5_flow_multilinear_regression.ipynb

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    "colab": {
      "name": "5_flow_multilinear_regression.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NourELMAWAS/SOS21Server/blob/master/5_flow_multilinear_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EO3x29FdCpWc"
      },
      "source": [
        "---\n",
        "20210824:\n",
        "# Creates two *Multiple Linear Regression* Models (for QR2 and QR3) based on any LimeSurvey sample issued from GdP (usually 'YYYYMMDD-partial_merge.csv')\n",
        "It is based on 4.2_flow_linear_regression_fabien but modified such as:\n",
        "* Conclusions are not specific to an input file but explained as to know what to look for.\n",
        "* Keeps a record on the model values for each GdP. \n",
        "* Omits most checks, for a more streamlined execution.\n",
        "* It supports most exception related to the particularities of each GdP. \n",
        "---\n",
        "Concerning the input sample:\n",
        "LimeSurvey creates CSV exports. This script accepts GdP exports of the shape 'YYYYMMDD-partial_merge.csv'.\n",
        "It supposes the following:\n",
        "* Maximizes sample size: doesn't care if the person finished the rest of the QR and admits items with a 'Sans réponse' answer.\n",
        "* Maximizes sample size: doesn't care about state of détrompeurs.\n",
        "* QR2 & QR3 are analyzed as independent experiments, with different samples.\n",
        "* Does not uses GdP scoring, nor any other column except the ones related to EduFlow and Flow2, Sexe and student_id\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfqBIbAFDXoK"
      },
      "source": [
        "## **Linear regression** \n",
        "**Linear regression**is a research method of analyzing the association or relationship between two or more variables to determine if the one or more predictor (or independent variables) have a significant effect on the response (or dependent variable).\n",
        "\n",
        "# Situations where to use Linear Regression:\n",
        "* I want to characterize the relationship between the dependent and independent variables by determining the extent, direction, and strength of the association.\n",
        "* I seek a qualitative formula or equation to describe (predict) the dependent variable Y as a function of the independent variables X1, X2,, ..., Xk\n",
        "* I want to describe, either quantitatively or qualitatively, teh relationshipbetween X1, X2, ..., Xk and Y, but control for the effects of other variables C1, C2, ..., Cp, which I believe have an important relationship with Y.\n",
        "* I want to determine which of several independent variables are important and which are not in predicting dependent variable Y. I might also want to control for other variables.\n",
        "\n",
        "A deterministic model (formula) does not admit error [epsilon] in calculation,\n",
        "whereas a statistical model (formula) does (by adding [epsilon] at the end of it and switching = (\"equals\") by ~ (\"distributed\"))\n",
        "\n",
        "---\n",
        "Multiple Linear Regression (on two experiments (QR2 & QR3)):\n",
        "* Response (y) : \"flow exists\" (Y/N), from LimeSurvey, \n",
        "* Potential predictor (x1) : Flow score D1\n",
        "* Potential predictor (x2) : Flow score D2\n",
        "* Potential predictor (x3) : Flow score D3\n",
        "* Potential predictor (x4) : Flow score D4\n",
        "\n",
        "This *Multiple Linear Analysis* comes originally from https://medium.com/swlh/multi-linear-regression-using-python-44bd0d10082d"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEl99ygCC0XE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ee70fdb-b01a-444f-98c9-8afdbaeacd95"
      },
      "source": [
        "# Load Librairies\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from pandas.plotting import scatter_matrix\n",
        "%matplotlib inline\n",
        "\n",
        "# USER VARS:\n",
        "save_files = True # unused\n",
        "flag_visualize_nans = True\n",
        "gdrive_project_path = \"/My Drive/Colab Notebooks/projets/gdp17\"\n",
        "\n",
        "csv1 = \"./data/20210518-partial_merge.csv\"\n",
        "sep1 = \",\"\n",
        "\n",
        "\n",
        "# Output files\n",
        "output_path = \"./data/\" # unused\n",
        "csv_out = \"lin_reg.20210818-fabien-tmp.out.csv\" # unused\n",
        "\n",
        "# DEV VARS:\n",
        "debug = True\n",
        "flag_nans_in_sample = False\n",
        "const_nan_tolerance_per_row = 4\n",
        "\n",
        "# Project definition\n",
        "current_gdp = gdrive_project_path[-5:].lower() # Try to use a regex for everythiong after the last '/'\n",
        "print(\"Current GdP project loaded:\", current_gdp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current GdP project loaded: gdp17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9M273tuFfyy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "131506f0-6a9a-4d32-ba6c-61a8fcc9bb18"
      },
      "source": [
        "# CONNECTOR to Google Drive \n",
        "from google.colab import drive\n",
        "\n",
        "mount_point= \"/content/drive\"\n",
        "drive.mount(mount_point, force_remount = False)\n",
        "gdrive_path = mount_point + gdrive_project_path\n",
        "\n",
        "# New Input file\n",
        "csv1 = gdrive_path + csv1[1:]\n",
        "\n",
        "# New Output files\n",
        "output_path = gdrive_path + output_path[1:]\n",
        "csv_out = output_path + csv_out\n",
        "\n",
        "if (debug):\n",
        "  print(csv1, output_path)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    618\u001b[0m         \"\"\"\n\u001b[0;32m--> 619\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    620\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-7a707ca3a687>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmount_point\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/drive\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmount_point\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mgdrive_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmount_point\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgdrive_project_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, use_metadata_server)\u001b[0m\n\u001b[1;32m    106\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m       \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_ms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m       use_metadata_server=use_metadata_server)\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, use_metadata_server, ephemeral)\u001b[0m\n\u001b[1;32m    285\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dfs-auth-dance'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfifo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfifo_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m           \u001b[0mfifo_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauth_prompt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m       \u001b[0mwrote_to_fifo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSKK7AW5FoKh"
      },
      "source": [
        "# <=================================================================================>\n",
        "# Program starts here\n",
        "# <=================================================================================>\n",
        "\n",
        "# Control delimiters, rows, column names with read_csv \n",
        "# Reads input data\n",
        "rawdata = pd.read_csv(csv1, sep = sep1)\n",
        "\n",
        "# Clean up the awful column names\n",
        "rawdata.columns = rawdata.columns.str.strip().str.replace(' ', '_').str.replace('[', '_').str.replace(']', '_')\n",
        "\n",
        "# Saves Table size in a new array\n",
        "tsize = []\n",
        "tsize.append(rawdata.shape[0])\n",
        "tsize.append(rawdata.shape[1])\n",
        "for col in rawdata.columns:\n",
        "    print(col)\n",
        "print(\"***********************************\")   \n",
        "    \n",
        "print(\"Loaded CSV successfully; QR1 = %s rows with %s columns:\\n\" % (tsize[0], tsize[1]))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYKlxgSdiER_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66654a63-3d9c-47d2-f3c1-bad17aad048a"
      },
      "source": [
        "# Preview the first 5 lines of the loaded data and Quick analysis on the loaded data:\n",
        "if (debug):\n",
        "  print(rawdata.head(5).to_string())\n",
        "  rawdata.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   id_x         submitdate_x  lastpage_x startlanguage_x  token_x          startdate_x          datestamp_x       ipaddr_x refurl_x QR1ExpFormLigne  QR1ExpFormLigneNbre QR1Intention_INTUNIQ_ QR1IntentAmaury_Q1_ QR1IntentAmaury_Q2_ QR1IntentAmaury_Q3_ QR1IntentAmaury_Q4_ QR1IntentAmaury_Q5_ QR1IntentAmaury_Q6_ QR1IntentAmaury_Q7_ QR1IntentAmaury_Q8_ QR1IntentAmaury_Q9_ QR1IntentAmaury_QA_  QR1SRL_STRAT04_  QR1SRL_PROC06_  QR1SRL_CTXTE03_  QR1SRL_PAIRS04_  QR1SRL_STRAT07_  QR1SRL_PAIRS06_  QR1SRL_CTXTE06_  QR1SRL_PROC05_  QR1SRL_CTXTE05_  QR1SRL_DET7_  QR1SRL_STRAT05_  QR1SRL_PAIRS01_  QR1SRL_CTXTE02_  QR1SRL_PROC02_  QR1SRL_PAIRS02_  QR1SRL_STRAT02_  QR1SRL_PROC03_  QR1SRL_CTXTE01_  QR1SRL_PAIRS05_  QR1SRL_DET8_  QR1SRL_STRAT03_  QR1SRL_PROC01_  QR1SRL_CTXTE04_  QR1SRL_STRAT06_  QR1SRL_STRAT01_  QR1SRL_PROC04_  QR1SRL_PAIRS03_  QR1OLEI_i1_  QR1OLEI_i2_  QR1OLEI_iF_  QR1OLEI_i3_  QR1OLEI_i4_  QR1OLEI_i5_  QR1OLEI_iG_  QR1OLEI_i6_  QR1OLEI_i7_  QR1OLEI_i8_  QR1OLEI_i9_  QR1OLEI_iA_  QR1OLEI_iB_  QR1OLEI_iH_  QR1OLEI_iC_  QR1OLEI_iD_  QR1OLEI_iE_  QR1OLEI_iI_ QR1BdA_Q1_ QR1BdA_Q2_ QR1BdA_Q3_ QR1BdA_Q4_ QR1BdA_Q5_ QR1BdA_Q6_ QR1BdA_Q7_ QR1BdA_DETA4_ QR1BdA_Q9_ QR1BdA_QA_ QR1BdA_QB_  courseid      surname QR1Sexe_SQ001_ QR1Sexe_SQ002_ QR1Sexe_other_  QR1AnneeNaissance  QR1PaysRegionResiden  QR1Statut  QR1NiveauScolaire  interviewtime_x  groupTime2120  QR1ExpFormLigneTime  QR1ExpFormLigneNbreTime  groupTime2121  QR1IntentionTime  QR1IntentAmauryTime  groupTime2107  QR1SRLTime  groupTime2108  QR1OLEITime  groupTime2122  QR1BdATime  groupTime2105  courseidTime  surnameTime  QR1SexeTime  QR1AnneeNaissanceTime  QR1PaysRegionResidenTime  QR1StatutTime  QR1NiveauScolaireTime  firstname_x   lastname_x                                email   id_y submitdate_y  lastpage_y startlanguage_y  token_y          startdate_y          datestamp_y       ipaddr_y refurl_y  QR2Intention_INTUNIQ_  QR2FlowMOOCac_FQRa_  QR2FlowMOOCac_FQRb_  QR2FlowMOOCac_FQRc_  QR2EduFlow_EduFlowD1a_  QR2EduFlow_EduFlowD2a_  QR2EduFlow_EduFlowD3a_  QR2EduFlow_EduFlowD4a_  QR2EduFlow_EduFlowD1b_  QR2EduFlow_EduFlowD2c_  QR2EduFlow_EduFlowDET4_  QR2EduFlow_EduFlowD3b_  QR2EduFlow_EduFlowD4b_  QR2EduFlow_EduFlowD1c_  QR2EduFlow_EduFlowD2b_  QR2EduFlow_EduFlowD3c_  QR2EduFlow_EduFlowD4c_  QR2FlowMOOCb_FQRb_  QR2SRLsp_STRAT04_  QR2SRLsp_PROC06_  QR2SRLsp_CTXTE03_  QR2SRLsp_PAIRS04_  QR2SRLsp_STRAT07_  QR2SRLsp_PAIRS06_  QR2SRLsp_DET5_  QR2SRLsp_CTXTE06_  QR2SRLsp_PROC05_  QR2SRLsp_CTXTE05_  QR2SRLsp_STRAT05_  QR2SRLsp_PAIRS01_  QR2SRLsp_CTXTE02_  QR2SRLsp_PROC02_  QR2SRLsp_PAIRS02_  QR2SRLsp_STRAT02_  QR2SRLsp_PROC03_  QR2SRLsp_CTXTE01_  QR2SRLsp_PAIRS05_  QR2SRLsp_STRAT03_  QR2SRLsp_PROC01_  QR2SRLsp_DET3_  QR2SRLsp_CTXTE04_  QR2SRLsp_STRAT06_  QR2SRLsp_STRAT01_  QR2SRLsp_PROC04_  QR2SRLsp_PAIRS03_  QR2RevenuRegulier  QR2ConditionExonerat ENGQ1 ACC_Q001_ ACC_Q002_ ACC_Q003_ ACC_Q004_ ACC_Q005_ ACC_Q006_ ACC_Q007_ ACC_Q008_ ACC_Q009_ ENT01 AMA2 LIB_LIB1_ LIB_LIB2_ LIB_LIB3_ LIB_LIB4_ LIB_LIB5_ LIB_LIB6_ LIB_LIB7_ LIB_LIB8_ LIB_LIB9_  interviewtime_y  groupTime2113  QR2IntentionTime  groupTime2112  QR2FlowMOOCacTime_x  groupTime2110  QR2EduFlowTime  groupTime2190  QR2FlowMOOCbTime_x  groupTime2109  QR2SRLspTime  groupTime2111  QR2RevenuRegulierTime  QR2ConditionExoneratTime  groupTime2123  ENGQ1Time  ACCTime_x  ENT01Time  AMA2Time  LIBTime_x  firstname_y   lastname_y     _merge status                                                                                                                                                                          info  id submitdate  lastpage startlanguage token startdate datestamp ipaddr refurl  QR3SRMOOC_Q1_  QR3SRMOOC_Q2_  QR3SRMOOC_Q3_  QR3SRMOOC_Q4_  QR3SRMOOC_Q5_  QR3SRMOOC_Q6_  QR3SRMOOC_Q7_  QR3FlowMOOCac_FQRa_  QR3FlowMOOCac_FQRb_  QR3FlowMOOCac_FQRc_  QR3Flow_FlowD1a_  QR3Flow_FlowD2a_  QR3Flow_FlowD3a_  QR3Flow_FlowD4a_  QR3Flow_FlowD1b_  QR3Flow_FlowD2c_  QR3Flow_FlowD3b_  QR3Flow_FlowD4b_  QR3Flow_FlowD1c_  QR3Flow_FlowDET2_  QR3Flow_FlowD2b_  QR3Flow_FlowD3c_  QR3Flow_FlowD4c_  QR3FlowMOOCb_FQRb_  MOT  ACC  ENT  LIB APP_Q1_ APP_Q2_ APP_Q3_ APP_Q4_ APP_Q5_ APP_Q6_ APP_Q7_ APP_Q8_ APP_Q9_ APP_Q10_ APP_Q11_ APP_Q12_ APP_Q13_ NET1 NET2  interviewtime  groupTime2116  QR3SRMOOCTime  groupTime2195  QR2FlowMOOCacTime_y  groupTime2115  QR3FlowTime  groupTime2196  QR2FlowMOOCbTime_y  groupTime2197  MOTTime  ACCTime_y  ENTTime  groupTime2198  LIBTime_y  APPTime  NET1Time  NET2Time firstname lastname\n",
            "0   1.0  2021-02-15 16:29:48         1.0              fr  28099.0  2021-02-15 16:28:40  2021-02-15 16:31:10   82.65.131.48      NaN               N                  NaN                  L001                L001                L001                L001                L001                L001                L001                L001                L001                L001                L001              1.0             1.0              1.0              1.0              1.0              1.0              1.0             1.0              1.0           1.0              1.0              1.0              1.0             1.0              1.0              1.0             1.0              1.0              1.0           1.0              1.0             1.0              1.0              1.0              1.0             1.0              1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0          1.0         A1         A1         A1         A1         A1         A1         A1            A1         A1         A1         A1       NaN  fbouchetGDP              Y            NaN            NaN             1905.0                   1.0        1.0                1.0            81.01           6.93                  NaN                      NaN          11.13               NaN                  NaN          19.22         NaN          11.45          NaN           8.65         NaN          23.63           NaN          NaN          NaN                    NaN                       NaN            NaN                    NaN          NaN  fbouchetGDP  francois.bouchet@gestiondeprojet.pm  436.0          NaN         NaN              fr  28099.0  2021-03-26 14:33:19  2021-03-26 14:33:19   82.65.131.48      NaN                    NaN                  NaN                  NaN                  NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                      NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                 NaN                NaN               NaN                NaN                NaN                NaN                NaN             NaN                NaN               NaN                NaN                NaN                NaN                NaN               NaN                NaN                NaN               NaN                NaN                NaN                NaN               NaN             NaN                NaN                NaN                NaN               NaN                NaN                NaN                   NaN   NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN   NaN  NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN              0.0            NaN               NaN            NaN                  NaN            NaN             NaN            NaN                 NaN            NaN           NaN            NaN                    NaN                       NaN            NaN        NaN        NaN        NaN       NaN        NaN          NaN  fbouchetGDP       both    NaN                      Date de naissance incorrecte. QR1: détrompeurs incorrectement répondus. QR2: détrompeurs incorrectement répondus. QR2: + de 10% 'Sans réponse' QR1: 90%  NaN        NaN       NaN           NaN   NaN       NaN       NaN    NaN    NaN            NaN            NaN            NaN            NaN            NaN            NaN            NaN                  NaN                  NaN                  NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN                NaN               NaN               NaN               NaN                 NaN  NaN  NaN  NaN  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN      NaN  NaN  NaN            NaN            NaN            NaN            NaN                  NaN            NaN          NaN            NaN                 NaN            NaN      NaN        NaN      NaN            NaN        NaN      NaN       NaN       NaN       NaN      NaN\n",
            "1   2.0                  NaN         2.0              fr  11676.0  2021-02-15 16:29:25  2021-02-15 16:34:38  88.124.206.21      NaN               Y                 19.0                  L003                L005                L004                L004                L004                L005                L005                L004                L004                L004                L004              NaN             NaN              NaN              NaN              NaN              NaN              NaN             NaN              NaN           NaN              NaN              NaN              NaN             NaN              NaN              NaN             NaN              NaN              NaN           NaN              NaN             NaN              NaN              NaN              NaN             NaN              NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN           NaN        NaN        NaN        NaN       NaN  JAMALTRAORE            NaN            NaN            NaN                NaN                   NaN        NaN                NaN           301.74          58.24                  NaN                      NaN         243.50               NaN                  NaN            NaN         NaN            NaN          NaN            NaN         NaN            NaN           NaN          NaN          NaN                    NaN                       NaN            NaN                    NaN          NaN  JAMALTRAORE             jamaltraore707@gmail.com  435.0          NaN         NaN              fr  11676.0  2021-03-26 14:33:02  2021-03-26 14:33:02  88.124.206.21      NaN                    NaN                  NaN                  NaN                  NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                      NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                 NaN                NaN               NaN                NaN                NaN                NaN                NaN             NaN                NaN               NaN                NaN                NaN                NaN                NaN               NaN                NaN                NaN               NaN                NaN                NaN                NaN               NaN             NaN                NaN                NaN                NaN               NaN                NaN                NaN                   NaN   NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN   NaN  NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN              0.0            NaN               NaN            NaN                  NaN            NaN             NaN            NaN                 NaN            NaN           NaN            NaN                    NaN                       NaN            NaN        NaN        NaN        NaN       NaN        NaN          NaN  JAMALTRAORE       both    NaN  Date de naissance incorrecte. QR1: détrompeurs incorrectement répondus. QR2: détrompeurs incorrectement répondus. QR1: + de 10% 'Sans réponse' QR2: + de 10% 'Sans réponse'  NaN        NaN       NaN           NaN   NaN       NaN       NaN    NaN    NaN            NaN            NaN            NaN            NaN            NaN            NaN            NaN                  NaN                  NaN                  NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN                NaN               NaN               NaN               NaN                 NaN  NaN  NaN  NaN  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN      NaN  NaN  NaN            NaN            NaN            NaN            NaN                  NaN            NaN          NaN            NaN                 NaN            NaN      NaN        NaN      NaN            NaN        NaN      NaN       NaN       NaN       NaN      NaN\n",
