Support_and_resistance.ipynb

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    "colab": {
      "name": "Support and resistance.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVfmDQwCwV8j",
        "colab_type": "text"
      },
      "source": [
        "https://saralgyaan.com/posts/python-candlestick-chart-matplotlib-tutorial-chapter-11/\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 230
        },
        "outputId": "5b9d2680-19a9-4093-bcb9-e770a876e517"
      },
      "source": [
        "!pip install yfinance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.6/dist-packages (0.1.54)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance) (2.23.0)\n",
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            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.18.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2020.6.20)\n",
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            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.15.0)\n"
          ],
          "name": "stdout"
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        },
        "outputId": "d714ac7f-5820-418e-dad1-cc82cb9714ca"
      },
      "source": [
        "!pip install mpl_finance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mpl_finance in /usr/local/lib/python3.6/dist-packages (0.10.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from mpl_finance) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl_finance) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl_finance) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl_finance) (2.4.7)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl_finance) (1.18.5)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl_finance) (2.8.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->mpl_finance) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
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    {
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      "metadata": {
        "trusted": true,
        "id": "DRwrGVy9iyCO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import yfinance\n",
        "from mpl_finance import candlestick_ohlc\n",
        "import matplotlib.dates as mpl_dates\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [12, 7]\n",
        "\n",
        "plt.rc('font', size=14) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6u85RbZpjIk2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "name = 'SPY'\n",
        "ticker = yfinance.Ticker(name)\n",
        "df = ticker.history(interval=\"1d\",start=\"2020-03-15\",end=\"2020-07-15\")\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKZn1fMevqLG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Date'] = pd.to_datetime(df.index)\n",
        "df['Date'] = df['Date'].apply(mpl_dates.date2num)\n",
        "\n",
        "df = df.loc[:,['Date', 'Open', 'High', 'Low', 'Close']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnZATktnv-1w",
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          "height": 441
        },
        "outputId": "cdc08020-9787-4f4a-8dd4-5fea3aac9740"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>737500.0</td>\n",
              "      <td>238.72</td>\n",
              "      <td>254.28</td>\n",
              "      <td>234.94</td>\n",
              "      <td>237.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>737501.0</td>\n",
              "      <td>242.54</td>\n",
              "      <td>253.56</td>\n",
              "      <td>234.65</td>\n",
              "      <td>250.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>737502.0</td>\n",
              "      <td>233.84</td>\n",
              "      <td>245.84</td>\n",
              "      <td>225.69</td>\n",
              "      <td>237.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-19</th>\n",
              "      <td>737503.0</td>\n",
              "      <td>236.81</td>\n",
              "      <td>244.86</td>\n",
              "      <td>229.85</td>\n",
              "      <td>238.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-20</th>\n",
              "      <td>737504.0</td>\n",
              "      <td>241.47</td>\n",
              "      <td>243.40</td>\n",
              "      <td>227.50</td>\n",
              "      <td>227.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-08</th>\n",
              "      <td>737614.0</td>\n",
              "      <td>314.61</td>\n",
              "      <td>316.30</td>\n",
              "      <td>312.70</td>\n",
              "      <td>316.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-09</th>\n",
              "      <td>737615.0</td>\n",
