GrowthModels.ipynb

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  "metadata": {
    "colab": {
      "name": "GrowthModels.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/AvijitGhosh82/Finance_Graph_Theory/blob/master/GrowthModels.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "DFO2ViFUfZh2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Remember to make github private after every coding session**"
      ]
    },
    {
      "metadata": {
        "id": "mI-IVnIwgWvo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Dataset Cleaning"
      ]
    },
    {
      "metadata": {
        "id": "8UeFtYYuOnOX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "5a22878c-3df1-4870-efb3-9aab86327988"
      },
      "cell_type": "code",
      "source": [
        "! rm -rf  Finance_Graph_Theory\n",
        "! git clone https://github.com/AvijitGhosh82/Finance_Graph_Theory"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Finance_Graph_Theory'...\n",
            "remote: Counting objects: 167, done.\u001b[K\n",
            "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
            "remote: Total 167 (delta 2), reused 15 (delta 0), pack-reused 144\u001b[K\n",
            "Receiving objects: 100% (167/167), 138.79 MiB | 58.15 MiB/s, done.\n",
            "Resolving deltas: 100% (56/56), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MjfEuBaM4o42",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1119
        },
        "outputId": "730ce3f9-44fe-464d-b98a-8901fe256e8a"
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('Finance_Graph_Theory/Country.csv')\n",
        "df.dtypes"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country    object\n",
              "AUS         int64\n",
              "AUS.1       int64\n",
              "AUS.2       int64\n",
              "AUS.3       int64\n",
              "AUS.4       int64\n",
              "AUS.5       int64\n",
              "AUS.6       int64\n",
              "AUS.7       int64\n",
              "AUS.8       int64\n",
              "AUS.9       int64\n",
              "AUS.10      int64\n",
              "AUS.11      int64\n",
              "AUS.12      int64\n",
              "AUS.13      int64\n",
              "AUS.14      int64\n",
              "AUS.15      int64\n",
              "AUS.16      int64\n",
              "AUS.17      int64\n",
              "AUS.18      int64\n",
              "AUS.19      int64\n",
              "AUS.20      int64\n",
              "AUS.21      int64\n",
              "AUS.22      int64\n",
              "AUS.23      int64\n",
              "AUS.24      int64\n",
              "AUS.25      int64\n",
              "AUS.26      int64\n",
              "AUS.27      int64\n",
              "AUS.28      int64\n",
              "            ...  \n",
              "ROW.26      int64\n",
              "ROW.27      int64\n",
              "ROW.28      int64\n",
              "ROW.29      int64\n",
              "ROW.30      int64\n",
              "ROW.31      int64\n",
              "ROW.32      int64\n",
              "ROW.33      int64\n",
              "ROW.34      int64\n",
              "ROW.35      int64\n",
              "ROW.36      int64\n",
              "ROW.37      int64\n",
              "ROW.38      int64\n",
              "ROW.39      int64\n",
              "ROW.40      int64\n",
              "ROW.41      int64\n",
              "ROW.42      int64\n",
              "ROW.43      int64\n",
              "ROW.44      int64\n",
              "ROW.45      int64\n",
              "ROW.46      int64\n",
              "ROW.47      int64\n",
              "ROW.48      int64\n",
              "ROW.49      int64\n",
              "ROW.50      int64\n",
              "ROW.51      int64\n",
              "ROW.52      int64\n",
              "ROW.53      int64\n",
              "ROW.54      int64\n",
              "ROW.55      int64\n",
              "Length: 2465, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "metadata": {
        "id": "3vAlJn9u4seF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "6ff64908-dd84-41d1-e3f8-c90b323bae8a"
      },
      "cell_type": "code",
      "source": [
        "df = df.groupby(['Country']).sum()\n",
        "df.head(5)\n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AUS</th>\n",
              "      <th>AUS.1</th>\n",
              "      <th>AUS.2</th>\n",
              "      <th>AUS.3</th>\n",
              "      <th>AUS.4</th>\n",
              "      <th>AUS.5</th>\n",
              "      <th>AUS.6</th>\n",
              "      <th>AUS.7</th>\n",
              "      <th>AUS.8</th>\n",
              "      <th>AUS.9</th>\n",
              "      <th>...</th>\n",
              "      <th>ROW.46</th>\n",
              "      <th>ROW.47</th>\n",
              "      <th>ROW.48</th>\n",
              "      <th>ROW.49</th>\n",
              "      <th>ROW.50</th>\n",
              "      <th>ROW.51</th>\n",
              "      <th>ROW.52</th>\n",
              "      <th>ROW.53</th>\n",
              "      <th>ROW.54</th>\n",
              "      <th>ROW.55</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AUS</th>\n",
              "      <td>22743</td>\n",
              "      <td>983</td>\n",
              "      <td>736</td>\n",
              "      <td>45918</td>\n",
              "      <td>44552</td>\n",
              "      <td>2679</td>\n",
              "      <td>5094</td>\n",
              "      <td>3962</td>\n",
              "      <td>3062</td>\n",
              "      <td>13648</td>\n",
              "      <td>...</td>\n",
              "      <td>194</td>\n",
              "      <td>0</td>\n",
              "      <td>173</td>\n",
              "      <td>190</td>\n",
              "      <td>1428</td>\n",
              "      <td>1172</td>\n",
              "      <td>580</td>\n",
              "      <td>533</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AUT</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>103</td>\n",
              "      <td>561</td>\n",
              "      <td>336</td>\n",
              "      <td>327</td>\n",
              "      <td>247</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BEL</th>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>135</td>\n",
              "      <td>0</td>\n",
              "      <td>168</td>\n",
              "      <td>205</td>\n",
              "      <td>947</td>\n",
              "      <td>549</td>\n",
              "      <td>637</td>\n",
              "      <td>489</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BGR</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>117</td>\n",
              "      <td>62</td>\n",
              "      <td>87</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BRA</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>52</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>149</td>\n",
              "      <td>166</td>\n",
              "      <td>774</td>\n",
              "      <td>488</td>\n",
              "      <td>521</td>\n",
              "      <td>548</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2464 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           AUS  AUS.1  AUS.2  AUS.3  AUS.4  AUS.5  AUS.6  AUS.7  AUS.8  AUS.9  \\\n",
              "Country                                                                         \n",
              "AUS      22743    983    736  45918  44552   2679   5094   3962   3062  13648   \n",
              "AUT          9      0      0     23     11      3      1      4      3      1   \n",
              "BEL         20      0      0     33     18      3      2      8      5      2   \n",
              "BGR          0      0      0      0      0      0      0      0      0      0   \n",
              "BRA         17      0      0     11     52      6      1      4      4      3   \n",
              "\n",
              "          ...    ROW.46  ROW.47  ROW.48  ROW.49  ROW.50  ROW.51  ROW.52  \\\n",
              "Country   ...                                                             \n",
              "AUS       ...       194       0     173     190    1428    1172     580   \n",
              "AUT       ...        90       0      87     103     561     336     327   \n",
              "BEL       ...       135       0     168     205     947     549     637   \n",
              "BGR       ...        10       0      16      22     117      62      87   \n",
              "BRA       ...        80       0     149     166     774     488     521   \n",
              "\n",
              "         ROW.53  ROW.54  ROW.55  \n",
              "Country                          \n",
              "AUS         533       0       0  \n",
              "AUT         247       0       0  \n",
              "BEL         489       0       0  \n",
              "BGR          54       0       0  \n",
              "BRA         548       0       0  \n",
              "\n",
              "[5 rows x 2464 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "metadata": {
        "id": "JBFm3TsAX3dA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "35d63eb6-7d02-498c-a77a-152e9bedcd8f"
      },
      "cell_type": "code",
      "source": [
        "df_trans = df.T\n",
        "# df_trans.index.names = ['index']\n",
        "df_trans.to_csv('temp.csv')\n",
        "df_trans = pd.read_csv('temp.csv')\n",
        "df_trans.rename(columns={'Unnamed: 0': 'Country'}, inplace=True)\n",
        "df_trans['Country'] = df_trans['Country'].astype(str).str[:3]\n",
        "df_trans = df_trans.groupby(['Country']).sum()\n",
        "df_trans.to_csv('Countrytrans.csv')\n",
        "df_trans.head()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\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>AUS</th>\n",
              "      <th>AUT</th>\n",
              "      <th>BEL</th>\n",
              "      <th>BGR</th>\n",
              "      <th>BRA</th>\n",
              "      <th>CAN</th>\n",
              "      <th>CHE</th>\n",
              "      <th>CHN</th>\n",
              "      <th>CYP</th>\n",
              "      <th>CZE</th>\n",
              "      <th>...</th>\n",
              "      <th>PRT</th>\n",
              "      <th>ROU</th>\n",
              "      <th>ROW</th>\n",
              "      <th>RUS</th>\n",
              "      <th>SVK</th>\n",
              "      <th>SVN</th>\n",
              "      <th>SWE</th>\n",
              "      <th>TUR</th>\n",
              "      <th>TWN</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AUS</th>\n",
              "      <td>904769</td>\n",
              "      <td>353</td>\n",
              "      <td>479</td>\n",
              "      <td>1</td>\n",
              "      <td>405</td>\n",
              "      <td>931</td>\n",
              "      <td>979</td>\n",
              "      <td>11328</td>\n",
              "      <td>0</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "      <td>48076</td>\n",
              "      <td>320</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>776</td>\n",
              "      <td>191</td>\n",
              "      <td>1766</td>\n",
              "      <td>10805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AUT</th>\n",
              "      <td>28</td>\n",
              "      <td>265580</td>\n",
              "      <td>1524</td>\n",
              "      <td>227</td>\n",
              "      <td>120</td>\n",
              "      <td>168</td>\n",
              "      <td>3440</td>\n",
              "      <td>1169</td>\n",
              "      <td>28</td>\n",
              "      <td>3874</td>\n",
              "      <td>...</td>\n",
              "      <td>134</td>\n",
              "      <td>838</td>\n",
              "      <td>7235</td>\n",
              "      <td>2029</td>\n",
              "      <td>2080</td>\n",
              "      <td>1009</td>\n",
              "      <td>942</td>\n",
              "      <td>580</td>\n",
              "      <td>242</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BEL</th>\n",
              "      <td>317</td>\n",
              "      <td>1351</td>\n",
              "      <td>337154</td>\n",
              "      <td>704</td>\n",
              "      <td>1565</td>\n",
              "      <td>991</td>\n",
              "      <td>3641</td>\n",
              "      <td>5304</td>\n",
              "      <td>65</td>\n",
              "      <td>1686</td>\n",
              "      <td>...</td>\n",
              "      <td>573</td>\n",
              "      <td>459</td>\n",
              "      <td>19054</td>\n",
              "      <td>1891</td>\n",
              "      <td>296</td>\n",
              "      <td>209</td>\n",
              "      <td>2839</td>\n",
              "      <td>1246</td>\n",
              "      <td>310</td>\n",
              "      <td>11299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BGR</th>\n",
              "      <td>0</td>\n",
              "      <td>379</td>\n",
              "      <td>99</td>\n",
              "      <td>45958</td>\n",
              "      <td>79</td>\n",
              "      <td>47</td>\n",
              "      <td>46</td>\n",
              "      <td>177</td>\n",
              "      <td>37</td>\n",
              "      <td>166</td>\n",
              "      <td>...</td>\n",
              "      <td>9</td>\n",
              "      <td>576</td>\n",
              "      <td>2759</td>\n",
              "      <td>3100</td>\n",
              "      <td>46</td>\n",
              "      <td>29</td>\n",
              "      <td>33</td>\n",
              "      <td>1048</td>\n",
              "      <td>33</td>\n",
              "      <td>158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BRA</th>\n",
              "      <td>1031</td>\n",
              "      <td>741</td>\n",
              "      <td>937</td>\n",
              "      <td>23</td>\n",
              "      <td>1182689</td>\n",
              "      <td>1043</td>\n",
              "      <td>1091</td>\n",
              "      <td>7430</td>\n",
              "      <td>0</td>\n",
              "      <td>192</td>\n",
              "      <td>...</td>\n",
              "      <td>390</td>\n",
              "      <td>25</td>\n",
              "      <td>49164</td>\n",
              "      <td>774</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>691</td>\n",
              "      <td>654</td>\n",
              "      <td>3057</td>\n",
              "      <td>15201</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 44 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            AUS     AUT     BEL    BGR      BRA   CAN   CHE    CHN  CYP   CZE  \\\n",
              "Country                                                                         \n",
              "AUS      904769     353     479      1      405   931   979  11328    0    72   \n",
              "AUT          28  265580    1524    227      120   168  3440   1169   28  3874   \n",
              "BEL         317    1351  337154    704     1565   991  3641   5304   65  1686   \n",
              "BGR           0     379      99  45958       79    47    46    177   37   166   \n",
              "BRA        1031     741     937     23  1182689  1043  1091   7430    0   192   \n",
              "\n",
              "         ...    PRT  ROU    ROW   RUS   SVK   SVN   SWE   TUR   TWN    USA  \n",
              "Country  ...                                                                \n",
              "AUS      ...     28    5  48076   320     6     7   776   191  1766  10805  \n",
              "AUT      ...    134  838   7235  2029  2080  1009   942   580   242   2009  \n",
              "BEL      ...    573  459  19054  1891   296   209  2839  1246   310  11299  \n",
              "BGR      ...      9  576   2759  3100    46    29    33  1048    33    158  \n",
              "BRA      ...    390   25  49164   774    17    10   691   654  3057  15201  \n",
              "\n",
              "[5 rows x 44 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "metadata": {
        "id": "1K1Vgw75NVc0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Growth Models"
      ]
    },
    {
      "metadata": {
        "id": "YUAIZj1pekdg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import collections\n",
        "import scipy.cluster.vq as vq\n",
        "import numpy.linalg as la # For finding eigenvalues and eigenvectors\n",
        "import itertools\n",
        "from networkx.algorithms.community.centrality import girvan_newman\n",
        "import pandas as pd\n",
        "import io\n",
        "import math"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "llm4YoRGY_1B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a5be4562-5e6f-4e58-b8a5-0471b5d708e8"
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('Countrytrans.csv',delimiter=',') \n",
        "countries = data.columns.tolist()[1:]\n",
        "data = np.matrix(data)\n",
        "data = np.delete(data, 0, 1)\n",
        "data = np.array(data)\n",
        "\n",
        "# print (countries)\n",
        "\n",
        "rows, columns = np.shape(data)\n",
        "n = rows\n",
        "print (n)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "44\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "05_p014dkfhg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_graph(g):\n",
