benvandyke · January 17, 2014 03:54
diff --git a/eia_analysis b/eia_analysis
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Exploring US Energy Trends, 1949 - Present"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ben Van Dyke, January 2014\n",
      "\n",
      "Energy data sourced from the [US Energy Information Administration](http://www.eia.gov/) (EIA). This notebook explores their excellent [Annual Energy Review](http://www.eia.gov/totalenergy/data/annual/index.cfm) dataset."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from __future__ import print_function"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The pandas read_csv method is one of the most useful parts of the library I've discovered thus far. Previously, I used NumPy methods such as load_txt or gen_from_txt which did not handle mixed text/numeric data well and often required additional wrangling to get in a usable format. The pandas read_csv method just works, I've yet to discover a case where it failed to correctly parse the data and pack it into a frame once given the appropriate parameters. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# read_csv returns a dataframe\n",
      "data = pd.read_csv('MER_T01_01.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Peek at the data.\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>MSN</th>\n",
        "      <th>YYYYMM</th>\n",
        "      <th>Value</th>\n",
        "      <th>Column_Order</th>\n",
        "      <th>Description</th>\n",
        "      <th>Unit</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> FFPRBUS</td>\n",
        "      <td> 194913</td>\n",
        "      <td> 28.748176</td>\n",
        "      <td> 1</td>\n",
        "      <td> Total Fossil Fuels Production</td>\n",
        "      <td> Quadrillion Btu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> FFPRBUS</td>\n",
        "      <td> 195013</td>\n",
        "      <td> 32.562667</td>\n",
        "      <td> 1</td>\n",
        "      <td> Total Fossil Fuels Production</td>\n",
        "      <td> Quadrillion Btu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> FFPRBUS</td>\n",
        "      <td> 195113</td>\n",
        "      <td> 35.792151</td>\n",
        "      <td> 1</td>\n",
        "      <td> Total Fossil Fuels Production</td>\n",
        "      <td> Quadrillion Btu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> FFPRBUS</td>\n",
        "      <td> 195213</td>\n",
        "      <td> 34.976732</td>\n",
        "      <td> 1</td>\n",
        "      <td> Total Fossil Fuels Production</td>\n",
        "      <td> Quadrillion Btu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> FFPRBUS</td>\n",
        "      <td> 195313</td>\n",
        "      <td> 35.349336</td>\n",
        "      <td> 1</td>\n",
        "      <td> Total Fossil Fuels Production</td>\n",
        "      <td> Quadrillion Btu</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "       MSN  YYYYMM      Value  Column_Order                    Description  \\\n",
        "0  FFPRBUS  194913  28.748176             1  Total Fossil Fuels Production   \n",
        "1  FFPRBUS  195013  32.562667             1  Total Fossil Fuels Production   \n",
        "2  FFPRBUS  195113  35.792151             1  Total Fossil Fuels Production   \n",
        "3  FFPRBUS  195213  34.976732             1  Total Fossil Fuels Production   \n",
        "4  FFPRBUS  195313  35.349336             1  Total Fossil Fuels Production   \n",
        "\n",
        "              Unit  \n",
        "0  Quadrillion Btu  \n",
        "1  Quadrillion Btu  \n",
        "2  Quadrillion Btu  \n",
        "3  Quadrillion Btu  \n",
        "4  Quadrillion Btu  "
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The text IO is awesome, sure, but some of the data frame methods are even more awesome. As an experienced Excel user, I've grown accustomed to the ease of creating a crosstabulated view of data but am often frustrated by the clumsy Excel implementation which throws a bunch of ugly default formatting on top and subtly changes the way you typically reference cells. The pandas pivot method offers a fast transform that can then be easily plotted in matplotlib or otherwise manipulated with other data frame methods.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Create a pivot with the year month as the row labels,\n",
      "description (energy type) as the column labels\n",
      "and value (BTU) as values.\n",
      "\n",
      "Annual criteria applied as the first 20 years of the\n",
      "dataset are missing monthly figures\n",
      "\"\"\"\n",
      "# Annual energy figures end with 13\n",
      "annual_crit = data['YYYYMM'].map(lambda x: str(x).endswith('13'))\n",
      "\n",
      "pivot = data[annual_crit].pivot(index='YYYYMM',columns='Description',values='Value')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Look at the last few rows.\n",
      "pivot.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Description</th>\n",
        "      <th>Nuclear Electric Power Consumption</th>\n",
        "      <th>Nuclear Electric Power Production</th>\n",
        "      <th>Primary Energy Exports</th>\n",
        "      <th>Primary Energy Imports</th>\n",
        "      <th>Primary Energy Net Imports</th>\n",
        "      <th>Primary Energy Stock Change and Other</th>\n",
        "      <th>Total Fossil Fuels Consumption</th>\n",
        "      <th>Total Fossil Fuels Production</th>\n",
        "      <th>Total Primary Energy Consumption</th>\n",
        "      <th>Total Primary Energy Production</th>\n",
        "      <th>Total Renewable Energy Consumption</th>\n",
        "      <th>Total Renewable Energy Production</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YYYYMM</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>200813</th>\n",
        "      <td> 8.426491</td>\n",
        "      <td> 8.426491</td>\n",
        "      <td>  7.062745</td>\n",
        "      <td> 32.993294</td>\n",
        "      <td> 25.930549</td>\n",
        "      <td> 0.128646</td>\n",
        "      <td> 83.551080</td>\n",
        "      <td> 57.587014</td>\n",
        "      <td> 99.292055</td>\n",
        "      <td> 73.232860</td>\n",
        "      <td> 7.202498</td>\n",
        "      <td> 7.219356</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>200913</th>\n",
        "      <td> 8.355220</td>\n",
        "      <td> 8.355220</td>\n",
        "      <td>  6.965738</td>\n",
        "      <td> 29.705994</td>\n",
        "      <td> 22.740256</td>\n",
        "      <td>-0.824350</td>\n",
        "      <td> 78.486601</td>\n",
        "      <td> 56.670274</td>\n",
        "      <td> 94.596186</td>\n",
        "      <td> 72.680279</td>\n",
        "      <td> 7.638177</td>\n",
        "      <td> 7.654785</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201013</th>\n",
        "      <td> 8.434433</td>\n",
        "      <td> 8.434433</td>\n",
        "      <td>  8.234230</td>\n",
        "      <td> 29.877211</td>\n",
        "      <td> 21.642981</td>\n",
        "      <td> 1.604199</td>\n",
        "      <td> 81.412091</td>\n",
        "      <td> 58.206642</td>\n",
        "      <td> 98.016397</td>\n",
        "      <td> 74.769217</td>\n",
        "      <td> 8.081239</td>\n",
        "      <td> 8.128142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201113</th>\n",
        "      <td> 8.268698</td>\n",
        "      <td> 8.268698</td>\n",
        "      <td> 10.458598</td>\n",
        "      <td> 28.719733</td>\n",
        "      <td> 18.261136</td>\n",
        "      <td> 1.103003</td>\n",
        "      <td> 79.896457</td>\n",
        "      <td> 60.563478</td>\n",
        "      <td> 97.366497</td>\n",
        "      <td> 78.002358</td>\n",
        "      <td> 9.074240</td>\n",
        "      <td> 9.170182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201213</th>\n",
        "      <td> 8.061822</td>\n",
        "      <td> 8.061822</td>\n",
        "      <td> 11.356824</td>\n",
        "      <td> 27.074759</td>\n",
        "      <td> 15.717936</td>\n",
        "      <td>-0.019155</td>\n",
        "      <td> 77.894266</td>\n",
        "      <td> 62.316795</td>\n",
        "      <td> 94.915113</td>\n",
        "      <td> 79.216333</td>\n",
        "      <td> 8.797769</td>\n",
        "      <td> 8.837715</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "Description  Nuclear Electric Power Consumption  \\\n",
        "YYYYMM                                            \n",
        "200813                                 8.426491   \n",
        "200913                                 8.355220   \n",
        "201013                                 8.434433   \n",
        "201113                                 8.268698   \n",
        "201213                                 8.061822   \n",
        "\n",
        "Description  Nuclear Electric Power Production  Primary Energy Exports  \\\n",
        "YYYYMM                                                                   \n",
        "200813                                8.426491                7.062745   \n",
        "200913                                8.355220                6.965738   \n",
        "201013                                8.434433                8.234230   \n",
        "201113                                8.268698               10.458598   \n",