              "      <td>316.84</td>\n",
              "      <td>317.10</td>\n",
              "      <td>310.68</td>\n",
              "      <td>314.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-10</th>\n",
              "      <td>737616.0</td>\n",
              "      <td>314.31</td>\n",
              "      <td>317.88</td>\n",
              "      <td>312.76</td>\n",
              "      <td>317.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-13</th>\n",
              "      <td>737619.0</td>\n",
              "      <td>320.13</td>\n",
              "      <td>322.71</td>\n",
              "      <td>314.13</td>\n",
              "      <td>314.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-14</th>\n",
              "      <td>737620.0</td>\n",
              "      <td>313.30</td>\n",
              "      <td>319.76</td>\n",
              "      <td>312.00</td>\n",
              "      <td>318.92</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>84 rows × 5 columns</p>\n",
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            ],
            "text/plain": [
              "                Date    Open    High     Low   Close\n",
              "Date                                                \n",
              "2020-03-16  737500.0  238.72  254.28  234.94  237.40\n",
              "2020-03-17  737501.0  242.54  253.56  234.65  250.22\n",
              "2020-03-18  737502.0  233.84  245.84  225.69  237.55\n",
              "2020-03-19  737503.0  236.81  244.86  229.85  238.06\n",
              "2020-03-20  737504.0  241.47  243.40  227.50  227.80\n",
              "...              ...     ...     ...     ...     ...\n",
              "2020-07-08  737614.0  314.61  316.30  312.70  316.18\n",
              "2020-07-09  737615.0  316.84  317.10  310.68  314.38\n",
              "2020-07-10  737616.0  314.31  317.88  312.76  317.59\n",
              "2020-07-13  737619.0  320.13  322.71  314.13  314.84\n",
              "2020-07-14  737620.0  313.30  319.76  312.00  318.92\n",
              "\n",
              "[84 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vz3tA1pnxRle",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def isSupport(df,i):\n",
        "  support = df['Low'][i] < df['Low'][i-1]  and df['Low'][i] < df['Low'][i+1] \\\n",
        "  and df['Low'][i+1] < df['Low'][i+2] and df['Low'][i-1] < df['Low'][i-2]\n",
        "\n",
        "  return support\n",
        "\n",
        "def isResistance(df,i):\n",
        "  resistance = df['High'][i] > df['High'][i-1]  and df['High'][i] > df['High'][i+1] \\\n",
        "  and df['High'][i+1] > df['High'][i+2] and df['High'][i-1] > df['High'][i-2] \n",
        "\n",
        "  return resistance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrD0cN7bxKKb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "levels = []\n",
        "for i in range(2,df.shape[0]-2):\n",
        "  if isSupport(df,i):\n",
        "    levels.append((i,df['Low'][i]))\n",
        "  elif isResistance(df,i):\n",
        "    levels.append((i,df['High'][i]))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yH3CyoKbvZpZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_all():\n",
        "  fig, ax = plt.subplots()\n",
        "\n",
        "  candlestick_ohlc(ax,df.values,width=0.6, \\\n",
        "                   colorup='green', colordown='red', alpha=0.8)\n",
        "\n",
        "  date_format = mpl_dates.DateFormatter('%d %b %Y')\n",
        "  ax.xaxis.set_major_formatter(date_format)\n",
        "  fig.autofmt_xdate()\n",
        "\n",
        "  fig.tight_layout()\n",
        "\n",
        "  for level in levels:\n",
        "    plt.hlines(level[1],xmin=df['Date'][level[0]],\\\n",
        "               xmax=max(df['Date']),colors='blue')\n",
        "  fig.show()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRS4Zyds3i80",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "68dd878d-ee4d-4496-e88d-b5cab6bd6c7d"
      },
      "source": [
        "plot_all()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZE2z86rVFqf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s =  np.mean(df['High'] - df['Low'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Pm8EsWIaGX8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def isFarFromLevel(l):\n",
        "  return np.sum([abs(l-x) < s  for x in levels]) == 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBz47_hO3dN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "levels = []\n",
        "for i in range(2,df.shape[0]-2):\n",
        "  if isSupport(df,i):\n",
        "    l = df['Low'][i]\n",
        "\n",
        "    if isFarFromLevel(l):\n",