        "    node_size = []\n",
        "    for i in range(len(countries)):\n",
        "        node_size.append(data[i][i])\n",
        "        g.add_node(countries[i], node_size = data[i][i])\n",
        "\n",
        "    for i in range(len(countries)):\n",
        "        for j in range(len(countries)):\n",
        "            if i == j or data[i][j]==0:\n",
        "                pass\n",
        "            else:\n",
        "                g.add_weighted_edges_from([(countries[i],countries[j],data[i][j] * 1)])\n",
        "    return node_size\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "D9gTjPx4mFIr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "f1a4c903-212b-4e68-a412-461b34712e89"
      },
      "cell_type": "code",
      "source": [
        "g = nx.MultiDiGraph()\n",
        "node_size = create_graph(g)\n",
        "pos = nx.spring_layout(g)\n",
        "nx.draw(g,pos, with_labels=True,node_color = \"red\", node_list=\"header\", node_size=[math.log(v)*50 for v in node_size])\n",
        "plt.show()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D9OnT8e6778LLywvDhw+HpaUlNm7ciD///BMAbrt9msFgQHZ2NoKCgnDq1CmEh4dj7969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Nu3bwfDMNiyZQvatWuH9957D3PnzrUaDHDw4EEwDINOnTohKiqKd+6PiIhAr1690KhRIzAMg4SE\nBLPmlblz5xospgqFwiR/VEVFhc3F06szZxpH21lrz5UZsqVPY3IYGRlpoMVJSEjgSUXnzp15MhQd\nHY3XX38d5eXlSE9PxwmyXpCck1NE8CHCRTP7iCxr0m4RwVvfJjs7m0+uaq6OqK3SqFEjuLi4ABUV\nuHv1Kl6fOhWRkZFgGAaenp4mRDcmJga//PILpk2bBh8fHwwcOBBFRUV89HB8fDwOHjyIr7/+2oCI\nRUVF4fDhwzh16pRJWguOiAUFBRl8F1wuvwkTJhj82Lt+/TpfocTd3R2zZs2CTCbjkw43atSInxuu\nX7+OlJQU3n/Q19cXSqXSwO+QiPWjfFrNd2fOnIFUKsWzzz5rsc3169cRFBRkseKAo7By5UpIpdIn\nXgLrfxVOkuaEw3H16lU+cSn3y97R5ZUKCwv53Ebt2rV7KskZwDpSc4ECzZs3t1ry6cKFCxg5ciRC\nQkJ4LUiHDh1sJrc+Pj6QSqX466+/8Pvvv0MqleK5557DgQMHEB4ejocPH0KlUmHMmDEGx33//fcY\nNmwYoqOj+QUqLCwMAwYMwFdffYWysjI+ZUZYWBh27dplcQxcIWohYRKaVa9cucLnp7O1NqcjxRbi\n4uPjg4yMDMjlct7XzsPDw8QvjXu+fXx8kJycDCJWA5ScnIzU1FQoFAocJesFyacRG+HZiNiAAXNE\nzBpJu0mE2OBgTJgwwSFJfjMyMvj0KcaZ80+fPm226D3DMHzy1wsXLhgcc/bsWdSvX9+gvVqthlKp\nhEqlQkJCAojIwKfRnPaUi8Ls0KFDVbCGRgMcPYrV8fGYQ4S5RNiZk4MhoaFQ6r8LFxcXbNiwgR/P\nW2+9BYlEgqioKPTr1w8MwyArK4v3KeTuYa9evZ5a0lFWVgY3NzdkZmZabFNZWYnWrVs/Fi1gdHQ0\nOnbsWOf9/FPhJGlOOBRc3TapVAqxWIwVK1Y49PxFRUWIiIgAwzBo06aN3UlWHyd27twJhmGwatUq\njBw50mzJp3379qFbt278IhEQEIAhQ4bg5MmTNvXx/vvv49VXX8X8+fNBRFi4cCG0Wi0CAwORmJgI\nAMjLy8OqVaswdepUKBQKfP/993j++ecRExPDL36hoaHIysoCwzB8tG1FRQWee+45SCQS+Pr6YtOm\nTdWOR0gUPD094evry+/bvXs3ZDIZEhMTERkZabXGpHBxNtauGLcxTnIr1NYYiy0pPxYtWoSRI0di\n+PDhIGI1PgzD8DUhOeG0MWlpaTyxNpYdejI1k8wXJO9AhBnE1s40R8KqI2knyLEVGDw8PHhN4mef\nfQYA+OWXX3itWHBwsEFghZD8Pvfccwbv4+nTpw00ZNz3lJSUxKcxEUr9+vWxb98+s9cTHx9f9U4c\nPw6MGIHb/v54ZOaeaInwOxH2BQejYvt2QKdDcXExn/aF81HjNNTCfmQyGXbu3GnTu/ckoNFoEBoa\nitDQUKupLt544w3k5ubWqR8awJbUIiKcPn26Tvv5J8NJ0pxwCCoqKpCXl8dn7Y6NjXUogdq1axe/\nWLZu3bpO03Y4AsXFxZDJZBg0aJBBySeNRoM1a9agWbNmfJRZbGwsXn311Ro5JkdFRfELjEqlQmVl\nJTp06ACVSoXy8nLs27cPQUFBGD58OK/xYBgGISEh6NOnDz777DNotVqDxLVarRZTpkyBXC6Hm5ub\nzTnshNnkiVh/tC5dugAA5s+fD4Zh0LdvX+h0Ot6PqTrSYIvfmj1mPc40bm4f5982cOBAtGrVCseO\nHTMZg63O+KQnT0slEp48mCtI/pBYx/0gMs2DNlNA0szlSVtKhK0OImfmJC8vjy94npCQgF27duHn\nn382INfcvR8wYACfeNbPz88gCW1AQAAKCwvx8OFDdO7c2ex3LBKJEBoaanYcSqWSfcAePABGjYLW\nzc32CFSpFKfj4xGmN7sWFBRAJBKhYcOGJv00atTosdXsrSkyMzPh5uZmNc/jt99+i4CAgMfyA9YZ\nMFD3cJI0J2qNgwcPwt3dnV/MpkyZ4rBz7969mydnLVu2NEnl8DRCo9HA398fSUlJ+Pbbb+Hr64sx\nY8YgISEBIn1W9oyMDKxcudKif5mtMPbJ4khEjx49EBcXxxMSLlrx3//+t1kzzvTp0yGXyzFv3jyo\nVCooFArMnDnTLpOPccZ3sViMLVu2oFevXhCJRBg2bBhefPFFREZGQkSE+kR4lggFROhMBC+ialN5\n1Fb69etnMSmul5cX/yPDHiJGRGY1Qw0bNsS6Z54xq+1xlLxKhPT0dLvvG/euChP8mrsvWVlZOHHi\nBH744Qd07twZAQEBSEtLw8CBAxEUFIRWrVohOjoaTZs2xcWLF82aNidPnoyRI0dCKpXyDvxEVVpN\nS7n1hMEbu5cuBdLTa3yfSjw8MNTHBzKZjNeQCmXgwIG1eg8fB5599llIpVKDQB1j3LhxA8HBwSgq\nKqrz8Wi1Wsjlcrz99tt13tc/GU6S5kStMG7cOH5hU6vVfM272mLPnj2oV68eGIZBixYtcPnyZYec\n93GgefPmUKlUBqWP3N3dkZ+fj6+++sqhfVnS7Pj7+yMnJwcBAQEoKyuDVCrFvHnzzJ7j/v37EIvF\nUCgUkEgkJslAbYUwGEC4AHPpLVrl5ODL7t1xOycH54lQYbSQ/kWEw8RqiBJqSMI453FLMnDgQKvm\n0JqKJXMnEeFALYmYJSkmwvDevW0e4yuvvMJ/Ly1atDAhZQqFgt8fFBSEZ599Fl9//TVatWqF8PBw\nLFu2zKB00/bt2yESibBx40aD83h5eWHTpk04efIkUlJSTMYhk8mwefNmLF261GpKlLlz50Kn02FC\np0446YD7VSKR4CVB0XVO5HI5goODa/wOPg7MmTOHr2hhCVqtFm3btrXq9+pIOAMGHg+cJM2JGuHa\ntWuIjo7mNQmcmay22LdvH2JiYvhEoZcuXar9YB8Tdu/ebeBYLZFIkJmZWTN/jfJyYNs24PXXgSlT\ngGnTgEWLgFOncPLECYwbN86gsoCxliQ/Px85OTlYt24dhg4dCg8PD7PfT2FhIU9a+vTpY+Isbiu0\nWq3ZsSiVSqxatQr3V6wAUlJsXlDLiE0FobZCOiw5mAv/1kbEYjHv2G5J28NJixYtLBaAf4nqpqD5\n+9UUnBfeg/T0dGRlZYGI8PLLLwNgHdCNtYbu7u4gMvTz8/T0RHZ2NsaNG4cdO3bwz8iVK1dMyjGt\nWbMGALuAe3h4QCKRQKlU8m2EqTW4bZbuGxFh7erV+EEqddg9u0CEeP3znpKSgtWrV2P//v1PRbJr\nS9iyZQufBNsa5s6di+zs7MdWlik6OtpiIXcnHAcnSXPCbqxZs4Y3kyiVSoeo1vfv38+b57Kzs3H+\n/HkHjLRuodFo8P777/MFt7mFpXPnzujUqZP9JZ8ePQJWrgTatQPUarOLzN9E+IUIHyiVGJKZCSIy\n0GCpVCq88sor2Lp1K2JjY1FaWgqxWIxly5YZdHXw4EGDSEwun5U90Ol0OHPmDBYtWmS2PJOvry80\nd++iondvaAS+WfbIr0ToZmEBb9++PU+mbCVetjjZmzP/iUQiuLq6olOnTgbExjiYwJJ84SCSwckx\nMs2PJhQ/Pz+DJLsbNmwAEVt1QavV4tatW+jfvz/fhgv28fLyglQqRcOGDbF+/Xps3boVY8aMQXZ2\nNgIDA3lCZXwfhw0bBoZhMHbsWD5xrDBf3IgRI3Dp0iWDSgFCMiw013LnFovFeNPB9w1E+Fwsxvf7\n99v9vD8JHDlyBGKxGGPHjrXabv/+/fD3939svroXL14EEVk1vTrhGDhJmhM2o6KiAm3btuUn09zc\nXAPzR03w/fff8yV2mjZtit9++81Bo60blJaWYsaMGYiLi+NLzDRt2hRvvfUWpFIphg0bVrOST0VF\ndvvc3JVK8UNaGubOmAEiwqRJk1BeXg6dToesrCx8/PHH6NatG9RqNd/N6dOn+VxnTZs2RcOGDVGv\nXj2bh3n//n188cUXePHFFxEVFYXg4GAMHz7crFkrMiAAXzFMrRfVm8T6rtlKxIxF6OguLDFkTeRy\nOaRSKS5evIgBAwaAiDB9+nSoVCq0aNHC7jEkSCQ44yCS8ScR2lvpKzAw0IBEcUEsRIStW7eiTZs2\nEIlE8PPzQ3h4uIn5NyYmxuyPixs3bvDEmCNRnLO/sD/h/5mZmSgpKUFOTo7J/ZXL5Rg6dKjJ/eRI\nt6/+Mxc0EUOExkTYLbgXwv1x+v8LiK2NWibYHkoEqeDzi/8FNSavXr0KhUKB9u3bW21XUlKC0NDQ\nOslDaQk9e/ZEWFjYY+vvnwwnSXPCJhw6dAhubm58eD5XF7KmOHjwIBISEsAwDDIzM5/qX2S//PIL\nhg4dyjsye3h4oGvXrtizZw8Alrz6+voiNTXV/pJPOh1uPfccHshkNV60fxSLcVZQwqmoqAgJCQnY\nv38/iFjz1pUrV5CbmwuGYZCSkoITJ07wiWsPHz5sZXg6nD17FosXL0bbtm3h6uqK3NxczJ07F6tX\nr8bEiRNNFmBOdnl7O0z78SexJZOq034RseYze5z/hcSB+//VV18FEWHDhg0oLy8HkfngAHskg6jW\nRO06EfpZ6SMgIMCAJH399dd8El53d3cwDIOoqChs27YNt2/f5hPUCo8Ri8UGZu+SkhLk5+fz+xUK\nBd58801otVqcOHGCz6vG/SVizdzCz8YErEOHDnxC5ZkzZ5q9lm16LaUw/cj3xAaY3KQqkibc/zex\nmtdpRvdtL7EEjt/Wq1cNZoLHh4cPH0KtViM+Pt6qG4lWq0XHjh0xadKkxzY2LmBg0aJFj63PfzKc\nJM2JasElyuRyRdWm5NLhw4eRlJQEhmGQkZGBX3/91YEjdRx27tyJjh078iab4OBgjBgxAufOnTNp\n27RpU3h4eOCHH36wqeTTxYsXMW3aNDRs0AArxWLH+CvFxABHjkCn06FJkybYsGEDn1LCxcUFDMMg\nMjIS3377LT+OyMhIZGdnm4zPWFsWGBiIli1bomXLlkhKSuL9lsRiMfz8/JCammqywA7XL5i1vi6B\nHCBWGyIkY9z/liI27SVpnB9Xjx494OXlBbVabdH3ryaSQIRdZFhU3VY5SoQ8K+f29fWFVCrlCdfY\nsWMxZcoUfn/9+vVx8OBBlJWVYc6cOXB1dYVIJMLRo0exdu1ag/vasmVLlJWVoVu3bgb3mIv4vXz5\nMpo2bQqiqsL1HBGUSqWYP3++gU8c979CoYCrqys/n3h6eprky8vLy0P37t1xgExJGIiQTlX558zt\nX0GEjkbb9pIRSfPzA27frv1EUQfQarVITEyEWq2u1lIxf/58ZGVl4dGjR49pdMB7773nDBh4jHCS\nNCcs4vr16waO8K+99lqNz3XkyBGkpKSAYRikp6c/dckPKyoqsHz5cjRp0oTXxCQmJmL27NkoLy+3\neNyECRMgFovx3XffISIiwmzC1+LiYsyYMQNpaWm8E7WPjw/WREdD6wBzIC8pKSjavBlJSUkYNGiQ\nwcJnXGVg/fr1EIlEuHr1qoG2LDs7GzKZDD4+PvD29ubJj1wuR2RkJL+AtmnTBlFRUWadvn2USvzp\n6elQgsbJG2Q9WW11wuWIs0TsGjdubEAq6kIYIkwhwk9kG1n7jQiLia1aYOmcXC1L7pr8/f0NcpUt\nWLAAN27cwNSpU+Ht7Y1BgwYhPDwcnTp14p8JrmqCsQZMKpViypQp0Gq1KCkpQfv27cEwDHx9fXnN\nmFgsRlxcHHbt2mVwrEwmg1gsRmBgoEF5Iq1Wi2PHjqFfv35mr8eN2Lqk5khYAyIUkXmSdovY+qdz\njI7ZS0YkjQj4978dMHM4Hvn5+VAoFNX6lx04cAB+fn6PPS1RVFSUwXPjRN3CSdKcMIu1a9dCLBaD\nYRio1Wr88ssvNTrPjz/+iNTUVDAMg7S0tKeqFt7NmzcxZcoUPppUoVAgOzsba9eutelX4rZt2/iI\nNmHJpytXrmDmzJlo3Lgx7xDt7e2NvLw8vPfee2zCzGPHgIAAh5OYz729DfyGpFIppFIp5HI572ek\n1Wrh4eGBhg0bolGjRnzqBU4D4+rqipSUFDzzzDMYPHgwOnfujKioKN4c6OLigoSEBDRp0sQs2Zko\nGNreAMYAACAASURBVE9TIqQKPl8igtjMuNcQoTVV40dEhONEkNiZYd842S1H0syVpeK0hJakOjOq\nTCZDy5YtbRrX9k8+wdZOnfAhEfYRW7fzDyJcJsIPRNhAhJfJOjnjJDAw0OA6JRIJ/P39+US0Y8aM\ngZeXF1544QVcunQJpaWlYBgGx44d45/n6dOnm5yXS8dy//599OvXDyKRyIC8KxQKbNmyBf/617/M\n3gulUomFCxeavDtlZWV8kXZjcXFxQScyT8J2EiGACHcF+zmftEgieBDhdSJojJ4vsyStFj866wrj\nx4+HWCy26oIAAH/99RfCwsKwY8eOxzQyFs6AgccPJ0lzwgAajQbt2rXjJ8xBgwbVSK19/Phx1K9f\nny9ufOLEiToYrf34+eefMWjQID4JrJeXF3r06IHvvvvOrvOcP38eEokEI0eOxMCBAxEXF4f09HTe\nxOjp6YlWrVrh3XffNa+Jy8+vE03TPSIM0mtQRo0aheHDh/Oln5o2bYrg4GAD/yOVSoUGDRpgxIgR\nGDduHLp3747o6GgDQhYfH48OHTqgX79+6NmzJ+rXr281y/8e/VhOElv2qBMRDpJtJK26hVVLhAFW\nyAqXN05IpowjETlyZktkpq0aNe57t0fMafJE1Rxj6b4Lr3HIkCHYsmUL/9nDwwMTJ07EtWvX+Mdv\nzJgx8PHxAcCatwcPHmwyNqlUioyMDIwaNQoSiQRubm78dYrFYsyfPx/3799Hdna2wbGci4Cfn59B\nsuZHjx7h1q1bBlUHevXqZXoPRCJszMkxIWExRGhDLFHnngchiSsh1l/NnN+fuWcJ48fXbBKpI6xc\nuRIMw2CzwL/UHHQ6HTp37ozxT2D8PXr0QHh4+GPv958MJ0lzgseRI0f4CVapVPKO8fbgxIkTaNiw\nIRiGQYMGDfDTTz/VwUhth1arxWeffYZ27drxvjChoaEYNWoULl68WKNzFhcXQ6lUwsXFhV9o3d3d\n0aJFCyxZssRqyRYAwI8/QqdS8YtFOBH2E2EwsXnB4ogQRqwGajEZmsSI2Og14wUoV/B5h4sLXF1d\nzSZ2DQoK4tOc9OrVy4CQKZVKBAcHIz4+HomJiQgLC+M1gQzD8AXHraWx8Hd1xV9iMUCsRu1DImwk\nwkhyDEkDseWVzJEvIrYwvK3kyFp+LnvFkobNnvQg1oiXrTJ79mx+sSdiieiSJUuwcOFCvPbaaxg3\nbhwKCgogl8sREhKCwMBAg+O5VBzVjasmgRn2CPe8CEmYOTHeP5MIXcy0M/ssPUUkbffu3RCJRHj9\n9derbfv222+jSZMmta5WYi+cAQNPBk6S5gQAYPLkyfwE2bJlSz7yylacPHkSaWlpYBgG9evXNzCj\nPG48fPgQ77zzDtLS0vhC7ykpKZg3b16NavPdvHkT8+bNQ9OmTQ0y/CcnJ8PV1bXaQAETvPCCwWIR\nTlUkbbZg+2/ERjQ+b7QoRRObJ0u4AOUKPt8gQqyF4uXc4sr5MHl6evIO5BxxcXV1hYeHB1xdXfkF\nWywWw9PTE2KxmC8ftXz5cgwZMsTg/D3kcoDY5K1RRCgntph4GLEVBi5R7UlaIVWRnwkTJhj0b08d\nT3NiT23O6qQu/drqSqylKJHJZAgKCjIxE3Pt5XI5RCIRGjdujDFjxsDf3x8uLi5o2bKlwb2whRxz\nz4K9JO0OsT90vrXlWZo1y+65oC5w7tw5SKVS9O/fv9q2hw4dgq+vb41/YNYGy5cvdwYMPAE4Sdo/\nHDdu3EB0dDS4hXjt2rV2HX/q1Ck0btyYT+1w5MiROhqpdVy/fh0TJ07k63wqlUrk5uZi48aNdk8q\nf/31F+bPn49mzZrxmkV3d3dkZ2ejWbNmEIvF2L17NwICAvD111/bP1i9KYeTcDJP0kCsI7Q7VZlw\niAj/ItY5WrgA5Rodt9xMclmhuLi4wMvLCz4+PnxEHkcs/Pz80KRJEwwfPhwfffQRrl27hsLCQj7J\nKUfgzaVYeJmqiFQfwXgGEVsM/BLVnqQdpSotU11rdJ4GMXeNQlLNbTPW2m3btg1//vknHjx4AJ1O\nh4qKCowePZrfzzAMevXqhfLycmg0GhCxpmCRSGSQIJlhGCQlJeHDDz80qB4gFIlEUiutobEoiXCV\n7CdpIMICYvOp6ap7lr780v5318G4ffs23N3d0aRJk2rb3rp1CxEREdi+fftjGJkpoqKi0Llz5yfS\n9z8ZTpL2D8aGDRv4iTUuLg4lJSU2H3v69Gmkp6eDYRgkJydX6+haFzh27BgGDBjAl+zx9vZG7969\ncfDgQbvOc+vWLbz99tvIycnhHcfd3Nz4JLVcyhGuPMuaNWuQkpKCxYsX2z/ohw+B4GCbSRqI9eta\nIViUQKyG7RPBApRrdMzb1ZjKpFIpAgICkJWVhZEjR2L9+vU4d+4cLly4gCNHjmDXrl3YsmULVq1a\nxScwTkxMREFBAXr06GFxQZ6s7783EVTEOnJ76P/vTGy9SZHRAgoirCZCW6NtZhdWYqMinxRhEhYm\nr815amLK5GTs2LEGYyEiPpKTI29paWn8I6fRaDB+/HjeN5GIEB8fjytXruDIkSOYNm0ab9auiSaS\nS6dRm2syJ9+a+e4dJsHBwL179r+/DoRGo0FYWBhCQkKqLeWk0+nQrVs3kyjtx4ULFy6AyBkw8CTg\nJGn/QGg0GoPKAbNnz7b52DNnziAjIwMMwyAxMRGHDh2qw5EaQqvV4t///jfy8vKgUqnAMAzCw8Mx\nZswYu8LQb9++jcWLFyM3N5fXBrm6uiIrKwtz5841m4j23LlzkEgkGD16NLp3725/yScOZ88CRmk3\nwsk6SetDhLfIkKT9SKw58SGZJ2kb6oggVCeriNX++ZJhAXUNEfyJjVwUE0vWhOOdTGxuNeG2vWSe\npB3S9zVo0CCMGzfOoH9zlQ8cQZ5s8XWz9fwdOnTA3bt3a3Q8p9kVErQDBw4AgAnBat++PZ8w2t5+\nRCKRXT579pI0sViM8PBwPkLYnEw38907TJ591v5318Hg3CdKS0urbbtkyRKkpaXZV8XEgejevbsz\nYOAJQUJO/KNw7Ngxys3NpXv37pGPjw8dOHCA4uLiqj3ut99+o8GDB9OhQ4coPj6e9u/fT82aNavz\n8f7999+0fPlyWr9+PZ06dYoAUHJyMs2cOZNefPFFcnFxqfYcd+7coXXr1tG2bdvo+PHjdPv2bVKp\nVJScnEwTJ06kgoICCgoKsjqGjIwMatiwIfn4+NCxY8do06ZNxDCMTdfw4MED+uqrr2jXrl1Uvm8f\nbQRsvn4iostE1N5oWyMiak5EC4moqZljPFUqkldWUkVFhcH28PBwCggIIFdXV3JzcyMPDw/y8vIi\nb29vcnd3J5VKRSqVioiIJkyYQLdu3aIPPviAwsLC6Oeff6bTp0/TuXPn6ODBg/TgwQODc/cgot5i\nMW3UaqkVEckE+yRE1I6IdhDRYCJ6jYje17c5TkRriWiXjffjd/3fdevWmew7efKkyTaxWExardam\nc8PCd/P777+TUqmkhw8f8tuMPzMMY/F4IiKZTEbr1q2jPn360MKFC62Ow9y5EhIS6NdffyUiMrge\nS+9hUVGRyTm5v1KplCoqKkgulxs8IxKJhLRaLel0OtLpdGbPKxaLSafTGYzP2nUL0aBBA6qsrKTT\np09TcXExFRcXW2z7DhENI6IIm85sB+RyoiFDHH1WuzB48GA6fPgw/fzzz+Tt7W217dGjR2nOnDl0\n6NAhksvlj2mEVdDpdFRYWEjz589/7H07QeTUpP2DIAwOGDx4sE2+WufPn0ezZs34agP7H0Nh4j/+\n+APjx49HREQEGIaBi4sLWrVqhU8++cSmMd+9exfLly9H69ateSdnFxcXpKenY9asWXYXIU5LS4OP\njw82b95cbcmnq1evYtmyZejVqxdfhoeINY+FhIRgSHa2SQLbcLKsSbtArH8Op3kiwb5rRAgkNnoy\n11hToHdCLi4uRnh4OP+9/9///Z/ZcVdUVGD//v1YsGABOnbsyDuQC7UzXPRnRkYGsrKyDLQecYIx\nZhBhkxntxXYipBGbJmQ8sdn344nNpbbHTPu9ZF6TNk/fpyOqDDwucXFxQWlpKbRaLXr27AmiutFo\nmjNDN27cGBkZGbxbgCURiUS1ShTMXVNubi7/3Qh922oiU6hm1RmsyoABdr3/jsbcuXMhEolQVFRU\nbdvbt28jKioKn3zyyWMYmXksW7YMMpnMGTDwhOAkaf8AlJSUIDIyEkSsz8m+ffuqPebixYvIyckB\nwzCIjY216Zja4PDhw+jTpw8/qavVavTt29cmX7f79+9j5cqVaNOmDV+iRqlUIi0tDTNmzMDly5dr\nPC4uR9Tnn39uUPJJq9XiyJEjmDlzJvLy8hAcHMybh1QqFeLj4w0CMjipFx6OYhtJ2iUiNCHCBME2\nMlpw5hAhlkxJ2gILPmMLFy7E1KlT0aVLFyQnJ0OtVvPjFi7SXl5eGDVqFD788EOcO3fOZIIuLS2t\nWpiJHFJI3Ra5R4RMK4u6LbnPhOJGbK3HGUT4P/39nEKEbCLIakEuTPpxc6uxY71cLjfJw6ZUKiGR\nSNCxY0e7r1lIDt3d3WtEzBiGgYeHB3+sUqnEDz/8gDfffNOhAQQMEb505DOUmAj89VeN54PaYuvW\nrWAYBkuXLq22rU6nQ69evTBq1KjHMDLLiIyMRJcuXZ7oGP7JcJK0/3Fs3LiRdybOzc2tNrfO5cuX\n+ULcMTEx2Lt3b52MS6vVYsuWLWjZsqVBbcmXX34Zf/zxh9VjHzx4gNWrV6Ndu3Y8qVMqlWjUqBGm\nTZuGS5cuOWSMGzZsAMMwWLFiBfz8/NCmTRs0btwY3t7evA+Ot7c30tPTUVBQgIkTJ2LYsGFo0qQJ\nfH19TTQlYrEYQUFB2CeRWCRpXJ60CGKTd75Nhk72xiTtof74XKPtz9qw8MfGxmLs2LH4/PPPUVpa\nitGjR4NhmKpizTod8OuvwJIlwEsvQTd0KFBQAIwfj+KpUxGqP9dIqgNthwXZVdPFXvBduBBhErGa\nupsW+tES4XditX999WTBUcTDXLQmwzBmM/C7ubnxBdJtDVYIDAw0qDrhiPFyATV+fn6QyWQGqUpa\ntWqF9PR0h/UnFHd3d/x58iTQpEntn5/ISEDvv/ck8OOPP0IsFmP06NE2tV+2bBkaNGhgdzokR+L8\n+fMgIrM1i514PHCStP9RaDQatG7dmp9kN27caLV9cXExWrRoAYZhEB0dbVBnz1G4f/8+3nrrLaSm\npkIsFkMikaBhw4ZYtGiR1ULCDx8+xJo1a9ChQwc+QatCoUCDBg0wZcoUnD9/3mFjvH79OlasWMEv\nctzCKBKJEBISgtatW2PgwIEYNGgQWrRogeDgYN60I5PJ4OXlxddNFKYrkEgkiIqKQn5+Pg46YsGx\nIleIjag0t+iFhobC3d3dYuqK1NRUbFqzBo/+7/+A5s0Bfd4zc3KHWC3HTf2xwlQIG4lQjwh/6j/r\niE3Mm0xV2eOfJ0OSlEus+ZYrAxVNbMTnWaoipH1tXNyNTXtcDdqJxOafs+d+VhIbrNDJxr7NES0i\nQrdu3fjaoLYQN0tiKQ2GsHRXbUQsFqOgoICPaubAFVTnhCO+dRWIIixlhhs3gDZtav5eNGgA/PCD\nw+YJe3H9+nUolUq0bdvWpvbHjh2DWq1+4uSoW7duiIiIeKJj+KfDSdL+B3H8+HE+pD46Otpq9NCV\nK1fQqlUrMAyDqKgo7Nq1y6FjuXz5Ml566SWEhYWBYRioVCq0adMG27Zts+jjUFFRgXXr1iE/P59f\nbOVyOVJTUzF58mSHTFxarRbHjx/H66+/jrZt2yIkJIQ3+ymVSohEInh4eCA8PBxqtRrR0dH8PRWL\nxXB1deWLkHPbidgo0djYWHTp0gXTp0+Hv78/Pv74Y8POz54F6qgAOYjNOWbLIpiSkoJJkyZBLpeD\nYRjIZDK0JcKRGvQpJGnfECGEDMnQVCKkC9poiI3oTCTCA6oiaeuNzvsWsT5rIEIpsYTtLLG1LT8m\n1kzpa+bajIlDBLF1HytrcV/vEJsKRWgGdWTyW6HExcVhwIABICLk5eXx2728vBxOijiC6OnpiS1b\ntpi8KyUlJZg+ffpjT85rMhdptcDs2dAGBdn+vXl4AM8//0TTbTx8+BB+fn6IjY21ya+rvLwc9erV\nw6ZNmx7D6CxDq9VCJpPhnXfeeaLj+KfDSdL+xyDMwP7GG29YbPfHH38gLy+PNzN+6cDEjgcOHECv\nXr14/zBfX18MGDAAx48fN9u+oqICGzZsQJcuXfiamnK5HCkpKZgwYQJOnz5dq/FUVFTgiy++wOjR\no9GkSRP4+PgYmCtTU1PRvHlz5OTkICkpyaTuo7u7u4H/DbctPj4e3bt3x5tvvomjR4/a7Fir1Wpx\nq3XrOiFoZUTIrcFiKhaJsCE8HBUuLjXqlyNpJ4klaEKiV0ps8MNZM8elEWElWSZpZ4g1T1rr+5r+\nuFgL15ZChBMOvMdfEmGWIAinpmIp8CElJQVJSUm1Pn914ubmBk99ZYqwsDD++dVqtdi+fTs6derE\nv8P8c1ILfzNjc645rV/btm3h5uaGhIQEi+/P29OmYX1kJJCVBXh7m35Hrq647OWF4zk5rLn+CUKr\n1SI5ORne3t64f/9+te11Oh369u2L559//jGMzjreffddZ8DAUwAnSfsfQWlpKUJCQsD9IrZkArx6\n9Sratm3L5xgrLCysdd9arRYbNmxA8+bNoVQqeZPppEmTzEZCajQabN68Gd26dUNAQACvxUlKSsK4\nceNw8uTJGo/lxo0bWLVqFfr164eEhATe4VoikSAwMBDJyclIS0tDUlISfH19+UVHKpVCpVKZaAtc\nXV2RmJiIXr16YcGCBfj5559tjjAtKirC7Nmz0bt3bzRq1AgBAQH8whRBbM4wRxEHTu4PHVqjYt/z\nifCoFv0SsSbBCCJ8ZbSvkFjzpbnjZhGhP5knaRpio0DzbBxDMbFBFsLrCifHEjROviA231u15Ff/\nfBmTHYZhLBIezjxqSWwxjTIMg549e7LBKnpTrznp2rUr/Pz8MHz4cIwbNw4xMTEQiUSQSCQmJmNH\nafDMaR8ZhuEz6VdWVlr0w9q5cyeICAO4CM2yMhyfPh2LwsOBt98G1qwBrl7F22+/DblcjvLy8hrP\nJY5Aly5dIJfLbc7juHLlSqSmplp1/3hciIiIQNeuXZ/0MP7xcJK0/wFs3LiRn0D79+9vlkRcv34d\n7du3B8MwCAsLw+eff16rPu/evYu5c+ciOTkZYrEYUqkUaWlpWLp0qckEq9Fo8Mknn6B79+4IDAzk\nSVlCQgLGjBmDEydO1GgMJ0+exJw5c9C+fXuEhobymgmFQoHg4GDExsYiJiYGgYGB/D6xWAyFQgGl\nUsmbNxmGgaenJ4KDg0FE6N27N9RqdbUln4qLi7FhwwaMHz8e7dq1Q3x8PLy8vAyy0nt7eyMhIQEd\nOnTAxIkTsXnz5qoUIO++C60Vny+7JSMDePgQ3377rV2L5nPE+nvVpm8i1ocsgFgzpHDfemKjJc0d\n9x4R2lMVSeN80mKJNSsOIcvO/eakktiEupyj/+eOurdmZI6N99ccuXGkyVJ4Lk5DvH//fpw9exYB\nAQFmj+F+LAg1dmq1Gg0bNrRK6uyR1NRUg7HFxsaa1Z7FxMTg9u3b1b7vS5cu5d/ZJUuW8NszMjIg\nkUgMkkt/8MEHIGJ9Ee/cuVOj+aW2mDhxIkQiEX6w0RfuxIkTUKvVT0VW/3PnzoHIGTDwNMBJ0v6L\nUVlZiZycHBCxmiBzOcxu3LiBDh06gGEYhIaG4tNPP61xfxcvXsSoUaMQGhoK7hdxu3bt8PnnnxsQ\nw8rKSmzbtg29evVCUFAQGIaBVCpFfHw8Ro8ejR9//NGufisqKrBz506MGTMGmZmZUKvVvDbBzc0N\nQUFBCA8PR0BAAO9UzRFBuVzOkybOvNmgQQMMGDAAy5Ytw2+//QaAJXxisRjjxo1DSkoKlixZAo1G\ng8OHD2Px4sUYOnQomjVrhrCwML7aAUcIAwMDkZaWhr59+2Lu3LnYvXt3taYNnU6H9957DwuUSlQa\nRXvWRE6IRCh67z0A7C/g+vXr27SQBhLhnAMICxEhWP+XC1qIJpZwBRFBToTdRu0LiNWkDdJvq09s\n3jSuTRax5aLsHYuWCMuJMI5YbVxtr82S3CC2RqS9BK2mRMzT0xMZGRn8NhcXF949gJsDrl+/ji5d\nupj0Wx1R5GreOmKsnp6eBhppa8EMM2fOtGkOGD16tIHvJ1cl5cKFCxCLxWAYBt9//z3fftasWSBi\ntY4NGjR47ERt9erVYBgGGzZssKn93bt3ERcXh/Xr19fxyGxD165dnQEDTwmcJO2/FCdOnOAnv4yM\nDDx69Mhgf0lJCfLz8/moxG3bttWon3379qF79+6874q/vz8GDx5sYJLUarXYsWMHnnnmGQQHB4Nh\nGEgkEsTGxuKFF16wq65nSUkJPvzwQwwYMABJSUm8aUQsFsPd3R1+fn7w9fU1MOlJJBLIZDKejIlE\nIqjVajRq1AiDBg3CypUrLeZKKysrwyeffAK5XA4fHx/e90yYO8zNzQ1RUVFo0aIFRowYgRUrVuDk\nyZN2+2rodDr85z//wbhx4/iUBiNGjAD+3/8DAgJqRBS0RPgjIQFR+kWRK3N18uRJPPPMMyAi9OjR\ng+/PWD5wEGEhYn3SiKo0TL/o990mgiuxOcluCtpHExs4sJbMk7S9xBK8+zUYzwNiC3Q74tqsyTYr\nZMUR5C0+Ph4qlQpXr15F8+bN+edd2KZ79+64e/cuoqOjTUo5Gfu9cZo2Y5O4vQTNkqnWeGxdu3Y1\na551d3eHu7s7+vbtW+17o9Vq0aJFCwOyN27cOAAwCKoQmua6detmMNbc3Fy73tXaYO/evRCJRHjt\ntddsaq/T6fDss8+ioKCgjkdmG7iAAVtyuTlR93CStP9CvPjii/zEavzLq6SkBF26dIFIJEJwcDC2\nbt1q17k1Gg0++ugjNGvWDAqFgs+XNnXqVL4Au1arRWFhIfr27YvQ0FCelMXExGDEiBE21/M8deoU\n5s6diw4dOiA8PJxfUKRSKdzd3eHl5QWVSsVP8pyvjPCzWq3m85R98MEHJjnWdDodzp8/j48++ggv\nvfQS8vLyEBMTAw8PD4OFhmEY+Pj4wMvLCxMnTsTWrVtNUhDUFnl5eZDL5fz4JRJJVS2+n34C8vIA\nqdRmgnCFYTCDCBK9ptISGRCaeoWLaLCLC56lqpQXEiKECT43IlNHflAVIeP+jzb620f/tw1V5Xj7\nf8QGD7wvOK4bseSNq/FpTNKgP8csG++HJblErP+Y8fY1RGit/38JVaUHiSRWu3fHqP3nxGoJNwi2\n3SI2nUhNiFp1BCk6OhoHDhzg2wg1uNyPMy5y21oSWZFIhNGjR6Nt27Y2+bP5+vqafZ5EIpHJO2Pp\nHJGRkQaVLoTSr18/aLVabN68GSKRyOYKIGlpafDy8oJMJsPw4cPx1VdfGYxHKpXizz//BADEx8fz\nz31WVha++OILh77LlnD+/HnIZDL06dPH5mM++OADJCUl2RRY8DjwzjvvOAMGniI4Sdp/EW7dusWb\nN0JCQlBWVsbvKy0tRbdu3SASiRAYGGg2lN4Sbt++jddffx0JCQkQiUSQyWRo0qQJli9fjoqKCmi1\nWhQVFaF///4ICwvjJ+vo6GgMHz7cwMxgDhqNBrt27cL48ePRtGlTg0SvXGJMFxcXfgHhnKq5NmKx\nGH5+fsjMzMTzzz+PdevW8ZMxwJpDv//+eyxYsACDBg1CZmYmQkJCDPJJcSWNmjRpggEDBmD+/Pn4\n7rvvMHDgQMhkMrz33nsIDQ01OK+j8e233xpoNnr37o379+/jxx9/xObNm5HfsSNmN2uGvUFBuCyX\n428zxOIGEb4jwmyJBAF25sRSKBTw8vJCYGAgxGIxphmdO5zYpLrc51yyjaRdMfrbjiMZxGrVuOOC\n9X3E6fc/T2z1gE8E55JSVR619sQmk3UlwnXBOeOM5D9mxmUPSfuSWHJYot/+NxF6E2GEUfueRFhG\nbO424faFdnwH1sTPz4//Xy6Xo3///jwJiY2N5fdJJBJcuHABALBq1SqLBF14PiLWPFpddKY54mV8\nfqVSidmzZ/NuD8YiNMkay5AhQwzeiXr16iErK6vad6e8vBwikQiFhYW4ffs2bt26hc8++ww5OTmQ\nyWRQKBTw8PDg03YsX74c//rXvxAaGor++hJpdY3y8nJ4eHggLS3N5mNOnToFtVqNX375pQ5HZh8i\nIiLQrVu3Jz0MJ/RwkrS6hE4HXLgALF8OTJ4MjB0LTJoELFsG/PYbu98KlixZgmXLlgEAPvroI34C\nnTZtGt/m1q1b6N69O0QiEQICAmzOrXPu3Dk8//zzvLO8u7s7OnbsiJ07d0Kr1eLrr7/GwIEDER4e\nzpOyqKgoDB06FN99953F85aWlmLt2rUYOHAgkpOTeXMlwzCQy+VQKBQGC4VIJDIgY/7+/mjatClG\njRqFTZs24S99CZeSkhJ8+umnmDZtGrp27YqUlBSTkkbu7u6oV68eWrdujRdffBGrV6/GmTNnLP4i\n5PxGli5dCrVabbevnCXcuHEDe/fuxapVqzB16lQMHDgQrVu35u+1uYWRI6hyuRyenp7w8fJCupsb\nxsrlmCKRYCyxyVRdBQu10EfHnHCm5w8//NDsOK+1aOEwkjaSqsiXiKoIlYwIrYjNURZAhLvE+qKR\n/vgfiRBFhAH6bQn6v+uILc/USd9uplG/5qSmJG2+oB9OyslQk1ZKbDCDTj/Ga4J9RVa+A0vEx3j/\n6NGjDaIeJRIJ3Nzc0K5dO34b99688cYbZjP8BwYGGrSzdRyWSJ6xqNVqfPfdd3jhhRfM7rdWXqph\nw4YYMmQIfHx8DJ7Bw4cPg2EYHDx40Oo7NWLECHh7e5vdFxwcjPHjx5vdN2nSJIvHORJarRYRm9ZE\npQAAIABJREFUEREICgqCRqOx6Zh79+4hISEBa9asqdvB2QEuYMCRCcKdqB2cJK0u8PffwIIFbMZ2\nhcL8oqJQANnZwJtvAmbCzW/fvs0v3ImJiSBia0Jyv6DLysrQs2dPiEQi+Pv72+SgumfPHnTp0oX3\nWwoMDMSwYcNw6tQpfPPNNxg8eDAiIiIgFoshEokQERGBwYMH45tvvjFLdM6cOYO33noL+fn5iIiI\n4Cd7LtrTkmmEI2M5OTkYM2YMtm7dilu3buH06dNYtWoVXnjhBbRs2RLR0dEG2fFlMhl8fX2RmpqK\nbt26Yfr06dixY4fVZL2W8NNPP/GBAhEREdi8ebPFthqNBufOncOOHTuwaNEijBkzBj179kSzZs0Q\nHx+PgIAAuLq6GpgSRSIRpFIpFAoFVCoV708jkUisJgX19PREYmIisrOz0aNHD7z44ovIz89H48aN\nDWo0fvPNN7hz545F8xV3v2fPno2SkhLzpbaMKh+EU81JmnAfl+w1mtiUHAyxZOi4vg1nHuRSfgyh\nqmCDBGLzrIUTYQexPmuguiVpx/RjHkQsmTQ2c4II7xLhVf3/bxBL7Lh958l82ajMzEwTAmXufeDM\n9sL9nTt3tmrCFn52c3Pjt0mlUnh6etpVwaA6iY2NxdWrV7Fv3z4THzd3d/dqtXPLly8HUKUN27Fj\nh8FjmJ2djaioKKvvq0qlwowZM8zu8/LywltvvWV2X0lJCYgIv9ZxvrScnByoVCr+R6UtGDJkCAYN\nGlSHo7IfXbt2RWRk5JMehhMCOEmao/HNN2wqBCuLiYk0aQIYlWHq3r27wUSXn58PgJ3oevfuDZFI\nBD8/P6xbt87iUDQaDVavXo2srCzeFyouLg4zZszAF198gaFDhyIqKoonZeHh4Rg4cCB2795tQMo0\nGg327NmDiRMnIjs7G2q1ml8URCKRxQWBI2PNmzfHyy+/jE8++QSFhYV488030b9/f6SnpyMoKMgg\nSayLiwtCQ0ORlZWFwYMHY9GiRTh06FC1NUftwd27d+Hq6opGjRohPj4ezZs3x/Dhw9GxY0ekpaUh\nKioKPj4+UCgUBtfGES9OI6hQKCCTySCRSEwWXaVSCR8fH0RERPD56xo0aIDJkyfj3XffRWFhIY4f\nP26Qq00mk/GaUw737t2Dv78/Dh48aFh/0sUFJ0+eNFsflFvcIyIiwDAMTxZiY2PRt29fDBo0CF26\ndMFVo/Qf4VQzksb5sUXoP3OE63dia49KiCVenMYsWb8/TX+O7wUkR0ms6XEEEbz0n8vIkKSFE0u8\nrI1LKJeoep+0I8TWO/Ul1uTajdi8a1zbdCL8qv//IrEJcrl9jwTXbosIyVt1Ys0HTPhZJpPxvqHm\nyJ0l06Q1adiwISoqKnDjxg3+GeYkJiYGYrHYIDG08fH+/v64evWqwfPcokULk0S1V69ehUgksvhD\nc/Xq1ZBIJBbnAKVSafVHakhICAYOHGjL1FAjFBQUQCKR4NSpUzYfs3btWsTHx+Pu3bt1Ni57wQUM\nvPvuu096KE4I4CRpjsQ77wBeXvYRNE48PNhkjGAnLeMJLyUlBX379oVYLIavry8++ugjs0MoLS3F\njBkzEBcXB5FIBLlcjszMTLzyyisoKChAdHQ0T8rCwsIwYMAAFBUV8aTs9u3b2LBhA4YMGYLk5GQD\nk5olcwnnM9ayZUuMGjUKU6dOxfjx45Gfn4/ExET4+Pjw2iOxWAxPT0/Exsaibdu2GDduHNauXYuL\nFy/W6tZrtVpcu3YNe/bswYoVKzBlyhT0798frVq1QkpKCkJCQuDu7m52AeMWNk77Z0w6OdNTQEAA\n4uLikJWVhS5dumDEiBF44403sHbtWnz//fe4efOmmUfiHTAMY/aXvk6nw+LFi+Hm5sablBcuXIgb\nN27g+PHjKCwsRO/evZGSkoL4+HirC7g9wlVMuCISWSVpLakq8pKTSv05OHMfEaE7sWbA9cSaOoP0\n218gQhMi9CKWZN0jNl9agH5/lP4cK8iwWoCUCO7EEjd/YlNpzNTviyaW9EURSwyF+dcskbRi/bh0\nRttXk6l/mY4IR4nQgVh/ORAbpSomlnxyIibWVMsd11Dfvw+xyXlfI8Js/biHU1V6kgcPHmDmzJk2\nfU/NmzeHu7u72Vqdxg7z5tJccGZUe54N7jnkku8ap3JJSEhAbGwsRCIRXF1drWrsevbsafLMcz8s\njM1pAwYMgKenp1mNfXh4OLp06WLx3ReLxdi3b5/F/ePGjTMxszoKb731FhiGsSsp+OnTp6FWq/Hz\nzz/XyZhqiiVLljgDBp5COEmao/Dee4BKVTOCxomLCyr0mbLNTXo+Pj5YvXq1SdenT59GQUEBb1Lx\n8PBATk4O8vPzERMTw2t6QkND0bdvXxQWFkKr1eK3337DggUL0KlTJwQHB1e76HMEMT09He3atUOH\nDh2Qk5ODyMhIuLq68iROLpfD398fDRs2RM+ePTFr1izs3LnT7uzfGo0Gv/76Kz799FMsWLAAL730\nErp3746mTZsiJiYGarXaRNvF+XeJxWKecJkjl3K5HBKJBElJSWjZsiX69OmDl19+GYsWLcL27dtx\n8uRJi1nPzUGr1aKsrAwnT57Erl27sG7dOnTu3BlEhCZNmqBnz57Iy8tDeno6EhISEB4eDl9fX6u1\nH4XaSnuJmDASz/j+SCQSdO3aFaVqtVWS1o8Irxs9o6eJ1W5xpkoiQgtiyVMDIgzT7ydic6MREfz0\nf8OIJVhcpn4v/Tk6U5WJlBHsE9bIbEpVJIwjPD8IxnVRv81c6an7+j6LjbZPJpZA7SfCH0b7fiaC\nStBuntH+hUQYK/j8HhEOEqv1M/du3yXCRXd3YPx4tE9IsPrdcVrv6Ohos+8g9785H7C0tDRs3brV\nYhF242dL2CcRq5E7evSoyTMXGBiIyZMnG/hCWuqDYRjeRcNcLeDo6Gi0b9/eYNvDhw8hl8sxffp0\ng+0nTpwAwzAWU+gAABFZzeh//fp1EBGfE9FR2L59OxiGweLFi20+5v79+0hKSsKqVascOhZHIDw8\nHN27d3/Sw3DCCE6S5gj88gsQHFw7gqaX60RoJJGgZcuWJhNlhw4d+C6LiorQsWNHPv+Vr68vEhIS\nEBERwZOy4OBgPPPMM/j000+xd+9eTJ48GRkZGRZzZgkXAnd3d0RERCAxMRGxsbHw8/MzMEuqVCqE\nh4cjOzsbw4YNw9KlS/Hjjz9W6zRbXl6O//znP1i/fj1mzZqFYcOGoX379mjQoAGCgoJMfLu4/oRi\nTEYUCgXUajXq1auH9PR0dOzYEQUFBZgxYwZWr16Nb775BleuXMHy5ct5rZafnx//a/7u3bs4c+YM\nvvnmG2zcuBGLFy/Gq6++ipEjR6JPnz5o164dMjMzkZSUxJMrlUoFqVRqlkAJxykkjNZII0eelEol\nPDw84OfnB09PT7i5ufHpQmQymUH6jnnz5vFmWaFPklKp5CPmJk6caNBPbm4uZs+ejVGjRuEnX1+r\nJO1bIoQSS4BArDN9ZyK8ImjDESfu8x1itVYjiVBPv/9H/d9dxNbhVBJrHpQS65AvpqqAiARiiVwM\nEVL1bbypirxdIVZb5U2EuYJ+V5sZi1AKiDCYqtJ9HCNWS3dCP9bO+usDsQlwpxKhI7GawyCqMnVy\ncpFY8qkh+4u2lxDhHaoisUSs+fr999+3+l5aktjYWKxZswaffvopIiMjzbax5N8mk8lw4MABAMCh\nQ4cs9lGvXj0+6tpS2bEWLVqgqKgIRITk5GTIZDK4uLiYmCk3bdoEsVhsknJi1qxZkMlkBttbtGiB\nxMREi/PJgwcPQESorKy0Ou8EBQU5NA8Z59P6wgsv2HXcsGHD0L9/f4PqCE8Dzp49CyJnwMDTCAYA\nyInaoWNHoi+/dNjpfo+Pp9iLF6ni0SOTfU2aNKGffvqJKisrycPDg0QiEZWXl5NWq6XAwEBq3Lgx\nRUdH040bN+jAgQN07do10mg0ZvthGIZkMhm5uLgQwzD06NEjevDgAel0OpJIJOTu7k5+fn4UGRlJ\nSUlJlJaWRtnZ2RQSEsKfQ6fT0bVr1+jMmTP022+/0eXLl+n333+n4uJi+vPPP6msrIwePnxIGo2G\ndDpdtdcuFotJJpORm5sb+fj4kK+vL/n5+VFgYCCFhIRQZGQkRUdHU0xMDEmlUrp27Rpdu3aN/vzz\nT7px4waVlJRQaWkplZaW0u3bt3kpLS2lmzdvklgsJq1WSwzDkLlHn2EYg/+5NsZtGYYhkUjEj1eh\nUJBSqSSlUkn37t2ja9euUWJiItWvX588PT3Jy8uLfHx8SK1Wk1qtJoVCQRUVFTRp0iRKTk4mX19f\nun79Ov31119UXl5O5eXldPnyZXJ1daW7d++STCYjIqLKykqb7qMtWEFEIwSfI4hoAxFlC7b9m4jm\nEdF9ImKIqC8RTSUiKXcfiOgKEYUIjvEmIgURRRLRQf3+KCIaT0Qriag9EW0jIi0RqYjoLhFJiKhS\nf14Q0WoiGkZEIiLyJaIb+va/E1ECEQ0iovNEtEvf5wAi2mhmLBzuE9EMIirSn9+biGYTUSsiqiCi\nV4noc31bDRG11F/3MSIaR0RnzJyzARHNIaJOZvbZgsP6cx/WP5P2ICgoiMaOHUu9e/emCRMm0Oef\nf06VlZUm7WJiYujGjRt0584dg+2enp50+/Ztat++PRUWFtJrr71Gb775psE4xowZQ++//z79/fff\n/DZvb2+6deuWwbkYhqGNGzdS3759iYioVatWtHfvXnruuefoww8/pDZt2tCXRvOjp6cnDR48mJYs\nWcJv0+l0pFarqV27drRp0ya6d+8eeXh40LZt26hr165m78OZM2coMTGx2nfipZdeoi1bttDNmzet\ntrMFN2/epMjISMrMzKQ9e/bYfNzHH39Mb7zxBh09epTc3NxqPQ5HomvXrnTq1Cm6cOHCkx6KE0Zw\nkrRagmEYiiZ2kdERUTQRLSN2USJiF4SlRPQBET0idgGIJXaBSNO3GUJEhUTko/9cSUTuERE06tVX\nafTo0VRRUcH3JxaLiYhIq9XyBEaj0dD169fp3r17ZokHd5xIJCKdTsdPxAqFgry8vCgoKIjq1atH\nKSkplJmZSWlpaXT16lU6e/YsXbhwgS5evEjnzp2jq1ev0q1bt+jevXv06NGjahcWhmFILBaTXC4n\nV1dXUqvVFBQURH5+fuTh4UEqlYoUCgXJZDKqrKyksrIyKi0tpZKSEp6s3Lt3j/7++2+qqKigyspK\n0mq1Fq/RVkgkEnJxcSGFQkEqlYrc3NzIxcWFv7eVlZWk0WiosrKSKioqSKPR0KNHj0ij0fDbNRoN\nabVafjyc1CUYhiGGYUwWJG9vb3JxcSGZTEYXL14kqZSlUBqNhsaMGUOhoaG0cOFCunPnDsXFxdFP\nP/1EEyZMoJxr1yj/449J5OBxRhDRaGLfg8vEEqckIlITkZhY4qUiol+JSEbsu3KLiIqJJWkaIgol\noqtE1JGIyojogP7cIUT0BxGF6fdHE9FLRDSXiK7rP0sEY1lNhqTzacMlYsnoXhvaymQyGjt2LE2Z\nMoXWr19Ps2fPptLSUrNto6Ki6O+//6Zr164ZbHd3d6c7d+5Qv379aNOmTUTEvg9CgtewYUM6fvy4\nTeOPiIig48ePk6enJ7+tsrKSXFxcSKvVUqNGjejo0aO0Y8cO6tKlC99m6tSp9O6779Ldu3cNzrdl\nyxbq378/FRcX07x582jTpk0mpFCIr776ijp37kyPzPygFeLatWsUHBxMly9fpvDwcJuuzRwePXpE\nYWFh5ObmRmfPniWRyLa35+zZs5SdnU27d++m+vXr17j/uoBOpyOlUkmLFi2iUaNGPenhOGGMJ6PA\n+98BGZlYhLmdoDebZFKVo3UlsY7SnlRVImcwsU7GQpPIdH2aDHNij4+SSqVCVFQUmjVrhq5du2LI\nkCEYMmQIOnbsiJSUFAQGBsLV1RVSqdSmfE6cSc7LywtBQUGIiYlBcnIybxaNjIxEcHAwfHx84Orq\najb60V7hzCycgz9Xk1Mmk0Emk0EqlUIikfAJcC1Fm9WFcD5wwkoIUqkUbm5u8Pb2hr+/P4KDgxEV\nFYX4+Hg0aNAAWVlZyMnJgY+PD6KiopCRkYFGjRohOTkZcXFxfG46znnb1dUVHh4efNJfc2OwNkZh\nMIQwJ5ubSoWjDOMQM71QwolNZcH5pkVTVe40MbFBAosF+wuoKmKzPrHmzv1EUBNrijzJPff6tlJ9\n29b6dqeJ9WHTOvg6HpdcJ0KSle+voKAA2dnZSExMRGZmpsXv28XFBW3atDG7Ty6X8/kNd+/ebbaN\nr68vSkpKsH79eoM5hnsOjeXVV1+1OC9+8803IGIjmrloaKEZs6KiAhKJxKyPLZfg1tXVFVOmTLE6\n/65evRoqlcqmuTogIADPP/+8TW3NQafTITU1FV5eXnZFZT548ACpqalYsWJFjfuuSyxevBhyudwZ\nMPCUwknSagljkraLqnI7leoXInMFrIW5mAaTKUk7amXSNhYua7+7uzvc3Nzg4uJi4L9kK9EQ+k4J\nM/7/t4k1osaRPC5LuVKp5NNpKBQKyOVygyhPa/dASNC4dpxjNedL5uXlBW9vb/j6+sLPzw+BgYEI\nDg5GQEAAn/YkMjIS0dHRqFevHurVqwdvb2+4ubnxwQ316tVDdHQ0oqKiTJy1w8LCEBISguDgYAQF\nBfEVKYwdzIUET3hvJlHdkZuZ+v6/I5aEbSWWaMUSm/6iEbFRnLOoiqTlEkvO9hPrpzZZf67h+nO5\nEuvLdYnYd+YNYqsA9Kyja3hccpsI06iKzApl8ODBVnPryeVyPPPMMxbrcY4dO5ZfgLds2QIfHx+z\n59m8eTOysrL4z5bIGRHZVNGkZcuWICJMmDDBbP3Mrl27IiwszOS4w4cPg4j9MVpd6p3XX38darXa\nprl65MiR8Pf3t6mtOXTr1g1yudxqEIM5jBgxAn369Hnq/NA4OAMGnm44SVptoE+UyJG0CmITYk7R\nfy4kNk1AdRP0YDIkaTpio8nEFibIf5oYBw6YE45QCT/bQ97+W4W7Vo5kCzV6QlEqlVCpVCaaOIbY\nHxaoIyFiAwi4uqC+xGrL1hCrCUsjNjghnAxJ2lpiNWYr9OcZSwQFVWmcLhFL5PKI0FfQ7r9ddhOh\nra+vzd99amqqyXaOoCckJODSpUsAgC+//NJixYspU6agbdu2/Ge1Wm02spSIJfxcwMzhw4etTo9a\nrZb/wcMRNmHS6OLiYjAMY/Y8crkcSqWy2il41KhRCA8Pt2m6Li4uBhHZXCtUiClTpkAk+v/sXXd0\nVNX6/e6dOzWZtEky6b0nQAoQCAklhN6L0nuvD2liBAVEitIUQUAsIAoCIliQKiJFHuXHAyIiIqAI\nPB5gjDHEOEz274+bezKTmUlmkhDAlb3WWcncem49+35lf3yFJfDKYvPmzYiIiHA4s72mcPHiRRAR\nE0mvxeOH2pi0quDQIeKaN2dxMLeJyJeIdpAYd/YBicHZh0sW/52IGpX8/ycRTSSi6WQZk/YLEbUi\nMU7nyEM/iMcDpgH7pr+lOCzT/8s2nufZX6k9ePCA8vLySKPR0F9//UXe3t4s7kwQBBIEgf0vl8tJ\noVCQIAikUChIoVCQXC4npVLJkgKk2Dm1Ws1+y+Vy+uyzz2j37t00cuRIatmyJduWtA3pt1KpZP/f\nu3eP2rRpQ6dPnyYfHx+2byKiWbNm0c2bN+ns2bNUVFRE58+fp/Pnz1OTJk1Iq9XSjh07KDU1lVQq\nFRERLVmyhMaMGWN27oqLi0mn01FiYiIdOnSI/Pz8SKfT0bFjxygrK4uOHz/OzicAqsvzdMrPj+S/\n/lr915XEmLR0EpMS3iSiT4nocyJqRkQ/ElEcic/POSKKJDHuzImIRhDRVCL6msSEhT+IyJ/EZAQv\nEmM8g0lMUjhApXGgTzp+JqKxRLSrnGW0Wi3l5+dbxJMREfE8Ty+88AK9+OKLdPToURo0aJBZQLh0\n3du0aUPdu3enUaNGma1vmjBTNsFm2LBhdODAAfr1118JAB0/fpzq169vs59ff/01tWjRglJSUuj7\n77+nBw8e0J07d8jFxYWIiBITE0mj0dCxY8fYOt999x0lJCQQx3G0YcMG6t+/v83tP/XUU3Tx4kU6\nf/58OWerFHq9nnr27EkrV660a3kionfffZeGDRtG7733Hg0cONDu9S5fvkxpaWm0Z88eSkpKsnu9\nmkTnzp3pu+++q00YeJzxCAnik499+8QvM5Mv4UMkinXeJMIeKhXsLNuGkegOAlla0noTYSaJsW30\nkJsUZyaly7u4uECn07E4qoSEBDRq1AhZWVno2rUr+vbti6FDh2Ls2LGYOnUqXnjhBSxatAgrVqzA\nO++8gw8//BDbt2/Hrl27cPDgQRw/fhxnz57FpUuXcP36ddy7dw+FhYUPNf4hNzcXGo0GmZmZFZZ8\nqgqmTZsGjuPw/vvvO7Te5MmTrdYavHv3Ljw8PLBz505wHIfTp09j48aNzFVkMBjQsGFDhISEgEhU\nhM/KyrK6j/feew88zyM5ORn+/v6stmnZ6x8eHo7ExES0U6lgCAh4KJY0qUrA4ZJ7vRcR6lOpuOy4\nkuWkygWS7EY0ifIXqSTGuDUi0ZJmuv2WRAit5j7bc0xlC71LoriDSLQEStMjiNCECMetbCeNRJkR\na/v4hQhNy3luTd2fZV3gHMdh9OjRqFOnjtl0ycLaqVMnKBQKTJo0ycwt7lXGgictHxMTA3d3dwQF\nBYHnebRr147pL8pkMpw8ebLc+z0zMxNEhBdeeAEymQypqals3v79+8FxHO7cucOmtWzZEtHR0eUK\n3Epo0aIF0tPTK3rkGIYNGwZfX1+7lz906BB4nrfQb6sIhYWFSEpKeqzV+yVLp1S2qxaPJ2otaVXB\nhQvExcdbpP03IqIpJEoN+BHRN0RU9jtqeMk6s0m0pEUQ0cySeT8SUf2S+RceUtcfd5S1lElyF6aW\nMLlczv5K02UyGf3www8EgFQqFXl4eFB0dLSZhcu0SdYypVJJSqWSVCoV+2vaJAuaJLOxZMkSeued\nd+jtt9+mQYMG2Z3l9dtvv1FERASdO3fOTMqEiCg7O5vu3r1Lx44dI7VaTenp6fTaa6/RM888Q0uW\nLKEjR45Q06ZN6dSpU9SwYUPq27cv7dq1i+7evWt1X0FBQRQSEkJHjx5lGaEdO3akzz//nJ1jmUxG\nOTk51KNHD3K/dIleMRiocSWuVzER/Vulol//+ou6kXmGZU2AI6KhJGZRS/iaxOfr65I2nETZDgkh\nJessIVGiQ2InCiJyJaJFJD6bRKJVO5aIOpOYMWpL6mMwmT/LRERbiGgyiVmpEnJItKLLiCibyOo5\nP0lETYnoLyvziMTr16tXL9qxYwf9/fffVFxcTAqFgtzc3MykJnieJ0EQSKVS0bZt2yglJYV0OtFu\nL5fLadCgQfT222+TtaFg2bJlNGnSJLp+/TpFRUVRSkoKnTp1iuLi4qioqIh++OEHIiI6ceIEJScn\nW+1ncXExqVQqAkDt27enTz/9lNatW0fDhg0jIiIfHx/KysqijRs30v3790mr1dJHH31EHTt2JDc3\nN5o2bRq99NJLVrednJxMgYGBtHPnThtnyRxXr16lsLAwunnzJvn6+la4bExMDHXu3Jm2bt1q1/Yl\njB8/nm7fvk1btmyx8BI8Lli+fDnNmDGD7t+/b/f7qxaPAI+UIj7pePCAWQqkL+AfSMzcPFfye1HJ\n1/+PJb+NRNhEBC2V1kUcRJaJA72JILMRO1W/fn0EBgbC1dWV1eyztlxtK9+C6EhzdPumMWKm9T6l\nTFQpqcDT0xN6vR56vR48zzNrhqQAHxwcjAYNGiAtLQ1KpRLu7u5o164dFAoFs6j1798fo0aNwoQJ\nEzB16lRkZ2djzpw5GDp0qFmfJEFTHx8fcByH1q1bs9JfaWlp4jJE+GPkSPzp4WG3ZekSEWZxHAt6\nb0uOC7xWtRGJFQL+z2TaQSotQXWQROsXUakVTIqTcyGxVuhoEpMSviZCBhFGkph56UligsU8Eq12\nZZ950zaILJ/lv0rW+Z/JtKlEeIcIH5bs19ZxrbBxf+l0OiQkJIDneYwePRpBQUFm97b0v7+/PziO\nQ8+ePWEwGPDBBx+w+S1btkRkZKTN+9fNzc0slurf//43ZDIZBg8eDBcXFwQGBqJu3brsXj9z5ozN\nV+U333wDIkJqaiq0Wi0EQcC9e/cAAIsXL2bliCZOnAhXV1e2njWBW1OEhYVh2LBhDr22PT098a9/\n/avcZfLz8+Hm5oakpCSHtg0AW7duRVhYGH7//XeH161JBAUFoXv37o+6G7WoALUkrYowfelHk+i+\n+KjMi/YtEoOlo0l0+7Qm0RVa3ov9QxsvZyLC4MGDcevWLav9KSwsxMmTJ7FkyRL07dsXqampCAoK\ngpubm10Zn6aZnqbB+I+aVP3TmmmCg63zrFAooFarWbYukSip4uLiwjJwiUTpBSmL1N3dHW5ublUu\nN+VCost9LxF+otIyUCCRdPxAYmLMMyQG9Jddv2wZpbKtrKvvKlVcBB1EeI3EAu3RJBKmgSRmShOJ\nhKyJybJ7SSRuRCJJk/om1f0USExa6EDi8zeCxOxRTckyMhJrj2aX9JWotHh8CIlEzpQUEokfX54k\nVkyoT4R9JJaQiiLxA215Sf/lJL43hpJY5kqqhNCMzIva3yCxwoKWxBqpc4iwVhDwJhHedXXFtSlT\nMLZ5c4vzn5iYyO6Lr7/+GkVFRSwZQC6XQ6PRmN1vZe+9zZs3w8/PD/7+/vjrr7/Y+2Xz5s3gOA6v\nvvoqfHx84O7ujuTkZJblfPbsWZvvypYtW4KIsHjxYshkMtSrVw+A6HZTqVSYP38+tFotpk6dytYx\nGo3w8PBAr169rG5Tr9cjOzvboXf2oEGD4O/vb3O+0WhEWFgYfH19K8wuLYuffvoJXl642AANAAAg\nAElEQVReFbqAHzW+//57EFGVaybX4uGjlqRVFUuWPBTLwHQ7iZFCoUDjxo2xb98+h7teUFCAkydP\nYsWKFRg4cCDS0tIQEhICNzc3Vj+wvIFekpvw9vZGVFQU0tLS0KZNG7Rq1QqNGjVCbGwsfHx8mA5b\nZWpQVrZVVIaptlXcpLgnngh1iNCGxA+MKCot02Tr3PJUaj0u286TWMC8I4n1LkH2kbQviRBDYlkl\nkEgWnyLRCkYkkiwvEq1jMWRe//OgSd8kkiYnsRD6IBKlPISSpiSR8Akly68lkXDJSJQTIRIJll/J\nMlFUKhHSk0pj0gJLprUhwmUSNRMbkJi52ovEklLTSSR+H5B1kiadr5tk+11RSISjRHieCKlxcWjc\nuDE4jkO/fv1gNBqxbNkydp3Cw8MtagNLHwByuRzffvstxo0bB57n8fHHH8PDwwNRUVFmcWHz5s0D\nz/PYuXMnYmJioFKpkJKSwt4J58+ft/q+kWKgBEFA//79QURYsWIFAGDw4MHQarWQyWQWNXM3b94M\nnuet1ufUarVsG/ZCymi8ffu21fnNmjWDk5OTWZycPfjrr79Qv359h2p5Pip07NgRYWFhj7obtbAD\ntSStqrh/H4iKqlaCdtfdHZNGjLA5eCYnJ6Np06ZWrSU8zyM0NBRz58616SKoDPLy8nD8+HGsWbMG\nw4YNQ0ZGBsLCwuDh4WFR5NwWaVKr1fD09ERkZCQyMzMxZswYrF69GgcOHMCNGzdw69YtnDhxAm+/\n/TbGjBmDrKwsxMXFwdfXF1qtllkCHwbpkiwBkqaY5Ir09PSETqdjA5mkrSaJ9FamP2WtaLbmP+nk\n0pUIv1m5v625+q5SxSTtFTIXigaJ9TYlS1ofEi1hwSQSlxZUWoz9E5N+Sa5KPxJJ3NMkyuYQiRbv\ncySSNA2JpMmZRFenjMRkB6JSC5oXifU/QaUkzdQq7lGyfUkz8QcSiaVTyflxJZHQSlbFZmRJ0hxp\n33Echjs749ixY8jNzWW6edbqbUoi0USEhg0bmtXdHTJkCGQyGbZv3w5nZ2ekpKSYEbWBAwdCEATk\n5OSgadOmkMvlqF+/PjiOg1wut0nUDh8+DCJCWloaPDw8wPM8bt26hdzcXEjvNmuQBG7LQqlU4pNP\nPnH4febh4YEpU6ZYTB8xYkS5RLM8/Otf/0LXrl0fWz00CRJZflzFdWthjlqSVh1YtQqQyaqFoBXz\nPNYmJCA8PBzPPPNMhYN9Wloa3nzzTUyfPp3FGpVdzs3NDT179kROTk6NnI7ffvsNhw8fxpo1azBq\n1Cg0a9aMibSqVCqzjDJbhEmKvwoNDWVF3JcsWYIdO3bg4sWLbEAxGo0ICQlBUFAQ4uLiMHPmTHz1\n1Vd48803MXLkSLRo0QIxMTHw8fGBi4sLFApFhfuvCtFTKpVwdXWFr68vIiIiUK9ePTRo0AChoaHQ\narVISUlBYmIiEhISEBMTg+DgYLNtSIK6tvTO7GnS8XEcB41GwwZoiWhKrSr7sLclk7lw8wMSLVl5\nRCgg0epVRPaRtP8jkVQNJDHb03S7RKLbVU9iPOfskv+lDMmDJn0yjUnjSbS4zSWRRKlILNaeRKJF\nbVXJOpLbVHJ3zirZrzsRLpr0oSxJiyWR7K0v2edvJBK7IpNlXijZ7/+o6iQNRChWKnE4Pp7FCI4b\nN87iuphmh7733ntWn+Onn34agiBg+/btUKlUFpnEaWlpcHZ2xr1799CnTx+WTSwRNVvvm6ysLBAR\n3njjDchkMkRFReHChQsgEgu5W8OJEyfAcRyOHTtmNp3neZw+fdrRVxT69etnIaS7ePFicByHzz77\nzOHtffLJJwgJCcFvv/3m8Lo1jaVLl9ZWGHiCUEvSqgPFxUCPHtVjSevSBSguxu7duxEVFYWIiAhG\nvPR6PTiOg4uLi6UoKcehfv36eOedd3Dv3j0sXboUMTExVgmJUqlEcnIy3nvvPbOv50eB27dv4+DB\ng1izZg3GjRuHFi1aIDIyEjqdzi5CZ0pMnJyc0LBhQ/Tt2xdz5szB+++/jxMnTthdwsVoNOLevXv4\nz3/+g88//xzLli1DREQEiMQAbL1ezyoBPKyKDKZuZE9PT2g0GgiCgPbt26Nv374YOXIkJk6ciGee\neQYKhQIZGRmsH76+vvD09AQRMYtlQEAAvLy8ylWsd7R/jq4jVQ0AiXFsvUx+DySxEsFVsi8m7SQR\n+pNIdORE6EqEn0v2E02ECSRavdyJkEki6SEifGVKUErW5Ur+9ieRWEWUTHMxWTaISl2mUv+IxKoi\njYkQQKUVG8qStF0kWuQGkxi/lk4i6TM9fhDhTRKteq9R9ZA0qW3VaDC8TPKIafPz82PB+7bQsWNH\nyOVybN26FYIg4KmnnjJ7XkJCQuDn54eioiImSSMJ7AqCgAsXLlh9ziS3p0QgIyIiEBgYCI7jbMZJ\npaenIzQ01Gw7RFThMVhDTk6O2bqS7M2SJUsc3tbVq1fh7e2N48ePO7zuo0BQUBB69OjxqLtRCztR\nS9KqCwUFQOvWVXuxZmYCf/7JNllUVIRFixZBoVAgODgY+fn5uHXrFvr06QOlUgmlUonY2Fir5WAS\nExOxYsUKFBUVoaCgAO+88w6aNGliYU0hEi1AAQEBmDhxYqXUuGsCRqMRN27cwL59+7By5UpMnDiR\nESilUmmzyoA1AqTVauHn54fk5GT06NED06dPx6pVq7B//37cuHGDfWEOGDAAMpkMu3fvtruff//9\nN65du4YjR45gy5YtWLhwIZo3bw4nJyeEh4fD29ubxehVN8Ere02lcmGmbvHg4GCzmCSe57Fx40bs\n378fJ06cYFl41dnGmdzjZV19TkToRCLR4qlUO01q66hUg8y0FZNYOq0difppRCIJOkoi0YkhkYxJ\n9UFNmxR3RiS6QwdJ58JG/xtRqds0xGRZPYkuWKlPROaJA62IcIZE96qSREKXSmJ2t+mxzCZCcxKr\nLzSj6iNpRhITDawdU6dOney+pzMzM6FUKplm35gxY9i8vLw8uLq6sizI5cuXg+M4REdHg0iMc7NG\n1I4ePQoiQkZGBnPJvv766wgNDUWHDh2s9uPGjRvgeZ7pEt4pqfhSWbi5uWHGjBk4e/YsBEHAiBEj\nHN5GUVERUlNTsXjx4kr3oyYhWSylShS1ePxRS9KqE4WFwJAhgEbj2AtVrQYGDhTj26zg119/RZ8+\nfRAQEIBNmzahuLgYRqMRixcvhq+vL/t6bd68uUVQMBEhLi4OixYtYgG5BoMB27ZtQ5cuXaDVaq2+\nxLVaLVq1aoW9e/c+lmZxSWRy0KBBCAoKwn//+182z2g04tq1a9i1axeWL1+OCRMmoHXr1oiJiYGn\npyfUarXdFjqlUgm9Xo+EhAR07NgR48ePx6uvvort27fjwoULFWZ/FRcXo1GjRtiyZQubtmPHDgiC\nAG9vb0yaNAkeHh7o168fm5+Xl4ecnBxER0fDx8cHc+bMQZ8+fdC4cWOEh4fD09PT6nWuiVYZS9r4\nkvvcmqvPQCLZuUaipernMs/GdBKD8kGiGO6vZeafo1JXZBiJ5C2qpAVTqSXNtIWTGCvGkZgh2tD0\n+Ei0ro2jUvkObyJ0L5nXmgivl6wfR6Klz5Sk2ZLm+J1EaZ7/WJlXn0R3KKh6SRqI8F8SXbfSdZPJ\nZAgMDHToWTMajWjcuDHUajXeeustC3HXK1euQKFQMDmHbdu2QSaTMYkYQRBw8eJFi+1K2Z5SSaqQ\nkBC8//77VpMHJJgK3J48eRI8zzt0LKbo1asXAgMDodFo0Lx580ptY8qUKejYseNjH4cmoUOHDggP\nD3/U3aiFA6glaQ8DH38MNG8OKBTlvkCLFQogIwOwo1gxIAbdJiYmolmzZmap7ocPH0Zqaio4joOX\nlxeeffZZZGRkmBER6SUdGRmJ2bNnm+kfGY1G7N27FwMGDIC3t7fVgVYulyMmJgYLFy585HEXt2/f\nhkqlQmZmJjw9PSsVkyLBaDTi8uXL2LlzJ5YsWYLx48fDx8cHRGIsn72ETiaTQaVSwdPTE9HR0WjZ\nsiWGDBmCwYMHQ6/X4+jRo8jLy8PMmTPBcRz69u0Ld3d3zJ07F4IgWCR5fPPNN+A4zkJ7ymg0onfv\n3iwe0TT4WxrYjEYj3nzzTSgUCri7u2PWrFno3bs3W87UmloTyQldS+53a64+kOjyfI1EOYpBVEri\n/o9EAicF5o8m0eqWR6UE7zkitC/Zj3PJOs9SKTGUyFNZkhZAohtTT6UkjyPRCvYuEcaSqJNGJLoi\n65FIIgUStdNal+y3LEn7mGxXHZhAYpbslZJlr5KYsJBEtiU4qqN9Wub4fX19HbaYG41GJCYmwtnZ\nmWWLLlu2zOx+5XkeM2bMAAAcOXIECoWCWckEQcClS5cstilZlKWkg5kzZ8LFxQWTJ0+22o/CwkIo\nlUrMnDkT27Ztg1KpdOg4THHy5EkQidblynyIfvbZZwgKCsLdu3cr3YeahHS+16xZ86i7UgsHUEvS\nHia++gqYOBHo0AFXXVyQ5+sL1KkDtGuHTV5eOLVwoRjP5gAePHiAVatWwcvLC+PHjzcjTPfu3cPg\nwYOZaGqPHj2wZ88exMfHs8HY1C0YEhKC6dOnW33JfPvttxg7dixCQ0NtZiB6e3ujX79+VSJJjsJo\nNCIwMBDBwcEIDg6u9pJPPXv2hEwmw8GDBy3mGQwGXLhwAdu3b8eiRYswZswYtGvXDvHx8fDy8oJG\no7HbQiclGXAcBx8fH/Tu3RtTp07FypUrsXfvXvj7+1t83f/000/w9fWFWq1GdnY2I1s5OTlQKpV4\n7733UFRUhDZt2oDjOAwbNgxFRUXo2bMnOI5Dly5dsHz5chARmjdvDo1Gg6FDh5qVi3Jzc0NcXBzi\n4uKqjaS5kCibYc3VBxIzL1OI8CeJumuxJLor04hwwGS5v4gwhUotZaEkErvbJfNtCUeb6rh5lKzr\nQmLcmhOJMW9Usk//ElIlK1kmikQ3ag8SXZzRJcRqQ8n/tkhaWd3Dj0q2vYJKi8SHkkgE75UsU0wi\ngZRi5uQkWt/mkWVsntRMSV1Zcii1Z4jQKTgYn332GVQqFbRaLTiOQ2xsLD6y8wMREJ+92NhYuLq6\n4qWXXgLHcVi/fj2bv379enAch3feeQeAKHXh5OQEV1dXSB96P/74o9k2582bByLR7RkSEgKO4zB0\n6FBotVqb/ZgzZw4UCgUWL14MFxcXu/tfFklJSSAiTJ8+3eF1f/nlF3h7e+Po0aOV3n9NY/HixbUJ\nA08gaklaDaFly5bYu3cv+x0eHg6tVuuwWKKEO3fuYNSoUdDr9Vi7di0ePHjA5hmNRrzxxhsICAgA\nx3GoU6cOdu3ahU8//RQBAQFmREH6kpVi0m7cuGF1fzk5OZg2bRoSEhJsBqFrNBqkpqbinXfewX0b\nrtuqon379tBoNGjcuLHDIpYVoVu3bhAEAYcPH67ytoqKirBt2za4u7tjxowZcHZ2Bs/zCAsLg06n\ns5vgSJlyknvT1DrRsGFDyGQyvPLKK4iPj2dq7m5ubjhy5AgOHTrEhG137doFQAwE5zgObdq0wfDh\nw0FEbBB1cnKCSqWyK6vY1jGoVCqr1jlrtSsfRrMmHD2vpA/aEvLFk2hFCyLRYnbL9NioNDatF4nW\nLgWJFryniLCFRMvaeRKzNt8y2XcwldYnrajqAJGla3Q6ia5XyaX7J4k1ftNJlCyxh6SV3a/UNnt7\ns4+tli1bYvv27WjRogVkMhmcnZ0xatQo5ObmVnhfGwwGJr0zZcoUppcmYdasWeB5nn3k3L59G15e\nXqy+qCAIuHz5Mls+OjoaHh4eICJs2rQJMpkMvr6+kMlkNrNOJYFbKWu7MujRowcUCgXatm3rsF7Y\n33//jbS0NCxcuLBS+35UCAwMRM+ePR91N2rhIGpJWg0hJSUFJ06cYL/DwsJAROjcuXOVvmxOnz6N\ntLQ01K9f32p20YkTJ9CkSRPwPA+dTodZs2ahqKgIS5cuhbu7OxucBEFgL1IfHx+MGDGiXDXqa9eu\nYfbs2WjYsCFUKpXVAVsQBAQHB2PKlCkWro7K4OWXXwbP8+jatSs6depUrV+EnTt3hiAIOHLkSLVt\ns0+fPpg4cSI0Gg2Cg4NZJtnw4cPx3HPPQa1WY/LkySgoKMDp06fxwQcf4MUXX4RcLoefnx8SEhLg\nbTK4WiNLtoiSdE08PT3Rv39/zJo1C2PHjgXHcZg2bRpb393dHWq1mpFAFxcXm/uTyWT46quv8Pnn\nn9skcM2tqN8TERZXAwGrbMst6cMuk2mHSHR1TiTCG2VI0x0qtbCBRCtWHxKTEFxJtMwJJBI606QI\nWyStmEqrDkiJEWVJ2r2S7X5fpu+FJLor36aqkbTCpk1x6tQpEBErIeXr64tp06ZhypQp0Ol0LEN8\n//795d7XhYWFCAgIgF6vx7BhwyCTycw+bJ5++mkoFApGxgoKChAeHs4+CAVBwJUrV3Dp0iVwHIdz\n585BLpdDLpdj4cKF4DgOISEhCAkJsdmHzZs3g0h0VTqK7Oxs8DyPw4cP4+TJk+A4zu4McAB49tln\n0a5duyfKIvXdd9+BiHDt2rVH3ZVaOIhaklZDiIiIwA8//MB+SwH7KpUKw4cPr1LgaXFxMTZs2AA/\nPz8MHjzYLIhewu+//46RI0cySYdOnTrh6tWrMBgMGDdunJmkh0qlYtYVnU6HAQMGWM3QMsWdO3ew\nZMkSNG/e3GYygmS16dChAz799FOH5D8OHDgAjuPw1FNPIS4uziymrqpo3749BEGo1hT6n376Cc7O\nzsxqJb3Qf/rpJ3h4eGD8+PFQq9UW52D69OlQqVQoKirC6dOn4ebmBldXV4wZMwY8z6NBgwY4ePAg\n1q9fjxdeeMEihtBa9m55cWf26qS1aNECHMdh1apVZu5R01bedQ8iwt1KkixrjUh0DUaR6EbsQKXV\nCyRipaFSlx+R6D5sXDI/m8SMyyQqTRy4bkJ6skkkYndLttWIxMoGIDEWLpYIviXrWyNppm5HNRE6\nk1h1wLT/piTtCxLdrLaO912qGkn7kQguTk6Qy+W4fPkyfv75ZwwaNAguLi4s8WjGjBmscoCHhwem\nT59uM4C/oKAAPj4+CAgIQI8ePSCXy83iZOvXrw8XFxdmnTMajWjUqBG73+RyOZo3b86sWN9++y2I\nCJmZmYiNjWX3zenTp1FUVGTWj927dyMyMpLFY/bv3x///ve/7XouJZesqZVOq9Vizpw5dq2/a9cu\nBAQE4H//+59dyz8uaN++vU0Nulo83qglaTUET09PVobk5s2bFtanjRs3VnkfeXl5mDp1KnQ6HZYu\nXYq///7bYpni4mKsW7eOxX/ExsZi+/btAMQXb6dOnczkIVxcXFjRbzc3Nzz11FN2xaDl5+djzZo1\naNeuHdPuskYWlEolEhISsGDBApv1SG/dugWlUolmzZrB29vbzF1SVbRt2xZyudzMylkdkGK6TLPg\nALH8TXZ2NpRKJV588UWzefn5+ZDL5ViwYAFefPFF8DyPjIwMJCUlQSaTWWg4TZ48mQ1UeXl5bABs\n3bo1CgsLkZubiyNHjmDdunXw8vJi59/Ly8shl2t5187UylbRNt6ugHg5StIkklNMohvSi0QLmTVS\nQyTKUcSSSOzcSHRr7qRSXTRTkvYHiW7OiSXb8iSxLJW0vV0l66iplHyakjRTstSbxDqotvqPkn02\nIdvHW5Z0mhLAimLSDpOYXeplch09PDzQp08fnD17FgcPHkTz5s0hCAKUSiWysrLQvXt3aLVa8DyP\n5s2bW61F+fvvv0On0yE0NBSZmZlQqVTM+m4wGBAQEICgoCCzD5Fu3bqZ3Uum97QkcvvJJ58woWU/\nPz9otVpMnDiRLffdd9+ZhVxwHIc9e/ZU+EweOXLELLlBQteuXREZGVnh+tevX4der8ehQ4cqXPZx\nwoMHD2oTBp5g1JK0GkBxcTHkcjkrVLxv3z42uCoUCrz11lvVahm6cOECWrVqhbi4OBw4cMDmcmfP\nnkWLFi3A8zzc3NzMvpx//vlnpKamsoGf4zjo9XoW56bVatGpUye747eKioqwadMmdOvWDb6+vjYH\nf57nodfrMWjQIBw6dAgGgwH+/v4ICQmBt7d3ha4YR9CqVSvI5fJqLYZcVFSExMREEFkquV+6dAk6\nnQ5DhgyBs7Ozhbuke/fu0Ol0jJSNGjUKarUavr6+Zu7ia9euISQkBHK5HHPmzAGR6NaU3FWmMBqN\naNq0KYgI0dHRLObw6tWrdhM0jUZTLZUJtFSaqVmdJE1qb1GppcwaSZMqDUSTKKvRwsr2JJIGEl20\n9YlwgUQiV0SW/ehCYjJAeSTtUsn6v1rZn/T7SxI12MojaVWxpP1JouXP9HpIVletVosuXbrg0KFD\nWLlyJaKjo1ncYevWrdlvPz8/LFiwwIx03blzB66uroiKikJycjKcnZ3Zx+hvv/0GrVaLRo0amd2T\nEyZMYH0QBIHV5DQVuS0rMWNadB0QLXXSPJVKVeFzee3aNSiVSnTr1s1i3rFjx8BxXLll9AwGA9LT\n0zFv3rwK9/W4YfHixVCpVE+Ue7YWpaglaTWAgoICsxfJn3/+iW+//Rb/93//ByJyKB7CXhQXF2P7\n9u0ICQlBz549rRYnlpCfn4/x48ezAsdt27Y1c80eO3aMuRekF2tERATCw8NZ6aHWrVs7JPpqNBrx\nxRdfoF+/fsyqJ1lkrFltVCoVevXqVW1kNjMzEwqFwkLioir4+eef4eXlBaVSib59+1rMHzBgAGbO\nnAlBECzEL69evcqSBPR6Pbp06WJWJFvCokWLIJPJUKdOHdy6dQsjSmq8JiUlYezYsdDr9WzZEydO\nMCtmx44dAYjnfdKkSQ6RK0EQ4OLiAl9fXyZSWtnWnkTtrodB0m6TaB27Xw6pkdqLJAbll0d6HG22\nSBpILP4+uJz+55JoFTtdZr2/SXS9rrRxPPaStLtkXkmhbJMsoWq1Gq1atcKmTZswYcIERv5DQ0OR\nnJwMhUIBuVyOjh07sg+HGzduwNnZGXXq1EFkZCQ8PDzYc3rx4kXI5XKL58HUkyAIAn755RcAYna1\ntf69/PLLZutv3bqVfehyHMcEbk1x7NgxRERE4PTp03B3d0fdunVtPrtOTk6YP3++zfnZ2dlo1arV\nE0l0AgMDzSpF1OLJQi1JqwHcvHnTbPA0hUajMdMbqm7cv38fs2fPhoeHB+bOnWszxkTCxo0bWSmq\nyMhIfPjhh2bzP/jgA6YjJpGn1NRUxMbGMlmJZs2a4eOPP3bohVZcXIzDhw9j5MiRiI6OLreMkSAI\nCA0NxTPPPIP//Oc/Dp+T5s2bQ6FQmMXQVBV79+6FQqFAXFwcdDqdRdLF999/D09PT/Tq1Qvu7u4W\n60tkql27dvDz84NKpTKrIXjnzh3UrVsXMpkM8+fPx5kzZ6DX65mkwnPPPYezZ88yi4CkpaZSqRAb\nGwsAOHjwIDw9PR2uXTpq1Ch4enriiy++cGg9W9a3PiRmVFY3SSssmX6bbLsHl9CjIWnXS/ojidmW\ntexFE2EImUuIFJBI7NpR1S1p/+fAdTO1tKWnp2P+/Pno0KEDqykbERHB3gFhYWFYu3Ytrly5ArVa\njYYNG8LPzw/+/v7sXbN//37wPI/Zs2cDALZv3w6e57Fu3TqzZ9rNzc1mX5YuXWr2vPz999/gOA7p\n6elmAremkMpUSQky5WXSd+jQATExMVbn7dmzB/7+/sxC+CRBKn9VmzDw5KKWpNUALly4gOjoaKvz\n0tPT0bBhw4feh6tXr6Jbt24IDQ3Fzp07K0xUuHDhAlq3bg2ZTMZiQsq6A+bOncsSDIjE+LUuXbog\nOTkZMpkMcrkcjRs3xoYNGxz+At27dy/L8tJoNMw1I33Jm77AOY6Dm5sbOnXqhM2bN9skokajERkZ\nGVAqlTh//rxD/SkPCxYsYAK1ixYtsmpF69OnD2bNmgWZTIa1a9ey6Tdu3GDu3w4dOkAmkyEpKQm/\n//47W+a9995jpcEuX77MsjTT09ORn5+PtLQ0pqkmZek6Ozujc+fOkMvl+Omnn9CqVStwHGdR0N30\nHEZGRlpMj46ORlBQED7++GNkZmaCiByudqBSqcwIG8dxaEmEE5UkQ7ZI2vckJgcUlUNqpDanhBSV\nnZ5OhA+r0K+qtteoNG4uhkRh3sJyjqcsSbMWk5ZJYsxeVSyg0n05dOhQJCYmguM4ODk5wd/fHzzP\nQ6VSoWPHjlAqlcjIyICHhwciIyPZc7927VpwHIeNGzciNjaW3a8HDhwo15WelpYGIirN9DQYgJMn\nga1bMd7PD8fXr0fh/ftQKpV4/vnnzZ45U60/lUplVvWjLA4fPgyO4yzeHTdu3ICPj49V3cQnAe3a\ntatNGHjCUUvSagDffvutTSK2Zs0au2Iqqgt79+5FTEyMhUvTFgoLCzF16lS4urqC53lkZmZaEByD\nwYAhQ4aYuTB8fHwwduxYpKamQi6XQyaTISUlBatXr64wq/PGjRvsK9605NNPP/2EWbNmoUGDBqxe\nqUKhsCABEpGoW7cu5syZgytXrsBoNCItLQ0qlQo5OTmVP4EmMBqN6N69O3iex/Lly1FYWAhfX18L\nC913330HLy8vdOrUCd7e3mz6xo0b2bmRgrTnzp3L5hcUFKBZs2bgOA4TJ05ETk4O/Pz8mHCthEmT\nJkGv17MyO35+fjh+/Dg4jkP//v2hVCrh6upqRqilNnDgQFbb0zQ7UyLCPM9j1qxZyM3NrXAgL3sN\nFAoF6tWrZ+G+ln6rSkjJVQdITAHZJmkvkVgvE+WQGqmtIfOYNJCYgOBDhCOV6M/j3qZWcO0caVLC\nUcuWLeHn5wciMWvbtEZsnTp14OzsjJSUFEbUpk6dyq696TMixXCabl/STgsODkZEQAAmEuG3Bg2A\n4GCA59lxGeVyIDISF2Jj0V8mQ0FJ6Mgff/zB9qVWq6FWq7Fo0aJyn2eNRoNXXzgenuwAACAASURB\nVH2V/X7w4AGaN29ud+bn4waDwQC5XI633nrrUXelFlVALUmrAXz55Zdo1aqV1XlFRUXgOK7aswvL\nQ1FRERYvXgydTofp06fjjz/+sGu9rVu3IiYmhsWoSMripsjLy0OrVq2Yu1Ky0CxYsABNmzaFUqkE\nz/OoU6cOli5dypIpJBgMBvj6+iIkJKTCkk+3bt3CokWLkJGRYaZqXl4B886dO2PXrl1Vji3Jy8tD\nVFQUVCoVy/Zas2YN2rdvb7Hs008/jZkzZ4LneWzevBlGo5FluUkDlE6nMyO/X3zxBTQaDTsHzzzz\nDHieR6NGjSxERydPngwiMWuzR48e0Ov1rKSVLeuZTqdDbm4uevTowdyf1mIBZTIZevfujb59+1qd\nV95gLggCzp49W+GgryXC80T4nMQg/fsm5CKPRBfhNhLjunaTJUkrJsJWIuiIcJLsI2l3SAykl7TT\npGLkaSX/20N8bpP1CgqPW7tMpYXmH0YZsICAAMTHx8PJyQkcxzG9RZ7nIZPJ0KxZM3avSkLMkvvt\nypUrVrf5wQcfoGnTpuhPhPthYXYd5wMifO/pCXzzDdMC9Pb2xsqVK+16x7Vt2xZxcXHs9wsvvIDM\nzEwzofAnCa+88kptwsA/ALUkrQawefPmcgM3g4KCMGjQoJrrUAlu3ryJAQMGwN/fHxs3brRbq+3y\n5cvo0KEDBEGAk5MTRo8ebTWg/9KlS8w1Ir20GzVqhPfffx+tWrViJCI6Ohrz5s3Dn3/+iczMTDg5\nOSEwMNDhkk95eXlYuXIlWrduzWK8pFR+awOURqNBRkYGli1b5lC8SU5ODrRaLXx9fVm25IMHDxAe\nHo5vvvnGbNlz585Br9cjKysLAQEBuHjxIry9veHk5IQOHTpAshZIL1KDwcAIXJ8+fXDhwgUEBgay\nLGBT/Pe//0VSUhI7vitXrrB6hESiNVMQBIvjlrLtZs2axaZFRERYdTt99NFHNgfnityekv6Wo4N+\nIBGSSawc4F1m3htUStKkmC4fEl16plUN3iXrMWnRVJpleYZEy1tMyfRBZH9SQyERppWQn1N2rvOo\n2hIHz39VmqurK/z8/CzuJXd3d+zZsweCICAgIABubm7Iy8vDjz/+iO3bt+Ppp59m5I6IoBIEFPXv\nj8JKHO/fWi1eVqvxwgsvOKQ/KcXOFRUVYf/+/fD19bUpC/QkICAgoDZh4B+AWpJWA1izZg2GDx9u\nc/6IESPg7+9fgz0yx5EjR5CUlISMjAyHAvGLioqQnZ0NDw8Ppully/J14MABhISEsJewQqFA165d\nsWfPHnTo0MHMVaLX620WWHYEBQUFCAkJAc/zTCdMJpPB3d0dbm5uFskJMpkM4eHhGD9+PI4ePWr1\nC3Tz5s2QyWRIT083c9tu3rwZaWlpFoNC9+7d8fzzz4PjOAwfPhwymQx169ZFUFAQeJ6HIAgsoPnY\nsWNwd3eHs7Mz9u7dixkzZoDneaSkpLBqBRLmzZsHQRAQFhaGH374gZX2kUSJrVWBkMvl8PHxQW5u\nLjIyMiARrcGDBwMAOx+ShUwul2Pnzp1WyVt5Omscx8HT0xNdu3atdhJgWjj9UbU/ifCCSZ9aU/Vk\nrIJKKxJUVzslk2FYv37suXI0nrAqzZaldfr06dDr9cy9Ln3YFBcXIycnB4l16mBzFY/7gUwGlNEo\ntAdqtRpz586Fn59ftcr91DTOnz8PIio3q78WTwZqSVoNYNGiRRY6P6Y4f/58hTo9DxsPHjzA6tWr\n4eXlhbFjx1qQgorw2WefISEhARzHITAwEKtWrbJpZl+7di2zdBGJFq3OnTtDGvwlshAYGIjJkydb\nraBQEYxGIxITE6HRaMwENnfs2IHevXszkiRlfvn4+JhVXTC1AHTo0AHr1q3DxIkTWXyYKYqLi5GY\nmIhPP/3UbPqZM2fg4+ODxo0bs7i5Ll26QBAExMTEQCaTYeHChTAajRg1ahQ4jkOrVq2Qk5PDdNBW\nrlxpts1Lly4hLCwMgiAwzabc3FyrfZeaZPHkeR7jx49nsgWenp5ITU1lLmdHsj5tLWsqpWI63Vbp\nMEeblhyLYavu9gOJCQdl+zVQJmN1OSvbvifCN9XZXx8fXH37bTPpnLCwMLRv3x6nT5/GhQsXMHbs\nWOa2rynyVtayq1AocPHixdLn6fnnq+f4NRrg448dem9kZWXByckJs2bNcmi9xw3t2rWzS6C3Fo8/\naklaDSA7OxsvvfRSuctoNBq8/vrrNdQj27h79y7GjBkDb29vrF692uF4jJ9//hndunWDXC6HWq3G\nkCFDyiV8M2bMMCMYPM/jlVdewfnz59GvXz9msfH19cXo0aPt+jI0Go2oW7cunJycyk09NxqN+Oqr\nrzB06FBEREQwcujh4YGgoCBmISxLeBITEzFjxgwWQ7Z7927Ex8dbkNIuXbpg4MCBIBKLlzds2BAc\nx2HGjBno1q0bvLy8cPHiRZYM8OGHH7IC1fXq1TNzwRqNRowfPx48zyMpKYm5YRYtWgS5XG61HJQg\nCOjRowc7BicnJygUCoSGhkKj0cDb25vpUxUUFFisLyVnSNfF1qBr6qYq20aPHu2w5EdFzbSoeU21\n/5KYRakvp1/jwsNxUaNxeNtGIuwlMatTQ4Q91dDfG0ToSYTY2FiLmESe53Hq1Cmbz8WlS5cwYcIE\nREVF1ZjlTaFQ4ObNm8CpU4C3d/Vduzp1ADtjbgFRy5CILGJlnyQYDAYIgoB169Y96q7UohpQS9Jq\nAGPHjsWKFSvKXaZJkyYWytyPEmfOnEGTJk2QnJyMY8eOOby+wWDA3LlzmZuxUaNGVrdjMBig1+sR\nFBRkIa8RGBiIzz//HJcvX8awYcNYnUovLy8MHjzYatF2g8GAhIQEODk5VcrUf/LkSUyYMAHx8fFm\nA5RUcaHsoMVxHBQKBVJTU7F161bmujx16hRz4apUKri4uMDNzQ0nT57ElStXWB1SnudRv359nD17\nlhHFsrp5x44dYyK5b7/9NgDR+mrqyrU28ElK6tLvjh07Yu7cueB5HjqdDvv27WP7WLFihcX6tuQ6\nTK2gthI0iEQrpD0kz9EWX0JCqm0gt9KKSKwU8CURFpIY91ZenySCryHC62S/te8ciWK1nMm2VCSW\n0MqvZN/P8Dxa2nEepXvXzc0NoaGhaNq0KUaPHo2VK1fiwIEDuHPnjtl9ePXqVUyaNAmRkZEPlbyV\nzdoFVaxrV0BijOI2k3lGEitQLG/Txq5n/+uvv4aPjw+USqWFBftJwqJFi2oTBv5BqCVpNYB+/fph\nw4YN5S6zatWqGpXisAfFxcXYuHEj/Pz8MHDgwEoH0e7du5clEPj5+WHp0qXsBdK0aVM4OzublXy6\nffs2mjZtapZ1mJCQgJycHFy/fh3jx4+Hv78/IwK9e/fG2bNnYTAYEBsbC2dnZ1y/fr3Kx3/kyBEo\nlUq4ubmhXr16zOKn1WoRERGBmJgYs1g6qWk0GjNyxHEc2rZtywhc3bp1Wfmb5cuX46WXXoJMJkN8\nfLzZOTYYDOjZsyc4jkNWVhby8/NhMBjQryTGyFRUWGo+Pj64d+8eBEFg54/neZw5cwYnTpwAz/MI\nCQlhrlKj0YjnnnvOYjtyubxSmYDlkbbqbLMqSWAqandIrLUZQubEqaJWNr7RmQhvR0RgC8fhJInJ\nCreJ8AuJEh/rS0iHYGVb2dnZ8PT0RDciHCWx6oA9fS/288OXYWHQEKFly5b48ssv2Tale8Hb2xta\nrRYcx8HHxweenp5WJWzKkjnJZe7r64vExEQ89dRTWLBgAXbv3o1Tp05hypQpCA8Pt2rRtbd5UeVJ\nGkis2+pHhN9Kfr9Bou6dsX59oIIEgtu3b8Pf3x+7d+9GixYtkJiYWOX3x6NCQEAAevXq9ai7UYtq\nQi1JqwF06NDBIl6pLAoLC8FxnF3Fy2saf/zxB6ZNmwadTofFixdbLdxuD27evIlevXpBoVBAqVQi\nLi4OPM8jMjLSpqv37NmziIuLM4t1atasGe7du4c7d+5g2rRpzOIjpfybKvVXFm+88QaLITP9Ir1+\n/TrmzZuHtLQ0uLi4MGISFRWFpKQkMyuT6SAXHh6O4cOHo0uXLiASrYQnTpxgsWllNZx27twJZ2dn\nuLi44IsvvgAAbNu2Dc7OzlCpVFZj0GQyGbZs2YK33nrLbHpOTg4KCgqg1WoRFBSEDh06wGg0Yt26\ndXBxcTFzR5qSLEcIlzWy+jAbT6XyGdXV/iLCc1XsF8dxGD9+vEVihUoQoC7pt+mytrYRGRmJcePG\nQSGXo3MJEfk3iSRS6u/fPI8fiHBIo8GdceOAkrCCspZX0+u7cOFCAGDxlS1btjQTcDUajbhy5Qq+\n+OILLFmyBAMHDkRqaiqCgoLg4uJSIXmXhG2lwuuBgYF2u7v/RVUjaSDCRBKzdH8l0TV9iQiQy4Fy\nxKuNRiNat26N5557DgDw6aefQiaTVajn+DiiNmHgn4daklYDSE9PZ1pa5SEwMJBl2j2OuHjxItq0\naYOYmBjs3bu30tsxGo0YPHgweznrdDp89dVXFa63c+dOBAQEsPWkGpkFBQUIDw+HUqlkdUCdnJzQ\nrl27cgvM28KAAQPAcVyFxZQvXLgALy8vvPrqq2jRooVVK0JoaKjVmC0pUNvb29tM2POPP/5A8+bN\nwXEcevfuDYPBgNu3b6NBgwYgIquuTY7jmDK6qSAtUWk5nZSUFLi4uCA4OBg7duxAYGAgeJ5noqFl\nm2Slq8iaJmXM1iRBk5ozEQ5YGbgr0/4iwit27LOiY23QoIGZ5Iu181fWjW4taF/aT+PGjdG9e3e2\nrCsREoiQQoSBGRnIOXeOCQb369ePEQvTGEYiMpNb8fPzg9FoxIkTJ+Du7g6tVlspRX2DwYBLly5h\n+/bteP7559GhQwfEx8fDy8sLGo3G4fviDao6SSsgQgQR6hBhsemy5ZTemz9/vkW2tlKpNKsM8qSg\nbdu2tQkD/zDUkrQaQEJCgl11IocNG/ZIpTjsQXFxMXbs2IHQ0FB0794dV69edXgb165dg1wuR2Ji\nIurUqYOUlBQW8zV//ny7YimWLVtmFvNERCzrsqCgAAsWLEBsbCwbBDMzM+2yZkquyC+//LLCPgwe\nPBgvvfQSfv75ZwQGBjKLmjTISlYHU7ej6V/TplAo4O/vD5lMBg8PDyZuLJWScnNzs1hPp9OB4zgs\nXboUbdu2hUT+eJ5nhNFoNGLatGmsqkFUVBQ4joOXlxe8vb3h6+vLkhukVl7d1PLIWnnzK7NNe5qG\nxDJOldHTktpNIkyv4rH6+PhYkC2e58vNul20aBH+97//YcqUKVaTVKT7iOM4dOzYEQ0bNrTYviAI\nyM7OxqZNm6DVauHk5GRWbPzw4cNsOy+//DLq1KnD1n/33XdhMBjQpUsXcByHoUOHVnsc0+zZs6FW\nq1FYWIicnBysXbuWWaDLtp0lx18VkgYSS2lxRPjJdPqUKVb7d/jwYej1eovwiKZNmyIlJaVaz8XD\nhpQwYE1kvBZPLmpJWg0gMDDQrgK3UoHsioqgPw64f/8+5syZAw8PD8yePRv379+3a72ioiJ4eXkh\nODjYrOTTnTt3MGDAAKhUKigUCjz11FNMKLa8bQUHB0OhUJhZq7y9vdmLqqioCK+99hrq1avHyEuT\nJk2wadMmswHp8uXL8PDwgE6ns+ta/fLLL3B3d8eKFStYwXc3NzfIZDIWR3bx4kW0bt0aRJZB+H5+\nfmjZsiXatm2LsLCwcslA2SBtmUyGDRs2wMvLCwEBAZDL5dDr9Rg0aBA4joNWq2U6cLNnz4ZEAomI\nacRJRbNHjx6NpKQks+2XJb+VbdaEdB9WG0SE76wM4uW1IhKzKuvZIF7lNYmQOTs7IzAw0Op1q6jN\nnDmT3YO//vorUlNTGfmyRtbKkz3x8PDAjh07MGTIEPA8j/j4eFy5cgXr1q2DWq3GvHnzIJPJEBYW\nho0bN5rdh0ajEdu2bYNKpYKfn59d5eLshaenJ4YNG2Y2rUmTJmb99/HxgbOzMwpbtYItkmZvrdUL\nRAggMRmjtcn0vDJ9AMR3TmBgIAsnMMW2bdsgk8meqOD7hQsX1iYM/ANRS9JqAFqt1qxgdnlQq9UV\nZoI+Trh27Rp69OiBkJAQfPLJJxUqfKelpcHZ2Rk6nc5q/J3RaMRrr70Gf39/pli/e/dui+UKCwsR\nEhICd3d3loVWUFDAiopLA0BYWBgTyzQYDHjrrbdQv359CIIAQRDQoEEDTJgwAYIgICUlxe7U+4kT\nJyI0NBQcxyE1NRU8zyMxMZGRz6SkJEaWPvvsMxarNmPGDOzZswcDBw5EWFiYWaxdeno6unfvXi5J\ncnV1RXZ2NrOqmOqlZWVlsUH79OnTZuLBgiDA2dkZkydPxt27dzFr1iykp6ejqKioWjMvTYlDRERE\njRA0qSlJjEnaT+axW6btARGukVjOqZ0JUXH02GJiYsymlSdDUh7RU6vVmDZtGsuGdnNzw5AhQ+Dq\n6lou2ZOqVpSdnpSUhEOHDiE+Ph48zyM8PByhoaEAxDJqiYmJTC+vrFUtNzcXKSkp4Hm+Qle/Pdi9\nezc4jmNlzHJzc3HgwAHUr1/f6jFJReCtkTR7aq0+IEIDImws+T+ZxMoTIMJ7fn5mfTMajWjfvj2m\nTZtmte9GoxFyudysRu7jDn9/f/Tu3ftRd6MW1YxakvaQ8eDBA/A8b/fXTVpaGho3bvyQe1X92Ldv\nH2JjY9G6dWt8//33VpeZPHkyZDIZ/Pz87Cr5dPz4caSlpYHjOOh0Orz44oswGAwoLCxkOma2NNiu\nXbvGyJNknUhOTsbly5cBiC/hDz/8kFlBOI5DvXr18Prrr7MsTFs4fPgwq1EYGhoKmUyGVatWoUWL\nFhgzZgwbdLp06YIVK1YwC8j06dPZNi5cuICQkBAIgoCxY8dizJgx8PX1tTkoS8K7ZacHBwejX79+\nmDRpEps/YsQILFy40Gy53r17M9f0tm3bmBWzqKio2kiZaUxeZRMJlCRaR3oT4Wki1CXzYHt7mysR\nupPoJltAhPlEmEFibU55FY+1Xbt2FrF/MpkMPM/bVQarT58+IBJd0dnZ2dBoNFAqlUhNTYWHhwcA\n4IcffkCfPn1YTVprzcvLC126dDEj2dI9MGDAALz99tssmcb0eVu3bh2USiW8vb2xatUqtq5kVXvl\nlVdYZQx7Ra1v3bqFXbt2Yf78+ejcuTOio6Nt3rO2mhQTaI2k2VNr9WUSibe0zmkSM0b/S4TBRGYS\nQK+88goaN25cbhJUkyZN0LBhQ7uO/1Hj3Llz4DiuNmHgH4hakvaQ8dtvv8HV1dXu5VeuXAm1Wv0Q\ne/Tw8Pfff2Pp0qXQ6XSYOnWqWVHjbdu2sTqdzz//vEPbzc3NxfDhw1kwskqlgru7u90DyNGjRxEZ\nGWmmvN6mTRtkZWWB53msWbMGO3fuRIsWLVhMV1xcHBYtWmRRBWL+/PngOA4uLi5QKpUICgrCtWvX\ncPDgQUb4eJ7Hhg0bmFUiMjISXl5eAERyOHr0aKaPdvv2bVy/fp0N7rbiddzc3JhOHJEo8yFZDMsO\nhGV/79y5k/X/3Llz8PT0ZGKmI0eOrBaSVhlLktR0RJhLhK9J1D8zLXBeRGKB8M+JMJYIimroqymx\nKm++VDrL9JzbWjY2NtaMUGk0Ggsil5KSYnZ9Xn75ZRiNRhiNRsyZM4ddz9GjR7OQh3PnzqFr167l\nkt7o6GgLF6IUi6lWq1G3bl1wHIfExEQ2iOfn5yMzMxMcx+Hpp59GQkICW3f9+vW4cuUKqxn72muv\nYfv27XjppZfYsjqdDgqFwm4SJvXHxcXF5r0yoOSvVJNVaodL7oXyaq2eL7mPfiZzcjeZCD0EAV0a\nNmQWxaNHj8Lb27tCQrN582YIgvBEuA/btGmDqKioR92NWjwE1JK0h4yrV68iKCjI7uUlKY4zZ848\nxF49XNy6dQuDBg2Cn58f3n//ffz4448QBAGxsbHo3LlzpV96+fn5ZgH0cXFxZgTEHmzcuNGM7CiV\nSgwfPtwss2v//v1o27YtnJycmBzCc889h+TkZDa4cxyHESNGwGg0ori4mOm2EYkB4QqFAoGBgdi3\nbx84jsOOHTtw5MgRpku1fv16GI1GTJ48GRzHWR2E27VrB6PRiJdffpkVipcGRScnJ6SmpmLSpEnl\nWlukZaUKEmFhYfjggw8wb968aiM7lU0KEIiwiBwTps0hwvBq6ndVjtGUnJQle1LyCBHhrbfeQkZG\nBurVq8eIUFBQENumRqPB5MmTUVRUhOeffx5OTk5M6mLIkCHsI+HkyZMsOcQaASISM0sjIyMt5vv4\n+GDLli3MujV69GhGDleuXMniQOPj4+0+H5KFTnoWpGnu7u6Ij4+Hh4cHK0MlkVsp0cHWMSTHx+N7\nB+4Fu1vbtrhx4wZ4nseqVasQFBRk13vDaDRCEARs3LjRsRdVDaM2YeCfjVqS9pBx5swZ1K1b16F1\nAgICMGTIkIfUo5rDsWPHkJSUBEEQoNfrER8fj7y8vEptKz8/H76+vvD29kZeXh7OnDmDZs2asYFh\nxowZFbopAeD06dNwcnJCYGAgnn32WTOC4+rqigULFlgcQ3p6utlgwnEctm7dCkB0q+r1ehCJ1iQp\nS2/SpEkAROmL6OhodOvWDRzHoXXr1igoKMDhw4fh5eUFQRAs9Mg8PT1x/fp1nD59GqGhoWxgXbdu\nHTiOw969e7F48WI0a9bMbF1TyYfyrESenp5VUoyvjhi2OCq1kDja/iIx7mwWEVaTGIO0kQiriDCV\nCEmV7JM1eZPY2Fiby5vG/JkSImlb0r3eo0cP1KlTBwCY251ItI5mZmbCyckJcrkcCQkJzOK6fPly\nuLu7QxAE9OvXj21LoVAgNjbWajyaROKbNm1qlbg7OztbXa+8plKpLLJWXV1dER0djfbt2+PZZ5/F\n3LlzMXLkSKSkpJjtV61Ww9nZucL7RaPRoFmzZvD09MRrlbwnbDaOA0rKI/Xr1w+CIOBf//qX3e+d\n1NTUxz78ZP78+VCr1U+Exa8WjqOWpD1kfP3118jIyHBonSFDhiAgIOAh9ahm0aBBA0YI+vTpg7t3\n7zq8jfz8fPj4+ECv11uQvPz8fIwdOxbOzs6QyWRo166d1XJRAPDuu+9CJpMhKyvL7IVmMBgwePBg\ns8HI19cXW7ZsYaKfkpyHqdVLck2qVCoWyO/j48Nqeh48eJANVq6urti9ezcKCgqYRaSs9Yzneaxd\nuxZ5eXlo06YNOI5D48aNcfPmTdy+fRtyuZyRv/z8fDPyyPM8oqOjGUkrq9Elk8mYZbCqBKuqrS6J\nWXjVOhibtCISlfqfJ4JTyT5tuZGlVjaYXTpPLi4uVknG5MmTzX6r1Wq23NixY83uu/HjxyMwMBCA\nqL+1ZcsWCIKAli1bsszkjIwMdv+1bduWueJWr14NT09Ps6zhffv2YdmyZQgJCamURp1MJmPxg0ql\nEv7+/vD09DSLKTS1eDk5OWH9+vVMv69evXpo1KgRPDw8mMSMXq9HbGws/P39rZ4vQRDQpEkTbNy4\nEQaDwaol19nZGbcvXsSPSmX13Q9t2gAlz/qCBQvAcRyeffZZu989GzZsgFwuf6wJUG3CwD8btSTt\nIWPnzp3o1KmTQ+ucOXPmiZHiKA8TJ06ETCaDTqfD9u3bMW7cOHh5eeHNN9+0u3B7Xl4e9Ho9fH19\nkZ+fX+6y69evZxmTUVFR+Oijj9i8sWPHguM4pipuC7m5ucjKyjIbpCRXY69evdCyZUvcuXOHubRM\nLVKurq4sMSEvL48NepLI6OrVq6FUKq3G5LRq1QoGgwHZ2dkQBAE+Pj6sTBYAxMfHIzQ0lLlITfvn\n5eWF3NxcdOnShfVn/vz5aNq06SMhYeU1LYmxRdU2CFfQzpKYQFCeNcdawoZpSTLTda3FpZlKpVy4\ncMHinlqwYAHc3d0BiJner7/+OlQqFTZv3gyDwYCZM2eybE6ZTMYygb28vNC4cWPUqVPHIr5N2q89\npNvaukTESpMREerUqYN169bh8uXLmDBhAiP9ZSsnmK6blZWF5ORkmzFmfn5+GDt2LC5evGhxTnbs\n2GFGCl1cXMQC6wAMH36IfI6r8rX/3dUVKLkex48fh5eXFyZNmgSFQmERa2oLkstTspw/bpBkm6qj\nDF4tHk/UkrSHjPXr16N///4Or6dWq5/oIr8fffQROI5DYGCgWcmn//znP8jIyEBSUhKOHDlS7jZy\nc3Ph7e0NPz8/u1+qAJCTk8OSArRaLXx8fCAIArZt22bX+tu2bYNCoWCWgrIDukwmQ3BwMJYtWwaO\n45horeT2NK3deeDAAVy+fBkxMTHgOM7Czejh4YFffvkFn332GQvGfvnll836M2fOHCZG6uzszPbH\n8zyUSiXy8vKwevVqUZHe1RUqlQorVqzAvXv3zGQwyhKOh91UKpXF8b7zkMhYeS2fxKLnjtTitFYS\ny5pYrel1tGVt2bRpE1QqFX799Ve4urqiRYsWEAQB0dHRiImJgZeXl101L01jv4gIer0eI0aMwKRJ\nk9CiRYtykzfkcrlVa2LdunXx4osvMoteYmIifH19ze57UzewUqm0mUChVCqRkJAAjuNs1vndvHkz\ns7alp6ezj5ayFUcM8+dXusA8iPBfQUBHIly/fh2//fYbQkJCsH37dhiNRnh4eDhU27J+/fpIT0+3\ne/maROvWrREdHf2ou1GLh4hakvaQ8frrr2P8+PEOr9e4cWOkpaU9hB49fFy6dIkJZw4fPtxCO624\nuBgffvgh/P390b9/f/YFbYrc3Fx4enoiICDAIYJWth/SwCUVKc/JybG5vNFoRO/evUH0/+xdeVxV\nZd7/neXulwsXuHDZd0QgEREXQEFFyV1TXMBdyiW31Ewro3LLrLFFLdManZzG1