            "2   3.0  2021-02-15 16:32:12         6.0              fr     53.0  2021-02-15 16:30:41  2021-02-15 16:32:12  85.169.198.26      NaN               N                  NaN                   NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN              NaN             NaN              NaN              NaN              NaN              NaN              NaN             NaN              NaN           NaN              NaN              NaN              NaN             NaN              NaN              NaN             NaN              NaN              NaN           NaN              NaN             NaN              NaN              NaN              NaN             NaN              NaN          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0          0.0        NaN        NaN        NaN        NaN        NaN        NaN        NaN           NaN        NaN        NaN        NaN       NaN      test-RB              Y              Y             fg             1921.0                  17.0        8.0                3.0           101.61           2.85                  NaN                      NaN          24.89               NaN                  NaN          14.68         NaN          21.79          NaN           8.03         NaN          29.37           NaN          NaN          NaN                    NaN                       NaN            NaN                    NaN          NaN      test-RB              test@gestiondeprojet.pm    NaN          NaN         NaN             NaN      NaN                  NaN                  NaN            NaN      NaN                    NaN                  NaN                  NaN                  NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                      NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                 NaN                NaN               NaN                NaN                NaN                NaN                NaN             NaN                NaN               NaN                NaN                NaN                NaN                NaN               NaN                NaN                NaN               NaN                NaN                NaN                NaN               NaN             NaN                NaN                NaN                NaN               NaN                NaN                NaN                   NaN   NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN   NaN  NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN              NaN            NaN               NaN            NaN                  NaN            NaN             NaN            NaN                 NaN            NaN           NaN            NaN                    NaN                       NaN            NaN        NaN        NaN        NaN       NaN        NaN          NaN          NaN  left_only    NaN  Date de naissance incorrecte. QR1: détrompeurs incorrectement répondus. QR2: détrompeurs incorrectement répondus. QR1: + de 10% 'Sans réponse' QR2: + de 10% 'Sans réponse'  NaN        NaN       NaN           NaN   NaN       NaN       NaN    NaN    NaN            NaN            NaN            NaN            NaN            NaN            NaN            NaN                  NaN                  NaN                  NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN                NaN               NaN               NaN               NaN                 NaN  NaN  NaN  NaN  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN      NaN  NaN  NaN            NaN            NaN            NaN            NaN                  NaN            NaN          NaN            NaN                 NaN            NaN      NaN        NaN      NaN            NaN        NaN      NaN       NaN       NaN       NaN      NaN\n",
            "3   4.0                  NaN         3.0              fr      9.0  2021-02-15 18:07:41  2021-03-24 09:11:42   93.25.176.76      NaN               Y                  NaN                   NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN                 NaN              NaN             NaN              NaN              NaN              NaN              NaN              NaN             NaN              NaN           NaN              NaN              NaN              NaN             NaN              NaN              NaN             NaN              NaN              NaN           NaN              NaN             NaN              NaN              NaN              NaN             NaN              NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN          NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN           NaN        NaN        NaN        NaN       NaN         Paul            NaN            NaN            NaN                NaN                   NaN        NaN                NaN            44.96          31.61                  NaN                      NaN           6.37               NaN                  NaN           3.83         NaN           3.15          NaN            NaN         NaN            NaN           NaN          NaN          NaN                    NaN                       NaN            NaN                    NaN          NaN         Paul              humbertpaul67@gmail.com    NaN          NaN         NaN             NaN      NaN                  NaN                  NaN            NaN      NaN                    NaN                  NaN                  NaN                  NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                      NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                 NaN                NaN               NaN                NaN                NaN                NaN                NaN             NaN                NaN               NaN                NaN                NaN                NaN                NaN               NaN                NaN                NaN               NaN                NaN                NaN                NaN               NaN             NaN                NaN                NaN                NaN               NaN                NaN                NaN                   NaN   NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN   NaN  NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN              NaN            NaN               NaN            NaN                  NaN            NaN             NaN            NaN                 NaN            NaN           NaN            NaN                    NaN                       NaN            NaN        NaN        NaN        NaN       NaN        NaN          NaN          NaN  left_only    NaN  Date de naissance incorrecte. QR1: détrompeurs incorrectement répondus. QR2: détrompeurs incorrectement répondus. QR1: + de 10% 'Sans réponse' QR2: + de 10% 'Sans réponse'  NaN        NaN       NaN           NaN   NaN       NaN       NaN    NaN    NaN            NaN            NaN            NaN            NaN            NaN            NaN            NaN                  NaN                  NaN                  NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN                NaN               NaN               NaN               NaN                 NaN  NaN  NaN  NaN  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN      NaN  NaN  NaN            NaN            NaN            NaN            NaN                  NaN            NaN          NaN            NaN                 NaN            NaN      NaN        NaN      NaN            NaN        NaN      NaN       NaN       NaN       NaN      NaN\n",
            "4   5.0  2021-02-17 22:57:55         6.0              fr  96656.0  2021-02-17 22:47:26  2021-02-17 22:57:55  91.172.74.231      NaN               Y                  3.0                  L006                L005                L003                L003                L004                L004                L003                L003                L003                L005                L005              6.0             4.0              7.0              4.0              7.0              5.0              7.0             3.0              7.0           5.0              6.0              4.0              7.0             1.0              2.0              6.0             2.0              7.0              4.0           3.0              6.0             2.0              7.0              5.0              6.0             3.0              1.0          1.0          1.0          0.0          1.0          0.0          1.0          0.0          0.0          1.0          0.0          0.0          1.0          1.0          0.0          0.0          0.0          0.0          0.0         A5         A4         A4         A4         A3         A2         A3            A4         A3         A2         A3       NaN     Marion-B            NaN              Y            NaN             1982.0                  63.0        5.0                6.0           668.66          13.23                  NaN                      NaN         141.46               NaN                  NaN         267.52         NaN          54.54          NaN          95.49         NaN          96.42           NaN          NaN          NaN                    NaN                       NaN            NaN                    NaN          NaN     Marion-B             beaudet.marion@gmail.com    NaN          NaN         NaN             NaN      NaN                  NaN                  NaN            NaN      NaN                    NaN                  NaN                  NaN                  NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                      NaN                     NaN                     NaN                     NaN                     NaN                     NaN                     NaN                 NaN                NaN               NaN                NaN                NaN                NaN                NaN             NaN                NaN               NaN                NaN                NaN                NaN                NaN               NaN                NaN                NaN               NaN                NaN                NaN                NaN               NaN             NaN                NaN                NaN                NaN               NaN                NaN                NaN                   NaN   NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN   NaN  NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN              NaN            NaN               NaN            NaN                  NaN            NaN             NaN            NaN                 NaN            NaN           NaN            NaN                    NaN                       NaN            NaN        NaN        NaN        NaN       NaN        NaN          NaN          NaN  left_only    NaN                                                                                                                                                   Date OK. QR1: OK. QR1: 90%  NaN        NaN       NaN           NaN   NaN       NaN       NaN    NaN    NaN            NaN            NaN            NaN            NaN            NaN            NaN            NaN                  NaN                  NaN                  NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN               NaN                NaN               NaN               NaN               NaN                 NaN  NaN  NaN  NaN  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN      NaN  NaN  NaN            NaN            NaN            NaN            NaN                  NaN            NaN          NaN            NaN                 NaN            NaN      NaN        NaN      NaN            NaN        NaN      NaN       NaN       NaN       NaN      NaN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqds03A2lxzn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "664f35fe-f47f-4335-e8d4-9af641e3f320"
      },
      "source": [
        "# Create working sets\n",
        "df_corrections = rawdata.copy()\n",
        "\n",
        "# Correcting columns names (from \"nothing\" --> QR2, only in GdP15)\n",
        "if (current_gdp == \"gdp15\"):\n",
        "  df_corrections.rename(columns={'FlowMOOC_FQRa_': 'QR2FlowMOOC_FQRa_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'FlowMOOC_FQRb_': 'QR2FlowMOOC_FQRb_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'FlowMOOC_FQRc_': 'QR2FlowMOOC_FQRc_'}, inplace=True)\n",
        "\n",
        "\n",
        "# Changing the column names (from EduFlow_EduFlow --> Flow_Flow), although the right name is EduFlow. (maybe change this for gdp15 only ???)\n",
        "if (current_gdp == \"gdp16\" or current_gdp == \"gdp17\"):\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD1a_': 'QR2Flow_FlowD1a_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD2a_': 'QR2Flow_FlowD2a_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD3a_': 'QR2Flow_FlowD3a_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD4a_': 'QR2Flow_FlowD4a_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD1b_': 'QR2Flow_FlowD1b_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD2b_': 'QR2Flow_FlowD2b_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD3b_': 'QR2Flow_FlowD3b_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD4b_': 'QR2Flow_FlowD4b_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD1c_': 'QR2Flow_FlowD1c_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD2c_': 'QR2Flow_FlowD2c_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD3c_': 'QR2Flow_FlowD3c_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowD4c_': 'QR2Flow_FlowD4c_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2EduFlow_EduFlowDET4_': 'QR2Flow_FlowDET4_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'id': 'student_id'}, inplace=True)\n",
        "\n",
        "\n",
        "if (current_gdp == \"gdp17\"):\n",
        "  df_corrections.rename(columns={'QR2FlowMOOCac_FQRa_': 'QR2FlowMOOC_FQRa_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2FlowMOOCb_FQRb_': 'QR2FlowMOOC_FQRb_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR2FlowMOOCac_FQRc_': 'QR2FlowMOOC_FQRc_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR3FlowMOOCac_FQRa_': 'QR3FlowMOOC_FQRa_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR3FlowMOOCb_FQRb_': 'QR3FlowMOOC_FQRb_'}, inplace=True)\n",
        "  df_corrections.rename(columns={'QR3FlowMOOCac_FQRc_': 'QR3FlowMOOC_FQRc_'}, inplace=True)\n",
        "  # **** MUST convert QR1Sexe_SQ001_, QR1Sexe_SQ002_, QR1Sexe_other_ into QR1Sexe *** 2DO:\n",
        "  # temporary patch to obtain the column, as long as the column is not used for any calculation\n",
        "  df_corrections['QR1Sexe'] = [0] * tsize[0]\n",
        "\n",
        "# Correct text strings to proper values:\n",
        "df_corrections.replace(to_replace=\"N\", value=0, inplace=True)\n",
        "df_corrections.replace(to_replace=\"Y\", value=1, inplace=True)\n",
        "df_corrections.replace(to_replace=\"NaN\", value=np.nan, inplace=True)\n",
        "df_corrections.replace(to_replace=\"null\", value=np.nan, inplace=True)\n",
        "df_corrections.replace(to_replace=\"NULL\", value=np.nan, inplace=True)\n",
        "df_corrections.replace(to_replace=\"None\", value=np.nan, inplace=True)\n",
        "df_corrections.replace(to_replace=\"none\", value=np.nan, inplace=True)\n",
        "df_corrections.replace(to_replace=\"NONE\", value=np.nan, inplace=True)\n",
        "\n",
        "\n",
        "#df_corrections.groupby(['QR2FlowMOOC_FQRa_'])['student_id'].count()\n",
        "\n",
        "# Corrected columns\n",
        "for col in df_corrections.columns:\n",
        "    print(col)\n",
        "\n",
        "# Keep only necessary columns:\n",
        "\n",
        "linear_reg_columns = ['student_id', 'QR1Sexe', \n",
        "                      'QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_', \n",
        "                      'QR2Flow_FlowD1a_', 'QR2Flow_FlowD1b_', 'QR2Flow_FlowD1c_', \n",
        "                      'QR2Flow_FlowD2a_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD2c_', \n",
        "                      'QR2Flow_FlowD3a_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD3c_', \n",
        "                      'QR2Flow_FlowD4a_', 'QR2Flow_FlowD4b_', 'QR2Flow_FlowD4c_', \n",
        "                      'QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_', \n",
        "                      'QR3Flow_FlowD1a_', 'QR3Flow_FlowD1b_', 'QR3Flow_FlowD1c_', \n",
        "                      'QR3Flow_FlowD2a_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD2c_', \n",
        "                      'QR3Flow_FlowD3a_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD3c_', \n",
        "                      'QR3Flow_FlowD4a_', 'QR3Flow_FlowD4b_', 'QR3Flow_FlowD4c_']\n",
        "\n",
        "df_csv1 = df_corrections[linear_reg_columns].copy()\n",
        "\n",
        "tsize = []\n",
        "tsize.append(df_csv1.shape[0])\n",
        "tsize.append(df_csv1.shape[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id_x\n",
            "submitdate_x\n",
            "lastpage_x\n",
            "startlanguage_x\n",
            "token_x\n",
            "startdate_x\n",
            "datestamp_x\n",
            "ipaddr_x\n",
            "refurl_x\n",
            "QR1ExpFormLigne\n",
            "QR1ExpFormLigneNbre\n",
            "QR1Intention_INTUNIQ_\n",
            "QR1IntentAmaury_Q1_\n",
            "QR1IntentAmaury_Q2_\n",
            "QR1IntentAmaury_Q3_\n",
            "QR1IntentAmaury_Q4_\n",
            "QR1IntentAmaury_Q5_\n",
            "QR1IntentAmaury_Q6_\n",
            "QR1IntentAmaury_Q7_\n",
            "QR1IntentAmaury_Q8_\n",
            "QR1IntentAmaury_Q9_\n",
            "QR1IntentAmaury_QA_\n",
            "QR1SRL_STRAT04_\n",
            "QR1SRL_PROC06_\n",
            "QR1SRL_CTXTE03_\n",
            "QR1SRL_PAIRS04_\n",
            "QR1SRL_STRAT07_\n",
            "QR1SRL_PAIRS06_\n",
            "QR1SRL_CTXTE06_\n",
            "QR1SRL_PROC05_\n",
            "QR1SRL_CTXTE05_\n",
            "QR1SRL_DET7_\n",
            "QR1SRL_STRAT05_\n",
            "QR1SRL_PAIRS01_\n",
            "QR1SRL_CTXTE02_\n",
            "QR1SRL_PROC02_\n",
            "QR1SRL_PAIRS02_\n",
            "QR1SRL_STRAT02_\n",
            "QR1SRL_PROC03_\n",
            "QR1SRL_CTXTE01_\n",
            "QR1SRL_PAIRS05_\n",
            "QR1SRL_DET8_\n",
            "QR1SRL_STRAT03_\n",
            "QR1SRL_PROC01_\n",
            "QR1SRL_CTXTE04_\n",
            "QR1SRL_STRAT06_\n",
            "QR1SRL_STRAT01_\n",
            "QR1SRL_PROC04_\n",
            "QR1SRL_PAIRS03_\n",
            "QR1OLEI_i1_\n",
            "QR1OLEI_i2_\n",
            "QR1OLEI_iF_\n",
            "QR1OLEI_i3_\n",
            "QR1OLEI_i4_\n",
            "QR1OLEI_i5_\n",
            "QR1OLEI_iG_\n",
            "QR1OLEI_i6_\n",
            "QR1OLEI_i7_\n",
            "QR1OLEI_i8_\n",
            "QR1OLEI_i9_\n",
            "QR1OLEI_iA_\n",
            "QR1OLEI_iB_\n",
            "QR1OLEI_iH_\n",
            "QR1OLEI_iC_\n",
            "QR1OLEI_iD_\n",
            "QR1OLEI_iE_\n",
            "QR1OLEI_iI_\n",
            "QR1BdA_Q1_\n",
            "QR1BdA_Q2_\n",
            "QR1BdA_Q3_\n",
            "QR1BdA_Q4_\n",
            "QR1BdA_Q5_\n",
            "QR1BdA_Q6_\n",
            "QR1BdA_Q7_\n",
            "QR1BdA_DETA4_\n",
            "QR1BdA_Q9_\n",
            "QR1BdA_QA_\n",
            "QR1BdA_QB_\n",
            "courseid\n",
            "surname\n",
            "QR1Sexe_SQ001_\n",
            "QR1Sexe_SQ002_\n",
            "QR1Sexe_other_\n",
            "QR1AnneeNaissance\n",
            "QR1PaysRegionResiden\n",
            "QR1Statut\n",
            "QR1NiveauScolaire\n",
            "interviewtime_x\n",
            "groupTime2120\n",
            "QR1ExpFormLigneTime\n",
            "QR1ExpFormLigneNbreTime\n",
            "groupTime2121\n",
            "QR1IntentionTime\n",
            "QR1IntentAmauryTime\n",
            "groupTime2107\n",
            "QR1SRLTime\n",
            "groupTime2108\n",
            "QR1OLEITime\n",
            "groupTime2122\n",
            "QR1BdATime\n",
            "groupTime2105\n",
            "courseidTime\n",
            "surnameTime\n",
            "QR1SexeTime\n",
            "QR1AnneeNaissanceTime\n",
            "QR1PaysRegionResidenTime\n",
            "QR1StatutTime\n",
            "QR1NiveauScolaireTime\n",
            "firstname_x\n",
            "lastname_x\n",
            "email\n",
            "id_y\n",
            "submitdate_y\n",
            "lastpage_y\n",
            "startlanguage_y\n",
            "token_y\n",
            "startdate_y\n",
            "datestamp_y\n",
            "ipaddr_y\n",
            "refurl_y\n",
            "QR2Intention_INTUNIQ_\n",
            "QR2FlowMOOC_FQRa_\n",
            "QR2FlowMOOCac_FQRb_\n",
            "QR2FlowMOOC_FQRc_\n",
            "QR2Flow_FlowD1a_\n",
            "QR2Flow_FlowD2a_\n",
            "QR2Flow_FlowD3a_\n",
            "QR2Flow_FlowD4a_\n",
            "QR2Flow_FlowD1b_\n",
            "QR2Flow_FlowD2c_\n",
            "QR2Flow_FlowDET4_\n",
            "QR2Flow_FlowD3b_\n",
            "QR2Flow_FlowD4b_\n",
            "QR2Flow_FlowD1c_\n",
            "QR2Flow_FlowD2b_\n",
            "QR2Flow_FlowD3c_\n",
            "QR2Flow_FlowD4c_\n",
            "QR2FlowMOOC_FQRb_\n",
            "QR2SRLsp_STRAT04_\n",
            "QR2SRLsp_PROC06_\n",
            "QR2SRLsp_CTXTE03_\n",
            "QR2SRLsp_PAIRS04_\n",
            "QR2SRLsp_STRAT07_\n",
            "QR2SRLsp_PAIRS06_\n",
            "QR2SRLsp_DET5_\n",
            "QR2SRLsp_CTXTE06_\n",
            "QR2SRLsp_PROC05_\n",
            "QR2SRLsp_CTXTE05_\n",
            "QR2SRLsp_STRAT05_\n",
            "QR2SRLsp_PAIRS01_\n",
            "QR2SRLsp_CTXTE02_\n",
            "QR2SRLsp_PROC02_\n",
            "QR2SRLsp_PAIRS02_\n",
            "QR2SRLsp_STRAT02_\n",
            "QR2SRLsp_PROC03_\n",
            "QR2SRLsp_CTXTE01_\n",
            "QR2SRLsp_PAIRS05_\n",
            "QR2SRLsp_STRAT03_\n",
            "QR2SRLsp_PROC01_\n",
            "QR2SRLsp_DET3_\n",
            "QR2SRLsp_CTXTE04_\n",
            "QR2SRLsp_STRAT06_\n",
            "QR2SRLsp_STRAT01_\n",
            "QR2SRLsp_PROC04_\n",
            "QR2SRLsp_PAIRS03_\n",
            "QR2RevenuRegulier\n",
            "QR2ConditionExonerat\n",
            "ENGQ1\n",
            "ACC_Q001_\n",
            "ACC_Q002_\n",
            "ACC_Q003_\n",
            "ACC_Q004_\n",
            "ACC_Q005_\n",
            "ACC_Q006_\n",
            "ACC_Q007_\n",
            "ACC_Q008_\n",
            "ACC_Q009_\n",
            "ENT01\n",
            "AMA2\n",
            "LIB_LIB1_\n",
            "LIB_LIB2_\n",
            "LIB_LIB3_\n",
            "LIB_LIB4_\n",
            "LIB_LIB5_\n",
            "LIB_LIB6_\n",
            "LIB_LIB7_\n",
            "LIB_LIB8_\n",
            "LIB_LIB9_\n",
            "interviewtime_y\n",
            "groupTime2113\n",
            "QR2IntentionTime\n",
            "groupTime2112\n",
            "QR2FlowMOOCacTime_x\n",
            "groupTime2110\n",
            "QR2EduFlowTime\n",
            "groupTime2190\n",
            "QR2FlowMOOCbTime_x\n",
            "groupTime2109\n",
            "QR2SRLspTime\n",
            "groupTime2111\n",
            "QR2RevenuRegulierTime\n",
            "QR2ConditionExoneratTime\n",
            "groupTime2123\n",
            "ENGQ1Time\n",
            "ACCTime_x\n",
            "ENT01Time\n",
            "AMA2Time\n",
            "LIBTime_x\n",
            "firstname_y\n",
            "lastname_y\n",
            "_merge\n",
            "status\n",
            "info\n",
            "student_id\n",
            "submitdate\n",
            "lastpage\n",
            "startlanguage\n",
            "token\n",
            "startdate\n",
            "datestamp\n",
            "ipaddr\n",
            "refurl\n",
            "QR3SRMOOC_Q1_\n",
            "QR3SRMOOC_Q2_\n",
            "QR3SRMOOC_Q3_\n",
            "QR3SRMOOC_Q4_\n",
            "QR3SRMOOC_Q5_\n",
            "QR3SRMOOC_Q6_\n",
            "QR3SRMOOC_Q7_\n",
            "QR3FlowMOOC_FQRa_\n",
            "QR3FlowMOOCac_FQRb_\n",
            "QR3FlowMOOC_FQRc_\n",
            "QR3Flow_FlowD1a_\n",
