the heatmap according to hierarchical clustering. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the clustermap\n",
    "a = sns.clustermap(years_correlation,  figsize=(8, 8), xticklabels = years_in_matrix, yticklabels=years_in_matrix)\n",
    "plt.savefig(\"Year_Correlation_Clustered.png\", dpi=300, quality=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Correlation of years over time <a class=\"anchor\" id=\"three\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see how related is each year in our matrix with the one before it. In this way we might more easily detect discripancies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove first year\n",
    "advanced_timeline = years_in_matrix[1::]\n",
    "corr_with_pre = []\n",
    "\n",
    "# iterate years and see their correlation \n",
    "row = 1\n",
    "col = 0\n",
    "for year in advanced_timeline:\n",
    "    corr_with_pre.append(years_correlation[row, col])\n",
    "    row = row + 1\n",
    "    col = col + 1\n",
    "\n",
    "# plot\n",
    "plt.subplots(1,1,figsize=(15,7))\n",
    "pal = sns.color_palette(\"Reds\", len(data))\n",
    "sns.barplot(np.arange(len(corr_with_pre)), corr_with_pre, palette=np.array(pal[::-1])[np.asarray(corr_with_pre).argsort().argsort()] )\n",
    "plt.xticks(np.arange(len(corr_with_pre)), advanced_timeline, rotation=90, fontsize=11)\n",
    "plt.title('Correlation of year with previous year')\n",
    "plt.ylabel('Pearson Correlation Index')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some years, such as 2006 or 2007 appear to have very low correlations with the years after. There seems to be an overall tendency of augmenting correlation with the years. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Research terms over time <a class=\"anchor\" id=\"four\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following part of the analysis wil focus on how certain process variables (Feedstocks, Processing Technologies and Outputs) evolve over time.\n",
    "\n",
    "This can help in answering questions such as for example:\n",
    "\n",
    "- Is the focus on a certain processing technology constant over time?\n",
    "- Is this evolution correlated with other external factors? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by creating a function such as: \n",
    "\n",
    "f(term, type of process variable) = [array with the number of records containing the term in each year]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "def get_records_of(startYear, endYear, term, process_type):\n",
    "    \n",
    "    # make query    \n",
    "    yearRangeQuery = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:{})\n",
    "                WHERE fs.term = \"{}\"\n",
    "                AND (toInteger(a.year)>={} AND toInteger(a.year)<={}) \n",
    "                AND NOT a.year = \"Null\"\n",
    "                RETURN  a.year, count(a)\n",
    "                ORDER BY a.year   \"\"\".format(process_type, term, startYear, endYear)\n",
    "    \n",
    "    # extract matrix\n",
    "    rawQuery = DataFrame(connection_to_graph.data(yearRangeQuery)).as_matrix()\n",
    "    \n",
    "    # create matrix to store years, docs and total docs\n",
    "    normalTimeline = np.arange(startYear, endYear + 1)\n",
    "    completeMatrix = np.transpose(np.vstack((normalTimeline, normalTimeline, normalTimeline, normalTimeline)))    \n",
    "    completeMatrix[:, 1::] = 0 \n",
    "\n",
    "    # add number of docs found by query to matrix\n",
    "    for i in range(len(rawQuery[:, 0])):\n",
    "        for j in range(len(completeMatrix[:, 0])):\n",
    "            if int(rawQuery[i, 0]) == completeMatrix[j, 0]:\n",
    "                completeMatrix[j, 1] = rawQuery[i, 1] \n",
    "    \n",
    "    # add total number of docs in that year to matrix\n",
    "    for i in range(len(completeMatrix[:, 0])):\n",
    "        for j in range(len(amountOfRecords[:, 0])):\n",
    "            if completeMatrix[i, 0] == amountOfRecords[j, 0]:\n",
    "                completeMatrix[i, 2] = amountOfRecords[j, 1]\n",
    "\n",
    "    # create a list of the normalized results \n",
    "    normalizedRecords = []\n",
    "    for i in range(len(completeMatrix[:, 0])):\n",
    "        if completeMatrix[i, 2] != 0:\n",
    "            normalizedRecords.append(float(completeMatrix[i, 1])/float(completeMatrix[i, 2]))\n",
    "        else:\n",
    "            normalizedRecords.append(0)\n",
    "         \n",
    "    # return a dictionnary for easy access to all variables\n",
    "    result = {}\n",
    "    result['range']  = completeMatrix[:, 0].tolist()\n",
    "    result['nominal'] = completeMatrix[:, 1].tolist()\n",
    "    result['total'] = completeMatrix[:, 2].tolist()        \n",
    "    result['normalized'] = normalizedRecords\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the function is built, we can plot virtually any evolution.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Evolution of output terms <a class=\"anchor\" id=\"four-one\"></a>\n",
    "\n",
    "Let us see the evolution of records of biogas Vs. ethanol as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "listOfOutputs = ['biogas', 'ethanol', 'biodiesel']\n",
    "start_year = 1990\n",
    "end_year = 2017\n",
    "\n",
    "# plot the graph\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.subplots(1,1,figsize=(16, 5))\n",
    "plt.subplot(111)\n",
    "plt.title(\"Evolution of Records with focus on Output\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Normalized Quantity\")\n",
    "\n",
    "for name in listOfOutputs:\n",
    "    nameData = get_records_of(start_year,end_year,name, 'Output')\n",
    "    plt.plot(nameData['range'], nameData['normalized'], label=name)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Evolution of processing technology terms <a class=\"anchor\" id=\"four-two\"></a>\n",
    "\n",
    "Let us develop the same procedure for some processing technologies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "listOfProcTech = ['fermentation','enzymatic hydrolysis','hydrolysis' ]\n",
    "start_year = 1990\n",
    "end_year = 2017\n",
    "\n",