DZNK9Mx38xMx5xy\ncnzNSMcxIyMzc4iY6/f94/A83nMXuCg0zRu/z+f5KOc855znbPf5nt/yrC+QzgAAIABJREFU/RKv\nBi0pKYHD4cB9993nJprOPAAGgwEzZszAuXPn3Piz2OTrOsELgoDVq1fjzJkzyMzMhCAIGDhwoNu5\nnjx5kleTiqIIs9mMhIQEfvw9e/bgxIkTkCQJISEhWLVqFVq1auXmHcqOisIEjQaziHCf0Yh+pMgq\n/VKAjUjhubqZifdmmoMIzzcwPj8/P49i8/fff7/bMtcwY3x8PLZs2YLy8nKUlpaiS5cuSEpKQmBg\noFtf57xB53XMMxYREcFzx/z9/bkUGnuWU1NTVbllgiAgODgYWVlZ6Ny5MwICAryeY0JCglfvGBsL\nKzTYunWr2/PsqQUFBfEPrszMTHTs2NHt3dq4cSPsdjtEUcSAAQNQWVkJQMkTc80DZVazejXOiGLj\nvaeShONPPglZlhEREYFBgwZxNRJA+YAURdFnuor169dDq9X+6kKerGDgv4nLrcUaby0grZnt4Ycf\nbjTlBAA8/fTT/5VUHNXV1bBarQgPD0dubm69yfxXrlzBPffcg6CgIDz66KO8b1VVFYKCghAVFXXD\nId89e/ZAq9UiNTUVV69exebNm7kkUnx8vNsP2+nTpxEWFgadToegoCDo9XpeAfbmm29ygPToo496\nFJnOzs6GJElITk7GhAkToNVqveYzZWZm4qeffsKYMWN4WMkTePz00095OMrPzw/t2rXjHjNJkjB9\n+nTU1NQgMDAQNpsNxcXFKC4u5seJJMJzFgs+FgR872EyqyTCPlJkdExU/0TcVC2BCMlEiCBCXyIc\ndBrP7+v6/I/LOMfSdVLSsURY5LTuAhHiiPCyD5P3v0nhTfM2NueqTAaQmA6lp/6uz4AkSdwzZDAY\noNPpVCBMr9dDkiT4+/tDq9Wiffv2WL9+PY4dO4arV69i2LBh8PPzQ3FxMTIyMtw8uez/LNS4d+9e\nvPLKK5xUOT8/HxUVFfj5559V/Ge+jJ2NPzk52Wdva3h4OE6ePImIiAjo9Xpe2OLsIV+7di1sNhsk\nScKQIUN8ps0BFB3b3IgIbJMkfOvD/UVkJDBlChYvWABBEDB+/HgQKR5HV5LqpKQkdOrUyadxOBwO\nSJKE119/3eex/xLWUjDw27AWkNbMNmfOHKxYsaLR2zEqDl80P39NlpWVxaVymORTQ/a///u/6N27\nN1q1aoUtW7YgMDAQMTExNwzQVqxYASZQ7mqnTp1C7969IUkSzGYzpk6discffxySJHHtzMzMTFy6\ndAm1tbUYPHgwiJSk5h49ekAQBIwbNw6vvfYaD+04T1zOXgxX75lOp0NcXJxqUpw7d67bGL/55hv0\n6NHjOtiKjMTWrVvx008/8XBmSkoKACA/P59X0bH+GlLIW7/2ZWKra38nRXanucCZX92/jKj0GikM\n8zYi7KfrIC2WFKZ4Z640byDtCimVnI2pCPyabrz607XpdDrV/TeZTIiJiUGnTp0wYsQIPPjgg3j9\n9dfxz3/+E0SEs2fPIi0tDcnJyTAajbDZbLDb7dzbyjxpqampGDBgAO6//37s2rUL58+fx8SJEzng\na926NQ9PTp8+HdXV1dizZw8SEhJApFTvOtPM1Ne8FVS4hnJHjx6N++67DzqdDjabTfUc/+EPf0BZ\nWRm/JrW1tXjqqae4F7GkpKTRVd3vvfceDAYDBEHAd999hwx/fzyh1eJ1IlyOjsaPgYH4kgjfREUB\nxcXAokVAnarLt99+y68nG6NrMdGRI0cgCIKK4LY+a9euHQoKChp1Ds1t4eHhGDly5H96GC3WzNYC\n0prZJk6ciOeee+6Gto2IiHDTvfs125QpUyBJEqxWKz7++ONGbXvt2jVs2rSJi1J7SjZuyBwOB4qL\niyGKIh5//PF6+9bU1GDu3LncU6XVaiGKIh566CEACvWG1WqF2WxGYmIiz1E7ePAgrl27hoyMDE5e\nm52djfPnz6Nt27YeJzxBELBy5UocOXIEsbGxvGqTSffodDrk5+dj8+bN3LvGcrnKyso4K3q3bt2g\n1Wqh1Wpx8eJFrsvpDPr6ZmZi/w0mXf9EhI2k8Jc1NUgbTGqQxto6InSm6yBtLBH6kBKarA+k1RKh\niBQVgcae5856xinLcoO8c+yZWbp0KVasWAEiQkREBHbt2gWHw4ETJ07gmWeewcSJE5GTk6Pi0GP3\nSaPRICwsDHPnzsW2bdtw8eJFzJw5s96q7mnTpsFiscBqtUIURYSGhnLgxv7VaDQc9ImiiA8//BA/\n/vgj97o05p7Fxsbyis4uXbqgqqoKV65c4R8rztXFERERkGVZNZ5x48bhxx9/bNQ7XFVVhZKSEv6B\nE14n51RZWQmLxcL1cffv34+HH34Yoihi9+7dbvtxrZzW6/Vufbp06cIJbhsyVvTza7G//vWvLQUD\nvxFrAWnNbEOHDsWWLVtuaNtx48bx0v1fu23atAmCIMBms6mqKn21yspKBAQEIC4uDuXl5QgMDMQD\nDzzgcz7alStXkJKSAp1Oh3379jXY/+jRowgICIDRaIRWq+WTS1RUFLp06QJBENCjRw907twZRITh\nw4fzsMKiRYt4/xEjRuCzzz7zKpLOQBqbNHJyclRJ1bW1tVi1ahXn13IGAkVFRbzfU089xT0DW7Zs\nwYABA1T7z87ORqzBgMM3AFpc2xYiSE0I0IgURQFPIK2SFNmnf9F1kPb3ur7xpDDLW4gQQoQJRCgh\nBaS1JUKih7HnE+HFuv9frOufXNdSifAcEX4kQpaHMZpMJjdA5dosFgv3IpWVlXHFBlEUVR4xk8mE\nuLg4dOvWDdOmTYNWq8WKFSswfPhwpKamIiMjA4MGDVI9kzNmzPD4vtfU1OCNN95AfHw8dDqdGwAx\nGAy8WKVHjx6oqKhAhw4d+Hh8KUpgz5xrPpsgCBg5ciTCw8MhyzIWLlwIANi5cycsFotHgXmmu+uc\nB+ar5eXlqULEubm5fN25c+dgMpkQEBAAWZZx7NgxjB49GhqNRiVFd+3aNZX+LSvocaVGYQS3Gzdu\nbHBctbW1EEURb775ZqPPqTmspWDgt2MtIK2ZrbCwEO+8884NbXv06NH/CioOlrgeERFxQ/l3Fy5c\ngL+/PxITE3le2tmzZ1FcXIyYmBi88sor9Qq3nzhxAhaLBXa7HefPn2/weOXl5RBFEeHh4RAEAYMG\nDUJtbS327t3Lv+CZALtGo+E/7jU1Nejfvz+IlBCQxWLBqFGjQERuVYwWiwUnT57EhAkTVMslSUK3\nbt24KPymTZu4d4B51BhlAiM+ffDBByGKIkRRdAODISEhivajVos/m0w3DdBYW9rEIG2NF5BWXbe8\n0gmkoW7ZVLruPXuOCIOIcAsRokgBbpGkDou6grThRJjn1OcUKdJBB0kR7HYeX0Mi60zSyfVeMoAi\nSRK6du2KkydPeswRstlsKC8vx5QpUxATE4POnTujsLBQ1WfatGmIjIzEnj17MH36dBU5rCzL0Ol0\nsFqteOKJJ7gH5eDBg8jKyoIgCAgICPBYoOI8Xn9/f/4seTvX4OBgtzw8s9mMkpISaDQahIaGYv/+\n/aipqUFqaqrb9pGRkdiwYQO0Wi3i4uIapSf5j3/8QyX8ftttt6nWV1RUcN5BvV6P06dPIycnB2az\nmee7rV69GkFBQRAEAdHR0bBYLCgsLAQRueV+jh49Gv7+/j7ldbVp0wY9evTw+Vyay1jBgC/gssX+\n+60FpDWzZWdn46OPPrrh7fV6PdasWdOEI2pau3r1Kvz9/RESEoKBAwc2Oon1/PnzsFgsSE5OVkkz\nMdu7dy/S0tJQWFjokYNqy5YtkCQJOTk5Hrd3tsuXL/NKysDAQGi1Wu71W7hwIURRRFZWFiebZTQM\nHTt2xNKlS2EymWA0GpGYmMgFxT1NditWrMCOHTv4MZYuXQpA4Yby5KnR6XQYMGAATp48iZdeeokn\nX2/dutVNP9LTtunp6biLlBBgU4G0b4jQsZ7jNpbK4xnyDNL+TkoOXQ25g7RQIlTQ9XDns6QUHAwn\nhajWRoT7yTtISyXCNpf1X5FSQHCQFA+Tp8pNTy07O5vrpbLKyrS0NADgCf71TeDx8fGYOHEiFi5c\nCJvNxr20Bw8exN13341OnTpxoC9JEsLCwtCrVy8sW7aMc++1adNG5X37+OOPMXXqVLRq1corqa0s\nyzzM6ewt3r9/Py5cuICZM2d6lWpypeIgUnRCc3Jy+DOg0+l4/qRGo1GB3TVr1iA9PR2SJOGJJ55o\n4JdAsZqaGhgMBvTr1w8RERGYN2+eW58TJ05Ao9HAz88PZrMZ//znPxETE4OIiAgcPnwYwcHBePvt\nt3HgwAHU1NQgICCAU5QEBASofqOqq6uh0+lw7733Njg2xm/3n7bFixe3FAz8hqwFpDWzJSUl3VB+\nFbNOnTqpXP6/NsvIyIDZbEZqaqpKUN0XO3fuHPz8/JCSklIvwPr555+xcuVKBAcHY/bs2TwJed68\neRAEwSeKk927d8NgMMBisUCSJKSmpuLixYs4f/48n+SmTp0Ki8UCi8WCPXv2YNKkSRgyZAj3KhgM\nBgQHB/Nwk+vEaLFYUFFRgbZt23IPnWu4tqKiwqNXxs/PD3PmzIFOp8PkyZMBKF/MrhWHHoGaKGIz\nEbqTEh5MIkInUhLyo0jJwXIFYQuIUEqEL0kJbTqvW0qEDnVgp77jukr+eAqrsUn+cS8gbREpotnw\nANIeIsKQumWriNCVlKT/RaRUaY4gQjARdngBaXNJAXJLifAxqb1uX5NvFCQsh/D48eOcnoWdF6u+\nZqAlIiLC6/OXlZWFLl26oKioSBUaFUURISEhKCgoQFZWFsLCwrzuIywsDOnp6WjdujX/gJBlmV/j\n0NBQpKamqsKh7Bm12WxYu3YtTp48idzcXAiCgMTEROzevRv/+Mc/cMcdd3gF3q6heHb+RqMRsiwj\nPDwcWVlZmDlzJkRRRExMDO8XGRnJPdcdO3ZskJC6uLiYe7b+/e9/e/1dOHbsGFfRCAwMxLlz52Cx\nWKDT6fCnP/1J1ffw4cMQBAFz5swBEWH06NGq9Q899JBPBLc1NTVec+B+SQsPD0dJScl/dAwt9stZ\nC0hrZgsJCfG5ytGTPfXUU79aKo6ysjLOkM6+9n21s2fPwmw2Iy0trUEPGLOvv/4a48eP5xI0kiT5\n5PKfNGkSiJTQoCAImD9/PgCF5kSWZSQlJaFv376cq6ympgZfffUVDAYDNBoNIiIi8M4773jNV2JV\nfqxoISUlBZ999plqDJcvX0b//v355B4bG4s33ngDtbW1uPPOO1XhpeDgYPTt21c1abLKU0/Hn0CE\nMCK86QRCXiFCIBHuIsJIF2B0jQjRRHjXA0h7kRSgd7GuxXgBL6GhoSom+4Y8a1NcQNo1ImwlJfx4\nxAtISyCClpRCBkMdaBvtBNImEmFX3Xme9gDSHKSESfNJ8daFkJIbx8Bajpex+vv749tvv+VhwZCQ\nEMyePRuvv/46P1cWhqytrcWMGTPAPElsoj916hQWL16MwsJCFcgxm80QBAFpaWmIiYlReUMmTZqE\n2NhY/vfJkycxZ84cpKenq0Adew6sVis6dOiADh06cO6zgIAAtGvXDqIoYtmyZQgKCuJFBqIowmKx\noLy8HKdPn+ZFAbGxsdixYwdOnz6NMWPG+CwdJkkSCgoKQKRwpR08eBCnTp1CYmIiJElSkeA+8sgj\nsNvtMBgMXsXNjx8/DkEQfKa6+PDDDyGKIgwGA8LDwzkAHjp0qFvfBQsWQJZl9O7dG0SEI0eO8HWM\n4HbYsGENHjM9PV2VK/pLG1Oj8SWto8X+f1gLSGtm02q1N5VTdvXq1V8lFceGDRs4w/3evXsbte2Z\nM2dgMplwyy23NNplX1lZiZCQEIiiiDZt2uDo0aNe+54/fx7x8fE85BMQEIAjR47g8uXL6NChA0RR\nRGlpKQICAmA2m7Fr1y4ACoBkOS3z5s3D0aNHvQK0sLAwaDQaSJIEPz8/bNq0STWG2tpaTJs2jeec\nWa1WrFu3zg2YLl++3M0L4dy8VeZZrVasrwNIrlxS54jwOSkcaM4ksu+R4mFzuIC0vXWg7EunvveT\ndy9ZY1qcE/BqRQQ7KZ6/Q07HcgVp5+pAGNWdw0m6Xt35ECmUISDCvaTogV4lQh4RXnK5DiClWOBl\nUjxrz9Yt603kFupzDlmmpaWBiFBUVISMjAzU1NSASPF6sudhz549nJOM5WuxfQYEBKB9+/aYNWsW\nevbsicTERLz//vuQJInnpjnbsGHD4O/vj4yMDB761Gq1KvWD2NhY9OnTB+np6dBoNBBFEcnJybj7\n7rt5ntr06dNVXr2tW7dyAmSmaqHT6TBp0iScOnUKvXr14uog27Ztw4kTJzBkyBCP91GWZTeRdkEQ\nuPe4qKgIV65cwbJlyyDLMk/aZ141BgKHDBni9g4kJCQgKyvL9x8DKPq4giBAlmVotVrs2rULoih6\nDF+mp6cjMjKSh0mdf3t8Jbh9/PHH/6MfzYWFhZx+p8V+G9YC0prRqqurodVqb3o/ERERKCsra4IR\nNY0dP36ce9CcJZ98sYqKChiNRmRkZDQaoB08eBAGgwHx8fGoqqrCunXrEBoaijvuuIMn4jPbtGkT\nNBoNAgICIAgCbr31VtTU1ODll1+GXq+H3W5Hnz59IAgC+vTpwwsW7r33Xu5B+fOf/4yhQ4d69BR1\n6NABq1ev5st1Oh2WLVumOqeVK1fCYDBAlmUYjUYsXrzYIyXBqVOnVJJNvsg3tWnTBm+99RaCg4Nx\n0mJBNilJ9etJyeNyBii5pFBrsL/LSMnpghNI+5SUfK+/umz7+5sAZq5trwfw5K05e91AiudsgNPf\na0lRMHDe5hop4O8AKaB0h4f9zibCnQykORV7iKLolm/03nvvgUgJj7GJmUldOXu2nD1LPXv2xM6d\nO90AyPz582G32/Hll1+CiHDPPffAZrPh/vvvd9O/ZABNlmXccsstKC0t5QUrDCT27NkTf/rTnzy+\nQzk5OcjPz3dbfuTIEXTs2JErWBiNRkiShMGDB+P48ePcexsaGorOnTtDkiSvz6Fer3dbl5aWxkHg\nY489hsrKSmRlZUEURVXV6L333guTyYSgoCD+kcXepRvxEK1atYpfr3bt2uGFF16AIAj4/e9/r+pX\nVVUFnU7Hr+XgwYNV630huGX8lb5UkDe11dbWQpIkvPjii7/4sVvsP2ctIK0Z7euvv0ZISMhN72fs\n2LG/GiqOK1euwGKxICgoyKPkU312+vRpGI1GZGZmNhqgPfPMMxBFEf369VNt+91332H69Omw2WxY\ntWoVampqMGjQIO7J0Gg0+P3vf4+amhoUFRVBEAT0798fVqsVRqORh14++eQTREZGQqPRoF+/fsjN\nzYXBYHCbiARBwAcffMCrxQRBwOzZs1FSUsIn79zcXB5m0ul0mDZtGr755hu3c3I4HFi6dKlq386S\nT96aLMvIy8tTAAMprPvfE+GBOqBGpCTNv1IHRtYRobDu/z8RIYAUDxsDaSIRWpOS38WIZVnbR40v\nEvDWZtwESPuhbnzv1f19kZQQ7y66Htp8iJQQpoMUz1kwXedXAyl5aLeQQjHyPSleNSKFHLaqqgpE\nhHfffZffn8uXL6tAk7M3kd2jpKQkPnGbTCYsXrzY4/O7evVqmEwmXqjAvGNMC5NdY1mWMXr0aMyc\nORPt27fnoXRW1fv+++83+K7Y7XaPJMnMzp8/j0GDBkGSJBURcm5uLg9fMjD4/PPP48MPP+RUNL60\n9u3bQxRFREdH48iRI9iwYQP0er0qPB4VFYWCggKIoohZs2ZBr9ffEGXHDz/8gFatWvGQsyRJ6N69\nO+677z6Iooj9+/er+r/22msQBAE9e/YEEeG9997j63wluG3dujX69OnT6LHerLGCgRb7bVkLSGtG\nO3nyJJKSkm56P+zH49dAxZGeng6TyYScnJx6JZ9cjeloZmVlNRqgjRs3DoIgcKJZT/bpp58iOzub\nU2fIsoyEhAScP38ee/fuhZ+fH/z9/dG7d2/+I11dXQ2Hw4Hx48dzgtm//OUvPFzlCSyNGDECsiwj\nLCwMOTk5KhB+9OhRVR6OxWLxqqv3+OOPq+gSoqKi+KTpCgTY5H3gwAEsXrxYBZoCiPCdC8D5mgiP\nkJKHdYIIl0lJkv+KlDywXKe+X5IiPH6cCNtJ8USdd1p/pgHeMNY8CZK79SHCBzcI0kCEx4jQnhSP\nGYhwjJSigxRSQqhjSa2y8AkpIc3EupZKChUISCkkcB47G7/FYkFKSopbeFmj0eC2227jYIA1q9UK\nAPDz84MkSRgzZgy/x5WVlViyZAk6derE7zX7l93DkJAQDBo0CCtWrEB8fDzPNzOZTMjPz8fvf/97\n1NbWcsDoy7sjyzLeeuutBvtVV1dj+vTpnNnf+Vl86623MHz4cEiShKCgIKxZswb79u3jIWDX5srd\nptPpuAzbgAEDcPHiRfTs2VPFGUhEuP3227nnuDFUHYDCh1ZaWooJEyYAAF588UV+bYcMGYLi4mJo\ntVp88cUXqu1GjBgBvV4Pq9UKvV6v8nr6QnC7fPlymEymRo21KSwsLAylpaW/+HFb7D9rLSCtGe2j\njz5CdnZ2k+xLr9fj2WefbZJ93aiNHTuWV3M1phji5MmT0Ov16NChQ6MAWnV1NTIzM6HRaLBz5856\n+65cuRJExBPwk5OTce7cOZSWlnJdw6CgIBgMBmzbtg2AQu/BPGovvfQSrxZ1nYC0Wi0iIyO5Z2z5\n8uU4ffo0BEHA9u3b8dVXX3GqDJ1Oh06dOuH5559Hp06dOMHvQw89xOVyPHFZ+fn5eZTyYXl/s2fP\n9lhlZxYE/JUI73sAOu3qQBlIqeRcSYTbSPGsOYM058KBu0nxRv1c9/d+8gxWXSfkhgAa80J1Iffc\nuf9Ee4nIq9B4VFQU5syZg6qqKrRv3x5EhLy8PHTu3BnHjx8H89iwZw1Q6DGIlLyr3Nxc7jViuZDs\nGrHlRUVFkGUZnTt3hl6v59WSFovFo47r0aNHIYpig+/MhQsXQEQ+fUCdP3+ef7T4+/vDYrHwZ5GI\nEBcXhxdffBGjR4/mRLe/+93vsHPnTq+yU67Ptt1uh7+/P3Q6HZ566ins2rULFovFLccxMjISsiw3\nim5o/fr1SEtLU1VlPvvss/y9mTRpEueac5alcjgcCAsL4xxvvXr1Ul2/hghuWZ6ws0Zpc9vHH3/c\nUjDwG7UWkNaM9s4777gRVt6odezYEXl5eU2yrxuxdevW8VyWxkg+nThxAnq9Hp07d24UQKuoqEBQ\nUBACAwNRUVHhtV91dTUHSAaDAX5+fnjnnXcwYcIEnlBcWFgIQRBQUFCAq1evorq6mldz9uvXD3v2\n7FF5wFgzGo2YNWsWn4y7du3KJ4R27dohJSUFI0eOhCAI0Ol0SEhIwFtvvaUKAVdVVWH8+PFunibG\nJ+VaBcfanXfeidDQUOj1eo88VnfccQe2bdsGQRCwkZQqyb84gZDDpIT7ztT9/S4ptBzBpHjWvIG0\nWlKS76c5gRlfPGkNNWfv3xxSkvz/UwCtlhSwajAYuN4lm9idxyqKIsLCwjhI8/PzAwAe2gwODgaR\nQnTrfH+DgoI40NHpdGjbti3uvfdeEBEX/WYtJycHa9asQXV1NUaPHo3k5GSPz/m2bdt8kiVav359\ngyGxs2fPcmmn6OhovPrqq3zd9u3bOe0LKxCw2Wx47LHH+HNssVgQFRWF2NhYj8+up5zK+Ph4iKKI\nuLg4HD16lL83zn0GDhwIURSRm5vbIB3G8ePHERwc7FZFDVz/YBMEAffccw8iIiIQExOj8ph9/vnn\nkCSJh3FZ0RDgG8FtcnIyBgwYUO8Ym9JaCgZ+u9YC0prRtmzZ4rEc/EbsiSeegNFobJJ9NdaOHTvG\naQcaI/n0t7/9DTqdDnl5eY0CaDt37oRGo0FmZma9Id6DBw/CYrFwT0R+fj6qq6sxe/ZsCILA+aRY\nlSYAbN68GUajEVarFbt27UKfPn1UEzRr8+fP57xYGo1GJcHy7rvvgoi4IkFQUBA2btyIf//7325j\nXL9+vSq840t+16effqoSsGbNZDLBbDZj+fLlKnDxSl3LJkX+KIGUkKZzor6DlIrOUhfA4grSQIR/\nkpKvtYmUSsobAWWunhIzKflg7evGN4/cw7S/VDvp7w+TC6M+AyMWiwVTpkzBs88+i65du7p5CW02\nm9s9NJvNqorH2NhYTJ48GYcOHcL69evRtWtXDvRDQkIgyzLXaHW2UaNGeQVpv/vd7+Dv79/guzN2\n7FjEx8d7XHf69Gl07dqV57jV553+5JNPOJ8ay800m82YO3cuxo4dC+Y1e+CBB7Bp0ya3cKenZ10Q\nBERGRvLqzunTp7ttExYWBpvNBqPRqAJOzvbjjz+idevWboUBzrZ48WJ+zEWLFsFsNqNz586qPitX\nrlRVvDLvoy8Et4sXL4bZbPa6vimtpqampWDgN2wtIK0Z7bnnnmsygfSrV6+CiHD8+PEm2Z+v9sMP\nP8BsNsPf379Rkk/Hjx/nMkeNAWgPPfQQBEHAuHHj6u03b948PkFKkoTVq1fjzJkziI2NhUajQbdu\n3SAIAnJzc/Hyyy8jJiaGV3qOHz8ea9euhVardQNncXFxePLJJ2EwGKDX63kiP6NAWb9+Pd/GYDBg\n+fLlHoHkxo0bVRMXY2/3FOpxnswyMjLcJq6YmBi8+OKL8Pf3d8uVEgQBy63WZgEz3jQufWkhISE4\n9sQT2KTV4kNS54r9TIQvSPH8nfdxLE3V/kWERzIzOcHpBx98wOlYmDdMFEUUFBRg2bJleOyxxxoM\n94qiyAtJLBYLhgwZgri4OO5hzc7OxsqVKyHLMl577TUEBwdj5syZIFL//JaWlnr1lsyePZuLjddn\nbdu2Rd++fVXLTp48ic6dO0MQBCQnJ2PPnj0N7odZZWUliouL+QcJI8/VarWYOnUq9Ho9jEYj5s2b\nh+eee84jVYurJ9hoNHLQWlhYiLvuuktF8EtEPFVgxIgRbr8fY8cYB0UUAAAgAElEQVSOVeX+eTPm\nvRQEAUuWLIFGo3HL6crLy+O/C86RioYIblnI82bUZHy1hx9++D/2gd5i/3lrAWnNaCtWrMCcOXOa\nbH/h4eG4/fbbm2x/vlhKSgqMRiP69+/vM9g6duwYtFotCgoKfD6Ow+FAv379IIpivXkpVVVVSE9P\nhyiKkGUZUVFROHPmDJYvXw5JkpCUlITQ0FDodDqetL9y5UpoNBr4+/vDaDR6zEXSarWIiIhAdHQ0\nVx+IiopCTEwMOnbsiL1796q40u644w6uFehsL730EiwWiwqssJwfV2+Dn58funfv7nXyT0lJwXvv\nvYfBgwfzycZTPxMR/qnRNDmgebuRwEyj0aB9+/YYEh6Od+sAkS/H+YGUooaaXwCkHWndGlqtFg6H\nA1arFXfddRcuXboEQRBw+PBhTJ48GUTuahLOraEcPL1ej+LiYnzwwQeqZ8Pf3x8rV65EVFQUysrK\n3EDayJEjvYK0kpISn8JdVqsVixYtAqAU07Rv3x6CICA1NdWnylBvVlNTgzlz5nBwJYoip6+ZOnUq\n/6iZMWMGnnzySY8eY09he0EQkJSUhLfffhtJSUmqdcyjZrPZ+Mfphg0bkJKS0qByATNW6CEIAtfA\ndS5Aqq6uhp+fHz/21q1bAVwnuC0uLva678TERDcaj+awsLAwjBo1qtmP02K/TmsBac1o9913Hx5+\n+OEm29+YMWMQHR3dZPtryEpKSiBJEpKTk32WfDp69Ci0Wm2jhIgvXbqEhIQEGAyGesvfd+zYAa1W\ny6vRJk6ciMrKSrRp0waSJCE3N5fLz1y6dAlnzpzhkk8LFizAnXfe6XFSnTlzJudxysvLQ2VlJdas\nWYMOHTqASNErZJOMJEkYOXKk29i2bt2q4oJiuToWi8Wj9+vdd9/FgQMH3BKtWcvLy+PyUQycJSYm\nehQCDwgIwKomBjM/kpK35Qs4EwQBU6ZMAa5dwxcjR6JKFG/omGeI8AYpeXAvEmEzKfQiTXVO7xEh\nso6W5dlnn8XYsWNhsVj4x4EzAAsJCeE5Z66AwxNIcyZtdQbhPXv2xMaNG1FTU4Pw8HDMnj0bKSkp\nPJTuDDaGDx+O1NRUj89+YWGhW7jOkwmCgA0bNnBy2TZt2jSpt+epp56CJEkIDQ3lHjXm+Zo8eTJM\nJhO0Wi3uuOMOFb2M6/Pi/Df7aBo5ciQWL17Mq7PZ+qSkJIiiiNtvvx3BwcH49NNPGzVmBohFUcSc\nOXMgCIJKOur999/nYU9Zlrln/OWXX4YgCF6rTsvLy2GxWG78YvpgR48ebSkY+I1bC0hrRps2bVqj\nyV7rMxaiaQz1xY3amjVrIAgCrFarz5JPR44cgUajUVVLNWTHjh2D2WxGZGQkKisrPfZxOBwYM2YM\n91IYjUbs3r0bGzZsgFarRXh4OGf+f+655wAo4VBRFJGWloY//vGPsFqtHr/sWdWmJElcOaG6uhph\nYWF8AtJoNMjNzcWoUaNgMBhUCcivv/46AgMD3SYcVtHmPCFZrVaIoojHH39cBejYWLxJPxkMBtx9\n991ehbBtRDhuMDQpSNvgI0AzGAxYsmQJUpKT8Twp4uU3e+xaUnLl9hDhaVKKIG52n8cCAxFA172b\nztc5KiqKg2oGyPR6PaZPn46//e1vcDgcHsGac0tOTubFAl27dsWsWbMQHR3NPU5s+4iICCQmJqJX\nr14gIlVRzLBhw7yCtHbt2qFfv371vkt//OMfOQjKysrCsWPHfH4PfbXIyEieZ/vWW2+hdevWILqe\ng9i6dWuUlZXBz88PGo0GY8aMwYIFCxp8jhiZsNFoxJNPPsmrap3fHSKlWvTChQuNHndJSQmIFA/p\nqFGjIEkSPvzwQ76eCc0LgoB27drx5fUR3F6+fBmCINSrenKz1qNHD7Ru3brZ9t9iv35rAWnNaKNG\njfJJW7IxptfrsXbt2ibdp6sdOXIEoijCZDL5LPl0+PBhaDQa9O7d2+fjbNy4kZNPegulnj17lnOI\nCYKATp06obKyEvn5+XwyEkURWVlZqKqqwscff4zw8HBotVqsWLHCYzhRr9dj+fLlsFqt3DvWrVs3\nfP/99/j555+5YDaRkg+2d+9eXL58GbIs47HHHgOgFDc4h03ZJGKxWNwqOVNTU7l8jaeJfuDAgWjX\nrp1qmdFo9Fg84Oqp8ffzwwcmU5MCtIPkm/i4s1fkMVILmDdl+5oIn5FCQNvYbX8ym7HBZoPJ6Z4w\nz6bz9WVVnGvXrsX69eshyzLuvfderhHL+kmShFWrViEmJgZarZZreLZr107lYYuPj8fYsWOxY8cO\n7N69G2VlZR655Hr37s3DkMXFxUhPT/f4HsTHx3vNb923bx/39oqi6JHCoyns0KFDHr06n332GX8f\n2TmGh4dzyTVZljF8+HCvnmxX0M+Kfh555BE3ZYOQkBBoNBqsX7++0eMfNGgQ9/5169YNOp1O5SVL\nTk7m7zQrSmiI4DY+Pr7ekOjNGCsYcJWaa7HflrWAtGa0/v37exUTvlHr0KEDunTp0qT7dLbvv/+e\nVxE+/fTTPm1z8OBByLLc4Je+s02fPh2CIODuu+/22ueFF17gX9iiKOLRRx/Fzp07eXVmeHg4NBoN\nVq9ezb1tLAF4yZIlHj1PEyZMQEZGBq8wYwS0Y8eOhdlsVm3Ttm1bDh4HDBiAkJAQ7N69W8URxTxi\nFovFzQvWoUMHVFRU8HCLc2Ogk3FTOYOFefPmIT09ne+f8Tk5N7PZjNjYWCwzmTi5a1O1P/kI0Bgo\n2DRmDH5qYk+ep/YFKXxwvvCsXRQEvESEPFHkJLHOoWVBEBAeHo7bbrsNRqMRDocDISEhCA8P555R\ns9mM/v37480330Tfvn1BpOQJDhw4kHt2mQdpzJgxWL16Nb+3I0eORExMDJdWioqKQnh4OKKiopCb\nm6vKW2TX0WAwwGq1etTptdlsWLhwoWrZ22+/zQtb8vLy0LNnT6Slpfn8DjbWcnJykJGR4XX9t99+\ni5KSEv7hwz5eBg8ejMDAQP7MM0BZX2O5b6WlpW4Eun5+fqpq7sYYK/AwGo1ISUmB1WrlIefKykou\nJSeKIl/epUsXlfC9s917770+Vd3eiD300EMtBQMt1gLSmtO6dOmikh1pClu5cmWzvbgOhwNJSUnQ\n6/WYMGGCT5JPBw4cgCzLGDhwoE/HqK2tRU5ODmRZ9krnUVtbi969e4NICQPa7XZ89tlnGDx4MARB\nQFpaGgRBQEZGBiorK/Huu+8iICAAJpMJTz75JJfQcW6tWrXCsGHDeBL13//+dwBKnt+IESNgtVo5\nWGLC1UwP9PPPP4cgCKoQJZtkXUEWEaFnz5743//9Xz4hOLf8/HxkZmaqJmf2b9euXREcHMxpD2Ji\nYryGOIkIobKMn2y2JgdDPxIhv4FJlHHQiaR43poboLF2gRTtzbUREdhAhP8hRT3gY1Jyzp4nwv2C\ngDSrVQWaGRkr8/SUlJTAZDJh4cKFHFgJggBBEDB//nwkJCSoPjouXrwIIsItt9yCsLAw7N69W3U9\nUlJScOrUKQ4AvvzyS/5O/c///A9uv/127nVj91sURYwbNw6bN29Gv379VPeaFcHcc889OHPmDEwm\nE/egv/HGG4iJiYEgCOjWrRvOnDkDAGjVqhVGjBjh03vYWGPFFW+//XaDfWtra7FgwQIOptjzHBQU\nxL3JvXv35tJM9TV2TcaPH6+6fkQKJY3JZGpUtSqggE32gWSz2ZCQkMA/xl566SV+f1iYsT6C20uX\nLoGoearu7XY7Ro8e3eT7bbH/LmsBac1obdq0wV//+tcm3Sej4miOkAaTgenQoYNPeW/79++HLMu4\n7bbbfNr/+fPnYbfbYbFYcOLECY99jh8/zsOQgiCgpKQEH3zwAVcGCA8PhyzLWLlyJf71r3/h1ltv\n5dIzo0aNcvuR1+l0mD17NgwGAywWiyph+PXXX+cgSaPRYMaMGQgNDYXRaIROp0NZWRm2b9+umjxZ\n3pGzDiFrw4cPx9GjR3mejnPLycnB3LlzVUUEWq2WgzzGOJ+Xl8c5t2JiYpCYmMhDQKxvYmIiBEHA\n74xGDl6IFP6xVk6tV926ciJMrPv/vrq+f3ABP+V1jf29sYHJk7UiUoTOWxEhiRTC3P1EsLqAt3/U\n9f/QaVkFEbR0HRSGuYy/FSk5aa5A7StSVBFcxxIUFOQx95CBaUmS3EKOjAA5JSWFA5Fjx46hvLzc\nzUPC+guCgNraWg4iJEniRLNEigfunXfecXu2lyxZgsDAQEycOJFzj7FnKzAwEDabDaGhoVi3bh26\ndesGo9Go+gCw2WwqxYJz586p9t8Y73djbeLEiQgODm70duvWrVNVRTOy2tDQUIiiiO7du7uF+j19\nEDCAPHDgQLf7JwgCxowZ43P1ucPh4B9KNpsNer1eJUg/aNAg7iFlOcX1EdzGxMR4LCa6GfvLX/4C\nQRBuKP+uxf5/WQtIa0aLjo7mX9RNaWFhYZg0aVKT7vOpp56CIAgICQnxmsDvbPv27YMkST7nY+zb\ntw86na7e8nlWDabVaqHX6/H6669j0qRJHJiwQoALFy5g06ZN/Ot86dKlHsk0i4uLOaXGjBkz+A/s\n2bNn0bFjRz4BjBw5EufPn8eKFSuQmZkJSZLw6quvqghKGbhyPY4oipgyZQreeustN6kcjUbDx8g8\nNWxdRkYG9wzIsowOHTrwsK7FYsHRo0cxbdo0/ndERARXSGCAzVkHk8hd69IZgDmDtEgixNSBI28g\n7R+kUHs05OGwEuFNp+1eIUIgEQYTYbHT8g2kqB0sdVq2vg6coe7fF72M31M7QISCnBwV6NLr9QgM\nDMSWLVsAABMmTGhw8tdqtWjXrh0EQcDx48cRHx+PoUOH4rvvvgMR4eTJk/z5ZCE4WZaxY8cOmEwm\n6HQ6/pzU1tZyHVBPYIk9s0888QQsFgsCAwOxdOlSnD59Gvfddx/8/f35R4PFYkFOTg7mzZvHK0Fd\nwUnHjh2xatUqXLlyhYPG+tQ5btQcDgcMBgMefPDBG95HUFAQv37sXWjbti3nD8zLy0NsbGy994t9\nuA0aNMitmlaSJNjtdq8ff57OiYVdIyIiIIoiz/lzOByc/kMQBHz33Xf1EtzOmzePa7g2lXXv3r2l\nYKDFALSAtGY1f39/XLp0qcn3O3r0aMTExDTZ/g4dOgRRFGE0Gn2SfNqzZw8kSfI5tLJy5UqIooih\nQ4d6/BK9evUqp7tgP96HDx/mBQBhYWGQJAnLly/HxYsXkZ2dzb+eGdhybnFxcSgoKIAgCOjSpQsH\nnVeuXMFtt90GQRAgSRIkScK+ffv4upCQEBiNRhU4c/2adwZgDzzwANauXetGsREcHIxFixZxEOYc\nRmXAzrWAwG63Y+zYsbDb7Th79izWr18PIiVHzd/fH4JwXVPRZDJh9oQJqDGbbwik5RNhKhEW1gPS\nQIQ+HiZKq9WKAwcOKBNt3TLXHLFzRHiWFA8bWzaGCPNJEURny0qcgFxjQRqI8JjBgPz8fBBdl1uS\nJAl33XUXSktL3XLQQkJCkJCQwMFCVFQUiEiV88TuVXZ2NoxGIwoLCzmhKfPUxsbGoqSkBG3btoUk\nSfy+7N27lzPqT5s2ze05P3jwICRJwubNm6HX62G327kSBqDkPWZmZuLChQtYtmwZkpKSVM9dmzZt\nMGXKFCxYsABt2rThahrOHxGbNm1q8urvZ599FhqNRlXV3BhjtBpVVVU4deoUl2lj70BiYiIHa9nZ\n2V41VZ0/jMxmM/r166dartVqIYqiz2CytraWF48wgLh8+XIAiloKGyMTXH/44Yeh0WjcCG6rqqpA\nRD4DxIaMFQy89NJLTbK/FvvvthaQ1kzmcDggiqJHqaCbtaak4qiqqoLBYIDBYPBJ8mn37t2QJMmN\nudubjRgxAoIg4NFHH/W4ft++fTAYDEp+Ux3R5MKFCyGKIqfHaNWqFc6dO4fHHnsMsiwjJibGo6SM\nRqPBsGHDOJ0Fq5pzOByYPXs2B2aJiYkYN26cihh4xowZKq8Mm8g9TRAPPvgg5s