            "QR3Flow_FlowD2a_\n",
            "QR3Flow_FlowD3a_\n",
            "QR3Flow_FlowD4a_\n",
            "QR3Flow_FlowD1b_\n",
            "QR3Flow_FlowD2c_\n",
            "QR3Flow_FlowD3b_\n",
            "QR3Flow_FlowD4b_\n",
            "QR3Flow_FlowD1c_\n",
            "QR3Flow_FlowDET2_\n",
            "QR3Flow_FlowD2b_\n",
            "QR3Flow_FlowD3c_\n",
            "QR3Flow_FlowD4c_\n",
            "QR3FlowMOOC_FQRb_\n",
            "MOT\n",
            "ACC\n",
            "ENT\n",
            "LIB\n",
            "APP_Q1_\n",
            "APP_Q2_\n",
            "APP_Q3_\n",
            "APP_Q4_\n",
            "APP_Q5_\n",
            "APP_Q6_\n",
            "APP_Q7_\n",
            "APP_Q8_\n",
            "APP_Q9_\n",
            "APP_Q10_\n",
            "APP_Q11_\n",
            "APP_Q12_\n",
            "APP_Q13_\n",
            "NET1\n",
            "NET2\n",
            "interviewtime\n",
            "groupTime2116\n",
            "QR3SRMOOCTime\n",
            "groupTime2195\n",
            "QR2FlowMOOCacTime_y\n",
            "groupTime2115\n",
            "QR3FlowTime\n",
            "groupTime2196\n",
            "QR2FlowMOOCbTime_y\n",
            "groupTime2197\n",
            "MOTTime\n",
            "ACCTime_y\n",
            "ENTTime\n",
            "groupTime2198\n",
            "LIBTime_y\n",
            "APPTime\n",
            "NET1Time\n",
            "NET2Time\n",
            "firstname\n",
            "lastname\n",
            "QR1Sexe\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FenINqRcU_gO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "6b180945-5e0c-49ca-c246-9f32531bcaa8"
      },
      "source": [
        "########### Visualizing NaNs\n",
        "if (flag_visualize_nans):\n",
        "  import missingno as msno\n",
        "  # https://towardsdatascience.com/visualizing-the-nothing-ae6daccc9197\n",
        "\n",
        "  %matplotlib inline\n",
        "  # df_csv1.isnull().sum(axis = 1)\n",
        "  msno.matrix(df_csv1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SItVOlwRRAhd"
      },
      "source": [
        "# How to read the matrix above:\n",
        "* White is NaNs.\n",
        "* Black is data.\n",
        "* One pixel-line is NOT a data row but a group-of.\n",
        "* Right bar is frequency APPROXIMATELY around this area.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k7SUzu1kJyfn",
        "outputId": "5190f086-9c3d-45ff-ef2a-881b985e8933"
      },
      "source": [
        "# Check if people actually answered the QR2 and QR3 (These basic 6 questions):\n",
        "df_csv1['tmp_row2erase'] = [np.nan] * tsize[0]\n",
        "# This checks if ALL of these are NaNs (at the same time)\n",
        "df_csv1['tmp_row2erase'] = df_csv1[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_', 'QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_']].isnull()\n",
        "# This checks if ANY of these are NaNs (at the same time)\n",
        "#df_csv1['tmp_row2erase'] = df_csv1[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_', 'QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_']].isnull().values.any(axis=1)\n",
        "\n",
        "# Remove ALL ROWS that have FULL NaNs in these 6 questions (or ANY, depending on the above)\n",
        "idx_tmp_row2erase = df_csv1[df_csv1['tmp_row2erase']].index\n",
        "df_csv1.drop(idx_tmp_row2erase, inplace=True)\n",
        "\n",
        "# Saves Table size in an array\n",
        "tsize = []\n",
        "tsize.append(df_csv1.shape[0])\n",
        "tsize.append(df_csv1.shape[1])\n",
        "\n",
        "df_csv1[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_', 'QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_', 'tmp_row2erase']].head(40)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>QR2FlowMOOC_FQRa_</th>\n",
              "      <th>QR2FlowMOOC_FQRb_</th>\n",
              "      <th>QR2FlowMOOC_FQRc_</th>\n",
              "      <th>QR3FlowMOOC_FQRa_</th>\n",
              "      <th>QR3FlowMOOC_FQRb_</th>\n",
              "      <th>QR3FlowMOOC_FQRc_</th>\n",
              "      <th>tmp_row2erase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     QR2FlowMOOC_FQRa_  QR2FlowMOOC_FQRb_  ...  QR3FlowMOOC_FQRc_  tmp_row2erase\n",
              "11                 0.0                0.0  ...                0.0          False\n",
              "14                 0.0                0.0  ...                0.0          False\n",
              "22                 1.0                1.0  ...                1.0          False\n",
              "33                 0.0                NaN  ...                NaN          False\n",
              "36                 1.0                1.0  ...                NaN          False\n",
              "40                 0.0                NaN  ...                0.0          False\n",
              "41                 1.0                1.0  ...                NaN          False\n",
              "42                 1.0                1.0  ...                1.0          False\n",
              "43                 1.0                1.0  ...                NaN          False\n",
              "45                 0.0                0.0  ...                1.0          False\n",
              "46                 0.0                NaN  ...                1.0          False\n",
              "51                 0.0                NaN  ...                NaN          False\n",
              "53                 0.0                1.0  ...                1.0          False\n",
              "57                 0.0                NaN  ...                0.0          False\n",
              "60                 1.0                1.0  ...                NaN          False\n",
              "62                 1.0                1.0  ...                NaN          False\n",
              "64                 1.0                1.0  ...                NaN          False\n",
              "66                 0.0                NaN  ...                NaN          False\n",
              "69                 0.0                NaN  ...                NaN          False\n",
              "73                 0.0                0.0  ...                NaN          False\n",
              "75                 0.0                NaN  ...                NaN          False\n",
              "81                 0.0                NaN  ...                NaN          False\n",
              "83                 0.0                0.0  ...                0.0          False\n",
              "84                 0.0                0.0  ...                NaN          False\n",
              "90                 1.0                NaN  ...                NaN          False\n",
              "92                 1.0                1.0  ...                NaN          False\n",
              "99                 0.0                NaN  ...                0.0          False\n",
              "100                0.0                NaN  ...                0.0          False\n",
              "101                1.0                1.0  ...                NaN          False\n",
              "103                0.0                NaN  ...                NaN          False\n",
              "104                0.0                NaN  ...                NaN          False\n",
              "106                0.0                NaN  ...                0.0          False\n",
              "107                0.0                NaN  ...                NaN          False\n",
              "117                1.0                0.0  ...                1.0          False\n",
              "122                1.0                0.0  ...                0.0          False\n",
              "123                1.0                0.0  ...                NaN          False\n",
              "125                0.0                NaN  ...                NaN          False\n",
              "127                0.0                0.0  ...                0.0          False\n",
              "129                0.0                NaN  ...                NaN          False\n",
              "131                0.0                NaN  ...                0.0          False\n",
              "\n",
              "[40 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1Ep6japJtFv"
      },
      "source": [
        "# Drop useless column\n",
        "df_csv1.drop(columns=['tmp_row2erase'])\n",
        "\n",
        "# Saves Table size in an array (NEEDED ???)\n",
        "tsize = []\n",
        "tsize.append(df_csv1.shape[0])\n",
        "tsize.append(df_csv1.shape[1])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piGFzlkaGdNo"
      },
      "source": [
        "# Split the dataframe in two sets, one for each QR\n",
        "df_csv1_qr2 = df_csv1[[ 'QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_',\n",
        "                        'QR2Flow_FlowD1a_', 'QR2Flow_FlowD2a_', 'QR2Flow_FlowD3a_', 'QR2Flow_FlowD4a_', \n",
        "                        'QR2Flow_FlowD1b_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD4b_',\n",
        "                        'QR2Flow_FlowD1c_', 'QR2Flow_FlowD2c_', 'QR2Flow_FlowD3c_', 'QR2Flow_FlowD4c_', \n",
        "                        'QR1Sexe' ]]\n",
        "\n",
        "\n",
        "df_csv1_qr3 = df_csv1[[ 'QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_',\n",
        "                        'QR3Flow_FlowD1a_', 'QR3Flow_FlowD2a_', 'QR3Flow_FlowD3a_','QR3Flow_FlowD4a_',\n",
        "                        'QR3Flow_FlowD1b_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD4b_',\n",
        "                        'QR3Flow_FlowD1c_', 'QR3Flow_FlowD2c_', 'QR3Flow_FlowD3c_', 'QR3Flow_FlowD4c_',\n",
        "                        'QR1Sexe' ]]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 828
        },
        "id": "3gNadW-cEWd5",
        "outputId": "5ce7241f-106c-4744-d840-008f4320ac4b"
      },
      "source": [
        "# Replace NaNs in New dataframe by 0's (ONLY if they are not ALL 3 questions for each QR) QR2\n",
        "df_csv1_qr2['flag_0'] = [False] * tsize[0]\n",
        "df_csv1_qr2['flag_0'] = df_csv1_qr2[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_']].isnull().values.any()\n",
        "\n",
        "# Remove ALL ROWS that have FULL NaNs in these 3 questions, for QR2 only\n",
        "df_csv1_qr2['tmp_row2erase'] = [False] * tsize[0]\n",
        "df_csv1_qr2['tmp_row2erase'] = df_csv1_qr2[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_']].isnull()\n",
        "idx_tmp_row2erase = df_csv1_qr2[df_csv1_qr2['tmp_row2erase']].index\n",
        "df_csv1_qr2.drop(idx_tmp_row2erase, inplace=True)\n",
        "\n",
        "if ( ~df_csv1_qr2[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_']].isnull().values.all() ):\n",
        "  df_csv1_qr2['QR2FlowMOOC_FQRa_'] = np.where((df_csv1_qr2['flag_0'] & df_csv1_qr2['QR2FlowMOOC_FQRa_'].isnull()), 0, df_csv1_qr2.QR2FlowMOOC_FQRa_)\n",
        "  df_csv1_qr2['QR2FlowMOOC_FQRb_'] = np.where((df_csv1_qr2['flag_0'] & df_csv1_qr2['QR2FlowMOOC_FQRb_'].isnull()), 0, df_csv1_qr2.QR2FlowMOOC_FQRb_)\n",
        "  df_csv1_qr2['QR2FlowMOOC_FQRc_'] = np.where((df_csv1_qr2['flag_0'] & df_csv1_qr2['QR2FlowMOOC_FQRc_'].isnull()), 0, df_csv1_qr2.QR2FlowMOOC_FQRc_)\n",
        "\n",
        "\n",
        "df_csv1_qr2[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_', 'flag_0']].head(40)\n",
        "df_csv1_qr2[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_', 'flag_0']].loc[~df_csv1_qr2['flag_0']]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>QR2FlowMOOC_FQRa_</th>\n",
              "      <th>QR2FlowMOOC_FQRb_</th>\n",
              "      <th>QR2FlowMOOC_FQRc_</th>\n",
              "      <th>flag_0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [QR2FlowMOOC_FQRa_, QR2FlowMOOC_FQRb_, QR2FlowMOOC_FQRc_, flag_0]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 828
        },
        "id": "UR7Qns44QmDy",
        "outputId": "8dc235fc-d93a-4aac-ec16-90d99884ac2f"
      },
      "source": [
        "# Replace NaNs in New dataframe by 0's (ONLY if they are not ALL 3 questions for each QR) QR3\n",
        "df_csv1_qr3['flag_0'] = [False] * tsize[0]\n",
        "df_csv1_qr3['flag_0'] = df_csv1_qr3[['QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_']].isnull().values.any()\n",
        "\n",
        "# Remove ALL ROWS that have FULL NaNs in these 3 questions, for QR3 only\n",
        "df_csv1_qr3['tmp_row2erase'] = [False] * tsize[0]\n",
        "df_csv1_qr3['tmp_row2erase'] = df_csv1_qr3[['QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_']].isnull()\n",
        "idx_tmp_row2erase = df_csv1_qr3[df_csv1_qr3['tmp_row2erase']].index\n",
        "df_csv1_qr3.drop(idx_tmp_row2erase, inplace=True)\n",
        "\n",
        "if ( ~df_csv1_qr3[['QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_']].isnull().values.all() ):\n",
        "  df_csv1_qr3['QR3FlowMOOC_FQRa_'] = np.where((df_csv1_qr3['flag_0'] & df_csv1_qr3['QR3FlowMOOC_FQRa_'].isnull()), 0, df_csv1_qr3.QR3FlowMOOC_FQRa_)\n",
        "  df_csv1_qr3['QR3FlowMOOC_FQRb_'] = np.where((df_csv1_qr3['flag_0'] & df_csv1_qr3['QR3FlowMOOC_FQRb_'].isnull()), 0, df_csv1_qr3.QR3FlowMOOC_FQRb_)\n",
        "  df_csv1_qr3['QR3FlowMOOC_FQRc_'] = np.where((df_csv1_qr3['flag_0'] & df_csv1_qr3['QR3FlowMOOC_FQRc_'].isnull()), 0, df_csv1_qr3.QR3FlowMOOC_FQRc_)  \n",
        "\n",
        "df_csv1_qr3[['QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_', 'flag_0']].head(40)\n",
        "df_csv1_qr3[['QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_', 'flag_0']].loc[~df_csv1_qr3['flag_0']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>QR3FlowMOOC_FQRa_</th>\n",
              "      <th>QR3FlowMOOC_FQRb_</th>\n",
              "      <th>QR3FlowMOOC_FQRc_</th>\n",
              "      <th>flag_0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [QR3FlowMOOC_FQRa_, QR3FlowMOOC_FQRb_, QR3FlowMOOC_FQRc_, flag_0]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pU2tFz0-YUcp"
      },
      "source": [
        "# Drop useless columns (flags and mask of rows)\n",
        "df_csv1_qr2.drop(columns=['flag_0', 'tmp_row2erase'])\n",
        "df_csv1_qr3.drop(columns=['flag_0', 'tmp_row2erase'])\n",
        "\n",
        "# Calculate dataframe sizes:\n",
        "tsize_qr2 = []\n",
        "tsize_qr2.append(df_csv1_qr2.shape[0])\n",
        "tsize_qr2.append(df_csv1_qr2.shape[1])\n",
        "\n",
        "tsize_qr3 = []\n",
        "tsize_qr3.append(df_csv1_qr3.shape[0])\n",
        "tsize_qr3.append(df_csv1_qr3.shape[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRKRSOtfUR7H",
        "outputId": "a7bc359b-af7f-4eed-b999-64b87844f216"
      },
      "source": [
        "# Must check if all math (12 more items/questions) will be done on numbers and not on NaNs:\n",
        "# FULL-NaNs rows:\n",
        "df_csv1_qr2['full_row_nans_qr2'] = [False] * tsize_qr2[0]\n",
        "df_csv1_qr2['full_row_nans_qr2'] = df_csv1_qr2[['QR2Flow_FlowD1a_', 'QR2Flow_FlowD2a_', 'QR2Flow_FlowD3a_', 'QR2Flow_FlowD4a_', \n",
        "                        'QR2Flow_FlowD1b_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD4b_',\n",
        "                        'QR2Flow_FlowD1c_', 'QR2Flow_FlowD2c_', 'QR2Flow_FlowD3c_', 'QR2Flow_FlowD4c_']].isnull()\n",
        "# Number of NaNs per row:\n",
        "df_csv1_qr2['partial_nans_qr2'] = [0] * tsize_qr2[0]\n",
        "df_csv1_qr2['partial_nans_qr2'] = df_csv1_qr2[['QR2Flow_FlowD1a_', 'QR2Flow_FlowD2a_', 'QR2Flow_FlowD3a_', 'QR2Flow_FlowD4a_', \n",
        "                        'QR2Flow_FlowD1b_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD4b_',\n",
        "                        'QR2Flow_FlowD1c_', 'QR2Flow_FlowD2c_', 'QR2Flow_FlowD3c_', 'QR2Flow_FlowD4c_']].isnull().values.sum(axis=1)\n",
        "\n",
        "# FULL-NaNs rows:\n",
        "df_csv1_qr3['full_row_nans_qr3'] = [False] * tsize_qr3[0]\n",
        "df_csv1_qr3['full_row_nans_qr3'] = df_csv1_qr3[['QR3Flow_FlowD1a_', 'QR3Flow_FlowD2a_', 'QR3Flow_FlowD3a_','QR3Flow_FlowD4a_',\n",
        "                        'QR3Flow_FlowD1b_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD4b_',\n",
        "                        'QR3Flow_FlowD1c_', 'QR3Flow_FlowD2c_', 'QR3Flow_FlowD3c_', 'QR3Flow_FlowD4c_']].isnull()\n",
        "# Number of NaNs per row:\n",
        "df_csv1_qr3['partial_nans_qr3'] = [0] * tsize_qr3[0]\n",
        "df_csv1_qr3['partial_nans_qr3'] = df_csv1_qr3[['QR3Flow_FlowD1a_', 'QR3Flow_FlowD2a_', 'QR3Flow_FlowD3a_','QR3Flow_FlowD4a_',\n",
        "                        'QR3Flow_FlowD1b_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD4b_',\n",
        "                        'QR3Flow_FlowD1c_', 'QR3Flow_FlowD2c_', 'QR3Flow_FlowD3c_', 'QR3Flow_FlowD4c_']].isnull().values.sum(axis=1)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6a1xX2AgIyd",
        "outputId": "708772fb-a2b8-49a5-d7f8-6b02cd93daf1"
      },
      "source": [
        "# Triple check for QR2: partial view (few, scarce NaNs) / specific column, existence of NaNs (most should be false) / sum of NaNs per row (most values should be 0) ...\n",
        "df_csv1_qr2[['QR2Flow_FlowD1a_', 'QR2Flow_FlowD2a_', 'QR2Flow_FlowD3a_', 'QR2Flow_FlowD4a_', \n",
        "            'QR2Flow_FlowD1b_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD4b_',\n",
        "            'QR2Flow_FlowD1c_', 'QR2Flow_FlowD2c_', 'QR2Flow_FlowD3c_', 'QR2Flow_FlowD4c_', 'full_row_nans_qr2']].head(40)\n",
        "df_csv1_qr2['full_row_nans_qr2'].value_counts()\n",
        "df_csv1_qr2['partial_nans_qr2'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     562\n",
              "12     28\n",
              "1      22\n",
              "2       7\n",
              "4       3\n",
              "3       3\n",
              "Name: partial_nans_qr2, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oA6FUetAgRUd",
        "outputId": "a63c79c4-9067-4925-cc9d-9ffec86ece33"
      },
      "source": [
        "# Triple check for QR3: partial view (few, scarce NaNs) / specific column, existence of NaNs (most should be false) / sum of NaNs per row (most values should be 0) ...\n",
        "df_csv1_qr3[['QR3Flow_FlowD1a_', 'QR3Flow_FlowD2a_', 'QR3Flow_FlowD3a_','QR3Flow_FlowD4a_',\n",
        "            'QR3Flow_FlowD1b_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD4b_',\n",
        "            'QR3Flow_FlowD1c_', 'QR3Flow_FlowD2c_', 'QR3Flow_FlowD3c_', 'QR3Flow_FlowD4c_', 'full_row_nans_qr3']].head(40)\n",
        "df_csv1_qr3['full_row_nans_qr3'].value_counts()\n",
        "df_csv1_qr3['partial_nans_qr3'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     252\n",
              "1       9\n",
              "12      4\n",
              "3       2\n",
              "2       2\n",
              "6       1\n",
              "Name: partial_nans_qr3, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UltlQQgTgKed",
        "outputId": "6d6580f1-143d-4d34-ab4c-391e7901f8b1"
      },
      "source": [
        "# Remove ONLY full-all-NaNs rows:\n",
        "idx_tmp_row2erase = df_csv1_qr2[df_csv1_qr2['full_row_nans_qr2']].index\n",
        "df_csv1_qr2.drop(idx_tmp_row2erase, inplace=True)\n",
        "\n",
        "idx_tmp_row2erase = df_csv1_qr3[df_csv1_qr3['full_row_nans_qr3']].index\n",
        "df_csv1_qr3.drop(idx_tmp_row2erase, inplace=True)\n",
        "\n",
        "# Remove ONLY rows with more than 4 NaNs:\n",
        "idx_tmp_row2erase = df_csv1_qr2[df_csv1_qr2['partial_nans_qr2'] > const_nan_tolerance_per_row].index\n",
        "df_csv1_qr2.drop(idx_tmp_row2erase, inplace=True)\n",
        "\n",
        "idx_tmp_row2erase = df_csv1_qr3[df_csv1_qr3['partial_nans_qr3'] > const_nan_tolerance_per_row].index\n",
        "df_csv1_qr3.drop(idx_tmp_row2erase, inplace=True)\n",
        "\n",
        "# Re-Calculate dataframe sizes:\n",
        "tsize_qr2 = []\n",
        "tsize_qr2.append(df_csv1_qr2.shape[0])\n",
        "tsize_qr2.append(df_csv1_qr2.shape[1])\n",
        "\n",
        "tsize_qr3 = []\n",
        "tsize_qr3.append(df_csv1_qr3.shape[0])\n",
        "tsize_qr3.append(df_csv1_qr3.shape[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjnDCE3gfUJS",
        "outputId": "2e7fd41c-dcdd-4d8f-bb64-1ee43701f2f6"
      },
      "source": [
        "# Check for changes\n",
        "df_csv1_qr2['partial_nans_qr2'].value_counts()\n",
        "#df_csv1_qr3['partial_nans_qr3'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    562\n",
              "1     20\n",
              "2      7\n",
              "3      3\n",
              "4      2\n",
              "Name: partial_nans_qr2, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XfqQU9KOYh9W",
        "outputId": "ee17e5fb-adb9-4645-aea9-31f227bd09db"
      },
      "source": [
        "########### Visualizing NaNs in QR2\n",
        "if (flag_visualize_nans):\n",
        "  import missingno as msno\n",
        "  # https://towardsdatascience.com/visualizing-the-nothing-ae6daccc9197\n",
        "\n",
        "  %matplotlib inline\n",
        "  # df_csv1.isnull().sum(axis = 1)\n",
        "  msno.matrix(df_csv1_qr2[['QR2Flow_FlowD1a_', 'QR2Flow_FlowD2a_', 'QR2Flow_FlowD3a_', 'QR2Flow_FlowD4a_', \n",
        "            'QR2Flow_FlowD1b_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD4b_',\n",
        "            'QR2Flow_FlowD1c_', 'QR2Flow_FlowD2c_', 'QR2Flow_FlowD3c_', 'QR2Flow_FlowD4c_']])\n",
        "  \n",
        "  msno.matrix(df_csv1_qr3[['QR3Flow_FlowD1a_', 'QR3Flow_FlowD2a_', 'QR3Flow_FlowD3a_','QR3Flow_FlowD4a_',\n",
        "            'QR3Flow_FlowD1b_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD4b_',\n",