    "# plot the graph\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.subplots(1,1,figsize=(16, 5))\n",
    "plt.subplot(111)\n",
    "plt.title(\"Evolution of Records with focus on Processing Technologies\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Normalized Quantity\")\n",
    "\n",
    "for name in listOfProcTech:\n",
    "    nameData = get_records_of(start_year,end_year,name, 'ProcessingTech')\n",
    "    plt.plot(nameData['range'], nameData['normalized'], label=name)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Evolution of feedstock terms <a class=\"anchor\" id=\"four-three\"></a>\n",
    "\n",
    "Let us develop the same procedure for feedstock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "listOfFeed = ['sugar','wood','paper', 'algae', 'waste']\n",
    "start_year = 1990\n",
    "end_year = 2017\n",
    "\n",
    "# plot the graph\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.subplots(1,1,figsize=(16, 5))\n",
    "plt.subplot(111)\n",
    "plt.title(\"Evolution of Records with focus on Feedstocks\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Normalized Quantity\")\n",
    "\n",
    "for name in listOfFeed:\n",
    "    nameData = get_records_of(start_year,end_year,name, 'Feedstock')\n",
    "    plt.plot(nameData['range'], nameData['normalized'], label=name)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Contextual relationships <a class=\"anchor\" id=\"five\"></a>\n",
    "\n",
    "### 5.1. Oil <a class=\"anchor\" id=\"five-one\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by comparing the evolution of the outputs above studied with the average oil price per gallon found in the [following](https://fred.stlouisfed.org/series/GASREGCOVM#0) website.\n",
    "\n",
    "We import the data, and convert monthly prices to yearly averages with the bellow code. \n",
    "\n",
    "- [Price per gallon in US dollars](https://fred.stlouisfed.org/series/GASREGCOVM#0)\n",
    "- [Price per barrel inflation adjusted in US dollars](https://inflationdata.com/Inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:2: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:16: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# get price per gallon in US dollars\n",
    "oil_data = pd.read_csv('Data/GasData.csv', delimiter=',', header=None).as_matrix()[1::, :]\n",
    "gallon = []\n",
    "oil_years = list(set([int(e[0:4]) for e in oil_data[:, 0]]))[:-1]\n",
    "for year in oil_years:\n",
    "    addition = 0\n",
    "    months = 0\n",
    "    for row in oil_data:\n",
    "        if str(year) in row[0]:\n",
    "            addition += float(row[1])\n",
    "            months += 1\n",
    "    average = addition / months\n",
    "    gallon.append(average)\n",
    "\n",
    "# get price per barrel data \n",
    "barrel = pd.read_csv('Data/GasDataNormalized.csv', delimiter=';', header=None).as_matrix()[:, 1].tolist()\n",
    "\n",
    "oil_index = {'gallon':gallon, 'barrel':barrel}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Relationship Over Time**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us visualize how the evolution of the price of gas relates to the normalized quantity of assets over time, in a chronological graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define subplots\n",
    "fig, ax1 = plt.subplots(figsize=(15,7))\n",
    "listOfOutputs = ['biogas', 'bioplastic', 'butanol']\n",
    "colors = ['b', 'y', 'g']\n",
    "start_year = 1990\n",
    "end_year = 2017\n",
    "price_type = 'barrel'\n",
    "\n",
    "# first axis\n",
    "\n",
    "for position, outputName in enumerate(listOfOutputs):\n",
    "    nameData = get_records_of(start_year, end_year, outputName, 'Output')\n",
    "    ax1.plot(nameData['range'], nameData['normalized'], label=outputName, color=colors[position], ls='--', alpha=0.5) \n",
    "    \n",
    "\n",
    "ax1.set_xlabel('Years')\n",
    "ax1.set_ylabel('Number of relative records')\n",
    "ax1.tick_params('y')\n",
    "ax1.set_title('Oil Price Vs. Asset Quantity')\n",
    "ax1.legend(loc=2, frameon=True)\n",
    "ax1.grid(False)\n",
    "\n",
    "# second axis\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(oil_years,oil_index[price_type], color='r', label='Oil Price')\n",
    "ax2.set_ylabel('Price of {} of oil $US'.format(price_type), color='r')\n",
    "ax2.tick_params('y', colors='r')\n",
    "ax2.legend(loc=1, frameon=True)\n",
    "\n",
    "# expose\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scatter Visualization**\n",
    "\n",
    "To study this relationship in a more in depth fashion we create a process that given a certain term gives us the relationship with the price of gas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Index:  0.8445465111603638\n",
      "P-value:  1.6031894575347735e-08\n"
     ]
    }
   ],
   "source": [
    "# define terms\n",
    "outPutToCompare = 'butanol'\n",
    "typeOfProcessVariable = 'Output'\n",
    "price_type = 'gallon'\n",
    "\n",
    "# get data\n",
    "data = get_records_of(1990, 2017, outPutToCompare, typeOfProcessVariable)['normalized']\n",
    "\n",
    "# plot the figure\n",
    "fig, ax1 = plt.subplots(figsize=(15,7))\n",
    "sns.regplot(np.asarray(oil_index[price_type]), np.asarray(data) ,fit_reg=True, marker=\"+\", color = 'g')\n",
    "plt.title('Gas price relation with quantity of Assets: {}'.format(outPutToCompare))\n",
    "plt.xlabel('Price of {} of oil in US$ in Year'.format(price_type))\n",
    "plt.ylabel('Quantity of Asset {} in Year'.format(outPutToCompare))\n",
    "plt.show()\n",
    "\n",
    "# get correlation indexes\n",
    "correlationIndexes =  stats.pearsonr(np.asarray(oil_index[price_type]), np.asarray(get_records_of(1990, 2017, outPutToCompare, 'Output')['normalized']))\n",
    "print 'Pearson Correlation Index: ', correlationIndexes[0]\n",
    "print 'P-value: ', correlationIndexes[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above graph each datapoint corresponds to a year. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Biggest Positive Correlations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:10: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The relationship between relative number of documents and price of oil over time:\n",