        "201213                                8.061822               11.356824   \n",
        "\n",
        "Description  Primary Energy Imports  Primary Energy Net Imports  \\\n",
        "YYYYMM                                                            \n",
        "200813                    32.993294                   25.930549   \n",
        "200913                    29.705994                   22.740256   \n",
        "201013                    29.877211                   21.642981   \n",
        "201113                    28.719733                   18.261136   \n",
        "201213                    27.074759                   15.717936   \n",
        "\n",
        "Description  Primary Energy Stock Change and Other  \\\n",
        "YYYYMM                                               \n",
        "200813                                    0.128646   \n",
        "200913                                   -0.824350   \n",
        "201013                                    1.604199   \n",
        "201113                                    1.103003   \n",
        "201213                                   -0.019155   \n",
        "\n",
        "Description  Total Fossil Fuels Consumption  Total Fossil Fuels Production  \\\n",
        "YYYYMM                                                                       \n",
        "200813                            83.551080                      57.587014   \n",
        "200913                            78.486601                      56.670274   \n",
        "201013                            81.412091                      58.206642   \n",
        "201113                            79.896457                      60.563478   \n",
        "201213                            77.894266                      62.316795   \n",
        "\n",
        "Description  Total Primary Energy Consumption  \\\n",
        "YYYYMM                                          \n",
        "200813                              99.292055   \n",
        "200913                              94.596186   \n",
        "201013                              98.016397   \n",
        "201113                              97.366497   \n",
        "201213                              94.915113   \n",
        "\n",
        "Description  Total Primary Energy Production  \\\n",
        "YYYYMM                                         \n",
        "200813                             73.232860   \n",
        "200913                             72.680279   \n",
        "201013                             74.769217   \n",
        "201113                             78.002358   \n",
        "201213                             79.216333   \n",
        "\n",
        "Description  Total Renewable Energy Consumption  \\\n",
        "YYYYMM                                            \n",
        "200813                                 7.202498   \n",
        "200913                                 7.638177   \n",
        "201013                                 8.081239   \n",
        "201113                                 9.074240   \n",
        "201213                                 8.797769   \n",
        "\n",
        "Description  Total Renewable Energy Production  \n",
        "YYYYMM                                          \n",
        "200813                                7.219356  \n",
        "200913                                7.654785  \n",
        "201013                                8.128142  \n",
        "201113                                9.170182  \n",
        "201213                                8.837715  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *\n",
      "\n",
      "# Plot total energy consumption\n",
      "\n",
      "# Extract the year from the labels\n",
      "labels = pivot.index.map(lambda x: str(x)[0:4])\n",
      "\n",
      "figure(figsize=(9,4), dpi=80)\n",
      "plot(pivot['Total Primary Energy Consumption'], color='b', \n",
      "     linewidth=2.5)\n",
      "\n",
      "xticks(np.arange(1, len(labels), 5), labels[np.arange(1, len(labels), 5)])\n",
      "\n",
      "xlim([0,len(labels)])\n",
      "ylim([0, np.max(pivot['Total Primary Energy Consumption']) * 1.15])\n",
      "\n",
      "grid(b=True, which=\"both\")\n",
      "\n",
      "title(\"US Annual Energy Consumption\", fontsize=16)\n",
      "ylabel(\"Quadrillion BTU\")\n",
      "\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rw0cfWQuTOFq10k07zz5rXmFiyrlzRLI5JldOhBBC5GrjRpg4Ef77X/v1HTvC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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4717b10>"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some observations:\n",
      "\n",
      "- Energy usage falls during period of recession such as the early 90's and around the burst of the dotcom bubble in 2000\n",
      "- Even as the economy has recovered from the 2008 housing crisis and resulting recession, overall energy consumption has continued to decline, bucking the observed trend\n",
      "\n",
      "The total US population has grown substantially since the 1950's, more than doubling from just over 150 million to nearly 315 million as of 2012. Consumption in per-capita terms may offer more to the story."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load population data from US Census Bureau\n",
      "us_pop = pd.read_csv('uspop.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Multiply to get millions of BTU's and divide by US population\n",
      "energy_per_cap = pivot['Total Primary Energy Consumption'].values * \\\n",
      "                        1000000000 / (us_pop[us_pop.Year >= 1949]['Pop'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *\n",
      "\n",
      "# Reproduce the previous plot with per-capita data\n",
      "\n",
      "figure(figsize=(9,4), dpi=80)\n",
      "plot(energy_per_cap, color='b', linewidth=2.5)\n",
      "\n",
      "xticks(np.arange(1, len(labels), 5), labels[np.arange(1, len(labels), 5)])\n",
      "\n",
      "xlim([0,len(labels)])\n",
      "ylim([0, np.max(energy_per_cap) * 1.15])\n",
      "\n",
      "title('US Annual Per-Capita Energy Consumption', fontsize=16)\n",
      "ylabel(\"Million BTU\")\n",
      "\n",
      "grid(b=True, which='both')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gAQDg8ePHKC4uhqWlJQDoDLJ//34EBgbC1NQUCoUCbdq0wdmzZ+Hh4WGIaIQI\n5fhxYPRo3kABgHbtgOhooFMnICiIH5b++2/+RX/hArB7N//AfV6FhcC+fcCmTbxRVPqtaWYG+Pnx\n/Q8aVNJVU1kyGRARAeTmAlFR/EtqxAggJqbq90Vqn5QUYOFCXqek0rQp4O/Pj27cusXrmG7fBvLz\ntW9fVMQbzDdvVrwvMzP+XnF1BV55hR/dU11KH4H58ktgyhS+f29vXtNF9VBiM0jjRKlUwt3dHdev\nX8fUqVPh7OyMPXv2ICIiAlu3bkXXrl3x6aefQi6XIzMzU6Mh4uDggIxnO6oBhISEQKFQAADkcjlc\nXV3h6ekJAPj88881llX9ZCIsl+6zEyFP6eVnM9Z0ntqULzExEbNnz5Z8e6USOHHCE8uXA4zx68eO\n9cT69cD58/GIjwe8vT3xyy+Aj088/vwT+PlnT7i7AwsXxsPdvez7L+/98OQJEBQUj+++A+7f59cD\n/Ho3N0+MHw+0bBmPxo2f//nassUT+flAbGw8jhwBxo3zxOTJ8eqjLyL9PVXL9H6V/n4ICZmNFSuA\nzz6LR1ERAHjCzAwYMSIeY8cCQ4Zo3/7xYyAmJh75+YCdnSfS0oBTp+KRkwM8esSX09L4+6V+fU+4\nugK2tvFwdARGj/aEkxPw00/aea5eBezt+bLq/fDJJ5547z0gJycevXoB58554uWXa/75e1G+v1S/\np6amolKYAeXn57Pu3buzuLg4lpOTw5RKJVMqleyDDz5gEyZMYIwxNn36dLZt2zb1bSZOnMj27t2r\ncT8VxYyLi9N7dn2hbNKJnO95smVlMdavH2P8eAVj9esztnEjY0ql7u0fPmRsypSS7Y2MGFuxgrHi\n4qplKypibNSokvsBGLOyYmzmTMYuXpT8cMpVUMBYz54l+/PziyvzcYqgrr7mDEWpZOzKFcbeeiuO\nNW2q+doaNYqxGzeefx9FRYxlZ/OfUpR+3j78sCSfoyO/35om4t9VxZDZKvpeN2jjhDHGli1bxj75\n5BONdSkpKczFxYUxxlhYWBgLCwtTXzdw4ECWkJCgGdKwbShCqs2VK4y1bFnyAdmhA2OXL1futlu2\nMGZuXnLb//s/3nCpjOJixsaPL7ltp06MRUdX/vbPIzeXMReXkn1/8IHh90kM5949xvbtY2zyZMZe\nflmzQQIw1qMHY2fO1HRK3ZRKxmbPLsnasSNjf/+t//08eMCfo6Agxpo35+/z0aMZ++QTxo4eZezO\nHf3vs7Yz+4PkAAAgAElEQVSp9sbJ7du3WV5eHmOMsQcPHrDevXuzY8eOsaysLPU2q1atYoGBgYwx\nxq5cucI6d+7MHj16xG7cuMFat27NlM/8a0WNE1IXxMUxJpeXfDCOHcuPLFRFYiJjrVuX3Efv3hV/\nuCqVjM2YodkwMcQHcnkyMxlr1aokw6JFZR/5eZEolYxduMDYnDmMOTjwL7IpUxj74QfGnjyp6XTc\n48eMnT/P2H/+w1j//oyZmmo3SADG2rXjDV6Rj4wxxvNNnFiSu1s3xu7eLbn+yRPGUlP5+3XzZsaW\nLGEsNJSx7dsZO3uWsadfb1oKChjbvZsfMbKw0P0clb60aMGYvz+//3PnxH/e9K3aGye//vorc3Nz\nY507d2YdO3ZkH3/8MWOMsaCgINaxY0fWqVMnNnToUJZd6njaRx99xF555RXWrl07Fhsbqx2SunUM\nQuRsjImdr6rZtm3T/FBfvlz6h9GdO4y99prml8L162VnW7hQc9uaOpR97RpjlpZx6izDh/P/wkVS\nXa+51FTeNdehQ9lfXjY2JQ2VoqLqyaZU8tfSjh38CEOPHppH60pfzMwY8/ZmbNUqxrZuFbe7Ttfz\nVlTEWECA5hEUT0/GFArGjI0rblg0a8bfgyEhjC1bxtiIEbx79tnt6tdnbOhQxl5/nTE7u7Luj78n\nnJwYCw9nLC2t+p+jstTpbh19oMaJYYicjTGx81U2m1LJ2EcflXwQmZoy9s03z7//wkLND9dmzRj7\n+WftbKX3rVDU/Aff1q1xzNFR80tBH3UJ+mLI19w//zD21VeM9emj/QUlk/E6pEGDGDMx0d1Q8feP\nY7GxVT/aVhalkrGUFMb+9z/GFi9mbPBgxqyty/9SbtWKsWnTGDtwQDNHbXyvPnrE2JAhFTdEqnqx\nsOBdrnv2aP+tsrIYO3SIvy/feIOxNm1KGielXwteXvxzovTtlUr+/j18mDcIJ07kjcfXXmMsJqZ6\nnzt9qOh7naavJ8RAnjzh85Ns2MCXmzQBvv0W6NdPP/evVPIhmx9/zJfr1wd27ACGDePLq1cDTwcU\n4aWX+PDJ1q31s+/nkZfHh0gfPsyXmzblE3U9Le6vkxISgOHDgexszfUuLiVDxlVDxHNzgf37+ZDy\nY8fwdORLCVNToEcPYMAAfunWja8rC2N89tSbN/kcIRcv8ktiou6hvKX34+oKdO/O9+HhAbRpo3vi\nvdqqsBAYNw44c4Y//woF0KoV/6n6/eWX+SSJ168DV68CycmaP3NyALmcD5V+4w0+VNncvPIZ0tOB\n7dv5XEO//655nYUF4OXF95GUBPzzT9n3M2ECnwupcWMJT0QNoHPrEFID7t3jE6mpvoBffhk4dIhP\nTa9v//0v8K9/8caKTMY/oBo2BN56i1/frBmfpbV9e/3vW6qiIj69+KpVfNnEBFizBpg6tWZzGcLW\nrfxv8fgxX37pJT63zdixfI6O8r7sc3P5fDS7d+tuqAD8C6xPH97oNTHhpwzIzOQX1e/375efUSbj\n835068Yv3bsDnTvTvDSVUVDAGyMmzzkxB2N8LqOtW/k/GX//Xf72lpb88+TXX3njE+Dzu2zerL9/\ngAypwu91gx2z0aOKYtbGQ4oiEDkbY2Ln05Xt9m3GjhxhbOVKzdEp7u68INSQDh5krGFDzT5sgBfg\nGmqYsBTPPm+Rkbx2QZV3yhR+uL2m6PM1V1TE2HvvlTw2ExPG1q6VPiT2wIE49u23jE2fXn6tSkUX\nU1PG3NwYmzCBsYgIxk6d0iwIlaK2vVdFoivfo0eM7d/Pa1latGCsVy8+Omr1asaOHePdQ6oan7/+\n4t1Apf/Gs2bxEUOGyKYvFX2vG2QSNkLqurw8PuPpL7+UHCZPS9PebsgQPkuqoc8uPHgwPzoyZEhJ\n10HDhsD33/ND86IKDuaz4qq6PP77X374et8+/p9hbXX3Lj86cvAgX9ZH15WFBb+9qtsuMxOIi+Mz\n/P7wA/DXX3y9mRmfsv2ll/il9O8uLkCHDoY5DQLRHzMz3k3k71/xti1a8CO069cD773Hu6pWrwZi\nY/lRmNp6LiHq1iGkCq5e5Sfo++Yb3g9dlpde4l+8y5Y9/+HeqvjrL16/kJrK+7FrSx1Hejr/0r1w\ngS+/9hpw9Civo6ltrl3jXyqq+gFnZ96QNWS9D2O8sWJuzk+yWJfqQkjlJSfzz52EBL5sbAy8/z4/\noahqZmZRUM0JIXrw++/8zKo7d5acNVilTRvAzY1f3N35TxubmsmpolSK92FUkcJCfgLEb7/ly/7+\nwN691du4e17Hj/Nao9xcvuznx0/gWFuKFEntV1QEfPIJsGQJL8oH+JnCly6t2VzPopqTGkbZpBMh\n36VLjL35Jh/eV7pPt3fvOPbDD4zl59d0Qm0iPG9lqSjbw4eaU/u/9Vb1Tk4l9bm7d4+xf/9bc46M\nBQuk15foM1t1oGzSGSpfYqJqqDK/HDhQ9fuoyZqTWva/FSHVIzGRdzN07sxHSjDGD5WPGsWr45ct\nA/r358ODif7Uq8ePnHTuzJe//pofktaH777j9Rft2wPTp/O6lvKG0lbGP//wI2oKBbBoEe/qq1eP\nd/uFhZWcFZeQ6ta5M+9OVNW7BQXx4dC1BXXrEPKMyEg+9FM1bNPYmBc3vv++WMNx67KsLKBnT147\nA/BivylTpN/fl1/yOWee7ZIzMuIFg15e/NKjR+WKRfPyeNHh6tWaDZwOHfhQzu7dpWclRJ/27OFd\njQAfun7mDNCgQc1mAqjmhJBKYwxYvpz31QK81iE4mE909sorNZvtRZSczAtj79zhjYg9e/ionqpg\njB/RWLGCL9evD7z6Ki8YVM07UlqDBrwB2ro1n4BL9bNVKz6HxL17fG6WL77gv6t06gQsXgyMGFH7\nan1I3TdvHq9DAfj8Olu3Vr5oWnXUWN+o5qSGUTbpqjPf48eaJwOztGTsxx/FyFZVdSnbzz8z1qAB\n/5vUq8fYyZOVv+2jR4yNG1fyN7W2Zkx1wvOCAsZiYxl7913GXF21z3Oi6yKTaZ/0rksXfvbZ6jiJ\nYV36u1YnkbMxVj35njzh5w5SvW6/+KL87e/f5yckbNw4jjVvzs9oHh1d9kkPpajoe53a+OSFd+8e\nH1WxcSNfViiAn34CevWq0VgEvMtFNWLn0SM+gue33yq+3d27fM6XrVv58iuv8MPZqu6Whg2BgQP5\nf5MXL/LpwXfu5Pfv48NHYD07JTxjJaMfevTgM/6eOwcMHUpHS4jYTEyAXbt4zRXAT2vx00/a2zHG\nT5vQvj0f3XP3Lu9i3bwZCAgArK2B3r35kciLF/n2hkLdOuSFlpXFv8QuXuTLXbrwwkk7u5rNRTR9\n8w0/BwrAJzQbNow3Hnv14g2P0oedMzP5pHSXLvHlbt2AAweqPry7uJhP/56Swi83bvACWH9/XgxN\nc4mQ2iYhgZ/q4MkTPhfThQsln3WJicCsWXwyRxVXV8DBgQ+Rf/BA+/6aN+enoZg5s+rvB6o5IaQM\nSUnA66+XzKw5eDD/78LQs7kSaT7+mH8QPsvOjjdSevfmRzymTCmZrdfPjx8RadiwerMSIqr163lx\nOMAbKlFR/CjJ11+XFIxbW/OjIxMm8AEBDx/yE4ceOsRnnf7zT837/Ne/eHF4VUanUc1JDaNs0hky\nX3w8P++Mqg/27bd5v6wI2Z5XXc2mVPJz8fj48NPSV3QOmSlTqvY3fd58hkbZpBE5G2PVn0+pZCw4\nWPOcT6V/nz27pLakrGw3bvDzRDk4lNx25EjGCgsrn6Oi73XqKSUvnKNHgUGDSoaArljBz+lSm2Yi\nfRHJZHz01OHDfCjvhQvA55/z09Tb2mpuGxYGrFtHf1NCniWT8aMnqnNuqaZMGDiQz+H02WeAXF7+\nfbRqxY++nDnDz9cE8NqwgQP5e1MvOZ+2YIRG3TpEX44e5TUDDx/yQ5CRkXxoHandGOMTTJ05A7z8\nMtC3b00nIkRsKSn8s9DYmE+h4OsrrY4qP58XhatqVZyd+UkHHRzKvx3VnBDy1LFjvAZB1TDZtQsY\nObKmUxFCSO328CGfgXbPHr7s4MAbKM7OZd+mou91vXfrPHz4EN27d4erqyucnJywcOFCAEBubi68\nvb3Rtm1b+Pj4IL/UtIphYWFwdHRE+/btceTIkSrvMz4+Xl/x9Y6ySafPfPpumIj83FE26UTOR9mk\nETkbIHa+ymYzN+eFtTNm8OX0dF6k/uOP0vet98aJubk54uLikJiYiF9//RVxcXE4deoUwsPD4e3t\njeTkZAwYMADh4eEAgKSkJOzatQtJSUmIjY3FtGnToHx2jmlCngMdMSGEEMMyNuYjdp5+tSM/H/D2\nBv73P2n3Z9BunQcPHqBv376IjIzEyJEjceLECdja2iI7Oxuenp74448/EBYWBiMjI8x/OkZw0KBB\nCA0NhYeHR0lI6tYhEj3bMImK4gWUhBBCDOObb/gw5KIifnLUlBTA0lJzm4q+1w1Sy65UKuHu7o7r\n169j6tSpcHZ2Rk5ODmyfltTb2toiJycHAJCZmanREHFwcEBGRobWfYaEhEChUAAA5HI5XF1d4enp\nCaDk0BMt03Lp5eJiz6cNk3gYGQFRUZ544w1x8tEyLdMyLdfF5RYtgIMHPREYCLz/frx6QsT4+Hik\nqs7mWZHKj0quuvz8fNa9e3d2/PhxJpfLNa6ztLRkjDE2ffp0tm3bNvX6iRMnsr1792psW1FMkcex\nUzbppOYrKmJsxw7GzM35+HtjY35eCBGyVQfKJp3I+SibNCJnY0zsfM+b7Z9/yr6uou91g85z0qRJ\nEwwZMgQXLlxQd+cAQFZWFmyeziVtb2+PNNV0jgDS09NhrzoBACFVcPcun/fC0REYPbqkK2fnzpJT\nhhNCCKkejRtLv63ea07u3LkDExMTyOVyFBYWYuDAgViyZAkOHz6Mpk2bYv78+QgPD0d+fj7Cw8OR\nlJSE0aNH4+zZs8jIyICXlxeuXbsGWakB11RzQspz/ToQEQFs2qR5Gnu5nJ/Mb8SImstGCCFEW7XX\nnGRlZSE4OBhKpRJKpRJBQUEYMGAA3NzcEBAQgI0bN0KhUCA6OhoA4OTkhICAADg5OcHExATr1q3T\naJgQogtjfNKfzz4DYmI0z47Zti0/gVVwMJ1ThRBCaqXn6lCqJhXFrMt9doYkcjbGys53/z5jo0Zp\nn0vFy4uxgwcZKy6uuWwioGzSiZyPskkjcjbGxM5nyGwVfa/TmSdIrZKRwadKvnCBL9erx2cmnDWr\n5BwPhBBCajeavp7UGufO8YZJVhZf7tePF7s+e9I3QgghYqv26esJMYSdO4E+fUoaJlOm8LPTUsOE\nEELqnjrROFFNAiMiyiZdfHw8lEpg8WLNocFffMFP+W1qWrPZREXZpBM5H2WTRuRsgNj5ajIb1ZwQ\nYT18yOcnUZ2bQS4HoqP5+RoIIYTUXVRzQoR05QowdiyQmMiX27YFDhzgPwkhhNRuVHNCapXMTOCt\nt4BOnUoaJt7eQEICNUwIIeRFUScaJ9RnJ41I2e7dAz78kE89v2EDoFQCMlk85s4FDh3SPqNlTRPp\nuXsWZZNO5HyUTRqRswFi56OaE/LCevKEN0ZCQ4Fbt0rW+/oCI0cCISE1lYwQQkhNoZoTUiMYA/bv\nBxYsAP78s2R9ly7AJ5/wOUwIIYTUTVRzQoRz4gTQsycwfHhJw0ShAHbsAM6epYYJIYS86OpE44T6\n7KSp7my//AIMGgR4evICV4DXknz6KfDHH0BgIGBU6hVJz500lE06kfNRNmlEzgaInY9qTkiddvUq\nn0ht166SdfXrA7NnA++9J16xKyGEkJpFNSfEYDIygGXLgI0bgeJivs7EBHj7bWDRIqB585rNRwgh\npGZU9L1OR06IXjEGnDzJp5f/3//4aBwAkMn4FPRLlwKvvFKzGQkhhIiNak4M7EXJlp8PrFkDODvz\nmpJdu0oaJr6+fEK1bduq1jB5UZ47faNs0omcj7JJI3I2QOx8VHNCaq3z54H//pefNfjBg5L1ZmZA\nQADwr38BHh41l48QQkjtQzUnpMoYA44dA5YsAc6c0bzulVeAyZOB8eMBa+uayUcIIURs1T7PSVpa\nGvr16wdnZ2e4uLhgzZo1AIDQ0FA4ODjAzc0Nbm5u+P7779W3CQsLg6OjI9q3b48jR47oOxLRo7g4\noE8fwMenpGFiZAQMGwYcPgwkJ/MRONQwIYQQIlWZjRMLCws0atRIfWncuDFat26NSZMm4e+//y7z\nDk1NTfHZZ5/hypUrSEhIwNq1a/H7779DJpNh7ty5uHjxIi5evIjXX38dAJCUlIRdu3YhKSkJsbGx\nmDZtGpRKZZUeBPXZSVOVbKdOAf3788upU3xdw4Z8htebN4Fvv+UNFiM9NnfrynNX3SibdCLno2zS\niJwNEDufkDUnBQUFWutyc3MRGRmJKVOmYPfu3TpvZ2dnBzs7OwC8gdOhQwdkZGQAgM5DOPv370dg\nYCBMTU2hUCjQpk0bnD17Fh5UqGBwhYXAjz/y4b0NGmhe6tcH6tUDfv6Zd9+UPqDVoAEwfTodISGE\nEGIYVSqItbKywty5c+Hm5lap7VNTU3Hx4kV4eHjg9OnTiIiIwNatW9G1a1d8+umnkMvlyMzM1GiI\nODg4qBszpYWEhEChUAAA5HI5XF1d4enpqb4+Pj5evaxq7Ymw7OnpKVSeJ0+ATz+Nx9GjQEKC59Mi\nVn494Pn0J1+WyTzB25N82dzcE1OnAr17x8PSErC2rvnHU5PLKqLkofdD3c8n8rKKKHlqw/tB9Hz6\nfD+ofk9NTUVlVLkg9smTJ+jSpQt+/fXXcrcrKCiAp6cnFi1ahGHDhuHWrVto1qwZAGDx4sXIysrC\nxo0bMWPGDHh4eGDMmDEAgEmTJmHw4MEYMWJEScg6VhDLGHDhAhAdzecCKSwEBgwAhgwBBg4E5HL9\n7k+pBH76Cdi+Hdi9GyinV04nMzM+cdrChcBLL+k3GyGEkBeP5EnY9u7dq3XjvLw87Nq1C2+88Ua5\nO33y5AlGjhyJsWPHYtiwYQAAGxsb9fWTJk2Cn58fAMDe3h5paWnq69LT02Fvb1/Bw9JUutUpGlU2\nxvhcH9HR/HLjhuZ233zDL8bG/KR4Q4bwi7Mzn8BMihs3gK++4sN8//pL87r69YEePeIxdaonLCz4\nMGDVpbCw5Hdzc2DcOKBFC2kZnkdt+LuKiLJJJ3I+yiaNyNkAsfPVZLYyGyffffedxrJMJkPTpk0x\ne/ZsDBkypMw7ZIxh4sSJcHJywuzZs9Xrs7Ky0PzpfOXffvstOnbsCADw9/fH6NGjMXfuXGRkZODq\n1avo1q3bcz0okeTk8Knao6P5OWae9dprvG7j2DHg/n0+zfuPP/LLggXAyy8Dfn7Am28CvXrxxkt5\nGOMjalavBg4cAEo3TI2NedHq6NF8dM3584Cg7wlCCCEvsDK7dSIiIjBjxowq3+GpU6fQp08fdOrU\nCbKn//KvWLECO3fuRGJiImQyGVq1aoUvv/wStra26us3bdoEExMTrF69GgMHDtQMWUu7dZKT+QRk\neXma63v04BOUvfEG4ODA1z16xKd9P3QIOHhQd0PGzg4YOVJ3Q6WwkM/AumYN8Ntv2vsbM4bv82nP\nGiGEEFJjKvpeL7Nx4ubmhosXLxosWFU8T+NEqQS++46f+fa11/Q71LU8+flA9+68gQIA3brxxsGb\nb/KjIRW5epU3VL77jh8JUZ04T0XVUPH1BU6c4N03ubkl15ubA0FBwMyZgIuL/h4XIYQQ8ryqfRK2\nmvBstbhKXh7vEhk6lE8c1qYNP0tuJYuFJSsqAv7v/1QNk3h88AEfkvvOO5VrmACAoyMwaxZw9CiQ\nnQ18/TXg7V1ytCQ7G1i7Fnj9dSA8vKRh4uDAl9PTeYOlvIZJWc+bKETOR9mkETkbIHY+yiaNyNkA\nsfPVZLYyGye//vqrxiRsz07IJrpLl4CuXfnRB5WUFD5nR6tWfHTMtm2a54NRYYzXipw5A+zYwc8d\nk55e+X2/9x6fLRXg3S/Llj3fY7G2BiZN4nONqBoqPj6a3TqvvcbrWlJSgPnzgaZNn2+fhBBCSE2p\nk90627bxoa+FhXx5+HBe+BkZCTz7kBo14l0tFhZ8dEtKCr8822hp3BhYv54Xk5Zn40bekACATp2A\n06f5fRvCnTt8tlaFAnB1Ncw+CCGEEH17oWpOHj/mXSdffMGXjYyAFSuAefNKhuNeugRs3szn/Lhz\np+pZxo7l3Sm6Dh6ppnh/8oQXnp47B7RsWfV9EEIIIXWZ5JqTiuYyEUl8fDwyM4F+/UoaJk2b8q6V\n+fM15wnp3Bn4/HMgI4NPgObnx6dvb9iQ12f4+/Naj88/B2JigMuX+Xaqadq3beNHKZ49G29qKjBi\nBG+YmJry27RsSf2Jz0PkfJRNGpGzAWLno2zSiJwNEDtfTWYrc56TuXPnIjIyElZWVvD19cUnn3yC\nkydPok2bNli8eDGsBTqpyqVLvAA1J4cvd+0K7N1bfvGpmRnv7hk+nI+EMTIqe7IzFxc+JDg4mBeo\npqQAvXvz+pX33+fdR0OHArdv8+3Xr+e1JoQQQgipujK7dd58802YmZnh/v37yMvLg4uLC3x9fXHq\n1ClcunRJa5I2g4Ys5/DPN98AEybwETIAr/eIiOBDafVNqeRHVBYu5F1IAG+ENGnC5yYBgDlzgFWr\n9L9vQgghpK6QXHPi4uKC3377DUVFRXBwcEB2drb6us6dO+PSpUv6T1tWSB0PgjFg5UreUAD4kZAv\nvgDeesvweRITgcBA4I8/NNcPHMjnJTGp0ukUCSGEkBeL5JoTU1NTAICJiYl62nn1japrJrMyFBcD\nM2aUNEwsLPhZdqujYQLwmpMLF4ApU0rWtWsHREVpN0yoP1E6kfNRNmlEzgaInY+ySSNyNkDsfELW\nnKSnp2PmzJlgjCEjI0P9OwBkZGRUW8BnFRbyETP/+x9fdnAAli/nk6xVpwYNeG3JsGHA8eO8saTv\nswkTQgghL6Iyu3UiIyPV58ZhjGn9HhwcXH0hnx7+yc3lhaenTvH1Li7A99+XnJ+GEEIIIeKTXHMi\nEplMhps3GQYNAn7/na/r2xfYt4+OVhBCCCG1TZ05t07PniUNkzffBGJjSxom1GcnjcjZALHzUTZp\nRM4GiJ2PskkjcjZA7HxCnltHNKoyl1mzeOGpIYYKE0IIIaTm1ZpuHYDhk0/49PRlTZZGCCGEEPFV\n1K1T4Ywct27dwtdff43U1FQUPZ3pTCaTYdOmTfpLWQnbt1d80j1CCCGE1H4VdusMHToUd+/ehbe3\nN4YMGaK+VLfyGibUZyeNyNkAsfNRNmlEzgaInY+ySSNyNkDsfELOc6JSWFiIlStXVkcWQgghhJCK\na04WLVqEHj16VPpoSVpaGsaNG4dbt25BJpPh7bffxsyZM5Gbm4tRo0bh5s2bUCgUiI6OhvzpcJuw\nsDBs2rQJxsbGWLNmDXx8fDRDVtA3RQghhJDa47nnObGwsMCDBw9gZmamntJeJpPh7t27OrfPzs5G\ndnY2XF1dUVBQgC5dumDfvn3YvHkzrK2tMW/ePKxcuRJ5eXkIDw9HUlISRo8ejXPnziEjIwNeXl5I\nTk7WmCKfGieEEEJI3fHc85wUFBRAqVTi4cOHuHfvHu7du1dmwwQA7Ozs4OrqCoA3bDp06ICMjAzE\nxMSoZ5UNDg7Gvn37AAD79+9HYGAgTE1NoVAo0KZNG5w9e7ZKD5L67KQRORsgdj7KJo3I2QCx81E2\naUTOBoidT+iaE4A3IE6ePAmZTIa+ffvCz8+vUneempqKixcvonv37sjJyYGtrS0AwNbWFjk5OQCA\nzMxMeHh4qG/j4OCg89w9ISEhUCgUAAC5XA5XV1d4enoCABITEwFAvax6Qmm5/GUVUfLUpnyJiYlC\n5Sm9TO+HurmsIkoeej+8GPn0+fqNj49HamoqKqPCbp0FCxbg3LlzGDNmDBhjiIqKQteuXREWFlbu\nHRcUFKBv375YvHgxhg0bBktLS+Tl5amvt7KyQm5uLmbMmAEPDw+MGTMGADBp0iQMHjwYI0aMKAlJ\n3TqEEEJInfHc85wcPHgQiYmJMDY2BsCPYLi6upbbOHny5AlGjhyJoKAgDBs2DAA/WpKdnQ07Oztk\nZWXBxsYGAGBvb4+0tDT1bdPT02Fvb1+5R0cIIYSQOqfCmhOZTIb8/Hz1cn5+vvoMxbowxjBx4kQ4\nOTlh9uzZ6vX+/v7YsmULAGDLli3qRou/vz+ioqLw+PFjpKSk4OrVq+jWrVuVHsSzhz1FQtmkEzkf\nZZNG5GyA2PkomzQiZwPEzleT2So8crJw4UK4u7ur+49OnDiB8PDwMrc/ffo0tm3bhk6dOsHNzQ0A\nHyq8YMECBAQEYOPGjeqhxADg5OSEgIAAODk5wcTEBOvWrSu38UMIIYSQuq1S59bJzMzEuXPnIJPJ\n0K1bN9jZ2VVHNjWqOSGEEELqDsnznPz+++/o0KEDLly4oHEnqqMa7u7uBohbRkhqnBBCCCF1huR5\nTlatWgUAeOedd/DOO+/g3XffxbvvvqteFgn12UkjcjZA7HyUTRqRswFi56Ns0oicDRA7n5A1J19/\n/TUAsZ84QgghhNQ9ZXbr7N27t9zC1NLzkBgadesQQgghdYfkeU4OHDggTOOEEEIIIS8QVgtUFDMu\nLq56gkhA2aQTOR9lk0bkbIyJnY+ySSNyNsbEzmfIbBV9r5d55OTTTz/VOuyiWpbJZJg7d241NJ0I\nIYQQ8qIps+YkNDRUZ7eOqnGyZMkSg4dToZoTQgghpO6QPM+JSKhxQgghhNQdkuc5WblyJQBgxowZ\nWpeZM2fqP+lzEHm4M2WTTuR8lE0akbMBYuejbNKInA0QO5+Q85w4OTkBALp06aJ1HZ37hhBCCCGG\nQt06hBBCCKlWkuc58fPzK/PGMpkMMTEx+klICCGEEFJKmTUnCQkJSEtLQ+/evbXOq0Pn1qk8yiad\nyH+PUZsAABQFSURBVPkomzQiZwPEzkfZpBE5GyB2PiFrTrKysnD06FHs3LkTO3fuxJAhQxAYGAhn\nZ+fqzEcIIYSQF0ylak4ePXqEnTt34t1330VoaCimT59eHdnUqOaEEEIIqTsk15wAwMOHD3Hw4EFE\nRUUhNTUVs2bNwvDhw/UekhBCCCFEpcyak6CgIPTs2RMXL17Ehx9+iHPnzmHx4sWwt7evznyVQn12\n0oicDRA7H2WTRuRsgNj5KJs0ImcDxM5Xk9nKbJxs374dV69exerVq9GzZ080atRIfWncuHGZdzhh\nwgTY2tqiY8eO6nWhoaFwcHCAm5sb3Nzc8P3336uvCwsLg6OjI9q3b48jR47o6WERQgghpLbS+zwn\nP/74IywsLDBu3DhcvnwZALB06VI0atRI62SBSUlJGD16NM6dO4eMjAx4eXkhOTkZRkaabSaqOSGE\nEELqDsnT10vVu3dvWFpaaq3XFWL//v0IDAyEqakpFAoF2rRpg7Nnz+o7EiGEEEJqkXILYvUpIiIC\nW7duRdeuXfHpp59CLpcjMzMTHh4e6m0cHByQkZGh8/YhISFQKBQAALlcDldXV3h6egIAPv/8c41l\nVT+ZCMul++xEyFN6+dmMNZ2nNuVLTEzE7NmzhclTepneD3Uz37MZazoPvR/qfj59vh9Uv6empqJS\nmAGkpKQwFxcX9XJOTg5TKpVMqVSyDz74gE2YMIExxtj06dPZtm3b1NtNnDiR7d27V+v+KooZFxen\nn+AGQNmkEzkfZZNG5GyMiZ2PskkjcjbGxM5nyGwVfa8b5Nw6qamp8PPzU9eclHVdeHg4AGDBggUA\ngEGDBmHp0qXo3r27xm2o5oQQQgipO6q95kSXrKws9e/ffvuteiSPv78/oqKi8PjxY6SkpODq1avo\n1q1bdUQihBBCiKD03jgJDAxEz5498eeff6JFixbYtGkT5s+fj06dOqFz5844ceIEPvvsMwCAk5MT\nAgIC4OTkhNdffx3r1q2DTCar8j5L92mJhrJJJ3I+yiaNyNkAsfNRNmlEzgaIna8ms+m9IHbnzp1a\n6yZMmFDm9u+//z7ef/99fccghBBCSC1lkJoTfaOaE0IIIaTuEKLmhBBCCCGksupE44T67KQRORsg\ndj7KJo3I2QCx81E2aUTOBoidryaz1YnGCSGEEELqDqo5IYQQQki1opoTQgghhNQqdaJxQn120oic\nDRA7H2WTRuRsgNj5KJs0ImcDxM5HNSeEEEIIIU9RzQkhhBBCqhXVnBBCCCGkVqkTjRPqs5NG5GyA\n2PkomzQiZwPEzkfZpBE5GyB2Pqo5IYQQQgh5impOCCGEEFKtqOaEEEIIIbVKnWicUJ+dNCJnA8TO\nR9mkETkbIHY+yiaNyNkAsfNRzQkhhBBCyFNUc0IIIYSQakU1J4QQQgipVfTeOJkwYQJsbW3RsWNH\n9brc3Fx4e3ujbdu28PHxQX5+vvq6sLAwODo6on379jhy5IikfVKfnTQiZwPEzkfZpBE5GyB2Psom\njcjZALHz1amak/HjxyM2NlZjXXh4OLy9vZGcnIwBAwYgPDwcAJCUlIRdu3YhKSkJsbGxmDZtGpRK\npb4jEUIIIaQWMUjNSWpqKvz8/HD58mUAQPv27XHixAnY2toiOzsbnp6e+OOPPxAWFgYjIyPMnz8f\nADBo0CCEhobCw8NDMyTVnBBCCCF1RkXf6ybVESInJwe2trYAAFtbW+Tk5AAAMjMzNRoiDg4OyMjI\n0HkfISEhUCgUAAC5XA5XV1d4enoCKDn0RMu0TMu0TMu0TMviLat+T01NRaUwA0hJSWEuLi7qZblc\nrnG9paUlY4yx6dOns23btqnXT5w4ke3du1fr/iqKGRcX9xxpDYuySSdyPsomjcjZGBM7H2WTRuRs\njImdz5DZKvper5bROqruHADIysqCjY0NAMDe3h5paWnq7dLT02Fvb18dkQghhBAiqGqpOZk3bx6a\nNm2K+fPnIzw8HPn5+QgPD0dSUhJGjx6Ns2fPIiMjA15eXrh27RpkMplmSKo5IYQQQuqMaq85CQwM\nxIkTJ3Dnzh20aNECy5Ytw4IFCxAQEICNGzdCoVAgOjoaAODk5ISAgAA4OTnBxMQE69at02qYEEII\nIeTFovdunZ07dyIzMxOPHz9GWloaxo8fDysrKxw7dgzJyck4cuQI5HK5evv3338f165dwx9//IGB\nAwdK2mfpghvRUDbpRM5H2aQRORsgdj7KJo3I2QCx89VktmqpOSGEEEIIqSw6tw4hhBBCqhWdW4cQ\nQgghtUqdaJxQn500ImcDxM5H2aQRORsgdj7KJo3I2QCx81HNCSGEEELIU1RzQgghhJBqRTUnhBBC\nCKlV6kTjhPrspBE5GyB2PsomjcjZALHzUTZpRM4GiJ2Pak4IIYQQQp6imhNCCCGEVCuqOSGEEEJI\nrVInGifUZyeNyNkAsfNRNmlEzgaInY+ySSNyNkDsfFRzQgghhBDyFNWcEEIIIaRaUc0JIYQQQmqV\nOtE4oT47aUTOBoidj7JJI3I2QOx8lE0akbMBYuejmhNCCCGEkKeo5oQQQggh1aqi73WTaswChUKB\nxo0bw9jYGKampjh79ixyc3MxatQo3Lx5EwqFAtHR0ZDL5dUZixBCCCECqdZuHZlMhvj4eFy8eBFn\nz54FAISHh8Pb2xvJyckYMGAAwsPDq3y/1GcnjcjZALHzUTZpRM4GiJ2PskkjcjZA7HwvVM3Js4dx\nYmJiEBwcDAAIDg7Gvn37qjsSIYQQQgRSrTUnrVu3RpMmTWBsbIzJkyfjrbfegqWlJfLy8gDwhouV\nlZV6WR1SJkNwcDAUCgUAQC6Xw9XVFZ6engBKWne0TMu0TMu0TMu0LN6y6vfU1FQAwJYtW8qtOanW\nxklWVhaaN2+O27dvw9vbGxEREfD399dojFhZWSE3N1czJBXEEkIIIXWGUJOwNW/eHADQrFkzDB8+\nHGfPnoWtrS2ys7MB8MaLjY1Nle+3dMtMNJRNOpHzUTZpRM4GiJ2PskkjcjZA7Hw1ma3aGicPHjzA\nvXv3AAD379/HkSNH0LFjR/j7+2PLli0A+GGeYcOGVVckQgghhAio2rp1UlJSMHz4cABAUVERxowZ\ng4ULFyI3NxcBAQH466+/yhxKTN06hBBCSN1R0fc6TcJGCCGEkGolVM2JoVCfnTQiZwPEzkfZpBE5\nGyB2PsomjcjZALHzvRA1J4QQQgghlUHdOoQQQgipVi9Etw4hhBBC6o460TihPjtpRM4GiJ2Pskkj\ncjZA7HyUTRqRswFi56OaE0IIIYSQp6jmhBBCCCHVimpOCCGEEFKr1InGCfXZSSNyNkDsfJRNGpGz\nAWLno2zSiJwNEDsf1ZwQQgghhDxFNSeEEEIIqVZUc0IIIYSQWqVONE6oz04akbMBYuejbNKInA0Q\nOx9lk0bkbIDY+ajmhBBC/r+9cwuJ4n3j+NdMQympwNTcSFpdV3ddd9MKk0Ips7qQopvVKElvKpTq\nQsKL7oqKIuhABJERmZ3p9IO0oPBAtOWhtJTaaC1P2cEE3Yy17fld+N/BrXb/P7ednbd4Pnfzzrzj\nh6/PyrMz74wMwzD/g9ecMAzDMAwTUHjNCcMwDMMwfxR/RXPC9+x8Q2Q3QGw/dvMNkd0Asf3YzTdE\ndgPE9uM1J7/JkydPlFbwCLv5jsh+7OYbIrsBYvuxm2+I7AaI7aekmzDNSXV1NbRaLRISErB///4J\nzR0cHJTJ6vdhN98R2Y/dfENkN0BsP3bzDZHdALH9lHQTojlxOp0oKSlBdXU12tvbcf78eXR0dCit\nxTAMwzCMAgjRnDx69Ajx8fGIi4tDSEgIzGYzbty48Z/nd3Z2yif3m7Cb74jsx26+IbIbILYfu/mG\nyG6A2H5KugnxKPGVK1dQU1ODkydPAgAqKythsVhw9OhRAGOPHDEMwzAM8/fgrf2YHEAPj/y/5kOA\n/olhGIZhmAAhxG2d2NhYdHV1SdtdXV1QqVQKGjEMwzAMoxRCNCfp6emwWq3o7OyEw+HAxYsXkZeX\np7QWwzAMwzAKIMRtncmTJ+PYsWPIzc2F0+lEcXExkpKSlNZiGIZhGEYBhLhyAgCrVq3Cixcv8OrV\nK5SXl6OoqAhRUVFISUmRjnn69CkyMjJgMBiQl5eHoaEhAGMrisPCwmAymWAymbB161ZpTlNTE1JS\nUpCQkIBt27b5xdVfbllZWdBqtdK+jx8/BtQNAFpbW5GRkQG9Xg+DwQCHwwFA+dy8ucmR20T9zp07\nJ/18k8mE4OBgtLa2AlA+O29uStfc169fkZ+fD4PBgOTkZOzbt0+ao3Ru3txEqDmHw4FNmzbBYDDA\naDSitrZWmqN0dt7c5Miuq6sL2dnZ0Ol00Ov1OHLkCABgYGAAOTk50Gg0WLFihdt7Ovbu3YuEhARo\ntVrcuXNHGvd3dv5083d2E3UbGBhAdnY2pk2bhtLSUrdzyVFzbpCg1NXVUXNzM+n1emksPT2d6urq\niIiooqKCdu3aRURENpvN7bjxLFiwgCwWCxERrVq1im7fvi2MW1ZWFjU1Nf22j69uo6OjZDAYqLW1\nlYiIBgYGyOl0EpHyuXlzkyO3ifqNp62tjdRqtbStdHY/usXHx0vbStfc6dOnyWw2ExHRly9fKC4u\njt68eUNEyufmzU2Emjt27BgVFRUREdH79+8pLS1NmqN0dt7c5Miur6+PWlpaiIhoaGiINBoNtbe3\nU1lZGe3fv5+IiPbt20c7d+4kIqLnz59TamoqORwOstlspFar6fv370Tk/+z86ebv7CbqZrfbqaGh\ngU6cOEElJSVu55Kj5sYjzJWTH1myZAlmzJjhNma1WrFkyRIAwPLly3H16lWv5+jr68PQ0BAWLlwI\nANi4cSOuX78uhJsL8vOTSBNxu3PnDgwGg/TNaMaMGZg0aZIQuXlyc+Hv3CbqN56qqirk5+cDEK/m\nqqqqYDab3caUrLmYmBjY7XY4nU7Y7XaEhoYiIiJCiNw8ublQuuY6OjqQnZ0NAIiMjMT06dPx+PFj\nIbL7lVtjY6M0z9/ZRUdHw2g0AgCmTp2KpKQk9PT04ObNmygsLAQAFBYWSjncuHED+fn5CAkJQVxc\nHOLj42GxWGTJzl9uLvyZ3UTdwsPDkZmZiSlTpridR66aG4+wzcmv0Ol00svZLl++7PaEj81mg8lk\nQlZWFhoaGgAAPT09bk/9xMbGoqenRwg3F4WFhTCZTNi9e7csXt7cXr58iaCgIKxcuRJpaWk4cOAA\nADFy8+TmIhC5efMbz6VLl6TmRITsPLm5ULLmcnNzERERgZiYGMTFxaGsrAzTp08XIjdPbi6UrrnU\n1FTcvHkTTqcTNpsNTU1N6O7uFiK7X7mNr0c5s+vs7ERLSwsWLVqE/v5+REVFAQCioqLQ398PAOjt\n7XXLSKVSoaen56dxf2fnq1tvb6+0LVd2/8XNxY+v+whEzf1RzUlFRQWOHz+O9PR0DA8PIzQ0FAAw\ne/ZsdHV1oaWlBYcOHUJBQYHb2gXR3IaHhwGMrQ149uwZ6uvrUV9fj7NnzwbU7du3b2hoaEBVVRUa\nGhpw7do13Lt3L6AvvZuoGxC43Lz5ubBYLAgPD0dycrJsDv50U7rmKisrMTIygr6+PthsNhw8eBA2\nm00WB3+6iVBzRUVFUKlUSE9Px44dO7B48WIEBwcL8Xn15AbIm93w8DDWrVuHw4cPY9q0aW77goKC\nFH2Bpz/c5MpO5Nxc/FHNSWJiImpqatDY2Aiz2Qy1Wg0ACA0NlS4/zp8/H2q1GlarFbGxseju7pbm\nd3d3IzY2VnG3ly9fAhhrXICxy2sFBQV49OhRQN3mzJmDpUuXYubMmQgLC8Pq1avR3NwsRG6e3IDA\n5ebNz8WFCxdQUFAgbYuQnSc3QLmai4+PBwA8ePAAa9euRXBwMCIjI5GZmYmmpiaoVCrFcvPm5ro1\nIULNBQcH49ChQ2hpacH169cxODgIjUaD2bNnK15zntwA+bIbHR3FunXrsGHDBqxZswbA2Lf+d+/e\nARi79TBr1iwAP79Lq7u7GyqVSrbP6++6uRzkyG4ibp4IxN+5P6o5+fDhAwDg+/fv2L17N7Zs2QIA\n+PjxI5xOJwDg9evXsFqtmDdvHmJiYhAREQGLxQIiwtmzZ6VfhtJuTqdTWnk9OjqKW7duua2KD4Rb\nbm4u2traMDIygm/fvqG2thY6nQ7R0dGK5+bJLZC5efNzjV2+fNltTYcINefJTcma27x5MwBAq9VK\nV8DsdjsePnwIrVaraM15c0tKShKm5kZGRmC32wEAd+/eRUhICLRarRA158lNruyICMXFxUhOTsb2\n7dul8by8PJw5cwYAcObMGSmHvLw8XLhwAQ6HAzabDVarFQsXLpSl7vzlJkd2E3UbP288Aak5vy6v\n9SNms5liYmIoJCSEVCoVnTp1ig4fPkwajYY0Gg2Vl5dLx169epV0Oh0ZjUaaP38+/fPPP9K+xsZG\n0uv1pFarqbS0VBi34eFhSktLI4PBQDqdjrZv3y6t0A6UGxFRZWUl6XQ60uv10gptIuVz8+QmV26+\n+N2/f58yMjJ+Oo8I2f3KzW63K15zX79+pfXr15Ner6fk5GQ6ePCgtE/p3Dy5iVJzNpuNEhMTKSkp\niXJycujt27fSPqWz8+QmV3b19fUUFBREqampZDQayWg00u3bt+nTp0+0bNkySkhIoJycHPr8+bM0\nZ8+ePaRWqykxMZGqq6ulcX9n5y83ObLzxW3u3Lk0c+ZMmjp1KqlUKuro6CAieWpuPEL84z+GYRiG\nYRgXf9RtHYZhGIZh/n64OWEYhmEYRii4OWEYhmEYRii4OWEYhmEYRii4OWEYhmEYRii4OWEYhmEY\nRij+BfQab0xfQqRuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3c04550>"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After remaining stagnant through the 1990's, consumption per-capita has fallen steeply in the second-half of the 2000's. We're now at levels not seen since the early 1980's after a severe recession, and if trends continue we should see levels dip to where they were in the mid 1960's by the end of the decade.\n",
      "\n",
      "So where are the declines in consumption occurring? The EIA dataset breaks out consumption by three categories: fossil fuels, nuclear and renewables."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "energy_consumption = pivot[['Total Fossil Fuels Consumption',\n",
      "                        'Nuclear Electric Power Consumption',\n",
      "                        'Total Renewable Energy Consumption']].values\n",
      "\n",
      "cons_per_cap = energy_consumption * 1000000000 / (us_pop[us_pop.Year >= 1949]['Pop'])[:, None]\n",
      "\n",
      "figure(figsize=(9,4), dpi=80)\n",
      "\n",
      "# fossil, nuclear and renewable\n",
      "plot(cons_per_cap[:,0], color='black', linewidth=2.5, label='Fossil Fuels')\n",
      "plot(cons_per_cap[:,1], color='r', linewidth=2.5, label='Nuclear')\n",
      "plot(cons_per_cap[:,2], color='g', linewidth=2.5, label='Renewable')\n",
      "\n",
      "xticks(np.arange(1, len(labels), 5), labels[np.arange(1, len(labels), 5)])\n",
      "xlim([0,len(labels)])\n",
      "\n",
      "title(\"US Annual Per-Capita Energy Consumption by Source\", fontsize=16)\n",
      "ylabel(\"Million BTU\")\n",
      "grid(b=True, which='both')\n",
      "\n",