        "      levels.append((i,l))\n",
        "\n",
        "  elif isResistance(df,i):\n",
        "    l = df['High'][i]\n",
        "\n",
        "    if isFarFromLevel(l):\n",
        "      levels.append((i,l))\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9KbA7dnB4M0g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "083d9a97-714d-459b-d4be-37f4a15479d4"
      },
      "source": [
        "levels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(5, 217.3),\n",
              " (11, 262.18),\n",
              " (25, 270.83),\n",
              " (31, 293.59),\n",
              " (58, 321.99),\n",
              " (67, 306.53)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOzjhPL04UjU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "8ce686f8-0c8d-445c-cc68-21c4368ca994"
      },
      "source": [
        "plot_all()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u45YkSZKG14DmpNryegqPSP1QSde7iHgF8LfA5zhUzW438NRyrNGlwIsi4gbg34DfIo9jehNASumTEfFu4LVldzqA1wLvSil9qoqYJUmS+q7TwjEzkx+qhoVHVIGqxijtBN5YLpeB64EfTCm9p9x+MXBP4A+A+wAfAZ7cNYcS5OTqVUDnOe8AfrmieCVJkqpji8dIqGpCW42mShKllNLUUbYnYKZ8rLfPV4Gf6WdckiRJtbDFYyRUNaGtRtMgxyhJkqQx4F13SVuBiZIkSTom3nWXtBWYKEmStBU0m1Zek6RjYKIkSdJW0G4718wwssiDNLRMlCRJkupikQdpaJkoSZIkjajmRJOV1RUWDyzSnLBLpdRPJkqSJFVofv983SFojLX3tJk+Z5pib0F7j10qpX4yUZIkqUILSwtrb5ifH2QYkqRjZKIkSVIdFhbqjkCSdAQn1h2AJEnjqDXXolguWLptibn9czQnmkPbNcoJZCXpcLYoSZJ0DDY65qhYLmhsb7Bj2w4a2xsUy0Xe0GrleYxmZ/Oy1aou2A1yAtnRZqIrVcMWJUmSjsG6Y442qiig0YDV1bwsir7Epa3LRFeqhi1KkiT1g8UZJGmsmChJktQPPcUZmhNNFg8sOseNJI0ou95JklSBTuGG+f3zG+oa1Sn+cMsdt3Cvk+5VTfGHycn+Hk+SxpiJkiQN2O7ddUegzViYhH0zh6+fWoD5fWs9Y4r57l9v+nP4yklM3f5a5r/+XFi9A3bDP3/+lWw74SRuf+hrueNjz+Wmg3ew+0/6Hf0Udw9GkgZn3766Izg2JkqSNGA37pxn541TdYehY7T/xn/htjtv487TP8eNn7+KU048hV07v/vYD3TKKXDbbfCNgzlJOuWU/gcrSdo0EyVJGrDzfmGBmd11R6Fj1bz06Xzb9gZf+PoXeOBTrmPxwCL79nZVrJuBqZmNHKlMrmbew9TMo+9a25p7wV3zLp322PfQnGiyb0jnXZKkrcBESZKkzWi1convpSWYm8tzI7WPPcHpjEea2TfDzO6ZPgcpSTpWVr2TJA2NdSdzHebS2515kXbscF4kSRojJkqSpKFx2GSurVZuobnwwrxstWqJS5K09dj1TpI0vDqtNaur49daY6luSRpqJkqSNCCdeXKWbltibv/c4fPkzM/D1FRd4WnQ1jnXk6dNDjQMSdLa7HonSQNSLBc0tjfYsW0Hje0NiuWe1pGFhVri6od1xxaNkeZEk8UDi6ysrrB4YJHmRLOS19nI5LSSpOrZoiRJ2rTDxhaNoU7r3/z+eZMZSdoCTJQkSZvSmmtx3U3Xrd2d8BiOccRuiUPEJEmStgYTJUnSphTLxfrdCY/hGI3tDVYPrm7qOJIk9YtjlCRJ2oxmExYX4fbb87JZzdglSdJg2aIkSQPSnGhSLBeHFwNotXLZ66UlmJvLF9rt4ex2pjV4riRpLJkoSVJVesp9d8bczOybYWb3zKH9xnmuIEmSRpRd7ySpCq0WXHhhbh1qteqOplLNiWblJbMlSRo0EyVJqkJRwI4d/Wkhmp/vS0hVae9pM33ONMXeYmgr1UmSdKxMlCRpwCZPm7z7ik4xgJWVtYsBjPBEtBs1qMlcJUnaKMcoSdKAHTYPT6cYwMxMfmxB647fkiSpJrYoSZI2bp1ugIe1kh2nfh1HkqTNMlGSJG3cOt0AD2slO079Oo4kSZtloiRJW92QF4uQJKkOJkqStNVtgWIRkiQdKxMlSRoWk5N1RyBJkkomSpI0LKam6o7g7uySJ0nawkyUJGmT5vfP1x1Cf7VaeS6nCy/MP0uStAWZKEnSJi0sLdQdQn8VBTQasGNH/rmj1YLZ2ZxEmUBJksaciZIkSZIk9TBRklSJseuOJmi3YXo6tzK123VHkzmOSpJUkRPrDkDSeBq77mjraM21uO6m65jbP0dzokl7z5AkEFUZtsp8ljaXJFXEFiVJfdeaazH74VmalzZpzW1yLMuQtxgUywU7tu2gsb1BsVwc/QnDpFO0YXZ242OOBl2Zr9mExUVYWcnLZnOwry9J2rJMlCT1XV+TB1sMqrNe0YZh0m7n2C67rC9d/uwSKknaKLveSdJW1Wzm5GNlBR7+8LqjObLelqxWK8e+tARzc/lvOUIS1ZprUSwXLN22xOv/5fXj30VSkrRptihJ0lbVaa2Znh6e4gwb1d0a1mgctUWsWC5obG+wY9uO0esiKUmqhYmSJFWh2XRcjSRJI8xESVLfNSearKyusHhgkebEFk0S2u3Nj6vpLrbgJK+SJA2UiZKkw2x2wHt7T5vpc6Yp9hZjPxbkiEnhZivEHWP3MuWxSM1Lm9z34vtuvuKiJGlLs5iDpMNslTmQ+qG9p838/nmmdk3VHYo4NBZp9eCqY5EkSZtii5IkbZJJkiRJ48dESZJ0d92TvFqIQpK0RZkoSbpLZ3zH7IdnaV7aPGyMx0hP1jk/X3cEtdvw+eue5HXUyoZLktQnJkqS7tI910xje+OwMR4jPXZpYWHN1SOd/G1QJwG+8N0Xrl3gYHJy7SduthiFJEkjzGIOkra0kU7+NuioBQ5MiCRJOoyJkqQtqTXXolguWLptibn9czQnmmNfylySJG2cXe8kbUlH62Y4FLqLKiwuWlhBkqQBqiRRiogXRsTVEfG1iFiMiHdGxFk9+9wrIl4VEZ+PiFsj4lMRMd2zz8nlPjdHxEpEvCMizqgiZkn9NXna5OYO0GrlxGB2Ni9bW3Dy0E5RhenpvLSwwnFrTjRZPLDIyurK4RMDS5K0hqpalHYDrwYeAzwBuBO4IiJO79rnEuCpwLOABwMvB14REc/q2udS4FzgfOBxwKnAuyLihIriltQnm55bqCig0YAdO/KyGMIWH42M9p42xd6C6XOm7WIpSdqQSsYopZR+oPv3MvlZBr4XeGe5+jHAG1JK/1D+vhARzwEeBbwhIiaA5wB7Ukrv6zpOATwReE8VsUuSJEnSoMYo3bt8ra92rfsn4GkR8SCAiHgMsAt4d7n9EcA24L2dJ6SUPgd8kpxkSRpnjs85ZL3y3VuZ/z8kSRUbVKJ0GbAfuKpr3a8A1wH/GRGrwAeAF6SU3lVu3wkcBG7uOdZN5bbDRMQFEXFNRFyzuLjYz/ilWmyFOX7W1T3p6VYfn2P57sMd5/itTY+dkyRtGZUnShFxCfBY4NyU0sGuTc8ntwz9CLn1aBr4vYh4yvG+Vkrp8pTS2SmlsxuNxmbClobCoOf46R7wvnhgcTgGvR9jkrClk0sd1abHzkmStoxK51GKiFngPOD7Ukqf6Vp/T+B3gWeklDpjlq6PiF3Ar5O7390InADcD+huHnoA8I9Vxi1tVZ1B7jP7ZpjZPVNvMMdpo8llc6JJsVwMV1JYke6/9eGnP7zucCRJGgmVJUoRcRnwTHKSdEPP5m3l42DP+oMcauW6FlgFngS8qTzmGeQKeR+qKGxJ46bVyl2zlpZgbi6PZWm3xyIp3Kit9LdKktQvlSRKEfEH5LLfTwe+GhGdMUW3pJRuSSl9LSI+QC4Hfgu5kt3jgWcDvwmQUlqOiNcBF0fEl4Avk0uKXw9cUUXcksZQp8z46qplxiVJ0oZVNUbpeeRKd+8Hvtj1+PWufc4Drgb+DPhX4CLgJcD/37XPXuBvgLcAHwRuAZ7WM9ZJUt3m5+uOQJIkqa+qmkcpNrDPjcCeo+xzO7now/P7FJqkKiws1B2BJElSXw2qPLgkSZIkjYxKq95JGnOXX54fX/gCPPCBcMEFtYXSmmtRLBcs3bbE3P45mhPNXMSg2czjkpyYdDznEOqZjNcKf5KkfrFFSdLxu+ACuOaavKwxSQIolgsa2xvs2LaDxvYGxXJZtMGJa+8ylnMI9cyz1d7TpthbMH3O9F3V/iRJOh4mSpLGwlEny11n4tqxbGWRJEmbZtc7aUi15lpcd9N1d+9GpnUd71xBY9nKIkmSNs0WJWlIFcvF4d3IdIglySVJUoVMlKQh1Zxort+NrF82mGxcfu3lnH352Vx+7eVcfu3l1cRyrCxJLkmSKmSiJA2p9p420+dMU+wtqut2t8Fk44JHXMA1F1zDBY+4gAseUW/RBkmSpEEwUZK2olYrl8menc3LVqvuiCRJkoaKxRykragooNGA1dW8LEZoDFSrleNdWoK5uZzodZX8toqdJEnqBxMlSaPlKEmeVewkSVI/2PVOkiRJknqYKElDYH7/fN0hSJIkqYuJkjQEFpYWBvuCzSYsLsLKSl42715+/JjH+Uwe4/6SJElDzkRJGmKVFSZot/PYnunpvGzfvfz4euN81o1nau39JUmSRpWJkjRoG5zkFYavMMGwxSNJklQVEyVp0DY4yaskSZLqY6IkSZIkST2cR0kalO6JUq+88rBxQZIkSRoeJkrSoHRPlNo1SWprrsV1N13H3P45mhNN2ntMoCRJkupm1zupZsVywY5tO2hsb1AsF0d/giRJkipnoiRJkiRJPUyUJI2Wo0yWK0GFc5BJkrYMEyVJo+Uok+VK4JxfkqTNM1GSJEmSpB5WvZM0kubnYX5f3VFIkqSN2rev7giOjS1KkkbSjadM1h2CJEkaY7YoSRpJF90wxUV1ByFJksaWLUrSVjY5WXcEkiRJQ8lESdrKpqbqjkCSJGkomShJkiRJUg8TJUmSJEnqYaIkSZIkST1MlCRJkiSph4mSJEmSJPUwUZIkSZKkHiZKkiRJktTDREmSJEmSepgoSZIkSVIPEyVJkiRJ6mGiJNWsOdFkZXWFxQOLNCeadYcjSZIk4MS6A5C2uvaeNjP7ZpjZPVN3KJIkSSrZoiRJkiRJPUyUpIrM75+vOwRJkiQdJxMlqSILSwt1hyBJkqTjZKIkSZIkST1MlCRJkiSph4mSJEmSJPUwUZIkSZKkHiZK0hCYPG2y7hAkSZLUxURJGpRmExYXYWUl/9xlatdUPTFJkiRpTSfWHYA0blpzLYrlgqXblrjys1fS3tPOG9rlcn4epqbqCk+SJEkbYKIk9VmxXNDY3mD14CrFcnH4DiZJkiRJQ8+ud5IkSZLUw0RJkiRJknrY9U5jY/fuuiPIbvr8n/OVE07i9oe+lnvs38Put9UdkSRJUv327as7gmNji5LGxlNunK87BEmSJI0JW5Q0Ni46b4GLZuqOApqXnkdje4MvfP0LbHvse9i3d42CDpIkSRpqtiipEvP75+sOQZIkSTpuJkqqxMLSQmXHNgmTJElS1SpJlCLihRFxdUR8LSIWI+KdEXHWGvt9e0T8dUQsRcSBiPhoRDy4a/vJEfGqiLg5IlYi4h0RcUYVMWt0VJmESZIkSVBdi9Ju4NXAY4AnAHcCV0TE6Z0dIuKbgQ8Cny33OQv4LeCWruNcCpwLnA88DjgVeFdEnFBR3JIkSZJUTTGHlNIPdP8eEc8CloHvBd5Zrn458N6U0q917fqZrudMAM8B9qSU3td1nAJ4IvCeKmLXcGvNtbjupuuY2z9Hc6JJe0+77pAkSZI0hgY1Rune5Wt9FSAi7gE8DfjXiHh32T3v6oh4ZtdzHgFsA97bWZFS+hzwSXJLlbagYrlgx7YdNLY3KJatJidJkqRqDKo8+GXAfuCq8vf7A/cCXgS8BLiI3P3uzyLilpTS3wI7gYPAzT3HuqncdpiIuAC4AODMM8/s85+gjWjNtSiWC5ZuW7LVR5IkSSOr8haliLgEeCxwbkrpYM/rvj2ldElKaX9K6RLgL4BfPt7XSildnlI6O6V0dqPR2FzgOi7FckFje2PdVh8r1kmSJGkUVJooRcQsuRDDE1JKn+nadDO5wMO/9jzlk0CnKehG4ATgfj37PKDcphFUWcW6VgtmZ6HZzD9LkiRJm1BZohQRl3EoSbqhe1tK6Q7gauA7ep727eRiDQDXAqvAk7qOeQbwYOBDFYWtUVUUsGMHNBr5Z0mSJGkTKhmjFBF/ADwLeDrw1YjojCm6JaXUKf99MfAXEfGPwJXA9wHnlc8hpbQcEa8DLo6ILwFfBi4BrgeuqCJuSZIkSYLqijk8r1y+v2f9y4AZgJTS28riCy8iF3v4d+DZZSGHjr3kLnpvAe5ZHu/ZXWOdJEmSJKnvqppHKTa43zwwf4TttwPPLx+SJEmSNBCDmkdJkiRJkkaGiZIkSZIk9b3jYwEAACAASURBVDBRkiRJkqQeJkqSJEmS1MNESZIkSZJ6mChpPDSbsLICi4v5527z84MNZaLJ4oFFVlZXaE40j/4ESZIkDR0TJY2Hdhump6Eo8s/dFhYGG8qeNsXegulzpmnvaR/9CZIkSRo6JkqSJEmS1MNESZIkSZJ6mChJkiRJUg8TJW3K/P75ukM4ZHKy7ggkSZI0JkyUtCkLSwt1h3DI1FTdEUiSJGlMmChppDQnmqysrrB4YNHS25IkSaqMidIgDXg+n3HU3tNm+pxpir2FpbclSZJUGROlQRrwfD6SJEmSjo+JkiRJkiT1MFEaAkNVOU6SJEmSidIwGKrKcZIkSZJMlCRJkiSpl4mSJEmSJPUwUdJAtOZaNC9tMvvhWVpzrU0da/K0yf4EtR7LuEuSJG15JkoaOVO7pqp9Acu4S5IkbXkn1h2AtobO5LAz+2aY2T1TbzCSJEnSUdiipC3LsuySJElaj4mStizLskuSJGk9JkqSJEmS1MMxStpyWnMtiuWCpduWmNs/R3OiedcYKkmSJAlMlGrlBXs9iuWCxvYGqwdXaWxvUCwXdYckSZKkIWOiVKOteMFe+RxIkiRJUh84RkkDVfkcSJvRakGzCbOzedna3MS4kiRJGl22KEkdRQGNBqyu5mUx/i18kiRJWpstSpIkSZLUw0RJkiRJknqYKEmSJElSDxMl9VVzosnigUVWVldYPLBIc6JZd0iSJEnSMbOYg/qqMw/U/P754a5wJ0mSJB2BLUqqhEmSJEmSRpmJkiRJkiT1MFGSOppNWFyElZW8bG5ufNXkaZP9iUuSJEkDZ6Kk8dVq5WRndjYvW60j799u50lmp6fzst3e1Mvb/VCSJGl0WcxB46sooNGA1dW8LIq6I5IkSdKIsEVJkiRJknqYKEmSJElSDxOlITa/f77uENbVmmvRvLTJ7IdnaV7apDV3lPE/kiRJ0ghxjNIQW1haqDuEdRXLBY3tDVYPrtLY3qBYdvyPJEmSxoctSpIkSZLUw0RpBA1zl7yh0ud5kSRJkrR1mCiNoGHukjdU+jwvkiRJkrYOEyVJkiRJ6mGipC2nOdFk8cAiK6srLB5YpDnR0yVvcrKWuCRJkjQ8rHqn49KcaFIsF+snG0OsvSd3wZvZN8PM7pnDd5iaGmg8kiRJGj4mSuNifn6gF/hHTTZGwORpk3WHIEmSpCFl17txsbBQdwQjZ2rXVN0hSJIkaUiZKGlDRrokuWOOJEmSdIxMlLQhI12S3DFHkiRJOkYmSjU6avU1SZIkSbWwmEONRqEgQmuuRbFcsHTbEnP752hONO+KW5IkSRpXJko6omK5oLG9werBVRrbGxTLRd0hSZIkSZWz6904aLVgdhaazfyzJEmSpE2pJFGKiBdGxNUR8bWIWIyId0bEWUfY/7URkSLi13vWnxwRr4qImyNiJSLeERFnVBHzSCsK2LEDGo38syRJkqRNqapFaTfwauAxwBOAO4ErIuL03h0j4ieA/wF8YY3jXAqcC5wPPA44FXhXRJxQTdiSJEmSVNEYpZTSD3T/HhHPApaB7wXe2bW+CVwGPBH4+57nTADPAfaklN7XdZyi3P89VcQuSZIkSYMao3Tv8rW+2lkREScCbwZ+O6X0yTWe8whgG/DezoqU0ueAT5JbqiRJkiSpEoNKlC4D9gNXda17GXBzSukP13nOTuAgcHPP+pvKbYeJiAsi4pqIuGZxcXGTIUuSJEnaqipPlCLiEuCxwLkppYPlut3AFLlrXd+klC5PKZ2dUjq70Wj089CSJEmStpBKE6WImCUXYnhCSukzXZt2A98EfDEi7oyIO4Em8MqI+Hy5z43ACcD9eg77gHKbJEmSJFWiskQpIi7jUJJ0Q8/mVwMPA3Z1Pb4AzALfX+5zLbAKPKnrmGcADwY+VFXckiRJklRJ1buI+APgWcDTga9GRGdM0S0ppVtSSl8CvtTznFXgxpTSpwBSSssR8Trg4oj4EvBl4BLgeuCKKuKWJEmSJKgoUQKeVy7f37P+ZcDMMRxnL3kOprcA9yyP9+zOWKeR0WrliWCXlmBuDppNaLfrjqovJk+brDsESZIkqe+qmkcpjuM5k2usux14fvkYXUUBjQasruZlUdQdUd9M7ZqqOwRJkiSp76pqUVIFWnMtiuWCpduWuPKzV9LeMx6tUpIkSdKwMVEaIcVyQWN7g9WDqxTL49MqJUmSJA0bE6UhZMuRJEmSVK/KJ5wVuXjD4iKsrORls3nE3TstRzu27bDlSJIkSaqBidIgtNu5gMP0dF6OScU7SZIkaVyZKI2Q5kSTxQOLrKyu0Jw4cquUJEmSpOPnGKUR0hmrNLNvhpndM/UGI0mSJI0xW5QkSZIkqYeJkiRJkiT1MFGSJEmSpB4mSpIkSZLUw0RpkCYn645AkiRJ0gaYKA3S1NSaqydPmxxoGJIkSZKOzERpCEztmqo7BEmSJEldTJQkSZIkqYeJ0hBqTjRZPLDIyuoKzYlm3eFIkiRJW86JdQegw7X3tAGY2TfDzO6ZeoORJEmStiBblHRE3a1biwcWbeGSJEnSlmCLko7I1i1JkiRtRbYoaUMsYS5JkqStxERJG2IJc0mSJG0lJkqSJEmS1MNESZIkSZJ6mChJkiRJUg8TJUmSJEnqYaIkSZIkST1MlCRJkiSph4mSJEmSJPUwUZIkSZKkHiZKI2jytMm6Q5AkSZLGmonSCJraNVV3CJIkSdJYM1GSJEmSpB4mSpIkSZLUw0RJdzc/X3cEkiRJUu1MlHR3Cwt1RyBJkiTVzkRJkiRJknqYKEmSJElSDxOlcdBswsoKLC7mnyVJkiRtyol1B6A+aLdzEYapqbojkSRJksaCLUrjYrNJUquVW6NmZ/Oy1epLWJIkSdIoskVJWVFAowGrq3lZFHVHJEmSJNXGFiVJkiRJ6mGiJEmSJEk9TJQkSZIkqYeJ0hCbPG2y7hAkSZKkLclEaYhN7ZqqOwRJkiRpSzJRkiRJkqQeJkqSJEmS1MNESZIkSZJ6mChJkiRJUg8TJUmSJEnqYaIkSZIkST1MlCRJkiSph4mSJEmSJPUwUZIkSZKkHiZKkiRJktTDREmSJEmSepgoKWs2YXERVlbystmsOyJJkiSpNiZKytptKAqYns7LdrvuiCRJkqTamChJkiRJUo9KEqWIeGFEXB0RX4uIxYh4Z0Sc1bV9W0S8MiKuj4iViPhiRLwpIs7sOc7JEfGqiLi53O8dEXFGFTFLkiRJUkdVLUq7gVcDjwGeANwJXBERp5fbtwPfA7y8XP4o8CDg3RFxYtdxLgXOBc4HHgecCrwrIk6oKG5JkiRJ4sSj73LsUko/0P17RDwLWAa+F3hnSmkZeFLPPs8FPgE8GPhYREwAzwH2pJTe13WcAngi8J4qYpckSZKkQY1Runf5Wl89wj6nlsvOPo8AtgHv7eyQUvoc8ElyS5UkSZIkVWJQidJlwH7gqrU2RsRJwO+TW5s+X67eCRwEbu7Z/aZy21rHuSAiromIaxYXF/sSuCRJkqStp/JEKSIuAR4LnJtSOrjG9hOBNwKnAXs281oppctTSmenlM5uNBqbOZQkSZKkLazSRCkiZsmFGJ6QUvrMGttPBN4MPAz4/pTSl7s23wicANyv52kPKLdJkiRJUiUqS5Qi4jIOJUk3rLF9G/AWcpL0fSml3uTnWmCVrqIPZWnwBwMfqipuSZIkSaqk6l1E/AHwLODpwFcjojOm6JaU0i1lS9JbgUcCTwNS1z7LKaVbU0rLEfE64OKI+BLwZeAS4HrgiiriliRJkiSorkXpeeRKd+8Hvtj1+PVy+xnkuZMeSG456t7nmV3H2Qv8Dbnl6YPALcDT1hrrJEmSJEn9UtU8SnGU7QvAEfcp97sdeH75kCRJkqSBGFR5cEmSJEkaGSZKkiRJktSjkq53Gl3z8zC/r+4oJEmSNG727as7gmNji5Lu5sZTJusOQZIkSaqdLUq6m4tumOKiuoOQJEmSamaLkiRJkiT1MFGSJEmSpB4mSpIkSZLUw0RJkiRJknqYKEmSJElSDxMlSZIkSephoiRJkiRJPUyUJEmSJKmHiZIkSZIk9TBRkiRJkqQeJkqSJEmS1MNESZIkSZJ6mChJkiRJUg8TJUmSJEnqYaIkSZIkST1MlCRJkiSpR6SU6o6hEhGxCBR1x9HjfsDNdQehvvO8jifP63jyvI4fz+l48ryOp2E9r82UUqN35dgmSsMoIq5JKZ1ddxzqL8/rePK8jifP6/jxnI4nz+t4GrXzatc7SZIkSephoiRJkiRJPUyUBuvyugNQJTyv48nzOp48r+PHczqePK/jaaTOq2OUJEmSJKmHLUqSJEmS1MNESZIkSZJ6mCj1UURE3TFIkiRJgxIR9+j5fWyuh02U+iAi/ntE3AsYm/8YkjRKIqLR/WU9Tl/UW1VEtCLixyJiV9c6z+uIi4gnRMTPl+d3e7nO69ER1Hk/ppS+Edl/K38fmwII/sfchIh4UERcCVwJ/CPwpoi4T81hqQ8iYk9E/GpEfG9ETJTrfL+MuIh4bkT8TkQ8LSK+qVznhdcIi4izIuIDwNuAv4uIp8J4fVFvNRFxekT8HfBO4AXAP0fE/46I+6aUku/Z0RQRExHxNuBvgPOB9wKXR8Q3dS60641Qx6rzORsRzwM+DbwzIt4YEU8u14/8ddPI/wF1KROiPwFuAn4cuAx4PPDaiHhYnbHp+EXEgyPi48CLyOf1L4F5yHdMagxNmxAR3xYRHwV+HdgFvAZ4e0Tc2wvq0VV+1v498G/A7wEnAbMR8Wvldr/jRtMzgQngEcD3AxcBPwxcUmdQ2rQnAU3gUcATgWcD3w78KXhzY1RFxC8CLwFeBvwhcDrw1ojYNQ7XTX6JHL9J4CzgtSmlq1NK88B5wIOBn4uIU2qMTcfvx4EvAd9N/lB/LvDoiHiVF10j7cnAQeBs4GnADwL3A+YiYmedgWlTngp8EXh+SulvgKcDbwBeERHfNQ5f0lvUTwBfTCl9OqW0Qr4R+X+BZ0bEU8pWJT+PR88zgOWU0g0ppW+klP6CnAS3IuI5YAv/MFvr3ETEyeTrpjenlP40pfRHwLPIvaxeHxH3H3CYfecHzfH7ZiCRmxqJiEgpdbp/PJp8IaYR0PPmfxZwdUrplpTSrSmldwC/Avwi+Q6YRkTPeX0m8ImU0jLwjZTS9cBPAT8G/EhEnFhHjDo+Xef2PsC2lNJtACmlrwGvBq4CZnv21RAqx/d2/35P4Ebglk4ylFI6CPwt8A5yy6Et/EMuIu4TESd0/R7AfwInRsS2rl3b5Pfs/we2Kg2jrnFIh52blNLt5OvhL3f2TSl9GfhlcmvhswYYaiVMlI4iInZGxExEXBgRP9a16f3AacBjy99PKpd/SP53fbStSsNtnUGHnwK+rWufKO96vQ94YWfdIOPUsYmIby2/iLvP07+RW3sp70afkFL6MPAm8gd6Y/CRaqMiohkRPxcRPxgRD+h6z34NuD0ivruzb/kl/dvAkyLiUY5pGU4R8U0R8UbgzRHxtnJg/4kppVuBrwDfCXxHZ/+U0pfIXbTuFRFPrydqHU15Xv8C+Gvgioj4mYg4uXzPLpKvlXZ39k8p3Qm8HjjZVqXhU14DdcYh/UhE/EpE/EDXGN8J8o2pR0fE9q7v1wXgUmB61M+nidIRRMRLyC1GjwB+EvjLiPiFcvMdwFs4dBfk9vJD/gvAPwBPSSndNur/QcZRRDw+Ij4EvCUiroyIn+/a/HGgERGPLH/vvEf+N7l7wC7veA2n8kLrI8CfA/9E+d4sfRg4tTPAFOjc6byI3IX2rPIYvl+HSGSXAJ8gtwr+Dbk7x+PLXT5I7g//+J6uWNeRP4efDd6lHjYR8VzgemAbuZXoQcAryd1iIbcafTfw1J4bjtcBN5PPuYZMROwBPlr++n+BA8BeoPMd+0byDeanRsR9u576n8BHyC0Tvl+HSJn4PKT8bv0D4IfINxhfExEnlb00Pka+2fjUztPK5RuAe5LHF44sE6U1RMQJEfECcve5n0wpPQ34PvJgtZcClHe95oD7R8Rvlk89WC7/BfimKCv0DDZ6rSciTomI3wHeSr6IeiX5zuVvRMQPlbtdAewAfqK8k9I5p/9B/pJ+3IDD1lFExEkR8VLyjYv3kQs2fAh4XkScX+52LfnL+H9GxD1SSneUd73+q9z3yeAX9DCJXDDnNRwa+P0U8hfxieQxLKSU/gHYT+4j/5jOc1NKN5Evwm8bbNQ6moh4BDkhmkkpPbMc09Aif+4+ECClVJC7Y/1Sua1jkXwxvQ0NlYj47+SL6EtSSj9Zjhk8F/gv4IyI2FbeSH4defzvuV1P/zq5BfHWAYeto4hcMOc15AR4FznpeS5wJvCz5W5/Rv6s/YmIeFBXt9hvALcAJw806D4zUVpDeXF8Evli+t3lujvIg9MORsR3lbv+M/B/gN+JiCcBp5brfwD427ILiIbHQ4HvAX4hpfTilNI7gWngs5QtCuWFV5v85fzTXc89mVzAoxhkwNqQSfJF8m+klH6rHCv4EuA95EqUpJT2k6ujfQfwq+W6g+VdzQcCN4AtSkPm/uSWv1eklD6csvcDX+XuLQovIX/2/kpEnA53jXM5CVgYbMjagIPkGxdvASgvoFeAJeBbu/b7VXJV2RdFxE+XXXx+Gvg8+btYw+Ue5Jb7NwKUPWxuI5/vM1NKq+V+F5NbE6fLblxnkluLbwM+MPiwBYd/93X9vpPcon9pSunLZVfJD5BvVqwClDcc58nfxS/pOszp5T43VBl71RzADETEqcA5wH+mlDon9PfKVqPuPpo7gQMppX8FKD/cXx4R30kuFb4QEavki+6RH8A2hoJ85+PdnRUppc9FRJN856PjMnIz8iUR8Q3yHeufJCdJnxxcuNqgVXIf97/rrEgp3VL2of7Prv3+jHzx/TsRkcgtSU8A7qTsLmKLUn3KFqSdwH+UN6Y+Tf5y/ni5/R7lncpFcneOzmfzpyLiYuBC4BMR8QZygnwqXe91DV55TqfJd5U/nFJqlzct9