      "TOP 10:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Output Name</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Pearson Correlation Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>butanol</td>\n",
       "      <td>1.603189e-08</td>\n",
       "      <td>0.844547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>bioplastic</td>\n",
       "      <td>2.463734e-07</td>\n",
       "      <td>0.804599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>biodiesel</td>\n",
       "      <td>7.978637e-07</td>\n",
       "      <td>0.784034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fatty acid ethyl ester</td>\n",
       "      <td>1.427601e-06</td>\n",
       "      <td>0.772960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>adipic acid</td>\n",
       "      <td>1.048009e-05</td>\n",
       "      <td>0.729790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bioethanol</td>\n",
       "      <td>2.862862e-05</td>\n",
       "      <td>0.704439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>syng</td>\n",
       "      <td>3.649295e-05</td>\n",
       "      <td>0.697899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>biobutanol</td>\n",
       "      <td>5.140385e-05</td>\n",
       "      <td>0.688369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cellulosic ethanol</td>\n",
       "      <td>1.301892e-04</td>\n",
       "      <td>0.660616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>biopolymers</td>\n",
       "      <td>3.263515e-04</td>\n",
       "      <td>0.630094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Output Name       P-value  Pearson Correlation Index\n",
       "7                  butanol  1.603189e-08                   0.844547\n",
       "19              bioplastic  2.463734e-07                   0.804599\n",
       "1                biodiesel  7.978637e-07                   0.784034\n",
       "21  fatty acid ethyl ester  1.427601e-06                   0.772960\n",
       "30             adipic acid  1.048009e-05                   0.729790\n",
       "3               bioethanol  2.862862e-05                   0.704439\n",
       "9                     syng  3.649295e-05                   0.697899\n",
       "15              biobutanol  5.140385e-05                   0.688369\n",
       "8       cellulosic ethanol  1.301892e-04                   0.660616\n",
       "14             biopolymers  3.263515e-04                   0.630094"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# query for data\n",
    "term_names_query = \"\"\"  MATCH (a:Asset)-[:CONTAINS]->(fs:Output)\n",
    "                        WHERE (toInteger(a.year)>=1990 AND toInteger(a.year)<=2017) \n",
    "                        AND NOT a.year = \"Null\"\n",
    "                        RETURN  fs.term, count(a)\n",
    "                        ORDER BY count(a) DESC\"\"\"\n",
    "\n",
    "# get data from past scripts\n",
    "oil_type = 'gallon'\n",
    "term_names = list(DataFrame(connection_to_graph.data(term_names_query)).as_matrix()[:, 1].tolist())\n",
    "correlations = []\n",
    "p_values = []\n",
    "\n",
    "# for every term, get its correlation with the price of oil\n",
    "for term in term_names:\n",
    "    data = get_records_of(1990, 2017, term, 'Output')['normalized']\n",
    "    correlations.append(stats.pearsonr(data, oil_index[oil_type])[0])\n",
    "    p_values.append(stats.pearsonr(data, oil_index[oil_type])[1])\n",
    "\n",
    "\n",
    "# create a pandas dataframe for pretty printing. \n",
    "oilDataFrame = pd.DataFrame(\n",
    "    {'Output Name': term_names,\n",
    "     'Pearson Correlation Index': correlations,\n",
    "     'P-value': p_values\n",
    "    })\n",
    "oilDataFrame = oilDataFrame.sort_values('Pearson Correlation Index', ascending=False)\n",
    "\n",
    "# print context\n",
    "print 'The relationship between relative number of documents and price of oil over time:'\n",
    "top = 10\n",
    "\n",
    "# print data\n",
    "print 'TOP {}:'.format(top)\n",
    "display(oilDataFrame[:top]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Biggest Negative Correlations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:8: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The relationship between relative number of documents and price of oil over time:\n",
      "BOTTOM -10:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Output Name</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Pearson Correlation Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>naphtha</td>\n",
       "      <td>0.677733</td>\n",
       "      <td>0.082145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>biodiesel blending</td>\n",
       "      <td>0.683315</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ethanol blending</td>\n",
       "      <td>0.683315</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>renewable diesel</td>\n",
       "      <td>0.716673</td>\n",
       "      <td>0.071767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>renewable fuel</td>\n",
       "      <td>0.944557</td>\n",
       "      <td>0.013770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>succinic acid</td>\n",
       "      <td>0.956893</td>\n",
       "      <td>0.010703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>rdif</td>\n",
       "      <td>0.629618</td>\n",
       "      <td>-0.095276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>electricity from biomass</td>\n",
       "      <td>0.456174</td>\n",
       "      <td>-0.146748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gasoline</td>\n",
       "      <td>0.371514</td>\n",
       "      <td>-0.175570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pellets</td>\n",
       "      <td>0.268436</td>\n",
       "      <td>-0.216520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Output Name   P-value  Pearson Correlation Index\n",
       "16                   naphtha  0.677733                   0.082145\n",
       "44        biodiesel blending  0.683315                   0.080645\n",
       "45          ethanol blending  0.683315                   0.080645\n",