2b51YokJqainnz\n5qm+8mVZRt++fVVeNmdQkJqaikOHDuHzzz9HSEgI9u/fj4MHD/Jkc9fjjx49GvPnz0eZv78KsDQW\npH1LhHBSPGbeQFo5KWHdiRMnerwW2x9+GNmkyD2tJyV8ybatIIK+rt8EUiSpzpHiTdtN18ObH5Gi\nJRpBhFBSQpypRFhCajoPInU4N4EIE6xW/Pj99x7DZbGxsZz1n4XVamtrkZ6ejtzcXN4vICAAd911\nF4qKipCcnMwlvzp06KAC63q9nk/krGjlgQce4PeYiDB37lwMGDAARITu3bu7Pessr415oWNiYjB5\n8mS+vl+/fmjXrh1WrVrFE+1HjBiBd955B0QKOz8DMDqdDmlpaRg9ejQmTZqkooFhY+zfvz927NjR\nKMUPTxYdHY1Bgwbd0LaXL1+GRqNBeXm5avmlS5cwduxY7h0jIk6lwz6ivHEIOre0tDSPVdBpaWk8\np7Q+Y/x1REpFriAIeOWVVwAonn02tsWLF9dLcBsVFdVk+WMPPvhgS8FAi3FrAWnNZN9//32zEh3q\ndDqsW7fupvbhcDgQHx8PrVZbr04ds127dkGSJJ8kWa5evYrU1FRotVqvNB6MDVySJAQHB2Pv3r2c\nfNZut0OSJCxatAgVFRVISkqCJEm44447VDkurBUVFcFqtUKn03GaDABYvXo1z/8JCgrCK6+8gh9+\n+AHBwcE4deoUD3Gx/TDg5TpBmM1mPPPMMx4r0woLCzFq1Cg3XrSIiAiP5LMs6X727NkAFOmt1NRU\nrFmzBl9++SUHrKxveHg4RFHk8jVmsxnztVo3kOaak1ZWD0gDEX5HhBH1gLRndTpMnjwZkZGRbudQ\nVlaGCw88gO+J8EAdUKM6gPVK3fYJdcuiiRBXt6wHEZ4iRcPTStcF3e11rRUREolgJIKFrgM/VxD6\nExEGEaHQA4msKIq4evUqDhw4gH379uHRRx+FVqtFbW0t1q1bh06dOnHPEwM+jF7i0qVLiI+Px5Ah\nQ3DixAkQEU6fPo0NGzbwik7n4zj/nZSUhEWLFoFIoYrwZESEgwcP8irHkpIS/i6mpKRAkiSu7cnS\nArZv3w6tVsv3UV1djQ0bNqB///6w2+08p1Gr1WLw4MEYNmwYYmNjVeOz2WwYOXIkDhw40OC762yM\nTNUboWtD1qNHD9jtdq/ra2trUV5ersrxdM6/TE1Nrbd4hp27M20OuzdardYnCqSrV69y4uK4uDhI\nksS5z7Kzs/nH29dff+2V4Pauu+5CUFDQDV0jV7Pb7T79xrbYb8NaQFoz2ZkzZxAVFdVs+8/OzkbX\nrl1vah+DBw+GJEm49dZbG/za3rlzJ0RRxPjx4xvc78mTJxEQEICQkBC35GZA8Si0atWK/8AOGjQI\nTz75JGRZRnBwMPd2VVRUYPbs2RBFEenp6ejfv7/bj3RkZCTS09MhCAKGDh3KS/J37drFJZkMBgOe\nfvpp/PzzzwAUseqioiKPgMsVnFmtVvzxj39Edna2W1+TycSP7ZzczMKcTJid9Q8JCUF+fj4kSUJe\nXh5qa2vhcDgwaNAgFBUVcYUB5/07c291794d+/btQ/v27bHAA0hrjCcNpGhotiLCB15A2oa66jxv\nE+RDgYGq/l8T4REiaIhwggiT6/rdQYrXDKQIto+vA2JDSMlniyVCV1KHOyvrgNrJes7vWSJ0DQzE\n/v370aNHD2g0GhiNRmg0Gnz++ef8eXM4HDAajZg/fz7+9a9/ITg4mHtfmH7rgQMHEBAQgLlz56qY\n5C0Wi0pGyBlMrF271uu1ISL06dMHy5YtUxX5SJLEvXVt27bFgAEDsGTJEl4JGRwcjH/961+q9+Xp\np5+Gn5+f1/ettrYWwcHBiI+PR1RUFA+nR0dHo2fPnigqKuIeKgZsIiIiMGnSpAarErt06eKVu60h\nY0z+vspRbdy40c17yd5hVixT3/X28/NDYmKi2/LCwsIGow6XL1/mFdvh4eEwGAw4f/48rly5wj/y\nmHaqJ4Lbr7/+GkSEU6dO3dC1YsZ42VoKBlqMWQtIayb75JNPcMsttzTb/h9//HGYTKab2l4QBMTF\nxTUo+fTGG29AFEWUlZU1uN9XX30VsiyjY8eOHn8Yt23bxiviNBoNXnjhBS5wHRISAlEUsXDhQhw5\ncgR2ux1arRYTJ050A0+yLPO8s9TUVJ7gffz4caSkpPA+99xzj6pQ4W9/+5vqy9yTIDRrI0aMQHR0\ntGqZwWDAhAkT3EIs2dnZqqpONlmyiaVVq1Z48cUXufxMZWUlzp49i1tuuYX3cwZlzhNqSEgIzwcq\nKiqCTqdD5ZIlNw3SQITtRMgmxRvmCtKW1jMhEhHGCALe93C8dkTYSoRX6/qNJkJK3bL3SRFtt9WB\nrLuJMIk856QVEOE5L+f3XR2wW5yYCEAJWxkMBs6V50o+Onv2bJjNZjgcDixYsAAFBQXca5qamoqC\nggKMHz8edrsdV65cgSAIOHLkCPr27atK4J42bRqIlBzCyZMnIzg4GBqNhj+flZWVICIup8byCZkn\nl9FmEBGvcNRqtZg2bRp69eqF7Oxst3fmnnvuqdcbBSgk14z01OFwYM+ePRg/fjwSEhJ4SNFutyMv\nLw+dO3fmVC9srAkJCbj77rs5nQegABdRFPHmm2/We2xvZrfbUVhY6HP/rVu3Ij4+Hrt27UJGRgZ/\nzpinXRAEt/fRU2NhS2dvm5+fH/bv31/v8auqqniag9Vq5YCZgWoiwvz58zmQctVmDQ8P9+kjtj7r\n1q2bV9WJFvttWgtIaybbv38/8vLymm3/V65cAdGNJaseOHCAh82++OKLevu++uqrEEVRlTvjzRYs\nWABBqNNxdDGHw8Gr1BiwWrduHfR6PadHiI2NxcmTJ1FSUgJBUNQFGKmrc+vUqRP0ej38/f2xefNm\nAAoRZUFBAf9RLy0txddff82PX1FRgVtuucUNnLnmlSUlJbmRZ7IJdeTIkSpPSkBAAPLy8njozHld\nq1ateJGCTqfDsWPHIEkSDAYD7rzzTp7fJEkSZs6cibZt2/JtnT0hwcHBPFG5tLQUsiwroZgvvwSc\nCGRvFKSBCIVESPYA0v52990eJ0F2nvFECCLCX5y2OUxK3tkZIlyq659IisRTPCn5Z4Gk5Kt9UTeu\n+72AtOGkeObY+bFwbhwR/InwEBE+9/PDfffdh23btuG+++6DLMvo1KkTEuvAG7OamhpoNBo8+eST\nOHfuHOfVI1Ly1yRJwhdffAHmDWHi2W+//TZEUeQguaamhoO0lJQUFBUVcfoUIqUaVJZlaLVantvk\ncDhw6NAhLFy4kK9zvp5xcXHo168fkpKSPH7YjRkzBklJSV7fO0bLU1VV5bXP4cOHMW3aNKSmpvIP\ngaCgIGRmZqq8v0SKBzg1NRXZ2dk3XLX4wAMPQKPR4PLlyz71/+KLL2Cz2XDkyBG+rKKigl9fNjbG\nZ2i32+sFaoIgeKwWnThxYr1RgwsXLnDAbTAYkJKSAofDgTvuuIOP48yZM+jatasbwe306dNhs9lu\n6HoB1wsG2G9ai7UY0ALSms22b9/eKAb+G7GwsDCfwJOzXbx4EXq9HjqdrkHJp23btkEURUydOrXe\nfg6HA4WFhZAkCRs2bHBbf/r0ae41EAQBc+fO5T++zLswb9487Nq1C/7+/jCZTLjttts8AiWmbzlz\n5kw4HA7U1NRg9OjRPNm4a9euqpDDmTNnVADIeSJy/ttut+P+++93yzNKS0tDYWGhyiuWmJiIdu3a\nQZIkmEwmxMXFce+cIAhYvXo1z+UhIrz55pucxVwQBOj1evTo0QMmk0nlfZNlmet7tm3bFgaDARcu\nXMCGDRt4uGr69Ol49NFH0S4zE8edAM3NgLTjRJBcQNpPej1C6s7nT3/6k8pzyIlfibBGEJBdB/IS\niJBLhL0u40qr+/84UgoC2tWBNBDhHlIKC/LJnSfNQITOHs7vIin5bCeJ8LlWC5vN5tEjmpSUhJKS\nEixZsgTvvPMOhg0bxifR4cOHo0uXLryvwWDA0qVLER0djeHDh+Ohhx7iIUatVqt6rtm9Ysudwf6j\njz4Km80GQRCwbNkyvk1NTQ1mzZrFtxVFEZmZmYiKisLYsWORkZHBnyHmdWvfvj0mT56Mtm3bIjMz\n0+v79/rrr0Oj0dT7jrraiRMnMG/ePGRmZnKAabFY0Lp1ayQkJKjCjjqdDllZWXj66afdKhs92Xff\nfQdZlrF48WKfxvLTTz8hKysLTzzxhMf1V65cQVlZmRtfoSAIbgUTrs1kMrm96+Hh4fWGJc+ePcu3\nkSQJRUVFuHbtGicQDg4O9khwe/78eRDRDdMulZeX31R0pMX+f1oLSGsm+8Mf/uBzBeSNWmlpaaOo\nOBwOB6Kjo7lHoT57+eWXIYoipk+fXm+/ixcvIioqCiaTyaPQ8OrVq3m4x9/fH2vWrIGfnx+v6IyK\nisLHH3+Mnj17QhAEFBQUuP3wSpKENm3aQBAEdO3aFRcvXoTD4UB5eTlXB0hOTsZHH33Ej3vu3DmV\nSDP70XXNa9Hr9R41FVkuDtuGUYNERUXxL3mWtB0dHY1evXpBlmVs27YNaWlpHMg4Jz77+flh6dKl\nnICXrSdS8tXMZjMEQUBubi5kWebyQEOGDPE4AW0PD/cIypqivVk3OZ88eZJXx7peu/DwcGxtYD/k\n9P+v6oDYS3QdKL5Xt+wnl+2+IYWn7UsPII0BzwFEOGWxIDMzEzqdTgWwAwICoNVq0apVK06O7AzI\nGL0L2yYpKQkREREoLy+H2WzmIc+PPvoIHTp0UHnFnQlzjx49qromffr04R7dsWPHorq6GlOmTIFW\nq4XZbIbdbke/fv1gNpvRsWNHJCcn8/0WFhaiY8eO3OvWs2dP/ryxZzU+Ph59+/bFsmXLeD7Z9OnT\nERER0fAPQD127tw5LFq0CJ07d1Z5hGNiYhAREaFKNzCZTL5iYkkAACAASURBVMjLy8OmTZs80nLk\n5+c3ajwzZszAoEGDGlQ4cTgcWLx4sYqrkHGZeSKXdm6sMMC5LV261OuxTp8+zYG3IAi48847ce7c\nOb5s6tSpHglu7Xa7qmq8McZoeFqsxZytBaQ1kz399NMNeqBu1lhFmq9UHP369YMoihg9enS9P4ib\nN2+GKIq466676t3f4cOHYTQaERsb6xZqqampQY8ePfgPYq9evTgdBwtZzJo1Cxs2bIBer0dQUBA6\nd+7s9kPKBK6dKTVefPFFVaL19u3b+fl89dVXnHONeS2cPR2s9ezZ02vVmLP8k9lsRtu2bWE0GiHL\nMiIjI6HX6/kX9vHjx3H8+HGIosg1RFlOnPNxzWYznygMdfxeFRUV0Gq1XINUFEXk5eVBFEXs2rUL\nf/7zn7nwt3MLCQnBN998g5kFBbjcTCDtLn9/zJo1C61bt3a7TpIk8Ws0lNRUGfWBNJDCi5ZMam9e\nX1JCm4xct4oUtYQ7XfbjDNJ+ICWsutJsdstBcgXhsbGxyMnJwbBhwxAZGQmTyYQRI0bAbDa7AXR2\nvyIjI2EwGBAXF4fS0lJotVo+GTPqDavVinnz5kGr1fLtQkJCUF5eztdrNBpYLBY88sgj3OPcuXNn\nBAUFITs7G9HR0fyd6dGjB3Jyctzes6SkJAwfPhwbN27kXjfnXDdZlmE2mzFp0iRs3brVK1l0Yyw6\nOhppaWno1q0bB6U6nQ5hYWGw2Wyq62axWFBUVISdO3diz549EAQBhw4d8uk4r776KmJiYvDdd981\nanybN29WvRusAKQh2g7X+52RkeE1TMy40ljfp556Cs8//zz/+69//asbwe3kyZMRGhraqHMBrtMq\ntRQMtJirtYC0ZrJFixb5RGtxs6bT6bB+/foG+y1fvhyCIKBNmzb1grpNmzZBFEXMnTu33v2tXbuW\na/C55ngcOXKEl9BLkoQHH3yQh6RkWUZYWBjee+89ZGVlQRRFdOvWzW2StVqtsFgs0Ol0ePzxxwEo\n1WKM0sJgMOCZZ57hPHSVlZUqkMcmTdfw5dChQ5GcnKxaZjAYuPeDtYiICE4marFYOPdZSEgIHnzw\nQX4NmTB3mzZt0Lt3bxUYdK5GNRgMmDx5MiZPnoyePXviypUrCAoKgs1m45MHq+5kHjrna5GamgpR\nFJGQkICLFy/itttuU8ItzQDQ9hBBJCVnqaioyM0Lwa5pdnY2tLKMjzQan0FaNSlqB84g7QoRZpKS\nu8Z40pYRwVEPSAMRHiNCbJ22qyuhMLumgiAgKioKUVFRsNvtbh4X5+us0+m4bFlAQADCw8NV+Was\nD7tnsiwjJCSEhzfZ8Zw1KV1DeKWlpWjVqhUiIyORlZWlmtC7d++O3Nxct3ctNDQU8+fPd1vOct1M\nJhNiYmIQFRXFw756vR5xcXFuXjdf7JNPPoEgCKqw3Y8//oj169ejT58+vOKSVWMHBASonnuNRoMh\nQ4a4Jda72pdffgmbzeYzoPNkhw4dUuWasiKJ+mg7nCum2TPgjTjWOW2BiLBz504UFRVxcLpo0SIV\nwe3Zs2dBRPjHP/7RqPMoKChAWlraDV+HFvv/ay0grZls7ty5nL26Oa19+/Yq3TlPtn//fgiCIhBe\nn+TTxo0beX5YfVZWVgZBENwIKoHrXgZBEBAfH88Tbll5+5133omlS5fyUKdrcq8oijz3o7i4GNXV\n1aioqODVXrIsY8GCBZym4OLFi6rcIucQhfMyu93uUUnAlQMsPj6e58nZbDaYTCaIooicnBw3jqkr\nV67wcCUReUxUFgQBU6dORceOHREfH4/IyEhUVVWhTZs2PGyj1WpVX/iMG00QrjPXb9myhRN0Dh06\nlOe3PThrFpCW1mQA7QIR7u3TB/3791eBs/T0dAwcOBABAQEwGo08PH3rrbdi2i23oKqJjt+optMB\n773H78e1a9ewceNGJCYmIj4+noeP7XY7wsLC+L1k58TAl+sHgusE7+fnx/MF8/Pz3QC9q5fWuTL3\nnnvuwY4dOzix6t13342wsDC0atUKbdu2VSXmFxQUeCw28vPzw6pVq7y+j7IsY9euXfzvqqoqbNy4\nEePGjXPzugUGBqJ9+/b1et0KCgoalCSqqanByy+/jCFDhnDJMnb+zteZXf9x48apqEhqamrQoUMH\nFafhzdjZs2fRq1cv1bVn5+wNrLnet1tvvdXjB+yBAwf4PkVRxLFjx/jvWWlpqRvBbUhIiMfiKW/W\nUjDQYvVZC0hrJisrK8PatWub/TiPPfZYvcmmlZWV0Gq10Gq1OHbsmNd+L7zwAgRBqNf7V1NTg6ys\nLGg0Gmzfvl217vLlyxxICYKAsWPH8qo5WZYRGhqKN954AwkJCVzE3fVHk+XfpKWl4dSpU7h8+TJ6\n9+7NwVtpaSm+/fZbAApjufNk6TwpsGVarRbDhg1zC3G4fvkzElpGh8A8IwEBAZg1a5bbRPbyyy+r\nihFyc3M50zzzfCUmJkKj0XCQdejQIZjNZgQFBSE+Pt7j5GEymbB9+3YcO3YMsiy7yW5du3YNxcXF\nHKDdf//9yngmT8bZJgA935NSYMAAjCAIyMjIQE1NDTZu3AhJkhAWFuYxh+/lhATU/tIgrVs3r88q\nS+zu06cPDAaDKneKTbr3338/OnbsqKps9DaZszAaA6nevHFESh4X+7/FYlHJgbFnzWQywd/fH1qt\nFvv27cPVq1eRn5/vkftQo9Fgx44dXs+TiBpMeXA4HPjoo49QXl6OXr16efW6lZeXQxAEvPbaa/Xu\nz9UqKyshiiLatWuner6NRiP0er2K7iM6OhqZmZno3r17g3lojbWrV6/i9ttvV73fDLDVd49ZMxgM\nHgl/d+/ezfel1+tVtByPPPIIBOE6wW1ZWRnCwsJ8HvMDDzzQUjDQYl6tBaQ1kxUXF/8iX0b1UXE4\nHA5ERERAluV6x7Ju3ToIgoCFCxd67XPmzBkEBwfDarXi9OnTqnW7d+9WTT6TJ0+GJEl8Mps4cSKm\nT5/Ow3Wuk5yfnx90Oh38/f2xZcsW1NbWYsqUKZyKIj8/n4deLl++jMLCQjdw5tyMRiMXIndenpqa\nqgqL+fn5qRQBmKctIyPDbVI8ffo0hg4dCoPBwEGmLMs8dMLOKSIiAvfeey8EQUBmZiZ+/vlnXLhw\nAZGRkSgvL/eoS6jVatGxY0cACu0Aq/50tWHDhvEJZ/z48Zg9ezafaAeazfgpKuqGAU+lIGBddjbe\neustFTiRJKleLjkGdnDtGnbGxPxiQK2aCB82kPNZUlICi8UCo9GIGTNmqNalpKRwXVeLxcLPMS4u\nDikpKejWrRtiY2PRv39/yLLMn7mQkBCPHlnn5vpMajQaWK1WREdH8+eN5TW6enQYkXO3bt0wadIk\nrF69GkTEi0hc7fnnn4fBYKj3OtRnrl43dh1Y5STzum3ZsqXeXLfc3Fw38u4DBw5g8uTJSElJ4UBV\np9OpPJWyLCM5ORkLFy6sl0KkseZwOLB8+XKv8m4NtSlTprilcbzxxht8vc1mw4IFCziwS0hI4AS3\nFRUVIFIqfQcMGOA1327NmjX45JNPEBoainHjxjXZubfY/y9rAWnNZD179sTbb7/9ixzLbrd7dK8X\nFRVBFEUuP+TJ1q5dq4TN6hElfvvtt6HVatGmTRs3NvRJkybxH67OnTsjPT0doihCo9EgODgYGzZs\nQGhoKHQ6nRvDP5sIJEnCrFmz4HA48Lvf/Y4DvuTkZC74fuXKFe5V8+S9YF6LTp06uR3DmT7D+cua\nSS2xiXTs2LGqcHBNTQ0WLVrEQ6JRUVEYMmTI/7H33eFRlfn3t0yfzKSXSe+NdEIKpBFiICQQmkBA\nOkgxIDUighBBkKKwCJFeBBQEEURwBQFZEFlYloUossAiiIioMbIYQhwm5/fH7Ptx7swkhOb6+27O\n89wHMnPnzm1z3/N+yjkSHbWnnnoKFRUVNMBu3bqV0rvXrl3Drl27bIqZmXYa2wcvLy8YjUbcuHED\nOp0OiYmJNgMEa7pgn7XscFOr1fjb3/6GYKUS7/E86u6T8BgzMoATJ1BVVWXXxorjOJIAGTlypKT7\nlu07AHx29Cim8Ty+/R1I2l+DgqBSqRolDrW1tVAoFORGYCkf8dFHH1Gh9sCBAykyys7xzp076X2e\nNzsSeHh4YODAgZTqZfdNcHCwJLpo736Ty+XkkWt9/wqCQI0pCoUC/v7+8PHxgZubm4RkyOVyuLq6\nIjo6GgUFBRg7dizatGkDX1/fh/bnZHBwcEBZWRmOHz9+z6jb7NmzUVlZSefSUuPMHiorKynKxc6X\nTCazEZeOi4vDa6+91iS5j6Zg+/btEu/cpi4uLi64dOmSZFubNm2i92NiYuiZlpWVBZ43u6ewa8bq\n4xoin5ZlDgsXLnxkx9uM/1toJmmPCSkpKfjss89+l+/q06ePjbDirFmz6OHR0AOcyWPMnDmzwW0z\nk2Fr8+Br166R+jfrGLVUXu/Xrx+efPJJ8DyPsLAwmwcgq/vKzs7GDz/8gB07dhBhcnNzoxqbmpoa\nSQG+PXLm7u5uYwej0WgkBuVyuVxifM4GS41GA0dHR8mx7d27F+np6RAEAVqtFu3atUNGRgY9VKOi\nomhQunHjBg0yK1asgEqloi5Qy30NCgqimiCW/vH394dWq4Wfnx+6du0KFxcXhISE2MgaMHFfnufx\nwgsv2DQ+WEt0dOc4bOPMJuoNkZxbHIdTHh742+DB6F5cLInwWXdLqtVqqFQqbN++HQkJCRRdU6lU\n2LBhAwCgpKSE1k/gOOzhefz7cZG0xESYrl+Hp6en3UJ7S0ybNg0KhQLOzs7kk8ng4+ODLl264NSp\nU3B3d6eIlpubGwYNGgRHR0eUlZUhIiICbdu2pfsnKCgIu3btomvi6OiI5ORkapRRKBQYP348OI5D\nQUEBjhw5goqKCowePZomGg2lTFk6zbJjlN2/er0erq6u8PDwgJubGzXnsHVUKhW8vLyQkJCArl27\nYsqUKdi2bRu+/fbbRs8RA0tpN5Q6raqqwptvvklRN+Yly/avZcuWGD58OLZs2WKXPP/6669IT0/H\nK6+8AsAcnZ8+fbrEzF4URUkkUq1WIyUlBevWrbMr93E/OHnypKTzuqmLdd0cm5RxnLlr3bq2zXJp\nLI1pWfPZVMHwZvzvoZmkPSaEh4fjyy+//F2+ixk2s4cYa4M3GAwNWj4tXrwYPM83qBXEPCUFQcDr\nr78ueW/jxo2kA2YwGJCamkodVS4uLpg/fz50Oh20Wq2N5hnzwfP19cWRI0dw6tQpKrhXq9VYsWIF\nTCYTamtrJYK29siZwWCwSR+yrlDL1yyjZey1rl27Yv/+/eB5Hrt378a1a9cwcOBAGvji4uKQm5tL\nA1F0dDSWL1+OGzduQC6X49lnnwUA8hRs3bq1TfG/p6cnAgIC8O2338LR0ZH0unieR2RkJFQqFa5c\nuYLq6mr63unTp+POnTt0rvv27UtkYNasWairq5OQTcvvszaed+c4DJPL8VXPnsDQobicn49XObOn\nZqxWSxEeFmVo3bo1TCYTRo0aRdtgA1BwcDDUajUMBgMuXryIN998E7Gxsbhw4QJ1TMpkMrzyyit0\nHpI4Dks5Dpf8/QEfH0AUH5qgmRISgP8IkbIuRHsCypb3sZOTE5FuVsAP/EZKfvnlF2RlZVEtmVar\nhU6nw4ABA+Du7k6TEfYvIyCWTSesYYYRdpYas+ffy/M8hg4dSvcuM4NnjSWFhYWIj4+Ht7e3JArL\nfmNssmAZgWLkjnWmOjo6UnSOva/RaODr64uUlBSUlJRg1qxZ+POf/0yuAMHBwejYsWOD59Iexo8f\nD7lcjilTpjQYdevYsSNmz56NgQMH2u0GZ7hx4wbmzZuHrKwsqhO0jkrqdDq0bdsW77333gNHD69d\nu0bWXE1dAgMDUV1dTduYM2eOhEBb/xbZebfUwbMGm1jK5XKkpaU12Z2hGf9baCZpjwmenp5NnsE+\nCiiVSqxduxbXr1+HXC6HUqls0PJp0aJF4Hm+we7TmzdvIiwsDCqVSlJEazKZJAXyTzzxBNRqNT2k\nevToQXIa1p2OPM9TROa1117DtWvXkJqaSgPblClTcOfOHdTV1eHJJ59slJz5+fnZpBA9PDwkg5ar\nqysRSZYaZNtiHn7x8fEwGAwICQkhUpWVlUWDsZeXFyZOnCh5eMbExFDUsqioyO7Mef/+/fjkk0/g\n4eGB8+fPIyIigvZBFEVER0dDFEWcOHECJpMJ0dHRcHJywqlTp1BcXIyQkBDs2rWLnBR4nkdqaqpE\n9Z/pQtkbUFxcXCAIAlq0aIHVq1ejW7dukvRoQEAAnnnmGRw/fhydO3eGIAiYM2cOfv75Z9q3lJQU\n6i7t2bMnDbrXrl2jczF16lTaZlJSEi5duoSkpCQapDQaDSZMmGAmmNOmAZcuAZs2AcnJ903O6hUK\nHDIY0N9qkB89ejQUCoVkALXGhg0bIAgCDAYD2rdvL3nP2dkZw4YNw7vvviuJxlrew8HBweA4DocP\nH4aDgwM5CWzcuBEcZ66zevnllyXXYM+ePXS9rcmEQqHA4MGDidyy99PT0228LpcvXw6tVktEjkXk\nCgsLKUVrXTPImnWYs4Hl62q1Glqtlnxi2fvsX19fX2RmZmLw4MFYuHAhjhw50mBk7bvvvoMoinad\nAqqqqrBhwwZKJbNIGStxuFfUDTA/h9544w0bGRjL+97Z2RlFRUVNNnG3RG1tLQYPHnxfZG3btm30\n+cmTJze4HptgNmbnxSL93bp1a7LWZTP+99BM0h4TlEqlTf3W40TLli2RnZ0NT09PiKKIffv22V2P\nGas31PpeWVlJBfWWwopnz56lqJhSqUTr1q1pMNbr9SgrK6Pif+uZJZNC6NWrF6qqqtCjRw+qx+rb\nty+qq6tRV1eHPn36NPqA9Pf3tynKtqw1YelEFlVgD/S8vDz07t0bHh4eiImJwbFjx2iAY2maFi1a\nUHqzR48edhsxysvLIQiCJI3KyBzHmW2rfv75Z1y+fBleXl7Yt28fOnfuTORRoVBQKnHHjh0AzAXX\nGo0GV69epe/Ztm2bjZaTh4eHXe03nueRm5tLf7M0Lqt5k8vlNEBqNBq6pjdu3IC/vz/UajUOHz6M\nQ4cOQaPRwNPTkyI4mzZtQlBQEBQKBTZs2EDEfe/evRRF4nkey5cvx8qVKyGXy+Hv748zZ87A19cX\nubm5AH5rTKG0el0d8MILQGzsPcnZHUEAcnKA9evJvNzJyQlbt26FyWSi5piUlJRGfx+BgYGIiYkB\nz/OSOqM5c+ZAqVTizp078Pf3l5x3tVoNd3d3lJSUIDIyEoWFhcjPz5f4azLyain/wHEcJk2aROlP\na80svV6P/v370z3KJgFpaWnIz8+XrDt16tQG/SC/+OIL8DxPJM+SyI0ZM4aInD0dOUEQbFKLbMKk\nVCqpwJ8dE5MgiYiIQF5eHp555hkEBQXBYDDcM6J19epVeHp64pNPPsHx48cxY8YM5Ofnw9/fv8Go\nm71GiTt37mDjxo3o0qVLgzVm7u7u6N27N86cOdPoPlmCNRncq0GGLS1btoTRaGy0Y7R169YoLCxE\nYkwMcPQoMG8eUF4OzJ4N7N4N/PIL3NzcEB8f/8jqCZvxfxPNJO0xoLa2FnK5/JG3lzeGuXPnUmqg\noQjZvHnzwPN8gx55b731FkRRRHZ2tqT+47XXXqOHT1RUFBwdHYkwFBUVIT4+HoIg2Dw4GVmLiYnB\nuXPnUFZWRtGtzMxMXL16FUajEf3792/0oWidxhMEQZLSZNEBltJhhCQ2NhZ1dXX4+uuvKWrBNKNE\nUYROp6Muu/T09AZlDt5//31Jasvy4Txv3jyKXJ4+fRo1NTVISEjAa6+9hmnTptG6jABx3G8Cp8XF\nxVAoFPj888+xe/dudO3alYRSLRfLwcPZ2ZmilVqtVmJ9ZbnOyJEj8Ze//IX2OzY2VpIOVyqVCAkJ\nQVVVFV544QXwPI/k5GSquVu2bBnkcjnCw8OpmcJkMkmEQ/38/PDll18iNTXVpkHlm2++kRRML1u2\nzDa9bjQCa9cCI0YA+flAXBwQHY2rHh446e+Pf3bqhGyOw+rVq+kjLHXNLJJ2796Ns2fPQhCERiVv\njhw5QtEx1knLjkmtVmPQoEE2pt08b9Y50+l0mDt3LrRaLXbs2CGJjrHfnJubmyQNnZSURHphH3/8\nsWRfDAYDevbsSSSIdUunpqbaRPoGDx6M4OBgu8e0YMEC6PX6Bo/ZHmpqavDpp59KiFxcXBwRMevj\ntyYi7LdjXYcll8vh7u6O2NhYFBUVYdKkSXjrrbdw4cIFZGRk4OWXX25wn6yjbqzEwDLqNmzYMJuo\nm8lkws6dO1FSUmLzjGD76uPjg2HDhpE8xr2wffv2e0qyNGVp4+KCW3374oIgoF4QbJt1PD2xTS7H\n7fuUOmnG/x6aSdpjwHfffdfg7PdxgYXe27VrZ5ccsgYA6/oyhnHjxoHneclAW1tbSyr+LOXGHtIO\nDg4YNGgQRFG0a3Isk8ko4rFy5UqayYeFheH06dMwGo0YMmRIow86a6V7pvfG/tbpdGSLw15PS0vD\n3r174enpiTNnzmDdunVkayOKImJjY4nIBQYGYsGCBXYLknfu3ImgoCCbwuxPP/2UnAFYDY5cLsf6\n9etRX1+PXr16oV+/fti6dSvts4ODA9lYMSkI1lTh7+9Pg56fn5+EfAqCIGm6SE9PR1ZWlg1RZIso\niqioqABglgxhUaHBgwfTcTEdrN69e6OmpoauaatWrcDzZuHdLl26SPYVMHe6WhvVP/HEE1AqlfD2\n9pYIlTaEiooK8LzUePxemDJlCkRRpC7fwsJCyT3GhJwnTpwIuVwuqTmzRkpKCmnxsU7EQ4cO0f3R\nsmVLaDQaii45ODhg7Nix4HkeH374IXiex8GDByWSNiwVynFm3052bbRaLXX+sWvCEBkZSRIxgiCQ\n4n5KSgo6dOggWbeoqAhJSUl2j6dbt26PRKV+7NixkuaZmpoaHD161IbINWRkb48cWdeSKRQKeHl5\noWXLlujRowemT5+OnTt3Nni9TCZTo1G3wMBAdOzYEbNmzaKom8lkwqFDhzB06FAEBgba/EYEQUBg\nYCAmTZpEeosN4fTp01T2cD+LwHGYxnG4zvNNS+VrtcCgQcAjsPJqxv9NNJO0x4B//vOfCA0N/d2+\n76OPPqJB2l6H0KxZs8DzPJYuXWrzntFoRGZmpo3i9dGjR4lYOTk5wd3dnQav3NxcEqq1TqOwrrTx\n48fj448/JqLl6uqKvXv3wmQyYeTIkY0+4C3rpzjuN2FV9jd7n6WL9Ho9SktLKW00depUeHl5Uc2N\ndQ2LTCaza2T83nvvSYiZIAiIi4tDp06doFKp8PPPP1PXrJOTEyn/s23NmTMHrVq1wokTJ0hWQaPR\nkB1WbGwsMjMzKcLo4OCA0NBQ+Pr62kQmxo0bh6+++orWtazBE0WRUphs4FIqlST58v7779MxrFmz\nhq5zTk4OBEHAsmXLcOrUKTg6OsLJyQkGgwEqlQorV64kHbBPLJT8Dx48SPvn5OSEv//970RAfH19\n76vr7vXXXwfP85g3b16TP5ObmwutVovq6mq6lznOnGb+9ddfab2AgAAkJiY2uJ3Lly+D53mEhobC\n39+fmj7S09Mhk8mwdOlSjBw5EiEhIXRu3d3dERcXRyr8HTt2RFJSEtr+R0iX/fZkMhnGjBlD14eR\nY44zu2xYonXr1iTELJfLqZM5OTkZBQUFknXT0tJs6tQYIiIi0KtXryafx4ag1+sxYcKEJq//7LPP\nQqlU4uDBgxIiFxMTAxcXF7tix/Z+55Z/M7Kfnp6Ofv36Ye7cuThw4ICNLAWLug0aNKhJUbeTJ09i\nzJgxVHdqPakJDw/HrFmzGixNuXHjBtXONrbMnTsXHfLysImT2pk1eWndGmjEDaYZ/7toJmmPAX/9\n61+RnJz8u3zXN998Q0XaXbp0QVBQkOT98vJy8DyPZcuW2Xz2+vXrMBgM0Ol0kkjIxIkT6eHDNMEY\nISssLJToi1k+8DiOQ05ODj777DOSiVCr1Vi1ahXu3r2LMWPGNFjHwdT+LV+zJIByuZy8Edl6sbGx\n2L59OwDz7H/KlCmUcmLK/uz7nJ2dceLECYwePRoajYaIxbvvviuZdQuCgPj4eIpuMEutrVu34tSp\nUzQgzJw5ExxnjgzeuXMHu3fvpoiSg4MDHBwcbNJHKpWKUinsPQ8PD3Tt2lXi9xgXFydJHbu4uMDN\nzY26aS3JKyOpLJrAIqoymYyiT5cvX4aHhwet9+qrr0IQBERHR1N6c+bMmRAEASkpKfjll18AmCMT\nlvInAwcOxKZNm6BSqeDp6Ym3334bWq3WpvPtXmCdxU21BGJ1Z6Ghofjoo4+g1+tRUVEBURQl8jHn\nz5+HIAhYvHhxg9tKS0ujax0XF0f1aSUlJfDw8MCXX34pubd53izyLJPJSBx14cKFEgFZdn8WFxdL\nrveiRYvAcWaPU0sUFxdTVFKlUmHjxo0AzHWlhYWFknUjIiJspEMYHBwcGoyMNxWsxKG2trZJ61+7\ndg2CINid8Fmv5+HhgSVLlhCRKygoQHh4OJycnBr11rRH6FQqFby9vZGVlUUiv8ePH4fRaITJZMKJ\nEyeaFHV7//33MXnyZERERNitx4uJicHSpUttJh51dXXUQNPQ8mV29sN1L+fkmEsAmtEMCzSTtMeA\nvXv32lWMf9QwGo1wc3ODKIo4deoUDh8+LJHiYDVRK1eutPns4cOHoVKpEB4eThGoqqoqSq8xI3Q2\noKWlpcHd3Z2IkvUDytfXFx9++CGys7OJtL3wwgu4c+cOxo8f3yA5YzVqlq9ZzsSZpphcLocgCFCp\nVOjXrx8VwO/YsYOM2rVaLdzc3MipIDo6miJQly9fRnV1NWQyGQYOHCix7hEEAQkJCTZinHV1ddDr\n9SgsLITRaKRU5PPPP0+RssuXL+PcuXNwd3fHu+++BCTN1AAAIABJREFUSzVvlgOMIAiS6GBISAgq\nKipw8+ZNbNu2TdK1yVIybBuzZs0iK620tDQa3Ng5cnFxwddffw2TyUTRGRcXF6oHe/fddyGXyxEX\nF4eff/4Z+fn54DizqwLP8xgxYgQyMjIgCIKkbujs2bOSJoRPPvkEubm5JB/BarJ++OEH+Pr62hD9\ne4F1GC9cuLBJ61+/fh0qlQpdu3alTrjFixdDEATJdZs6dSpkMpmk6QUwN2OwVCfP8wgKCpLIY1RV\nVUEQBGzfvh35+flE1HQ6HXJzcyGXy7FkyRLwPE/ircePHwcAmqD4+PhIJhrl5eXgOHP00RLDhw9H\nYGAg3bOMaCUlJaGoqEiyrsFgsBvlMhqN4DjORmz1fhEWFmZTB9cYkpOTERIS0ug6d+/eRU5ODsrL\ny++5PevUal5eHoKCgqDT6RrVH7NH4nx8fJCdnY0xY8Zg3bp1OHbsGNavX99o1K1Pnz7o2bMnwsLC\n7NbZJSYmYvPmzXS/N7QP3pxZe9CSdDGLNfY3x3G4arVONcch4j+LH8dBLgiIiIhARESETQS2Gf+b\naCZpjwHvvPMOunfv/ti/p02bNjY6UQqFAuvWrSNrIpbussSf/vQnCIKAbt260cNn165dRAC8vLyI\n6Fh2ctrrEFOpVJg/fz769+9PD8C+ffvi559/xnPPPdcgObOXErFcl9VTsfVCQkKwatUqmEwmXL58\nGSUlJdQs4O3tTXVxcrkcffv2xe3btzF48GDo9Xq0b98e77zzjiRlKAgCEhMTcfLkyQbPb4cOHeDo\n6Ii6ujqyBcrIyICrqytkMhl27tyJJUuWkD9hQw/w2NhY9OnTh8559+7dyUDdcjY/adIkHD9+nF57\n7bXX4OnpCZlMhvbt29P5cXR0hCAIiI2NRUpKChISEuj4U1JS6JqyyOWIESNw4cIFMoz39PQkKRSd\nTgdXV1dJN9zYsWNpn/Ly8rB582ZoNBq4uroSMbEES5nLZDKKbDYFrNP4T3/6U5PWP3ToEARBkKRK\n27VrB0dHR0kkKCQkBDExMQDMkSKDwQBBEFBYWIjr16+TnZYgCCTGCwD5+fkIDg7Gnj17JI0iMpkM\nbdu2RYsWLRAdHY0OHTogKCiIUo0s2szzPKVKOc5cH8pet5RYeOmll+Dq6gqFQgGdTkfRwMTERHTu\n3FlyzHq93i6RPXz4MARBaNJ5awjnzp0Dz/M4d+5ck9Z/7733wPM8KisrG13vxRdfRG5uLu7evftQ\n+8fAiNwbb7yBUaNGoU2bNvDx8aHff1NIHEunZmZmol+/fujZsyeys7MlHb1KpRL+/v6Ijo6Gh4dH\no5NH68WP43DTgnzVcRzimkDSLJeDHIcQUWxOezZDgmaS9hiwcuVKSbH248Bzzz0HjuMwbNgwyetJ\nSUnw9/cHz/NYv369zeeYOCor3jaZTBLpC0tboPj4eDg4ONikIdnDq3fv3njxxReJ3GVkZODbb7/F\niy++aNdTkw141pE0S+JkuchkMhQXF+PixYswGo2YO3cuRcCcnZ3pQcokDdauXYu4uDjU19fjwoUL\ndolTaGhoo0bzDJs3bwbPm+2Ali9fDo4z15AlJSWBRVgYKVWpVDaSGVqtluqcLI3gWcfkiBEjSEuJ\n53lUVFRQapXjOCxZsoTMuS3TaEwMuE+fPjAajTh79iydw8jISHz33Xeora1Fy5YtqcB97dq1EEUR\nQUFBlN4cPXo0eJ5HQUEBRV5/+OEH6i4VRRHbtm0jctivX797SgWMHDkSPM83KYLCsGDBgkYbWqzB\nUrUHDx4EYI52uri4ICMjg9ZhtWesu7Jr166SAnWTyQQHBwcEBwfD1dXV5nNHjhxBaGgonVeFQkHH\nNnv2bKjVaowfP54+e/z4cbp/WfMAx5kjmqwu0lLSZf369dSR7OjoiIkTJwIAEhISUFxcLDlepVKJ\nd9991+Y8TJ06FW5ubk06Zw0hPz+/ybWzJpMJjo6OePLJJxtdb9++fTAYDDaRzMcNRuSWLVuGgQMH\nomXLlmRH15RonFwuh4ODA9zd3SX+rOw+amgSxjqCh3Ic1lkQrve5pkXSbEgaxwGTJ/+u564Zf2w0\nk7THgPnz5zfql/mw+OCDD8BxHBISEmwGzszMTHAcR3UuDDU1NYiNjYVCocDevXsBAFeuXKFBWa1W\nkySAXC5HdHQ0dXLaiwwtXLiQHmRhYWE4e/YsXnrppQZTFNakzdryxpLAGQwGzJs3DyaTCQcPHqTG\nBqVSCQ8PD6pPy83NJWFaduylpaU2OmYtW7ZEeHi4RN+qMVRXV0OpVGL48OE0AFseA9NjY39bdmSy\n/WQPeHacgYGBVOcGgFT9Wb0gEz9lDR4qlQpyuVxSE8a6QGfOnIn6+nps27aN3lu7di0mTJgAJycn\nqNVquLq64vz58yQMHBkZSanKFi1aQCaTYdWqVbQ/r7/+Om0rJiYGW7duhVarhbOzs0TQ+F5YtmwZ\nEaOm6j/NnTu3wcYWe+jRoweUSiWJRVdWVkIQBMydOxcVFRVkOcZxXIMpWCYJIpPJJLVxycnJSEhI\nwJIlS0jMVhRF+Pv7Q6/XY9KkSRAEAVu2bDEPuv/Rt2PrMYFjFiVlHYJMFw8wR8FYV7STkxOGDx8O\nwCyu3KVLF8l+CoJAtYWWyM/Pf6i619raWoiiiE2bNjVp/VGjRkGtVjcquspqXK0lR/4oYERu8eLF\n6N69u42c0MMsH3Mc8i0IVwnH4c8PStL+oy/YjGYAzSTtsWDq1Kn3FU24H1y5cgWiKMLJycnG8onJ\naHAchwsXLtDr58+fh7OzM9zd3UkvaPXq1bSupdRFWFiYxPjXcnFycsKcOXOoeN3V1RX79+/HK6+8\ncl/1I9ZEjXVgtm3bFidPnsSNGzcwbNgwKrJ3dnamDk9mz2RJADZt2mQjassIVHV1NdUQ2ROotYbR\naERAQACUSqWEfFlGE/V6PWJjY+Hi4oIpU6bYPc68vDzs3LkTarVaEuUBgNLSUjoXK1euJLkOnuex\nePFiyGQyIqGW25TJZNiyZQtda0Zw2XGtXr0agiDA0dERISEh8PDwIGKrUqlQXl4OlUoFPz8/XL58\nGYB5sGZ+hiyi1blzZ/C8WXz4QfwSDx8+DKVSiejo6CabRs+ZM6fBBhdrmEwmhIeHw2AwUOE4I7PM\nR/bWrVuIjIxEREREg9vx8fGBn58ftFot3U8nTpwAz/PU/Wp5/ouLi+Hj44MWLVqgffv2cHV1xbhx\n4wCAJhiWkhwcxxFpY36VAEiU18fHBy4uLigpKQEAxMXFoWvXrpLj5DjOrrVbQECATRT9fjBx4sQm\na6xduXIFgiDYrW1luHv3LnJzczFt2rQH3qc/Apgg8EsvvYR27drBx8eH6mIbe67d5Tj4cBxucBxq\nOA4BHAfjg5K0wMDmBoJmEJpJ2mNAaWlpg4KxDwOj0QgnJyeIomhj+TRmzBia4Xt4eKC0tBSAubBe\nJpOhVatWqKurIykGNigzeyGZTEZpUntRsGHDhiEmJgYs6rZy5UosWLDgvrq0LEkaI1EuLi54/vnn\nUVNTgzfeeIO6QjUaDZEke/ZMGzdulNQNcZzZ7ujzzz9H3759oVar0bdvXwCAt7e3jf6UJU6dOoVh\nw4ZJrKwcHBwk0T9GJKdNm4a8vDxKdVpGTRiRmzNnDqqqquDo6IiYmBgJoWQyDTzPY9WqVVizZg1t\nn6XyFAoFEhMTJedLrVbjs88+g8lkIu06Ly8vEvdkFlKTJ0/G9u3byRKI1Un16tULPM+jf//+kjpE\ndoze3t545513oNfrodfrceDAgYe6V69evQp3d3e4uLjgq6++atJnmLxGY6K0DDdv3oSDgwOCgoIo\nLezl5UU1hIC581kUxQYnTPv27QPHmWuRysrK6PXw8HDk5ORg/PjxRP6VSiWZo0+dOhVqtRq9e/em\nbupWrVrRxMOS3DMtQMsaVUa+QkJC4OLiQs0CsbGxkvWuXLliHuTtwLIr9EHg6OhI/rP3QkJCQqMe\nlIC5izw7O/uR1aH9EWEtNM3M3xkRG81xeJ3jsIXj8LQFMbtvkqbVAr9zurgZf1w0k7THgH79+jVq\n+vygYIKj1sr4zzzzDARBwNatWwEAvXr1QnBwMHV3snRKZWWlRJGfPWyYTpe9aFhmZiaZEYuiiOef\nfx4LFy5skhaSNdGz/LdVq1bYv38/Tp48ifbt20Mul9MA15A905tvvilRFmcdjzt27ICnpydu376N\ns2fPwsHBAaIooqamBq+//jpEUZSo31dVVWHOnDlITk6mWhONRkONEawZgUWvLCMqzBMxNzcXbm5u\nZJ/DulSHDRuG2tpaeHl5wd/fXxKJevbZZ2n7a9aswcKFC2mbM2fOpPo2liJjBNjNzQ0XL17EzZs3\naaDIycmByWTCzZs3ERERAYVCgT179mD48OEU0eF5ntwgZDIZ+Q6aTCaJwXRZWRm6desGnufRpUuX\nB4qe2UNtbS0SEhKgUCianAJjkjGNRW1MJhOmT59O1ysxMRG1tbWoq6uDk5MTcnJyaN0FCxZAFMUG\nuyDj4+Ph4eEBhUJBzQd79uyh7k3LrlyVSgWDwYDevXuT1AfP87h9+zbOnTtH67HJDMdx5HVr7RrA\nNLqcnZ3JPismJgY9evSgdQ4cOACZTGazzzU1NeA4TnJP3w+2bt0KQRCaFOV855137tlccPDgQXh5\neUl8Xf8v4ebNmzh58iSioqIkzzOFQiEhaZ9yHLI5Dj04DgcelqT9jr7Pzfhjo5mkPQZ06tQJ7z1i\nuw/Wcff8889LXh8xYgRJBzAcPHhQEq0BzB1l7AHDaqZEUZTU71guPj4+6NKlC0VjevfujUWLFt03\nObPctlarxYgRI3DlyhWMHz+eCIdWqyWSZm3PtG7dOhtilp6ejn/+85+0Tr9+/chuqGfPnlAoFBg+\nfDiMRiO0Wi2GDx+O7du3o3PnznB1dQXHmdOXWq1WIp/ArJssjdPZ/gcEBODQoUPIycnB5MmT0aZN\nG5IjYXV8TzzxBIxGI4KDg+Hm5kZ6Y8Bv6UnW0MF01hQKBSZNmkTngUVhWFQuMjISVVVV+Pzzz4m0\nTZkyBYBZj0+r1cLHxwdnz55FZGQkRFGEm5sbVCoVxo4dC5lMhvDwcHTs2BHBwcFYtGgRbVun02Hj\nxo1wcnKCg4MDieE+apSUlEAQhCZHl2fMmGG3M/nu3buYPHkyddOOHTsWq1atAs/zRED//ve/U1SS\nITY2tkFrJUautFotBg0aRK8bDAZ0794dxcXFdM8LgoBOnTpBr9cjJiYG+fn50Gg0eO211wCA1mnd\nurVkAsRx5rS0JdRqNaKiouDk5EQ2VS1atJAU5q9ZswYajcZmn3fs2GGzvftBREREkySC7t69C51O\n16BOG2BO3fr4+Dy2e+dRo6amBqdPn8b27duxcOFCTJgwAX369EFeXh4SExMRFBQENzc3aDQaSZZA\nEATJs4/Vm3bp0oWIWD3HIZzjEMn9Jmj7QCTNx8fsb9uMZqCZpD0WZGVlUffZo8COHTvAcRyys7Ml\nlk/Dhg2DIAjYuXMnvVZVVUUdkDNmzEBNTQ3NAC0jZe7u7nbJmVKpRJcuXWggb9269QORM0uCExUV\nha1bt2Lz5s3kE6hQKCiKFRoaKrFnWrNmjaTLVBRFtG7dmjwOLXH58mW4uLiguroalZWVRPhOnz6N\n2NhYSkUKggC1Wi3RF2vfvj3Gjh2L7t27UxOEKIq0Trt27cDzZv+/mpoalJaWomPHjnj66acpIsik\nSaKjo2E0GpGQkACdTifpJhw/fjytu2HDBhIL1mg0GDZsGDjOXHfHvpeZo7dv355Mpdm5+OCDDwCY\n/VQFQUBBQQE+/vhjqNVquLi4QCaT0SDM87wkjcc00jiOQ2FhIXr37g2e59GxY8dGC8IfBVhzgCUR\nagwsCrxu3ToYjUaMGzcOKpUKarUakydPlkT7RowYAZlMhvPnzwMwW6AJgkBSEdevX4dMJiNya42C\nggLo9XqIokiivKtWrYIoivjwww+pZpPneWrAGD16NFQqFdq2bUu2Tez6MTcGy8grx3H4+eef6Ttd\nXV0RFRVF9Y0AEB0dLXEQKC8vl3SfMowePRre3t5NOo/WuHjxInieb5Km3ZAhQ6DRaBq8N0wmE/Lz\n820mjr8X7ty5g7Nnz2LXrl14/fXXUVZWhn79+iE/Px8tW7ZESEgIyc5YEi4myeHo6Ahvb29ERkai\ndevW6Ny5M55++mnMmDEDa9aswcGDB3H16lUqDzh69Cg9t4qKivDss88iLS1NQsSe4ziUWhEzy/+H\ncL/pokVwHA7bI2lZWf+V89mMPyaaSdpjQFxcnN2OrAfB5cuXIQgCPD09JQ/LQYMGQRAE7N69m147\nceIENBoNAgICEBsbi8TERHo4MdLFWsrtEbSsrCyK5AQHB2P27NlNbmG3XhQKBUpKSnD48GF069aN\niAdLUbm6umLkyJFk3r1mzRqJmr5MJkObNm3uKdZZWlqKsrIy3Lp1y65RNCOmarUaqampGDBgABWA\ns1o8Fu148cUXqWYsPDwcSqUSCoUCFy5cwKpVqxAeHk4q8uxc6vV6eHp6ora2Frm5uVCpVFSUD/zm\n3sDzPDZu3Iinn34aHGduPujevTulVtn1YKK9Y8aMQX19PVloKZVKXL58GSaTCZ07d6ZuRuYwwIRa\ne/fuDVdXV+h0Onz66acAgG+//ZZStnK5nL5XLpdTI8Lvgd27d0MmkyElJaVJKVV2bMwGa/r06Q12\njCYnJ8PZ2ZlSlq1bt4arqyt9z+uvvw5BEIjIWeKnn36iVLulThnrvIyOjpZc88jISLLYmjx5MmQy\nmSSFzGQ32GdYXZul6G5AQAAiIiLg4OCAsLAwAEBUVBQ1EQBm0duAgACb/W3Tpg15lt4vWET1Xvjq\nq68gCEKjZRsvv/wyMjIyHkl63Gg04vz589izZw8qKiowZcoUDBw4EAUFBWjVqhXCwsLg6ekJBwcH\niaA2q6vV6/Xw8vJCeHg4UlNTUVhYiCFDhmDatGlYsWIF9u3bh6+++uqB9vXbb7/Fli1bMGTIEDg6\nOkKj0aBdu3YoLy/HgQMHcGfFiodzGrBeLDxzm9GMZpL2GBAQEPDQSuCAWQNKp9NBLpcTmQGAAQMG\nQBRF8vwDfuvsa9++PUwmE5mAWy6WBc2WS2hoKBEkFxcXvPDCCw9Mzvz9/bFkyRK8+OKLlKZkg5ZS\nqUTHjh1psFq5ciXpDDFylZmZ2aRCc5PJhLfffhtyudzGiN2SoBUXF6OgoEBCysLCwjBs2DAcOXIE\nRqMRzs7OyMvLw6FDh4jIBAcHQxRFbN++HUePHoW7uzs2bdpEg4MoinB0dIRWq8UPP/yAXr16UQSP\noaysjAaSt956C7169SKCmpWVZSN+ylIoFRUVMJlMpMnm5+eH2tpafPfdd/Dz84NarcbHH39MNYou\nLi5QqVQYMGAABEFARkYGkZWXX36Ztp+ZmYmnnnoKPM8jKysLJSUl8PHxwaZNmyQR2seJ8+fPw9HR\nEV5eXvjuu+/srnP79m0MHToUcrmcJheWorP2UFdXB1dXV/LurK2thV6vxxNPPEHrMA1BexgxYgRN\nJFgH9MyZM6FUKrFy5UqJfhzzX42NjUVubi5EUcSOHTtw7do1OteWRebMAspS7iI+Ph6hoaFQq9Xk\nfBAZGSlJLXbp0gXx8fE2+8oaae4XdXV1EEWxSfWysbGxiIqKavD9v/zlL/D09CQJEkswwemPP/4Y\nK1euxLRp0zBkyBAUFhYiNTUV4eHh8PLygl6vp8Yldv8zvTJPT0+EhoaiVatWKCgowMCBA/H888+j\noqICe/bswfnz5x9L9Le+vh4XL17EmjVrMGjQIISEhMDZ2RmdO3fG/PnzcezYMdTW1qK2thb//ve/\n8eOPP+IvBw/iok73aAiakxNgUcrRjGY0k7THAEdHxwcu6rVEXFwceJ7H0aNH6bW+fftCFEXSOgPM\nAwzP85g6dSq+//57CfFhi710JRP05DhzUfTIkSMbVc5vaGEinsuXL0dqaiqRDaa7lpiYiLfffhsm\nkwkrVqywIWZZWVmS6FNDuHTpEiZMmEC1V+y7LaOCgYGBVMzLtm9JyqxRXFwMnU6HqqoqIqYdOnQA\nz/MYNWoUvvnmG3h7e2P9+vWkUi+Tyci25uzZsxg1ahREUZRotjGxYZ7nsXnzZuoM9PHxQWxsLARB\nkBQis2v00Ucfobq6Gs7OzuA4jrwc9+3bB6VSidDQUOzfvx96vZ78QSMiIpCUlARBEEjz65dffqEG\nBPa6h4cH1Go1NZgAwJEjR5CQkICsrCz84x//uP+b9AFw69YthIWFQa1WSxwMbt26hX79+kEmk8HJ\nyQmvvvoqTCYTysrKIAgC3nrrrUa3e+nSJcjlcgwZMgQAyL2BCeVWVVVBLpfb1TA0Go1Qq9VwdHRE\nZmYmADPZUKlUmDp1Kl0PNtmRy+Xo06cPVCoVYmNjkZ+fDwB0zZm9GiPHHMdJiFW7du1I6sXDwwOA\nuVaMdSQD5ogZM3K3hEwmk0zQmorJkyfDwcHhnutt3LgRgiDg4sWLMJlMuHr1Kg4ePIg1a9ZgxowZ\n6N+/P1QqFaKiohAZGQlvb284OjoSybX87Wm1Wri7uyM4OBgtW7ZEfn4++vXrh7KyMixevBjvv/8+\nvvjiiyZ7hz5qmEwmnDlzBkuWLEGvXr1gMBjg7e2N3r17Y+nSpaisrJREb1euXElk3bJ85ECPHqgX\nxYcnaUOH/lfOQzP+uGgmaY8YJpMJgiA8dAqApcUsLWF69+4NURSpU66urg6tWrWCTCbDjh07SCXf\nOprUUPSMvd+1a9cHImdubm547rnn8NRTT1FNFyODfn5+mDFjBm7fvo3ly5eT8Tl7eOfk5ODrr79u\n9BzU1tZi2bJlyMrKou2zTkq2rdjYWKo9Y+8rFIomibBu374dPM/jwIEDJHnRoUMHcJzZWP7WrVto\n1aoVZs6cSR6pMpmMUrb79+/HjBkzbBo3WJqO53ls2bIFGRkZ4Diz5IKfnx8EQSBtMkvCXFlZiVOn\nTtHxMW2tF198ETzPo6SkBK+88go4joOnpyd1Y7KBkHXCvv3227Td8PBwDB48GDzPIzs7W9LMwHD3\n7l288cYb8PDwwDPPPPNIJhj3gslkQseOHcmsu1evXtTIYk/UlonIbt68udHtvv/++5KGmfLycoii\nSN2JK1euhCAIdmuyFixYQKSf1bOVlpZCr9eT7Aa7rnFxcYiJiYEgCBg0aBC0Wi0AENFnvy+O48ib\nNS0tjb6rpKQEBoOBCClglv7o168frRMdHY2ePXtK9vH69evgOO6BokhOTk4YPHgwjhw5gg0bNmDW\nrFkYOXIkunbtioyMDERHR1P021JrkP2umDWYWq2Gh4cH2rVrh5KSEowfPx4LFy7E9u3bcfr0aZKF\n+SPi119/xbFjxzBv3jx06tQJzs7OCA0NxeDBg7F27Vr861//ajSqfO3aNUmWgazF6uuBbt0ejqAl\nJgJ2fp/N+N9GM0l7xPj555+bNFttDJs3bwbHcRL18SeffBKiKGL//v0AfnMLcHJywrlz5yRF4Y0t\nvr6+9PBt06aNjR9nU5bWrVtj3LhxpCvGasCYPdPly5dRUVFhQ8zatm1rNz1iiUOHDqFPnz6UfhUE\nQVJXFhUVhcTERErdssGD4ziUlpbiz3/+c5OEa2/evEkpwlmzZhGxZNu7fv06BgwYgJ49eyIpKYl0\nx9ixrlu3DkuXLrWRi2DitoygsVQXE78VRVGS4mTRtW+//ZY003iexyeffAKj0Yjs7GxKgTIvSCcn\nJ6hUKhQXF4PneRQXF8NkMuHu3buk18VxHCZPngyDwQClUtkkTa0ff/wRI0eOhIeHB5YvX/7YNa9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SEhIfj+++/J0zE2NhYmk4nU/8eOHYvw8HASyAwMDISjoyMcHR1x4sQJGI1GsiZiemmiKCI6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\nlJKSQleuXKH09HS6fv06ZWZmSlmuvLw8KculVCpJo9GQX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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26814b6e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rT31GYhBnFN7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "75c3f1ae-0341-4dca-adee-532d6bca758b"
      },
      "cell_type": "code",
      "source": [
        "# Calculate degree distribution histogram\n",
        "#degree_sequence = sorted(nx.degree(g).values(),reverse=True) # degree sequence\n",
        "degree_sequence = sorted([d for n, d in g.degree()], reverse=True)  # degree sequence\n",
        "\n",
        "degreeCount = collections.Counter(degree_sequence)\n",
        "deg, cnt = zip(*degreeCount.items())\n",
        "plt.bar(deg, cnt)#, width=0.80, color='b')\n",
        "#plt.scatter(deg,cnt)\n",
        "\n",
        "# PLot both graphs together\n",
        "#plt.axes([0.4, 0.4, 0.5, 0.5])  \n",
        "#nx.draw(g,pos,node_size = 100)\n",
        "plt.show()\n",
        "print (deg,cnt)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26839984a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(86, 85, 84, 83, 82, 81, 80, 79, 77, 76, 74) (23, 5, 2, 1, 2, 3, 2, 2, 1, 2, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "u9JYr2R1sSj5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "e70fcb8b-cbe6-4c6d-900f-5e9753410012"
      },
      "cell_type": "code",
      "source": [
        "plt.loglog(deg,cnt)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2681910630>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RpVRgzhQ2c10JA5mIiFzmTGMvOnoHMGtiPEKD/KQuR9YYyERE5DKOZq4FBWzmuhoGMhER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\nyERERDLAQCYiIpKB/w+Kd0zSnyrmoQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26839985f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XVFxIb-hqsFw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "45acd33c-81be-4461-f464-7d165a512b84"
      },
      "cell_type": "code",
      "source": [
        "r=nx.degree_assortativity_coefficient(g)\n",
        "print (\"Assortavity  :\" + str(r))\n",
        "\n",
        "# #Calculate clustering\n",
        "# clustering = nx.average_clustering(g)\n",
        "# print(\"Clustering :\" + str(clustering))\n",
        "\n",
        "# Calculate density\n",
        "density = nx.density(g)\n",
        "print (\"Density :\" + str(density))\n"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Assortavity  :-0.08865177759337561\n",
            "Density :0.9730443974630021\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eAj7BUG2x6Jv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import numpy as np\n",
        "from scipy import integrate\n",
        "\n",
        "\n",
        "def disparity_filter(G, weight='weight'):\n",
        "    ''' Compute significance scores (alpha) for weighted edges in G as defined in Serrano et al. 2009\n",
        "        Args\n",
        "            G: Weighted NetworkX graph\n",
        "        Returns\n",
        "            Weighted graph with a significance score (alpha) assigned to each edge\n",
        "        References\n",
        "            M. A. Serrano et al. (2009) Extracting the Multiscale backbone of complex weighted networks. PNAS, 106:16, pp. 6483-6488.\n",
        "    '''\n",
        "    \n",
        "    if nx.is_directed(G): #directed case    \n",
        "        N = nx.DiGraph()\n",
        "        for u in G:\n",
        "            \n",
        "            k_out = G.out_degree(u)\n",
        "            k_in = G.in_degree(u)\n",
        "            \n",
        "            if k_out > 1:\n",
        "                sum_w_out = sum(np.absolute(G[u][v][0][weight]) for v in G.successors(u))\n",
        "                for v in G.successors(u):\n",
        "                    w = G[u][v][0][weight]\n",
        "                    p_ij_out = float(np.absolute(w))/sum_w_out\n",
        "                    alpha_ij_out = 1 - (k_out-1) * integrate.quad(lambda x: (1-x)**(k_out-2), 0, p_ij_out)[0]\n",
        "                    N.add_edge(u, v, weight = w, alpha_out=float('%.4f' % alpha_ij_out))\n",
        "                    \n",
        "            elif k_out == 1 and G.in_degree(G.successors(u)[0]) == 1:\n",
        "                #we need to keep the connection as it is the only way to maintain the connectivity of the network\n",
        "                v = G.successors(u)[0]\n",
        "                w = G[u][v][weight]\n",
        "                N.add_edge(u, v, weight = w, alpha_out=0., alpha_in=0.)\n",
        "                #there is no need to do the same for the k_in, since the link is built already from the tail\n",
        "            \n",
        "            if k_in > 1:\n",
        "                sum_w_in = sum(np.absolute(G[v][u][0][weight]) for v in G.predecessors(u))\n",
        "                for v in G.predecessors(u):\n",
        "                    w = G[v][u][0][weight]\n",
        "                    p_ij_in = float(np.absolute(w))/sum_w_in\n",
        "                    alpha_ij_in = 1 - (k_in-1) * integrate.quad(lambda x: (1-x)**(k_in-2), 0, p_ij_in)[0]\n",
        "                    N.add_edge(v, u, weight = w, alpha_in=float('%.4f' % alpha_ij_in))\n",
        "        return N\n",
        "    \n",
        "    else: #undirected case\n",
        "        B = nx.Graph()\n",
        "        for u in G:\n",
        "            k = len(G[u])\n",
        "            if k > 1:\n",
        "                sum_w = sum(np.absolute(G[u][v][weight]) for v in G[u])\n",
        "                for v in G[u]:\n",
        "                    w = G[u][v][weight]\n",
        "                    p_ij = float(np.absolute(w))/sum_w\n",
        "                    alpha_ij = 1 - (k-1) * integrate.quad(lambda x: (1-x)**(k-2), 0, p_ij)[0]\n",
        "                    B.add_edge(u, v, weight = w, alpha=float('%.4f' % alpha_ij))\n",
        "        return B\n",
        "\n",
        "def disparity_filter_alpha_cut(G,weight='weight',alpha_t=0.4, cut_mode='or'):\n",
        "    ''' Performs a cut of the graph previously filtered through the disparity_filter function.\n",
        "        \n",
        "        Args\n",
        "        ----\n",
        "        G: Weighted NetworkX graph\n",
        "        \n",
        "        weight: string (default='weight')\n",
        "            Key for edge data used as the edge weight w_ij.\n",
        "            \n",
        "        alpha_t: double (default='0.4')\n",
        "            The threshold for the alpha parameter that is used to select the surviving edges.\n",
        "            It has to be a number between 0 and 1.\n",
        "            \n",
        "        cut_mode: string (default='or')\n",
        "            Possible strings: 'or', 'and'.\n",
        "            It works only for directed graphs. It represents the logic operation to filter out edges\n",
        "            that do not pass the threshold value, combining the alpha_in and alpha_out attributes\n",
        "            resulting from the disparity_filter function.\n",
        "            \n",
        "            \n",
        "        Returns\n",
        "        -------\n",
        "        B: Weighted NetworkX graph\n",
        "            The resulting graph contains only edges that survived from the filtering with the alpha_t threshold\n",
        "    \n",
        "        References\n",
        "        ---------\n",
        "        .. M. A. Serrano et al. (2009) Extracting the Multiscale backbone of complex weighted networks. PNAS, 106:16, pp. 6483-6488.\n",
        "    '''    \n",
        "    \n",
        "    \n",
        "    if nx.is_directed(G):#Directed case:   \n",
        "        B = nx.DiGraph()\n",
        "        for u, v, w in G.edges(data=True):\n",
        "            try:\n",
        "                alpha_in =  w['alpha_in']\n",
        "            except KeyError: #there is no alpha_in, so we assign 1. It will never pass the cut\n",
        "                alpha_in = 1\n",
        "            try:\n",
        "                alpha_out =  w['alpha_out']\n",
        "            except KeyError: #there is no alpha_out, so we assign 1. It will never pass the cut\n",
        "                alpha_out = 1  \n",
        "            \n",
        "            if cut_mode == 'or':\n",
        "                if alpha_in<alpha_t or alpha_out<alpha_t:\n",
        "                    B.add_edge(u,v, weight=w[weight])\n",
        "            elif cut_mode == 'and':\n",
        "                if alpha_in<alpha_t and alpha_out<alpha_t:\n",
        "                    B.add_edge(u,v, weight=w[weight])\n",
        "        return B\n",
        "\n",
        "    else:\n",
        "        B = nx.Graph()#Undirected case:   \n",
        "        for u, v, w in G.edges(data=True):\n",
        "            \n",
        "            try:\n",
        "                alpha = w['alpha']\n",
        "            except KeyError: #there is no alpha, so we assign 1. It will never pass the cut\n",
        "                alpha = 1\n",
        "                \n",
        "            if alpha<alpha_t:\n",
        "                B.add_edge(u,v, weight=w[weight])\n",
        "        return B   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FD3eGxWlRK7J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "0f2e18a5-e423-4c7c-9ba7-ef648e96f03e"
      },
      "cell_type": "code",
      "source": [
        "alpha = 0.05\n",
        "G1 = disparity_filter(g)\n",
        "G2 = nx.Graph([(u, v, d) for u, v, d in G1.edges(data=True) if ((d['alpha_in'] < alpha) or (d['alpha_out'] < alpha))])\n",
        "print ('alpha = %s' % alpha)\n",
        "print ('original: nodes = %s, edges = %s' % (G1.number_of_nodes(), G1.number_of_edges()))\n",
        "print ('backbone: nodes = %s, edges = %s' % (G2.number_of_nodes(), G2.number_of_edges()))\n",
        "print (G2.edges(data=True))"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "alpha = 0.05\n",
            "original: nodes = 44, edges = 1841\n",
            "backbone: nodes = 44, edges = 165\n",
            "[('AUS', 'CHN', {'weight': 33353, 'alpha_out': 0.1381, 'alpha_in': 0.0002}), ('AUS', 'ROW', {'weight': 42485, 'alpha_out': 0.3877, 'alpha_in': 0.0}), ('AUS', 'USA', {'weight': 10805, 'alpha_out': 0.0116, 'alpha_in': 0.591}), ('AUS', 'IND', {'weight': 19018, 'alpha_in': 0.0104, 'alpha_out': 0.0168}), ('AUS', 'JPN', {'weight': 28457, 'alpha_in': 0.0009, 'alpha_out': 0.0438}), ('CHN', 'BRA', {'weight': 19782, 'alpha_in': 0.0008, 'alpha_out': 0.3125}), ('CHN', 'IDN', {'weight': 8080, 'alpha_out': 0.0138, 'alpha_in': 0.5499}), ('CHN', 'IND', {'weight': 12119, 'alpha_out': 0.4923, 'alpha_in': 0.0215}), ('CHN', 'JPN', {'weight': 37730, 'alpha_out': 0.015, 'alpha_in': 0.0568}), ('CHN', 'KOR', {'weight': 32487, 'alpha_out': 0.0036, 'alpha_in': 0.0856}), ('CHN', 'ROW', {'weight': 254606, 'alpha_out': 0.0024, 'alpha_in': 0.0}), ('CHN', 'TWN', {'weight': 13187, 'alpha_out': 0.0302, 'alpha_in': 0.3752}), ('CHN', 'FIN', {'weight': 3545, 'alpha_in': 0.77, 'alpha_out': 0.0451}), ('CHN', 'RUS', {'weight': 5405, 'alpha_in': 0.671, 'alpha_out': 0.0422}), ('CHN', 'USA', {'weight': 54146, 'alpha_in': 0.0153, 'alpha_out': 0.0768}), ('ROW', 'AUT', {'weight': 13842, 'alpha_out': 0.7361, 'alpha_in': 0.0016}), ('ROW', 'BEL', {'weight': 31111, 'alpha_out': 0.5007, 'alpha_in': 0.0004}), ('ROW', 'BGR', {'weight': 4211, 'alpha_out': 0.9112, 'alpha_in': 0.0}), ('ROW', 'BRA', {'weight': 45902, 'alpha_out': 0.3589, 'alpha_in': 0.0}), ('ROW', 'CAN', {'weight': 44863, 'alpha_out': 0.3675, 'alpha_in': 0.0004}), ('ROW', 'CHE', {'weight': 39331, 'alpha_out': 0.4163, 'alpha_in': 0.0}), ('ROW', 'DEU', {'weight': 111720, 'alpha_out': 0.079, 'alpha_in': 0.0007}), ('ROW', 'DNK', {'weight': 21669, 'alpha_out': 0.6184, 'alpha_in': 0.0}), ('ROW', 'ESP', {'weight': 40486, 'alpha_out': 0.4056, 'alpha_in': 0.0}), ('ROW', 'EST', {'weight': 1267, 'alpha_out': 0.9724, 'alpha_in': 0.0}), ('ROW', 'FIN', {'weight': 11684, 'alpha_out': 0.7723, 'alpha_in': 0.0}), ('ROW', 'FRA', {'weight': 77982, 'alpha_out': 0.1728, 'alpha_in': 0.0}), ('ROW', 'GBR', {'weight': 105233, 'alpha_out': 0.0919, 'alpha_in': 0.0}), ('ROW', 'GRC', {'weight': 18040, 'alpha_out': 0.6705, 'alpha_in': 0.0}), ('ROW', 'HRV', {'weight': 4636, 'alpha_out': 0.9027, 'alpha_in': 0.0}), ('ROW', 'HUN', {'weight': 5258, 'alpha_out': 0.8904, 'alpha_in': 0.0038}), ('ROW', 'IDN', {'weight': 38994, 'alpha_out': 0.4195, 'alpha_in': 0.0}), ('ROW', 'IND', {'weight': 84985, 'alpha_out': 0.147, 'alpha_in': 0.0}), ('ROW', 'IRL', {'weight': 34711, 'alpha_out': 0.4619, 'alpha_in': 0.0}), ('ROW', 'ITA', {'weight': 49519, 'alpha_out': 0.3308, 'alpha_in': 0.0001}), ('ROW', 'JPN', {'weight': 144224, 'alpha_out': 0.0366, 'alpha_in': 0.0}), ('ROW', 'KOR', {'weight': 70119, 'alpha_out': 0.2069, 'alpha_in': 0.0}), ('ROW', 'LTU', {'weight': 3646, 'alpha_out': 0.9227, 'alpha_in': 0.0}), ('ROW', 'LUX', {'weight': 14956, 'alpha_out': 0.7181, 'alpha_in': 0.0}), ('ROW', 'MEX', {'weight': 10031, 'alpha_out': 0.8011, 'alpha_in': 0.0311}), ('ROW', 'MLT', {'weight': 901, 'alpha_out': 0.9803, 'alpha_in': 0.0022}), ('ROW', 'NLD', {'weight': 56291, 'alpha_out': 0.2837, 'alpha_in': 0.0001}), ('ROW', 'NOR', {'weight': 10184, 'alpha_out': 0.7984, 'alpha_in': 0.0221}), ('ROW', 'POL', {'weight': 11643, 'alpha_out': 0.773, 'alpha_in': 0.0027}), ('ROW', 'PRT', {'weight': 11463, 'alpha_out': 0.7761, 'alpha_in': 0.0}), ('ROW', 'ROU', {'weight': 8026, 'alpha_out': 0.8375, 'alpha_in': 0.0}), ('ROW', 'CYP', {'weight': 1017, 'alpha_in': 0.9761, 'alpha_out': 0.0}), ('ROW', 'CZE', {'weight': 5711, 'alpha_in': 0.0219, 'alpha_out': 0.8815}), ('ROW', 'LVA', {'weight': 834, 'alpha_in': 0.9804, 'alpha_out': 0.0003}), ('ROW', 'RUS', {'weight': 16753, 'alpha_out': 0.0, 'alpha_in': 0.6707}), ('ROW', 'SVK', {'weight': 5909, 'alpha_out': 0.0005, 'alpha_in': 0.8689}), ('ROW', 'SVN', {'weight': 1957, 'alpha_out': 0.0011, 'alpha_in': 0.9546}), ('ROW', 'SWE', {'weight': 20403, 'alpha_out': 0.6361, 'alpha_in': 0.0002}), ('ROW', 'TUR', {'weight': 27082, 'alpha_out': 0.0, 'alpha_in': 0.5232}), ('ROW', 'TWN', {'weight': 51647, 'alpha_out': 0.0, 'alpha_in': 0.2882}), ('ROW', 'USA', {'weight': 307314, 'alpha_out': 0.0, 'alpha_in': 0.0003}), ('USA', 'BRA', {'weight': 12703, 'alpha_out': 0.5553, 'alpha_in': 0.012}), ('USA', 'CAN', {'weight': 154475, 'alpha_out': 0.0004, 'alpha_in': -0.0}), ('USA', 'FRA', {'weight': 26689, 'alpha_out': 0.0462, 'alpha_in': 0.2695}), ('USA', 'GBR', {'weight': 46233, 'alpha_out': 0.1129, 'alpha_in': 0.0035}), ('USA', 'GRC', {'weight': 3403, 'alpha_out': 0.0218, 'alpha_in': 0.848}), ('USA', 'IRL', {'weight': 19344, 'alpha_out': 0.407, 'alpha_in': 0.0008}), ('USA', 'JPN', {'weight': 34873, 'alpha_out': 0.195, 'alpha_in': 0.02}), ('USA', 'KOR', {'weight': 16776, 'alpha_out': 0.4591, 'alpha_in': 0.0411}), ('USA', 'LUX', {'weight': 7904, 'alpha_out': 0.0009, 'alpha_in': 0.6811}), ('USA', 'MEX', {'weight': 77468, 'alpha_out': 0.0242, 'alpha_in': -0.0}), ('USA', 'NLD', {'weight': 23586, 'alpha_out': 0.0128, 'alpha_in': 0.3146}), ('USA', 'NOR', {'weight': 4340, 'alpha_out': 0.0251, 'alpha_in': 0.8102}), ('USA', 'SWE', {'weight': 7427, 'alpha_out': 0.0274, 'alpha_in': 0.6971}), ('USA', 'CHE', {'weight': 12097, 'alpha_in': 0.0287, 'alpha_out': 0.5712}), ('USA', 'IND', {'weight': 10187, 'alpha_in': 0.0406, 'alpha_out': 0.6243}), ('USA', 'TWN', {'weight': 13197, 'alpha_in': 0.0371, 'alpha_out': 0.5427}), ('AUT', 'DEU', {'weight': 30401, 'alpha_out': 0.1042, 'alpha_in': 0.0}), ('AUT', 'SVK', {'weight': 2080, 'alpha_out': 0.3598, 'alpha_in': 0.0315}), ('AUT', 'SVN', {'weight': 1379, 'alpha_out': 0.0096, 'alpha_in': 0.5508}), ('AUT', 'HRV', {'weight': 1150, 'alpha_in': 0.6085, 'alpha_out': 0.0152}), ('AUT', 'ITA', {'weight': 7332, 'alpha_in': 0.0379, 'alpha_out': 0.3397}), ('DEU', 'BEL', {'weight': 30627, 'alpha_out': 0.1024, 'alpha_in': 0.0005}), ('DEU', 'BGR', {'weight': 897, 'alpha_out': 0.937, 'alpha_in': 0.0287}), ('DEU', 'CHE', {'weight': 23467, 'alpha_out': 0.1764, 'alpha_in': 0.0008}), ('DEU', 'CZE', {'weight': 21944, 'alpha_out': 0.1978, 'alpha_in': 0.0}), ('DEU', 'DNK', {'weight': 12464, 'alpha_out': 0.0007, 'alpha_in': 0.4709}), ('DEU', 'ESP', {'weight': 27885, 'alpha_out': 0.0017, 'alpha_in': 0.1819}), ('DEU', 'FIN', {'weight': 8396, 'alpha_out': 0.0004, 'alpha_in': 0.603}), ('DEU', 'FRA', {'weight': 66453, 'alpha_out': 0.0003, 'alpha_in': 0.0153}), ('DEU', 'GBR', {'weight': 42762, 'alpha_out': 0.0046, 'alpha_in': 0.0712}), ('DEU', 'HUN', {'weight': 12781, 'alpha_out': 0.0, 'alpha_in': 0.4619}), ('DEU', 'ITA', {'weight': 38693, 'alpha_out': 0.0024, 'alpha_in': 0.0922}), ('DEU', 'LTU', {'weight': 649, 'alpha_out': 0.0412, 'alpha_in': 0.9619}), ('DEU', 'LUX', {'weight': 4593, 'alpha_out': 0.02, 'alpha_in': 0.7588}), ('DEU', 'MLT', {'weight': 832, 'alpha_out': 0.0167, 'alpha_in': 0.9514}), ('DEU', 'NLD', {'weight': 35803, 'alpha_out': 0.0011, 'alpha_in': 0.1107}), ('DEU', 'NOR', {'weight': 4774, 'alpha_out': 0.0171, 'alpha_in': 0.7506}), ('DEU', 'POL', {'weight': 25994, 'alpha_out': 0.0, 'alpha_in': 0.2047}), ('DEU', 'PRT', {'weight': 4192, 'alpha_out': 0.0086, 'alpha_in': 0.7774}), ('DEU', 'ROU', {'weight': 4215, 'alpha_out': 0.0017, 'alpha_in': 0.7763}), ('DEU', 'RUS', {'weight': 11974, 'alpha_out': 0.0006, 'alpha_in': 0.4851}), ('DEU', 'SVK', {'weight': 5725, 'alpha_out': 0.0006, 'alpha_in': 0.7087}), ('DEU', 'SVN', {'weight': 2023, 'alpha_out': 0.0009, 'alpha_in': 0.8857}), ('DEU', 'SWE', {'weight': 13422, 'alpha_out': 0.0012, 'alpha_in': 0.4441}), ('DEU', 'TUR', {'weight': 9330, 'alpha_out': 0.0112, 'alpha_in': 0.5698}), ('DEU', 'EST', {'weight': 550, 'alpha_in': 0.9676, 'alpha_out': 0.0063}), ('DEU', 'GRC', {'weight': 2893, 'alpha_in': 0.8406, 'alpha_out': 0.0396}), ('DEU', 'HRV', {'weight': 1473, 'alpha_in': 0.9155, 'alpha_out': 0.0043}), ('DEU', 'CYP', {'weight': 405, 'alpha_out': 0.0242, 'alpha_in': 0.976}), ('DEU', 'LVA', {'weight': 403, 'alpha_in': 0.9761, 'alpha_out': 0.0253}), ('SVK', 'CZE', {'weight': 5522, 'alpha_out': 0.0009, 'alpha_in': 0.025}), ('SVK', 'HUN', {'weight': 2093, 'alpha_out': 0.2025, 'alpha_in': 0.0308}), ('SVK', 'RUS', {'weight': 3262, 'alpha_out': 0.0178, 'alpha_in': 0.6073}), ('SVN', 'ITA', {'weight': 1747, 'alpha_out': 0.0025, 'alpha_in': 0.7373}), ('BEL', 'FRA', {'weight': 24672, 'alpha_out': 0.0588, 'alpha_in': 0.0023}), ('BEL', 'NLD', {'weight': 18244, 'alpha_out': 0.0357, 'alpha_in': 0.0123}), ('BEL', 'LUX', {'weight': 5273, 'alpha_in': 0.2937, 'alpha_out': 0.0108}), ('FRA', 'CHE', {'weight': 7662, 'alpha_out': 0.0476, 'alpha_in': 0.3772}), ('FRA', 'ESP', {'weight': 23173, 'alpha_out': 0.0702, 'alpha_in': 0.0013}), ('FRA', 'ITA', {'weight': 24441, 'alpha_out': 0.0244, 'alpha_in': 0.041}), ('FRA', 'LUX', {'weight': 3878, 'alpha_out': 0.6486, 'alpha_in': 0.0311}), ('FRA', 'NOR', {'weight': 10039, 'alpha_out': 0.323, 'alpha_in': 0.0234}), ('FRA', 'PRT', {'weight': 2722, 'alpha_out': 0.0484, 'alpha_in': 0.709}), ('FRA', 'GBR', {'weight': 24844, 'alpha_in': 0.0389, 'alpha_out': 0.0478}), ('FRA', 'MLT', {'weight': 642, 'alpha_in': 0.9223, 'alpha_out': 0.0443}), ('NLD', 'LUX', {'weight': 4159, 'alpha_in': 0.0239, 'alpha_out': 0.4786}), ('BGR', 'RUS', {'weight': 3100, 'alpha_out': 0.0, 'alpha_in': 0.6226}), ('BGR', 'TUR', {'weight': 974, 'alpha_out': 0.6392, 'alpha_in': 0.0208}), ('RUS', 'CZE', {'weight': 5708, 'alpha_out': 0.0278, 'alpha_in': 0.4162}), ('RUS', 'FIN', {'weight': 6715, 'alpha_out': 0.0023, 'alpha_in': 0.3559}), ('RUS', 'GRC', {'weight': 3510, 'alpha_out': 0.0192, 'alpha_in': 0.5846}), ('RUS', 'HRV', {'weight': 1650, 'alpha_out': 0.0021, 'alpha_in': 0.7776}), ('RUS', 'HUN', {'weight': 4175, 'alpha_out': 0.0388, 'alpha_in': 0.5276}), ('RUS', 'ITA', {'weight': 22701, 'alpha_out': 0.0322, 'alpha_in': 0.0273}), ('RUS', 'LTU', {'weight': 3624, 'alpha_out': 0.0, 'alpha_in': 0.5744}), ('RUS', 'POL', {'weight': 10735, 'alpha_out': 0.0062, 'alpha_in': 0.1894}), ('RUS', 'TUR', {'weight': 10634, 'alpha_in': 0.1924, 'alpha_out': 0.0057}), ('RUS', 'LVA', {'weight': 760, 'alpha_out': 0.0007, 'alpha_in': 0.8908}), ('TUR', 'MLT', {'weight': 993, 'alpha_out': 0.0072, 'alpha_in': 0.6212}), ('CHE', 'ITA', {'weight': 9371, 'alpha_out': 0.0234, 'alpha_in': 0.19}), ('CHE', 'LUX', {'weight': 3461, 'alpha_out': 0.2597, 'alpha_in': 0.0459}), ('ITA', 'ESP', {'weight': 14875, 'alpha_out': 0.0374, 'alpha_in': 0.0694}), ('ITA', 'GRC', {'weight': 4451, 'alpha_out': 0.0062, 'alpha_in': 0.4581}), ('ITA', 'HRV', {'weight': 1455, 'alpha_out': 0.8089, 'alpha_in': 0.0032}), ('ITA', 'LUX', {'weight': 3414, 'alpha_out': 0.607, 'alpha_in': 0.0479}), ('ITA', 'ROU', {'weight': 2960, 'alpha_out': 0.0126, 'alpha_in': 0.596}), ('LUX', 'GBR', {'weight': 12151, 'alpha_in': 0.243, 'alpha_out': 0.0}), ('LUX', 'MLT', {'weight': 646, 'alpha_in': 0.5722, 'alpha_out': 0.0434}), ('IDN', 'JPN', {'weight': 15230, 'alpha_out': 0.193, 'alpha_in': 0.0011}), ('JPN', 'KOR', {'weight': 35639, 'alpha_out': 0.002, 'alpha_in': 0.0183}), ('JPN', 'TWN', {'weight': 27160, 'alpha_in': 0.0491, 'alpha_out': 0.0005}), ('CZE', 'POL', {'weight': 5465, 'alpha_out': 0.0326, 'alpha_in': 0.0695}), ('POL', 'LTU', {'weight': 671, 'alpha_out': 0.0368, 'alpha_in': 0.7272}), ('DNK', 'GBR', {'weight': 5410, 'alpha_out': 0.5254, 'alpha_in': 0.0374}), ('DNK', 'SWE', {'weight': 8726, 'alpha_out': 0.0141, 'alpha_in': 0.0044}), ('DNK', 'NOR', {'weight': 3932, 'alpha_in': 0.0942, 'alpha_out': 0.036}), ('ESP', 'PRT', {'weight': 11467, 'alpha_out': 0.0, 'alpha_in': 0.0419}), ('FIN', 'EST', {'weight': 952, 'alpha_out': 0.4447, 'alpha_in': 0.0003}), ('FIN', 'SWE', {'weight': 5589, 'alpha_out': 0.0687, 'alpha_in': 0.0079}), ('GBR', 'IRL', {'weight': 21465, 'alpha_out': 0.0005, 'alpha_in': 0.0795}), ('GBR', 'MLT', {'weight': 760, 'alpha_out': 0.0243, 'alpha_in': 0.9166}), ('GBR', 'NOR', {'weight': 5251, 'alpha_out': 0.0111, 'alpha_in': 0.5458}), ('GBR', 'CYP', {'weight': 443, 'alpha_out': 0.0168, 'alpha_in': 0.9505}), ('HUN', 'ROU', {'weight': 2531, 'alpha_in': 0.0748, 'alpha_out': 0.0245}), ('LTU', 'LVA', {'weight': 561, 'alpha_in': 0.092, 'alpha_out': 0.0054}), ('MLT', 'CYP', {'weight': 611, 'alpha_in': 0.0109, 'alpha_out': 0.0518}), ('NOR', 'SWE', {'weight': 7181, 'alpha_out': 0.031, 'alpha_in': 0.0705}), ('SWE', 'EST', {'weight': 636, 'alpha_out': 0.7436, 'alpha_in': 0.0049}), ('GRC', 'CYP', {'weight': 450, 'alpha_in': 0.54, 'alpha_out': 0.0157})]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mbmIRlBrSKPW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "f1b7a50f-6199-401d-d2db-8d8e21bbf93a"
      },
      "cell_type": "code",
      "source": [
        "pos1 = nx.spring_layout(G2)\n",
        "nx.draw(G2,pos1, with_labels=True,node_color = \"red\", node_list=\"header\", node_size=[math.log(v)*50 for v in node_size], arrows = True)\n",
        "plt.show()"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w3miRkTS4fJlhEGvvuODgYJ4/f07hwoUTzd68eXPu3r3LpUuXYu3pBpA/f37G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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2683aa4c18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jB0z5uT_cb7U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "079926c3-ab00-4260-8f6c-e36a98f2d401"
      },
      "cell_type": "code",
      "source": [
        "r=nx.degree_assortativity_coefficient(G2)\n",
        "print (\"Assortavity  :\" + str(r))\n",
        "\n",
        "# #Calculate clustering\n",
        "# clustering = nx.average_clustering(g)\n",
        "# print(\"Clustering :\" + str(clustering))\n",
        "\n",
        "# Calculate density\n",
        "density = nx.density(G2)\n",
        "print (\"Density :\" + str(density))"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Assortavity  :-0.37932882535249246\n",
            "Density :0.1744186046511628\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "k8WFIG0OcvUo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "c4db5298-a976-46af-f40d-dd0d3f701666"
      },
      "cell_type": "code",
      "source": [
        "# Calculate degree distribution histogram\n",
        "#degree_sequence = sorted(nx.degree(g).values(),reverse=True) # degree sequence\n",
        "degree_sequence1 = sorted([d for n, d in G2.degree()], reverse=True)  # degree sequence\n",
        "\n",
        "degreeCount1 = collections.Counter(degree_sequence1)\n",
        "deg1, cnt1 = zip(*degreeCount1.items())\n",
        "plt.bar(deg1, cnt1)#, width=0.80, color='b')\n",
        "#plt.scatter(deg,cnt)\n",
        "\n",
        "# PLot both graphs together\n",
        "#plt.axes([0.4, 0.4, 0.5, 0.5])  \n",
        "#nx.draw(g,pos,node_size = 100)\n",
        "plt.show()\n",
        "print (deg1,cnt1)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2683f44d68>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(43, 31, 19, 15, 12, 11, 10, 7, 6, 5, 4, 3, 2) (1, 1, 1, 1, 2, 1, 2, 4, 5, 12, 9, 3, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "N9sU-G33c0Od",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "47390ed7-b328-4532-d2f8-35d29a1e7819"
      },
      "cell_type": "code",
      "source": [
        "plt.loglog(deg1,cnt1)"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f268426ea90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qPN9jdRwgaSVFIZumqZ+/1qRv/6BOZsTUn36oRh++eakc3NQEyOV06L7bqyWN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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2683f74c18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HfEeWkYDgAdb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "711e25c8-1a9b-496f-b5c9-4860ea0c9251"
      },
      "cell_type": "code",
      "source": [
        "print(len(list(nx.isolates(G2))))"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}