nf2SSmtRsS9gQdQXiiXydNq5LlZLgD+mDzO8FuAF3S+g1WP8ry+mPzZux94a0rpU+Qxvh0Hy7GiOzmUPHXO64vJ10i/TW6d+G/A/0op/dMA/wyVuj5bKcd73ky+cfF18nCEj6aUbu7ad7FMpLpb7P+UfEP5/5TH+Djwo+Tu0p/pHus0amJE4+6biPgNcne6fwe+i9yv9nUppRviUMWdzn+g3wOaKaVnlB8Ad5b9N08ifzGfQ76rcnEnyVI9yjFGe8kXyv+WUppbY597kOfq+GfgwpTS33XezOVdrt8id8kK8sX0BeUdbdWkPK8vJpeE/mxK6eI19uncALoaeE1K6bVd5/WU8vk/Rr7Y/gbwvJTS+wbzF2gtZaKzh9wVdgl4ZUrpr7u2dw8o/gTwqpTSazoXXuX6neRk6UHk/x8vSFZGq01EPIOc4FwN3Jd8k+KtKaW95fbui7Mzyd1mn5pS+nTPcYJ88/FBwJVlK4VqEnlC5z8FriF/N343ucv6L6eUvtxzXu9FPv97yuI5vcc6k9yF65rOeR3lC+pRFhHfTL6Z+C3AMjlJ+hngU50CDeSKsansZnkVcE5K6dNlN/aD5XEeRZ777Czgr1JKf1XH39NXKaUt+wAeSc56zyd/kP8C+S7z+3v221YuPwy8sGfbyXX/HT7uOhedxH8v+Q7mn5Bndr+T3EXyjHL7iV3P+Q7ynesHrnPM+wKPrPtv28qPrvP6HPIH+B8Df0S+m/lnwMPXOK8PILcAfuc6xzwZOGut1/Ex8PP76+SuGY8l939/LXmswo8C9+j5P/Cd5HnOvusIxzthrZ99DPy8/jU5oYU8Z9m55BsTv9T53uycn/I7+FPAjq7nf9s6xz2xqph9bOi8zgNvKH++J3luuluBVwD3Ldd33rdPIHeVPL3r+Wd17+N5rf9Rfh++HXgzOVH6/vJa+EPAE9fY/5fICfBJG/neHPXP4a0+RunJwKkppTen3PfyNeQB/g+JiBfBXf1sVyOXkv5WciEAIpdHfDv5Q0JDIKW77no8E3hZSulnUy7EcT558PfPl3c+7uzqf/s04EspDzIlIk6NiMny5xPK/xdXl7/bVbUGqfykJZ/HV6eU/mdK6efJA4K/Dfi18n3afV6fDHw9lV1py/O6q/z5Himl29OhLl0n9ryOBiAi7lG2xj8J+EBK6Z9SSu9KKT2XPP3Cb5ILOXQ7C1hIZderiPjRiHh99w4pjz2LnmIsGoDO+6/szvxk8txHpJRuTvnO8u+RW3QfXq7vnJ/zgHenlFbK79ZPABev9Zmb8hgJDVDXeb0/uffMVeWmO1JK15DHpfw45VyD6VBL7vnAP6aUvlKe10+RJ/nelnpaez2v1TvCGNwzyNeyb08pfSblnjPPIPeQ+rny+rf7Gugc4KqUu0ifHBGviYhfWeP1uudBG1lbJlFa5z/I7cDn4+5lKt9Pvlt9UUSc2vXmfTy59Wk1Iv4OeBe5SfKDVcatY/ZQclP+XYMHU0pvJZegfTK5Kg8cmmPnx8l3Uijf6EvAy8rn3e3N7Qd5Pcpr3jPJRRc+11mfUtpHLgV+FvBz5erOZ9q55K48RMSF5PN6UXe3kK7jeF4HpPtzOKX0jfKL9tvI4086RRggtwrvBH44InZ0JbHfD3wwIr45Iv6BfP7/fY1jJxPfwYiIbZ2bS13/5l8iT0D5sHKfk8rtv0l+j55f3tTqzMNyGvDJiPhrci+At6eUftz3Zn3K8/pdEXHPznlNeS6rWzg012DnJtPvkYus/Ejneqo85w8EPh0Rf0m+ZnprSumR6VBhBw1I+d233mfi6cC9Kcdgl4nsf5GvhR9Mbt2n62bkQ4H3RZ5btCBfW32k96C937WjakskShHxy8BfRMQfRcRTImJHuWmJ3G/64Z19U0q3kAefLZAHoXa+gJ9MTpb+ndx9YGf5oa+aRMQZ5Qd5d6J7A3Af8hcvcWgOjsvK5ZPKC69vRESD3OR8ekRcQx6TdH5K6WdRbSLizIh4VJkcdS56/5M8OP8+5T6dO1tvJned/KHI5fgPRsR2cte70yLiavId7PNTSueNywf3KIo8MP/iiLigc2FduoKyzGxK6dayJffT5Nb7Z5C7vxIRp5FvdJxHTqy+SO7S87vlc02MBiwifpU8bcJfRcT7IqJT8vme5EIaP1zecLwjIjolgl9OPt+d8/Ut5GkXXk2+JvmmlFKnR0dnvjMNUHleP0meL+cjUc4fGXls9luBn4yI+6Q8f2TnvF5CvqDu3OzYSZ5i5cXkG5M7U0q/VR7H3hkD0rmBVF7zTEbEZRFxcUTs7exT9pr5Krk3Dhy6kTxHrjz5mCgripIrAj+M3AXzT4DfSil9S0rpI0dosRppY50oRcSuiPhn4JfJE2I9hPxm3gOQUvpj8oXy+ZGruHR8gtxf+gGRJ9RK5PEQHwf+R0rph5Olv2sTESdHxB+TJ5v8C2B/RPwgQMoDQv8K+I3O7+WF12fI5d53A/cqD/Ut5IGovwr8fUrp/imlTslav6AHrDyvf0QeC/hH5ApmPxd5QDDk8UgXlHc47yy7Vt1IrpT17eSWRMh3MR9Frqrkea1ZRDwhIv6DXL2sUyTlbV27XAHcJyI6lUI7F1G/S36Pfnv5+/3JpYavB3allH6qTKy86Bqw8mbGO8jn9GXA75OrEb42Iu6dUvoKuXvWDnJVs//X3pnHXT7X/f/5mrGNwcgyttxk3/d9SXbFTaGQfV8jIVvirn5lK1liEkZE4S5hLClbZSJCNJaKFAY/2ceQGfO6/3h/vnO+c+Zc1wzmOudc4/18PM7D9V3ne7zP5/P9vHeIXFGIsv3vUUr3F64hcg0/6ygIMEMZ3/06ZKe/IWlBSVcTcj2OCH/9DXC+pKHFE3Q34TE8pVxWyeh6Ikpn3bI9M9GgdBXbOzTJNT2FbaIyIEk6hVjbzkt4BL8t6dxybAYi9/fwumGjGBavJHrYvVtuOQQYDZxnew5HH7QqTWX6NFa5CxKl+uJDWDMuIRZcc9b2jyj7ZinbuwNvATs3XX87cHVte7ZOf6f8GKIP0khC6VmDSBYdTjS1q4pubFFkum/ZrvYvQXgDlyrb6wHHNv0+MqG0M3Jdvoy5O4imdssRC+VXa/JaiihbelLZrhL85yxyXbMm50NSrp3/lDH6ByKHYUZCCVoaeAPYs5yzCGGZHFWN1bJ/QaLH2QG1fSvW/h5IU0J4ftom188TYerL1PYtTxgkP1+25wQuJMpHr1w7b9syX09WrIEw3qZMOyfXzcv7daXavjmAR4nKsACzEl6iV4ANauetR1SZXSvl2l0f4GDCALlVbd82hPFi3rK9EhGRc0XTtZsS0VfLlO35Pmrv1unZozSOWCwPs/1azT38Z2A1l1KUti8nchmOlLR/sWovR3gdrq1u5gjJSzrPOsBYQrG93/btRNWswTQslPcRyvB3JS3qRjz0WsREUMl+pO3Tyu8jLV2dZdXy371tP2T7UdvHE+E51bEngW8DJ0ra2GWWJpTnJyld3W3/3fb5Kdeu4F1CNpfZHlfk8CKhFA0FsP1P4HJCibqshMRCxMG/S8k1K+c+AhMLrbznDKXsFM8AZ7jRdxDCqLEQUeUM268RVuoXiNC8XSStTlSvvJ8InZxIlT+YMu0ojwHftv1wbd8EQjn6B4DtscR4vZmQ6wmS1ifew09TcgYrUq5dwZuUis4wMRzvH4RnaNlyzqNEq5xdJB2mKMgCEU55VzXWbb9Y3q0DPyrv1umyj1IRniXN4qba/JIuA161fYQazc8WIZKHjyB+TKsS8dV72X6zY18kmQxFE9Elbf+2tm9ZQl5bu1HJbCgRCjCYSA7+PVHR8E/APvVJu/pttO9bJBW1cTkYWL1JrkMJuR1o+47a/usJ79I9RFWtk4jF+M62/9N87zZ9laQFaiTxv1u2K3k/PAdWigAAHUpJREFUBRzt0itJUR3pk0QI1utE6POmROjOscB7KcvupchvVaIk+Kau9UJS9LcaTihR8xLGyj0chQGSLqaEKi9MhNt91qUCbDk2A3AB4YkYShgh93aEQydtpjIeTem9V5uD1yWcAUvW17mSvgocThgeXyOqPe9p+4Y+/gpdS79XlIpmrJ6sFc0/Gkl3ARfZvrzFsbWImPh/2v5Di9slXURlqZK0GfEiXh14qZJpsUyfRPTLmhsYYfsrHXvgZKqoyXVlIj5+HdtP1vbPRsTQb03I9TbbR3XymT/qTMXLud6EcgXCGr0uEYJFbcxWYZcrEc0K72t9x6RbqC289iPCndetHavG7IxElMbclRKlFhUok+6hJtftCS/TMrVjlVwHECG18xfPcMq1A0jaEfiy7Q2mcF59Hj4G2NL2Zqo17i7HViYiNeYAfvBR8Br1Rr9WlOovZ0XFq14LLEhaHHiQiKt9uOybz/aLff+0ydSiRu39Xr0+NQvKtwlvxJb1e9QmhNmJ3/ob9eva8V2SBq2MGj3ItXoJHwl80faarc4v4bQz2H6rbKdc+wGKMvx7AuvWPE0Tvf9N5042FyR9zwcZS5JGAH+tjFGSlid61L3UNG5F5KvkWG0zH1Cuwwl57Vm2VyDm8Ueazku5dghJnyJyfPew/ZPeDFdq9By8iWhxc2Tt2Oytoqiqa/rq+budfp2jVJt4jwUelbROq/PKAAbYkvAWPSxpLkkXA7+RtGB7njjpDRVc4pklraNGNbvJBn1tQt6I0tiw3GczwhpSMcb2G4oGl1lJqc0UsQ5wMEHS+sX63JNcq0XxhoTnobrPp4kE1Ip3HQ0qU65dgKRDJW3Sy/GqOt2ngHsclZUWUDTuPlRNFQmVuQ0doxpLipLslL97LP0raR5K6J2kIeXd+giwcbnfxHFe5oEcqx2gJtf5qn2VMaKZMm8PIvKCr6vJ9WEi37f53inXNlMbkyOBc4j2CzP35t0H3pM0B1E06eflPtsqKkR/qtW/8VFWkqCfK0qSVpd0IBFatTvwQKvzaj+apYEbFQ0onyZCPLa3PboNj5tMgTLRuix8jyLKPi+g6IvTkuIlXBC4VdJqku4mys9W/ZMmyr8suvqvC7WfUlOQZijjdQQwWI2y35NRFl7LEHJdRdJIIuds4oSdcu0MrRZWxcp8PNG0sCXFijkAWBQYUcb434F5iEpLzQ2eU0FqI3W5Stpd0iOEnE5Safrb06IaWJJoRLoj8W5dEljW9tV9/dxJ7zTJdTdJfwVuVvSVnKMWQjcJZU79BNEXaWtCrksRcr24PU+f9IRqDWSLZ/4H5dDJ5XhLw0a5ZnUi7Pl1SbcCVwG3ukUeUr5bG/0qup5mV6KkxYiu7LMCl9q+tZdJHEX3788BHyd+IHvY/mVP5yedQdI+RM3+l4AtbN/Vw3nV72FFYiI/hShZewmw0UfdAtJtSNoV2IuolrWb7Rt7v4JlibF6LNFkdDjwyZRr52kVOmn7L5LGEgvk3vIUNiUsmTcQ8/B2tn9Tv1fff4OkFWXBvDDRWuNworT3SsBuZd+hvVy+FiF7E8VyroWJxQDSkNFBilwXI2RzINFLcn5gD6Jp6Pa9GCVWInqfrUMUavglpFy7ATdSC9Z05HI+DZwKnCHpQttP93L5Zwhl6QHgOqJE+Jhyv5yHm+h6j1IVrtEsOEcD0R8SvRrGlX29WSAHEN3cD7K9cCpJnaV4jVr9/l4krJJbEFV0WlL7PaxLVNyZFVjO9v7Fct1vjADTE73I9QFikbwNUYa011Aewks8GzG2l7O9X8q1O5A0i6TzJG1SjUNFdbvfA6tU4bM9XP4mjd5Ii9j+TQnxGZgv5/bSPE4lbUD0MbuQKHh0LqEwfQ/YS9IaZdHdqmnzKGB328vUlSRHCfeUaxtpIddliGqvvwKutz0M+CbRW2c7SVuX81rNxw8SeaLL15WklGv76cGTvx9wr6QFHcUYriJCXs8qx9V0frU9mmijsqLtHW2PUaOVRsq1iX5TzEHSoURp0aeAG2y/Kmku4H+Jfklfsv10K0umGsnhmezdBdQHY7F0zQz8w41S7pcRpYI3sP1sb/eQtASwsEv56PISd4bttJ8muS5FNKb7M/CWo+jG94hqdZvZvqe3e0iaBVjfdtX3IeXaAVrNmZIWJZoBbwScavucsv8iohHhjj15lBQ5D+/Uficf6SThbkDS8rZHSVqaCJ/ckWg4+lQ5vghwPvAx2+s1XduqGEvKtAuoPA2K6q9HAMcAm9i+uxwfBAwjqoouPRX3S7m2iRYRVD3NpwsR+dkP296jKFPbAL8kyvTfMaUxqiyYM0X6g0fpUyWm9stEPfczgdMkLW77FeAnRIjOttBa2NW+VJI6S2XNqBbCimo6fyFyVm6StHk59VtEaMB2Pd2rGviO5qJ31KzS2YSyQxS5zizpR4RV6zKiEs++5ZTjiPCPz6mHvLNyD9l+x/ZtKdfOUP6/T6xgpcgTWxrA9tO2dyGaOu9bFCSIcLpNFMnELWVl++0i44FlOxdebaKFdXkGSScCj5SF0xNEH6TXgb2r8xxln4cBSytCaHuM9Cj7UqZtZAqehgVsv0TI9Rki/LlaeL9NrKfmUVQY7bGwA6Rc20k1R6pR9GhCmZO/IWk3mCjD54gm7LtJWrfMu3cQ8j6ruleL+1dK0gzOgjlTpGsUJUmLSPqdpN3L9sCiLR8H/NT2krZ3JxLVPg0cBGD7EqLZ5JaKHhy9Dvakc9SsyBsD+wBzEVVWDiRCrM5QhPP8FTgXOFERMz9V905FuLNI2hQ4hJDr2kTO2J+AkyXt6Eg4Pbmcs1pP96lP7CnX9lNZIMvLeWVJ9xKe+zskXShp7XLqt4m8lS2KF3hJ4F4i9r1XUqbtRbXE74qyWLod+Btwetn9e+AKYMfira+4m7BSD69dm3SYnjwNRLXQh2nI9VHgbGBXSSvUrnmU8BZ+V9KcuWDuKrYBLpT02bI9lMjdPQ0mcQrcDNxEQzF6k/D4Lyap176ROY6nEtsd/xBFJc4GJhDx6wPL/pmJMIDZy9/fJyrrPEhYrDct520GPFTuoU5/n/xMJl/V/l69yPlZ4Au1/SsC1wC3lO3BhAXszE4/f34mk+eAFvuWB94jcswOqe1fALiI6K9S7Xsc+CkwT6e/S34mymR2YKe6fMvc+/cyr36cCIe9hSgpO7h27cZEXssE4FUi7n2ScZ+fjsh0ILBjbXsA8FVg69q+WYiQrDHAEmXfp4DfAZc03W8dIrcsZdt5ue5X2xbwDWDXSs7lvzuVMblu2V6CUIx/03S/hYhGwR3/bvmZRC7zEYaJJ2r7NiCKNnyzSdabFlnvXLZnIRTgETlWP/yno56XWijWeMKjcCfwDqEQQSy8fkEoSdcTfRo2JJoVzkUJD3BUTbofeJ6YRJIOIml+SSMlna1SVrbsH2j7T0TIzoLEyxkAR/O6XwMLSlrf0UT0f4CvKBoXJh2kuP3nUfRauLRubS5WzVGEh2FeYsIGwPbzRILpDIru4RAJ4jsxFZ6HpO+R9Ali7vyppLXdsFSOBYbbPsKRK7gCkZe0JuEVBMD2HbYPIF7MsxJVK6nGfdJ+yrv1EGC4Gr2tliJkc1x1niMv9DrC0HhO2X03cC2wgUofu3LuPbYvLH+nbDtH5Wn4XNkeSrRSOB0m8TTcVD5nl+2ngPOANSTtUt3M9nPOct9tpzkUthnbLwI/BuaQdELZ/QhheDxCUcChkvUrwLvAd9Ro3n207W1yrH54OqIoSVpJ0oz2JH0ZHidykM4lKuwsbnt8+SFsQtTz38f2g8ALhGK1laLLO8Chtk91uhK7gcUID9FWxMDdoOyvZP01QgleV5P20nkAWISwSgNcTFRSGtX3j5z0Rpls5yJC5hYAviXpv6vD5b/fI8Jg15C0QO3yfwEzEUVXsH0r8Hnbv2rHsydTZAjhOfoHjcUyRBjWdyUtJ+l+YuF9MBFet3NRsJA0czn/FKKy1swkHaMKnSQMj78mPEbYfpyYU+eSdFjtkqeIMtGbl9Dnymj5PLA5SbdxD7GA/g5MXFCfA4yT9E2YaLx6kwjHWkPSTmUtdQ8h22078eDJpLnaUzqHCF2/GDhG0ly2XycMG0/QUIABFiYMkjMRzgRsjy33SufBh6StipKkGSWdSVivLpG0bE0jfpiIl32W+HFUlZQGEArU68SPAKL52b1Ez5xHAGz/p13fI5kig4DLCcvlm8DPJa1KhAjgSC49jVh4bVi7bh5CSaoKNdj2FW187mTK/IQYdyOBy4pVcxCA7VcJi+XmwPa1a4YQXqaXqx22q47gvVrVkmmPpBnLf6tS688QHt7hwPySDir7xxTL5FeJ+Xlz25cSpaCXB46EmHtLUvC/gRmJEJ+kQ1QLsOKlv4HIVTigHL6TaOS9v6Sh5bzxRDjPQMLYge0HgD1t95rjkLSfmqdhyFR6Gv4DnFo8DaOB/R3FWJIOUIuw2VnSCYrqzZO8C2tj+E3gamKOriKtniDKu+8gaYSieNK5RJjdYrZ/3fTvZT7oh6TdHqVZgfHlsxSRrL9DOSbiZf0AUWFnY0mfKQP+USKP6SpJI4jJ/Ce2v+pSFjrpKp4gmvs+aPtEwsr8TeCk6gTbXyPCLC+TdIakLxNJxHcRlu2k+3iNCJd7ylESehhRRenc6gTbZxPGjtMl/VjScUT50vuAx5pvmGEB7aMUyDkHGCHpY5X33fbLRO7CXIQifHJVDUnSfxHNRJ+y/XyxTn6cWJitpigdjaPH1YLA3IQilXSQ2qLrV4Rn8EBJQxxVskYQi+cTapcsSFiqh0paH6K6YblXWqS7hCZPwyXA0TVPw/W09jRczaSehpfKvVKuHULSesCVwC5ANd56ehc+RoQ176joZfau7euJHNIXaERbXVOMVlnMbBrT1v+hZTBfBfyB8DTcCPyo5C7cV55nHSI+egRRuhLb1wFHEYnEzwIr276hnc+eTB3F5f8ssVjaDcD2HoQl80RJX1U0wAP4ErGwGgKsAnzF9p7Fip10EUWu/58Io9sLwPbxRFGGvSWdI2mdcvrJhHV6JqLM+xG2dyjWsaRzbENUqducCJ2syswOIvIFXyAqm71Do1rWOELmG0rahijdvxBwLLCFo3R0xWmEBfuCvv8qiaQvSNpd0XNsEmoW6dFEnq9o5CbdRRilDpN0saSzCXleCizu0mendq+0SLeR9+FpuIpYD1WehsdJT0NXU5PleGIdDKEAfbyna0oo7M3Ab2nIGtu/AA62vZkbrTR6a/adfEA6oXmOIjTp9YkfyjFEqe8ryvZ8JYzuh8CcxSKN7duLB+kg2//qwHMnU0GxQs9KLLqeAFA0Gv0GkST838D5xaL9CyKEcl7gONs/LuenpatD9BQKV+Q6kAib/YeiD9b3iHCt24m8tEslLeMornItMAfwY9tXlnunXDvL/cTC6Q1ibJ5WchfeBv5JNAK+j1B0DpC0hKMYx6WEwnQBkXd4UpmPx0oaUJPrAba3sj2GpE+RtB3wM0L5WbKHc6qxfBdhZNxe0Vz2LeL9ehgx964GHG77Ottvp0W6c9Q8DV9k6jwNP6BnT8OipKehq6jJciPivfllomrzlvXzmt/DxSB1AbCiSiEORXGscbW/nREafcM0HziS5pK0SRUH34yjl8otRDjARY5qK4cTfVf2Iyq4QLzUbwD2yQVWdyDpIEm/lDR/L+eoJBG+CfxA0kvERLCD7Q2JfIf5gQeLxewgQnnaSiUpPC1d7aXI9QFJi/Q20Ra5jCNkOJqQ6/a2NyN6Yb0E3Fk8hqcQeSzbSBpSuz7pECXs6npCURpA5AgeLem7hJFqkKR5iJ5JD1H6chCW622JHKVVbd9fvcgdzQrfK3+/3dYv9BGkttgdT4RULUh4dAc3n2tPbN48hpD7aODEcuwd28OAL9reMC3SnaWFp8GEAtRjH8GySL6F8DScXdtfeRo2T7m2n94U0tqxccQQvQ34C/BZRbXglaFH5XgkYRypckMnvk/z3dq3TFNFSVFx5WmibPcCPZ1XtOPzgLUk7W77UeALwBFELGYVpvc/wHL5I+gskjaQ9BhRZedntl+YistuJSrbnQmsZvtmANt/IGKlv2D7Fdt/JrwPZ9JQkpM2UJPrt4CzmsKoms+tXuQ3ES/x7xEhsJVcnyO8DVvZftz2E0TOwyFEPmLSRpotkrXt+4g8pK8Ri6zDiXLf1xC9lKrE/vOBrSVtWQyVExxV0yZaL9vyRZJJqC12NyMW1PsQlQjX7uH8Sk73EuNxTUlbwcRw2jHl77RId5AmT8NtNDwNk1Qd7MXTsIKknco56WnoICX6YnlJx0tasvlY+bM+Xvcv2/cCt0mar4f7vkTk8A+WtGIfPHrSA9NEUSoGi2FEAv/niYn7uSlcdjehHf8/ANv32z7X9ouV1m17tLPcd8eQNJOkcwmL1bXA3LZ/1nRO88RdTcgDgX8D19ZlWCxbL9v+Y83ruDeRn/RMX32XpIGkGSR9i5DrL4Chti9vOmeSuaEm15mIYht/rL98i1zfsv2QGtXUjifK9t/XV98ladCbZ7DaduQ2/IxoDHxBMVx8kZiPRwMTysv8TsJw9afmfycNV+1D0kxN21V0xX+AN21fSyi2B5Tjy7a4R1Uu/BbgzzT6D9b7naVMO0RZP1VyfReg5mn43PvwNBxVzklPQ4co79bjgT8Sa9uN69FVtfXS28B1xRN8MlFIZxDxvnyxl39iJiKf9Km+eP6kNdPKo7QUYZXc29EbZW5gaUlzVye0WHj9m4h9Hy/p1HLOwHIsXcTdgYn49duArxdLyR6S9pO0BUw+cdcmgl8Tv4tFyv4BzefbHleFhti+rO+/TlJjYeBB4PtFrvtLOkrSFyXN1MsYvIco/zwf9CjX8VUIZgkDSfqQ9+MZLDxGtF/YWdJajuIre9j+jO03YKKR6lzb/242hiR9j6SVJd0MXC7prOpdWlv4rgZUPeg+B3xB0t+BK+rv3XJNpSQ/DvwNWEjSQu34HsmkSFpR0qWSzpN0Us3jU8l1ndrp6Wnof6wBrEeEol9JFM+pilfV35NLEPllo4mqddsRrVHWUoQ/T4akjYCvEMU5xua83D6mlaL0cWAw8ISiT9JI4kcyUtKB0KPy8xAR8rGnpFnT+tF56hbM4r4/FZgTuFLSE0QC8JeAayRd1dNLmfg9PA0sW/a3XHhnSED7KR6+HxIVyn5eFtn7Eb2PLgRukbRK83VFKRLRnHTJcq+Ua4f4IJ5BmCS34S7gjLLvlWLZnixkL2XZXhSNnG8nFlGPAbsDV0vashwfTMlRUhTOOZoIc/4YUZTh5Rb3rOT6Vrn2+b7+HkmDEp1xJuG5HUe0SjkJOE+T5pelp6F/Mxr4EVEN9iDgv4CdJM1enVDG7BPE+vswYGvbNxKOg32ApXu49z+BTW1/M8Mp28v7VpR60HYXJSbe/YCVgZ2JEsK3AUdK2rVc2+xVGkskDC9W/k46RAsL5jwAjjLso4jy3RdR8lCALYiF9f61UKs6LwDbOfrtJB2ihQVzRgDbI4mX9hAiZ2ULQq7LEhXs9pE0Z/1ejjyVccDnbX+9rV8k6YkP5BksXqdhRNnvXcu+yV6++TLuCDsDI2zva/sUIqdzDHCKpNkdVevmJBZWLxDjdQfCOLW2SlGcOrataA59MBEOPSEt0u2h/H8+kOhBt53t/W3vQ3gC96ThGYT0NPRrbP/L9vW233Pk/50G7EvIvjpnLGGA3qIYtt4p+79D/D7ubnFrbD9tOxXhDjDVipKkzSSNAo5VqWJVi6v9NbABYSG50fbvbD9ExGjeA+yu0sCw+b62X3BWS+ooPVgwr1L0TYEY7N8hchpesf287XuJmv57uEUemSdN/s6ypG2mFwvmOdX4BS4mxugPHMVTxjjyxM4llOCWi+QqtEtZjbJjlPl0PKHsvC/PYI2RhEXzt339vMnUUTwJ8wGvV/tsP0YYqWYjKk5CLK7mIUJ7tnaUhb6AmKt7qpT2N8IifXa5byrBbaD8f34b+F35VO/EUYSHb/GyLz0N0wm1NJJTiSqj+9ZDJ20/afu18rdrIew5F3chmtKYKt6CvQiLhYkF11627yrHB9p+T9G07kvAQbYvrF1/KhFnu3V6jboTSVcA423vWbaXJSweQ4mSwGMkzehGJZ0BxSJ5AlE1awVHzlnSBRSL4mHEgvkbtu8o+7cmQl0/UYVx1EOramN5DyJ8YElnz7KuQNLyRInu/7X9tya5nUzI+mqimugEwuPwO6Is9Nerl3Iv988Quy5B0p1ECNWXivcISbMRpb23ATYmepS94ZJDVvstbGX7ls48eTI1VPKStDGRorB4tTaStDjwsu3XmuT6yVxE9y8qY5ai59kVRA7/NYq2KLPYHl2tpTr8qMkUmBpL/yzlcyWwLlGVZb+adly9XE8nrGBbSfqv2vUzA/9OJak7mYIFczCl7wYRA18dn1BibtcHLkklqbuYCgvmYk3nVn+/J2kQ0dfqolSSOo8mr6K0STFauBbyegkfwDNY+zdSSeouLifaZdTH6RhiLA8ANrL9VDXvNlmkU0nqYsrCuBprqwEPORo3VyHR6WmYTqgibWxfR/QNPaQ4Du4nxncWLusnTFFRcqNZ3fcdFZGOAT4LfLK8YCcUS/RzhHdhFSLxfydJZwA7ERN/0oUUi+UMwGxNSaV3ADcSvVQWLXIeLGk9SZ8mwnXmIRTopMuwfZHtr7tRgW4CEfM+niiiMhFJgyRtUTxOfyDi5C9p/1MnLWiuonQIpYpS7UX8DHBNzWtUzesvAfMSeWg9kkpS1zGcaK9xdPEkAWD7JiKsrlWBjlxw9QOq9VLZ3JQoAV5VgB1YS2uY2NC5M0+aTAtqaQfXET2y9gXOtP39zj1V8n6ZqtwRR4LamGINuZ7IOzqEEltLhHrgSEw7gOjRcQCNxqLXT/MnT6YlvVkwRSzWIJoIn0Asom+2vbbtv7T5WZP3QQ8WzLeb8osGEkaOYcBNtle1PVn/nKQj9FpFqbagSs/gdEJZHB9J5IruWnkbJC1DFG94pYOPl3xIyvicl3ivXg1Qwp3/BHy6nJPGi+mDmSX9mCjQcarteW2fD5m73Z+YYo7SJCc3chiWJdyHxwPDbL9bSy6uzp3Ldk7o/YAyYB8jFOBDi5JUHXsD2Nf2NWV7TeDvtl8t2wOdZd27mtq4vQkYZfuYaj8wxFEaenngucorkXLtTiQdRzSC3cX2nU3HBhHGqRmJUDwD+6XS2z+RdA6wGVFR9grCODmWqIz1aiefLflwSNoc+AbRJPYsotro15xVYqcrypy8IzDS9pNl3yRr5aT7eV8abVlsDSw5LMOJijvLFUvXwaVqS3VuKkn9hKmwYL5aO/c+26+WMAHlYrr76cWC+QBRFhzbo0oCccq1C5lSFaVCeganL44GjiPyR/cC7rb9yVSSpgs2J4pc3Ul4+eeolKTKS5z0f2y/bfty20/W3q2pJPUz3pdHCSapeDYj8AzwMmEN+SlwQMl5SfohacGcfkkLZv+nlypKcwODbD8raQXg2fQMTj8Uj//MLm00Uqb9H0lHERUMdyv53elpSJIu5X0rSjCxVOm2RK7Ko8Bxtm+dxs+WtBlJMxFNR/ciCjU8aPuIjj5UMk2QdDphoR4PDLd9YO1YVj3rZ0i6hagoei9RMOcc22fVjg8EJqRcpx+KwuSU6fRFGavOwg1J0p18UEVpW+AXwAm2T5/mT5V0lLRgTn+kBXP6oObRP5hIEH4ZOLlKEE6SpP+Q79Yk6X4+qKI0BBjr0oA0mT5JC+b0SVow+y8lOXgYkU94qu0TaseyeWGSJEmSTEM+kKKUJEn/JC2Y/ZusopQkSZIk7SMVpSRJkn5I5iElSZIkSd+SilKSJEmSJEmSJEkT2Rk4SZIkSZIkSZKkiVSUkiRJkiRJkiRJmkhFKUmSJEmSJEmSpIlUlJIkSZIkSZIkSZpIRSlJkiRJkiRJkqSJVJSSJEmSJEmSJEmaSEUpSZIkSZIkSZKkiVSUkiRJkiRJkiRJmvg/plp3avcL+4wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6sznBke5KBb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}