       "18          renewable diesel  0.716673                   0.071767\n",
       "11            renewable fuel  0.944557                   0.013770\n",
       "17             succinic acid  0.956893                   0.010703\n",
       "40                      rdif  0.629618                  -0.095276\n",
       "34  electricity from biomass  0.456174                  -0.146748\n",
       "5                   gasoline  0.371514                  -0.175570\n",
       "10                   pellets  0.268436                  -0.216520"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same approach but value negative correlations\n",
    "term_names_query = \"\"\"  MATCH (a:Asset)-[:CONTAINS]->(fs:Output)\n",
    "                        WHERE (toInteger(a.year)>=1990 AND toInteger(a.year)<=2017) \n",
    "                        AND NOT a.year = \"Null\"\n",
    "                        RETURN  fs.term, count(a)\n",
    "                        ORDER BY count(a) DESC\"\"\"\n",
    "oil_type = 'gallon'\n",
    "term_names = list(DataFrame(connection_to_graph.data(term_names_query)).as_matrix()[:, 1].tolist())\n",
    "correlations = []\n",
    "p_values = []\n",
    "for term in term_names:\n",
    "    data = get_records_of(1990, 2017, term, 'Output')['normalized']\n",
    "    correlations.append(stats.pearsonr(data, oil_index[oil_type])[0])\n",
    "    p_values.append(stats.pearsonr(data, oil_index[oil_type])[1])\n",
    "\n",
    "oilDataFrame = pd.DataFrame(\n",
    "    {'Output Name': term_names,\n",
    "     'Pearson Correlation Index': correlations,\n",
    "     'P-value': p_values\n",
    "    })\n",
    "oilDataFrame = oilDataFrame.sort_values('Pearson Correlation Index', ascending=False)\n",
    "\n",
    "\n",
    "\n",
    "print 'The relationship between relative number of documents and price of oil over time:'\n",
    "bottom = -10\n",
    "\n",
    "print 'BOTTOM {}:'.format(bottom)\n",
    "display(oilDataFrame[bottom:]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2. Sugar <a class=\"anchor\" id=\"five-two\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part we will make the same analysis but taking an example of a feedstock: sugar. \n",
    "\n",
    "Data was obtained [here.](http://databank.worldbank.org/data/reports.aspx?source=global-economic-monitor-commodities#)\n",
    "\n",
    "We start by importing the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:1: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sugar_data = pd.read_csv('Data/Sugar_Price.csv', delimiter=';', header=None).as_matrix()\n",
    "sugar = {}\n",
    "sugar['years'] = [int(e) for e in sugar_data[:, 0]]\n",
    "sugar['nominal'] = [e for e in sugar_data[:, 1]]\n",
    "sugar['real'] =  [e for e in sugar_data[:, 2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Relationship Over Time**\n",
    "\n",
    "Let us see the evolution of Sugar prices side by side with the evolution of certain feedstocks in our database. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define subplots\n",
    "fig, ax1 = plt.subplots(figsize=(15,7))\n",
    "feedstock_list = ['sugar', 'wood', 'sugarcane', 'sugar beet', 'cellulosic sugars']\n",
    "colors = ['gold', 'mediumblue', 'm', 'green', 'k']\n",
    "\n",
    "start_year = 1990\n",
    "end_year = 2017\n",
    "sugar_price_type = 'real'\n",
    "\n",
    "# first axis\n",
    "for position,feedstock in enumerate(feedstock_list):\n",
    "    data = get_records_of(start_year, end_year, feedstock, 'Feedstock')\n",
    "    ax1.plot(data['range'], data['normalized'], label=feedstock, ls='--', color=colors[position])\n",
    "    \n",
    "ax1.set_xlabel('Years')\n",
    "ax1.set_ylabel('Relative number of records')\n",
    "ax1.tick_params('y')\n",
    "ax1.set_title('Sugar Prices Vs. Asset Quantity')\n",
    "ax1.legend(loc=3, frameon=True)\n",
    "ax1.grid(False)\n",
    "\n",
    "# second axis\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(sugar['years'], sugar[sugar_price_type], color='r', label='Sugar Price', ls='-')\n",
    "ax2.set_ylabel('Price per kilo of sugar in $US (inflation adjusted)', color='r')\n",
    "ax2.tick_params('y', colors='r')\n",
    "ax2.legend(loc=1, frameon=True)\n",
    "\n",
    "# expose\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scatter Example**\n",
    "\n",
    "Let us see a scatter plot where each point is a year and the x and y axis correpond to the price of sugar and quantity of assets respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outPutToCompare = 'sugarcane'\n",
    "typeOfProcessVariable = 'Feedstock'\n",
    "price_type = 'real'\n",
    "\n",
    "\n",
    "data = get_records_of(1990, 2017, outPutToCompare, typeOfProcessVariable)['normalized']\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(15,7))\n",
    "sns.regplot(np.asarray(sugar[price_type]), np.asarray(data) ,fit_reg=True, marker=\"+\", color = 'b')\n",
    "plt.title('Sugar price relation with quantity of Assets: {}'.format(outPutToCompare))\n",
    "plt.xlabel('Price of sugar US$ per kilo in Year ({})'.format(price_type))\n",
    "plt.ylabel('Quantity of Asset {} in Year'.format(outPutToCompare))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Biggest Positive Correlations**\n",
    "\n",
    "Which are the feedstocks who are more related to the price of sugar per kilo in what regards the number of records? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:7: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The relationship between relative number of documents and price per kilo of sugar:\n",
      "TOP 10:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feedstock Name</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Pearson Correlation Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>sugarcane</td>\n",
       "      <td>6.074365e-07</td>\n",
       "      <td>0.789014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>cellulosic sugars</td>\n",
       "      <td>1.263220e-06</td>\n",
       "      <td>0.775341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>jatropha</td>\n",
       "      <td>1.521222e-06</td>\n",
       "      <td>0.771713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>sorghum</td>\n",