        "            'QR3Flow_FlowD1c_', 'QR3Flow_FlowD2c_', 'QR3Flow_FlowD3c_', 'QR3Flow_FlowD4c_']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8kltm+Njrnh0Qvff3tdb+LsM523f23r80/lyjVdB7/3Rr7fcyvBG4bOZH/zXJnTJ8LPYGSba11n639/7U3vsbW2t/keGN+mW9988mGq223vsnW2sHJ7lX7/1VrbUbZPg4+f9srb0tyc+31h7We/9ga+1DSY6N19Cqu5bbojazrntPa+0JGc5VfM7M82m0ePu9LZp7z/CW1tqNMpxP8ryVJ9No8a7ltuh3eu9PHx93emvtqAyTESuT302j1bUf26OHZPiI+Ud7729urd0wXker6lpsi3atrP967xdnOAXHW2efy/uFVbNf26JxwnXlvcJnM5x640o0Wh3Xclt0eu/9Kb33v86wg+9KNFp9+7Etelhr7eG997OSvHz+8RrV5whwNrzxKLor/eOmtbZpvG1Xa+3+Sd6W4YiH5/fePzre59Ak3+5zLyJHfi/eyt+0tbY5yS1775+e+dnmsdMvZfgY4B2SXLgyObS3Fhot3vzRqHM/e3qGUzI8PMOFE2+Q4fypT89wpOo79vZ8Gi3O/N9z5Q1aa+0tSb7ee//F8fX1iCS/keSGSV7ee3/y7NFdV/d8HLgD2BYdkmHH0e7559Nosa7rtuianm/1l3zjOIBt0f37zJHgrJ4D3B4d1Hvfua/n48AcwLbo+km+s6/33yzGIrZFe3tvx+IcwLboAb33d473M5m6ig5wW7Sl9365RuvPpmUvACzbuDd899xtu8c3D22cnDs1w8fFXthau/044fDEJC/c2/OtyYJvICt/0/GN2mfmfrby5m1nhg3T5pkG92+tPeTqno/FGV9HV9kZ1Fo7KMNpM36k9/7G3vu/9d6/kOEK9f+e4eiUPW8UZ59vjRZ9Q9jL33Pl+3cnuVNrbev4Wtqd4XV00Xj7USvrwmt4Pg7QAWyLnpzkBRqtvuu6LWqtPcC2aG0cwLbo1ktY3A3pALdHO8fJ2H09HwfgALZFT8r47yJNVtcitkUmv1fXAWyLbjXzHCZWV9EBbosuH9eHGq0zToHChtVau3mSE5L8YIZzpv7jylEMK2aOYnhHa+3UDEc8tCT/nOGN3ovWeLE3lP1pNOOyDDv1Vt7kPSjDudWeswaLumHtq9H4xmJna+1P9/IGYVOGC/R8fua+LNjV9Zn5e381yXFJvtNa+5kMF/P9tQxv8p6e5H2ttdsnuXitl32jWNS2yGto9dgW1beobRGrx/aoNv8uqs+2qD7bovpsizY2E+BsSK21X8lwld8fzLC39cjx9l/NcIG38+fuv/Jm76EZLjJ2Soarmv/m2i75xrG/jWY+wndhhqtpH9aGi4e8PskLe+8vXsbybwT722j+Td549MNPZnij97m1XOaN5Br6vKb3/sXe+/9urf1jkj8d7/uiJC8Y/5G7Ocm/9t6/tZzfYPpsi+qzLarPtqg+26PabIvqsy2qz7aoPtsi0ns3jA01kvx+ki9kuKrvnZNcP8NVft+Y4Y3C65Mcv5fHfXeSR2f4GMwTZ27ftOzfaWrjujQaf/6NDBcVuSTDeQk1KtRofNy2DOdSuyjJk5b9e0x17Gefm2U44uRd43rtmUkOm3mOtrevjTVtZFu0zhrZFtVvND7OtqhWI9uj2n1si9ZZI9ui+o3Gx9kW1WpkWzTxsfQFMIy1HEleleTrSX5idmU2/uzIcSV3eZJXzL45yHCV7VMzfJzsBTO3ewNRp9E9xw3V7iTP0ahkox/J8HHZCzIcKbRyuzcQy+nzB+NthyV5QJIjNCnXyLZo/TWyLarfyLaoXiPbo9p9bIvWXyPbovqNbIvqNbItmvhY+gIYxlqNJC/LcEGQu8zdPrshOiLJi8c3Cw+cu9+PJnnI3h5nLL9RhnN5fTrJkzWq1yjDOSLvm+S/JXmQRmX6/Nyyl3mjDdui+sO2qP6wLao/bI9qD9ui+sO2qP6wLao/bIuM2XGlq2jDVLXWjkvyAxmOVOizP+sz5+HqvV+U4Vx25yR5bGvt+uO5ntJ7/5ve+5+Mz7epu+rvQh1Io/FH/5zhDcTLxufTaMEOoNGhvfee4arav957/9Px+TRaoOvY57+M6znvB9aAbVF9tkX12RbVZ3tUm21RfbZF9dkW1WdbxDxR2Si+kORpSf4hyZ+11n746u7Ye/9Ukr9LcmySS/twIZH5+9g4Ld51bjTetqv3/k/JnovzaLR417XRd8bbeu/9kpn7aLRYB7Ke02Jt2BbVZ1tUn21RfbZHtdkW1WdbVJ9tUX22RVyJCXAmb9yb2nvvH0ry9AxXV35na+0ue7lvG7/cOf7vljVazA1t0Y3GveoskNdRbfrUp1F9tkX1eR3Vp1Ft+tRnW1Sf11F9GrE3JsCZvLmPt3w4yVOTnJ3kXfMrwN57b61tT3LrJG/uvV/m4y+rT6P6NKpNn/o0qk+j+jSqT6Pa9KlPo/o0qk8j9kZUNoTW2oNaay8ev/1who/CrKwAf3jmfluS/GSSGyU5M/FxpLWiUX0a1aZPfRrVp1F9GtWnUW361KdRfRrVpxFX0QtcidMwVnNkuMry8zJc1feuM7fdKcmHknwtyZ3H2382yTeT/PKyl3sjDY3qD41qD33qD43qD43qD43qD41qD33qD43qD43qD42Mvf53sewFMIxFjyRtL7fdLskHkvxJkhvP3P5D4wrwgiQvTPLtJM/b13MZGm2EoVHtoU/9oVH9oVH9oVH9oVHtoU/9oVH9oVH9oZGxX/+dLHsBDGO1RpL/mOS2M98/bVy53XPufndM8sEMewefM3P7pmX/DlMfGtUfGtUe+tQfGtUfGtUfGtUfGtUe+tQfGtUfGtUfGhn7Gm2MDJMyntPp/Uk+luTPe+8vGm9/Z5ITM6wUL5m5/92SHNZ7f8/4/abuvE+rSqP6NKpNn/o0qk+j+jSqT6Pa9KlPo/o0qk8jromLYDJVhya5MMMevYe21j4+Xu33dUkuSvLi8WIHSZLe+5lWfGtOo/o0qk2f+jSqT6P6NKpPo9r0qU+j+jSqTyP2yQQ4k9R7/6skr09yfJJ7JTk3yUsyXODgkiS3zvDxmL091opvDWhUn0a16VOfRvVpVJ9G9WlUmz71aVSfRvVpxDUxAc5ktNZOba09vLX2PeNNT81wYYMn9N5PTfLqJJcmuUuSeyR59HKWdOPSqD6NatOnPo3q06g+jerTqDZ96tOoPo3q04hrwwQ4k9Bau0GS+yR5bZI/bK39XB9OcP/SJCe31u7Se39tkqckeVySy5KcvbQF3oA0qk+j2vSpT6P6NKpPo/o0qk2f+jSqT6P6NOLachFMJqW1dt8kz0pyXJK3JfmjJK9K8one+2Nm7neT3vuXlrKQG5xG9WlUmz71aVSfRvVpVJ9GtelTn0b1aVSfRuwvE+CsO621Nu7ZS2vtsAwXO7goye7e++WttVsl+bEkL0jymQwfgXlAkgf33t86+xwudrA6NKpPo9r0qU+j+jSqT6P6NKpNn/o0qk+j+jRiEZwChXVlbsX3wCR/luSfknw8yStaa0f03v85yR8kuVWSf02yfXz4c1trRybJynNY8S2eRvVpVJs+9WlUn0b1aVSfRrXpU59G9WlUn0YsiiPAWZdaaw9N8pok/zPJZ5P80Di+muROvfdvjPfblGHP38OT/HXv/WXLWeKNR6P6NKpNn/o0qk+j+jSqT6Pa9KlPo/o0qk8jDpQJcNaF2Y+ptNaOSfI3GVZ8v957v6S1tiXJw5K8KMm5SX60937JzOMPXfl+dg8ii6NRfRrVpk99GtWnUX0a1adRbfrUp1F9GtWnEYvmFCiU1lq7YzJ8TGXck5ck/yHJTZJ8cGXF13u/PMmbkrwsyUlJfnx8fBsfb8W3SjSqT6Pa9KlPo/o0qk+j+jSqTZ/6NKpPo/o0YrWYAKes1tqTk/xla+1xyZXO1bQ7ySFJVs7ldPm4ArwsyUvG+5w8/uxKKzorvsXSqD6NatOnPo3q06g+jerTqDZ96tOoPo3q04jVZAKcyv4iw0dZTmutPXbm9guTfCrJL7TWTkr2rAA3JdmW4RxQF6zxsm5UGtWnUW361KdRfRrVp1F9GtWmT30a1adRfRqxakyAU05r7WGttaN77/+S5KeTXJLkaTN7Ac9PcnqSOyV5dmvth8eHHp7kR5N8d5Kz137JNw6N6tOoNn3q06g+jerTqD6NatOnPo3q06g+jVgTvXfDKDOSvC7JOUlukWTTeNv2JP+QYYX2izP3PS3Jt5J8LclfJ3lvkm8mefayf48pD43qD41qD33qD43qD43qD43qD41qD33qD43qD43qD42MtRqtd6fDoYbW2quSPCDJA3vvfzPetrn3vqu1tj3J25N8V5LTe++vGH9+nyR3T3KXJJ9I8r7e+5+MP9tz1WAWQ6P6NKpNn/o0qk+j+jSqT6Pa9KlPo/o0qk8j1pIJcEporf1RhhXfg3rvf9PaFVfqXfl6bgX4u733/z7z+IP7cAGEle+t+BZMo/o0qk2f+jSqT6P6NKpPo9r0qU+j+jSqTyPWXC9wGLqxsUeSP0qyK8k9x+83z/zssUmOyhU7a2Y/CvPYZS/7Rhka1R8a1R761B8a1R8a1R8a1R8a1R761B8a1R8a1R8aGcsYLoJJBbdP0pJsb619V+99V5K01l6a5A+S3LT33sePwuzIsJfwwiTPaq09eWlLvbFoVJ9GtelTn0b1aVSfRvVpVJs+9WlUn0b1acSaMwHO0rTWWpL03u+Q5P1JfjPJw8efvSTJLyS5V+/9Y+P9ds2sAH82yeUZVoKsEo3q06g2ferTqD6N6tOoPo1q06c+jerTqD6NWCbnAGepWmtbeu+Xj1//XZITk3w8yR2T/FTv/b17eczKRREO773/+9ou8cajUX0a1aZPfRrVp1F9GtWnUW361KdRfRrVpxHL4ghwlqr3fnlrbcv49Y8k+fsk/znJm5J8OLliL+HMY3aNX35zbz9nsTSqT6Pa9KlPo/o0qk+j+jSqTZ/6NKpPo/o0YllMgLN0cyvAH83wUZiHJnlUa+2w3nvf2wqujx9fWPlfVo9G9WlUmz71aVSfRvVpVJ9GtelTn0b1aVSfRiyDCXBKmFsB3i3J/0ny4lzDCpC1o1F9GtWmT30a1adRfRrVp1Ft+tSnUX0a1acRa805wCmlXfl8UO/PcD6oFyZ5de/9W0tdOJJotB5oVJs+9WlUn0b1aVSfRrXpU59G9WlUn0asFRPglDO3Avxwkh9M8h97759Y7pKxQqP6NKpNn/o0qk+j+jSqT6Pa9KlPo/o0qk8j1oIJcFZda61d23M0za0AT+29v211lo5Eo/VAo9r0qU+j+jSqT6P6NKpNn/o0qk+j+jSiIhPgrJnW2vFJvtx7/85+3n/PCnD8flPvffeqLSAarQMa1aZPfRrVp1F9GtWnUW361KdRfRrVpxGVuAgma6K19p+SfDzJzcfvN1/TY2ZXfOP3u1tzEYTVolF9GtWmT30a1adRfRrVp1Ft+tSnUX0a1acR1ZgAZ618JMnFSZ6fJL33Xde0Ipv9eWvtZuPjfGRh9WhUn0a16VOfRvVpVJ9G9WlUmz71aVSfRvVpRCkmwFm4uZXWlvHLnUmel+RHWmuPSfa9ImvtinNGtdZOS/Lq1tqxq7fUG4tG9WlUmz71aVSfRvVpVJ9GtelTn0b1aVSfRqwHJsBZuJmV1v2S/FZr7XvG8zb9RZJPJ3lQa+2oq3v83IrvCUlemuTNvfcvrP7Sbwwa1adRbfrUp1F9GtWnUX0a1aZPfRrVp1F9GrEu9N4NY+EjyZFJLkiyO8mOJA9IcnCS/zje9tTxfm3ucW3m6ycm2ZXkUcv+faY4NKo/NKo99Kk/NKo/NKo/NKo/NKo99Kk/NKo/NKo/NDKqj9a70+lw4Gb32I3fH5bkmUnukuTfk5yU5INJ3pDk1kmem+Qneu8f3dtztNaemOT0JI/tvb96zX6RCdOoPo1q06c+jerTqD6N6tOoNn3q06g+jerTiPXGKVBYiJmV1hGttU2994uTvCvJTZO8I8kDM1wA4fVJHprk60ke01rbupfnWFnxPcaKb3E0qk+j2vSpT6P6NKpPo/o0qk2f+jSqT6P6NGLd6QUOQzemMZLcN8lnkjwpyQ3G2x6V5NIkJ47f3z3JP2X4CMylSW6xl+fYHR950WiDDqy7O/4AACAASURBVI1qD33qD43qD43qD43qD41qD33qD43qD43qD42M9TScAoWFaa1tT/LGJN+T4SMvj0ny+SS/leSIJE/pvX953ON3WpJze+9/PPcc35Xkzr33v1zThd8gNKpPo9r0qU+j+jSqT6P6NKpNn/o0qk+j+jRiPTEBznUyf76nmdsPTvLgJI9Ocockv5Pk+klumeQVvff/Nd5vc+991/j1pt777tnbOHAa1adRbfrUp1F9GtWnUX0a1aZPfRrVp1F9GrHemQDnWptd8bXWbp3k2AxX9z2v937WePtBSZ6f5P4ZruJ7QpK/673fdTlLvbFoVJ9GtelTn0b1aVSfRvVpVJs+9WlUn0b1acQk9ALnYTHW50jy8CTnZ7iYwa4kO5M8O8kRM/f5sSQvyXDxg91J7rbs5d5IQ6P6Q6PaQ5/6Q6P6Q6P6Q6P6Q6PaQ5/6Q6P6Q6P6QyNjPQ9HgHOdtNZ+Kslbk/xGkvck+e4kp2a44MHpSZ7Ze9853vfQJCcluVWfO98Tq0ej+jSqTZ/6NKpPo/o0qk+j2vSpT6P6NKpPI9Y7E+Dsl5lzNLUkmzKs+A5K8nO994vH+2xN8ktJXpjkYb33N423X+lcUSvPtea/xMRpVJ9GtelTn0b1aVSfRvVpVJs+9WlUn0b1acTUbFr2AlBba+1prbWTxxXfyn8vm5PcNsk3eu8Xt9a2JEnv/etJXp/k40l+obV2g/H2K+1lseJbLI3q06g2ferTqD6N6tOoPo1q06c+jerTqD6NmCoT4Fyt1todkjwtyZtba7cdV1qt935ZknOT3C5Jeu+Xt+GCB+m9n5fkM0m2ZTjfE6tIo/o0qk2f+jSqT6P6NKpPo9r0qU+j+jSqTyOmzAQ4V6v3/vEkv5Lk20n+tLV2u5m9gG9Pcnxr7b+P910519P1k2zJsAJklWlUn0a16VOfRvVpVJ9G9WlUmz71aVSfRvVpxJSZAOcqWmuvbK09Nkl6729I8ttJLk/y1tbaSeNewHck+fMkp7bW3tpaO6q1dlKSByf56STv6b1fsqRfYfI0qk+j2vSpT6P6NKpPo/o0qk2f+jSqT6P6NGJD6L0bxp6R5FVJLkpy14wXSR1vf2iSf0ryz0luP972vUlenuRrGVaOFya5IMPVf1ce19Zy+TfC0Kj+0Kj20Kf+0Kj+0Kj+0Kj+0Kj20Kf+0Kj+0Kj+0MjYKKP1fqVz07OBtdZeneQBSe7fe//b8bY9V+9trT0sw8dhtmS48u8/tNYOT3LTJPdI8pUkX+69f2C8vyv9LphG9WlUmz71aVSfRvVpVJ9GtelTn0b1aVSfRmwkJsBJkrTW/iDJqUkelOTMPvMfRmvtRr33fx2//vkkz8iwAnxw7/3/XM3zWfEtmEb1aVSbPvVpVJ9G9WlUn0a16VOfRvVpVJ9GbDTOAU5aa09J8tgkv9d7/9u5Fd9vJvnF1tphSdJ7f2OS38zwcZc3jud8SmutzT6nFd9iaVSfRrXpU59G9WlUn0b1aVSbPvVpVJ9G9WnERmQCnCQ5L8MVe09trf3Uyo2ttZcmeXqSD/feL27DlX9XVoAvTnJwkjNaazdcwjJvNBrVp1Ft+tSnUX0a1adRfRrVpk99GtWnUX0aseFsWfYCsDyttc29912997e11i5N8t+SvKi1dmGSH0/ymCT37r3/VTLs0Vv5WEvv/U9aa4ck+U7v/d+W91tMm0b1aVSbPvVpVJ9G9WlUn0a16VOfRvVpVJ9GbGTOAb6Braz8Zr6/b5JfT7ItyRFJ/nPv/YN7edxVzu3U2hUXSmBxNKpPo9r0qU+j+jSqT6P6NKpNn/o0qk+j+jRiI3ME+AbUWntIkjskuWVr7W299z9Okt77n7fWeoaPtlySZOvMY/as8OZXfONtVnwLpFF9GtWmT30a1adRfRrVp1Ft+tSnUX0a1acRmADfcFprr0hy3yTnJzk8yatba4f13l+eJL33d7fWWpIXJXnxuFfvz/vw0Rd7+NaARvVpVJs+9WlUn0b1aVSfRrXpU59G9WlUn0Yw6r0bG2QkeUmSrya5T5LvSvI9Sd6Y5GNJDkmyaea+90vyySSfSnKfZS/7Rhka1R8a1R761B8a1R8a1R8a1R8a1R761B8a1R8a1R8aGcYVY1PYEFprD0xyryRPSXJG7/2bvfevJfl8ki9n2BN4i5X7997fleS5SS5L8tLW2qlrv9Qbi0b1aVSbPvVpVJ9G9WlUn0a16VOfRvVpVJ9GcGUmwDeOXUn+Icnf9t4vn7n95klun+Sfk3ystfaG1tqJyZ4V4AuTXC/D3kFWl0b1aVSbPvVpVJ9G9WlUn0a16VOfRvVpVJ9GMKP17nQ+G0Vr7Xt771+e+f6Pkjwkya8m+UKSGyb57SRv6b0/cuZ+23vvO9Z6eTcijerTqDZ96tOoPo3q06g+jWrTpz6N6tOoPo3gCi6CuQG01jb33ncluWDmthslOTbJQ5P8WR+v6ttaOyzJC1trL+29fzJJVlZ8LoCwejSqT6Pa9KlPo/o0qk+j+jSqTZ/6NKpPo/o0gqsyAT5RrbVbZ7jAwdlJvpHk27Mrrt77v7bW7td7//bcQy/JcM6nb84/pxXfYmlUn0a16VOfRvVpVJ9G9WlUmz71aVSfRvVpBPvmHOAT1Fr7jSR/keTMJJ9I8oLW2k3m7ze/4hv3/B2T5INJLmqttdVf2o1Jo/o0qk2f+jSqT6P6NKpPo9r0qU+j+jSqTyO4ZibAJ6a19uokj0nyyiSPSvJ3SZ6e5NGttU1Xt0JrrR2S5AFJHpHk7b33C+3tWx0a1adRbfrUp1F9GtWnUX0a1aZPfRrVp1F9GsF+6r0bExlJXp7k60l+OMnmmdvfmeTzSQ4av98097gfz3ARhAuTPHPm9rbs32lqQ6P6Q6PaQ5/6Q6P6Q6P6Q6P6Q6PaQ5/6Q6P6Q6P6QyPD2P/hCPCJaK09JckvJfnj3vsHeu+7WmvXG3/8jiS7kxycJP2Kix201toNkvxkkvtkWPG9ePzZpt67vX8LpFF9GtWmT30a1adRfRrVp1Ft+tSnUX0a1acRXDsugjkdf53kX5L8ZGvtU7331/beLx1/dmiSo5L8ZWttZ5L/leSveu+fSPKt1tqvJDm29352smfFt3sJv8PUaVSfRrXpU59G9WlUn0b1aVSbPvVpVJ9G9WkE10Kzg2f9au2Kczn13ntr7bZJ3pJhx8Zv9d5f1Vq7TZIPJTknw5WAtya5TYa9gR9K8rHe+1Nnn9Nev8XRqD6NatOnPo3q06g+jerTqDZ96tOoPo3q0wiuO6dAWf9ulOSQ1toNeu//mOTUJDuT/HJr7YVJPpzkTzJ8xOUevffbJrlnkudm2CN4weyTWfGtCo3q06g2ferTqD6N6tOoPo1q06c+jerTqD6N4DpwBPg61Vp7YpIfS3LXJN/OsBJ7WZL/keQmSd6d5PuTvC/JKb33nfN79lpr39V7/+aaL/wGoVF9GtWmT30a1adRfRrVp1Ft+tSnUX0a1acRHBhHgK9DrbU/SPKMJF9J8vQkr09yUZI/TvK2DB9t+ckkn0pydJKfT/Z8RGbzzFN9a3y+FhZKo/o0qk2f+jSqT6P6NKpPo9r0qU+j+jSqTyNYgN67sY5Gkmdl2NN3SpJDZ26/QZJfybDi+99JtiX5vgwrwH9J8shlL/tGGRrVHxrVHvrUHxrVHxrVHxrVHxrVHvrUHxrVHxrVHxoZxmLG0hfAuBaxhosXvDfJi5Jcb7xt09x9njCuAH93/P52Sf4xyY4kj1/27zD1oVH9oVHtoU/9oVH9oVH9oVH9oVHtoU/9oVH9oVH9oZFhLG44Bcr68r1JfiTJWb33S5Ok9747ueIjLL3330/yh0ke01q7be/9E0kekuTgjB93YVVpVJ9GtelTn0b1aVSfRvVpVJs+9WlUn0b1aQQLYgJ8fTksyaW54rxNW1Z+0HufvZrpO5JcP8k9xp99MsmJvfc3rt2iblga1adRbfrUp1F9GtWnUX0a1aZPfRrVp1F9GsGCmABfX85JcnGSn02S3vvlsxcvmPn6veP9bjxz+zfGrzVfXRrVp1Ft+tSnUX0a1adRfRrVpk99GtWnUX0awYJ4Iawv30rywST3aa2dmuy5qu/KR19W9gCemOTyDBc+SJItKx+TWflfVo1G9WlUmz71aVSfRvVpVJ9GtelTn0b1aVSfRrAgJsDXkd77t5M8N8mWJM9qrd1zvL2v7NVrrW3OcI6oLUme01p7a5LXttYOWdJibyga1adRbfrUp1F9GtWnUX0a1aZPfRrVp1F9GsHimABfZ3rvn0pyapJbJHlZa+1prbVNvffdrbVjkjwyyUuTfCDJx5JsS/LO3vt3lrbQG4xG9WlUmz71aVSfRvVpVJ9GtelTn0b1aVSfRrAYrV/pvPmsF621OyV5Y5Ljk/xbkv+bZHOSQ5K8vPf+O+P9VlaMrYu9pjSqT6Pa9KlPo/o0qk+j+jSqTZ/6NKpPo/o0ggNjAnwda619b5IfSvKfkhyeYW/fJ3vvHxh/viXJbud8Wh6N6tOoNn3q06g+jerTqD6NatOnPo3q06g+jeC6MwE+Ufb21adRfRrVpk99GtWnUX0a1adRbfrUp1F9GtWnEeybc4Cvc60NV/+dZ8VXh0b1aVSbPvVpVJ9G9WlUn0a16VOfRvVpVJ9GcN04AhwAAAAAgElyBDgAAAAAAJNkAhwAAAAAgEkyAQ4AAAAAwCSVmgBvrZ3aWnt5a+3vWmv/3lrrrbU3LXu5AAAAAABYf7YsewHmPCfJ7ZJ8K8n5SW613MUBAAAAAGC9KnUEeJJfTnKLJIcnefySlwUAAAAAgHWs1BHgvfe/Xfm6tbbMRQEAAAAAYJ2rdgQ4AAAAAAAshAlwAAAAAAAmqdQpUBbhbne7W1/2MnD1Tj/99CTJaaedtuQl4epoVJ9GtelTn0b1aVSfRvVpVJs+9WlUn0brw5lnnjnFcxxvmLnHM844Iy95yUvy5je/Odu2bVut/5tV/2/EEeAAAAAAAEySCXAAAAAAACbJBDgAAAAAAJNkAhwAAAAAgEkyAQ4AAAAAwCRtWfYCzGqt3S/J/cZvVy4teqfW2uvGr/+19/60NV8wAAAAAADWnVIT4ElOSvLwuduOH0eSfCGJCXAAAAAAAK5RqVOg9N5f0Htv+xjHLXsZAQAAAABYH0pNgAMAAAAAwKKYAAcAAAAAYJJMgAMAAAAAMEkmwAEAAAAAmCQT4AAAAAAATJIJcAAAAAAAJskEOAAAAAAAk2QCHAAAAACASTIBDgAAAADAJJkABwAAAABgkkyAAwAAAAAwSSbAAQAAAACYJBPgAAAAAABMkglwAAAAAAAmyQQ4AAAAAACTZAIcAAAAAIBJMgEOAAAAAMAkmQAHAAAAAGCSTIADAAAAADBJJsABAAAAAJgkE+AAAAAAAEySCXAAAAAAACbJBDgAAAAAAJNkAhwAAAAAgEkyAQ4AAAAAwCSZAAcAAAAAYJJMgAMAAAAAMEkmwAEAAAAAmCQT4AAAAAAATJIJcAAAAAAAJskEOAAAAAAAk2QCHAAAAACASTIBDgAAAADAJJkABwAAAABgkkyAAwAAAAAwSSbAAQAAAACYJBPgAAAAAABMkglwAAAAAAAmyQQ4AAAAAACTZAIcAAAAAIBJMgEOAAAAAMAkmQAHAAAAAGCSTIADAAAAADBJJsABAAAAAJgkE+AAAAAAAEySCXAAAAAAACbJBDgAAAAAAJNkAhwAAAAAgEkyAQ4AAAAAwCSZAAcAAAAAYJJMgAMAAAAAMEkmwAEAAAAAmCQT4AAAAAAATJIJcAAAAAAAJskEOAAAAAAAk2QCHAAAAACASTIBDgAAAADAJJkABwAAAABgkkyAAwAAAAAwSSbAAQAAAACYJBPgAAAAAABMkglwAAAAAAAmyQQ4AAAAAACTZAIcAAAAAIBJMgEOAAAAAMAkmQAHAAAAAGCSTIADAAAAADBJJsABAAAAAJgkE+AAAAAAAEySCXAAAAAAACbJBDgAAAAAAJNkAhwAAAAAgEkyAQ4AAAAAwCSZAAcAAAAAYJJMgAMAAAAAMEkmwAEAAAAAmCQT4AAAAAAATJIJcAAAAAAAJskEOAAAAAAAk2QCHAAAAACASTIBDgAAAADAJJkABwAAAABgkkyAAwAAAAAwSSbAAQAAAACYJBPgAAAAAABMkglwAAAAAAAmyQQ4AAAAAACTZAIcAAAAAIBJMgEOAAAAAMAkmQAHAAAAAGCSTIADAAAAADBJJsABAAAAAJgkE+AAAAAAAEzSlmUvAAAAAAAAy3fppZfmvPPOy44dO/KBD3xg2YuzECbAAQAAAAA2kF27duVLX/pSduzYkR07duScc87Jueeemy996UvZvXt3kuSggw7KiSeemK1bty55aQ+MCXAAYMM56aSTcuaZZy57MdiHs846a9mLAAAAk/KNb3wjr3zlK3P22WfnvPPOy86dO5MkmzZtyk1ucpMcd9xxufvd757t27dn+/btuelNb5rNmzcveakPnAlwAAAAAICJu+iii/LRj340X//61690+61udavc8pa3zPHHH5/t27fnuOOOy2GHHbakpVw8E+AAwIZz1lln5bTTTlv2YnA1Tj/99GUvAgAATM6xxx6bt73tbbnwwgv3nPpk5fQnZ5xxRr7zne/sue+Nb3zjHHfccbnNbW6ThzzkIWmtLXHJD4wJcAAAAACADaC1lq1bt2br1q05+eST99y+e/fufPWrX73SxPinP/3pfOQjH8kpp5ySI488colLfWBMgAMAAAAAbGCbNm3KUUcdlaOOOip3vvOdkyRnnHFGXvKSl+y5KOZ6tWnZCwAAAAAAAKvBBDgAAAAAAJNkAhwAAAAAgEkyAQ4AAAAAwCSZAAcAAAAAYJJMgAMAAAAAMEkmwAEAAAAAmCQT4AAAAAAATJIJcAAAAAAAJskEOAAAAAAAk2QCHAAAAACASTIBDgAAAADAJJkABwAAAABgkkyAAwAAAAAwSSbAAQAAAACYJBPgAAAAAABMkglwAAAAAAAmyQQ4AAAAAACTZAIcAAAAAIBJMgEOAAAAAMAkmQAHAAAAAGCSTIADAAAAADBJJsABAAAAAJgkE+AAAAAAAEySCXAAAAAAACbJBDgAAAAAAJNkAhwAAAAAgEkyAQ4AAAAAwCSZAAcAAAAAYJJMgAMAAAAAMEkmwAEAAAAAmCQT4AAAAAAATJIJcAAAAAAAJskEOAAAAAAAk2QCHAAAAACASTIBDgAAAADAJJkABwAAAABgkkyAAwAAAAAwSSbAAQAAAACYJBPgAAAAAABMkglwAAAAAAAmyQQ4AAAAAACTZAIcAAAAAIBJMgEOAAAAAMAkmQAHAAAAAGCStix7AQAAAAAAWJ6dO3fm/PPPzznnnJMdO3Zkx44d+exnP5sk2bx585KX7sCYAAcAAAAA2AB2796dCy64YM8k98r44he/mF27diVJNm3alKOPPjq3uc1tcsIJJ+RGN7rRkpf6wJgAB67ipJNOyplnnrnsxeBqnHXWWcteBAAAAGCd2bFjR574xCfm4osvvtLtJ554Yn7mZ34mxx9/fLZv356b3vSmOfjgg5e0lItnAhwAAAAAYOJueMMb5pRTTsnZZ5+dHTt27JkI/+QnP5kLLrgg27dvv9I49thjc8ghhyx5qQ+cCXDgKs4666ycdtppy14Mrsbpp5++7EUAAAAA1pnDDz88T3rSk5Ikvfd87Wtfu8qpUN75zndm586dSZLWWm5+85vn5S9/ea53vestc9EPiAlwAAAAAIANpLWWI488MkceeWTueMc77rl9165d+fKXv5wdO3bkve99b973vvflwgsvzLZt25a4tAfGBDgAAAAAANm8eXOOPvroHH300bn44ovzvve9b9mLdMA2LXsBAAAAAABgNZgABwAAAABgkkyAAwAAAAAwSSbAAQAAAACYJBPgAAAAAABMkglwAAAAAAAmyQQ4AAAAAACTZAIcAAAAAIBJMgEOAAAAAMAkmQAHAAAAAGCSTIADAAAAADBJJsABAAAAAJgkE+AAAAAAAEySCXAAAAAAACbJBDgAAAAAAJNkAhwAAAAAgEkyAQ4AAAAAwCSZAAcAAAAAYJJMgAMAAAAAMEkmwAEAAAAAmCQT4AAAAAAATJIJcAAAAAAAJskEOAAAAAAAk2QCHAAAAACASTIBDgAAAADAJJkABwAAAABgkkyAAwAAAAAwSSbAAQAAAACYJBPgAAAAAABMkglwAAAAAAAmyQQ4AAAAAACTZAIcAAAAAIBJMgEOAAAAAMAkmQAHAAAAAGCSTIADAAAAADBJJsABAAAAAJgkE+AAAAAAAEySCXAAAAAAACZpy7IXAAAAAACAGi655JKce+65+dSnPrXsRVkIE+AAAAAAABvMZZddlvPOOy87duy40vjqV7+65z5bt27NEUccscSlPHAmwAEAACbopJNOyplnnrnsxeBqnHXWWcteBAA2mIsvvjjvfOc787nPfS47duzI+eefn927dydJtmzZkmOOOSYnnHBC7n3ve2f79u3Zvn17tm3blk2b1vdZtE2AAwAAAABM3Ec+8pG85jWvyVFHHZWb3exmuetd77pnovumN71ptmyZ5lTxNH8rAADWNUeu1ufo1frOOuusnHbaacteDPbi9NNPX/YiALAB7dq1K0nyuMc9Lne4wx1y6KGHLnmJ1oYJcAAAAACAidu6dWuS5PnPf36SZNu2bXuOAD/uuOOyffv2HHPMMTn44IOXuZgLZwIcAIByHLlam6NXAQDWn5NPPjlvfvOb8/nPf/5KF7386Ec/uufo8E2bNuXoo4/eMyF+wgkn5A53uMOSl/zAmAAHAAAAANgAtm3blm3btuUud7nLntt27tyZ888//0qT4meffXb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            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CxNtttKhjRRH",
        "outputId": "867f7be3-f48e-40f5-c56a-709758bccc95"
      },
      "source": [
        "# Replace all remaining NaNs to 0 :\n",
        "print(\"NaN values in QR2=%d and QR3=%d before replacement.\" % (df_csv1_qr2.isnull().values.sum(), df_csv1_qr3.isnull().values.sum()) )\n",
        "df_csv1_qr2.replace(to_replace=np.nan, value=0, inplace=True)\n",
        "df_csv1_qr3.replace(to_replace=np.nan, value=0, inplace=True)\n",
        "\n",
        "\"\"\"\n",
        "df_csv1_qr2['QR2Flow_FlowD1a_', 'QR2Flow_FlowD2a_', 'QR2Flow_FlowD3a_', 'QR2Flow_FlowD4a_', \n",
        "            'QR2Flow_FlowD1b_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD4b_',\n",
        "            'QR2Flow_FlowD1c_', 'QR2Flow_FlowD2c_', 'QR2Flow_FlowD3c_', 'QR2Flow_FlowD4c_'] = df_csv1_qr2[['QR2Flow_FlowD1a_', 'QR2Flow_FlowD2a_', 'QR2Flow_FlowD3a_', 'QR2Flow_FlowD4a_', \n",
        "            'QR2Flow_FlowD1b_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD4b_',\n",
        "            'QR2Flow_FlowD1c_', 'QR2Flow_FlowD2c_', 'QR2Flow_FlowD3c_', 'QR2Flow_FlowD4c_']].replace(to_replace=np.nan, value=0, inplace=True)\n",
        "\n",
        "df_csv1_qr3[['QR3Flow_FlowD1a_', 'QR3Flow_FlowD2a_', 'QR3Flow_FlowD3a_','QR3Flow_FlowD4a_',\n",
        "            'QR3Flow_FlowD1b_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD4b_',\n",
        "            'QR3Flow_FlowD1c_', 'QR3Flow_FlowD2c_', 'QR3Flow_FlowD3c_', 'QR3Flow_FlowD4c_']] = df_csv1_qr3[['QR3Flow_FlowD1a_', 'QR3Flow_FlowD2a_', 'QR3Flow_FlowD3a_','QR3Flow_FlowD4a_',\n",
        "            'QR3Flow_FlowD1b_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD4b_',\n",
        "            'QR3Flow_FlowD1c_', 'QR3Flow_FlowD2c_', 'QR3Flow_FlowD3c_', 'QR3Flow_FlowD4c_']].replace(to_replace=np.nan, value=0, inplace=True)\n",
        "\"\"\"\n",
        "\n",
        "# Calculate ALL NaNs\n",
        "nans_in_qr2 = df_csv1_qr2.isnull().values.sum()\n",
        "nans_in_qr3 = df_csv1_qr3.isnull().values.sum()\n",
        "print(\"NaN values in QR2=%d and QR3=%d AFTER replacement.\\n\" % (nans_in_qr2, nans_in_qr3) )\n",
        "if (nans_in_qr2 != 0 and nans_in_qr3 != 0):\n",
        "  print(\"==> This is not right: bith values MUST be ZERO before continuing.\\n\")\n",
        "  flag_nans_in_sample = True\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NaN values in QR2=51 and QR3=18 before replacement.\n",
            "NaN values in QR2=0 and QR3=0 AFTER replacement.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4389: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  method=method,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gek2i4b1wh12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "456574ad-f1f9-44c6-fda2-6f5e2dc63831"
      },
      "source": [
        "if (flag_nans_in_sample):\n",
        "  import sys\n",
        "  sys.exit()\n",
        "\n",
        "\n",
        "# Must create 5 new columns (for each experiment): y; \"Flow exists\" (ANY(a, b, c)), x1, x2, x3, x4; scores per each Flow dimension (AVG (a,b,c))\n",
        "df_csv1_qr2['QR2_y'] = [np.nan] * tsize_qr2[0] \n",
        "df_csv1_qr2['QR2_y'] = df_csv1_qr2[['QR2FlowMOOC_FQRa_', 'QR2FlowMOOC_FQRb_', 'QR2FlowMOOC_FQRc_']].any(axis=1)\n",
        "df_csv1_qr2['QR2_x1'] = df_csv1_qr2[['QR2Flow_FlowD1a_', 'QR2Flow_FlowD1b_', 'QR2Flow_FlowD1c_']].astype(float).mean(axis=1)\n",
        "df_csv1_qr2['QR2_x2'] = df_csv1_qr2[['QR2Flow_FlowD2a_', 'QR2Flow_FlowD2b_', 'QR2Flow_FlowD2c_']].astype(float).mean(axis=1)\n",
        "df_csv1_qr2['QR2_x3'] = df_csv1_qr2[['QR2Flow_FlowD3a_', 'QR2Flow_FlowD3b_', 'QR2Flow_FlowD3c_']].astype(float).mean(axis=1)\n",
        "df_csv1_qr2['QR2_x4'] = df_csv1_qr2[['QR2Flow_FlowD4a_', 'QR2Flow_FlowD4b_', 'QR2Flow_FlowD4c_']].astype(float).mean(axis=1)\n",
        "\n",
        "df_csv1_qr3['QR3_y'] = [np.nan] * tsize_qr3[0] \n",
        "df_csv1_qr3['QR3_y'] = df_csv1_qr3[['QR3FlowMOOC_FQRa_', 'QR3FlowMOOC_FQRb_', 'QR3FlowMOOC_FQRc_']].any(axis=1)\n",
        "df_csv1_qr3['QR3_x1'] = df_csv1_qr3[['QR3Flow_FlowD1a_', 'QR3Flow_FlowD1b_', 'QR3Flow_FlowD1c_']].astype(float).mean(axis=1)\n",
        "df_csv1_qr3['QR3_x2'] = df_csv1_qr3[['QR3Flow_FlowD2a_', 'QR3Flow_FlowD2b_', 'QR3Flow_FlowD2c_']].astype(float).mean(axis=1)\n",
        "df_csv1_qr3['QR3_x3'] = df_csv1_qr3[['QR3Flow_FlowD3a_', 'QR3Flow_FlowD3b_', 'QR3Flow_FlowD3c_']].astype(float).mean(axis=1)\n",
        "df_csv1_qr3['QR3_x4'] = df_csv1_qr3[['QR3Flow_FlowD4a_', 'QR3Flow_FlowD4b_', 'QR3Flow_FlowD4c_']].astype(float).mean(axis=1)\n",
        "\n",
        "\n",
        "# Intended to replace in Y columns only but it is done in the entire DF\n",
        "df_csv1_qr2.replace(to_replace=False, value=0, inplace=True)\n",
        "df_csv1_qr2.replace(to_replace=True, value=1, inplace=True)\n",
        "df_csv1_qr3.replace(to_replace=False, value=0, inplace=True)\n",
        "df_csv1_qr3.replace(to_replace=True, value=1, inplace=True)\n",
        "\n",
        "\n",
        "# New, smaller, calculated dataframe:\n",
        "cols_sample_qr2 = ['QR1Sexe', 'QR2_y', 'QR2_x1', 'QR2_x2', 'QR2_x3', 'QR2_x4']\n",
        "cols_sample_qr3 = ['QR1Sexe', 'QR3_y', 'QR3_x1', 'QR3_x2', 'QR3_x3', 'QR3_x4']\n",
        "df_sample_qr2 = df_csv1_qr2[cols_sample_qr2].copy()\n",
        "df_sample_qr3 = df_csv1_qr3[cols_sample_qr3].copy()\n",
        "\n",
        "tsize_sample_qr2 = []\n",
        "tsize_sample_qr2.append(df_sample_qr2.shape[0])\n",
        "tsize_sample_qr2.append(df_sample_qr2.shape[1])\n",
        "\n",
        "tsize_sample_qr3 = []\n",
        "tsize_sample_qr3.append(df_sample_qr3.shape[0])\n",
        "tsize_sample_qr3.append(df_sample_qr3.shape[1])\n",
        "\n",
        "df_sample_qr2.head(20)\n",
        "df_sample_qr3.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  app.launch_new_instance()\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4389: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  method=method,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>QR1Sexe</th>\n",
              "      <th>QR3_y</th>\n",
              "      <th>QR3_x1</th>\n",
              "      <th>QR3_x2</th>\n",
              "      <th>QR3_x3</th>\n",
              "      <th>QR3_x4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>3.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>5.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>4.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     QR1Sexe  QR3_y    QR3_x1    QR3_x2    QR3_x3    QR3_x4\n",
              "11         0    0.0  7.000000  4.666667  6.000000  6.000000\n",
              "14         0    1.0  5.000000  6.333333  3.333333  7.000000\n",
              "22         0    1.0  5.666667  6.000000  7.000000  5.666667\n",
              "33         0    0.0  5.666667  6.000000  5.666667  6.000000\n",
              "36         0    1.0  5.666667  6.000000  7.000000  5.000000\n",
              "40         0    0.0  4.666667  4.333333  5.333333  3.333333\n",
              "45         0    1.0  6.666667  6.666667  7.000000  6.666667\n",
              "46         0    1.0  5.666667  6.333333  6.000000  5.000000\n",
              "53         0    1.0  7.000000  4.333333  4.666667  7.000000\n",
              "57         0    0.0  6.000000  5.333333  6.000000  3.666667\n",
              "83         0    0.0  7.000000  6.333333  7.000000  7.000000\n",
              "99         0    1.0  5.000000  5.666667  6.666667  5.333333\n",
              "100        0    1.0  4.666667  5.000000  7.000000  5.333333\n",
              "106        0    0.0  5.666667  4.666667  7.000000  4.000000\n",
              "117        0    1.0  6.666667  7.000000  6.000000  7.000000\n",
              "122        0    0.0  6.000000  5.333333  7.000000  3.666667\n",
              "127        0    0.0  6.333333  4.666667  7.000000  3.666667\n",
              "131        0    0.0  5.000000  4.333333  2.666667  4.666667\n",
              "133        0    1.0  6.000000  6.000000  7.000000  6.333333\n",