      "legend(loc='center right')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6jImrq2t9Z6PeoNpEQ5K1AZKtj2oTDUnWBki2PqpNNCRZGyBefU1iETYWiyVU\nHxWFQqFQKBTJp6bverNYTCQ2NlbcEqqEahMNSdYGSLY+qk00JFkbINn6qDbRkGRtgHj1NQvjhEKh\nUCgUSvOBdutQKBQKhUJpVL6Lbh0KhUKhUCjNh2ZhnEhyvx3VJhqSrA1oeH11WXpbksuOahMdSdZH\ntYmGJGsD6JgTCoXy/zx69AgWFhbQ09PDlStXxC2HQqFQxAIdc0KhSAhhYWGYPXs2CgsLAQAqKiqI\ni4tDx44dxayMQqFQ6hc65oRCkXCKioowa9YsuLi4oLCwkFmEMC8vD05OTnj9+rWYFVIoFErj0iyM\nE0nut6PaREOStQH1py85ORmWlpYIDg4GAKirq+P06dNYuXIlAOD58+cYMWIEPn/+3OjaGgKqTXQk\nWR/VJhqSrA2gY04olO+SiIgI9OzZE/fu3QMA9OvXD4mJiXBwcICvry+mTJkCALh+/TpcXV3rNEiW\nQqFQmhJ0zAmF0sh8/vwZXl5e2L59O+O2aNEirF27FtLS/213VVxcDAcHB1y+fBkAsHTpUvj5+TW4\nvvLychQVFeHTp08Cj48fPyI3N7fKg8Ph4MiRI+jfv3+Da6VQKE2Tmr7r1DihUBqRd+/ewcnJCfHx\n8QAAVVVV7Nu3D05OTlX6t7S0RHJyMgAgJCQEM2bMqHddhBDExcVh48aNiIqKQmlpaZ3i09HRQWJi\nIjQ1NetJIYVCaU58FwNiJbnfjmoTDUnWBoimLysrC1ZWVoxhYmFhgbt371ZpmABfjJeYmBioq6sD\nANzd3XH+/Pl601ZSUoL9+/ejd+/e6Nu3Lw4fPlwrw0RNTQ1GRkawsbHBuHHjMG7cOABfdid3cXGp\n1BUlyfdVkrUBkq2PahMNSdYGiFefdM1eKBRKXXnx4gXs7e3x4sULAMD48eOxb98+cDicGsMaGhoi\nKioKtra2KC4uxujRo3H9+nV06dJFZD1v3rzBzp07sW3bNmRlZTHuHA4H48ePR/fu3SEnJyfwkJeX\nh4aGBtTV1fm6oYAvLTDS0tLYv38/zp49i/Xr12Px4sUi66RQKN8ntFuHQmlgHjx4AAcHB2RnZwMA\nZs+eja1bt0JKSqpW8URERGDChAkAgNatW+PmzZvQ0tKqVRx5eXlYvHgx9u3bxzcDSFNTEx4eHpg9\neza0tbVrFee3fPz4Eaampnj27BmkpKRw6dIl9O3bt05xUiiU5gUdc0KhiJH4+HgMGTIEeXl5AIAl\nS5bAz88hIkMfAAAgAElEQVSPWcuktgQEBGDp0qUAgD59+uDChQuQlZUVKuznz59hZ2eHa9euMW49\nevTA/PnzMWHCBHC5XJE0CeLu3buwsLBASUkJWrVqhbt370JNTa3e4qdQKE0bOuZEzFBtoiHJ2gDh\n9J0/fx52dnaMYbJ+/Xr4+/uLbJgAwOLFizFt2jQAX6YYe3h4VKrggrTxeDxMnz6dMUwGDBiAixcv\n4u7du3B1da1XwwQATExM8McffwAA0tPTMX36dBBCJPq+SrI2QLL1UW2iIcnaALrOCYXS7Pj777/h\n5OSEwsJCsNlsBAcHw9vbu87xslgs7NixA5aWlgCAPXv2YOPGjTWG8/HxwcGDBwEA/fv3x6lTp2Bj\nY1MnQ6km5syZg1GjRgEAjh8/jk2bNjVYWhQKpXlBu3UoFBEoKyvDq1evkJmZiaysLL6/mZmZuHDh\nAng8HmRkZLB//36MGTOmXtPPycmBmZkZMjIywGazERMTA0dHR4F+Q0ND4erqCgBo37494uLiGq2L\nJT8/HyYmJkhNTYWMjAyuXbsGMzOzRkmbQqFILnTMCYVSj+Tl5WHNmjX4888/UVRUVK1fOTk5REZG\nwsHBoUG03L17F3379kVRURGUlJRw48YNdOrUic9PbGwsHBwcUFpaClVVVcTHx6N9+/YNoqcqbty4\ngX79+qGsrAxt2rTB3bt30bJly0bVQKFQJAs65kTMUG2iIWnaysrKEBQUhPbt22PDhg0CDRMpKSno\n6urC3Nwc48ePx+XLlxvMMAG+jOsIDQ0FAHz48AHDhg1DXl4eU3aPHz/GyJEjUVpaCg6Hg6ioqEY3\nTACgd+/eCAwMBACkpKTAzc1NIn9sSNoz9y2SrI9qEw1J1gbQMScUikRz9uxZGBsb46effsLbt28B\nAN27d0dQUBCio6Nx69YtZGVlobi4GBkZGbhx4wYOHjyInj17Nri2sWPHwsfHBwDw9OlTjBs3DuXl\n5Xj9+jWcnJyQn58PANi9e7dYl5NfsGABs9jckSNHsGLFCrFpoXxfvHr1Cjk5OeKWQaktpAnQRGRS\nmhmPHz8mzs7OBABzGBgYkCNHjhAejydueQzl5eVk9OjRjEYPDw/Sp08f5tzX11fcEgkhhLx+/Zro\n6+szuvz8/MQtidIMycjIIH/99ReZPn06MTAwIACIrKwsOXr0qLilUb6ipu96vX/1i4qKiLm5OenR\nowfp3LkzWbx4MSGEkLdv3xI7OzvSvn17Ym9vT/Ly8pgw/v7+xNDQkHTs2JGcOXOmskhqnFAakcLC\nQjJ//nwiLS3NfEgVFBRIYGAgKSoqErc8gRQUFBBjY2M+QwoAmTJlikQZUk+fPiU6OjqMvg0bNohb\nEkXM8Hg8UlRUVOvnlMfjkby8PPLo0SNy8OBB4u7uTjp06FCpDlQcbDabhIaGNlAuKLWlpu96gwyI\n/fTpE+Tk5FBWVoZ+/frht99+w7Fjx6Curo6FCxdi3bp1yMvLQ2BgIB4+fIhJkybh1q1byMzMhJ2d\nHZKTk8Fm/9fjVNPAmdjYWNjY2NR3NuoFqk00xKUtLy8PQ4cOZdYDYbFYmDFjBtauXcu3cqoklt3L\nly9hZmaG3NxcAICVlRXOnj0r9CJtjUFsbCw0NTVhbW2NN2/eAAB27tyJH3/8UczKJPOefo0k66tO\nW3l5Oa5evYqUlBRkZ2dXOnJycvDp0yfIyMhATU1N4CEnJ8d0z1SEyc7O5lvl+Fvk5eXRv39/qKqq\nIjIykhkntnXrVvz0008NUQy1RpLvKdCw+mr6rjfI3jpycnIAvmwqVl5eDhUVFRw7dgyXLl0CAEyb\nNg02NjYIDAxEdHQ0Jk6cCBkZGRgYGMDQ0BA3b96EhYUFX5yurq4wMDAAACgrK8PY2JgptMTERABg\nzisG8dDz6s8rkBQ9X58nJiY2evodOnSAo6Mj7t+/DwCwtLTEtm3b8P79ezx+/JgxTsSlT5jzyMhI\n2NnZQVdXF3///TdkZWUlSh8A5ObmIiAgAN7e3sjPz8esWbOQmpoKPz8/seqrQNzl0xT1VVUfzpw5\nAw8PD6SkpKAmSktLkZOTI/L4EC6Xi759+8LAwAAmJiaYNWsWZGRksHHjRvTu3RsrVqzAhw8fMHfu\nXCQmJiI4OLje8k/PhXt+Y2NjkZqaCmFokJYTHo8HU1NTPH/+HB4eHli/fj1UVFSYlTIJIVBVVUVe\nXh48PT1hYWGByZMnAwDc3NwwePBgjB49+j+RdCoxpYF5+vQpHBwcmIozZswYhIWFSVSrg7DweDyw\nWKwGXWCtPrhx4wbs7OxQUFAANpuNQ4cO8dV7StPl/v378Pb2xpkzZypdU1JSgo6ODrS1taGjowMd\nHR2oqanh48ePePv2Ld/x5s0bvH37FmVlZVBWVuYL9/VfAwMDmJmZVVtf79y5AwcHB2ZQ+6JFixAQ\nECDx9aS5IpaWEzabjcTERLx//x6DBg3CxYsXK4mq7oGgDwulMblz5w4GDx7MdIeIujGfpPB1l6gk\n07t3b2bxuKKiIkycOBGRkZHMrB5K0yMrKwsrV67Enj17wOPxAACKiopYsmQJxo0bB21tbcjLy9cq\nTkIIMx2+LpiamuLy5cuwt7dHVlYW1q1bhw8fPmDr1q0NVmcIISgsLISCgkKDxN+cadC3WMuWLeHk\n5ITbt29DS0uLaa7Lzs6GpqYmAEBXVxfp6elMmIyMDOjq6tYqnW+bPSUJqk00GkvbxYsXYWNjwxgm\nK1asQFBQUI2GCS070fhWm5WVFaKjo8HhcFBaWorRo0fjn3/+kQhtDQ0hBE+ePMHevXvh7u6Ovn37\nwt3dHUlJSRKhrzacOnUKvr6+aN++PUJCQsDj8SAlJYU5c+bg2bNnWLJkCdq1a1drwwT48mO1LobJ\n1+VmZGSEK1euoE2bNgCA7du3Y9q0aSgrKxM5/m8pLi7G2bNn4enpCQMDAygqKqJVq1aYMGECNm/e\njNu3bzPpSfI9BcSrr95bTt68eQNpaWkoKyujqKgI586dg4+PD4YNG4bQ0FAsWrQIoaGhGDFiBABg\n2LBhmDRpEry8vJCZmYmnT5/C3Ny8vmVRKJWIjIzEhAkTUFJSAgDYvHkzPD09xazq+8Pe3h5HjhzB\nqFGjUFxcjOHDhyMxMRGGhobillavFBQU4MaNG4iLi0NcXBzi4+Px7t07Pj/Xr1/Hzp07MWjQIHh5\necHe3l7sLcllZWVISkpCamoqcnNz+Y7Xr18jNzcXz58/x6dPn5gwQ4cOxbp169C5c2cxKhdM27Zt\nceXKFdjb2+PRo0cICwvDtWvXoKysDDabXemQkpKClpYW2rZtizZt2jB/W7duzRhNb9++xcmTJ3Hs\n2DGcOXMGHz9+5EszIyMDERERiIiIAPBlXGbv3r2hq6uL0tJS2NraNpkWz0ajvqcH/fvvv8TExIT0\n6NGDdOvWjaxfv54Q8mUqsa2trcCpxH5+fqRdu3akY8eO5PTp05XibACZlO+c4OBgwmazCQAiLS1N\n9u/fL25J3z0RERHMtE8rKytSXl4ubkn1xv79+4miomKV01zl5OSIiYkJYbFYfO5dunQhwcHBjTaF\nncfjkZSUFBIREUG8vLxIv379SIsWLarU/e1hampKLly40Cha68rr16+Jqamp0Hn79mCz2URfX5+Y\nmJgw75KvD2lpaWJra0uWL19ORo4cSTQ1NauMq3v37iQ6Olqipv03NDV91+neOpTvjq83wpOTk8PR\no0er3DSP0rjMnTsX27ZtAyBZUz5FpaysDIsXL8bvv//O525oaAgLCwtYWlrC0tIS3bp1g7S0NJ4+\nfYrNmzdj9+7dfC0RGhoa8PDwwOzZs6Gjo1Nv+l6/fo3bt2/j1q1buHXrFm7cuMF0cVaHoqIiNDU1\nmUNDQwO2trYYN25ck2oBeP/+Pfz9/fHkyRPweDyBR2lpKTIzM5GWllZj94+KigoGDx6MYcOGYdCg\nQVBWVmauEULw4sULXLt2DdevX8e1a9eQlJTE920zNzfH2rVrYWdnJ/YWs4amxu96g5tH9UBNMi9e\nvNg4QkSAahONhtJ27tw5ZnE1ZWVlcv36dZHi+R7Lrj6oSdvHjx+ZVWTl5eVJSkpKo+gipP7L7c2b\nN8TOzo75dayiokLCwsLIq1evagz77t07sm7dOqKnp8f3C1tKSoqMGDGCnDx5kpSVldVKT15eHjl/\n/jwJCAggo0eP5lutV9AhLS1NevXqRebMmUNCQ0PJzZs3SVpaGvn06VOluJvyMycspaWlJDU1lVy8\neJGEhISQZcuWkUmTJpEBAwYQLy8vEhsbS0pLS2sV57Fjx4ifnx9RVlbmK3tra2ty9epVgWFyc3PJ\n2bNnyfr164mLiwtZunQpKS4uro8sVqIh72tN33VqnDQwVJtoNIS2e/fuMU3rHA6HXL58WeS4vrey\nqy+E0Xbu3DnmJW1nZ9doTd31WW6JiYnM0ukASLdu3cjz589rHU9JSQnZv38/6dWrVyXjoXXr1mTV\nqlUkPT29UriysjJy79498ueff5Jp06ZVu3JqxdG2bVsyceJEsnHjRhIXF1errqSm/syJiwpteXl5\nZPny5UReXp7vngwePJiEhoaSpUuXkiFDhpAffvhB4L0bMmSIQKOxvvQ1BN+FcUKh1ER6ejrR1dVl\nKvPBgwfFLYlSDT/++CNzr4KDg8Utp1YcOHCAb5zGuHHjSEFBQZ3i5PF4JD4+nsycOZPIyclVGvvg\n7OxM9uzZQ5YtW0YGDhxIFBQUqjVEWrVqRUaNGkX8/f3J2bNnydu3b+sp95S6kJubS7y8vIisrKxQ\n4164XC5fa8v79+/FnQWhocYJ5bvn/fv3pHv37kwlrhikTZFc8vPzmS4NJSUlga0DkkZZWRnx9vbm\nMxrWrVtX7y0/79+/J9u3bycmJiZCfcCUlZWJo6Mj8fHxITExMSQnJ6de9VDqn4yMDDJ79mymC5rF\nYpFOnTqRCRMmkMDAQHL69GmSnZ1NPnz4QAYOHMjc6169epHXr1+LW75QfBfGSVNotpNEvgdtJSUl\nxN7enqm8c+bMqZePxfdQdg1BbbTFxMTwNVvX9b4VFxeTK1euVPnrsi7llpaWxje+RFlZWeDMw7og\nSF9CQgKZNWsW01LCYrFI165diZubGwkJCSEPHz5slFlPzeWZa2xq0paTk0MSEhKqbXkrKioiQ4cO\nZZ49IyMjkpmZ2Sj66kJN3/WmM6yaQqklhBC4u7vj3LlzAL6svbBp06ZmPwq+uTBkyBBMnToVAHDy\n5En89ddfIseVl5cHKysr9O/fH1paWhgzZgwOHz7MNyNGFMrLy7Fp0yYYGRnh/PnzAICuXbsiISEB\ngwYNqlPcwtCzZ0/s2LED2dnZuHXrFvLy8nD//n0EBwdjxowZ6Ny5c5OaPUPhR0tLCz179qx28Tou\nl4ujR49i0qRJAICHDx+iX79+ePHiRWPJbBgazCyqR5qITIqEsWrVKubXhJmZWZ37/SmNz9u3b4m2\ntjbTGpGVlVXrOF6/fk2MjY0FdnnIy8uTSZMmkWPHjpHPnz/XKt579+4Rc3NzvvimTZtGPn78WGuN\nFEpdKS8vJ7Nnz2aeRR0dHfLgwQNxy6qSmr7rTeKrT40TSm3g8XgkODiYqaRt2rSh/exNmMjISOZe\nDh8+vFbdO9nZ2aRLly5M+GHDhpFp06YRJSUlgWMzXF1dSWhoKHn69GmV6Xz69IksXryYGQ+A/5/p\ncu7cufrKMoUiEjwejyxatIh5LtXU1MitW7fELUsg34Vx0pT7FMVJc9NWWFhIgoODSY8ePZjKqaqq\nSh4/fiwR+hqL5qhtwoQJzD0VdjXf9PR0vim0Li4uzDoURUVFJDIykowfP77KFVA1NTXJiBEjyPr1\n68nVq1dJUVEROX/+PGnXrh3jR0pKiixatIgUFhaKlK/a0Bzva2PwPWoLCAjgewdmZ2eLFA8dc0Kh\n1IHnz5/jl19+ga6uLn788Ufcu3cPAKCgoICoqCh07NhRzAopdWXz5s3Q0NAAAEyZMgXjx4/H7du3\nq/SfmpoKKysrJCcnAwDc3NywZ88eSEt/2U6My+VixIgROHjwIHJzc3HgwAH07dsXcnJyTBy5ubmI\niorCwoUL0a9fPygpKcHOzg7Pnz8HAPTq1QsJCQkIDAzkC0ehiJvFixdj69atAIB3795h3rx5YlYk\nAg1mFtUjTUQmpREpLy8nMTExZPDgwZX2I9HV1SWrV68W+dcCRTL5+++/K+1hMnDgQHL69Gm+Lpjk\n5GTSqlUrxs9PP/0k9IyV0tJScvv2bbJ582YyYcIE0rp1a4HjVDZs2FDrFVoplMZm9OjRzHN77Ngx\nccvho6bvOt1bh9LkePfuHYYOHYrr16/zudvY2GDu3LkYNmwYZGRkxKSO0pDcv38f//vf/3DgwAG+\nfU66d+8Ob29vdO/eHY6OjsjOzgYA/Prrr1i/fn2dZmhlZGTg+vXruH79OqSlpeHp6Ql9ff0654VC\naWiys7PRuXNnvH//Hnp6ekhKSoKSkpK4ZQGge+uIHapNNKrS9u3MC3l5eeLh4dHoo9KbYtlJAvWl\nLS0tjSxYsKDalVBXrFhRq8GzklxuhEi2PqpNNBpD286dO5k6MXfu3FqFpWNOKBQhePXqFWxsbJCY\nmAgAGD9+PDIzMxEUFIQuXbqIWR2lMWndujX++OMPvHz5Ev7+/tDS0uK77ufnh9WrV9M1bSjfPTNn\nzoS1tTUAYNu2bYiLixOzIuGg3TqUJkFmZiZsbW3x5MkTAICLiwv27NkDKSkpMSujSAKfP3/GX3/9\nhaNHj2Ls2LGYOXOmuCVRKBJDcnIyunfvjuLiYnTp0gV37twBh8MRq6aavuvUOKFIPC9fvsTAgQOZ\nWRJubm74888/qWFCoVAoQuLv749ly5YBAFavXo0VK1ZU6ZcQghMnTuDo0aMYNmwYRo4cWe+tkDV9\n15tFt05sbKy4JVQJ1SYaFdpevHgBKysrxjCZM2cOduzYIXbDpCmUnSRCtYmOJOuj2kSjMbV5e3uj\nW7duAIC1a9fi0aNHAv0lJyfDyckJw4YNQ2hoKEaPHg1LS8tGL8dmYZxQmifJycmwsrJCWloaAGDB\nggXYunUr3SuEQqFQaomMjAx27doFFouFkpISzJo1Czwej7leUFCAJUuWoGvXrjh16hRf2Bs3bmDA\ngAEYMmQIs45Ug9NgQ3HrkSYik1KP/Pvvv8yeKgDIkiVL6n3reQqFQvnemD9/PvNe3b59O+HxeOTA\ngQNEV1eXcWexWMTNzY08ffqULFu2jG8VZRaLRaZMmUJevHhRJx01fdfpmBOKRFFQUAA/Pz/8/vvv\nKC0tBQD4+vpi5cqVdOYFhUKh1JGCggJ07doVaWlpUFJSgrGxMS5fvsxc7927N7Zs2QIzMzPGLTs7\nG6tXr0ZwcDDKy8sBfGmJ8fDwwJo1a0RaO4WucyJmqDbh4PF4JCIigujp6fFZ6IGBgeKWJhBJKrtv\nodpEQ5K1ESLZ+qg20RCXtpMnT1ZaF0hDQ4Ps3r2bbzXlb/U9efKEjB07li9c165dSVpaWq011PRd\np533FLGTlJQEW1tbjB8/HhkZGQCAzp074+bNm1i0aJGY1VEoFErzYvDgwZgyZQoAQEpKCvPmzUNy\ncjKmT59e7Zi+Dh064NChQ7h58yasrKwAAA8ePEDv3r2r3etKJGpt7tTAy5cviY2NDTEyMiJdunQh\nmzZtIoQQ4uPjQ3R1dYmxsTExNjYmJ0+eZML4+/sTQ0ND0rFjR3LmzJlKcTaATIoE8P79e+Ll5cW3\n9by6ujoJCQkRei8UCoVCodSekpIScuDAAfLo0SORwpeVlfGNX5GTk6vV/j01fdfrfcxJTk4OcnJy\nYGxsjIKCAvTs2RNRUVE4dOgQFBUV4eXlxef/4cOHmDRpEm7duoXMzEzY2dkhOTmZz3qjY06aFzwe\nD2FhYVi0aBFycnIAAGw2G3PmzMHq1auhoqIiZoUUCoVCEYYtW7Zg/vz54PF4YLPZ2LhxIzw9PWsM\n1+jrnGhra8PY2BjAly3rO3fujMzMTAAQKCQ6OhoTJ06EjIwMDAwMYGhoiJs3b9YqTTqPXTTEoS0h\nIQF9+/bFtGnTGMOkX79+uHPnDrZs2cIYJpJcboBk66PaREOStQGSrY9qEw1J1gYIp8/T0xORkZGQ\nk5MDj8fDzz//jAULFjADZ0VFuk6hayA1NRV3796FhYUFrl27hi1btmDfvn3o1asXfv/9dygrKyMr\nKwsWFhZMGD09PcaY+RpXV1cYGBgAAJSVlWFsbAwbGxsAYPZaqTivKFB6Xv15BY2RXl5eHmJiYrB7\n927GSNXR0cH69euhq6uLvLw8Pj2JiYliL5/qziVZH60Pop1XICl6mpI+Wh++73MlJSVcvnwZzs7O\nyMnJwcaNG5GSkoLw8HDcunULFcTGxiI1NRXC0GBTiQsKCmBjY4Ply5djxIgRyM3NhYaGBgBgxYoV\nyM7ORkhICDw9PWFhYYHJkycD+LI0+ZAhQzBq1Kj/RNJunSZLaWkptm3bBl9fX7x//x7AlyloXl5e\nWLZsGRQVFcWskEKhUCj1QVpaGpycnJCUlAQA6NWrF44fPw5tbe1KfsWyfH1paSlGjx6NKVOmYMSI\nEQAATU1NsFgssFgsuLm5MV03urq6SE9PZ8JmZGRAV1e3IWRRGpnz58+jR48eWLBgAWOYVDy4gYGB\n1DChUCiUZoS+vj6uXbsGOzs7AF+68WfMmCFSXPVunBBCMHPmTBgZGWH+/PmMe3Z2NvN/ZGQks8b/\nsGHDcPDgQZSUlCAlJQVPnz6Fubl5rdL8ttlTkvgetcXFxcHBwQH29vbM/g3t27fHiRMncOLECbRv\n315s2uoLSdZHtYmGJGsDJFsf1SYakqwNEE1fy5YtcfLkScycORNaWloICgoSKe16H3Ny7do1hIWF\noXv37jAxMQHwZTfEAwcOIDExESwWC23atMGOHTsAAEZGRhg3bhyMjIwgLS2NoKAguhJoE+XmzZvw\n8fHB6dOnGTcFBQWsWLEC8+bNg6ysrBjVUSgUCqUxkJGRQXBwMLKyskTuCaHL11PqzJ07d+Dj44MT\nJ04wbhwOB+7u7liyZAl0dHTEqI5CoVAokkZN3/UGna1Dad7cu3cPvr6+iIqKYtxkZGTg5uaGpUuX\nQk9PT4zqKBQKhdJUaRbL10tyv11z1JaWlgYXFxcYGxszhom0tDRmzZqFp0+fIigoqM6GiSSXGyDZ\n+qg20ZBkbYBk66PaREOStQHi1UdbTihCk5eXh4CAAGzevBnFxcUAvuzLMG3aNCxfvhxt2rQRs0IK\nhUKhNAfomBNKjRQXFyMoKAhr167Fu3fvGPcxY8bA399fqNk3FAqFQqFUQMecUKqkoKAAaWlpaNGi\nBXPIyclBRkYGLBYLPB4Phw4dwtKlS5GSksKE69u3L3777Te+lX0pFAqFQqkv6JiTBkYStT148AAe\nHh7Q0NBA165d0a5dO/zwww9QUVGBrKwspKWloaioCDU1NUycOJExTDp06IDIyEhcuXKlwQ0TSSy3\nr5FkfVSbaEiyNkCy9VFtoiHJ2gA65uS7hcfj4f79+1BTU2vwmS1lZWWIjo7G1q1ba3zgeDweCgoK\nmHNNTU34+vrCzc0NMjIyDaqTQqFQKBQ65kQMpKenY+/evdi9ezezCVKvXr0watQojBw5Ep06daq3\ntHJzcxEcHIw///wTGRkZjDuLxYKTkxPGjh0LAPj06ROKiopQVFTE/P/p0ye0a9cO7u7udKl5CoVC\nodQbNX3XqXHSSJSUlOD48eMICQnBmTNnwOPxqvTbqVMnxlDp2bOnSCvmZmZmYvXq1di7dy9KSkoY\ndxUVFcycORMeHh5o27atSHmhUCgUCqUuiGXjv8ZGkvvtwsLC4O3tDT09PYwZMwanTp1iDBNjY2Ns\n2rQJy5Ytg5GRERPm8ePH8Pf3h5mZGfT19bFkyRK8ePFCqPTy8/OxdOlStG/fHjt37mQMk+7duyM4\nOBgZGRn43//+h7Zt20p0uUmyNkCy9VFtoiHJ2gDJ1ke1iYYkawPomJNmy6VLlzBjxgyUlpYybi1b\ntsTkyZMxc+ZMmJqaMu5r167FkydPEBkZicjISGbX5vT0dAQGBiIwMBD29vZwd3fHsGHDKo39+Pz5\nM4KCguDn58c33dfJyQmLFy9G37596Z5FFAqFQmkS0G6dBuL9+/fo1q0b0tPTAQDW1tZwc3PDqFGj\nICcnV2P4jIwMREVF4cCBA7h+/TrfNW1tbcyYMQNubm5o3bo1wsPDsWLFCrx8+ZLxY2FhgXXr1sHK\nyqp+M0ahUCgUSh2hY07EhIuLC8LCwgAAW7Zswdy5c0WO68GDB9i5cyf27duH9+/fM+4sFgu6urp8\nA107duyIgIAAjBgxgraUUCgUCkUiqfG7TpoANcm8ePFilddyc3PJtm3byN27d+tZVdVEREQQAAQA\nMTMzIzwer17iLSwsJHv27CGWlpZM/BWHjo4O2bFjByktLRU6vurKTdxIsjZCJFsf1SYakqyNEMnW\n1xjaVFRUKr336CH5h4qKisD7WdN3vVmPOUlNTcXAgQOZRcQsLCzg4eGBcePGgcvlChVHWVkZSkpK\nhOqKAb7Mkpk9ezYAQFVVFQsXLqy3Fgw5OTm4urrC1dUV//77L3bu3ImrV69i7NixmD9/PuTl5esl\nHQqFQpE08vLymlwLOgUif/+abbfO06dPMXDgQL4ujwpUVVUxY8YMuLu7w9DQkO9aQUEBbty4gatX\nr+Lq1auIi4tDaWkpAgMDMX/+/GoLmsfjYfDgwTh79iwA4MiRIxg9enStdFMoFAqlMk2xe59S9X37\nLsecJCUlwc7ODjk5OQCAefPmQV5eHrt27UJubi6fXwcHB4wePRqPHj3C1atXcffuXZSXlwuMd/z4\n8di1axcUFBQEXt+6dSs8PT0BAFOnTkVoaKjQmikUCoVSNdQ4aZqIapxU2ekjLy9PFBQUmENRUZG0\nafjaeyIAACAASURBVNOGzJw5k7x586bW/YV1oRqZhBD+/s47d+4QdXV1pr/Lx8eHGfNRXFxMDh48\nSKytrYXqK+vUqROZMWMGad26NeNmZGREHj9+XEnDo0ePCJfLJQCIvr4+yc/Pr6RN0qDaREeS9VFt\noiHJ2giRbH2Noa2m7wBFMqnqvtV0P6tchK2goAAfP35kjg8fPiAhIQFGRkbMmApJ48aNGxg4cCDe\nvHkDAAgMDISvry/TFcPhcDB+/HjExsYiKSkJnp6eUFJSAgDIyMjAwsICv/76K6KiopCbm4tHjx4h\nJCQEt2/fhr29PQDg4cOHMDMzw99//82kW1JSgilTpuDz589gsVgIDQ1Fy5YtGzn3FAqFQqE0E0Sx\nhIyNjUUJJjLCyLx06RJRUFBgWjg2b94sVNwFBQXk3r17pLCwsFp/ZWVlZNmyZXwtKwsXLiSlpaVk\n+fLljJu3t7dQ6VIoFApFeET8XDUr/P39iZubGyGEkJSUFMJisUh5eXmjpW9tbU127dpVqzBV3bea\n7metx5yUlpaiZ8+e+Pfff+vVSKqOmvqmzp8/j2HDhqGoqAgsFgs7duzAjz/+2CBajh8/DhcXF2a9\nEXNzcyQkJIDH46F79+64efMmZGVlGyRtCoVC+V6R5DEnBgYGyM3NhZSUFIAvWpOTk6Gtrd1gaaam\npqJt27YoKysDm125E6QhNA0YMAAuLi6YMWOG0GFEHXNSZbfO0aNH8ffff+Po0aPMsWvXLgwZMgRj\nxowRWlhDc+bMGQwZMgRFRUVgs9nYt29fgxkmADB06FAkJCSgW7duAICbN2+Cx+OBw+EgLCyskmEi\nyXsnUG2iI8n6qDbRkGRtgGTrk2RtjQGLxcKJEyf4hkE0pGHSVDXVhiqNkxMnTuD48eM4ceIETpw4\ngZiYGDx58gTz58/HypUrG1NjlVy/fh0jR45EaWkppKWlERERgSlTpjR4uoaGhoiLi8PkyZMZN39/\nf8ZgoVAoFAqluLgY8+fPh66uLnR1dbFgwQJmM9Y3b97A2dkZKioqUFNT49tqZN26ddDT04OSkhI6\ndeqECxcuAAB8fX3h4uJSJ00GBgb4559/mPNv44yPj0efPn2goqICY2NjXLp0SWA8z549g7W1NZSV\nlaGhoYEJEybUSde3VLkIm6mpKTMttjakp6dj6tSpyM3NBYvFwqxZs/Dzzz/j3bt3GD9+PNLS0mBg\nYIBDhw5BWVkZABAQEIDdu3dDSkoKmzdvhoODQ43p3L9/H05OTkyLSUREBEaNGlVrvaIiLy+Pv/76\nCxMnTkRBQQHGjRsn0J+NjU2jaaotVJvoSLI+qk00JFkbINn6xK1t/vz5SExMrJe4jI2NsXHjxlqH\nE9RF4efnh5s3b+LevXsAgOHDh2Pt2rVYvXo1fv/9d7Rq1YqZwBEfHw8AePLkCbZt24aEhARoa2vj\n5cuXKCsrA1D7Bc2q6k75Op6v/8/MzISzszPCwsLg6OiI8+fPY/To0Xjy5AnU1NT44lmxYgUcHR1x\n6dIllJSUICEhoVbaaqJK42T37t0iGScyMjLYsGEDjI2NUVBQgJ49e8Le3h579uyBvb09Fi5ciHXr\n1jE77T58+BARERF4+PAhMjMzYWdnh+TkZIF9aBWkpKRg0KBByM/PBwAEBwc3qmFSAYvFgpOTU6On\nS6FQKJT/SExMrPIXfmNACMGIESMgLf3lkzpgwAD8/fffCA8Px7Zt26Curg4A8PHxgbu7O1avXg0O\nh4Ps7GykpqaiXbt26Nu3LwBASkoKxcXFSEpKgpqaGlq3bs2XTl01CfJXQVhYGIYMGQJHR0cAgJ2d\nHXr16oWYmBhMnTqVLxyHw0FqaioyMzOhq6uLPn36CK1NGOp9+XptbW2mX0tBQQGdO3dGZmYmjh07\nxjw806ZNg42NDQIDAxEdHY2JEydCRkYGBgYGMDQ0xM2bN2FhYSEw/pycHNjb2yM7OxsAsH79erRt\n27a+s1FvxMbGiv1XRVVQbaIjyfqoNtGQZG2AZOsTtzZjY2OxxsVisRAdHY2BAwfyuWdnZ0NfX585\nb926NbKysgAA3t7e8PX1ZXoKZs2ahUWLFsHQ0BAbN26Er68vkpKSMGjQIPzxxx/Q0dGpF03VkZaW\nhsOHD+P/2rvz+CiqfP//r05nISudkJUEiCYsSSCEVUAZIgooasQNCYggOjPOjFy5boiK4h2v4Ije\nAed6dRgQFcXR4SeLPlxmlLA4XwGRiCxC0CRkI2QhZN865/dH0ZU0WSBNkq6Ez/PxOI+q6q6ufnd1\nJXW6zqmqbdu26Y/V19e3uIw//elPLF26lLFjx+Lv78+jjz7Kfffd166MbWm1cnLw4EF8fX1bfM5k\nMlFaWnrBhWdkZHDgwAGuuuoq8vPzCQkJASAkJIT8/HwAcnNz7SoiERER5OTkNFvW/PnzCQ0NZf36\n9fprn3jiCR5//HH9EJztj8PWOUum2562MUqeptOpqamGytOd8tkObxslT3eZtjFKnu6Uryv+Htri\nSDNMV+jbty8ZGRnExMQAcPLkSfr27QtoP95XrlzJypUrOXz4MJMnT2bMmDFMnjyZ5ORkkpOTKSsr\n47e//S2LFy/mnXfe6ZBM3t7eVFRU6NOnTp3Sm3b69+/P3Llz+etf/3rB5YSEhOjzffPNN1x//fVM\nmjSpxYMFtu8wJSWFjIyMiwva2jnGl3otk7KyMjVy5Ej18ccfK6WUslgsds/b7lT40EMPqQ0bNuiP\n33///WrTpk128wKqoqJCXXPNNfr1RO6///4Ou9uvEEIIY2tjd+V0kZGR6quvvmr2+DPPPKMmTJig\nCgoKVEFBgbr66qvV0qVLlVJKbdu2TaWlpamGhgZ18uRJFRYWplJSUtSxY8fUV199paqrq1VNTY26\n77771Pz585VSSj333HPqnnvuUUpd+DonrWWaM2eOmj17tqqrq1P79u1TgYGBau7cuUoppbKyslRo\naKj64osvVH19vaqqqlLbt29X2dnZSimlEhMT1dq1a5VSSn344YcqKytLKaXUoUOHlKenp0pPT2/2\nfq19bxf6Plvv2HEJ6urquOOOO5g7dy4zZswAtFqW7V43eXl5BAcHAxAeHk5WVpb+2uzsbMLDw5st\n8+6772b37t0A3H777bzxxhsddrdfIYQQoqM988wzjB49mvj4eOLj4xk9ejTPPPMMoJ3tMmXKFHx9\nfZkwYQJ/+MMfmDRpEjU1NSxZsoSgoCDCwsIoLCxk+fLlQNudWS/WH//4R37++Wf8/f1ZtmyZ3Vmn\nERERbNmyhRdffJHg4GD69+/PK6+80mJfl++++45x48bh6+vLrbfeyurVq4mMjGx3nla1Vmt54YUX\n2qzVtKahoUHNnTtXLVq0yO7xxx9/XK1YsUIppdTy5cvV4sWLlVJKHT58WA0fPlzV1NSoX375RV15\n5ZXNjojQ5KqskydPVlVVVXbPX+73nHCUZHOckfNJNscYOZtSxs4n99YRrWnte7vQ99lqn5NHHnmE\n9evXExAQwM0338zLL7/Mzp07iY6OZunSpXrv4/N98803bNiwgfj4eEaMGAFopwo/+eSTzJw5k7Vr\n1+qnEgPExsYyc+ZMYmNjcXV15fXXX2+1Njh69Gg2b95Mr169HK+NCSGEEMLQWr18/V133YW7uzsV\nFRWcOXOGoUOHcvPNN7N7925++OEHPvnkk64LaTIxePBgdu/e3WqlSAghRM9l5MvXi9Y5evn6Visn\nQ4cO5dChQ9TX1xMREaH3FwEYPny4flGZrmAymcjMzLQ731sIIcTlQyon3VOH31vHzc0NAFdX12bn\nV7d1gbTO0lbF5GJONXMWyeYYI2cDY+eTbI4xcjYwdj4jZxPdU6t9TrKzs/mP//gPlFLk5OTo40CL\n1yERQgghhOgIrTbrrF+/Xu+YqpRqNj5v3ryuCymH84QQ4rIm+4HuqcP7nBiJbJRCCHF5k/1A99Th\nfU66EyO3d0o2xxg5Gxg7n2RzjJGzgbHzGTmb6J56ROVECCGE6EkiIyP56quvnB3DaaRZRwghhOEZ\ndT8QGRlJVVUV6enpeHl5AfC3v/2N9957j+3btzu83CuuuIK1a9e2667CRuRos06rZ+vYnD59mjVr\n1pCRkUF9fb2+0HXr1l1CXCGEEKJnaGhoYNWqVSxZssTZUS6ooaHBKZcDaa8LJrz11lspLS1lypQp\n3HTTTXoxEiO3d0o2xxg5Gxg7n2RzjJGzgbHzGTlbZzOZTDz22GOsXLmSs2fP2j2XmZmJi4sLDQ0N\n+mOJiYmsXbtWn16zZg2xsbH4+fkRFxdHampqs/dQSrFixQqio6MJDAzk7rvv5syZM/rzd911F2Fh\nYVgsFiZNmsSRI0f05+bPn8/vfvc7pk+fjo+PT7f5ri545KSqqoqXXnqpK7IIIYQQ7bdoEbSwU3dI\nQgL8+c/tesno0aNJTExk5cqV/PGPf2xz3qZ3Fv7oo494/vnn2bJlC6NGjeLnn3/WL4Da1OrVq9m6\ndSs7d+4kKCiIhQsX8oc//IH3338fgJtuuon169fj7u7OE088wZw5czhw4ID++o0bN/LZZ58xfvx4\nampq2vXZnOZCdxR8+umn1SeffHKh2TrVRcQUQgjRg7W5H5g0SSnomDJpUrtyRUZGqq+++kodOnRI\n9e7dWxUUFKg1a9aoxMRElZGRoUwmk7Jarfr8iYmJau3atUoppaZOnapWr17d5nKVUiomJkYfV0qp\n3Nxc5ebmZrdcmzNnziiTyaRKS0uVUkrNmzdPzZs3r12fqSO19r1daL9+wSMnf/7zn3nxxRdxd3fX\na3Qmk4nS0tJOrTQJIYQQFyUhwenLiouL4+abb2bFihXExMRc1Guys7OJioq64HwZGRncdtttdn1F\nXF1dyc/PJzg4mKeffpp//OMfFBQU6PMUFhbi6+uLyWQiIiLCoc/kTBesnJSXl3dFjkuSkpJCYmKi\ns2O0SLI5xsjZwNj5JJtjjJwNjJ3P6dna2QzTWZ5//nlGjhzJo48+CoC3tzcAlZWV+Pj4ANjdRLdf\nv36cOHHigsvt378/b731FuPHj2/23LvvvsvWrVv56quvGDBgACUlJQQEBBjyzKb2uKguu1u2bOHR\nRx/lscceY9u2bZ2dSQghhOh2oqKiuPvuu1m1ahUmk4nAwEDCw8N59913sVqtrFu3jp9//lmf/4EH\nHmDlypV8//33KKU4ceIEJ0+ebLbcBx98kKeeekp/rqCggK1btwLaAQQPDw8CAgKoqKjgqaeesntt\nd62kXLBy8uSTT7J69Wri4uKIiYlh9erVhjtdyqi/JkCyOcrI2cDY+SSbY4ycDYydz8jZutqzzz5L\nZWWlPr1mzRpefvllAgMDOXLkCFdffbX+3J133snTTz/N7Nmz8fPz4/bbb7c7C8fm4YcfJikpialT\np+Ln58f48ePZu3cvAPfeey8DBgwgPDycoUOHMn78eL3DLdh3wO1OLngRtmHDhpGamorZbAbAarWS\nkJDAjz/+2CUBwbgX3xFCCNE1ZD/QPXXavXVMJhMlJSX6dElJieFqYUY+b1uyOcbI2cDY+SSbY4yc\nDYydz8jZRPd0wQ6xS5YsYeTIkfphux07drBixYrOziWEEEKIy9RF3VsnNzeXffv2YTKZGDt2LKGh\noV2RTSeH84QQ4vIm+4HuydFmnVYrJ0ePHiUmJob9+/fbLcTWpDNy5MiOyH1RZKMUQojLm+wHuqcO\n73Py6quvAvDoo4/qpxE/9thj+rSRGLm9U7I5xsjZwNj5JJtjjJwNjJ3PyNlE99Rqn5M1a9YAstEJ\nIYQQomu12qyzadOmNs/Kuf3221t8fMGCBXz66acEBwfrpxsvW7aMv/3tbwQFBQHw4osvcuONNwKw\nfPly1q1bh9lsZvXq1UydOrV5SDmcJ4QQlzXZD3RPHd7nZP78+W1WTt56660WH9+1axc+Pj7ce++9\neuXk+eefx9fXl0ceecRu3iNHjjB79mz27dtHTk4O119/PcePH7e7f8DFfAghhBA9m+wHuqcO73Oy\nfv163nrrrVZLayZOnIi/v3+zx1sKsWXLFpKTk3FzcyMyMpLo6Gj9qnftYeSmJ8nmGCNnA2Pnk2yO\nMXI2MHY+I2cTLVu/fj0TJ05s9fnExETWrl3bhYnstdrn5JVXXmlWs7FNm0ymZkdBLuS1117jnXfe\nYfTo0bzyyitYLBZyc3MZN26cPk9ERAQ5OTktvn7+/PlERkYCYLFYSEhI0K+9kpqaCjReQtn2hyLT\nbU/bGCVP0+nU1FRD5elO+eTvwbFpG6Pk6U75uuLvwagiIyM5ffo0ZrMZb29vpkyZwv/+7//i5+fn\n7GiXpCMve2/7DlNSUsjIyLi492+tWWfZsmUtBrNVTp577rlWF5qRkcEtt9yiN+ucPn1a72+ydOlS\n8vLyWLt2LQsXLmTcuHHMmTMH0G6CNH369Gb9WeRwnhBCXN6Muh+44oorWLt2LZMnTyY/P59p06Yx\ndepU/vSnPzk7WpvWr1/P2rVr2bVrV4vPX3vttcydO5cFCxZc0vt0eLPOsmXLeO6555oV2+PtERwc\nrNfCHnjgAb3pJjw8nKysLH2+7OxswsPD27VsIYQQwghCQkKYOnUqhw8fBuDbb79lwoQJ+Pv7k5CQ\nwI4dO/R5ExMTefbZZ7nmmmvw8/Nj2rRpFBUV6c+39trt27cTHx+vzzdlyhTGjh2rT0+cOFG/Y/GK\nFSuIjo7Gz8+PuLg4Nm/ebJdXKcXChQuxWCzExMTw9ddft/rZ1q1bR2xsLAEBAdxwww0t3j25I7Xa\nrPPSSy+xePFiFi5c2Ow5k8nE6tWrL/pN8vLyCAsLA+Djjz9m2LBhACQlJTF79mweeeQRcnJySEtL\ns1vJFyslJUU/BGg0ks0xRs4Gxs4n2Rxj5Gxg7HzOzrbo80WknkrtkGUlhCbw5xv+3K7X2I4AZGdn\n8/nnn3PnnXeSk5PDzTffzIYNG7jhhhv417/+xR133MGxY8fo06cPABs3buSzzz4jIiKCG2+8kZUr\nV7J8+fI2Xztu3DjS0tIoLi7G19eXgwcP4u7uTkVFBS4uLuzfv1/vSxIdHc3u3bsJDQ3lww8/5J57\n7uHnn38mJCQEgD179nDXXXdRVFTEpk2buP3228nIyMBisdh9vi1btrB8+XI++eQTBg4cyPLly0lO\nTuabb7651NXdqlYrJ7GxsQCMGjWq2XNttUMlJyezY8cOCgsL6devH88//7zeJmkymbjiiit48803\n9feYOXMmsbGxuLq68vrrrxvupoJCCCGMLfVUKjsyd1x4xk6glGLGjBmYTCbKy8u59dZbefrpp1m5\nciXTp0/nhhtuAOD6669n9OjRfPrpp9x7772YTCbuu+8+oqOjAZg5c6Z+xGPDhg1tvnbMmDHs2LGD\nsLAwEhIS8Pf3Z/fu3bi7uzNw4ED9pJQ777xTzzlz5kyWL1/Onj17SEpKArRWjYcfflh//pVXXuGT\nTz7hnnvusfuMb7zxBkuWLGHw4MGAds+9F198kaysLPr169cp67XVysktt9wCaB1R22Pjxo3NHmur\nzeqpp57iqaeeatd7nM+ovyZAsjnKyNnA2Pkkm2OMnA2Mnc/Z2RJCE5y2LJPJxJYtW5g8eTI7d+7k\nlltuYf/+/WRmZvLRRx+xbds2fd76+nomT56sTze9T52npyfl5eUAF3ztpEmTSElJISIigkmTJuHv\n78+OHTvw8PCw+y7eeecd/ud//kfvhFpeXm7XdHR+N4oBAwaQl5fX7DNmZmby8MMPN7s6fE5OjnMq\nJ211ZLHV8IQQQghnam8zTGf51a9+xcKFC1m8eDHTpk1j7ty5/PWvf233cvr379/maydNmsQjjzzC\ngAEDWLJkCRaLhQceeIBevXrx0EMPAVqF4je/+Q1ff/0148ePx2QyMWLECLt9+vlnx2ZmZnLrrbe2\nmGfp0qUkJye3+7M4qtUOsd9++y1ZWVlMnDix2X115N46F0+yOcbI2cDY+SSbY4ycDYydz8jZutqi\nRYvYu3cv11xzDdu2bePLL7/EarVSXV1NSkqKXYWgtbNV7rnnnjZfO2HCBI4dO8a+ffsYO3YssbGx\nZGZmsmfPHn71q18BUFFRgclkIjAwkIaGBt566y0OHTpk9z6nT59m9erV1NXV8dFHH/HTTz8xffr0\nZnkefPBBXnzxRY4cOQLA2bNn+eijjzpkfbWm1SMneXl5/POf/2Tjxo1s3LiRm266ieTkZOLi4jo1\nkBBCCNFdBQYGMm/ePF599VW2bt3K448/TnJyMmazmauuuor/+7//0+dt2sey6XVFIiIi2LJlC088\n8YTda19//XUAvLy8GDVqFJ6enri6arvxCRMmcOTIEQIDAwGtT+ejjz7K+PHjcXFx4d577+Waa66x\nez9b59qgoCBCQ0PZtGlTixdRnTFjBuXl5cyaNYvMzEx69+7N1KlTueuuuzp+BdrytXadk6ZqamrY\nuHEjjz32GMuWLdMPG3UVo57fLoQQomvIfqB7cvQ6J60eOQGorq7m008/5YMPPiAjI4OHH36Y2267\n7dLTCiGEEEK0otU+J3PnzmXChAkcOHCAZ599ln379rF06VJDXiTNyO2dks0xRs4Gxs4n2Rxj5Gxg\n7HxGzia6p1aPnLz33nt4e3uzatUqVq1aZfecyWSitLS008MJIYQQ4vJzUX1OnE3aGoUQ4vIWEBDA\nmTNnnB1DtJO/vz/FxcXNHr/Qfl0qJ0IIIYToUg7f+K87MXJ7p2RzjJGzgbHzSTbHGDkbGDufZHOM\nkbOBc/P1iMqJEEIIIXoOadYRQgghRJe6LJp1hBBCCNFz9IjKiZHb7SSbY4ycDYydT7I5xsjZwNj5\nJJtjjJwNpM+JEEIIIYRO+pwIIYQQoktJnxMhhBBCdCs9onJi5HY7yeYYI2cDY+eTbI4xcjYwdj7J\n5hgjZwPpcyKEEEIIoZM+J0IIIYToUtLnRAghhBDdSo+onBi53U6yOcbI2cDY+SSbY4ycDYydT7I5\nxsjZQPqcCCGEEELoOrzPyYIFC/j0008JDg7mxx9/BKC4uJi7776bzMxMIiMj+fDDD7FYLAAsX76c\ndevWYTabWb16NVOnTm0eUvqcCCGEED1Gl/c5ue+++/j888/tHluxYgVTpkzh+PHjXHfddaxYsQKA\nI0eO8Pe//50jR47w+eef8/vf/56GhoaOjiSEEEKIbqTDKycTJ07E39/f7rGtW7cyb948AObNm8fm\nzZsB2LJlC8nJybi5uREZGUl0dDR79+5t93saud1OsjnGyNnA2Pkkm2OMnA2MnU+yOcbI2cC5+Vy7\n4k3y8/MJCQkBICQkhPz8fAByc3MZN26cPl9ERAQ5OTktLmP+/PlERkYCYLFYSEhIIDExEYDU1FQA\nfdq2QmW67Wkbo+RpOp2ammqoPN0pn/w9ODZtY5Q83Smf/D3I9MVsvykpKWRkZHAxOuU6JxkZGdxy\nyy16nxN/f3/OnDmjPx8QEEBxcTELFy5k3LhxzJkzB4AHHniA6dOnc/vtt9uHlD4nQgghRI9hiOuc\nhISEcOrUKQDy8vIIDg4GIDw8nKysLH2+7OxswsPDuyKSEEIIIQyqSyonSUlJvP322wC8/fbbzJgx\nQ3/8gw8+oLa2lvT0dNLS0hg7dmy7l3/+YU8jkWyOMXI2MHY+yeYYI2cDY+eTbI4xcjZwbr4O73OS\nnJzMjh07KCwspF+/fvzXf/0XTz75JDNnzmTt2rX6qcQAsbGxzJw5k9jYWFxdXXn99dcxmUwdHUkI\nIYQQ3YjcW0cIIYQQXcoQfU6EEEIIIS5Wj6icGLndTrI5xsjZwNj5JJtjjJwNjJ1PsjnGyNnAufl6\nROVECCGEED2H9DkRQgghRJeSPidCCCGE6FZ6ROXEyO12ks0xRs4Gxs4n2Rxj5Gxg7HySzTFGzgbS\n50QIIYQQQid9ToQQQgjRpaTPiRBCCCG6lR5ROTFyu51kc4yRs4Gx80k2xxg5Gxg7n2RzjJGzgfQ5\nEUIIIYTQSZ8TIYQQQnQp6XMihBBCiG6lR1ROjNxuJ9kcY+RsYOx8ks0xRs4Gxs4n2Rxj5GwgfU6E\nEEIIIXTS50QIIYQQXUr6nAghhBCiW+kRlRMjt9tJNscYORsYO59kc4yRs4Gx80k2xxg5G0ifEyGE\nEEIInfQ5EUIIIUSXkj4nQgghhOhWekTlxMjtdpLNMUbOBsbOJ9kcY+RsYOx8ks0xRs4G0udECCGE\nEELXpX1OIiMj8fPzw2w24+bmxt69eykuLubuu+8mMzOTyMhIPvzwQywWi31I6XMihBBC9BiG6nNi\nMplISUnhwIED7N27F4AVK1YwZcoUjh8/znXXXceKFSu6MpIQQgghDKbLm3XOrylt3bqVefPmATBv\n3jw2b97c7mUaud1OsjnGyNnA2Pkkm2OMnA2MnU+yOcbI2cC5+Vy78s1MJhPXX389ZrOZ3/72t/z6\n178mPz+fkJAQAEJCQsjPz2/xtfPnzycyMhIAi8VCQkICiYmJAKSmpgLo07YVKtNtT9sYJU/T6dTU\nVEPl6U755O/BsWkbo+TpTvnk70GmL2b7TUlJISMjg4vRpX1O8vLyCAsLo6CggClTpvDaa6+RlJTE\nmTNn9HkCAgIoLi62Dyl9ToQQQogew1B9TsLCwgAICgritttuY+/evYSEhHDq1ClAq7wEBwd3ZSQh\nhBBCGEyXVU4qKyspKysDoKKigi+//JJhw4aRlJTE22+/DcDbb7/NjBkz2r3s8w97Golkc4yRs4Gx\n80k2xxg5Gxg7n2RzjJGzgXPzdVmfk/z8fG677TYA6uvrmTNnDlOnTmX06NHMnDmTtWvX6qcSCyGE\nEOLyJffWEUIIIUSXMlSfEyGEEEKIC+kRlRMjt9tJNscYORsYO59kc4yRs4Gx80k2xxg5Gzg3X4+o\nnAghhBCi55A+J0IIIYToUtLnRAghhBDdSo+onBi53U6yOcbI2cDY+SSbY4ycDYydT7I5xsjZasCu\nhQAAGihJREFUQPqcCCGEEKIbKa8t50jBEYoqizql24X0ORFC9Ai11loySzJxMbng5ealFzezm7Oj\nic5SXQ2//AJ5eVBV1XqprYWGBrBa7Ye28fMfb/pYQwP4+ECfPs1LQAD4+2s5SktbLlVV0KsXeHqC\nl1fz4uEBLi5aMZmaD+vroaJCK+XlzYegLaNXL600HW/6vt7eje9pG3d3v+hVXVZTxjdZ37Ajcwcp\nGSl8l/sd9Q31AHi6ehLhF0GEXwT9evfTxn218WHBwxhgGdBseRfar0vlRAjRrZTVlPFT4U8cLTyq\nlQJt+HPxz1iVtdn8ri6uekXFx92HSEsk0QHRRPtHa8OAaK70vxJPN08nfJpupq4O8vO1ykDTcuqU\nthN1dQU3N3B1RbmaOeNaz0m3SnJcK2kIDsItMgq3yCtx9/TFzeyGm4sbbmY3PF09GWAZgLu5hZ1l\nZSWkp8OJE5CWZj/MygLZNzjObNYqQaCvR6UaKPWAXF/42R92RruxY4Bif59arA60tSybtIznEp9r\n9vhlUTlJSUnRb89sNJLNMUbOBsbO11Oy1TfUk1aUxsH8gxw8fZAfTv3AwfyDZJVmdUq2wPxABo4a\niLvZHbOLGReTC2bTueG5aT8PP4YFDyM+JJ7hIcMJ9QnFZDJd0vsWVRax++Ru9uXuw9XFlWDvYL0E\neQUR7B2Mv6c/Oz/7nMTBg6GgoLEUFjaOl5RoCzSZGovt17fJpFUcvL0bi4+P/bhSUFwMZ85ow/PH\nT5/W3qfJ/+IqVzjeB/4/T/CMgMzecLI3ZFq08XKPi18PrpgZYg5hWLUf8UWuDDtZw7CjRfRLL+ZS\n1nAKkOju3rgjtg2bjrc0tI2bTFBWBkVFWkWpvVxctKMvrWVz/KM1fretLL8pBZzxhDwfyPO1H+b6\naiXv3LDKdrAxA4i0X45LAyScgkmZ2rDIE7J6Q7afVrL8tGU0nKvI/M1/Pvf/x1stRG97v95l99YR\nQlwerA1WCisLya/I51T5KfLLzw0r8vnh2x947fRruJvd9eJh9tDH3cxu5JTmcDD/IIcLDlNdX93m\ne5lNZqICoogJjGFI4BAG9xmMq4srFXUVVNZVNisl1SWkl6STVpTG2ZqzdssqrCqkMLuwXZ81yCuI\n4aHDGR4ynPiQeAb1GUQfzz4EeAZg6WXB7GJunPlcc0FWeS67sr9h18ld7MrcxeGCwxd8H3MD+B4D\n377auFmdN/TUjtCHlEPfMvsSdu6x3tVQ6gElvVou9S7gU9tYfGvtpwu94GgCHA2Co4HaMMMCykSL\nO7H2qsfKIWsuh9xy2RgKhAJjtdyDisCjXvu8LmZXzL28MHt5Y/byxsXbB1+fPgz0u4LB/tEMDhzC\n4JBYfPwCtSaN//f/4NprLy2cTXW1VkmxleJirVLo6Ql+fs2Lr6/WzFJXpzXvVFY2lqoq+Pe/YfBg\nrcKnlLaNnD80mxsrkucPPc8d7auvh5oaqstLOJZ/hKMFRzhafIyjJSfIrsgjt7qAU3VnqFF17f7I\nLsrEyJoAEs9amJTvyTVZJiyF5XD2rPbZW6gY1bvAKR+tshK5KNKhVd0jjpwI0VNZG6zkV+STXZpN\ndmk2WWezyC7Lprq+Gi83LzxdPe36V9iKq0vj7w5Tk9+dJpMJEyaiAqKI8o+66F/9JdUlbE/fzhc/\nf8GBUweorq+m1lpLrbWWOmudPl5rraWqvooGdeFfcu3hYnJhUJ9BxIfEMzRoKDFBMcQExhAdEI2H\nazt+np+jlKK4qpgTxScay5kT5JTmYFVWGlQD1gar3XiDatArXBfDhAmLmy8B9W70KavHUlDOcYuV\nDP9WPmND46/N7srSy8KA3gMYYBnAgN4D6N+7PwN6D6CfZwiuOXnU/Xycup/TqMv8hbqTGdSdyqXW\nRVHmAUeC4MdgOBiq/fK+VOG+4QwOHMzgPoMZETqC6QOnE+4XfukL7iBKKWqttZTVlnGq/BR5ZXna\nsLxxmFeWR1V9FT7uPvi6++Lj7mNXvN28Kawq5EjBEY4WHCW9JL3df3tebl709e1LX9++hPmE2Q37\n+vZlVN9R+Hn4tfzihgatgpafb19On24cX7gQpk1r9tIe06xzvPA4vVx70cu1Fx6uHvRy7YWbi9sl\nH1IVwkYpRUFlASfPniSnNIcG1aBvax5mD7txd7M7lXWVlNaU2pWzNWcprSmlrLaM6vpqauprqLHW\n6OPV9dXUWGuos9bpFQUXkwsm07khJkwmE5V1lWSXZpNblqt3Outo/r38GRM+hrHhYxnTdwxj+o4h\nzDcM0JpUvsv9ji9OfMGXv3zJnuw9LfbnaI9err0I8Q7Bx92HuobGCk1NfU3juLWGPp59iA+J15tO\n4kPiiQ2KNUyfkNMVpzmYrzUz/ZCvlaMFR6lraN+vUs86GJcNEzNh4klt3KMeirzgtPe5Em6hoF8f\nTof6UGjxoM7LA6uHG1YPd+rdXbG6u2J1MWFVVqrqqvSdWmFl+44AtYebixsD+wwkJjBGryTGBMYQ\nFRDV+k6sNdXV8NNPkJEBwcEQGQmhoRRVn+HQ6UMczD/Ij6d/JKMkA6uy6hXG8yuORVVFZJ3NQtH2\n7mxk2EiSBiVxy+BbGBE6os39h1KK3LJcThSfoKS6hPLacvtS1zjetHLetNRZ66ix1rT6f6AzmTAR\naYkk0hJJmG9jpSPMJ4ww38ahr7uvU/ajPaZywrIWHsdEL9demE+a8Rvsp/+K9HTztPtV2XTa081T\nGzYZ93b3JtArkECvQIK8ggj0CnTo11hLekr7f1dzJJtSijPVZ8gvzye/Ip/SmlLqrHXUNdRR31Df\nbLygsoCs0ixOnj2pHZEozb74fxgZXPJhbEe5m93xcvOiqq6q5bwZOJwtwi+CwX0Gsz9vPyXVJc2e\n9zB7cFXEVQR4BmjNMC5udk007mZ3PN08CfEOIcQ7hFCfUEJ8tKGvuy87duxo83tVSjntB8cFt7ma\nGsjN1TphZmdrw3OlNjuTnyoyyW4oodhTa4cv9tQqGsWeUOzfi+IgH0I8+jBR9WOiNYJR1mDcrTSe\nGWK1QmgoDByoleho7fD9xeZrGrW+hlPlp8gtyyW3LJe88jxKa0rp7dEbSy+LXfH39MfSy4Kri6vd\nzrespkwb1mpDH3cfYgJjuNL/ymZnQBnhf0llXSVpRWkcKzrGscJj2rDoGIf3HqYqoqrZ/OG+4dw8\n6GaSBidh6WUhrSiN48XHtWHRcdKK06isc6CPSXtk0OLfqpebF2E+YYT6hOLt7k1FbUWzylFFXQWg\ndfgeGDBQryjGBsUSExjD4MDBeLl5XVK8zvxee3SfE4Wiqr4KaqG8rLxDl+3r7qtVVryD6OPZR/8D\ntvSyYPGw2E17u3k360Bn60R38uxJfir8iQbV0GpxdXFt1vbu4do4DuiHlc8vCmX32q74x66UorKu\nkrLaMgD9qIKH2cO+jf2819gO+VfWVVJVV6Uvo6ymrNkRiMP7DvNB+QfNltP089XU15Bfka/3aThd\ncbrdv147i+0oX9OjLh5mD/3In5uL9s/d9h0qpVAobVopPFw9Gk/N8+unj0f4RRDkFaSvB2uDler6\nart+Fbt27CJhfAKA/sff9BdlrbWWQ6cPsTdnL/ty93G04Kj+vK35qKmhwUOZGjWVaVHTmNh/Yqce\nwXBKxUQpraPngQPaWSCnTjU/TJ2fr7Wxt8IdiD9XAO0002uvheuug+uvh6goreNiF/Fw9dCaVlo4\nhbMtAZ4BBHgGdFKqzuXl5qX1/wkdbvf419Ff4zPIh23Ht7Ht2DZ+yP8BgJyyHN7c/yZv7n/T4fez\nNa00/X99fqXdzexm97/A9j/Aw+xBrn8uw8YOI9QnlFCfUP2Iho+7zwX/FqwNVirrKrVWhB54uny3\nOXKy4YcNVNdX2xdr43hVXVWzHV9VfVWL40bZgXWGlpofWqs46eMms908ZhezPqy11uqVB1tFoqy2\nrNV2TVcXV7udcV1Dnf6dXOiQa1dzMbkQ5hNGv9796OfXj/69+9PPr59+nr672d3uEOz5497u3vh5\n+DUrvu6+3eqfRWlNKd/nfa9XVo4VHmNYyDCmXjmVKVFT6Ovb19kRO4atEnLkCBw+bF+KihxbZu/e\nEBEB/fppwyFDYPJkGD688RRNYSiZJZl8cvwTth3fxvaM7dRaa/XnXEwuRFoiGdRnEAMDBurDIO8g\nu34fXm5erf4QExenxzTrdGRMa4NVr6xU1VdRVlNGUVURBRUFFFYWUlhZSEFlgT4sqiyipLpEL5fa\n9i4uzNvNGy83L1xMjf/gz6/cuLm4EewdTIiP1oSgD8+NW3pZcDe74+riql9Poel4d6tEGEJ1tdZD\nv08f7XoWXUUprVmlulo786G+vrFYrY3jtbXadTdszS5Nh9nZF38qqMUCISH2JTRUK7aKSESEdkaG\n6LbKasrYkbkDpRQD+wzkSv8rW77Wiuhwl0XlpCvbO5VSlNeWU1JdwpnqM5RUl1BZV2nXo79BNeid\ntX7c8yNxY+P0oxTnFxNaZzZbp8Aaa02zjoK2zpJNi+0oiELp89ZY7Ttd2ZbVWraCwwVYhlianZ3Q\ntLOZq4urfjTA18MXX3dfu2kTJvsjC9bG966pr8HN7NbY3+dcP5+m/X9aOvLg4+7D7p27nd6G3RYj\ntLG35pKynT4NP/zQuEPPyWkc5uRo19UArYkiKAj69tVKeHjjeFCQdvqku7tWgXF318dTUlNJTEjQ\n3qe1UlamnWZZXd04rG77lGKH+PhAbCzExUFcHClWK4kzZ2oVkF69Ov79LlGP3eY6mWRznPQ56UZM\nJpO2k/bwpV/vfhecP7ggmMShiZ0fzAFG/8MQnay8HL7/Hvbu1cq+fdpZExdDqcbKRGpqp8Z0mMkE\nYWGNRzpsw5gYrULSr59900tKina2iBDC6XrEkRMhRBM1NVrfCttFoppeMKqoSKtQHDig9b240JUl\nAwO1HXp4eOMwIEBbfm6udjQlN1crp087ltds1t4nOFgrvXtrF5fy9Gy8N0jTobu7drVTWzGb7aeD\ng7WsYWFd2/QkhLhocuREiJ7u9Gn45hvYvVsbfv+91i+jPcLC4KqrYMwYGD1aO421b9/2NW/U1mpn\ntRQWau9fW2tfbI/ZKhC24u8vnUeFEHZ6xJETIzdPSDbHGDkbODFfaal2F9bvvmuskJw4YZ+NNu7X\n0auX1pl1yBAYO1YrY8ZoR0S6gJG/VyNnA2Pnk2yOMXI2cG6fkx7xcyXVqG3eSDZHGTkbdGK++nrI\nzNT6P6xbB888A8nJ2lGNoCCtyWPECPj1r2H9evuKiasrjB1L6qRJ8Oab8I9/wPbtjR1cbffzyM6G\nf/0LXnwRZszosooJGPt7NXI2MHY+yeYYI2cD5+YzRLPO559/zqJFi7BarTzwwAMsXry4Xa8vKWl+\nJUujkGyOMXI2cCCfUlBRod3ltaREa4rJzNRKRkbjMDtbOzX2YvTuDRMmwNVXa2XsWPDyomTZMvjN\nb9r5ibqGkb9XI2cDY+eTbI4xcjZwbj6nV06sVisPPfQQ//rXvwgPD2fMmDEkJSURExPj7GhCaJWK\n2lr701qrqrRraXz9tVbZaFpst5m3VUJsw5IS7ahIe7m4QP/+cOWVWomK0oa2M06kr4YQogdyeuVk\n7969REdHE3nuFL5Zs2axZcuW5pWTV15pdRkZn36q3ZragC452/ltcu3tImSbv4VhxrZt2pkPLd2m\nu+mw6WvPH29ams5vm3bws2Vs365dRryhobFYrfbTtsdsj58/rK9v7ITZdGgbt1pb/hy27LZKSQvr\nPAPgr39t11fRItvprpGRMGCA/TAqSquYuLfvolAZF3s6sBNINscZOZ9kc4yRs4Fz8zm9Q+w//vEP\nvvjiC9asWQPAhg0b2LNnD6+99po+j9x5WAghhOhZDH0q8cVUPLrBCUVCCCGE6CBOb7AODw8nKytL\nn87KyiIiIsKJiYQQQgjhTE6vnIwePZq0tDQyMjKora3l73//O0lJSc6OJYQQQggncXqzjqurK3/5\ny1+YNm0aVquV+++/X87UEUIIIS5jTj9yAnDjjTdy7NgxTpw4wZIlS1iwYAEhISEMGzZMn+eHH35g\n/PjxxMfHk5SURFlZGaD1Jvb09GTEiBGMGDGC3//+9/pr9u/fz7Bhwxg4cCAPP/xwh+XtqHyJiYkM\nGTJEf67QdsfXLsoGcPDgQcaPH8/QoUOJj4+ntrYW6Jx111HZnL3e3nvvPf29R4wYgdls5uDBg4Ax\ntrm28jl73VVXV5OcnEx8fDyxsbGsWLFCf42zt7m2sjl7vdXW1nLfffcRHx9PQkICO3bs0F9jhG2u\nrXydse6ysrK49tpriYuLY+jQoaxevRqA4uJipkyZwqBBg5g6darddTqWL1/OwIEDGTJkCF9++aX+\neEevv47M1tHrrr3ZiouLufbaa/H19WXhwoV2y+qs7U6nDGjnzp3q+++/V0OHDtUfGz16tNq5c6dS\nSql169appUuXKqWUSk9Pt5uvqTFjxqg9e/YopZS68cYb1WeffWaofImJiWr//v0dksmRbHV1dSo+\nPl4dPHhQKaVUcXGxslqtSqnOWXcdlc3Z662pH3/8UUVFRenTRtjmzs8XHR2tTzt73b311ltq1qxZ\nSimlKisrVWRkpMrMzFRKOX+bayubs9fbX/7yF7VgwQKllFKnT59Wo0aN0l9jhG2urXydse7y8vLU\ngQMHlFJKlZWVqUGDBqkjR46oxx9/XL300ktKKaVWrFihFi9erJRS6vDhw2r48OGqtrZWpaenq6io\nKNXQ0KCU6vj115HZOnrdtTdbRUWF2r17t3rjjTfUQw89ZLesztrubAxx5OR8EydOxN/f3+6xtLQ0\nJk6cCMD111/Ppk2b2lxGXl4eZWVljB07FoB7772XzZs3GyafjergM5Hak+3LL78kPj5e/2Xk7++P\ni4tLp627jshm48z11tT7779PcnIyYMxt7v3332fWrFl2jzlz3YWFhVFRUYHVaqWiogJ3d3f8/PwM\nsc21ls3Gmevt6NGjXHvttQAEBQVhsVjYt2+fYba5lvJ99913+us6et2FhoaSkJAAgI+PDzExMeTk\n5LB161bmzZsHwLx58/R1sWXLFpKTk3FzcyMyMpLo6Gj27NnTKeuvo7LZdOS6a282Ly8vrr76ajw8\nPOyW05nbnY0hKyctiYuLY8uWLQB89NFHdmf4pKenM2LECBITE9m9ezcAOTk5dmf9hIeHk5OTY5h8\nNvPmzWPEiBG88MILXZ7t+PHjmEwmbrjhBkaNGsXLL78MdO26a282G2eut6Y+/PBDvXJipG2upXw2\nzlx306ZNw8/Pj7CwMCIjI3n88cexWCyG2OZay2bjzPU2fPhwtm7ditVqJT09nf3795OdnW2Yba6l\nfE23x85cdxkZGRw4cICrrrqK/Px8QkJCAAgJCSE/Px+A3Nxcu/UUERFBTk5Os8c7ev05mi03N1ef\n7qx1dzHZbM6/5EdXbHfdpnKybt06Xn/9dUaPHk15eTnu566a2bdvX7Kysjhw4ACvvvoqs2fPtuu3\nYMR85eXlgNY34NChQ+zatYtdu3bx7rvvdmm2+vp6du/ezfvvv8/u3bv5+OOP+frrr7v0onftzQbO\nX282e/bswcvLi9jY2E55/87I5+x1t2HDBqqqqsjLyyM9PZ2VK1eSnp7eKRk6Mpuz19uCBQuIiIhg\n9OjR/Od//icTJkzAbDZ3+QUq25sPOnfdlZeXc8cdd7Bq1Sp8z7sSt8lkcuoFPDsiW2etOyOvN5tu\nUzkZPHgwX3zxBd999x2zZs0iKioKAHd3d/3Q48iRI4mKiiItLY3w8HCys7P112dnZxPeiXdfbU++\n48ePA1rFBbTDa7Nnz2bv3r1dmq1fv3786le/IiAgAE9PT6ZPn87333/fpeuuvdnA+evN5oMPPmD2\n7Nn6tFG2udbygfPWXXR0NAD//ve/ue222zCbzQQFBXH11Vezf/9+IiIinLbNtZXN1jTh7G3ObDbz\n6quvcuDAATZv3kxJSQmDBg2ib9++htjmWssHnbfu6urquOOOO5g7dy4zZswAtF/9p06dArSmh+Dg\nYKD59bSys7OJiIjotL/ZS81my9AZ66492VrTFf/ruk3lpKCgAICGhgZeeOEFfve73wFQWFiI9dxd\nXH/55RfS0tK48sorCQsLw8/Pjz179qCU4t1339W/CCPks1qtes/ruro6tm3bZtcrviuyTZs2jR9/\n/JGqqirq6+vZsWMHcXFxhIaGdtm6a282I6w322MfffSRXX8Oo2xzreVz5rp78MEHARgyZIh+BKyi\nooJvv/2WIUOGOHWbaytbTEyMIba5qqoqKioqAPjnP/+Jm5sbQ4YMMcw211q+zlp3Sinuv/9+YmNj\nWbRokf54UlISb7/9NgBvv/22vi6SkpL44IMPqK2tJT09nbS0NMaOHdsp211HZeuMddfebE1f11SX\nbHcd2r22g8yaNUuFhYUpNzc3FRERodauXatWrVqlBg0apAYNGqSWLFmiz7tp0yYVFxenEhIS1MiR\nI9Unn3yiP/fdd9+poUOHqqioKLVw4UJD5SsvL1ejRo1S8fHxKi4uTi1atEjvod1V2ZRSasOGDSou\nLk4NHTpU76GtVOesu47IZpT1tn37djV+/PhmyzHCNtdavoqKCqevu+rqajVnzhw1dOhQFRsbq1au\nXKk/5+xtrrVsRtjm0tPT1eDBg1VMTIyaMmWKOnnypP6cEba51vJ11rrbtWuXMplMavjw4SohIUEl\nJCSozz77TBUVFanrrrtODRw4UE2ZMkWdOXNGf81///d/q6ioKDV48GD1+eef64939PrrqGydse4c\nyTZgwAAVEBCgfHx8VEREhDp69KhSqvO2Oxun3/hPCCGEEKKpbtOsI4QQQojLg1ROhBBCCGEoUjkR\nQgghhKFI5UQIIYQQhiKVEyGEEEIYilROhBBCCGEo/z9jSEgIbfEPFQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x413c890>"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The consumption declines have been fossil-fuel driven, which dominates the overall energy mix. Renewable and nuclear sources have remained fairly steady with renewables showing growth in recent years.\n",
      "\n",
      "The [World Bank](http://www.worldbank.org/) offers energy consumption statistics [by country](http://data.worldbank.org/indicator/EG.USE.COMM.FO.ZS), with 2010 being the most recent complete year. Here's how the US compares with some of its peers with regards to fossil fuel consumption."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fossil_country = pd.read_csv('country_fossil_fuel.csv')\n",
      "figure(figsize=(9,5), dpi=80)\n",
      "\n",
      "N = len(fossil_country)\n",
      "ind = np.arange(N)\n",
      "width = 0.4\n",
      "\n",
      "rects = bar(ind, fossil_country['Fossil_fuel_%'], color='gray')\n",
      "ylabel(\"Fossil Fuel % of Total Energy Consumption\")\n",
      "title(\"Selected Country Fossil Fuel Consumption Mix, 2010\", fontsize=16)\n",
      "xticks(ind + width, fossil_country['Country'])\n",
      "\n",
      "def autolabel(rects):\n",
      "    # attach some text labels\n",
      "    for rect in rects:\n",
      "        height = rect.get_height()\n",
      "        text(rect.get_x()+rect.get_width()/2., 0.90*height, '%d'%int(height),\n",
      "                ha='center', va='bottom', fontsize=14, color='w')\n",
      "\n",
      "autolabel(rects)\n",
      "\n",
      "grid(b=True, axis='y')\n",
      "\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rj7y7eJbXR3muX3muG+tX9h+sX9l9lOe66VP9XkenCUrepR3g5WRmeeO0q1Wr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\nERERqQ4LFCIiIlIdFihERESkOixQiIiISHVYoBA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zZU888QS2bt2KuLg46HQ6zJkzB2az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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3643c50>"
       ]
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The USA is actually below several of its peers in fossil fuel consumption as a percentage of total consumption, although in absolute terms it is a larger fossil fuel consumer due to the population size and consumption level. Australia's percentage is among the highest in the world, and France has a much larger nuclear mix than the rest of the group. These statistics are probably worth further analysis, as well.\n",
      "\n",
      "The US is also producing more energy after many years of declining production, with additional fossil fuel production being driven by fracking."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "energy_production = pivot[['Total Fossil Fuels Production',\n",
      "                        'Nuclear Electric Power Production',\n",
      "                        'Total Renewable Energy Production']].values\n",
      "\n",
      "prod_per_cap = energy_production * 1000000000 / (us_pop[us_pop.Year >= 1949]['Pop'])[:, None]\n",
      "\n",
      "figure(figsize=(9,4), dpi=80)\n",
      "\n",
      "# fossil, nuclear and renewable\n",
      "plot(prod_per_cap[:,0], color='black', linewidth=2.5, label='Fossil Fuels')\n",
      "plot(prod_per_cap[:,1], color='r', linewidth=2.5, label='Nuclear')\n",
      "plot(prod_per_cap[:,2], color='g', linewidth=2.5, label='Renewable')\n",
      "\n",
      "xticks(np.arange(1, len(labels), 5), labels[np.arange(1, len(labels), 5)])\n",
      "xlim([0,len(labels)])\n",
      "\n",
      "title(\"US Annual Per-Capita Energy Production by Source\", fontsize=16)\n",
      "ylabel(\"Million BTU\")\n",
      "grid(b=True, which='both')\n",
      "\n",
      "legend(loc='center left')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/H7Nnz0bDhg3FKwiDwWDIOcznRMYwbeJRl9oEAgHWrVsHGxsbzjCxsLDAxYsX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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4109e50>"
       ]
      }
     ],
     "prompt_number": 154
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This increased production is driving increased fossil fuel exports, the US may be a net energy exporter by the end of the next decade."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "energy_impexp = pivot[['Primary Energy Exports',\n",
      "                        'Primary Energy Imports']].values\n",
      "\n",
      "# Extract the year from the labels\n",
      "labels = pivot.index.map(lambda x: str(x)[0:4])\n",
      "\n",
      "figure(figsize=(9,4), dpi=80)\n",
      "plot(energy_impexp[:,0], color='b', linewidth=2.5, label='Exports')\n",
      "plot(energy_impexp[:,1], color='r', linewidth=2.5, label='Imports')\n",
      "\n",
      "xticks(np.arange(1, len(labels), 5), labels[np.arange(1, len(labels), 5)])\n",
      "\n",
      "xlim([0,len(labels)])\n",
      "ylim([0, np.max(energy_impexp[:,1]) * 1.15])\n",
      "\n",
      "grid(b=True, which=\"both\")\n",
      "\n",
      "title(\"US Annual Energy Imports/Exports\", fontsize=16)\n",
      "ylabel(\"Quadrillion BTU\")\n",
      "\n",
      "legend(loc='center left')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RT548QZs2bQBAVh31fqFA2kvIy8uryDzSq3CFnQuxWIyRI0eic+fOsvNAyia6EkK0okuX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\nmjo1l+LiLP9m1rSAAKLHHyf64AOia9es+z5yPV6J7qGYEMF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       "text": [
        "<matplotlib.figure.Figure at 0x43fd250>"
       ]
      }
     ],
     "prompt_number": 164
    }
   ],
   "metadata": {}
  }
 ]
 }