       "      <td>3.083429e-06</td>\n",
       "      <td>0.757299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>dry biomass</td>\n",
       "      <td>3.454736e-06</td>\n",
       "      <td>0.754884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>beets</td>\n",
       "      <td>4.105286e-06</td>\n",
       "      <td>0.751165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>dedicated energy crops</td>\n",
       "      <td>6.915371e-06</td>\n",
       "      <td>0.739525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>algae</td>\n",
       "      <td>1.103076e-05</td>\n",
       "      <td>0.728562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>hybrid poplar</td>\n",
       "      <td>1.490683e-05</td>\n",
       "      <td>0.721206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>soy</td>\n",
       "      <td>2.631077e-05</td>\n",
       "      <td>0.706675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Feedstock Name       P-value  Pearson Correlation Index\n",
       "26                sugarcane  6.074365e-07                   0.789014\n",
       "107       cellulosic sugars  1.263220e-06                   0.775341\n",
       "43                 jatropha  1.521222e-06                   0.771713\n",
       "34                  sorghum  3.083429e-06                   0.757299\n",
       "64              dry biomass  3.454736e-06                   0.754884\n",
       "75                    beets  4.105286e-06                   0.751165\n",
       "99   dedicated energy crops  6.915371e-06                   0.739525\n",
       "1                     algae  1.103076e-05                   0.728562\n",
       "100           hybrid poplar  1.490683e-05                   0.721206\n",
       "25                      soy  2.631077e-05                   0.706675"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "term_names_query = \"\"\"  MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock)\n",
    "                        WHERE (toInteger(a.year)>=1990 AND toInteger(a.year)<=2017) \n",
    "                        AND NOT a.year = \"Null\"\n",
    "                        RETURN  fs.term, count(a)\n",
    "                        ORDER BY count(a) DESC\"\"\"\n",
    "price_type = 'nominal'\n",
    "term_names = list(DataFrame(connection_to_graph.data(term_names_query)).as_matrix()[:, 1].tolist())\n",
    "correlations = []\n",
    "p_values = []\n",
    "for term in term_names:\n",
    "    data = get_records_of(1990, 2017, term, 'Feedstock')['normalized']\n",
    "    correlations.append(stats.pearsonr(data, sugar[price_type])[0])\n",
    "    p_values.append(stats.pearsonr(data, sugar[price_type])[1])\n",
    "\n",
    "sugarDataframe = pd.DataFrame(\n",
    "    {'Feedstock Name': term_names,\n",
    "     'Pearson Correlation Index': correlations,\n",
    "     'P-value': p_values\n",
    "    })\n",
    "sugarDataframe = sugarDataframe.sort_values('Pearson Correlation Index', ascending=False)\n",
    "\n",
    "\n",
    "\n",
    "print 'The relationship between relative number of documents and price per kilo of sugar:'\n",
    "top = 10\n",
    "\n",
    "print 'TOP {}:'.format(top)\n",
    "display(sugarDataframe[:top]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Biggest Negative Correlations**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:7: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:13: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The relationship between relative number of documents and price per kilo of sugar:\n",
      "Bottom 10:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feedstock Name</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Pearson Correlation Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>wood fuel</td>\n",
       "      <td>0.336664</td>\n",
       "      <td>-0.188530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>waste oil</td>\n",
       "      <td>0.329527</td>\n",
       "      <td>-0.191282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>particle board</td>\n",
       "      <td>0.289545</td>\n",
       "      <td>-0.207425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>durum</td>\n",
       "      <td>0.279399</td>\n",
       "      <td>-0.211741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>citrus residues</td>\n",
       "      <td>0.260452</td>\n",
       "      <td>-0.220080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>beef tallow</td>\n",
       "      <td>0.240798</td>\n",
       "      <td>-0.229158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>sawdust</td>\n",
       "      <td>0.223542</td>\n",
       "      <td>-0.237542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>trap grease</td>\n",
       "      <td>0.212719</td>\n",
       "      <td>-0.243025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>wood waste</td>\n",
       "      <td>0.210637</td>\n",
       "      <td>-0.244102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>wood</td>\n",
       "      <td>0.137684</td>\n",
       "      <td>-0.287685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feedstock Name   P-value  Pearson Correlation Index\n",
       "93         wood fuel  0.336664                  -0.188530\n",
       "72         waste oil  0.329527                  -0.191282\n",
       "136   particle board  0.289545                  -0.207425\n",
       "156            durum  0.279399                  -0.211741\n",
       "178  citrus residues  0.260452                  -0.220080\n",
       "123      beef tallow  0.240798                  -0.229158\n",
       "53           sawdust  0.223542                  -0.237542\n",
       "138      trap grease  0.212719                  -0.243025\n",
       "79        wood waste  0.210637                  -0.244102\n",
       "5               wood  0.137684                  -0.287685"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "term_names_query = \"\"\"  MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock)\n",
    "                        WHERE (toInteger(a.year)>=1990 AND toInteger(a.year)<=2017) \n",
    "                        AND NOT a.year = \"Null\"\n",
    "                        RETURN  fs.term, count(a)\n",
    "                        ORDER BY count(a) DESC\"\"\"\n",
    "price_type = 'nominal'\n",
    "term_names = list(DataFrame(connection_to_graph.data(term_names_query)).as_matrix()[:, 1].tolist())\n",
    "correlations = []\n",
    "p_values = []\n",
    "for term in term_names:\n",
    "    data = get_records_of(1990, 2017, term, 'Feedstock')['normalized']\n",
    "    correlations.append(stats.pearsonr(data, sugar[price_type])[0])\n",
    "    p_values.append(stats.pearsonr(data, sugar[price_type])[1])\n",
    "\n",
    "sugarDataframe = pd.DataFrame(\n",
    "    {'Feedstock Name': term_names,\n",
    "     'Pearson Correlation Index': correlations,\n",
    "     'P-value': p_values\n",
    "    })\n",
    "sugarDataframe = sugarDataframe.sort_values('Pearson Correlation Index', ascending=False)\n",
    "\n",
    "\n",
    "\n",
    "print 'The relationship between relative number of documents and price per kilo of sugar:'\n",
    "bottom = -10\n",
    "\n",
    "print 'Bottom {}:'.format(bottom * -1)\n",
    "display(sugarDataframe[bottom:]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NON SERIES TIME ANALYSIS IS A LIMITATION.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Comparing Years <a class=\"anchor\" id=\"six\"></a>\n",
    "\n",
    "In this part of the analysis the goal is two understand what exact capabilities differ from year to year. More exactly, how does one particular capability evolve over the course of two or more years. \n",
    "\n",
    "For example, if in year X1, Y1% of the assets related to sugar, what is the percentage Y2% in year X2? \n",
    "\n",
    "### 6.1. Visualizing the differences  <a class=\"anchor\" id=\"six-one\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us visualize two different years side by side. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:36: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:53: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1224x1224 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## call functions\n",
    "first_year = 2010\n",
    "second_year = 2017\n",
    "colors='binary'\n",
    "\n",
    "graph_holder = 0.005\n",
    "\n",
    "fst_year_matrix = get_year_matrix(first_year, normalization=False)\n",
    "scnd_year_matrix = get_year_matrix(second_year, normalization=False)\n",
    "\n",
    "# create a subplot\n",
    "plt.subplots(2,1,figsize=(17,17))\n",
    "\n",
    "# first heatmap\n",
    "plt.subplot(121)\n",
    "sns.heatmap(fst_year_matrix , cmap=colors,  cbar=True,cbar_kws={\"shrink\": .2}, square=True, xticklabels=False, yticklabels=False, vmax=graph_holder)\n",
    "borders(1.5, 'k')\n",
    "plt.title('Capability Matrix: {}'.format(first_year))\n",
    "\n",
    "# second heatmap\n",
    "plt.subplot(122)\n",
    "sns.heatmap(scnd_year_matrix , cmap=colors,  cbar=True,cbar_kws={\"shrink\": .2}, square=True, xticklabels=False, yticklabels=False, vmax=graph_holder)\n",
    "borders(1.5, 'k')\n",
    "plt.title('Capability Matrix: {}'.format(second_year))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the very high number of rows, visualization is rather hard. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to create a matrix of absolute diferences between the two examples, for this, we start by subtracting them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cap_diff = np.absolute(fst_year_matrix - scnd_year_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we plot these differences. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,1,figsize=(13, 13))\n",
    "plt.subplot(111)\n",
    "sns.heatmap(cap_diff, cmap=colors,cbar_kws={\"shrink\": .2}, square=True, yticklabels=False, xticklabels=False)\n",
    "borders(1.5, 'k')\n",
    "plt.title('Differences between {} and {}: Normalized Differences'.format(first_year, second_year), size=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seem to be some areas where differences clearly exist. Let us investigate these areas in a more in depth fashion. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2. Understanding the differences  <a class=\"anchor\" id=\"six-two|\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's understand what exact capability pairs are the most 'popular' in each year. \n",
    "\n",
    "We start by creating a function that returns given a year X, the most popular capability pairs of that year as absolute numbers and percentage of total documents. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_top_hits(yearMatrix, year):\n",
    "    \"\"\"\n",
    "    The function prints the top occurences if fed a matrix of occurences, it also prints other types of valuable info.\n",
    "    WARNING: Percentages are shown as 0 to 1. \n",
    "    \"\"\"\n",
    "        \n",
    "    # list where all the values and indexes of matrix are stored\n",
    "    top = 10\n",
    "    values = []\n",
    "    indexes = []\n",
    "    no_duplicates = np.triu(yearMatrix, 1)\n",
    "    total_documents_q = \"\"\" MATCH (n:Asset)\n",
    "                            WHERE n.year=\"{}\"\n",
    "                            RETURN count(n)\n",
    "    \"\"\".format(year)\n",
    "    \n",
    "    total_documents = DataFrame(connection_to_graph.data(total_documents_q)).as_matrix()[0][0]\n",
    "    matrix_axis_names = axis_names\n",
    "    \n",
    "    \n",
    "    # loop through the matrix\n",
    "    for row_n in range(yearMatrix.shape[0]):\n",
    "        for col_n in range(yearMatrix.shape[1]):\n",
    "            values.append(no_duplicates[row_n, col_n])\n",
    "            indexes.append((row_n, col_n))\n",
    "    \n",
    "    \n",
    "    # order the indexes and get the top\n",
    "    Z = [indexes for _,indexes in sorted(zip(values,indexes))]\n",
    "    extremes = Z[-top :]\n",
    "    \n",
    "    \n",
    "    # create dataframe\n",
    "    term_Dataframe = pd.DataFrame(\n",
    "        {'First Term': [matrix_axis_names[e[0]] for e in extremes],\n",
    "         'Second Term': [matrix_axis_names[e[1]] for e in extremes],\n",
    "         'Number of Documents': [int(no_duplicates[e[0], e[1]]) for e in extremes], \n",
    "         'Percentage' : [no_duplicates[e[0], e[1]] / float(total_documents) for e in extremes], \n",
    "        })\n",
    "    \n",
    "    # prepare dataframe\n",
    "    term_Dataframe = term_Dataframe[['First Term', 'Second Term','Number of Documents', 'Percentage']]\n",
    "    term_Dataframe = term_Dataframe.sort_values('Number of Documents', ascending=False)\n",
    "    \n",
    "    \n",
    "    # print everything\n",
    "    print 'The top hits for the {} matrix: '.format(year)\n",
    "    display(HTML(term_Dataframe.to_html(index=False)))\n",
    "    \n",
    "    \n",
    "    print 'The total number of documents is {}.'.format(int(total_documents))\n",
    "    print 'Note: Percentages are as 0-1 in this table. '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us use this function to try to understand each year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us get the top term pairs for the year of 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top hits for the 2010 matrix: \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:17: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>First Term</th>\n",
       "      <th>Second Term</th>\n",
       "      <th>Number of Documents</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>fermentation</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>319</td>\n",
       "      <td>0.350935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hydrolysis</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>225</td>\n",
       "      <td>0.247525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>transesterification</td>\n",
       "      <td>biodiesel</td>\n",
       "      <td>168</td>\n",
       "      <td>0.184818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anaerobic digestion</td>\n",
       "      <td>biogas</td>\n",
       "      <td>152</td>\n",
       "      <td>0.167217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>catalysis</td>\n",
       "      <td>biodiesel</td>\n",
       "      <td>131</td>\n",
       "      <td>0.144114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fermentation</td>\n",
       "      <td>bioethanol</td>\n",
       "      <td>120</td>\n",
       "      <td>0.132013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sugar</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>106</td>\n",
       "      <td>0.116612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sugar</td>\n",
       "      <td>fermentation</td>\n",
       "      <td>102</td>\n",
       "      <td>0.112211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hydrolysis</td>\n",
       "      <td>bioethanol</td>\n",
       "      <td>95</td>\n",
       "      <td>0.104510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>enzymatic hydrolysis</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>85</td>\n",
       "      <td>0.093509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of documents is 909.\n",
      "Note: Percentages are as 0-1 in this table. \n"
     ]
    }
   ],
   "source": [
    "get_top_hits(fst_year_matrix, first_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us get the top term pairs for the year of 2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:17: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top hits for the 2017 matrix: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>First Term</th>\n",
       "      <th>Second Term</th>\n",
       "      <th>Number of Documents</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>fermentation</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>154</td>\n",
       "      <td>0.229851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anaerobic digestion</td>\n",
       "      <td>biogas</td>\n",
       "      <td>137</td>\n",
       "      <td>0.204478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pyrolysis</td>\n",
       "      <td>bio-oil</td>\n",
       "      <td>101</td>\n",
       "      <td>0.150746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hydrolysis</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>76</td>\n",
       "      <td>0.113433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fermentation</td>\n",
       "      <td>bioethanol</td>\n",
       "      <td>76</td>\n",
       "      <td>0.113433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sugar</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>60</td>\n",
       "      <td>0.089552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>waste</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>58</td>\n",
       "      <td>0.086567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sugar</td>\n",
       "      <td>fermentation</td>\n",
       "      <td>57</td>\n",
       "      <td>0.085075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>waste</td>\n",
       "      <td>biogas</td>\n",
       "      <td>53</td>\n",
       "      <td>0.079104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fermentation</td>\n",
       "      <td>biogas</td>\n",
       "      <td>53</td>\n",
       "      <td>0.079104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of documents is 670.\n",
      "Note: Percentages are as 0-1 in this table. \n"
     ]
    }
   ],
   "source": [
    "get_top_hits(scnd_year_matrix, second_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make two observations: \n",
    "- These two particular years have generally the same term pairs in their top table. \n",
    "- However, the percentages can differ greatly. \n",
    "\n",
    "*Note: There is a high difference in number of documents. *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now finally create a side by side comparison. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:31: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:36: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n",
      "/Users/duarteocarmo/.pyenv/versions/miniconda3-4.3.30/envs/tech-cap/lib/python2.7/site-packages/ipykernel_launcher.py:53: FutureWarning:\n",
      "\n",
      "Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>First Term</th>\n",
       "      <th>Second Term</th>\n",
       "      <th>2010 Percentage</th>\n",
       "      <th>2017 Percentage</th>\n",
       "      <th>Difference in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>hydrolysis</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>0.247525</td>\n",
       "      <td>0.113433</td>\n",
       "      <td>0.134092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>transesterification</td>\n",
       "      <td>biodiesel</td>\n",
       "      <td>0.184818</td>\n",
       "      <td>0.050746</td>\n",
       "      <td>0.134072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fermentation</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>0.350935</td>\n",
       "      <td>0.229851</td>\n",
       "      <td>0.121084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>catalysis</td>\n",
       "      <td>biodiesel</td>\n",
       "      <td>0.144114</td>\n",
       "      <td>0.023881</td>\n",
       "      <td>0.120234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pyrolysis</td>\n",
       "      <td>bio-oil</td>\n",
       "      <td>0.041804</td>\n",
       "      <td>0.150746</td>\n",
       "      <td>0.108942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vegetable oil</td>\n",
       "      <td>transesterification</td>\n",
       "      <td>0.080308</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.072845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>catalysis</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>0.079208</td>\n",
       "      <td>0.010448</td>\n",
       "      <td>0.068760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>transesterification</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>0.089109</td>\n",
       "      <td>0.023881</td>\n",
       "      <td>0.065228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>catalysis</td>\n",
       "      <td>methanol</td>\n",
       "      <td>0.061606</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.060114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anaerobic digestion</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>0.008801</td>\n",
       "      <td>0.067164</td>\n",
       "      <td>0.058363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gasification</td>\n",
       "      <td>syng</td>\n",
       "      <td>0.005501</td>\n",
       "      <td>0.059701</td>\n",
       "      <td>0.054201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vegetable oil</td>\n",
       "      <td>biodiesel</td>\n",
       "      <td>0.084708</td>\n",
       "      <td>0.032836</td>\n",
       "      <td>0.051873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vegetable oil</td>\n",
       "      <td>catalysis</td>\n",
       "      <td>0.063806</td>\n",
       "      <td>0.011940</td>\n",
       "      <td>0.051866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>transesterification</td>\n",
       "      <td>methanol</td>\n",
       "      <td>0.073707</td>\n",
       "      <td>0.022388</td>\n",
       "      <td>0.051319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fermentation</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0.055006</td>\n",
       "      <td>0.004478</td>\n",
       "      <td>0.050528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pressing</td>\n",
       "      <td>ethanol</td>\n",
       "      <td>0.045105</td>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.042119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>solvents</td>\n",
       "      <td>biodiesel</td>\n",
       "      <td>0.033003</td>\n",
       "      <td>0.074627</td>\n",
       "      <td>0.041624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>seed oil</td>\n",
       "      <td>biodiesel</td>\n",
       "      <td>0.038504</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anaerobic digestion</td>\n",
       "      <td>bioethanol</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.038806</td>\n",
       "      <td>0.037706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anaerobic digestion</td>\n",
       "      <td>biogas</td>\n",
       "      <td>0.167217</td>\n",
       "      <td>0.204478</td>\n",
       "      <td>0.037261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentages are as 0-1 in this table for easy viz.\n"
     ]
    }
   ],
   "source": [
    "# list where all the values and indexes of matrix are stored\n",
    "frst_perc = get_year_matrix(first_year, normalization=True)\n",
    "scnd_perc = get_year_matrix(second_year, normalization=True)\n",
    "differences = frst_perc - scnd_perc\n",
    "differences = np.absolute(differences)\n",
    "values = []\n",
    "indexes = []\n",
    "no_duplicates = np.triu(differences, 1)\n",
    "matrix_axis_names = axis_names\n",
    "\n",
    "\n",
    "top = 20\n",
    "\n",
    "# loop through the matrix\n",
    "for row_n in range(differences.shape[0]):\n",
    "    for col_n in range(differences.shape[1]):\n",
    "        values.append(no_duplicates[row_n, col_n])\n",
    "        indexes.append((row_n, col_n))\n",
    "\n",
    "# print the table \n",
    "Z = [indexes for _,indexes in sorted(zip(values,indexes))]\n",
    "extremes = list(reversed(Z[-top:]))\n",
    "\n",
    "\n",
    "term_Dataframe = pd.DataFrame(\n",
    "    {'First Term': [matrix_axis_names[e[0]] for e in extremes],\n",
    "     'Second Term': [matrix_axis_names[e[1]] for e in extremes],\n",
    "     '{} Percentage'.format(first_year): [frst_perc[e[0], e[1]] for e in extremes], \n",
    "     '{} Percentage'.format(second_year): [scnd_perc[e[0], e[1]] for e in extremes], \n",
    "     'Difference in %': [no_duplicates[e[0], e[1]] for e in extremes]\n",
    "    })\n",
    "\n",
    "term_Dataframe = term_Dataframe[['First Term', 'Second Term', '{} Percentage'.format(first_year), '{} Percentage'.format(second_year), 'Difference in %']]\n",
    "\n",
    "\n",
    "display(HTML(term_Dataframe.to_html(index=False)))\n",
    "print 'Percentages are as 0-1 in this table for easy viz.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this visualization we can easily compare the term pairs and see their evolution over the course of the years. "
   ]
  }
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