              "139        0    1.0  5.000000  6.333333  5.666667  6.000000"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qv4ndtjms49x",
        "outputId": "c6aa65e0-a296-4937-fef4-9ec6d730216d"
      },
      "source": [
        "# Creating the Scatter Matrix\n",
        "cols_exper_qr2 = ['QR1Sexe', 'QR2_y', 'QR2_x1', 'QR2_x2', 'QR2_x3', 'QR2_x4']\n",
        "cols_exper_qr3 = ['QR1Sexe', 'QR3_y', 'QR3_x1', 'QR3_x2', 'QR3_x3', 'QR3_x4']\n",
        "\n",
        "\n",
        "# Code that actually works for plotting: https://seaborn.pydata.org/index.html\n",
        "sns.pairplot(df_sample_qr2[cols_exper_qr2], hue='QR1Sexe', corner=True)\n",
        "sns.pairplot(df_sample_qr3[cols_exper_qr3], hue='QR1Sexe', corner=True) # diag_kind=\"hist\"\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fba8bb55610>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 953x900 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ld1aGfDmZTkeRJJX43TrfM+p0DAAAAAAOo8xeYWp2Xs9d7NeeyvWX2d2VQb3YNKDx6bkoJHPO+Z4xVYQSY1RWksqDHp3pZmQWAAAASHeU2Ss8fb5PtYW58rnXPwrpzc7UpuI8/fxcXxSSOed8z5hKE2C97KLykEfnKbMAAABA2qPMXuHhhu6ojMou2lUR1MMNyT3V+EzXqMoSYCfjRWUBj1oGJzQ3v+B0FAAAAAAOosxeZq3V46d7tKcqGLX3vKU6qCfO9Gh+wUbtPePtbNeoKvK9Tsd4SXamS4W52Wrqn3A6CgAAAAAHUWYvO9E+opwsl0qjOAoZznMr4MnS0bahqL1nPFlrda53NKHWzEpSRb5XZ7vZBAoAAABIZ5TZyx491a3dldEblV20uzKoR08m5xE9ncNTysnMkN+d5XSUJcqDHp3pGnE6BgAAAAAHUWYvi12ZDSXtebNnukdVlUBTjBdVhjw62UmZBQAAANIZZVZSz8iUmvsntLkkL+rvvbHIp67hKXUMTUb9vWPtdOeoyoOJNcVYkirzvTrTxY7GAAAAQDqjzEp68kyvdlUElOmK/sfhchntrgrp8dM9UX/vWDvRPqzKBByZLQ141DM6pYmZ5D7DFwAAAMDaUWYlPXSyS7tiMMV40e6KgB5JwnWzJztHVF2QeGU2w2VUGfLqdBebQAEAAADpKu3L7NTsvJ672B/TMruzIqgXmwY0OTMfs3tE29TsvNqHJhNymrF0aarxyQ7WzQIAAADpKu3L7HMX+1WdnxvTHXtzczJVX+TT0+f7YnaPaDvddWm9bFZGYn6LVIa8Ot4+7HQMAAAAAA5JzKYSR4+c7NauykDM77OrIqiHk2iq8Yn2YdUW5jodY0U1hZRZAAAAIJ2ldZm11uqxUz3aUxmK+b32VAX1+OluLSzYmN8rGo62Dqk6ATd/WlRTkKsLPWOamVtwOgoAAAAAB6R1mT3ZOSKXkSpCsV8XWhrwyJOVkTSjicfbh1WTwCOz7qwMFfvdOtvNJlAAAABAOkrrMvtwQ7f2VIdkjInL/fZUhfRQQ1dc7rUeU7PzauwbV01B4pZZSdpQmKtjbcnxxwEAAAAA0ZXWZXZ/Q5duqYr9FONFt1QnR5lt6Lh0vmx2ZmJ/e9QW5upQy6DTMQAAAAA4ILHbSgy1Dkyoe3hKm4vz4nbP+iKfBidm1dg3Hrd7rsXhliHVJfAU40X1RT4dpswCAAAAaSlty+xDDV26uToolys+U4wlyWWM9laHtP9EZ9zuuRYHmwdVG/Y5HeOGqgq86hia0sjUrNNRAAAAAMRZ2pbZHx/r1N7q/Ljf95bqkH58PHHLrLVWB5oGtaUkfiPWa5XpcmlDOFdHWoacjgIAAAAgztKyzPaMTOlcz5i2l8f+fNmrbS3zq2VgQu1Dk3G/dyTaBic1t7Cgorwcp6NEZFORTy80DjgdAwAAAECcpWWZ/cnxTu2tDikrI/7/52e6XNpXna8fH+uI+70jcaB5QFtK/XHb4Xm9NpX49dzFfqdjAAAAAIiztCyzPzjSoX018Z9ivOjW2nz94Ehiltmnz/XHdVOs9dpU7FND54imZuedjgIAAAAgjtKuzLYNTuhC75h2VsR/ivGibWUBtQ9NqikBdzV+9mK/tpb6nY4RMW92pqrzvRzRAwAAAKSZtCuzDx7p0G21+cp0YIrxogyX0e0bCvT9w+2OZVhO68CExmfmVBHyOB1lVW4qydPT5/qcjgEAAAAgjtKqzFpr9a2DbbqrPux0FN1VV6jvHGqTtdbpKC/52dle7awIJs162UXbygN68myv0zEAAAAAxFFaldmjbcOanp3XpmLnz1CtC+dKkg40J8702CfO9Gh7WfJMMV60uThPTX3j6h+bdjoKAAAAgDhJqzL7ny+06GWbwgkx8miM0cs2hfX1F1qcjiJJmpqd13MX+7WrIuh0lFXLzHBpe3lAP2N0FgAAAEgbaVNmx6bn9JPjnbpno/NTjBfdXV+ohxu6NTw563QUPXuxX9X5ufJ7spyOsia7K4Paf6LL6RgAAAAA4iRtyuz3D7drW5lf+bnZTkd5SdCbrZ2VAX3nYJvTUbT/eJf2VCXfqOyim6tCevpCH0f0AAAAAGkiLcqstVZferpRr9xS7HSUa7zqpmL96zNNWlhwbiOo2fkF7W/o0m21zp29u15+T5bqwj49cbrH6SgAAAAA4iAtyuzPzvbKWmlbAm5utLk4T9mZLj3uYAn7xfk+lfjdCue5HcsQDbfVFuh7CXbcEQAAAIDYSIsy+9knzus120sSYuOnqxlj9NrtJfrsE+cdO6bnmy+26q76AkfuHU231ebrmQv9GhyfcToKAAAAgBhL+TL7QuOA2gYmdWddodNRVnRbbYF6x6b17IX+uN+7f2xaPz/XpzsS+POJVG5Opm6uCuq7h5xfgwwAAAAgtlK6zFpr9bH9p/WmPWXKcCXeqOwil8vozbvL9ff7T8d9dPbrL7bo1tqQfDmZcb1vrNy7pUhfebbZ0TXIAAAAAGIvpcvsIye71Ts6rbvrE+c4npXcsaFAY1Nz+mkcj5eZnpvXvz7drF/aWhK3e8ba4hrkx9gICgAAAEhpKVtmp2bn9ZEfndQ7bq1K6FHZRS6X0Ttvq9Lf/OikJmbm4nLPbx1oVWW+VzUFuXG5XzwYY/T6HaX61GNnHVuDDAAAACD2UrbM/tMjZ1WV79WuyuQ5O3VbWUAbi3z6+ENnYn6vyZl5ferR83rznvKY3yve9tXma3x6Tg81dDsdBQAAAECMpGSZfaFxQN862KZ331HjdJRVe9ft1XrwSIeePt8X0/t85vFz2lTiU32RL6b3cYLLGL19X5U++uOTmpqddzoOAAAAgBhIuTLbPTKlD/7HIf3uPbUKeLKcjrNqfneW/q+X1+kPvn5YHUOTMbnHifZh/cfzLXrnrdUxef9EsLMiqKp8rz4Rh1FuAAAAAPGXUmV2ZGpWv/mlF3TfliLtrgw5HWfNdpQHdP+2Er37Sy9oeGI2qu89MjWrD/zHIb3r9mrl52ZH9b0TzXvurNH3j7TrCTaDAgAAAFJOypTZgfEZvfP/e041hbl6YFeZ03HW7bU7SnVTaZ7e/oVn1TM6FZX3nJqd1+9+5YC2lvp1d33ynyt7I353ln7/vo36o28e0bG2IafjAAAAAIiilCizx9qG9MBnf6H6cJ5+4/ZqGZP4uxffiDFG79hXpR0VAT3wmad1oGlgXe/XPzatd33xeeVkuvTrt6Xu9OKrbSrO02/fVav3fOkFPXMhtuuQAQAAAMSPSbDjS1YVZnB8Rp95/Jy+e6hd776jRnfUFcQql6MONA/oy0836dVbi/WHr9qkkoA74tdaa/XTE1368IMNurOuQG/dWylXCpT91TrRPqzPPXleb7+1Sv/9vo3yZGdc7+np9wEBAAAASSbpyuzs/IJebBrQDw536CfHO3V7XYF+ZU+5gt7UXv85Nj2nB4+268kzvbqzrkCv21mm2zfkqyjv2mJrrVVj37iePNOr/3yhRfMLVu+6vVpbS/0OJE8cgxMz+upzzTrbPap/fe+t2l4eWOmplFkAAAAgwSVNme0fm9b7v3ZIx9uHVRb0aF9NSPdsDCuU4iX2auPTc3qusV9HW4d0pmtULmNUEnDLl5MpK2l0albtQ5PyZmdqR3lAt28o0PYyf0pMvY6WLzx1Qb+0tUTvvK1qpafwYQEAAAAJLqHKrDFmv6QrdyYqlNQnSVmFVTllv/257ZJkFzg79BIj41p+2TOf0XUYlwb2f/ri2LFHBi9feen77LI+a+39DiQDAAAAEKGEKrNXM8YcsNbudTrHchI1W6LmkhI3W6LmAgAAALCylNjNGAAAAACQXiizAAAAAICkk+hl9gtOB7iORM2WqLmkxM2WqLkAAAAArCCh18wCAAAAALCcRB+ZBQAAAADgGpRZAAAAAEDSocwCAAAAAJJOptMBrnT//ffb/fv3Ox0DMBE8h8XmcFok36cAAAApK6FGZvv6+pyOAAAAAABIAglVZgEAAAAAiERMy6wxZrMx5sgV/xkxxvxhLO8ZawNjUzrZPqy+0SmnoySN6elpne4cUVv/mNNRAAAAAKSImK6ZtdaekbRbkowxGZLaJX0vlveMpRca+/VvzzbrxaYB3VIV0nvurNFtGwqcjpXQDjUP6MGjnXqooUvVBV79t5fX6RWbi5yOBQAAACDJxXMDqFdKumCtbY7jPaPmVOeI/uK7x3Whd1yS9JMTXTrRMaLPvetmbS8POJwuMXUMj+vLzzTrh0c7JEmdw1M62jqsL//mPt1exx8BAAAAAKxdPNfMvl3Sf8bxflHV3D/+UpFd1DIwoZb+CYcSJb7OwWn9+FjHkmuTs/O60Mt0YwAAAADrE5cya4zJlvSApG8t89j7jDEHjDEHent74xFnTTxZGctfz17+OqSsDCNfzrWD/+4VPktgray1+rdnmvSrn39G//unpzU9N+90JAAAAMRYvEZmXyPpkLW2++oHrLVfsNbutdbuDYfDcYqzerWFuXrT7rIl1163o1Q1BR6HEiW+nZUhffC++iXXNhX5VBfOdSgRUtVnHjuvLz/TpFduKdLBlkF94GuHZC1HAQMAAKSyeK2ZfYeSeIqxJFUV5Oq9d9XorvpCnesZU33Ypy0leaoN5zkdLaHdu7FI5e/06kT7sEoCOdpeFtDuqpDTsZBCTnWO6EtPN+pv37xD+bnZ2lEe0F//6KS+eaBVv7avyul4AAAAiBET69ELY0yupBZJG6y1w9d77t69e+2BAwdimgeIgIngOQz7JYj3fOkF1RR6df+20peune8Z02ceP6en/uzeVJ7WHsn3KQAAQMqK+TRja+24tbbgRkUWAFbrXPeojrUN6b7NxUuu1xf5VFXg1XcPtTuUDAAAALEWz92MASCqvvJMk+7bUqTszGt/lP3y1hJ98ecXWTsLAACQoiizAJLS9Ny8fnisUy/ftPzGcdvK/JqeW9Dh1qE4JwMAAEA8UGYBJKUnz/SqMt+jcJ572ceNMbpnY6G+8WJrnJMBAAAgHuK1m3FK6B6Z0sGmQR1tG9KO8oD21YRUHOBonus52Tmic92jOtg8qIqQRzdXhbS3Jt/pWEgBDx7p0K01Bdd9zp11BfqrHzToo2/arqwM/nYHAACQSiizERqbntPHHzqtbx/8rw1l3rCrVP/Pm3coz53lYLLE9sSpHn384TMvfV1bmKt/ettu7a4KOpgKyW5qdl4/O9urj79l53WfF85zqyTg1tPn+/SKzUVxSgcAAIB4YKgiQo2940uKrCT98GinLvaOO5Qo8R1pGdLnf3ZhybXGvnGd7x11KBFSxTMX+lRd4FXQm33D5+6tCeknx7vikAoAAADxRJmN0Mz8/LLXp+eWvw5pwS5oavbaz2dmbsGBNEgl+090a0+Eo/t7q/P16MkuzS+wqzEAAEAqocxGqLYgVzeV5i25Vl/kU21hrkOJEl9VgVdv3F225Fpudobqi3wOJUIqsNbq8dPdurkqFNHzi/1u+b1ZOtwyGONkAAAAiCfWzEYo35ejz7xjj/792WY9ebZX92ws1LvvqFlxJ1VIhT633nVblcJ5OXqooVs1BV69965a3Vp7/U17gOtp6BhRTmaGSlex+dqeiqAeOdnN5mMAAAApxFibOFPv9u7daw8cOOB0jOuaX7AanZqVLydTmeyOGrFz3SPyezJV7Pc6HSUSJoLnJM4/nDTzqUfP6mz3mH799uqIX3Oue1RfebZJj/3JK2KWywGRfJ8CAACkLNrYKmW4jILebIrsKm0s9idLkUWCe+xUj3ZVrm437LqwT/1jM2obnIhRKgAAAMQbjQxA0hgYn9GF3jFtKcm78ZOv4HIZ7aoM6okzvTFKBgAAgHijzAJIGk+d7dX28oCy1jAzYkd5QI+f7o5BKgAAADiBMgsgaTx6qls7ygNreu3OioBeaBzgaCgAAIAUwW7Gq9A6MKamvkl1DE2qNOhRbb5blYWrm+6YbiYmJnS8a1LNAxMKerNUle/VlhK/07GQhOYXrH5+rk8ffdP2Nb0+z52liqBXB5oGdGd9YZTTAQAAIN4osxHqHxvXQw09+tufnJK1kjHSn/3yZr3lZpfCfs6aXclTF4f1B18/ojSB1cwAACAASURBVOnLo2G/sqdcv31PrbaVrW10DenrSOugQrlZKvTlrPk9tpf79eTZXsosAABACmCacYTO9UzpY/vPaPEkI2ulf3j4rM71TDobLIGd7BjWR3986qUiK0nfPdyu5v5xB1MhWT12qke7Kla3i/HVdlYE9cTpniglAgAAgJMosxHqH5vWzPzStXZzC1Z949MOJUp84zNzahu8tuz3jc44kAbJ7pGT3dpTGVrXe9SFfeoamVL3yFSUUgEAAMAplNkIlQU88nuWzsr25WSqPMDZqSvJ92Zrd8W104krC/jMsDqtAxPqHZ3WxiLfut4nw2W0vTygp85yRA8AAECyo8xGaE91SB//1V0qyru0Xi/sy9HH37JTt9Ssb6QoldUV5elDr9mi+ssFJDc7Q3/1hq2qCnkcToZk8+ipbu2uCsrlMut+rx1lAaYaAwAApAA2gFqFX95eotKgW/1jMyrIzdLOdU55TAe31xXq/7xjjzqHp+RzZ2p7iVdut9vpWEgyPzneqZdtDEflvXZWBPSNA62aX7DKiEI5BgAAgDMos6u0c50b0KSjzaV+bS7lOB6szcD4jE52jOiD926MyvsV+HIU9GbpePuwdlfy7xkAACBZMc0YQELbf6JLuyqDys6M3o+rHeUB/ewMU40BAACSGWUWQEJ78Gi7bq3Nj+p77qwI6nHWzQIAACQ1yiyAhNUzMqUT7SPrPpLnapuL83SuZ0xDExwTBQAAkKxiXmaNMUFjzLeNMaeNMaeMMXfE+p6xdKpzRM9e6NOpzmGnoySNC71jeu5iv461DjodBUnmwaMd2lcTiuoUY0nKznRpa6lfPz/XF9X3BQAAQPzEYwOoT0nab619izEmW1LSHjL6xJkefeSHJ9XYN67qAq/+1+u36r6bip2OldBebBzQ3+0/pUPNQyrIzdaf379Fd23MV3kw1+loSHDWWn3zQKveektlTN5/e/mlI3resKssJu8PAACA2IrpyKwxJiDpZZL+RZKstTPW2qFY3jNWjrQO6k+/eVSNfeOSpOb+Cf3RN4/qcAujjStp6hvTJx4+rUPNl/5f3j8+oz//7jGd7xl3OBmSQUPHiEYmZ7W1LDY7Ye+uDOpnZ3u1sGBj8v4AAACIrVhPM66V1Cvpy8aYw8aYLxpjknJIrn1wUv3jS9fXDU/Oqm1w0qFEia93bFrPNy4t+9ZKzX0TDiVCMvnqc8162aawXCY2Z8EW+93yZGfoZOdITN4fAAAAsRXrMpsp6WZJn7fW7pE0LulDVz7BGPM+Y8wBY8yB3t7eGMdZu5A3W5mupb9Uu8yl61ieNytTpQH3NddDuXxmuL7RqVn9+FinXr6pKKb32cWuxgAAAEkr1mW2TVKbtfb5y19/W5fK7UustV+w1u611u4Nh8MxjrN2tWGvPnhf/ZJr739FnaoKri1ruGRbeUAfes0WZVzxR4BX3VSkmsKkXTaNOPn2wTbtqAgoP8Z/+NhZEdCjp7pjeg8AAADERkw3gLLWdhljWo0xm621ZyS9UtLJWN4zVkoDXr1+Z7G2lwXUOTypkoBbNfleVeX7nI6W0O7aUKivvHefmvonFPRkaUNhrraWB5yOhQS2sGD1r0836TfvrIn5vW4q9evTj51T/9i0Cnw5Mb8fAAAAoiceuxn/vqSvXd7J+KKk98bhnjFRXxRQfRFFbDUK/Tm62x/W3RudToJk8eTZHmVmGG0uyYv5vbIyXNpREdDPzvbqV26uiPn9AAAAED0xP2fWWnvk8jTindbaN1lr2f4XwIr++WcX9cvbSmRitPHT1XZWBPXISaYaAwAAJJuYl1kAiNSJ9mFd6B3THXUFcbvnnsqgfnG+T7PzC3G7JwAAANaPMgsgYXzhqYt69dYSZbri96Mp6M1WacCtFxoH4nZPAAAArB9ldg36RqecjpB0ekenNDE563QMJLDukSk9caZH922J7XE8y9ldGdTDDV1xvy8AAADWLh4bQKWMA00D2n+iS8819mtfdb5eu7NU+2rynY6V0A63DOqZC/16qKFLGwpz9da9FbqrPnGPYIJzvvJMk+6sK1BuTvx/LN1Sna9PPnpWH35gW9zW6gIAAGB9KLMROtM1qo/88KSOtQ9Lkk60j+j5xn79w9t266ZSv8PpElP/xIS+fbBNX3u+RZJ0rG1YT5zp1Rffc4v21cRvTSQS3/TcvL7+Qov+5+u2OnL/ypBHknSyc0TbytixHAAAIBkwzThCLf3jLxXZRSc7R9XcP+5QosTX1Dutbx1oW3JteHJW53v4zLDUT453qrLAq7Kgx5H7G2N0S3VI+08w1RgAACBZUGYjlJmx/EcVz41qkk2GkTJc107ZzFzmGtLbV59r0b2b479W9kqUWQAAgORCE4tQZb5Xr9i0dK3nXfUFqs73OpQo8d1U7NVv3V2z5FpZwK36sM+ZQEhIF3vHdLF3TLdUhRzNsakoT31j02rsY+YAAABAMmDNbITqi3z6/VfW6+6NhTrUMqjdFUHdUhPSxpI8p6MlrJycHN2/vURVIa9+fr5PtYW5uru+UHuqnS0tSCzfPdSuu+oLV5z9EC8ul9GtNfn66fFOvf/eekezAAAA4MaMtdbpDC/Zu3evPXDggNMxgEjmQSfOP5wkZq3VnX//uP77KzeqpiDX6Thq6BjWdw616ad/8DKno0SC+foAACCtMc0YgGMOtw4p02USZrr+TSV+dQ1PsbEbAABAEqDMAnDMD4926Lba/IQ529XlMrqtNl8PHu1wOgoAAABugDILwBHWWu0/0aV9tYl15vBtGwr0/cPtSqQlGAAAALgWZRaAI052jkiSKkPOnC27kk3FeRqbnnspHwAAABITuxmvwom2QV3om1Dn8JRKA27VhXO1vTzodKyEdr53TBd7xtTUN66gN1sbi33a4/ARLEgMj57s1s1VoYSZYrzIZYzurCvUdw62aVtZwOk4AAAAWAFlNkKNveP6+ott+urzLS9d+7W9Ffq9V2SoppDjeVby7Pk+/eUPGl76endlQB9543btrOCPAOnu0VM9esOuMqdjLOue+kJ99Mcn9RevvUlZDh8ZBAAAgOXxW1qE2oYm9LUXWpZc+8aBNjX3TzqUKPEdbxvSPz5ydsm1I63DutAz5lAiJIqB8Rld7B3TlgQ9p7k06FFJwKPHT/c4HQUAAAAroMxGaHxqTsvtBzM2PRf/MEliZt5qaHL2muvjM/MOpEEiefp8n24q9Sf0qOfLN4X11eeanY4BAACAFazrN0ljzJZoBUl0lfleVVy1UU1pwK2qBDkfMxGVBXL0qi3FS65lZ7i0oTDXoURIFE+d7dXWMr/TMa7r9g0FOto2pNaBCaejAAAAYBnrHRZ5OCopksC28oA+8daduruuUJ6sDN2+IV//+LZd2sHazxWVBr36b6+o05t2lyk3O0M3lebps+/coz3llNl09/T5Pm1P8M2VsjNdevmmsL7ybJPTUQAAALCMG24AZYz59EoPSUqrJnf7hkKVB9wanJxT0JOlqgJK2Y3cUh1Sdb5bv3V3rbxZGaovTsw1koiftsEJTczOXzPTIRG9akux/urBBv3hqzbJl8N+eQAAAIkkkt/O3ivpTyRNL/PYO6IbJ/FVFvhU6XSIJFOY51FhXuIXF8TH8xcHdFOpP+GO5FlOkd+tbWV+feOFFv32PRucjgMAAIArRFJmX5R0wlr7zNUPGGM+HPVEAFLasxf6tTmJRuhfu6NUn37snH7jjhplZybuhlUAAADpJpLfzN4i6chyD1hra6MbB0Cqe7FpIGGP5FlOXdin8pBH3zrQ6nQUAAAAXOGGZdZaO2CtnTDGFF39mDFmc2xiAUhF/WPT6hufVmUouXYBf/Pucn368XOanuNYKQAAgESxmjlzPzfGvG3xC2PMn0j63o1eZIxpMsYcN8YcMcYcWEvIRDE8OaUXLvbrB0fa9fzFfo1MLreMGFc73DKoHx7t0M/P9aq1d9TpOHDQweZBbS7Ok8uV+Otlr7SxOE+VIa+++iznzgIAACSK1WzP+QpJXzDGvFVSsaRTkm6N8LX3Wmv7VpktoUxOTuqhhj795fdPaHpuQTmZLn34Ddv0up3F8ntynI6XsJ4806M/+sYRDU7MymWk371ng968p0xbShP7WBbExsHmQdWFfU7HWJO37q3U3//0lN62r1J57iyn4wAAAKS9iEdmrbWdkvZLukNSjaSvWGvHYpQr4ZzomtD/+kGDpucWJEnTcwv68A8bdKozbT6CVTvbNaqP/viUBidmJUkLVvrnpy6qZWDS4WRwysHmQdUXJWeZrcr3aldlUJ9/8oLTUQAAAKBVlFljzKOSbpO0XdLrJH3SGPOJCF5qJT1sjDlojHnfMu/7PmPMAWPMgd7e3kjjxF336LQmZ5eul5ueW1D36JRDiRLf0OSMzvdcW/a7hvnM0tHc/IJOdo4k7cisJP3qzRX66nPN6h7hexgAAMBpq1kz+1lr7buttUPW2uOS7pQ0HMHr7rbW3izpNZI+YIx52ZUPWmu/YK3da63dGw6HVxEnvor9bnmzM5Zcc2e5VOx3O5Qo8YW82dpUfG1xKQnwmaWjs91jys/NVm7OalY3JJZCX45evjmsTz561ukoAAAAaW8104y/f9XXc9bav1n82hjz7Aqva7/83z26tGFUpOtsE8r2Yo8+8sbtLxVad5ZLH3lgu7aU5DqcLHFtLM7T//26rSr0ZUuSMlxGH7y3TtX5fGbp6FjbUFKPyi56w84y/fhYp1oHJpyOAgAAkNaiOURyzXCbMSZXkstaO3r5f79a0keieM+48Xg8et3WAlXl36qe0SkV5eVoZ2mu3G5GGa/nZZvC+pf37FPb0ISCnizV5PtUnu9xOhYccLhlSDUFyf+HjDx3ll55U7E++8R5/e9f3el0HAAAgLQVzTJrl7lWLOl7xpjFe/2HtXZ/FO8ZVx6PR7fWUsRWa1dlULsqg07HgMOOtg3pHbdWOR0jKu7fXqI//dZR/cmrN6kojz9oAQAAOGE1a2ZXzVp70Vq76/J/tllr/zaW9wOQmKbn5tXYN54SI7OS5Hdn6c4NBfr3Zzh3FgAAwCnRLLMmiu8FIIWc6RpVWdCj7MyY/v0srl55U7H+88UWzc4vOB0FAAAgLa3pN0tjTP4yl39jnVkApKgT7SOqLvA6HSOqKvO9Kspz6/HTPU5HAQAASEs3LLPGmLuMMaeMMQ3GmNuMMY9IetEY02qMuWPxedbaEzFNmiAae8d0tG1ITX2jTkdJGt1DUzreNqSz3SNOR4FDjrYNqTo/tcqsJN1VX6hvH2hzOgYAAEBaimQDqH+S9DZJPkk/lvQma+0vjDE3S/qMpLtimC+hPHuhX5989KwOtQxqd2VQf/iqTbqrvtDpWAntYNOAvvx0kx451a3KfK/+x6s36ba6kIIeNs1JJyfah/WWWyqcjhF1t2/I19eeb9bI1Kz87iyn4wAAAKSVSKYZZ1lrj1trn5XUa639hSRZaw9JSputfY+3D+lPv3VUzzcOaHbe6sWmQf3xN4/oWNuQ09ESVvvApP7Pkxf0o+Odmp5b0PmeMX3wPw+roW3M6WiIo7n5BV3oHUvJ84W92ZnaVubX46eYagwAABBvkZTZK5/zF1c9lh3FLAmtdWBC7UOTS651j0yrZWDCoUSJr3NkUk+cWfpL/uy81cU+ymw6udg3roLcHHmyM5yOEhO3VOfrR8c6nY4BAACQdiIps39pjPFKkrX2+4sXjTF1kv4tVsESTZ47S2aZ/ZqZWriynEyXQt5r/97hc0fzeGMkuoaOYdUUpt562UV7KoN69mKfpufmnY4CAACQVm5YZq21D1prrxl+tNZekPT1mKRKQNX5Hr37juol1955a6Uq89NmpvWq7agI6k9fvWnJtVuqg6oLp950U6ysoWNElaHULbN+T5Yqgl692DjodBQAAIC0EtEQ2eVdi8slPWWt7THG7JT0IUn3SKqMYb6EUVXg01tvrtS+6nx1jUyp2O9WbYFHtYU+p6MltLtq8/XF9+xVc/+4Ap4s1Yd92lkRcjoW4uhE+7BetjHsdIyY2lHu18/O9urujWwIBwAAEC83LLPGmI9Ler2kI5L+3BjzkKTfkfR3kn4rtvESy/aKgLZXBJyOkVSqi/JUXZTndAw4xFqr052j+o3ba5yOElPbygL61sFWSTc5HQUAACBtRDIy+zpJe6y1U8aYkKRWSduttU0xTQYg6XWPTMtKCnlTe215fZFPTf0TGpqYUXCZdeIAAACIvkg2gJqy1k5JkrV2UNI5iiyASJzqHFFNgVdmud3TUkhmhkubi/N0oIl1swAAAPESycjsBmPMg1d8XXv5ayPJWmsfiE00AMnuZOeIKvNTd/OnK9UX+fR844BetbXY6SgAAABpIZIy+8bL/50raaOkRyWdkTQVq1AAUkNDx7Cq89Nj9+rNJXl68EiH0zEAAADSRiRl9llJH5P0bklNl68VS/qMtfbvjTG7rbVHYpQvoRxrHdKzF/v1YtOgbqkK6s76Au2qZGfe62kZGNczF/r1xKke1RX59KqbinVzNZ9ZujjVOZryOxkvqgv7dLZnVNNz88rJzHA6DgAAQMqLpMx+QpJXUrW1dlSSjDF+SZ8wxnxe0v2SamMXMTG0DUzo4w+f0c/P9UmSHj3VrdvO5utjb9mp6oL0GHlarbm5BX39hVZ97skLly6c7NZ3D7XrS7+5T1vL/M6GQ8xNzc6rfWhS5cH0OIvZnZWh8qBHJztGtKeKP9gAAADEWiQbQL1W0u8uFllJstaOSPo9SW+X9I4YZUso53rGXiqyi55vHNC5njGHEiW+c71j+tLTjUuudY1M6VTniEOJEE/ne8ZUHvQoMyOSHzOpoS7s0+GWIadjAAAApIVIfstcsNbaqy9aa+cl9Vprn4t+rMQzv7Cw7PWFhWs+Gly2YK2W+9gWrv12Qgo62TmiylB6jMou2hDO1cFmdjQGAACIh0jK7EljzLuvvmiM+XVJp6IfKTFtLPJpT1VwybVtZX7VFzHFeCWbivL09lsrl1wLebO0pTTPoUSIp1OdIyoPpcdOxos2FPp0vH3Y6RgAAABpIZI1sx+Q9F1jzG9JOnj52l5JHklvjlWwRFNd6NNfvW6rHj7Zrecb+7W3Jl/3byvWhjDFbCVZmS69544aVYa82t/Qpfpwrn71lgrtKA/e+MVIeifah/XKLel1TE1Z0KPe0SmNTs0qz53ldBwAAICUdsMya61tl3SbMeY+SdsuX/6JtfaxmCZLQHuqQ9pTHdLkzJw82ZH8HQB1RT7VFfn07juqlZPFDq/pwlqrs91jeu9dKb833BIZLqOaQp9OtI/ojroCp+MAAACktIgbmbX2cUmPxzBL0qDIrh5FNr10j0zLSAp60m90srrAq4aOYcosAABAjKXPNqMA4uZU54iqC7wyxjgdJe6qQl4da2PdLAAAQKxRZgFE3cnOYVXmp9fmT4tqCnN1soPjpwAAAGKNMgsg6hraR1SVpmW2IuRR6+CEpmbnnY4CAACQ0uKy+NMYkyHpgKR2a+3r43HPWOgfm9axtmG1D02qLODWjoqgwnk5TsdKaNMzczrUOqzGvjEFPFnaXh5QdQHHGaW6k50jetmmsNMxHJGV4VJZ0KNz3WPaURFwOg4AAEDKitdORn+gS2fS+uN0v6ibmJ7Vvz3brE89du6la7/38jq9/xV1ykvDTW4i9dOGbv3xN49owV76+t7NYX34gW0U2hQ2NTuvzuEplQc9TkdxTGXIo1NdI5RZAACAGIr5NGNjTIWk10n6YqzvFUsNnSP67BPnl1z756cu6FQXa+NW0tg3pr/76amXiqwkPXGmVw2sJ0xpp7tGVRHyKDMjfVcxVIS8OtnBJlAAAACxFI/fNj8p6c8kLSz3oDHmfcaYA8aYA729vXGIszZDE7Oav7KVSVqw0uD4jEOJEt/Y1Jy6R6avuT44wWeWyk52pO962UWXjufhjzYAAACxFNMya4x5vaQea+3BlZ5jrf2CtXavtXZvOJy4a+yq83NV6Mteci3kzVIV02VXVB706PYN+UuuuYxUW8hnlspOtKfvTsaLKvO9Ots9JmvtjZ8MAACANYn1yOxdkh4wxjRJ+rqk+4wxX43xPWNiU0mePvlru7WxyCdJqgvn6lNv36ObSpN2GXDM5fty9KH7t7xUaMN5OfqHt+7Snsqgw8kQSyfah9N+TXTQkyVrrXpGr52ZAAAAgOiI6QZQ1tq/kPQXkmSMeYWkP7XW/nos7xlLd28M6yu/tU89o9Mq9OWoIpTeo0+R2F0V0ufeebNaByeV587UhrDP6UiIofkFq3M9Y6opSO9/G8YYVRfk6nTXqIr9bqfjAAAApKR47WacMsqCXpUF0/sX9dXK9+Uo38cRRungYu+YQt4sebP50VIR8uhM14henqZHFAEAAMRa3H7jtNY+KenJeN0PQPw1dIyohjXRkqTykEenOtkECgAAIFbS9+wMAFF3rG1I1Wk+xXhRVcir052jTscAAABIWZRZAFFzpHVItYWsi5YunTV7sW/8miO9AAAAEB2U2VXqGJrU0dYhtQ9OOh0FSCjzC1anu0Y5eukyT3aGQt4stQxMOB0FAAAgJbFLyyr84lyv/uDrR9Q/PqP83Gz949t26RWbi5yOBSSEi71jCniy5Mvhx8qiynyvznSNUPABAABigJHZCLX0j+v9Xzuk/vEZSdLA+Iw+8LVDauobdzgZkBiOtA6pjqOXligPenSmi3WzAAAAsUCZjVDH8JRGpuaWXBufmVfHMNONAUk63DLECORVKkJenWRHYwAAgJigzEao0Jet7IylH1dWhlFhLuenApJ0uHVQ9UWMzF7p0lmzjMwCAADEAmU2QrWFPv3tm7fLZS597TLSXz+wTbVhRqKAyZl5NfaNq6aAfw9XKg961DE8panZeaejAAAApBx2aolQhsvojbvLtK0soK7hSRUH3KoP+5SVwd8DgGNtQ6rOz1V2Jv8erpSZ4VKJ363zPWPaXh5wOg4AAEBKocyuQnZmhraW+bW1zO90FCChHGxmivFKqvK9OtM1SpkFAACIMoZRAKzb840DlNkVlIc8OtXFJlAAAADRRpkFsC7zC1aHWga1pSTP6SgJqTLk1Sl2NAYAAIg6yiyAdTndNaKAJ0tBb7bTURLS4jRjAAAARBdldpXGpmfV2DuqselZp6MkDWutBsZn2NE1RT1/cYBR2eso9GVrcnZe/WPTTkcBAABIKWwAtQovNg3oa88164XGAd1cHdJ77qjRvtp8p2MltNbBCX3rQKu+e6hdG4t8+v37Nurm6pDTsRBFT53r1U42N1qRMUY1Bbk63TWqu+o5lxoAACBaGJmN0IXuUX3oO8f1/SMd6hie0o+Odep/fPuoznWzFm4lM3Pz+twT5/Xpx86rbXBST5zp1a//y/M6182Uy1QxO7+gF5sGtK2MMns9lfmsmwUAAIg2ymyELvSN60Lv2JJrTf0TutA77lCixNcxNKVvHmhbcm1iZl5nKbMp40jrkIr9bvk9WU5HSWhVIa+Otw07HQMAACClUGYj5MnKWPa6O3P565CyMlzyLvO5uVf4LJF8Hj/Vo11MMb6hmsJcNXQwMgsAABBNlNkIbSr26YFdZUuu3b+tRJtKOFtzJeUhj/7sNZuXXNtS4tNNpX6HEiHaHjvdrV2VrIG+kYqQR62DE2yCBgAAEEVsABWh4oBH7793g+6qL9CZrlFtLM7TroqAyoJep6MltDfvLldNfq4OtgyqIuTRvpp8lQU9TsdCFLQOTKh7ZFr1RfxB50ayMlwqC3p0tntUOyuCTscBAABICZTZVdhSEtCWEqZUrobPnaV7NoV1z6aw01EQZQ81dOmW6qAyXMbpKEmhtjBXx9uHKbMAAABRwjRjAGvyw6Md2lvN0VSRqi7w6kjLkNMxAAAAUgZlFsCqtfRPqLl/QjsqmKkQqQ2XR2YBAAAQHZRZAKv2nUNtun1DvjJd/AiJVFV+rpr6x9kECgAAIEr4TRTAqswvWH3jxVa9fHOR01GSSnamS5Uhr04wOgsAABAVMS2zxhi3MeYFY8xRY0yDMeavY3k/ALH32Klu+T2ZqinIdTpK0qkv8ulIK+tmAQAAoiHWI7PTku6z1u6StFvS/caY22N8TwAx9IWfX9Srt5Y4HSMp1Rbm6sWmAadjAAAApISYlll7ydjlL7Mu/8fG8p4AYufFpgG1DUzqtg3sYrwWm4vzdKBpUNbyYxAAAGC9Yr5m1hiTYYw5IqlH0iPW2uevevx9xpgDxpgDvb29sY4DYI2stfrY/tN6YFcZGz+tUTgvR8ZIzf0TTkcBAABIejH/jdRaO2+t3S3p/2fvvsPbOs+D/38PCILEILj3JkWKIrVFSZbjve0sO7HrtHbdrDrN781skma2aZ3hJE3etm/rtInTJE5cx46TOI3jIY/YlodsidqiSIl7TwDEJEGCOL8/AIEEQYqUTBAgdX+uy5eJ8xyccxN8DoQb53nupwjYpSjKxjntP1ZVtV5V1frs7OxohyOEuEDPnxpi0D7BFdVynV4oRVGoyTNzQIYaCyGEEEK8bSt2e0VV1THgJeCmlTqnEGJ5uL0+vv6HRu7ZU0aCRol1OKva+rwUXmsZjXUYQgghhBCrXrSrGWcripIW/FkPXA80R/OcQojl909PNrIhP4WNhamxDmXV21SYyhttozJvVgghhBDibYr2ndl84CVFUY4DBwnMmf1jlM8phFhGvznUw+utFu7aXRrrUNaEnJQktBqFM0OuxXcWQgghhBAL0kbz4KqqHge2RfMcQojoeaNtlG8+1cRXbt6AQRfVt4uLhqIobClK40/NQ6zPS4l1OEIIIYQQq5aUJBVCzOu1llE+/vBhPnn1OoozDLEOZ03ZWpLGc6eGYh2GEEIIIcSqJsmsECKMqqr8/PUOPvHIYT59bRW1BTJPdrnV5qfSMuRixOmNdShCNkJBDwAAIABJREFUCCGEEKuWjBsUUaWqKu0jbrqsHtL0iVTnmjAlJ8Y6LLGAE712vvnUKazuSb7+7jryUpNjHdKapNNq2F6SxjMnB7hnT1mswxFCCCGEWJUkmRVRtb/NwocfOsjElB+AD15axmeuqyLNoItxZBcXv19lwDHBqNPL5LQfBdBoFKZ8fizuSZoHHPypeZghxwTv2lLAtTW5sgRPlO2uyOTxhl5JZoUQQgghLpAksyJqLC4vX37iRCiRBfj5G53cWJfHnsrMGEZ2cZia9vPsyUF+d6SXAx1WkhMTSDfoSEzQgKriB7QaBVOSlvzUZN69pYC6glRJYlfIlqI0fvpaB6cHnVIISgghhBDiAkgyK6LGPj5Fl8UTsX3YORGDaC4e036V3x7u5f8+d4Ysk44rqrP5wM4SzDK8O64kaBSuXp/NT1/v4Lvv3xzrcIQQQgghVh1JZkXUZJuS2F6SxuHusbDtpVIZN2oOd9v46hMnQIWPX1VJda7c8Ytn19fm8fnfHOOz11XL/GQhhBBCiPMk1YxF1KToE/nGrRupzDYCoE9M4Dvv20RNvjnGka09I04vn//1Mf76oQauqcnl799VK4nsKmDWJ3L1+hy+v/d0rEMRQgghhFh1FFVVYx1DSH19vdrQ0BDrMMQys7i89I+Nk5KcSGmmAUWJ+zmZSwkwLi4ci8vLT1/v5OE3u7iiOotbtxZi0MmAi9XE7fXxxd8e54d3bWd3xXnNJY/7C0kIIYQQIpokmRUiUlwns1b3JPvbLDx1op99Z0a5pCKDd28uIMcsw1RXq8PdNn7xRid/+ORl5C797yjJrBBCCCEuanILR4g45Jv202sbp8Piptviocvipm3ETcuwC7tnkpp8M1uK0vi/f7aFFCnstOptL0mnz+bhzh/t56EP76I00xjrkIQQQggh4p4ks0LE0LRfpc82zpkhJ02DDpoGHJwZdNFt85BhSCQvVU9OShJZpiR2lKTzni0F5JmT0cjyOWvOu7cUkpSYwHv+43X+5soK/mJXKakG+aJCCCGEEGIhMsxYiEhva5jxoS4rT58YZNqvMjntxzvlZ2JqGqfXx5hnEovLy5DDi88/cwitRqE4w0Bxup6idAMFaXqStFKf7WLUPzbOE0f6GBuf4u7dJXzztk0L7SrfaAghhBDiohZXyayiKCNA16xNWcBojMJZTLzGFq9xQfzGNjeuUVVVbzrXExRFeTb4vMiDvfvzJcbaq7IXO+n0uNM37bJOqj6v/7yijQX/tBZNgi/WYbwtq+x30CQZEzytB2xjL/33wKzNs/vqov1UCCGEEGIti6tkdi5FURpUVa2PdRzzidfY4jUuiN/Yoh1XvP7e50N+h/iwFn4HIYQQQojlIuMYhRBCCCGEEEKsOpLMCiGEEEIIIYRYdeI9mf1xrAM4h3iNLV7jgviNLdpxxevvfT7kd4gPa+F3EEIIIYRYFnE9Z1YIIYQQQgghhJhPvN+ZFUIIIYQQQgghIkgyK4QQQgghhBBi1dHGOoDZbrrpJvXZZ5+NdRhCKIvtIH1VxIFF+ykg80hErC2lnwohhBAXJK7uzI6OjsY6BCGWRPqqEEIIIYQQsRVXyawQQgghhBBCCLEUkswKIYQQQgghhFh1ojpnVlGU9cBjszZVAP+gquq/RvO80dRr8zDi9JKdkkRRuiHW4awKjf12BsYmMCdrWZeTQoZJF+uQxDKZ9E3TMerB65umJMNAmmHl/7b9Yx5ah10kJmioyjGSlaIPa3dNTNE86MTrm6Yy20Re6pz2cS9Ngy7s4z4K0pKpLUiNOMeRbhujTi9ZpiS2laZHtJ8edNBrHSclWcvGwlQMSXFVjkAIIYQQYk2K6icuVVVPA1sBFEVJAPqAJ6J5zmh65fQwn37sKGOeKVL1ifzLnVu4en0OiiL1LRbyeusoX/rdcXqs4yRpNXzmuipu2JBHZa4p1qGJt8nmnuTBV9v50b52pv0qW4pT+cEdW1mXs3J/26PdNr639zRvtFlQFHjX5gI+fmVFKCHtGnXzo31tPHqwB78K24rT+Kf31LG5OA2A9hEnLzQN84PnzuD1+SlK1/Pd92/mHeuyQuf44/F+vvb7k6Hr/r731nHThiySkpIAeKNtlC/99gTdVg9JWg2fuqaK27bmU5BhXLHXQQghhBDiYrSSw4yvBdpUVe1awXMumy6Lm088coQxzxQA9vEpPvHIETotnhhHFr9ah5x8++kmeqzjAHh9fr777Gk6re4YRyaWw9GeMX74chvT/kDB3GM9dh7c186Uz79iMTzbOMgbbRYAVBWePNbPwU5rqL2hy8YjBwKJLMCRnjEeOdDNZDDGzlEP3366GW/wca9tnPuePMWZIQcAh7usfPm3J8Ku+y/99gTHBwJ9uNvi5jtPN9NtDbwPeH1+/vm50zQOOqP/ywshhBBCXORWMpn9APCrFTzfshp0TOD0+sK2eSanGbCPxyii+Gcbn6Sx3xGxvc8mr9la0DxPwvZi8xBj45Mrcn6bx8trrZFVpQ922EI/n+izR7S/1jrKqGsCgL6xiYj200NObO5A8tpvj7zux6emGRgL9OFR1yTH5zlH/zzHFWIpJqam8ftlRSUhhBBiKVYkmVUURQe8B3h8nrZ7FUVpUBSlYWRkZCXCuSBZxiSStOEvly5BQ7YpKUYRxb+UpETKMiPnFeeYk2MQzdu3WvrqSinPivzb1pdlYE5OXJHzm5O0bClKi9i+sdAc+rlqniHPW4rSSNcH5vZmp0TO8S3O0JOSrA22R173iQkK2SmB6z5Fr6UyO3I4cU6KvC+I8/fjfW1s+8bz7Pz2Cxzqsi3+BCGEEOIit1J3Zm8GDquqOjS3QVXVH6uqWq+qan12dvYKhXP+yrKM3P++TWg1gfmxCRqFb966kfIsmRe3kJp8M1995waMuoTQtrt2l1CSoT/Hs+LXaumrK2VbSRo3b8oLPc42JfHJq9eRlJhwjmctn4SEBG7dWkDprC9MthanckllZujxzrJ0Lls38zjPnMyH3lGGPligqTTTyD17SkPtBl0Cf//O2tCc2015Jr72zg1h1/2Xbt7A+tzAOatyUvjKLRswzSr49Oc7i6lewXnDYm3Y2zjIT1/r5J/fv5kPX1rOx37ZgM29MqMchBBCiNVKUdXoD2dSFOVRYK+qqj8713719fVqQ0ND1OO5UFPTftpGXAzaJ8g1J1OZbUKnldWNFnOw00qP1UOaIZHidANVuSmxDmkxi1b0ive+ulLGxidpHXIxPjVNeZYxJhW+mwcctAy70CYoVGWbWDenf/XZPDQPOpmYmqYqN4XqOe2tQw56bONYPVOUpOvZWZ4Z1j7i8NAy7GHAPkGeOZmqXAM55vDfs6HTSnewj1dlGynOXJFkdimV52S86iowMTXN1d9/mY9eVh76IuVnr3dQkmnga++sjXF0b5tUSBRCCBE1UU9mFUUxAt1AhaqqkZPLZpEEQcQJSWbFaiDJ7Brx2MFuHj3Yw9/dWBPaNuL08rXfn+D1L11DygoN3Y8SSWaFEEJETdRvK6qq6lZVNXOxRFYIIYS42KiqyoOvdnBTXV7Y9uyUJGryzTx7cjBGkQkhhBDxT8bICiGEEDFyvNeO2+tjY2FqRNullZn89nBvDKISQgghVgft4rsIceFGHF5ODznptrox6xMpzzRSN8+HNhFpxOmlecCB2+ujMse0GuYan7eJqWlODzrptXnINSdTk2fGlDzztqSqKmeGnLSNuEnVJ1KTl0LmnArijX12mgcdJGg0bMhPYX2eOaz9ZJ+dliEn3mk/lVkmdpZnhLV3DLtoGnJic09SlmVkW5EZQ/JMlWOb28uJPju9tgkK05LZVJhKxqwYJianOdxjo3N0JsbKnLX3txLR8ZtDvVy2LguNEjkad1txOg/ua8fumSLVsKqHGgshhBBRIcmsiKr97aN89tfHmA6um3h9bQ6fubZaEtpFDNjH+dyvj/FGmwWA5EQNv/zw7ohEbDWb9qv87nAvX3niZGjbp6+t4m+urECvC7w1vdFm4UM/O8jktB+A6zbkcP/7NoeWxjnYYeVjDx/CGqz6WpJh4N//fBtbigNL9hzusvKV352keSiwJm5KkpYf3r2dy6sC1ag7hl1897nToaGcCRqFH9yxhVu3FQIwPuXjf97q5vvPnQnF+Mlr1vGxyysw6QPJxQtNQ3zmsaP4gn382g05fPmmmohCVELMNe1XefrEAF+9ZcO87TqthtoCM6+0jPCeLQUrHJ0QQggR/2SYsYiaU/127n+mOZTIAjx/apj2UXcMo1odjvfaQ4kswMSUn/ufacI5MRXDqJZXp8XNPz15Kmzbv73YQutwoH/Y3JN8/Q8nQ4kswAtNw5zsC0y/9/n8PHqwO5TIAnRbPbxyZmYN4CM9Y6FEFsDp9fHfr3Vgc08AcGrQETYncdqv8u2nm2gedATa+xz86wstYTE+8FIrpwYDx2wddnL/M82hRBbgxaZhmgadCLGYhk4rqfpE8tMWXq5sc1EaLzZFrGonhBBCCCSZFVHknvQxYJ+I2C5rJy5u2BH5urUMuXB5fTGIJjrsnim8Pn/EdqvHCwT6T/tI5BcfFnegfdw3zZkhV0T7mVnJa691PKK9bcSF1R34UuDs/2cbdnpDXxpYPZNhiSqAXwWLKxCDc8JH31jkOazSx8USPNs4yI7S9HPus7Eglf1tFlZiGT0hhBBitZFkVkRNtknHrrLwD2qKAqWZK78W6Woz3/zYd27OJ2vOfNHVrDBNT545OWybQZdASYYRgGxTEjfOqfAKUJ4VaE9JTuSG2tyI9surskI/bw4ON57tug25lGYE7oSVZRnQzJmquK0kjaK0QB8tyTCSadSFtafqE0N9uCBNz56K8KHfigJl0sfFIlRV5bnGoUWT2VxzEirQISNahBBCiAiSzIqoKctK4fM31rC9JJBQZBh1fOd9m1iXa4xxZPFvU2Eq99+2iZSkwNzRG2pz+diVFSQmrJ1LNjc1mf/6y+1U55oAKErX85O/qg8lq0mJCXzuhvVcEUxOU/WJ/MufbaGuYGa+9Y11udy+o4gEjUKSVsO9V1SwpzIz1L6p0Mxnrq1Cn5iARoGbN+bxni0FaLWB13VbcSrfu30zWSZd8HEaX7m5JjTsc31eCv9y59ZQTCUZBv71A1upDcaQa07mczesDyUkGUYd99+2iW3zJNFCzNYy7GJq2k9Jxrm/+FAUhdp8M2+2W1coMiGEEGL1UOJp6FJ9fb3a0NAQ6zDEMmsbdjHsmsCg07KlaFV8yI8sKzrHSvXVHqsHr89PYZoevS4h6ueLBat7klGXl3RDItkpyRHtbq+P/rFxDDothemRcws9Xh8tw060Gg1VuSnotOEJv8/n40S/A9+0SnmWkax5znGq347T66M4XU9BWmRy0WPxMOyaINuURElm5Jcx/TYPPWPjpOi01K5ccbNF+ykQP2/wIsx/vtzK0Z4xPnhp+aL7vtg0xKjLy79+YNsKRLbsltJPhRBCiAsi1YxF1FXmmKjMMcU6jFWpeJG7NmtBhlFHxpyhvLMZk7TnXJbIkKRlS/HCQzW1Wi3bSs5dBbq24NwJaHGmgeJzDB0uSDdQkL72/1Zi+Tx3aojrN0QOk59PdW4Kz8wqVCaEEEKIgLUzZlEIIYRYBcY8k5wedIYNmT+XwnQ99vEpRpzeKEcmhBBCrC6SzAohhBAr6JUzI9QVmCOGxC9EoyhU5Zg42jMW5ciEEEKI1UWGGZ+H1mEnBzqsnOi1U1doZld5JtXnGP4o4FT/GKeH3BzssFCcYWBHaTq7yjMXf6K4KDQP2jnSbed4zxjr81LYUZrOplnzqvtsHg512Xirw0pBqp5dFRnsLJsZMmxzjXO4x8H+Ngs6rYZLKjK5ojo77BxNAw5eaxnFPj7F5VVZbC1OIylxZv7x4S4bBzutdFnc1JdlsK0klfKsmev6WLeN430OTvbbqcs3s6kolW0lM8Oae6xuDnWNcbDTSlFaIMYdpece1iwubi+cGmJT4fnVD6jINnK028b181TwFkIIIS5WkswukcXp5QfPnZmZt3QQrt2Qw/23bSLHHFlQRgS82DTCD54/E3pclmngX+/cytaScy9HIdY+i2uCH+/r4HeH+0Lb9lRm8u1bN1KeHZhjvbdxiPv+eCrUXpCazA/v2h7qPw3ddv7m4cNMB9eC/enrHTx4Tz2XVwUS2qYBB3f+aD+OicD6vA+83MrPPriTq9bnANDYZ+fzjx+jPbjsySMHevj0tev41DXrSEhIoNvi4kf72nl61nzFa2py+OotNVTmBBLeJ48N8L29p0PtJRkGHviLbWFJuRBn+ab9vHJmhG/ftum8nleRZeLV1pEoRSWEEEKsTjLMeImah5wRBThebBrm9KAzRhHFv2M9Y/xoX3vYtk6Lh9YRV4wiEvGkedDJE0f6wrbtb7PQMhzoH6cHHTzwUmtYe799gubgNWf3eHn4za5QIgswMeXnT83DM8drt4QSWQBVhX97oQXPpC8UQ/uc9TsffLWDk/0OANpHPWGJLMCfmofptHiAQDL8ny+3hbV3Wz2hGIWY63D3GFkpSWSe55rRFdlGGvsdxNMKBEIIIUSsSTK7RJO+6Xm3exfYLsDn9zMxFfn6TPr8MYhGxJtJn8p8n8vP9g+fX8U96Ytsnw60T/v9uLyR/cs5K3l1eyOf75jw4QsmwGePNZvX5w8lyN6p+fvqTIx+JuZ5D5jvuEIA7G0cZPsFrEOcZtCRrE2g2+qJQlRCCCHE6iTJ7BKtyzFRM2d+bGW2iXUyZ3ZBJRl6bt1WGLbNlKSlMluW6RGwLsfI1uLwaq5F6XoqcwLruFZlmfjzXSVh7cmJGqqCyzxlmPTcsaMo4riz5xReWpmJZs4ql/deUY45OTFwjhwTKUnhsy3evTmfquAQ4vJsAzV5c697I6XBZXoqc0y8b1t4DEZdAtU58r4gIqmqynONg2y/wDnVFdlGTvTZlzkqIYQQYvWSObNLVJxh5Du3b+LRAz0c6LBSX5bOn+8qoSzTGOvQ4lZ2ip67dpWQk5LE3sYhyrMM/NWlZeyukAJQInBN/cO7avnd4T7eaLOwpSiVD+wqYUN+IMHV6RK4Y3sRqcmJ/PHEAEXpej50aRl7KrNCx9heksa3bt3IIwe60SVo+NA7ytmYP5NIbi5K45cf2c0DL7VidU/ykcvKuW7W2p71ZRn811/u4Oevd9I+6uKG2jxu3phHij6Q7FbnmrnvvXX89nAfBzus7ChN5/b6otC6tKakRO6+pJTsFB3PnByiLNPAB99Rxs5yKQAlIjUPOpmc9lN2jjWLz6U008CxHjvv2lywzJEJIYQQq5MST/Nv6uvr1YaGhliHcU4+nx+Lx0u6IWnJyyoI6BhxkmpIJMO4KoplKYvtsBr66moxOTnNsHuCbFNyWJXh2QbGPJiSEkNJ5ly9VhfaBIW81Pm/XPJOTePzqxiT5v/+zjU+hWNiioL0+ZOMiYkpBpwT5KQkY0yeP4Z+m4eU5IVjjIJF+ykQP2/wgh/sPU2Hxc1du0sv6PlHe2y8fHqExz62Z5kji6ql9FMhhBDigsid2fOk1WrINetjHcaqU54twy7F/HS6BIp05x7hkJ927jtZRRnnHrqelJjAucrtmPSJmM6RhCYnJ1K+QBJ71kKJsBAQGGL8+6N93HtF5QUfoyzTSNNAG6qqoiiSIwohhBBya1EIIYSIsqM9Y/jVwJzrC5Vm0JGYoKFvbHwZIxNCCCFWr6gns4qipCmK8htFUZoVRWlSFGVVjY+ayzs1TZfFhcc7FetQVpWWIQdDDqnCuRq5vVPzVgVeKo/Xx4Ddg98/f4Vfn8/PgN3D+NTC5+i1uhlzTy7Y3jnqpse2cP/qtbrpGFl4uZxRp4e2YceC7VM+P/1jHrzniLFjxIXFNbFgu7i4PXawh3esy3zbd1TLs4yc7Fu4rwohhBAXk5UYZvxvwLOqqt6uKIoOWLVj8Ro6rTx6MLwA1M4yKfRyLoe7rOxtHGJv4yBlWUY+/I5yrqjOjnVYYglcE1O8fGaEH77cRoICn7imisursjDolv628Va7hf9+rYPTQ06u25DD+7YVUVc4U8H4eO8YvzrQzeutFrYWp3HPnlLqZ11Tjf129p4c5MnjAxSmJfPRyyu4an1OqP1Un51D3Tb+561udFoNH7msnM2F5tCw9sExN0d6HPzk1Q7sE1PcWV/M7ooMNhfNLI3yassIP3u9k/YRFzfU5XFTXR7bS9ND7UeDx3+rw8rOsnTuvqSUbSXh7XtPDfHMiQFKM4185DLp4yKcy+vjqRMDfOd9m9/2sYozDDT227lpY94yRCaEEEKsblFNZhVFSQWuAD4IoKrqJLDw7ZU41j7s4itPnODMkAuAbquHo91j/Ogvd1Aly/PMa9Q1wf+81c1vD/cB0GnxcLDDys8+tJNd5VLRON7tb7fwiUeOhB5/7JeHeOjDu7hyiYnayb4x7v3lIezjgVEM//1aJ10WDz+4YwupBh39Yx6+9sRJjgeXGum2ejjUZeOhD+9kXU4Kk5PT/PpgDw/t7wKgY9TNwU4bP//QLvZUBvrPsV47f/+/jaFzfvrRo/zn3dtDyezJARf/3yOHQ+vZfuvpJr56y4ZQMvtWu4WPP3wYV/DO84/3tTNgn6AoPYkcs4Eei4e/++3xsOv+cPcYP/tgPWVZJhzjkzxyoJtfN/QCwT7eaeVnH9wpVbtFyK8PdrOxMJUMo+5tH6s008DRnrFliEoIIYRY/aI9zLgcGAF+pijKEUVRfqIoyqpcy6Zt1BX6QHtW+6ibthHXAs8Q3RYPvz/aH7bNPTlN67C8ZvFOVVUefrMrYvvvDvcu+RinB12hRPasF5uHaRtxA9A24g4lsmf1jY3TErzOWkZdPNbQE9bu9flpGQ4MF7a4x3n8UGQ8LzYNhX4+2GFlbsH2Rw920xG8bttH3aFE9qynjvfTbQ0MF24dcUZc9x2jblqCfbh9xM3vgl/WnOWZnKZV3hdEkNc3zY/3dXDLxvxlOV5ZppFT/TLMWAghhIDoJ7NaYDvwn6qqbgPcwJdm76Aoyr2KojQoitIwMjIS5XAuXJJ2/iVDFtouQKNoMMyz1MpqXdJotfTV5aAoCpnGyPq/medxZyk5MfLvrEvQkKgNzBlM0mqYb/pgUrB/JGoUTPMspaMLtWtI1Ue2p+pnYpzv+SlJ2tAxdAmRMSYnJpCgORvj/Nd3cnB7gkbBoJunj89zXHFx+p83uylM17Mu59wVt5cqJyUJ96QP6znmkAshhBAXi2h/4uoFelVVfSv4+DcEktsQVVV/rKpqvaqq9dnZ8TvPrCrXxDs3hX+zfn1tDtW5y/MBZS3aWpLG31wZvgxFRZaRdat0mZ7V0leXy127S0hMmMk2k7Qa3rO1cMnP35BvpjbfHLbtw+8oZ31uYFtNnonbtxeFtV+2LosNwedU55n5xDVVYe1F6Xo25AX6j1mfxF27S0OJJ4A+MYGr1s/8bXaUpWOelfAqCnz0igoKg8voVGYbqcwOv4Y/dkUFdXmBASQ1+Sm8a3PkdV+TH4hhU1EaH78qvI+XZRqolqkHAhh1efn3P7XwZ/XFy3ZMRVEol7uzQgghBACKOncM3nKfQFFeBT6qquppRVH+ETCqqvqF+fatr69XGxoaohrP23Gq387RHjtNAw5q8lLYVpJGbUHq4k+8iJ0acNA65ORQt43CND3bS9LDCvzEqUXLjcZ7X10Ofr/KiT47r7eOkqBRuLQyi42F5vOqxnqq386BDisdo262l6azszQ9bD3WjlEXhzptHO+zsz43hZ1l6VTnzSTAA2PjHO620dBpIy81mfrSdHbM6j8jDi8n+8d4q8OKLkHDzvIMLq8K/6LhjbZRGjptOCem2F2RyYYcE4WZM7MdDnVZOdI9RrfVw/aSdGryTNTkz1zXbSNOGjptNPY72JBvZmdpGutyZ2LsGHFxst9BQ5d1pfv4Uv4Q0X2DFwtSVZWPPNRASrKWD+wsWdZj//LNTrYUpfGxKy98zdoVJAviCiGEiJqVSGa3Aj8BdEA78CFVVW3z7XsxJAhiVZBkVqwGkszGsQdeauWJI318/V21aJd52PkrZ4bpsY7zwF3bF9859iSZFUIIETVRX5pHVdWjQH20zyOEEELEgz8c6+fnr3fw9XfXLXsiC4EiUM+eHFz24wohhBCrjVQpEUIIIZbJvjMjfP1/T/L5G2vINEUWUVsOhel6BuwTeCZ9i+8shBBCrGFRvzO71jQNOLC4vKQbddTJfNklaR1xMmz3YkhKYGtxeqzDESvszJATx/gUhenJ5KcaItqbg9dU2gLXVOeoi377BPrEBLaVRPaffqubnrEJNBqF8pxksgzh5xgfn+TUkJspv5+SjGQK0iJXBzvRM4bT66MgLZmyrMiibl0WN8POCbJNyZRlRT5/2DFBt9WDKVlLTZ45ol1cHJoHHXzq0SN8+toqSjIi+/py0Wo0lGQYaBpwsqNU3lOFEEJcvCSZPQ9/ah7i639opMc6TlG6nq+/u5bra/NiHVZcO9Bh4Zt/bOJ4n500QyJ/d+N6LqvMpiQreh/0RHzwTvp4pnGQb/yxCYt7kpq8FO57bx27yjND+7xyeph/fPIUHaNu8lOT+fq7armqKp3k5GQAGjqs3P9sM4e6bJiTtfzt9dVcsz6bkmDCebjbxi/e6OQPx/rRajTcdUkJt24tYEvwS5Pmfjt/PDHIT15rx+vzc11NLn9zVQU7SgMFmmxuL3tPDfHdZ5qxeabYVGjma++sZXdFeIxf+9+Toev+vvfUcc2G3FD74S4b9z3ZyNFeO6n6RL50Uw3v3pyPSZ8Y9ddYxA/nxBR//VADd+8uXZEvNEozDTT22yWZFUIIcVGTYcZLdLR7jM/9+hg91nEAem3jfO7xYxzpmreWlQDah11879nTHO+zAzDmmeKrvz9Ju8UV48jESjj9MC15AAAgAElEQVTaZ+dzjx/HElwPs3nQydd+f5L+MQ8Ax3vH+MJvjtMx6gZgwD7BZ399jBMDwcc2Dw+83Mqh4DXmmPDxj0+e4syIO3SOV06P8Puj/fhVmJz287PXOzk14Ay1Nw25+I+XWpmY8qOq8HzTEH88NsDk5GQwBjtf/t0JbJ4pAE70Obj/6SZ6LO5gzA4+O+e6/8xjRznVH+jTVpeX7z7bxNHewGP7+BRffuIER3vHovCKinj2T0+eYn1eCu9Yl7Ui5yvJMHCsR/qZEEKIi5sks0vUN+YJfeA9yzHuoyf4wVxEsni8NMxJ9lUVui3yml0Muiwepv3hxXTPDLnotQUSw0H7BMNOb1j7+NQ0PcH2Ydckr5wZiTju2f4zZJ/gxeahiPbXW0dDPx/rifyy6YXmITqtgfN2W8eZW9D9aK+dfscEAD1WD9ZgMn6WY8JHVzCGfvsEb3VEnuNsgi4uDg2dVl45Pcyf71reJXjOpTzLxIngF4VCCCHExUqS2SXKMOrQasJXGEjQKGQao1PgYy0w6LQUpCZHbM806WIQjVhpmcbIv3O6IZHU4PDbNEMiSdrwtyBFmekf+sQEyueZv5phDDzfpNNQk5cS0V6dO7OteJ55i1U5KWQYE8KONVuuOYmUJG2wXUdiQuR1nxUs7GNO1lKUro84RlaUCv+I+KOqKvf98RR31Bdj0K3czJ2SDANdFg8TU9Mrdk4hhBAi3kgyu0TrsvV85rqqsG2fuHodlZky93MhdQWpfPmWDWFfAty0MY+yzMgCOmLtqSswc8eOotBjjQJfe+cG1gfnE9blmfjCjevDnnPv5RWUpQeuqeq8FL540/qwhPfK6mwqsgMJrlGv433bi8ia9eVIWaaBPRUZocdbi9OozZ9Jbs16LR9+RzlZKYFzVOWaeOem/FC7VqPwlVs2UBssRFWbb+az11WHxfjJa9ZRVxA4Zkmmka++c0NYwntjXS51hVIE6mKxr2WUMc/Uig0vPkun1VCYpqd50Ln4zkIIIcQapahzx9jFUH19vdrQ0BDrMBbUOeKi3eJmYGyC/NRkyjKTqciRisbnYh+foLHfReeomzSDjtIMPXWFabEOazHKYjvEe1+NF0P2cRr7HYw4vZRlG9lcmIp+1t2rnlEn7ZZxem3j5KUmU5JhoGrWndXJyUkO9zrpGHFj1mspzzJQWxDefw53W2kbdpOgUViXbWTznIrZJ3vHaB1x4/X5qcw2Ul+WEdbeOuykZciFzTNJWaaRrcVpGJJmYrSPeznR66DXNk5hup6NBWbSZ43I8Pn8HO0do23ERbpBR12+mcIoVrKdZdF+CsTPG/wadcd/vcHOsgwur8pe8XP/92vtXFGdzT17ylb83OdhKf1UCCGEuCCSzAoRSZJZsRpIMhtjpwed/MWDb/KvH9iKVrPyA51ebBrC4vbyL3duW/FznwdJZoUQQkSNDDMWQgghLsDDb3Zx9frsmCSyABXZJo71SBEoIYQQFy9JZoUQQojzNDE1zR+O9XNF9coPLz6rOEPPgH0C58TU4jsLIYQQa9DKlV5cAyyuCZoGnAw6JsgzJ1OTm0KWObJarwh3oMNCj9VDmkFHUXoy6/NknrEIcE5McrLPSd+YhzxzMrX5ZjLmVAI+3GWj0+LGnKylJi+FoozwAmKNfYE5sQkahapsE+vzw4svNQ/Y6Rj1MOHzU5FlYMucObUur4/WYSc29xQlGQYqso0oyszIyEmfn7YRFwP2cfLMyazLMaHTJoQdo6HTSpfFTao+kapsE6XZkVWYxdry8ulhSjMMZKfE7t8ArUZDRbaR4732FS9AJYQQQsQDSWaXyDUxxZPHBrjvj6fwq4HKrF++eQN37ijAbJSEdiHPNQ7y6UePMh5cPuLOncXcs6eUugJJaC9209PT/PHYAH//v434/CqKAp+7vpq7d5eQFiyw9PLpYT75yBGcXh8A796cz6evq2JdTqBI1IF2C3/7+LHQ2rUbC8zc9946tpcGijwd6bbxnWeaQmvB5qQk8W8f2MaeykwAHOOT/MdLbfx4XzsAyYkafvJXO7ksmBhM+1X+cKyfv/vNsdB1/61bN3F7fRGJCYGBLS+cGuJTjx7BMxno43fsKOLeK8upypGKxmvZE0f62F2RGeswKM8ycrjbJsmsEEKIi5IMM16ixn4H9z/TjD9YTsWvwvf2NtM46IptYHGsecDBN59qCiWyAI8d7KHb4olhVCJeHOt18I2nmvAFLypVhf/7/BkaBwJLjfRY3Xz32eZQIgvw5PEBmgYcAPh8Pn5/tC+UyAKc7HfwVrs19PhEnz2UyAIMO708tL+DMbcXgKYBZyiRBZiY8vOl3x5nxBlo7xx189UnToRd9//wh5N0jLgBaBly8K2nm0KJLMDjh3ppGZL3hbXM7fXxWssou+ZUxo6FddkmGjpti+8ohBBCrEGSzC7RqMuL1+cP2zY1rTLi8sYoovjn9E7RbY1MXM8mCuLiZnF7w5JACCSLw84JAOzjU/OuoTninAy0T/g43hdZ/Kax3xH6uX0kMqk82edgxBU4xvA8fbHXNo59PDAHcaHrfjSYDDsnpukYdS8Yo1ibXjkzQnVuCqbk2A9uqs5L4Ui3Db9fClcLIYS4+Egyu0T5qcmY53xwMeoSKEjTxyii+Jdu1LGpMHKoZdHKrMEp4lxBqp5Moy5sW5JWQ1F6oH/kpiRzSXnkna+i9MA1l2lK5vJ1kcV3dlXMPGe+4eyXr8uiODUwNaBknr64qdBMtikQ13zXvUGXQEFqIIZsk46tRZHnOBujWJueOj7AtpL0xXdcAekGHaYkLa3zfHEjhBBCrHWSzC7R9tIMvvP+zWQEP3ynGxL5zvs3s6UgJcaRxa912Sl8+ZYNlGYGEobkRA1fvrmGkkxJZgXUFabynfdvIjtY8Mms13L/+zaxJZiAZpuT+ez11azLCRRTStJq+Ox1VWyc9QXJ9bW5XF4VmCuoKHDr1gK2F6eF2mvzU7j7khISNIGCTvWl6bx/RxHJyYkArM9L4bvv24Q+MVDQqTTTwLdv20SqIXCdl2QaeeCu7WHX/Q/v2h7q08WZRr548wbKs4yhGL9403pq86UA1Fo1Ne1nX8sIO0rjI5mFQD9+q8O6+I5CCCHEGqOoavwMTaqvr1cbGhpiHcY5HeuxMeqaJNOoY2ucfDMf70712xl0eElJ0rI+z4hZn7T4k2JLWWyH1dBXV4tT/XYG7RNkmZLYPCsRPat91EW3xYMpScvGglSSdeGVhHutHjpGg9WMc00R1WWtzglOD7uY9PkpyTBQPqfSsKqqdFrcOCd8FKTqyUqJ7J99Yx5GnZNkmZIonOeu65lBB71jE6QkadlcYCYpaUWGny7aT4H4eYNfI95oG+Xr/9vIfe/dGOtQQl45M0yXxcN/3r0j1qHMZyn9VAghhLggsZ/ws8rMXdZDLK62IJXaglhHIeJVoH8sXN26IstERdbCdzqLMgznHLqekZLMnnMsn6IoCuXnOD5AYZqBwrSFz1GdZ6Y6T6oXXwxebBpmyzxfusRSbX4qvz7Yg6qqYctKCSGEEGudDDMWQgghluhPzcNsjbNkNjsliaTEBM5IFW0hhBAXGUlmhRBCiCXotXmwuSdDc6TjyabCVF45MxzrMIQQQogVFfVhxoqidAJOYBrwqapaH+1zRsuJXjttIy66LG6KMwxUZhtl2PEiWofttA57aBlykZmioybXzPY4KpyyltnHJznea6dlyEVRup4txWnkmmeG2076pjnR56Cx306aXseW4lRKM8/vQ/rx3jGO947hnPCxsTCVXaXpJOlm3laOdNloGXYx6BinMttEdY6RqryZIcWN/XZah110jropStezLscUdk3129w0Drg4Pegg3ahjQ14K20tnqhVPT0/zZruVxn4H2gQNGwvN7CrPDIvxQIeVUwMOxienqS1IYVtxGmb9TBXl/rFxjvaMMez0UpOXwubCVAwrM+dVrDL7zoyyqSgVTRwO5d1YmMpLp0e494rKWIcihBBCrJglf2JTFCVRVdWpOduyVFUdXcLTr17ifnGrY9TFw2918djBntC227YV8H+uSWRdtlQuXcirLVb+6clTocd1BWbuv20jm+VLgKjyTfv55f4uvv/cmdC2G+ty+c77NpMerMz7yplR7v1lA2drwJVnGfn5h3YuOaE93jPGxx4+xIA9sC6sRoEf3rWDmzbmAXCyz863n2niYKct9JzP3VBNSXoySUlJDIy5eLyhl5+/0Rlqv6kuj7+9fh3VwYT31VYLX/ztiVB7ZbaJf75jM9uDxddebbXwsV8eCq0Fa9Zr+dHdO9hTGahw/Fa7hU88ciS0HrRWo/DAXdu5sS4Q47Bjgk/96ggNXTMxfud9m/jArpIlvQbi4vLy6WE2nmN+dyzVFZj5r1facHl9mOTLGCGEEBeJRYcZK4pytaIovcCAoijPKYpSNqv5uWgFFm96reP8uqEnbNsTR/rptXhiFFH8O94zxr+92BK2rbHfQeuIO0YRXTy6LJ6I135v4xAtw04AbO5Jvv30KWYXM+8YdXOi177kcxzutoUSWQC/Cv/+pxZGnYFt7SOusEQW4IcvtXFy0B083wS/2N8Z1v5s4yBd1nEAmvsd/Mvz4b9D24iL1uC8QO/UNI+81R1KZAEc4z5ePj0SenyoyxZKZAF8fpUH97UzaA/EcGrAEZbIAnz76Sb6bONLfh3ExWHar/Jmu4VN86wrHA8MOi3rc1N49czI4jsLIYQQa8RS5sx+D7hRVdUs4MfA84qiXBJsW+rSEM8pinJIUZR75zYqinKvoigNiqI0jIzE7z/Cbq+P+VYxck/6Vj6YVWLKr+IYn4rY7pmcjkE0b99q6asA41PTTE1HdliPN/Dae31+Rp2TEe0u79L7s32ev63FNRn6+45P+SPax6emmQwmn57JafzzXVPBGCenp7G6I2P0TJ39HaYZdnoj2occMwn2fM+3uCdD55ivLzq9Pry+1dlHRfSc6LOTYdSRbtAtvnOMbC1J45mTg7EOQwghhFgxS0lmdaqqNgKoqvob4FbgIUVRbmVpaxhepqrqduBm4P8oinLF7EZVVX+sqmq9qqr12dnZ5xn+yinOMFAyZ/mPwjQ9JZkLL9dxscs3J3FDXW7YtiSthsrs+CueshSrpa8CFGfo2V4SXnE1VZ9IefC1z0lJ4u494UNpEzQKNXkpSz7H5qI05k4dfP+OQkqCw5TLs4wY56wJe2llJvmpgXm7Rel6qnLCh+hnm5IoC15TRRl6bt0WvqaTVqOE+o9Zr+PWrZFrPl27ISf0886yyOHst20roDInsIzOuhwTSdrwt8FbNuZRkLbwUj7i4vRaywh1cTrE+KydZRm8dHqYiSn5MkYIIcTFYSnJ7JSiKHlnHwQT22uBfwSqFnuyqqp9wf8PA08Auy4o0hirK0zln2/fzDU1OaTqE7myOpvv37GFTYXxtURDPClIN/DXl1dyZ30x6YZEthal8sO7trMlX74AiLZUvY7v3b6F23cUkapP5IqqLH75kV2h+bAajcJfXlLGJ69ZR6ZRR12BmZ9/aCcbC5f+YX13eTr//oFtVOWYyDYl8fErK7lta2GofVd5Bj+8ezv1ZemkGRK5dWsBn7uhmrLgmq41+Wa+fdtGbqzLJVWfyOVVWfzbB7ayNTgfNsOYzAd2lnDPnlIygjE+cNd2dswa5nlZVRZ/d+N68szJlGQY+NatG6mfVSCqrtDMD+7YQmW2keyUJD51zTquqp5JdqtzU3j4o7tDMf7VnlK+cGMNyYky51CE23dmhNqC+F5LON2goyzTGDbUXgghhFjLFHW+sbOzd1CU64ARVVWPzdmeBvwfVVW/dY7nGgGNqqrO4M/PA/epqvrsfPvX19erDQ0N5/s7rKg+mwerZ5K0ZB3Fcld2Scbck/SOeTDqtJSvjmJZiw6fXw19FQIVi23uKVL0Wgy6yARNVVVGnF6SExMw6xMv6ByDY+OMT00v+LftHHXh9PrINyeRlaKPaB+yuxl2TZFh0FKYHnmM8fFJ2q3j6BM1VOTMf+e4ZchJYoKGsgWWTGkdcjI57ad2gTtrrokp3JPTZBp1aBNWzYplS53mId6mialptt33PA/8xXb0c0YbxJs/NQ/TOermwb+Km4UD4q/0sxBCiDVj0dsPqqq+AKAoSk7w7urZ7WOKovxmkafnAk8ogbGIWuCRhRLZ1aIw3UBhuiSx5yPNqCPNGL/zzNYynTaB3NSFP3wrikKO+e0Nqc1Li0xQZzt7J3YhualGcs9xQ1iv11FXeO7+U5V77uHR6xZpNyUnYkq+sGRerH2HumyUZhriPpEFuKQig18d6Mbi8pJpSop1OEIIIURUnc8tiFcVRfmzsw8URfkcgWHDC1JVtV1V1S3B/+rOdRdXCCGEiEevt46yIT++hxifZdBpqS9N5zeHemMdihBCCBF155PMXgX8paIojyuKsg+oZpXOf327HOORFVLFudnlNYsZ33RkVeGV5vZGVj6ebXyRquDeqWn8/oV/D+/UNFPnKHrj9/vxTi1+jnPHeO72xZ4vVq/XW0epXSXJLMDVNTk8/FYX/vnKhQshhBBryJKrnKiqOqAoyrPAlwE/8CVVVV1RiywONXRaeKFpmLfarewsS+eGujzqyzIWf+JF7GiPjbfarextHGJdjpFbtxVyaWVWrMO6KPTaPDx7cpCnTwxwSUUmt20vpGqBOafR8ma7hSeP9tM06ODq9TlcUZXFlpKZCsNHu2280DTMG22jbC1O45ZN+WHXVOeoi5dPj/Dk8QFKMw3csaOYPZWZofZB+zhvtlt5vKGH5MQE7txZzKWVGZiSA8OSfT4/r7eN8qsD3YyNT3H7jiIur8oi1zwzNLqhw8rjh3tpGXJxY10u19bkhA1LPtFr5+kT/bzZbmVneQbv2pzP5qKZwm9tI05eah7hmZODVGQZuaO+iF3lMzGK1c3t9XFmyEX1IkPV40lVjgldgoZXzoxwdU3O4k8QQgghVqklJ7OKorwA9AMbgWLgvxVF2aeq6uejFVw8OT3k4FtPN3OkewyAIz1jvNFm4Z/v2LJqhp+tNKdniicO9/HQ/i4ADnfbeP7UEA/eUy9fAkSZZ9LH/U838dSJwJqTh7vHePrEAL/660vIX2SO63I51mPj048eYcjhDcXQNuLiHzL0ZJiS6bd5+N7e07zRZgm1v3JmlP+8exvVuWb8fj+PH+rlgZfagMC8xecbh/jFR3axLZgQv9lu5TOPHQ2d86XTwzx4Tz3XbggsCfVWh5WP/qIhtObum+1Wvn3bRv5idykAJ/vsfOzhQ1iC69Ee7rbRbfXwtXdtQJ+opX/Mw9f/cJLDs67711tH+dHd2ynKMDLp8/M/b3bz09c7QzE+d2qIX35kV1jCK1avhi4blTlGdNpVUxgMRVG4vjaPB19tl2RWCCHEmnY+/zr/h6qq96iqOqaq6gngUsAepbjiTo9lPJTInnWy30GP1ROjiOJf64iLXx3oCdtm80zROnJR3dCPiW6LJ5TIntVp8azoa98y7Aolsmf94Vg/LcPuUPvZRPasthEXLUOBGNtH3Dz0RldYu9Pro2nACQQqEP/qQHdYu1+F508NhR7vb7eEEtmzHnqjixHnBADNg45QInvWrxt6aBsOxNAy5Aolsmc19js4E4yxbcTF/7wVHoN9fIrmYIxi9dvfNkrNKrore9allZk0DzppHnTEOhQhhBAiapaczKqq+vs5j32qqn7j7GNFUfYvZ2DxRllgcQFloQYBCiRoIl8fjazUEHWKMn+f1axgf53vXBpF4WyX0MzTN2CmzyiKMn//ObtJBe087bPvoM3Xrk1QSAjGtlCMZ6/rhWLUhGJcoI9LF18z3mi1ULMKR98kJmi4bkMOP9nXEetQhBBCiKhZznFTb299jzhXkqHn8qrwuZ67y9MpyViZIZurUXW2gQ9eWhq2Lc+cTFXOqlhrdlUryzRyZ31x2La6fPOKvvZVOSZK56zFfOfOYtbnBe5yVeeauLEuN6x9c1EqVbmBGCtzTHzsioqw9kyjjrqCQGJh0idy1yXh/SsxQeHaDTPDKi+pzCQ5Mfxt7iOXlZMRXLJkQ76ZgtTwt66/2lPK+uDc4prcFK6IuO4zWB+8U1edY+Kjl5WHteekJFFbsPqSHxHJ7fXRMuxa8bnmy+XamlyebRzE4vIuvrMQQgixCimqujzVDhVFOayq6va3c4z6+nq1oaFhWeKJhkNdVho6bTR02theksau8kx2lKUv/sSL2Mk+O8f7xnileYSKbBNXrc9md0XcF8dZ9L5avPdVgCH7OK+1WXipaZjtpelcU5NDWZZxRWM42GHl1ZYRmgedXFaVxY7SdOoKZhaVPdk3xpvtVt5qt7K5OJXL12WxdVaBqP4xD/vbrDzfNEhZhpHra3PZMWu+9Zjby4FOG0+fGCA5MYGbNuZxaXkmulnrgb7RNsrek4NYPVPcsjGPPRUZpBln1t882j3GC01DtAw5uaomh0srMimd9To1Dzh4tWWUAx1WdpSlc2VVFhtm/Q69VjdvtFl5sWmIyhwT123IZXvpirwvLOX+r5SzfRv2nRnhu88287V31sY6lAv24KttbC9J5xPXVMUqBBmnIIQQImokmRUi0ppIZsWaJ8lslH33mWaGHBPcMWeUw2rSMerm/73YwutfumbeIfErQJJZIYQQUbOcw4zlHywhhBBrxv721TlfdrbyLCOmZC37WkZiHYoQQgix7C4omVUUZb51Vf7ybcYihBBCxIXxyWlODzrXxBz/K6qyeWxOZXkhhBBiLVg0mVUU5R2KojQpitKoKMpuRVGeBw4qitKjKMqes/upqnoyqpHGiY5hF0d7xmgdlqU3lmrUOcGxHhstQ/KarTT3pI9uiwe7Z3Lxneehqir9Y+MMjI0vuM+wY4Jem4epaf+87S1DDo502+i1uudtHxzzcKJ3jJ4F2q2uQP85vcASIz6fnzODDlpj2L+8U9N0Wz1SaGcNOdJjozTTQHJiwuI7x7k9FZm81jqKY2Iq1qEIIYQQy0q7hH3+BfgzwAQ8BdyqqupriqJsB/4deEcU44srr7WM8M97T3Os187GAjNfvLmGy6uyYx1WXGvotPLDl9t46fQwhWl6vnhTDdfW5GBIWkrXE29H84CDbz7VxGuto6zPS+Eb761jV/nSi2+Nurw8eqCbH77chkZR+PS1Vdy+o4h0ow6A8UkfexuH+OZTp7CPT/Hnu0q494oKitIDFYwnJyd5qcXKd55ppmPUzeXrsvjUtevYOSuGN9stfG9vM4e7xqjJS+HLN9dw5fqZasSHu2z8+NU2nmscIteczBduXM/V67NJDxZwahtx8qu3enj4rS50CRr+5spKbttWSH7aylUZbx9x8YPnTvPMyUEK0vR8470bubwqC23Ccs7iECvtQLuV6lW4vux8TMlaagvMPNc4xO07imIdjhBCCLFslvJpK1FV1ROqqu4HRlRVfQ1AVdXDwEWzLs2J3jE+9/gxjvXaATjZ7+BvHzvGsR5bjCOLX0P2cf7fn1r5U/Mwqgq9tnE+89hRDnfLaxZtVvckn3nsKK+1jgJwetDJB392kPYR15KP8crpEb7/3Bk8k9O4vD6+9XQT+9tHQ+3Heu185rGjjLommZpW+cX+Lh490M3ZonKHe5186ldH6BgN3HF9tXWUbz3VRHtwVEPLkJO/+81xDneNAdA86OSTjx4JXVN29yQ/fa2DZ08O4VdhwD4Rdg0CvHhqmJ+81sHElB/HhI/v7T3Nmx2Wt/HKnZ/xyWm+9+xpnjoxiD/Yxz/6iwaaB2UUwmq3v90SWkZqLagvTeep4/2xDkMIIYRYVktJZmfv8+U5bbpljCWu9dg8DDnChxCOuLz02BYefnmx67GNs+9MeNGRab8aSm5E9PTbxiMSKs/kNF0Wz5Ke7/erPH4oco7dk8cGQj8f6xmLaP91Qy8WV2BIc+eoG68vfOjx0V47A8HrqMviodsaHo9j3EfHaGBbl83Ds42DYe2qSqj/uCam+OOJAeZ6uXnlCt0MOSbYeyo8xmm/Stt5fGkg4s/UtJ/jffbQesJrwfaSdN7qsOL2+mIdihBCiBhSFKVIUZT/VRSlRVGUdkVR/kNRlCRFUa5SFMWuKMpRRVGaFUX5/qzn1CiKsl9RFK+iKJ+fc7yvBqejHg8+d/dK/j5LSWb/XlEUA4Cqqr8/u1FRlErgF9EKLN6k6nXMXdVAUSA1OTE2Aa0CBl0C2aakiO1mvbxm0WZISiBJG3l5m/VLG96t0SjU5EVWca3KnSmGk2tOjmgvzTSiD67xmpIcea6UJC2GRE0olsSEyCLoaYZA/9AnashPizyHOXjNJWkTKMkwRMaQFbktWhbq42nSx1e1E3128s3JGNfQdAhjkpZ1OSb2t63cyAUhhBDxRVEUBfgd8HtVVauAKgIjbb8X3OVVVVW3AtuAdymKcnY6qRX4FPD9OcfbA7wL2K6q6mbgOmBFKw4umsyqqvoHVVUjbueoqtoGPBqVqOJQSWYyH76sPGzbPXtKQ/MDRaTaglS+cON6lFn5yiXlGWuiOmi8K8s08tVbNoRtu7O++Lxe+zvqi8KS30yjjls25ocebytJY/2s5FaXoOHzN1aHEoDKbCPXbpiZ/wrwuRuq2VYaKIZeW2Dm41dWzomxKDS0syrXzBduqAn7EmlrcWoooU7Uarh7dwmmWQlHTkoSV60PP2c05ZiT+cZ768L6+OVVWWxY5cu5XOwOdFipXkNDjM/aVJjKC01DsQ5DCCFE7FwDTKiq+jMAVVWngc8C9xCoj0Rw+zhwFCgMPh5WVfUgMLeSYD4wqqqqN7jfqKqq/QCKouxQFOUVRVEOKYqyV1GUfEVRUhVFOa0oyvrgPr9SFOWvgz9/QVGUg8E7vP+01F9IOTu/7Zw7BbLuQmCfqqrDiqJsBr4EXK6q6rKtJl9fX6wGF88AACAASURBVK82NDQs1+GWXWO/nS6LhyHHBLnmJMoyDdQWpMU6rLhmc3k50jNGt9WDWZ9Ida6JjYVx/5otumZyvPdVALfXx6l+B902D7kpSdQWmMkwRt5FPJfWYRfNAw4UjcKGvBQqssOT4V6bh1P9DjyT01TnmtiQb0aZldmd7B2jbdSN1T1Jcbqe6hwTJVkzx+izeTjZ76DPNk6uOYnafDPls87h8nhp6LHTZXFjSgr0n01F4f3ncJeV5kEnWo2G2gIzGwtTz+t3/P/Zu+/4xq4y8f+fI8lWde/dnu7pmZZGAklISIGEFgiB0HY37LLsF5aWEBY2odddAssPNrALYUMqhJreG5mWyVR7PO69FzWrWvf3hzwa29LMyJORJdnP+/XyK5l7patH8rnyfc495zxvlD84RUO/k7ZhF7mWDNaV58S8a50A8dT2Pv0XvIjy0V/tZmNFLucvj3/BtHTQPTbJnc8088qtly7ky0oNeiGESBFKqf8H1Gma9q9ztr8O3A28VdO0tyul8oCngWs0TRuY8bjbAZemaT+Y/rcNeBmwTD/+AU3TXlBKZQAvANdpmjaslHo/8DZN0z6ulLoc+BpwJ/BRTdOuVEpdAbwX+AThvxt/Br6nadqLp3tPpx1DpZT6PuHbx/uBW5RSTwB/D3wb+Pjpnr+YrCvPYV35wl4op7s8m5FL60uSHcaSZDUa2F6Xz/a6WGWh47Oi2MaKU9zNrcyznHJ0wvrKXNZXnrzzoiLPQsUpnm+znP5O65aafLbUnPl7fKMyDXo2V+WyuSrlO2lEHEIhjX2d47xv21nrp00ZlXlmJv1BuscmqYoxRF8IIcSSd5FS6gDh4cc/mpnIxqJpmksptRW4CLgEeEApdSuwF1gPPDV9k0MP9E8/5yml1PXAT4FN04e6Yvrn9el/26ZjeOPJLHANcI6mad7pLL0bWK9pWkcczxVCCCHSRvOQC5vRQJ5l8a1vqJRifUUOr7SMcMOO6mSHI4QQYuE1EL4DGqGUygZKgSbCc2bfrpSqA3YqpR7UNG3/qQ44PVT5eeB5pdQh4CPAa8ARTdPOn/t4pZQOqAcmgTygh/Dd2G9rmvbf831D8SwA5dU0zTsd7DjQLImsEEKIxWh3x9iiKskz1+qSLHa2ySJQQgixRD0DWJRSHwZQSumBHwL/BURKtGia1g58B7jlVAdTSq1WSq2csWkz0Ek4MS6anqqKUipDKbVu+jH/CjQCNwK/mh6S/ATw8elhyyilKpRScS2CEs+d2WVKqT/P+Hfd9L8VoGmadu3pDjD9Qe0FejVNe3s8gaUiv9/P/l4ngw4fJdlG1pdmYTEvvt57sXh0jrrpt3spsGZSV2jFoI+n/+rs8fiDHOlzMOr2U51vpr7s7A/TH3P7aR9xYdDpqCu0znu1bH9wirYRN/bJAJV5Firy5l8+u2/CQ/f0vPBlhVaMGfp5H0Okhl1to6xcRCV55lpdmsVjT59y1JgQQohFStM0TSn1LuCnSqmvAEWE57l+Uyn1ljkP/znweaVULeAlnMtlAyGl1GeAtYSHA/9EKZULBIEW4GZN0/xKqfcCP1ZK5RDOOX+klAoSnq66Q9M0p1LqReDfNE37d6VUPfDq9LBkF/AhYOh07ymeZPa66f9aCY9dfppwtu2N47nHfZpwBp62S3z6/X7+fGiIr/zxMJ7AFKYMHbdfu45r15dKQitS0kvNw3zynn04fUEy9Tq+ft063rWlgkzDwiRado+P3+7s5j+eOkYwpJFjzuA/37+JS9ecvTnUbSMuPvvAAfZP17y9Ym0Jt1+7jvLc+BJSty/IPTs7+d4TTUyFNPKtmfziw1vZOo85uPu7x/mHu19j2OVDp+Bf37qKj11Yi03KdqWlPR1j3HLlmmSHkTDluWac3gADdi+lOQuyUJkQQogUomlaN3AtgFLqAuA+pdQWTdOeJzxc+PjjPEyvZjytMsbhXgMuOMnr7AcujrGrfsZjPjvj/+8kvCjUvMRzm+ZV4F3APYTHQH8U+F/g/OnVqjaf6slKqUrC825/Od/gUsmBPlckkQXwBkL8+5+OcGjAmeTIhIjWN+HhM/fvx+kLAuCfCvGlPxyieci1YDEc6nHwvSeaCIbCC+raPQG+8scjtI+cvRj+sK83ksgCPNkwyMstI3E//+iAk28/dpSp6RjH3H6++LuDjLl9cT3fPunny384zLAr/PiQBj986hgN/fK9kI56Jzz4giFKF2Y16qTQKcXq0iz2dY0nOxQhhBBJpmna3zRNq9E0bV+yYzlT8SSzPyB8C7lG07QtmqZtIZxRL1NK/Qz4w2me/yPgi0DoDUWaZIMObySRPc4XDDHomM8NaiEWxojLx6jbP2tbSINB+8K1174JT9S23gkPw874EsXT8QamePZo9OiT3e1jcR8jVoytw27G3HPLqMU2NhngSJ8jruOK1Le3Y4w1pbPLSy1GdYVWSWaFEEIsCvEks1cD/6BpWuRWg6ZpDuCfgBuAD5zsiUqptwNDmqa9dorH3KyU2quU2js8PBx/5AusJNuEec48OKNBR0nW4u3BF7OlS1sFKLQZKbDOHv6uU1CygMMKYw31rcg1U5w1v1q3J2PK0HNpjLI9O+ZRiqgsN/rzWF5kJd8a3xDhPEsGa8uiZ0+UxziuSH0720ZZeYpSVIvF8iIb+zolmRVCCJH+4klmQ5qmaXM3Ti/DPKxp2s5TPPdC4FqlVAdwP3CpUuqeOce5S9O0bZqmbSsqKppH6AtrU7mNr123DlNG+CMzGsJzZjeULd6FQsRs6dJWIZxI/uf7N2MzhqfFZ+gV33zXhgW9UN9Qmc3nr1iFQRe+y5VtNvD169ZRW3j2YnjXlgo2VZ5YVOqt9cVcuLww7ufXl2Zzy5Wr0U/HmGfJ4Lvv2Ui+Nb6EO9eSyTfftZ5CW7jjQKfgs5evipngitS3p318Ua9kfNzyIhuN/U6CU2k9YEoIIYRAxchTZz9AqT8CD2ua9ps52z8EXK9p2nWxnxl1nLcAnz/Vasbbtm3T9u7dG8/hkmLS4+fQgJMhh4/ibCMbZDXjxeq0YwxTva0e1znqpm/CQ6HNmJTVjCd9QRr6HYy4fNQUWBK0mrGPtmE3Bp1iWZHtjFYzbh12Y/cEqMwzU5lnmXcMveMeuscnyVnY1YzjGQt76i94ETEx6eeC7zzLXTdti3RuLGaff+gA//vR7QuRvC/+D1MIIUTSxLOa8T8DDyulPk54xSqAbYCZ8MJQS4bFnMm5dQXJDkOIuNUUWKkpsCbt9S1GA9tq4x/2eybyrca476TGkmnQU/8G76RW5JnPqKSPSB2vdY6zqiRrSSSyEJ43e6jXviTuRAshhFi8TnubRtO0Xk3TzgW+BnRM/3xN07Qdmqb1xvtCmqY9n841ZoUQQixeu9rHWLEE5sseV1Ng4WDPxOkfKIQQQiSAUupKpVSTUqpFKXXrmR4nnjuzAGia9izw7Jm+kBBCCJGqdrePcc2GsmSHsWDqCq389WB/ssMQQgixBCml9MBPgcuBHmCPUurPmqY1zPdYcSezAloGnbQMueh3eCnNNrGiyMrKUlno5VT8wSka+p20D7vItWayriyb4kVcw3GxaR1y0TjgQCnF2rJs6grP7pDlEaePAz0T9Ix7KM02saEym/Lc+c1Zfb1rnMZ+BwadjrXl2ayvOPvzck/FHwzR2O+gbdhFriWTdeXSxtONNzDF0QEHn75sZbJDWTA1+VaODToJhTR0S2RotRBCiPmrvfWRG4FvAdVAF3Bbx3euufcNHnYH0KJpWhuAUup+4DpAktlEGXZM8uBrPdz1Yltk28cuqOXmNy+jLEfmyp3M041D/PO9+zi+ztgla4r4/ns2UigljVLekT47N/5iF3ZPuOZqoS2T3/79uaw+Sx04Hn+Qe3Z28qNnmiPbPrC9is+/bTUFtvjmwL7aOsLNv3kNpy8IQEm2kZ9/aCvnVOedlRjj8dzRIf7xt69F2vibVxbyg/dtpugslSASifd61wQ1+VZMC7NwV0qwmQzYjAa6xiapPcudVEIIIRaH6UT2F8DxOw01wC9qb32EN5jQVgDdM/7dA5x7Jgda2KVN09ixoUl++VLbrG2/frWDlkFXcgJKA4MOL//+pyPMXDD7uaPDNPQ7T/4kkTIe2tsTSWQBRlx+Hj00cNaO3zjg5KfPt8zadt+ebhr7HXE9PxAMce/u7kgiCzDo8PFc09BZi/F0hpxevvKnw7Pa+AvNIzT0xfceRGrY3T7KqpKlM1/2uLpCK0ekrQohhDi5b3EikT3OMr09JUgyGye7x09oTpELTYOJGRf7YrZJX5Bhly9q+4THn4RoxHyEQhpHB6IvcpsGz15HhNMTIDAVXTlmfDK+c8oXnKJz1B21vXNk8g3HFq9J31TsNj4pbTyd7GwbO2sjDtJJZZ457s4jIYQQS1L1PLfHqxeomvHvyult8ybJbJyq8yyUZM8eNlhoy6Q6X4YYn0xpjpm31hfP2qbXKZYXLr07IOlGp1Ncv7Uqavs7Np69BXJqCqxUzTl/ss0GlsU55NFmyuDtMRbsefPqorMSXzxKsk1cUV8ya5tep1i+hFbFTXeBqRAHeiaWZImaqnwLR/rsyQ5DCCFE6uqa5/Z47QFWKqXqlFKZwA3An8/kQJLMxml9ZS4/vH4TG6cXl1lXns1/vG8zm6oWbm5eujFn6vnS1fVctb4UpaAq38z/fGQba95gTU+xMN68qojPXr4Kc4Yem9HAbVfXc/7ys1dnubbQyg+v38Q51bkArCnN4qc3bmHdPBZwumxtMX//pjpMGTqyTQa+cMUqzl+2cLWgzZl6brlqDVdvCLfxyjwzv/jwVtYswcQoXR3pc1CcbcRmXHpLSFTnWWgakGkfQgghTuo2YO6Qt8np7WdM07Qg8CngCaAReFDTtCNnciyladHD/JJl27Zt2t69e5Mdxim1D7uY8ATIMRtYViQXrPHwBoIMOfxYjfq4F/ZJstMu7ZkObfVs0DSNvgkPSinKcxMzCmHE6WXQ6aPAkknpGbxGMBiidcSFXilWlCTnnPQFphh0+Ba6jcezBG3qfMGnqJ8938KBHjsfOb822aEsuFBI4+9+s4c9X34rWaaMRL2MLJUshBBpLEGrGZ81S68r+g2qK5Lhg/NlyjBQXSBNLR0ppajIm1+pnPkqzDK9odWtDQZd0uc7GjP0VBck9nMSifFq6yhbFnD161Si0ymq8iwcG3SytSY/2eEIIYRIQdOJa8okr3PJMGMhhBBLUnAqxL6uiSU99aEq38JRGWoshBAiTUkyK4QQYkk60uegwJZJjjlhQ2xTXkWumaNSLk0IIUSakrGf89DY7+Dl5hH2dIyxtSaPi1cWUl8e/2I1Qoj5mZj0s6t9jMcO9bO8yMYV60rmPaT4YM8Ejx0eYMzl4+2bytlWk4c5U776BPytdYT6JXxXFsJ3Zp88cvbqRwshhBALSa7o4jTo8PLtRxt5sXkEgCcbBnmuKZ8fvX8zpTlSnkeIRPjzgT6++qcTi9vd/WoHD37ifJbFOXf9SJ+d9//3TjyBKQAe2NvDXTdt5Yp1pYkIV6SZV1pG2V67tOeKVuWZOTboRNM0lJK1moQQQqQXGWYcp2ODzkgie9zOtjGZayREggzYvfzwyWOzto24/DT0O+I+xquto5FE9rgfP9OM2xc8KzGK9OUPhtjXNc7aJX5n9vgQ62GXL8mRCCGEEPMnyWycpkKxK1yEUqi0kRCLiaZpBKdCUdtD0ZtOKhDj+YFQSM5bwf7uCSpyzdhMS3uAklKK6gILxwZcyQ5FCCGEmDdJZuO0qsTG5qrZ82PXlWezqlhK9QiRCKU5Jj55yYpZ27KMBurL4q8le/7yAgy62UMn/+nNKxJZU1OkiZeODS/5+bLHVeaaaRqUUUZCCCHSz9Lukp6H8lwLX79uPY8c7Gdn+xjba/N5x6YyKvOtyQ5NiEVJKcX7tlVSlGXkgT3drCqx8YEd1awsiT+Z3ViRy303n8fdr3Qw4vbxkfNruXBFYQKjFunihWPDvGNTebLDSAnluWYa++Ifvi+EEEKkCklm52FDZS4bKnPx+KcwZ+qTHY4Qi15Rlon3bavi3edUoNepeS9Qo9Mpttfms7U6D03T0OtlMIoA+2SAlmEXq0vj7xhZzKryLDzc0ZvsMIQQQoh5k2T2DEgiK8TCMrzBJFSnU4Cs1CrCXmoZZm1ZNhnSuQFAZZ6F1mGXrGgshBAi7chfciGEEEvKM42DbKiUGuHH2UwGzJl6eic8yQ5FCCGEmJeEJrNKKZNSardS6oBS6ohS6o5Evt5CcPkCtA27cHoCyQ5FiJTg8AQYdnoTdvxQSGPI4cXllXNOvHFTIY0XmobZXJmb7FBSSnW+hSYpNSeEECLNJHqYsQ+4VNM0l1IqA3hZKfWYpmk7E/y6CfFaxxg/e6GVvZ3jbKnK5ZOXrGBbbX6ywxIiKfzBKV5uGeF7jx9l1B3goxfU8N4tlZTkmM/aa/SMT/LbXV08uKebqnwzX7xyDefVFUwPGxZi/vZ3T5BtzqA425TsUFJKRa6ZY4MuLqsvSXYoQgghRNwSemdWCztevC5j+ictCzx2jLj47EMHeLpxiInJAM82DfOZB/bTNiS1+cTSdLDHzt/dvZejAy6GnT6+/8QxHn797C0iE5wK8cuX2vnZ862Muv3s77bzkf/dTUO/rLoqztzTDYNsrpK7snNV5plp6LMnOwwhhBBiXhI+Z1YppVdK7QeGgKc0TduV6NdMhNZhN52jk7O29Yx7aB2WZFYsTfu7J9DmdE3d/bdORl2+s3L8QYeXe3d1zdoWmNI4JvUwxRnSNI3HDvfLiJoYKvMsHJVhxkIIIdJMwpNZTdOmNE3bDFQCO5RS62fuV0rdrJTaq5TaOzw8nOhwzpj1JCsYW4yyIPRSkS5tdaHkWjKitpVkGzEazs7XSqZBR541+jWscs6JM9Q85GLSP8WyQqkPPldlnpnOsUkCU6FkhyKEEELEbcFWM9Y0bQJ4Drhyzva7NE3bpmnatqKiooUKZ97WlGXx/m2Vs7a965wK6sukTuFSkS5tdaFsqc6jLOfEvEOdgs9dsRqbKToBPRNFWSa++va1s7atLrGxrjz7rBxfLD1/OdDH9rp8KT8Tg9Ggp8hmpH3EnexQhBBCiLgl9BaHUqoICGiaNqGUMgOXA99N5GsmSq7FyD9fuoILVhTSMeKmpsDK5qpc8q3GZIcmRFIsK7Jx7z+cx+td4zi9QTZW5rC+4uyWO7msvoQHP3E+R/rsFNiMbK7KpTLPclZfQywNmqbxp/193HzxsmSHkrKqC8JDjVeVSCetEEKI9JDo8XplwN1KKT3hu8APapr21wS/ZsJU51upzpfhaUIcV1dopS6BQzZNGXp21OWzo07mOIo35mCPneBUSIYYn0JFrpnGPgfXbipPdihCCCFEXBKazGqadhA4J5GvIYQQQpzOg3u7edPKQhlifArVeRZe6xpPdhhCCCFE3BZszqwQQgiRDN7AFH892M+bVhQmO5SUVl1goVFKXwkhhEgjkswKIYRY1B491M/yIitFWabTP3gJK8oy4vIFGXf7kx2KEEIIERdJZoUQQixqv3qlg7esLk52GClPpxR1hVa5OyuEECJtSDIrhBBi0Xq9a5whp5et1XnJDiUtVOdbaJBkVgghRJqQZFYIIcSi9fMXWnnbulJ0Oln4KR5V+RYOdE8kOwwhhBAiLpLMCiGEWJRahlzsahvjEhliHLdlhVYO9dqTHYYQQggRF0lmhRBCLEp3PnOMt60rxZShT3YoaaMiz8yAw4vLF0x2KEIIIcRpSTIrhBBi0Tk64ODl5hGuWFeS7FDSikGnoybfyhG5OyuEECINSDIrhBBi0fnmI428Y1M5lkxDskNJO7WFFg72SDIrhBAi9UkyK4QQYlF5rmmItmE3l9fLXdkzsbzIxmud48kOQwghhDgtSWaFEEIsGt7AFF/902E+dF41Br38iTsTK4ps7JcVjYUQQqQB+UsvhBBi0fivZ1uoyDWzuUrqyp6p0hwTk/4gA3ZvskMRQgghTkmSWSGEEIvCkT479+zs5KbzapMdSlpTSrG6NIu9nWPJDkUIIYQ4JUlmhRBCpD1vYIpP37+fD5xbTb41M9nhpL1VJVnsbpdkVgghRGqTZFYIIUTa+/ajjRTaMrloRWGyQ1kUVpVk8WrraLLDEEIIIU5JklkhhBBp7fHDAzx+eIC/e9MylFLJDmdRWFZkpW/Cw6jLl+xQhBBCiJOSZFYIIUTaahlycevDB/nUpSuxGaWm7Nli0OmoL8tmZ5sMNRZCCJG6JJkVQgiRliYm/Xz813t4/7YqVhTbkh3OorO2PJvnmoaSHYYQQghxUpLMCiGESDse/xQf+/UeNlfl8pbVxckOZ1E6pyqP55uG0DQt2aEIIYQQMUkyK4QQIq34gyH+8Z7XsBkNvH97VbLDWbRKc0yYMvQc6rUnOxQhhBAiJklmhRBCpA1vYIqb/29v+L8XL0MnCz4l1LaaPB491J/sMIQQQoiYErpahlKqCvgNUAJowF2apt2ZyNdMpLZhF3s7x2noc7CmLIvtNXksL85KdlhCJEQopHGo187fWkfR6+CC5YWsr8hJdlgpx+UN8HrXBLvbx6jMN3NeXQE1hdZkh7Uo2T0B/uHuPRj0Oj516QoMOumPTbQddQX89LkWbrlyjawULYQQIuUkeunHIPA5TdP2KaWygNeUUk9pmtaQ4Nc960ZcPv7jqWP89eCJHuor1pbwzXetpyjLlMTIhEiM17vGueEXOwlMhefLGQ06HvzE+Wyqyk1yZKnlT/v7+PIfD0f+vbzIym8+voOKPEsSo1p8OkfdfOzXe1hdksWHzq1Bp5PEaiHUFljQ62BPxzg76vKTHY4QQggxS0K7tTVN69c0bd/0/zuBRqAika+ZKMcGnLMSWYAnGwY5OuBMUkRCJNZvd3VFElkAXzDEnw/0JjGi1NM34eG7jx+dta112E1DvyNJES1OzzQO8s6fvsJbVhXx4fNrJZFdQEop3ryqmHt2diY7FCGEECLKgo3RUkrVAucAu+Zsv1kptVcptXd4eHihwpk3X3Aq9vZA7O1i8UmXtno2aJrGqNsXtX3E5U9CNKkrMBVi0h/9HeAPhpIQzeLjDUxx+58Pc+vDh/j0Zau4fG1pskNaki5eWcRzTUMM2L3JDkUIIYSYZUGSWaWUDfg98BlN02bdstA07S5N07ZpmratqKhoIcI5IyuKbaycU8ewrtDKCpkzu2SkS1s9G5RSfPC8mqjt796SlgMrEqY818yN51bP2mbO0LOyRL4X3qj93RNc/eOXaBp08a13bmB1qXymyWIzGbhoZSE/f6El2aEIIYQQsyR6zixKqQzCiexvNU17ONGvlyhV+Va+996N3LOzi90do2ytyePD59dSKwu9iEXqgmUF/NcHzuGnz7egV4pPXbqCbTUyZ26mDL2OT1y8jOIsIw+91sPKYhv/fMkKVkkye8Yc3gA/fKKJPx/o44Pn1nDB8gJZeCgFvGNjObf8/iAfuaCOOvm7J4QQIkWoRBZDV+ErkLuBMU3TPnO6x2/btk3bu3dvwuI5G3yBIKNuPwXWTIwZCe8LEMlx2ivndGirZ4vLGwAFNmNGskNJaROTfswZeowZ+oV6yXgyvMR9wc/gDUwx5vYzFdLINmWQbTbMOwH1Bqa4b3cXP3m2hS3VuVy/rYpsk7S5VPLooX4a+h08cPN5GPRxD+ySngghhBAJk+hs7ELgJuCQUmr/9LbbNE17NMGvmzDGDAPluZLEiqXDJglFXHItmckOYcEM2L08e3SIl5qH2d89wajLT5bZgF4p3L4gKFhZlMWWmly21eaztSaPkuzoVd81TaN12MUfX+/jvt1d1BVZ+cLbVlNbIHf+UtGV60o52Gvnjr808LXr1skdcyGEEEmX0Duz87WU7naJlCZ3ZkU6WNA7s23DLp44MsAjh/rpHJ1kc1Uu68pzWFVioyTLNGuFYZc3SMeom9ZhFy1DLo4NOsk06KgrtFJgM6JXijG3n+YhJ0opttfm8ZZVxVTlSzmjVOfyBfnu40fZXJXL165bhyXztJ27kvEKIYRIGElmhYgmyaxIBwlLZv3BEB2jbg732tnTMcbLzSNM+qfYUpPHtpo81pZnY9DFv35geHVsPwN2L3ZPAA2wGQ1U5JootBnlDl+a8finuPvVDo4OOPj7N9VxzcZyynPNJ3u4/HKFEEIkTEols0qpYWBmMbtCYCRJ4ZxOqsaWqnFB6sY2N64RTdOuPNUTYrTV+Rw/Hcl7SA0z30M87fTx6edEMVZtsJTc8I16pTv1HF8tFCIw2j0ZtA+evi5LKGhEZ4iu6ZRq0iVOSOlYM/LLzRn5lWaA7p98sDs0aR+K8bDTtlMhhBDiTKVUMjuXUmqvpmnbkh1HLKkaW6rGBakbW6LjStX3PR/yHlJDqr+HVI/vuHSJE9In1nSJUwghxOKyIHVmhRBCCCGEEEKIs0mSWSGEEEIIIYQQaSfVk9m7kh3AKaRqbKkaF6RubImOK1Xf93zIe0gNqf4eUj2+49IlTkifWNMlTiGEEItISs+ZFUIIIYQQQgghYkn1O7NCCCGEEEIIIUQUSWaFEEIIIYQQQqQdSWaFEEIIIYQQQqSdlEpmr7zySg2QH/lJ9s9pSVuVnxT4OS1pp/KTAj/xSnac8iM/Qog0lFLJ7MjISLJDECIu0lZFOpB2KoQQQojFLKWSWSGEEEIIIYQQIh6SzAohhBBCCCGESDuGRB5cKbUaeGDGpmXAVzVN+1EiXzdRjg7Y6Rzx0G/3UJpjprrAwtqy7GSHldJ6J9w0D7rpGp0k25xBXaGVTVW5yQ4rLfTbPTT0OXD5gqwstrGmNBudTiU7rFn6Jjw09NmZ9E+xqiSL1aVZKHUixuZBJ0cHHIy6A9TkW9hUmUO+zRjZP+rycaBngp5xdKsjDAAAIABJREFUD6XZJjZU5FCWa47sn/T62ddtp23YTbbZwOrSLOrLcmbFsLt9lOYhFwadYkWxja01+bP27+8e59igC38wxIoiK+ctL5y1/1CPnbYRF/bJANUFFpYV2KgutET2Nw3Y6Zhx3tcWWFgz47wfHHfRNOShY9RNtjmD5UVWNlQunTbu8gZp6LfTO+6hLNfM2rJsss0Zb+iYmqbRNOjk2IATc6aedWU5lOedaBetQy6ODjjQ6xRry7KpLrBG9h3ps9PQ52AqpLGmLIvNVXmRfYd7Jzja78QTCLGyOLotnEzzoJOj/Q5GJ6Pb8dE+O22jkww5vJTlmqkrNLOqJNxGWwbstI156Bv3UpSVSW2hlXXl4X3945M0DbnoGJ0kz5LBskIbGyrD+yY8Xg73OGkfcWPJNLC82DrrfexsG6VlyEWmQcfKYhvnVJ/Yt7djjOYhFwCrim1srT1xPrzeOUbzsJtAMMSKYhvnLiuI7xcihBBCpKiEJrOapjUBmwGUUnqgF/hDIl8zUbrGXfzx9T5+/kJbZNvfvakO63nV1BTakhhZavtb6xhf/N1BtOmlFS5cUcAtV65mY2XeqZ+4xPWOT/LJ3+7jQI8dgAy94u6P7+CCOC++F0L32CQ3/+Y1GgccABgNOu75u3PZXhe+eG4fcfH9J5p4smEw8pyvXbeOD59fC4DPH+S3Ozv5j6ebI/tv3FHN565YRcF0ovBs0wiffmA/U6FwA9pSncs33rmetdMJwcstw3zynn04vEEASrNN3HnD5shF+t6OMT7/0AE6RicBMGfo+dmHtvCW1cVAOLn5/hNNvNgcnluqFHzn3RuoLqwGYHDCwx9inPcfPb+aqoLwef9K+wSff+gA0yFy/vICvnTVGjYugYTWH5ziN6928L0nmiLbPnXJCj516XJMGWf+52Vvxzgf+p9d+IIhANaU2rjrw9upzrdwqMfOB3+5M/I7L8sx8ZuP72BlSRb7Osf5x3teY8jpAyDbbOCum7Zx3rICXu8a5/MPHaB12A2E2+vPb9rCJatLThlLrHZ8x7Xr+MgFtXSMuLh3Tze/ebVz1vv/6PmZZOj1PNEwzPefPPHZvG9bJTdfVMuKkhxebBnh1ocPRb4bL15ZyBevXM36ilx2to7zL/e9TmAqvHN9RTZfv24951Tn8eKxYf7xnteY9E8BUJVv5j/et5nttfn8rXWET937OmNuPwAF1kx+cuM5XLC8kN3to3z2wQP0jHsAsGbq+dmHtnLxqqIz+yUJIYQQKWAhhxlfBrRqmtZ52kemoO5RL3e92DZr2/++0k7XmCdJEaW+w70TfO/xpsjFGsArLaN0jEwmL6g0cbDHHklkAQJTGt9+tBGHJ5DEqGZ7rXM8ksgC+IIhfvBkE25fOMk42u+clQAA/OCJJg72TABwpN/JT55rmbX/3t1dNPaHj9k27OJ7TzRFElmAfV3hO2sAHk+AB/b0RJIagAGHl1daRmY8fjySyAJ4AlP88qU2Buye6ddwRxJZAE2D7z3exKHpGFtH3PzipfZZMf7vK+20j4aff7jXzncfP8qMEHm1dZT26YRpsWsbcfPDp47N2vbT51toHTrz9+/2B/nBk02RRBbg6ICLfZ3jhEIa9+zsmPU777d7efboEABPNw5GElkAhyfIQ3u7CYVCHOieiCSyEG6vP3++jVGn95TxNA1Et+MfPtnEwe4JOscmZyWyAD9/oZVjw26ahpz8+NnmWfse3NtD55iXQz3R340vNo/QNuymZcjFfz7VHElkAQ73Ojg26GLAMcn/vtIeSWQBusc87O0YA+CRg/2RRBZg1O3niSMDAOxpH48ksgBuf7gjYsx96vcvhBBCpLKFTGZvAO6bu1EpdbNSaq9Sau/w8PAChjM/do9/1gUrhC98J1IouUg13sAUwzMuLI+zp+lntpBtdXTGBelxnaOTuP3BGI9OjkFH9EVw+4g7cqEd6/fs8AYjya7TG5h1wX7c+GT4eZP+IL0T0Z1Fdm94vzs4RedodNI0M3ntt0fH2DXmwek9HkP05znq9uMJhBOpCY9/VjIN4fPe7gn/fnzB0KzkKRJjEtv4QrZTpzcY+/Pxnvn79/im6Ijxe+23ewmGQhwdcEbtaxkOD6uN9bz2ETe+oMagI/r31D02edrv8InJ2O3Y5QvG3BcMaTg8AZze4KyEfObxJgNTMc9xhzeIJxCkZzy6w8/u8eP2TdE9Fr2vdzpJ7RiJ/f6BmMfsHJ3E7k6d7xSx9Giaxq62UQ7N6LwVQoj5WJBkVimVCVwLPDR3n6Zpd2matk3TtG1FRak73Kkyz0JxlnHWtgJrJlX55pM8QxRnmbhoxexhsXqdonbG/LZ0spBtdXVpVtS2d59TSZHNGOPRybGxMidq2/XbKim0ZQJQV2glUz/7K2ZTZU7knKkpsFCZN/v8yTYZWFYUbh9VuWauWDt7CKhS4eMCFGaZuHJdaVQMF6080ea2VEcPZ79qfSkrS7KmY7CinzMP+dy6fIqywu+h6mTnfV54Tm2xLYOLV85uCzoFtYXJa+ML2U6r8syUZM/+fPIsGZHP50wU2DJ579bKqO2bqnLINOi5fltV1L7L68Pt5JLp4eMzXbW+DHOmPmZ7vXpDGbUFp471ZO24usBMdb6FXMvs+cHlOSYq88yU5ZgibfU4a6aemgILJVlGzls2e2738e/GsiwjV66PbtfLCq0sL8qKue/40P7L10YPmX7r9GezI8b82Ks3lFFXLNNkRPL851PH+MwD+/nIr3bz+OH+ZIcjhEhD+ttvvz3hL3LHHXe8HajSNO3np3rcXXfddfvNN9+c8HjOREm2ifqyLJoGnQw7fdSXZfGNd26QBTROIceSSXW+hUGHj66xScpzTHz9netZX20jy5iZ7PBO5Y7TPSDRbbXAmsmqEht7OsbwBUO8Z0sln3jzMvKtqZPMFtgyqSsMxxic0rhhezUfu7COHEv4d1uWbWRVSRaHeu3YPQHOW5bPl66qZ830Ak65lkzWV2RzbNDJoMPHymIb33n3BrbXhc8pU6aBilwzYy4/7aNuSrKN3H7tet60vABjhh4Ag15hztDT2O/AnKHnny9Zzo66AoqzTdNRhqjJt3Kwx05I07h+axXv3FwRWWTKnKFnfUUOh3rtOLxBLlpZyGffuop1FeEYS7JNrCnL4thJzvscS7hDa9jpo3O6jX/tuvVsqrVhy0x4G096O7WZMji3Lp/GficDDi/ryrO584ZzWF165gvjKaWozrfgC4Zo7HeQY87gG+/cwJtWFJJp0FGSbcJo0HGwx445Q88tV67m8rUlmDL05FoyyDZmcLjPgVLw0QtquW5zOXnWTGxGAxV5Zg72TBCc0nj3lgret72KstxTJ7OnasdlOWbWlGbR2O9k1O1nQ0U2X7tuHdtqCyjKMrG6JIu2ERcDdi/Li2x8690beNPKInItmdTmW+m3e+kam6Qyz8w33rme7TW55GeZKLQacfkCtA67KLQZ+co1azmnIpscqxFLph6dCg9/tpkM/OvlK9lWnUdhlgmlU+SYM2joc5Ch13Hzxcu4aEUBJTnh9l6Ra+ZQrx1Ngxt2VHH1+rJZC64lyGnb6bTbExmESD2Heuzc8ZcjfOOdG6gvy+Y/njrGTefXYNAlrdBGvG1VCJFClKZFD/M76y+i1P3AE5qm/epUj9u2bZu2d+/ehMfzRrQMOnF4g9iMela9gQu2paRrzMWoK4DRoGdteVp8ZqddMnih2uqgw4s3MEVpjgmjQZ/w1zsTA3YP/qBGWY6JDEP0RUjbkAuHN0hZromSSJJ5wpDDy5DTS741k/IYicW4O9wZYs6Ifc5NuMPJrl4pNp5kpexDPRNMaRqVOSYKs6Mv3hv67PiCIQqtGZGFnWY63Xl/vI1nGnSR1WoXQMq0U7snwITbT64lI9KZ8UYFgiH67V4yDIqynNm/s6mQRt+EB71OUR4jGTs24CSkaawosmGY0yaP9jvwB0MsK7ZiM8a/6nLrkAvnSdrxsUEHLu8UOWYDy4tnj6poG3YxMRnAYtSzZk7b6R51MeIOYDToIouaHddvdzNg95OpV6yrmN2uRycn6R71oSO6zft8PhqHJlEo1hSbMRpnd4Ad7JlgKqRRVWik0HLmd9DnId4l2BN/MSJSyk3/s4uVxVmREQXffLSBf7l0JW+LMeJmgaRWuQAhRFwSnswqpaxAF7BM07RTTopIh2RWLAkpkyQIcQrSTkU6kGRWRGkZcnL9z1/lR+8/h8zpDqenGgYZdHj56Qe3JCssSWaFSEMJLc0DoGmaG1gUY3HdviANfQ56JzyU55pYV56NdR49+0vVnvZRusY85FoyqM61sLIsej6oWJwm/UFahlyMuvxU5ZtZXmSbVYfW5w9yqM8RrjObY2R9WQ62OTVKO0bddI64yTJnsLLYRpZp9v4jveFVavU6HSuKrfMe4jrq8tHQ72DM7ae2wMqGimx0M4a5xXPeH+yZoH3ETZ4lg/qyHIqyUmc4uIiP0xugeciF0xOgpsA6a97z3Ha8rNAWqfnsC0zROuxi0OGjLNfE8iIbGfrTD5P0+/3s73XSPeYh35pJTYGFZUUnRgU09NlpGXaRZTSwpix71t3pvgkPrcMujHodK0qyyLfGdze8e8zN0QEngWCIlSVZkbnjQiy0+3Z38+ZVxZFEFmB7bR5f+N1BpkJa1FoGQghxMglPZhcLfzDE7/f1cMdfGpgKaegU/Ns1a/ngjiqMmfIxnsxTDQN8+v79kRVuP7C9ig+fX019+eKvwbnUuXxBfvlSGz+ariNrNOj475u2Rmq8hkIh/nCgj3/7w2GCIQ2l4AtXrObD59VEEtrXOsf52K92R0qxfGBHFV942+rI3OHd7aN87qEDdE+XyNpQkc3t165ja03+3HBiGnR4+K9nW/i/nV1AeA7tjz+wmcvXhoe5+YMhHt7Xw+2zzvt6PrCjGvP0ef/c0UE+de/ruKfb+A3bK/l/l62MOWRapKYxt48fPNnEvbu6AcgyGvj1x7eztSYfty/IL07Sjv3BEL/b18NX/niYkAYGneIH12/i2k3lkWT3ZJ5vHuPT9+/HEwi3m5vOream82tYVZrN31pG+OS9+yKrJV+1vpRbrlxNbaGNo/0OPn73Hvomwit1v2lFAd99z0YqTrPoVmO/g3/74yFe6wyXnSrPMfGTG8+J+1wR4mwJTIX4w+u9/NvV9bO251oyybdm0tjvYH3Fgk3XEEKkuaTNsk83R/rsfPORxkgZipAG336skSP9jtM8c+k62u/gG480zqqJeN+ebjrHpK7hUnBs0BlJACBcxuYLvzvIgCOceB7pc/K1vzQQnD6nNA1+8GQTDdPnlN0T4I6/HJ5VU/S+3d0c6QvvDwaD/On1vkgiC3Co18Hu9rG4Yzzc64gkshCuQ3vHXxromi7x0tBn5xtR5/3RSIw9Y26+/tfGSCILcP+eHg73yvdCOjnS54gksgBOX5A7/nIEu8dP08nasd1D24iLr/7pSKRsWzCkcevDB2OWCJqpsd/B1/7aEElkAf5vVxdtI25GnT5++NSxWWV/Hjs8wMEeO8GpEL96pSOSyAK83DLKrjja/K620UgiC9Bn9/LbXV34AlKaRyysV1pGKM4yxlx8bHVp1ry+w4UQQpLZOA07fVE1AwNTGoMxakyKMKcvQOdodG3DoRj1ScXiE6sO7bDTx4Q7fJE+4vLN6uiAcLI44Aw/z+kNcChGUnj8uHZvkAO9E1H7j8wjkYwVY8+4J1IDdOgk5/3QdM3SCU+Qthi1PWMdV6SuWL+vQ70OHJ5gzO+rYaeP8ckAI05fVJ1dbyDEWIwasjM5PAF6xqNrKIeP66ehL1a7D58vuzuiL/Qb4uhUbRyIfsyBbjsOjySzYmH9aX8vO+pijwhYWWyL2caFEOJkJJmNU0WumSzj7OHElkw9VYkva5C28syZrI+xenFFnnxmS0FVngU1Z6TlskJLZD5pea45aq6f0aCL1CjNs2bOqhk787gABTYTb1oRvX/7SS6SThbjXGtKsyibXq22Is9Mtin6vD9eH7c4y8imGPVLq/JliHE6idUOLlpZSL4tk8qTtOOSLCPluWZMGbP/jOaYMyjNiV61e6YCawZrYtSSrswzU5Jt4vxl0W24Kt9ClskQVXsZYGuMespzxaq5fNHKQgpsKV0mTSwyvuAUTzcOcW5d7KVUagusMTtzhBDiZCSZjdO6ihy+f/1G8izhuXy5lgy+996N6VJqJilWlGRx2zX1VE9f2BsNOm69ajU1edbTPFMsBqtKbPzgvZuwZIZLCpXnmPjh+zZTYAsns6tLs/jeezZSOH0xnW0y8J33bGDDdIkSa6aB266qZ0VxuL1k6nV8+er6SA1YgCvWlkQSXqXgnZvL2VId/3zsTVW5fOWa+khCUpVv5vZ3rKV0upNqXXkO33vvptnn/Xs2sm76vC/ONvHla+qpKTjRxm+5ck3MBFekrnUVOfzbNfVkTi/ctKLYypeuqseaaWBViY0fXh/djvNtRuoKrfzXjVvINoc7PAqsmfz0xi1Unmb+6oqSbL7y9rWRThFTRrhtryqxkWXO4F8uW8nq6cWZMvSKf37Lcs6pzkEpxQ07qjl/eTjZ1Sn4+IW1bK89fQfOjtp83r+tkuNTec+ty+O9WytmLXYmRKK90jJCVZ7lpIuWleeaGXJ6cftkxIAQIj4LUmc2XulQRqKp38mg00Nxlok1ZZLIxqOhz06/3UuWyUB9qY0sc8qv9ColT84STdPoHJ1kwhOgPMdEcYw6s8cGnQzYPRTZjNTHqNE66vLRO+7BajRQW2iNWuWye8xN+4gbvU6xsthGcYw6sqcSDIZoGHBgnwxQXWChpiC6s6VpwMmg4+Tnfdeom87RSbLNBtaV5UTVNk0Qaadn0VRIo2PEjdsXpCLPHOl0gdO34+6xSUbdfoqyMqmYx8JfjX0O+uweskwZbCi1YTafuMDvn/DQOuLCmmlgbVkWxowTIwScngCdY5Nk6BW1hda4a1C7vAEaB5wEp0KsKLJRFON8TAApzSMiPvvAfmwmA1etLzvpY776p8N85z0b2Vpz+hEHZ5ksoSxEGpJkVohokiSIdCDtVKQDSWYFEF4dfts3nuJb79owq7Norl+81MZl9cV88NyaBYwOkGRWiLQkNWXm6UD3OEMOH0VZRjbHMU9JQEOfg76JSbLMGawrtWFL/TuzYgE1Dzrpm/BQlGVkbYw7s82DDrrGPNhMBtaV2LBZZrefEaeX5iEXBp1idamNbPPc/R6ODbrxBUPUFJhZViS1NdOBfdJP59gkpgw9tQUWMuO8++jxB+kYnSQU0qgtsGI1pe6fucY+O70THrLNGWwsy8Y0o4Zyz/gk7SNurEYD9WVZmDPiex+DDi/Ng04UsLIka9ZdZF9givZRN8EpjZp8C1lmqZMuFs6rbaOU5ZhPmcgClGabaB1yLVBUQoh0l7p/5VPQIwf7+fIfDzExGSDHnMHXr1vH1etLMBjkYzyZv7WOcMvvD9I95sFo0PGZt67kivpSlpfYkh2aSAFPNQxy6+8PMur2k2U0cMd163j7xnIyp4fp7mwb5baHD9E24iZTr+Of3rKcd20pp7Yg3H6O9Nr51qONvNI6ilJw7cZy/uWyFawoDiesh3smuHd3F/fv6SakwTnVudx2dX1ccwxF8rQOOfncQwfZ3z2BXqf4xMXL+IeLlpF3knl2xw3YvfzwySYeeq0HgMvWFPPv166LzNtPJa+0DHPL7w/RMx7+bvzcFau4en0plflWXu8a55bfH+TYYLiT5h8uWsZHLqihNOfUQ+gPdk9w5zPNPHN0CAjPKf/UpSvYWJnLsNPLT55p4f92daJpcOHyAr7xrg3UFcoaBmJh/GV/30lXMZ6pPNfMrvbRBYhICLEYyMoPcdrXOcatvz8Yqf1n9wS49eFD7O+RVfdOpmXQybceaYzUAfUFQ3z38SY6xk9dg1EsDU39Tm6ZTmQhXNvzSw8f4nCfHYC+CQ8/evpYpPSNfyrEnc8009R/osf+rwf7eKU1fNGjafCnA3282nriIqih38m9u7sjdUBf75rgd6/14PaeqOEpUktgKsQvXmpnf3e47NJUSOP/e76V17ujyzDN9XLLcCSRBXjm6BCPHuxPWKxnqmXIwdf/2hgpz+MLhvjWo0c5NuTC6Qnwk2eaOTYYbufBkMbPXmiNfB6n8kLzcCSRBXiyYZCXm0cA2N0+zm92hhNZgFdaR/nd3m5SaaqRWLz8wRBPNQ7GmcyaaB2W6wQhRHwkmY1Tv92Lc87qepP+Kfrs0bUCRdi4x8/hGEvs947JZyagz+6JqsfpC4boGQ/XJh5x+djVHl1vsHci3H6c3gAvtYxE7Z/5nIZ+e9T+V1pG6LNLHdhUNTEZmJWQHdcYRy3VF48NR217/MgA/qmpGI9OnlFXgKMDzqjtveMeBh1eXm2LbvddMWp2z/W3lui7WX9rDZ8jezujj/n4kUFcXlk1ViTeS83DVOSaKTzNEGOA4iwTI04f3kBqnbdCiNQkyWycirKMGOesUJqhVxTH8cW8VGUZM6gtiB7eV5Qtn5mAQpsxUu7kOJ2C0qzwHL9sk4H60uiVgwuzwkNNrZl6NlVGl+HZMKN0z/Ki6OHsGytyKJLamikr22SIWV5pWRzDYbfVRN/1uXB5AZn6+ObbLpRssyFSlmemoiwj+dbMSOmnmU5XuxZgU1X0nPPj58jaGKtwn7csH4tRpsmIxPv9vh7OjVE/ORa9TlGSbaR77PQdOEIIIclsnNaX53Db1fWRsiB6neJLV9WzvkwWkzmZNWXZfPmaeqwzEpYbd1RRkz+/0ilicVpXnsW/v2MtGfrwOaVT8MW3rWF9Rfiiu7bQxi1XriZ7xgI+7zynPFJ/U6fT8d6tlbPmQ26qzInUnQVYX5HDBcsLIv8uzTZx0/k15FqlQyVVGTP0/L9LV1I0o6PwbetK4qof/ObVRZxTdeJxy4usvGtLRULifCPqy3L46tvXzurMuem8GlYUWcm3GfncFavItZxYnOnq9aVsjKN28eX1JawoPtGBs7rExmX1xQBcsLyAC2ecC5V5Zj58fk1UqSshzjaHN8CLx0Y4f1nB6R88rTjbRJcks0KIOEhpnnmwT/o43OdkwO6lNNvI2vJs8uSi+LT2dIzRPTpJrjWDqnwLK4tTvgNASp4sEJ8/yOF+J91jk5TmmFhflo1tzgqr+zrH6Rh1k20ysKoki+o5dWBbh50cG3Ci1+lYUxq9/9iAg7YRN95AiLpCC5uqFs0q5Iu6nfaMT9I24sacoWdlsY1cS3x300ddPpqHXEyFNFYU2yhZmFqqZ2RP+yhdYx7yrBmsKLLNarsNfQ5ah13T9bmzKYnjzixA06CDlkE3KFhZZGNV6Ynv2zG3n+ZBJ4GpEMuLbZSdZkGps0RK8yxx9+/u4o/7e/n0Zavifs7df2vn3GUFfOzCugRGFkV6doRIQ5LMChFtUScJYtGQdirSgSSzS9w7fvIyV64rZUtN/B2Jjxzsx6CH269dn8DIokgyK0QaSvhkGaVULvBLYD3hP1Yf1zTt1US/bqK0DLmwewJkmfSsKomegySidY+6GHYFMGboWBejjqhY3NqGnTg8QcpyTJTEuBPUOz7JsNNHniWTmhjzIgftk/ROeDFm6GO2H+ekn5YRN3qdYmOMObQAB3smmAppVOWZKMyKjqGxz4EnMEVxViaV+dExtAw6sXuDZJv0rIxx3g/aPfRNeDFn6lhTJm08mUKhEM1DLkJaeM505py1DvonPASmNMpyTGTM2dc+4mLSP0VVnimqXnFTvx2XP0RJjDbSOepizB2g0JZJ1Zx97UMuxjwBbJl6Vs+Zt9o7Psmgw4clU8+aOfv6xtwMOP1kZijWl89u1y5fgLYhNwa9iqrNHAyGaBl2oRSsLLah081+jwe7xwkBdQVmciype9daLA6N/Q767R42VZ1+msBMxdlG9nWNJygqIcRishArP9wJPK5p2nuVUplA6hX8i9NLzcN8+9GjNPQ7WF2SxZevqefiVUXJDiul7Wkf48fPNvNS8whlOSZuvWoN25fnUJ4ldWYXO79/imeahvjWY+HyTOfW5fP5K1azfUZphldbR/j2o0c52GtnRbGNL1+9hkvWlET27+0Y42fPt/Js0xBFNiNfeNtq3ryykOLppHh/1zh/eL2X+/d0R+pxXr6uJJL0Hh2w8+KxEf7ruRYmfVO885wKbtxRHblD4PD4eeHYCN957Ci9Ex4uWlHIp9+6km0z6tDOPe9vu3oNb15dPCvGnzzbwgvHhinNNnHLVWu4dHUhORaZgrDQesYmeei1Hn75UhvBkMaN51bzofOqWV6UhdsX5K8H+/n2Y424vEHet72KT75lOZV5Fty+AI8fHuC7jzcx7PJx6epiPnP5KjZU5ODxBHi+ZWRGO87jc1esZkddeP7f801DfPORRpqHXKwvz+a2q+u5YEV43varraN897FG9vfYWV5k5bar67msPty+97SPcefTzbzcOkJFrplbr1rDJauLsJky2Nc5zi9eauOJIwPkWzP57OWruGB5PrWFWRzqsfPbXZ38fl8P5gw9//SW5bxjYxmV+VY6Rlzct7ubu1/tQKH46IW13LC9ipoCK039dp4+OszPn2/FG5zivVsred+2Ks6pXjTD7kUK+vXfOrh0TfG852YXZ8kCUEKI+CR0ASilVA5wMfA/AJqm+TVNO32xvBR0sGeCzz54gIbp8hBNg07+9YH97O+WnsOT6Rp1c+fT4UQWwuWNPvvgAVr7pTTPUvB6zwSfvn9/pM7wrvYxvv7XBnqm6ww3Dzr4wu8OcrA3XD6nZcjFp+/fz/7p3vhh5yS/fLmNZ44OoWkw5PTxxd8f5Ej/iZImr7SOcvernfiCIdz+KX70TDMNM8pBNQ+6+dajR3F4ggRDGr97rYcnjgxE9h/scfCZB/ZHyv0pPBMDAAAgAElEQVS81DLC959ooms0XOPzUM8En5t73j94IBLjoN3Dj6cTWYABh5fPPbifA1J/OilebRvlzmeacfun8AVD/OqVDp5tDP9u9ndPcMt0rfBgSOPeXV08uCdcZ3Vf5wSf/91Bhpw+NC1cn/anzzXj9gXY32ef047H+cZfG2kbdnKwZ4LPPLCf5qFweznc5+BzDx2gacBBQ5+dLz18kP094fbdOuzm/933Ons7xugZc/ODJ5t4ebpsTu+Eh888sJ/93XZGHB5+u6uTxw4PENJgxOXntj8cpnkofN48fqSf+/d0E5jScHiDfPfxJvZ1hf+svtg8wn+/2IY3EMITmOJnz7dG6sw2DLj4/hNNOH1BAlMa9+3u5rkYJZCEOFsmJv08crCfS2Z0/sWr0Gak3+6VOshCiNNK9GrGdcAw8Cul1OtKqV8qpU5fXyEF9UwPhZxp1O2PXOCIaENOb+Ri7bipkEbHqBRDXwraR9z4p0Kzth3stUfOmc5RDz3js88fhzcYaR99dj9PNcy+2NY0IvtHnN5ZielxLzafaHOxhqk9dniA5kHndAxupkKzL5Z2tY8x4AjXv+0am2Roznk/5vbTPV0Lt3fCG1XbNKRBx4i08WR4rik6OXv0UD8e/xT7u6L7UR96rYcxt5+2ETdzr5mfahiie9xD5+hkzHbcN+GjY9TNxGRg1r5+u5fOUTd9E1465tSGdfun6BydpNfujaqhfPy7sd/p4/HD0e26fcRNz7g75r7d08d6Isa+JxrC23a3RdegfezwAB3DrqjtQpwN/7ezk221eXEv3jbT8ZW+HVIHWQhxGolOZg3AFuBnmqadA7iBW2c+QCl1s1Jqr1Jq7/BwdMH7VJFjzmTuKBmlIHfOyqviBFOGnqKs6KGWOWn6maVLW00VsX7P2SYD1um6ltlmQ6Qsz0zHL3yMeh0VudHzW7NN4eNaDXpqCqL7xupm1DYui7ECbE2+BZsxfKGUZYqeaVFgzcScoZuOJSNqeFz4vA/HaM7UURKjbnIy2/hSbqe1MdrDsiIrRoOiLDe6LSwvsmLJNMT8fVXkmrGeZF+2yYA1U0dejIt0g06Rbc4k22yIqk0O4TZlydBTGKPWcbbJgDnDQGVe9GycHHMmOaZMqvKj91VM16xdVhTj/U/PQ491zJoCC7kWqTMrzj5vYIpfv9LB/8/efcfHcdf5H3/NNm3T7qqsuqwuWbItdzux0yE9Ibl0AoSSEH650O4OOOCOSwIHhHocnQRCCzkgpJIe0p04jrtlWy6S1Xvbpu278/tjpbXWu5LWttrK3+fjwQNrZstk9zuz8535fj/vy5blndLzJUnCmp5Gt03cMBAEYWqz3ZntBDplWd429vffiHRuo2RZfkCW5XWyLK+zWhfu/NOiTC13nlces+z2zWUUi8zUSS0vtPDvly1FmtAXOKcym7Ls1JwvmyptdaFYmp/OpSecyHzx0ppokabl+el8+sLKmPW3biiO5sguzTfxpUtrYjqTa0ssVFoj7UenU3PzuuKYHNp8s5ZzJuTMri7OoHRC51anVnL7uWXkWyLLyrMNnD9h3rskwZcuq2HF2DZW5xi589zYaIhPbC6Ldhpq8838+2VLYy50barIojJ3/tr4mdxO31+XG3NxwaRTcdO6YhQKBWtLMliad/x7SVMp+Pz7q9FplKwoNLFuQqVVpULiy5fVUJyppyxbH9eOv3BpDatLMlmaa+S2s0ti1t11QQV1eSZqctL53PuqYtbdtK6IJRlaVhRZ+NKlscfG86utVFgNVOYY+ddLqmMu9CwvNFGVayRdp+YTm8ti8mmXZOrZMDYP/QMrC8k0HO8kZxk0XFVfAMCGsgyKMo7/Xhk0Sm7bVIrFIIpACTPv8V1dlGQZEl5wTFa2MY2uEdGZFQRharMezSNJ0lvAHbIsH5Yk6V7AIMvyFxM9dqHHSBzutdM66KHH7iHPrKM0U8dSUZ13Sl0jozT1j9I65MaiV1OWrae+aMEXHBGRJzOkqd/JoV4nQy4/JVl66gvNZBqPdzZ67R72d9npHPGQa9JSV2CKOflxePzs7bDTMjiKSaem3Brfft5rGeJovwuVQqIyx8jaksyY9XvaRzja78IXDFNpNXJWRVbM+v2ddpoHXdjcAUqz9CzNTSd3wh3hpn4HzQNuemyR/b48W0913vHKs3a3j72ddloG3Vh0aqpyjXEVZmeJaKcJHOy2c6DbQSgsU5dvon5CFdXuEQ8HeyKVq6tzjdRM+B6b+10c6Lbj8AapsBpZW5IRrYR8sMtG86CboVE/JZl6luYZoxdE2odGOdDjoM/upcCiY3mhiYKxdcf67TQPeuga8ZCTrqUkS8+ywkjbGHB4OdDjoG3s2FhpNUbXOT1e9nW5aB5wYdCoqMwxxOQjv9cyxJE+F2mqSLbyiglVvBu6bDR2O0GCuvx0lhceX7erfYSmfheBUGRf2Fgeuy/MEhHNc4YJhWUu+P5rfHxTGbX5p5768NDbLZxblc1tZ5fO3MZNTUTzCEIKmovO7Coi0Twa4BjwcVmWE1ZNOhNPvIQFSXQShFQg2qmQCkRn9gzz7L4efvzqUe65qg5JOvX+4ZO7uzDr1Hz1ytoZ3Lopic6sIKSg2R5mjCzLe8aGvNXLsnztZB3ZVOHyBWjud+H0BKZ/sACAz+fjcK+DbjFc6Iw0Murj2IATvz+UcL3d7eVQj4Mhp3fS12gZdE25vnN4lB6biHEQZo/TE6BjeJRgMBy3zjbq50ivA4fHf1KvGQwGaep3MuBI3La7RjzY3Ilfs33IRa9dHFOFhUWWZX72WhNXrcg/rY4sQJZRQ7do44IgTENUfjgJO1qH+f3WNra3DLO2JIOPbiqJZg0Kie1qG+Hv+7p5cX8vJVl6PnV+BRecQpl+ITW9fXSAX73VwtE+JxctzeHGtUWsmpBrub1liP/b3sHW5iHqi8x8bHMZZ08Y+tjQaeeZfd08s6+HAouWuy6o4PyqbJTKyJzB1kEnrxwa4E/vtqNRKbjj3DLOr8rGahJz2YWZs7V5kF+8cSzajj+4YQnLx4YEv9s8xO+2trCn3c5Z5Zl8aOMS1ifxu7C7bZgn93bz8oE+yrIN3Hl+RXT+dlOfk6f2dvPE7i5yTGncfUEl51dZUakUNPU7+UdjP39+rx1Dmoo7zyvn/KpsLAaRayzMv3ePDePwBqJZ3qcjQ6+Jq/otCIJwItGZTVJTv5N/f6yB5rEYg2cbetjfbefBj6yNmT8nHNdv9/L7d1p5am83AN12L3s67Pz24+s5a27magnzaFfbMHf9aVc0WuFP29rpsnn4/g31ZKdrOdrn5N6/H+TAWC5sj93Lvk47v/rwWuqLLfgCIf6yvZ2Ht7UDkSzOfQ/v4rcfW8+mykiRp7ebhvnGM43R9/zCo/v4xYfWcPkK0ZkVZkZDl41PPbwTh+d4O+62efjhTavotnv4wt/2RiOmntzTTWOPk599aDWVOemTvuaQy8uDW1p4riESmxM5Ntp46GPrWV+awV93dPDAWy0AdI54+NQfd/L7T2xgc2U2rx0e4P7nD0Vf63N/3sODH1nLxadYNVYQZtKDbx3j0mV5KE7zrixECpj1TTJqQRAEYdysDzNeLI4NjEY7suPahtw0D4g8ycl02Nw809ATs8wTCNHcL3INzwRN/aNxGYGvHx7g2FgGa/uwO9qRHddj99IyliPb1O/k0Z2dMet9wTBHx9qP0+PnsV2x6wH+0dg3Y/8NgnCk1xXtyI577fAALYMuWgZG47KSD/c5aZnmd6Fl0B2XFzvqD9E8MErr0Ch/2RHbroNhmcO9TnpsHh7d0RH3eu80x2fICsJcax9ys7NthHMnVJQ/HRkGDf1OH7Nd20UQhNQmOrNJ0qmVJ7VcALVCwqCJ/3zS1KLZnQm0Cb7nNJUCtUIRXZ/o4v34PqVWKTElyPgcbz8qhZQw5zPRMkE4VbrJ2rFKkfD4L0mgTXDcm0itlNBr4gdGaVUKNColJl38Op1GSZpagUUfv0+YEywThLn28LY2zq3KJk01M+dFWrUSjVKBzS1qlAiCMDnRq0hSVa6Ra1YWxCy7YkUe1fOYJ7nQ1RdncPcJOaJVOUaqcsRndiaoyUunvig2ouaT55azbCyqoSLbwM3rimPWX1BtjebCVuem87mLYtvPkkw9tWPD+nVpaj60cQmqCSGvBo2Si2rFnGxh5tQWmFgZ147LqM01UZ5j4OK62PZ2/ZoiqnOmztZcWZzBXRdUxCxbmptOVa6Rogw9nz8hn7bArGV5gZlMQxq3n1Mek2ts1qnFtA1h3vmDYR7d0cFFM1wTI8uooVcMNRYEYQpizmyS8sw67rqggs2V2Rzpc1KVa6S+yExBhn6+N21Bu6AmmwLLahq67OSbtawoNMfkJQqLV02eif++Zjl7O210DLtZVmhmZZEZzdhdqzyLng9vLGZdaQaHep2UZxupyzfFzEG/pC6XrPQ09rTbyDGlsbo4g5UTckPPqcjmNx9dx652G2qlxJqSDDZVzMwQN0EAKMs2cv/19exqG6Ft2E19oZm1JRmoVArKso185qIq3rc0l6Z+FzV56dQVpJNrnv534YrleSzJ1NPQaaMwQ8eKInM0L/biujx+fZuGXe0jZBk0rCvNYMVYh/rcikx+89H17GwbRq9RsaYkQ3RmhXn36qE+CjN05Ftmtl5BlkFDr917Wnm1giAsbrOeM3syRCaisECI/E4hFYh2KqQCkTN7Bvj4b9+jJs8Urcg9UyIFpXK5ef2SGX3dSYicWUFIQWKYsSAIgiAIgnBKhkf9bG8dYWNZ5oy/tkmros/hm/HXFQRh8RDDjE/CgW47/zjYx7vHhllflskldTksL7RM/8QzWEOnjR1tI/zjYB8VOUYuX57H2WIY6Blje8sQLx7o40CPgwtrrJxTmU1dgXn6J45p6nfy7rFhXtjfQ0mmgStX5p/UMGJZltnTYePRHR0MOH3csmEJG8uzMKaJQ58Q672WIZ7e003r0ChXrMjnnKpslmROPfcVIse4lw72saN1hLMrsnh/bc5JtfFEWgZcvNsyzPP7e8g36fjAqnw2V87sHS9BmCnPNvSwqtiCdhYKYmboI8OMBUEQJiPO6JLUa/dw39MHeK91BICtx4Z46+gAP711NYUWMW82kVFvgCf3dPGbLa0AvN08xHMNPfzqw+tYWyrmzS52DV02Pv+XvXTZItElW5uHuGFtEfdcVUd6girFifx9bzf/+0oTAFsY4pmGHn790XWsL03uDkBDp51bHngXXzAMwMuN/fzs1jVcWZ9/Cv9FwmK1p2OET/5hJ3ZPpGrqlqYhPve+Kv7l4uopn9cxPMpXnmhgf1ckYmrrsSG2tQzxw5tWkWvSnvL2vHCgl++8cDj697MNPfzmo+vYKObGCgvQU7u7Znx48TiLXsPeTtusvLYgCIuDGGacpCN9rmhHdtzudhtHekVm6mSO9Lt4+N32mGWDLj9NA8552iJhLh3tdUU7suOe2N3F4b7kvv/GbgcPjV0IGWf3BDjSm3z72XpsKNqRHffT147i8oqoB+G4xh5ntCM77qEtLTRN01aP9LmiHdlxbzcNcbT/1I9xR/scPPR2a8wyly9IY48j8RMEYR71O70c6nVSXzQ7o9Qy9Gr6RTVjQRCmIDqzpylRTqYQIU9az0N8aGey5L99OfH+dZrNR0Lst8LskmbhGCeJRissQC8d6GP1Egsa1eycTlr0GvqdYs6sIAiTE53ZJFXnGtlYFjs0ds0Si8iZnUJdTjofObskZpnVmEalyJk9I9TkpVN4QkzD9WsKqc5NT+r5tQVmPrG5LGaZRa9O+vkAZ1dkkXbCSdbdF1ZhSEtumLNwZqjNS8eij20Tt59TRuU0ba0m18iKwtjIkHOrsqk+jWNcVa6J209o9+lpKpbmJd/uBWGuPNfQw9olszdtyKJXMzzqJxwWxa4FQUhMzJlNUp5Zxz1XL+OVQ/1sOzbE+tJM3l+bS4GYLzsprVbNtasKKM7Q88qhPiqsRi6py2VtiZgveyZYVmjmRzev4uWDvTT2ODm/xsrmiqyk58sCXL0ynxxTGi8e6KUky8Dly/OSni8LsKLQzF/uPIvHdnUy4PJz87pi1s9CxU0hta1aksEDH1nLM/t6aBsa5bLl+ZxbOf381KJMA9++rp6XD/axs22YsyuyuLAmB+tpzJcFuGx5DhkGNc/v76XArOPK+nwxX1ZYcOyeAHs6bHzy3PJZew+1UoE+Tcmw20+2MW3W3kcQhNQlcmYFIZ7I7xRSgWinQioQObOL1NN7u/nDO6382yU1s/o+X358H7/88Fpq803TP/j0iLH8gpCCxDBjQRAEQRAE4aS8uL+XVUtmP54wQ69hQMybFQRhErM+zFiSpFbACYSAoCzL62b7PWfL3o4RGnudHO51Up2bztI8I6uXiCGLUznUa+do3yh7OmwUWLSsLDKzrlQMl5sLw6M+drXb2NthozLHyPrSTApOmMN6uo72OXmvZZgRt5+NZZnUF1lIm5A12NhjZ0frCK1Do6wssrC+NJP8CdtwuMfBvi47jT0OKnKMrCyysLzweEZnc7+DQ72j7G4fIdekZVWxJWaYsMsTYHv7CDtbh9EolawttcTlcb7bPMjOdhsuX5B1JRmsK7Fg1h8frrazbZh9nXY6RzzUF5lZUWii3Jr8/MQBp4/trcPsbh+hwKJjfWmGyJ9OQS0DLt5rHabH7mV9aSarii0YxvKId7UN09Blp23IzYpCM7X56SzNj7TTPe0jHOxxcrTfydK8dJbmpbOyODKVoqnfyZ52Gwd7HJRbjawqMrN8rOrrkV4Hjb1O9nXaKbLoWFFkZt3YEPpeu4c9HTZ2tdvIMmhYs8TC+rLIcdPjD/JeyzDvtYxgSFOyoSwz+jxZlmnosvNeyzAAG8szWV5gjhaPOtTj4N2WIdy+EGeVZ1FfZEalFNe0hZMXCIV56+gA376uftbfy6xTiyJQgiBMaq7mzF4oy/LgHL3XrGgZcPGbLa08vbc7uuyyZXl84RL1tEVCzmSvHRqIyUussBr54U310ZM9YXYEgmF+s6WFn73WHF12blU2P7p5FVkzNO+oqd/JBx98l0GXP7rsoY+t46KluQC0DLr44t/2xUSXfPrCCj7/vmpUKgXDoz4eeqeVv2zviK4/v9rK169ZRkmWAYB3mof52lMHouuLM3X8782rWTM273pb6zCf+uNOgmPFQQwaJQ98ZB2bq7KBSEf2//1pFzZ3JHZFkuBnt67hihWRnNmGThtffbyBw33HI7b+7ZJqPnNR8vv0U3u6+O9nG6N/l2cb+MWH11CTN+tD4oQZ0jHs5uO/207rkDu67Ic3reS6NUUc6LJzz9MHaJjQjv/5ggpKM/R02j389LUm/tHYH113/ZpC/sWoxqxN43fvtMbEk22qyOKb1y6nzGrk5YP9fO+l48fGmlwj371hJSuLLbx5ZJAvPbYvui7frOWnH1zN2tJMtjQN8qk/7mS8Ho5Jp+I3H13P+tJM9nTYYnKV01QK/vKps1hVnEFjj4Obf7UVhzcIgEKCP92xkbMrsmf2wxTOCDtaIxcYMw2aWX8vk1Yl7swKgjApcUk2Se3D7piOLESC7TtGPJM8Q9jXMcLPJ3SmAJoHXBztF9m8s611eJRfvnEsZtlbRwdpmsHPflf7SExHFuD7Lx3BOZbXebDbEZfB+eBbLTSO5cQe6nHy1x0dMevfODLAkbFszwPddn78SlPM+o5hD0fGMjxdHh9/2tYe7cgCjPpDvHb4eMdiR9tItCMLIMvw4JvH6LNH9tujfa6YjizAL19vZm9HbKb0ZJr6nPz41aMxy44NjnKgW2SCppID3faYjizAt587RL/TS/OAK6YjC/CbLS3s73XSOuSJ6cgCPL67i5ZBDwd7HDyyLTZn+53mIY70udjdMcLPX49t24f7XDT3u2jqd/KT12LbVI/dy6E+Jw6PnwfePMbEwq4OT5B3miLXiv+6oyMmV9kXDPPYzi4A3jwyEO3IAoRl+PnrzfgCoWQ+IkGI8Y/GPuqLzNM/cAaYdRr6RNasIAiTmIvOrAy8JEnSTkmS7jxxpSRJd0qStEOSpB0DAwNzsDmnxhsIT7JcnAhMJhCWcSf4fCaebKWSVGmrAP5gmFCCKIOZbK8ub/xr2Ub9+EPhsfeK/559wTCBsfW+YJhE9ec8/sj6UFjG4Q3ErR9vP4GwjM3tj1s/MmHZxJP348sC0W3zJmiLnkCIQCi5WjS+UBi3b2G18VRqpwuFJ0FbdXgDBILhKdtxov1JliP7mTcQIlGaiC8YIhQK40nwXG8wjD8YxuGJb7cef6Rd2j3x+4RjbFm/I/7u1fgdrUT7ypDLH3MxSBCS9UpjP6tnMZJnIoteLTqzgiBMai46s+fIsrwGuBy4W5Kk8yaulGX5AVmW18myvM5qtSZ+hQWgOFMXl49amqWnJEtE80ymyKLn6vr8mGV6jZJKa2rmzKZKWwVYkqlnU0Xs3ORso4aKGcz4Xb3EguKE2o93nFseHcZcnWfEpIudyXBRjZUKa2QIcblVz/KC2KG4+WYtlTmR9WWZem5aXxSzPk2liLafDIOW69fErgd4f21u9N8byjKRTtjGG9cVU5IdeY9KqwGDRhmz/orl+VRYk9uvy7MMXLOqIGaZTq2c1/zpVGqnC0VNrjEuj/ijm0rJM+uosBri2vGFNVZKM/WUZuspyoidh740L52STD3lVgOrTyiOk5OeRoXVSHGGLjrUfZxBo6TCaqA6x8BN64pj1qmVEtU56WQZ07hl/ZK47d9UGRkq/MENxXHrblwX2UfOr8mJW/eJc0qj84IFIVltQ6M4vAHKxo6js03MmRUEYSrKe++9d1bf4N5773WO/f/offfdlwdY77333ncSPfaBBx649847427eLgjWdC1L89IJhMI4vUEuqLbyb5fUzNmVyVRk1KrISdeSrlXR7/SxssjCf11Vx+aqBX+Cfd90D1jIbRUgTaVkzRILKqWCfoePC2qsfPOfVlCZM3Pzu63GNNaXZdEx7EGjlPj8xdVcVZ8fPTkeL9g0MuonEJa5dnUhnzyvnOLMyAmQRa+hJi+dsBzJKzy3MpsvX740uk+lqZVk6DVk6jX0Ob3U5Zu456o6zio1o1JF3kOrVFCRY6RjxEO+WctXrljK2pIMTGNZthadmhVFZjpHPOg1Kv7f+RWcX52NNT2SA1qYoWdFoZlBp4+wLHP92iJu2VCcdAEotUpBSaYevUZJn9NHfaGZ+z6wjLPmZh5iyrfThSLbmMZZFVn02r3IyHzq3ApuWb8Es15NgUXHikIzNnekHV+zqpDbzi6lOt9EjklLXUE6Hn8Ytz/ExXW5fOaiKuqLLZh1Gqpz0gEZmzvApoosvnpFLWtKMjBq1eSmp2FIUzHoGjs2Xr2MzZXZKBQKMvRqrEYtPXYvVblG7rm6jk1lWahUCrKNGgotOjpHPBRl6Pmvq+s4pyIbjVpJTrqW2nwTxwZdWI1p3PeBZWyusqJRKbAa01hTkkHr4CgGjYovX7aUi+vy0J1wMWcWTNtOx9w7mxshzJzHd3URCIVZWzI3BTD9oTCvHxng45vLZvutkm2rgiAsILOaMytJkgFQyLLsHPv3y8DXZVl+IdHjUyETcdDpYcgVINuoJit9ZivDLmaH+xyYtWryzCnxmS2a/M5wWMbmCWBMU6JRzc5Jq9sfJBAMY9YnLgTi8gWwuQMUmLUoFPGDQXyBEF12DzmGNIxjndATHel1YkyTKMhIfMezddCFUiFFO8on6hlx4wuFKc1O/PxBpxe7J0BJpi7aUT5ZXSMe0rVKTLrZL4gyZtG004XC4w/hC4awJGjLAw4PI54AZZk61OrYdjrs8jLg8pNn0mDWa2PWef0huh0erIY00k9o336/n9YRL2atilxz/GiAYwMujBolOQmOm30OD2qFgswEBd2cY8Pz07Xx+9OoL0gwHMa8gNrpGDHeOUXc8sC7bKrIYn3p3HRmXd4g//roHhruvXS230rkzApCCprt8UW5wBNjsQAq4JHJOrKpIjtdR7boxJ60mlxR2XU+KBTSrFeb1GtUMMVbGNPUGNMSd1Ihcge2fJJO5rjqvKnvlE7WSR2XnzH1sOHsdC3Z6dopHzOdwgxxXEh1Oo1y0juVVpMOqynxd5xp1JJpTNx+tJrJ27dGo6E6d/Kdp3yKKRm5k2wLJO7EjhPDioXT4fQG2Nth41Pnlc/ZexrSlHj8kXnoWvWsjyQQBCHFzOqvmizLx4CVs/kec8nlDXCg20GXzUOBRUddvik6nFFIbMDp4UC3k9ZBNxa9mnKrgfoikcGZKo4NuGjscSBJEnX5JkpPco7UkMvHwW4Hgy4fpdkG6vJNMTm0o74gB7sddIy4yTNrWZZvxqw/uX2qqd/JoV4nKoVEbb4pGuszrnPEzcFuB95AiJq89JOOzJluv/cHwxzqddA84CJDr6FubPipkFoae+w09jhxeYOUWQ2sLTaj10Y6mkMuHwe6HQyNtePafFNSJ9WjviCNPQ7ah93kmrQsKzAlvOt7so70OTnU4yRNraAu30Rx5vGLNS0DrmjF8Np805zNaxTODG8eGaQ2P31OO5WSJJGh1zA06qdwhrPSBUFIfeISbZJ8gRAPvd3KD18+El322Ysq+ecLK8WVwim8fjiSlzg+mn1zZRZfvqyGFUVirvFCd6Dbzq0PbotWT802avjTHWdRM81d0nEjo36+/veDPDUh0uoHN67k+rWRgjTBUJhHtrXzzeeOZ7TecU4Z/3JxddJ3jxo6bdz64Dacvkj113yzlj/eviE6N7h1cJTbf7ed5sFRALRqBY/csZE1Sc718gdD/O6dVr7/0vH9/jMXVXL3hP3+tcP9/L+Hd0bb+AU1Vr53w0qs6TOT5yvMvoPddv7zyf3sarcBoFRI/OjmVVy9sgCb2883njnIk3sSt+PJhMIyj1jOHwsAACAASURBVO7s4N6nD0aXffTsEr546VKM2lP/6d3dPsKHfr0Ntz9SDbk0S89DH1tPudXIwR47H3pwGyNjcVRZBg0P37GR2nwxMkaYGS8e6GVl8dxfkLbo1Qw4faIzKwhCHJEzm6SWoVF+9I8jMct+8loTzQMiM3Uy+7tsfPeFwzHxK283DdE84J78ScKC8bednTExIIMuP8819CT9/MN9zpiOLMB9fz9Ax3Dk+28ZHOW7Lx6KWf/rLS00JblPhcMyf3y3LdqRhUge56uHjud+bmsZinZkIRIX9JNXm5LO1jw2MBpzAQvgpxP2+36Hl/96an9MG3/98AAHu+1Jvb6wMDT2OKMdWYh0RL/74iFaBlwc6nXGdGQhth1Ppm1olG8/F9u+f7+1jaNjOcmnwhcM8cvXm6MdWYDWITdbm4cAeHJ3d7QjCzA06ufvJ+yDgnCqAqEwrx/uZ+08FL4069QMiorGgiAkkFRnVpIkhSRJirF/ayRJWiNJ0tzM/F8gHJ5gXGagLJMwD1CI8AZCDLjif3ycCbJDhYUlHJY52O2IW364L/kTcbsnPtfS4Q3i9kf2GZcvmDDP1e5Orn0EQmEO9cZvT3P/8c5w54gnfv3AaMKMz0Qc3qn3e7c/lDAywpbkf4OwMCTKM+62eXH5g5O241Hf1Md+pzeYMG84UU5ssnyBcMKLPe1jHevGnvh9trHn1DvPgjDR9pZh8szaaPzaXDLp1AnPJwRBEKbtzEqSdC3QA3RJknQN8BbwPWCfJElXz/L2LRjFmTpyThg2mGXQsCRT5MxOJs+k5dzK2IgSpUKiNEvM4VroFAopmk850VUnZGNOpTTLiEYZe4hZuySD/LHKrIUZOoozY4eMmbSqpLOb09TKuDxOgPedkDN7ohvXFSU9b3FJpp5cU/x+P77duSYtF094PwCFNHXhHmHhKcs2xOURX1KXyxKLjrLsxO24YJrhjkUZumim8jhjmipuTvfJMOnUCdv82WOZ0v+0ujBu3bWrC+KWCcKpeK6hZ97iCE1aFQPizqwgCAkkc2f2HiJFnDYBfwRuk2X5fcDmsXVnhHyzjgdvW8fKYjMA9UUmfv3RdaKC6RSKMg187v1VXFBjRZKg0KLjf25axfICcaKfCs6vtvIvF1ehVSswaJR8+fKlbKrMSvr51blGHvrYekqz9dHX+/b1K6LFk3LStfziQ2tZVxI5OarNT+ehj60/qZP9i+tyufvCCtJUCtLTVPzXVbUxHdjVxRncf90KLHo1aqXExzeVct3qqec6TpRn1vLAbetYNTZHbHy/LxqrjqzTKPn3y5dy2bI8JAkKzFoevG0dtfkzl+crzL7VxRa+d8NK8kxaFBJcsiyXO88rx2xIoyonvh1/67oV0xb/yzKm8ZMPrmbjWHusyUvntx9bf9oFma5eWcAd55ShUSow69R885+WR4d9nldt5QuXVKNTK9FrlHzp0hrOqZyTzGNhkQuHZV440MvGOYrjOZFZp6bf4Z2X9xYEYWGbNmdWkqTdsiyvHvv3flmWl09Yt0uW5TUztTGpkIlo9wSwuf1Y9Oq5zOlLad22UfocfrRqZaoUIhH5nWPCYZluuwdJkigwa5FOvH2VhCGXD5cvSHZ6GgZNfOEbpzfA8Kgfs059SpVeQ2GZHpsHhUKa9G5Zr91LIBQm36xFpTz5UgHT7ffeQJA+hw+DRkX23BV+Eu10hh3tc+D2hyjJ0mPRx36P07Xjybi8AYZG/Zh0ajJmoJIxRIqn9di9qBQS+Se0eVmW6bJ5kIACi+6U9tkZJnJmF4Ftx4b498f28e3r6ufl/bc2D3Koz8kDH1k3m28z7zuLIAgnL6lfZEmSFLIsh4FPTFimZMp0ycXJrFNjFnE8J6XAYqDAIoYWpyKFQorehTxVWca0KedYpWvVU+ZiTkepkCiaZrh/nvn0onKm2++1ahUlWaI4fKqrmiIPe7p2PBmjVo3xNNp3IiqlIiaOZyJJOv19VhBO9OSeLs4qT35kzkwTBaAEQZhMMmdfdxLptHplWX5vwvJi4P5Z2aoFyu72c6DbQbfdQ4FZx7ICE+YZutK+mL3XMkTHsBuLXkNJpo7KKU4YhcWlY8hF06CbIZeP4gw99fkmdBM6hU63j/3dTrrsHnLTtdTkGckxLayh+/5gmOYBFz12D3kmHZU5BjQqEce12PQ5vDT2OLB7ApRnG1gxIQ/b7Q/S1O9iyOUfmwtrRKGI3MTpt7s53D9Kn8NLvllHbZ6RTOPp5wy3DY3SOjiKQauiKic95mJKt81D04CLNKWCqtx0Mg3id0iYPf5gmOcbernvA8vmbRvMeg2DrvhibIIgCNN2ZmVZ3g4gSVKOLMv9E5a3SpJ0xgQpev0hntjTzTeeOUgoLKOQ4KtX1HLr+iL0WnEiMZmXD/byuT/viUZJ3LK+mI9tKmFpvnmet0yYba0DTh7e1sGvt7QAkKZS8IMbV3LVykhBmmAwyDMNfXztqf0EwzKSBF+4uIZb1heSlb4wOrShsMzf93bzpcf2Rff7b/3TCq5fW4T6FIYrCwtTx/AoP3jpSDSCJz1Nxc8+tIbzqq24fEEefPMY//vKUSDSjn/1kbVcUJPDkMvLE3t6+M4LhwjLkVEC915dxw2rCmMu2pysvR0j3PbQ9mjl42tXFfAfV9ZiTddyqMfBx3+3nR57ZP7gOZVZfOf6egrF3Vhhlrx2uJ/CDB05ptO/SHOqzDo1Q6PizqwgCPFO5mzsLUmSbhr/Q5KkfwOemPlNWpgauux8+7lGQmM5HWEZvvPCIRq6RezBZA71OPjvZxtjMhH/vL2D1kGRM3smaBpwRzuyAL5gmPueOcj+zkie555OB9949iDBsX1KluEHLx+msXfhZDe3Do7y1ScaYvb7rz21n5aB0WmeKaSShi5HTJas0xfk/ucP0evwcKTPGe3IQqQdf/Fv++i1ezjc6+K7Lx6OxjeFwjLffK6R/b3xETnJGvUG+fZzh2IifJ7c001Dl51gKMxv326NdmQBtjQNsa1l+JTfTxCm89ftHWyumN9CYgaNEn8wjDfJWDVBEM4cJ9OZvQD4iCRJj0qS9CZQDWyYla1agAZcvrjMwEBITpgxKUQ4fQHahuI7ruIzOzMk+p4HnD5sYyfpw6P+mAsdEOksLqT2MTjJfj8o7hAsKomqpDb2OrCNBhKuG3D6GHEH6Hf6ohc6xnkD4dMaDunwBtjbaY9b3mv34vaHeK81vuN6MEG+rCDMhF67l20tw9H4p/kiSRIWvYZBkTUrCMIJku7MyrLcA7wAnA2UAr+XZXnh3EKZZYUWLSZt7Khsg0ZJ0TRZg2eyTIOGFYXx82OLMsVndiYoytDFZXeWZRuwjlX7zTVp4+b6pakUFC6gfSrfHL/f6zXKaFausDgkKqa0sSyTHFMaRRn6uHZcYTWQm55GgUWLVh37M2rRq0+r4FimQcOFNda45SVZBtK1Ki5blhu3bu08ZX8Ki9//vdfO2RVZaNXzXyfAoleLrFlBEOIk3ZmVJOkfwEZgOXAl8CNJkr4/Wxu20KwszuD+6+ujJ98WvZr7r1tBfZEoZjSZCms6X7m8lpKsyImiVq3gK5cvpeI0cxaF1FCbl859H1iGXhM5CSowa7nvA8uoyYvsMyuLLdx/3QqyjZF9yqRT8e3rVrCyYOHMp16SZeBnt66J2e9/dusaSrPE/MTFZGWRhX+9uIo0VeQnscJq5IuX1pBpSKM618gPblwZ046/f+NKMo1prC4ycf919dHiTFkGDfdft4LVp9G5TFMr+fzF1SwriOwnaqXEly6tYUWhGUmSuHn9EjaN3SVTSPCJzaWsn6fsT2Fx8wfDPPxuGxfXxl9AmQ9mrVoUgRIEIc60ObPRB0rStbIsPznhbxXwFVmWvzFTG5MKmYj7u+z0O7xY09Niql0KkzvYbafH7iVdq2Z5ngG9bsHXDRP5nTMkEAiwp9OJ3RMg36JlWYKOakOnjT6Hjyyj5rQ6AbOpa8TNoMtPllGzkGJPRDudQf5gmIM9dlzeICVZeoozj190k2WZtiF3pB2btXGFcHa3jzDk8mNNT2Nl8cz8Loy4/XQOu9FpVJRm6WPykR2eAO3DbtRKBaXZetIWdnVtkTOboh7d0cHD77bx5ctr53tTAHjwrWNcXJfLBzcsma23EDmzgpCCkg5GnNiRHfs7CEQ7spIkbZVl+ewZ3LYFaXmhGQoXzp2jVFBXYKZuAd1tE+aOWq1mfdnUd41WFFlYMUfbc6oKM/SiWuwip1EpWFWc+GKKJEmUTjGiZDYuwmToNWRMEv1m0qkjv0WCMEtCYZmfv948mx3Hk2bWqkTWrCAIcZLuzCZh0klCkiQpgR1AlyzLV83ge8653e0jDDp9ZKenLdi7SAvNgW47PTYvJq2Kmtx0zCITcc702D302b1k6DWUzMLwbn8wRMugG18wxJJMPZZTyF1u6LDR6/CSna5h9ZL4ju+Qy0fniAdDWvwdKoBum5umfhdqpYKqHAPZsxDr0zniZsDpw5qelvDObNvQKG1DbkxaFXUFZjQqEduTaoKhMK1DbkZ9QYoydGQZj48gCYfD7O92MDLqpzhTT7nVGPPcvR0jDDh95Jq1rCiMvTPbY/PQ5/CSYdBQkhW7Dx7sttNl82DRqVmel45OJ46NwsLw/P4eNEoFywsWzlQqk069oAoECoKwMMxkZ3aqIUKfAxqBhXNUPAXP7OvmP5/cj80dwKxT8/VrlnHFshzU6lPPE1zs3m4a5MuP76Nj2EOaSsHn31/FpXV5lOcYp3+ycFreaxni7kd2M+D0YdAouf/6ei5bnjdj+agjo34efOsYv3rzGKGwzMpiMz+4cRWVJ/Hdvri/l68+0cDQqJ/0NBX3fGAZVyzLQz9WdKmxx8GnH9lF88AoGqWCf7u0mg9tKME4tn5P+wjfffEw7zQPIUlwVX0Bd51fPqMjAd443M/n/rInut//z80rubAmB2msKtC2Y0N88W/7aB92k6ZS8Ln3VfHBDcVkGBb8cHphjMsb5JH32vn+i4fxh8JUWA389NY11OabcHkCPLa7i/ufP4QnECLfrOV7N9RzTpUVvz/Es/t7uOfpAzi8QTINGr557XIuX5EPwLvHhvj0I7sYdPkxpqn47g31XFKXi0qpYMvRAb78eAOdI5Fj479dUs1ldbksyRbHRmF+BUNhfvDSEW5aVxw9zi0EZp2aw30iDlEQhFizfvtAkqQiIgWjfj3b7zWbdrYN85XHGrC5I7Eidk+ArzzewJ4ucWCdTFOfk28910jHsAeI5DN+54XDtAyJjM7Z1mf38pn/2x2t/DjqD/H5v+yheWDmCpDv6bDx89ebo9EkezvsPPjmMQInRNlMZm/HCF9+fB9Do5GCHk5fkK8+3sC+LvvYNgf51rONNI9luvpDYb793CEOdB+PLXnhQC/vNA8BkZzav+/tZnuC6JJT1T40yqcf2R2z39/9p920jkVO9Tu8fPPZRtqHI3/7gmG+++LhhNEqwsJ1oNvOt55rxB+KtN3mgVG+9Wwjo74gDd0O7v37ATxj+ZY9di//8eR+OoZHaei285UnGnB4g0AkburfH9/Hvg4b3TYPn3lkd7RgjcsX5HN/3s2xwVGO9jn4xjONdI4cPzZ+67lDNIn8YmEBeHxXF3qNkpVFC2sou1mvEdWMBUGIM5Od2cku3/0I+BKQ3BnuAtVr9+L0BWOWuf0heuyeedqihW/E4+dAd3z+YdeI+MxmW5/TS58j9kc/FJbpnsHP/lBv/IWcVw71YfMkV22yzxHJ6pzIHwrTPbZPjYz62dI8GPe8jpFIx3HE7WNLU/z67S0jSb1/MnoS7PeewPH9vt/pi3a+Y7ZxOD5fWVi4xtvURFuaBxke9dMx4ubEOoltQ2667V46bR68gdifNocnSLfdQ7/Dy8AJmZiBkEzXiIfh0UDCO0xdNnFsFOaXxx/i+y8dXnB3ZSFyZ1ZUMxYE4USn1JmVJClRRZePJHjcVUC/LMs7p3itOyVJ2iFJ0o6BgYFT2Zw5kW1Mi8Y2jNMoFeQYxVDCyaSnqRNGmJxYCTRVpEpbhUjxmPG4kHGSBFbTzLXXsuz473ZdaSYmbXLD7rMMGgya2CqsCglyxnJo07Vq6hMUuckdaz+mNBUrE1QUX54g2/hUJdrv1UqJ7LH93qJXU2GNn4ucO49tPJXa6UKRl+D7qi80Y9KpyUuwz+Skp5Ft1JBr0qJSxJ7w69RKrMY0Mo1pcRnFkgQ5pjTStSqKE+Rtj2cwC8J8+fVbx6jIMVKdmz7fmxLHrFMzPCo6s4IgxJq2MytJ0mZJkholSTogSdJGSZJeBrZLktQhSVK0erEsy/sTPH0z8AFJklqBPwMXSZL08MQHyLL8gCzL62RZXme1xgfFLxTLCkz8x5W10RMXpULiy5cvpS5PzG+azNL8yGc2scPyoY1LKElwEpcKUqWtAhRn6vn+jSvRjM2PlST46hW1JzWfdTqrl1i4fEVe9G+rMY3PXFhJmjq5mJCVRSa+dlUdamVkn1JI8MVLa1iWH+mMmnVq7rl6GSbd8Q7BhzcuieZvKpVKrl1VEM0xBlhVbOassQzOmVCWbeD+61bE7Pf/fe0KyseKaRVl6PnPq+owph3fxls3LGHFPFaaTaV2ulAsKzDz4bNKon+btCruuXoZZp2aZQVm/t955dF1WrWCez+wjAprOssL0vnSZTWM92dVComvXVXLqmIzSzL1fPeG+ug+qJDgv66qo9JqpK7AzNeurItm1wLcdnZJtF0Jwnzod3p58K1j3LyueL43JSGDRok/GMY7NuRfEAQBksiZlSTpPeB2wAj8HbhWluUtkiStAX4iy/LmpN5Iki4AvjBVNeOFnok47PLS2Oui1+4lz6Rlab6RLGNq3mWcS9tbh+kYdmPRqynO0FO1AK/4nmBR5HeGwzLNAy66bB6sxjQqc4xJdzSTZfP4aepz4QmEKMs2nHQGq9PtY3+Pky6bh9x0LXX5RrJOqEY8Xik4XauiKseI8YQ7v4d6HBztd6FSSlRZjVTOcPsKhMI0D0T2+1yTlgqrMa5acUOnjZahUTL0GmrzTGTPzR22RdFOFwqXN8DRfhfOsZzZiZWHbaM+DvY4GXD5KMnUU19kRqGItAGH28/+bge9Di8FFh31BenotZGqxKGwzLEp9sHtLUO0j3jI1KspyzZQujiLP4mc2RTxxUf34g2GuHVDyfQPnief/fNunvjnTbOV972wxlULgpCUZDqzu2VZXj3270ZZlmsnrNsly/KapN5oEXRmhTOG6CQIqUC0UyEViM5sCjjc6+SWB7byvRtWYkibyaCLmXXP0/v5zvX1sxWNKDqzgpCCkjliTbwF8ZUT1iUdiifL8uvA68k+Xlg83P4g/Q4f+jQlOeniTraw8Aw4vYz6QuSY0tBrFu6JnCBMJxyW6bZ5QIICsw6FQpyfC9P75rONXL2yYEF3ZAEsOrWoaCwIQoxkjlpfkyRJL8uyW5blJ8cXSpJUAfxh9jZNWAya+l1867mDvHpogHyzlm9cu5wLqq2oZijrVBBORygs88aRfv7zif10271cVGPlK1fWUpWz4IfCC0KcQaePh7e18cs3mgH45wsquXXjkmjBMkFI5N1jQxzpc3LHuWXzvSnTMomKxoIgnGDaHoUsy0/LshyXWyDLcjORok6CkJDbH+Rbz0Y6shCJObnzDzsSRroIwnw43Ovgk3/YSbfdC8Crhwe47+kDjJ4QxyMIqeDNowP86B9H8QbCeANhfvjyEd4+Gh9fJQjjZFnmOy8c4ro1hahT4CJzulbNgNM735shCMICktSRS5KksyVJukGSpJyxv+slSXoEeHtWt05IaX12L68ejo0GCctwbNA1T1skCLGODY4SCsdO1dvSNESvQ5wsCannid1dccue3NM9D1sipIq3jg4y6PSxuSJ7vjclKWadOi5DXRCEM1sy0TzfAx4CrgeelSTpv4GXgG1A1exunpDK9GmqaGboRBm6pKdaC8KssujiM3GzjfH5t4KQCuoK4jOW6wrEkHkhMVmW+cFLh7lmVWHKzK226NT0izuzgiBMkMyd2SuB1bIsfxC4BPg8cJYsy/8ry7I4ogiTyjVp+cY1y5Em/EZeXJdLbX78CZcgzIfafBOXLz+elStJ8PVrlpNnTs0sZOHMdu2qQjINxy8WZhs1XF1fMI9bJCxkW5uHGBz1c3b5zGVzzzazXhSAEgQhVjIFoLzjnVZZlkckSToqy3Lr7G6WsFhcVJvD03dv5tjAKBaDmmX55rnK4BSEaWUZ0/jGtcv54IYljIz6KbMaWJonLrYIqak238Tjd22isdeBBCzNN1E6IS9XECb68atHubo+P2XuygJYdBoGRAEoQRAmSKYzWy5J0tMT/i4b+1sCZFmWPzA7myYsBmqlghVFFlYUWeZ7UwQhoWxjGudVW+d7MwRhRpRmGyjNFh1YYWoNnXaa+13cfUHlfG/KSbHo1Qy5fMiyjCSlTidcEITZk0xn9pqx/zcQmSP7D+AwcEYOMXZ6AvQ7veSmazEmmG8nxAuFQrQNuzHr1GQZRc7sQjM86kerVsxbvqovEKTf4SfLqEE/Scbh8KgPrVo56Tba3X4UCol07fzsk7IsMzzqR69RoRPzbeeE3e1HIUmkz+Bx2O0P4g2EyDSc3OgRfzCE0xvEpFOfVEXY02k33TY3GqWCbJHdLZyCX7zexGXL81MuJk+rVqKQJJy+IKZ5Ot4LgrCwJHP2uhX4LnAb0Dq2LBf4iSzL90uStEqW5T2ztH0LyvaWYX6/tZXtrcOsXZLBxzaXsqEsdeaazIe9HSM8ububFw70UpKl567zKzi/Jme+N0sAeu1entjdyZ+2tZNv1vGvF1exsSxrToec7ekY4Q/vtPFO8xD1RWbuPK+cdaWZ0fXdNg9/29nJn99rpyRLz79cXM360szoFXmb289LB/r4xRvNaFUKPvf+Ks6vtqKbw455x4ibv27v4LGdnVRYjXz+4irWlmRO/0ThlNjcfl4+2MfPX5+571yWZba3jvA/Lx+mbcjNLRuWcMPaIgos08+dPtTj4JdvNLP12BAX1ORwxzllVOVOX3SpY9jNI++18+TuLmry0vnc+6pYvSRj2ue1D4/yfEMvj7zXjkGj4q4LKriw2iourgpJ6xxxs6VpkP+5edV8b8opyTRoGHD6RGdWEAQAJFmWp36AJP0Y0AP/Isuyc2yZCfg+EAIuk2V5RpK2161bJ+/YsWMmXmrGNfU7+dQfd9I8MBpdVpKl58GPrKVazLFLyOkJ8B9P7ufpvcejIXRqJb//xPqFfhFg2t7cQm6ryZBlmf/9x1F+9MrR6DKVQuLxf95E/RwNCe+2ufnkH3ZyoNsRXZZn0vLH2zdQlZtOKCxz//OHePCtY9H1GqWCJ+/eRF2BGYCn93Tx2T/HXkt7+PYNnFM1N8OGA8Ew9zx9gEfea48u06oVPHX3OdTkzXoV2UXfThP5+94uPvN/M/udH+y2c+3P3sEfCkeXffLccr58+VKUU1zc6XV4uOEXW+kc8USX1eWbePiODVPe3fUFQ3zlsQYenxClo9coeeruzdN2hH+z5RjfeKYxdtlH1/G+2twpnzePkr06NvXJiDBjvv73A/Q7fXxoY8l8b8op+e9nD/KfV9ZxdsWMn0eIccuCkIKSGV9yBfDJ8Y4sgCzLDuAu4Bbgg7O0bQvKsYHRmI4sQNuQO26ZcFzzgItnG3pilnkCIY72i5zZ+dbv9PHQOy0xy4JhmcYexyTPmHnNA6MxHVmAXoc32j567R7+sLU1Zr0/FOZwb+RQ5A+G+P0J6wFe2N87C1ubWLfdw192dMQs8wbCHOlzTvIM4XT4gyF+/05b3PLnTvM7P9zniunIAvxhayu9dk/iJ4xpGXDHdGQBDvY4aB1yT/m87hEPT+6JzYR1+0M0TXNsHHR6+cv2jrjlW5oGp3yeIIxzegP8bWcnly7Lm/7BC5RFr2bAJSoaC4IQkUxnNiwnuH0ry3IIGJBl+d2Z36yFR6dOPJ9JzI+bnFqpSJjXOdlnKcwdtVLCnGBY4lzOm9WqFCS66TXePtQqBaYE2zi+zykkiZwE8wWtc1gtW6NUYEwwz1cvjguzIvKdx3+/VuPpfec6dfxPoUmnRq2a+idSm+B5kgRa1dTfv1qVeI66dppjo0alIEMfv09kiCHGQpIe3dHB8kIz2ae5z8wnk1ZNv+OMLNsiCEICyXRmD0qSdNuJCyVJ+jDQmODxi1JNXjrXrorN67tyRT41OSKQfjLLCs18+qLYSonVOUZqkphPJsyuTEMaX72iNmZZkUXLikLznG1Dbb6JW9YXxyy7sMZKbX6kfeSka/mPE7axPNvAsrEhxiqlgk+cU4ZaebxHbExT8f66uRtumW/R8ZXLl8YsW5pnFFnKsyTRd27QKLl42el958sKzJSfUAH4P66oTXixZKLKHCPXnPC7cNtZJZRb9VM+ryhDz5cuq4lZtrzQFG37kzHpNNxxbkXM0GezTs2myuwpnycIAKGwzG+2tKb0XVmItPl+h7gzKwhCRDJzZguBxwEPsHNs8TpAB/yTLMtdkz33ZC30+V2Hehzs67TT1O+kIsdIfZGZ2vy5O/lPRV0jbnZ32GjotJNv0bKqyMKqJIqczLMzYi6iNxBib4eNnW0jZBk1rC/NpNxqnNNtaB8aZWfbCI29TiqsBtYuyaBywsUOtz/I3g4bu9ps5JjSWF+aGRM7Eg7LNHTZ2d46jFqpYH1pRnQ+7Vxx+ca2sX2EAouOdSUZlMxNtucZ0U5PdOJ3vqEsc0YuHrQOjrKjbZg+u4/VJRZWFVuSGqnQ5/Cyq22EI31OagtMrFmSkdRdL6cnwJ4OG3s6bBRl6FhXmkFx5vTtxucPsq11hJ2tw+jSVKwvzVjoBcfEnNkF4qUDvXz/pcPce/WylI61efPIAJ0jbn5y65qZfunU/VAE4Qw2bWc2giAfMQAAIABJREFU+kBJughYNvbnQVmWX5npjVmMJ15CSjojOwlCyhHtVEgFojO7QNz0q61sKM1kc4rfyd/XaeOVQ/389VNnz/RLi86sIKSgpCfIybL8KvDqyby4JEla4E0gbey9/ibL8j0ntYWCIAiCIAjCKTvY7aC538VnTpj6k4oy9BoxZ1YQhKjZrvbiAy6SZdklSZIa2CJJ0vOpWjRqV9swbxwZZFvLEOtLM7mgxrrQh3fNuwGnlzeODPLk7i6WF5q4dlUhS8V8wjnRbfPwyqE+nm/o5azyLK5ckU9FzswOI97TYePRHR1027zcsr6YsyuyYoo2Helz8szebna0jfCBlQVcUJNDnnnqeYgTDY36ePvoII/u7GRpXjrXri6MzpkF8ASCbG8Z4ZFt7eg1Sm7ZsIQ1SyyolMmUAxDONHvaR/jrjg56Hb5Iey3PIn2svb7TPMgze3toGxrl0uV5bKrIonIR1UQIh2V2t4/w5+0d2Dx+bt1QwoayTAwJCpgJi8+v3zrGxXW5qBSpf2zM0GsYdPnnezMEQVggZvVXbKwK8njWgHrsfyk5lKhlwMn9zx/ivdYRAN49NsxbRwf4wY0rqVhEJzwzKRSW+cPWNn7yahMQiY/4285OHrtr01zNKTxjefwhvvfCIZ7YE8n4fad5iKf3dPOnOzaSexKdyakc6LZzywNb8QYikSavHe7nhzet5Lo1RQB0jXj4xG/fo9PmjW7DbWeX8J9X1qKZptrruMd2dvGt5yJ15t46Oshfd3Ty2F1nRzsZ7zYP8/HfbY8+/qm93fz1U2eJi0xCnP1ddm5+4F18wUh7ffVQP/97yyquWVXIjtZh7np4F3ZPAIC3m4f47EWV/OslNVO9ZErZ12XjlgffJRCK/AS/fLCfBz6ylktSvBiQML1+p5eXDvbxw5tWzvemzAhDmhJ/MIzHHxKJEoIgJFXN+LRIkqSUJGkP0A+8LMvyttl+z9lwbNAd7ciO29NhFzmzU+gacfPAm8dilg26/BzqERmcs61teDTakR3XNODi6MDMZfzuarNFO7LjfvzKUWzuyBXzI33OaEd23J+2tdMxPHV257gem4efvHI0ZpndE6BxrP0EQmF+syU2KzcUluc0Z1ZIHdtbh6Md2XE/fqUJu8fP4T5ntCM77rdvt9LYY5/LTZxVrx8eiHZkx/3ijWY8gdA8bZEwV/74ThubKrJI1y6OCCdJksg0qOl3iqHGgiDMQWdWluWQLMurgCJggyRJyyeulyTpTkmSdkiStGNgYGC2N2fGpXBBwFk3abXEFP3MUq2tJvr4Z/KjT/RakiRNWJ54EEbS+4w0/WMn24YzWaq10/mkUIA01V4hL562lHBfmfOtEOaaxx/i4W1tKR/Hc6IMQxp9Ip5HEATmoDM7TpZlG/AacNkJyx+QZXmdLMvrrFbrXG3OSSu3GthYFjt0cc0SS1w2oXBcoUXHXedXxCyzpqexNC81h2WnSlsFKMnUc93qwphlNblGqmZwzuyaEgs6dewQr8++rwqzXgNAda6J4gxdzPrbzi6hOGPqDM5x+WYdn31fVcwyi15N3dica7VSwe3nlsWsVyokLltkJ20nK5Xa6VxaX5qJVh37k/fZi6ow6dTU5KZj0cfetfrEOWXUFiye+f3nV1tj8nkB7rqgMm4fFhaXv+3soCo3nQKLbvoHp5AMvZpeUQRKEAROIprnlF5ckqxAQJZlmyRJOuAl4DuyLD+T6PELPUZiV9swbzcNRQtAnVuVzRoxN29Kg04fW5oGeWpvN8sLTFxVX0DNwu/MLorIk26bhzcOD/DigV42lmdx6bLcGc+R3ddp44ndXXSPeLhxXTEbyzKjBXUAmvqdPN/Qy862Ea6sz+e86mxyTcmfVA2P+ninaYgndndRnZvO1SvzY3JkvYEQO1qH+ev2DvRpKm5YW8TqJRkoFWfEPadF0U7n0t4OG0/s6qLX6eXGNUVsLM/CqI2UjtjaPMjz+3sjBaCW5bGxLHNR1UMIh2X2dNh4bFcnNneAm9cXs640I6ks3dMkonnmSSgsc/73XuP2c8pYmrd4LswA/PHdNlYXW/jkeeUz+bJnxA+HICw2s92ZrQd+DyiJ3AX+qyzLX5/s8eLES1ggRCdBSAWinQqpQHRm58mz+3r4yatHuefqZfO9KTPumX3daFSKmf5vE51ZQUhBs13NeB+wejbfQxAEQRAEQThOlmV+9loTV67In+9NmRUZeg1N/TNX0FAQhNQlAuZOwsFuO/u67BzqcVCTZ2JVkWVRzamaDU5PgN0dI+xoHaEoU8/GskwRyzNHhkd97GqzsbfTRmWOkfWlmSc1byocDrP12DDbWoZQShIby7M4qzxrRrdx0OVjV9sIDV12anLTWVuaQb55cc3tEubWgS4777YMEQjKnFWeSX2RBUUSw85bBlxsaxmmx+5lQ1kmq4st6JPIYO21e9jZNkJjj5PlhSbWlmRgTT+9+CtvIMjudhvvtQyTbUzjrIosKmZ4ioCwuG1pGsTpC7CmJGO+N2VWZBo09NrFnFlBEERnNml9dg+/fOMYT+89Hndy2bI8/uvqWgosyRW0ORM9uaeLrz11IPr30jwjD31sw6IrRrHQBIJhfv1WCz9/vTm67NzKbH50yyqyjGlJvcbbzUPc/rsd+EOROBOtupmHPrqeTZXZM7KNvkCIn73axG/faY0uu7guh+/dsBLLWBEpQTgZDZ02bvrVu9G4GZVC4s93nsW60qlrG3QMu/n477bTOvT/2bvv6DauK3/g30EjQIAVhb13iios6rJsWS5yi7uduMmxHTu9bJJNfsnuZjebxNlNNptkU+zYjrsd927LluWiXkhRVGHvHUQnAIKo8/sDFMghQBKkCAIg7+ccniO9QXkg3wzmzrx375ivbWrN5JmM2pz4z/ea8N7pIV/bXZuy8dOryyC5gLWo+5o0+MYLJ33/T0sQ44WvbESeggJaEpw/7mvDtavTwVum2d2TpSKoqTQPIQRLmM042rWoLZxAFgD2nBtG6zBNc5nJgNGG/97TwmlrHragaWg0TD1aObr1Vjw6rcbvgXbtvKZlvXSizxfIAsC404N3Tw/O8oz56dJa8dSRbk7b3sYRdNDUMbJAH5wd5tRNdXlYPHWoGx7P7Msxzw2aOIEsAPzq/SZo5jhZ7tBYOIEs4K2l3KUdm+EZc9NZ7Hj4gyZO25BpHGcH6LhJglPbrUef3oYthYs7kyaSJMWKoLXY59y3CSHLHwWzQRqfobA8FZyfmdPlwViA3w/9zkLP4fLAHeBLfqZxPJ3H44HB6vBr11udF9y38+wuDwLlnxt3efwbCQmCPsCY1Vjt8MyR6NDm9B9z5nEXHHOMxfEAz2NZYNy18GOcy+OBedzl1z7m8G8jJJA/fNyGa9ekQcBbvqd4IgEPUpEAWgvVmiVkpVu+R7pFVqiSonBajc5ceSwKVbT+cybpiWLcVpPFaYsV8VGSsnzKXUSq7ORYbCngXpVXymJQEGSdWR6Ph5ur/adYXrt28ZKJ5MhjUZmVyGnLSBQjX0n7FFmYa9b4j8/dm3Mh4M/+VVeaGocYAfcx927JReoc67fzlVJkJ3MfszojHrkXkBdAFSfG/dtyOW1CPuOrr0zIbBr6jGgeHsX24uVfY1oRF4NBWjdLyIpHa2aDlK+Mw8M3rsYLx3pwoseAquwk3LUpB0UpdIIxE5GAj2/sKEBKfAxeO9mPYlUcvnlpIYoomA25OLEQv7yxAs8f7cWec8OoyknCQ9vzkZkU/PrubQVyPHzjajx5uAt8HoP7t+VhyyImgEqMFeG3t67FM0e7sa9pBJvykvHARfmUAIosWE1OEv52dzX+uK8NdpcHX99RiG1BTLUsTY3D8w9sxB/3taFbN4bb12fhxsqMOesVp8SL8dg96/H4gU4c6dRhR4kSu7fkIlm68DXfDMPg9posSEQCPHe0BxmJEnx7ZxFWTamvTMhMfv9xK65Zkw7hHBdwlgO5VIQhow3rpl0UJYSsLCGtMztf0VATcczuxLDZDlVcDGQxwnB3J2oYrA7EiviIEfLD3ZVgLJv6nR4PC6PNCVkMHyLBwn73mtFxMAyguMAMrTNxe1iM2pyQxQggFCz/E7BFtGzG6WKz2p3weIA4yfyO0eNON2xON5LmmYDM6fZODY4XC+a8CzwfxjEHxAI+xKKoOG7OhOrMLpGzAybs/vtx/O62dRCtgGPp00e6sT43Gfdvy1usl1ye2bIIWebozuw8xcYIkU9B7LwlXcCdCrJwPB5zQXeJAEAZH5og9jw+j6HxQRaVdIHHaLGQD/ECLrgJ+bwL3s8CoazeZD5+t7cV165NWxGBLAAkx4owaLSFuxuEkDCjYHYezDYnGodGMWC0IT1BgvL0OMRL6GRjNla7E6d6TejUWpAYK0JFRgLyFLQmknhZ7C40Do6i3zCG1HgxytPj530C36Y2o2XYDP7EusLFrmNM+z3RWuxoHByF1mJHrkKK8rR4X9BrsjlwbnAUw6ZxZCRKsCo9ATLxhX21ejwsmoZH0aa2IE4sQHl6PE2/J7M6O2BCQ58RuzfnhrsrS0YuE6FNTdnvCVnpKJgNkt3pxpOHu/G7va2+tm9dWohv7Chc0JX8lWLP2WH84NXTvqy1Wwvk+NWNFciheokrnsvtwYvHevHL9yfLkNy/LQ//dHkxpDHBHZrO9Btxx2PHYLZ7M72mJsTg2fs2Ltq6bLvTjacOd+N//Pb7AoiFdPhcCYxjDvznO414a0pptt/euha3VGdizOHCXz/rwCOfT5bB+tGuEjywLf+Cpswf7tDh3iePwzWRkXxdViL+fEcVMpIooCWB/fbDlhV1VxYAFLIYfNo8Eu5uEELCbOUc9S5Ql9aK33/cymn706ft6NDQVcGZdGos+PUHLZzyK4c6dDgzSPUSiXef+u8PmzltTxzsQnuQ+5Tbw+KZoz2+QBYAhk12fNqyeCc3XVor/jfgfm9dtPcgka152MwJZAHg5++cQ59+DB0jVk4gCwD/81ErOrULHx8mmwO/fL/RF8gCwKk+I84MGBf8mmR5O9VnxLlBEy4tSQl3V5aUQhaDAZpmTMiKR8FskEbHXZhetpNlgVEb1f6bidXugiZADTiTbfFqlZLoZbG74HT753wxjQU3PlxuD1qGzX7tHSOLd4FpdNxJ+/0KZ7L5164dHXfBandhdNx/rLo8LMwB2oM1ZnejVzfm124Y8+8HIQDwmz3NuH5dxoq6KwsAibFCWO3uoOunE0KWp5V15LsAWckSqOJiOG0KmQjZycGXOllpMpNicVGRgtPG5zEoUNIUYwJkJkn8anTGSwTIkQe3T8UI+X51jAHgsrLFuzuRlRyLlHjufi+X0n6/kuQpZH41aKuzk5CeKEF2ciySYrnJptITxMi6gPGhjIvBTVUZfu1FKippRvwd6dChU2vFxSugrux0PIaBIk6EfgPdnSVkJaNgNkhpCRI8trsGlRP1zNZkxuPx3TW0hmkWSVIRfnhFCS4tUYJhvMHLH7+4DlXZSeHuGokAyjgx/nJnNdbnesdDeVo8/r57/bwSOF1enoJvXlqIGAEPcTEC/Nu15Vifl7xofUxLkOCxe7j7/RP30n6/khSpZHji3vXIV3gD1EtKlHj45tWIlwiRlRyLJ+5dj4p0b73x6pwkPHp3DVIuIAO4gM/DAxfl45bqTPB5DJRxMfjzHZVYnUF1ZgkXy7L49Z5m3FiZsagloaKJKk5MU40JWeGozuw8jdqcMI45kBArQsI8axiuVKM2O/oM44iLESB7kTPNhgjV71xClnEndFYHEiVCJCygFInHw2LQZAOfYZCWGJogc3K/FyIhcjIZ0zhdQnqrHZZxFxRxMYgVcZN/mcYcMNmcSIwVIX6RvhccLjeGR+0QC3lQhajG8xKhOrMhsufsMP5rTzN+cUMFeMzKLJH6+IFO7ChV4a5NOYvxcivzl0hIlKN0nPMULxEu2snKShEvicEqSczcDyQrkkwshEy88H2Kx2OQmRTaab+035NkaQySpYGPYwmxogVdiJmNSMCn6exkRk63B796vwl3bMhesYEs4E0CFWiNOSFk5QhpMMswTBaAZwCkwHvV9W8sy/4hlO8ZSgar3VtPcHQcqfFirEqLR5KMgrS5NPQZ0K0dQ5JUiLK0BCjj6HcWLdSj42gfMYNhGBSqZH53iAxWB9pHLLA53chTSP3WChqsdjQOmjFiGUeOXIo16QkQTFl/6HR70KGxYNBoQ0q8GIVKGWLmWepqyGRDx4gFfB4PRSkyKGifXFIOl/dvOGSyITVejIIF/A2XisFqR9uIBeNOj994HRkdR+PQKEw2J/IVUqzOTAzqNe1ON9o1FqhHx5GWIEGhSgZhGKd8evdZbxK0IpUMqguY8kwi1zOHuyGXibA2K7hxulylxMegaZgqJBCykoX6zqwLwPdZlj3JMEwcgDqGYfayLNsY4vdddGN2F946NYifv9sIDwvwGOD/XVWGL9VkQBZLJ88z+bhRjW+9WA/bRLbBL63Pwrd3FoVsOihZPB0jFjz0bJ2vVM6qtHj86c5K5E3UCB422fCvb53F3kZvKRy5VISn79uAiom1fVqzHY983oHHD3YBAGIEPPzvbWtx9Zp0AN71XnvODuN7L52Cy8OCYYCfXVuOL23IDjoYalWb8cDTJ9Cr966ZqsxKxO+/uG5e627Jwnk8LN5pGMQ/v3Yabg8LHgP84oYK3FqTFdaALpAhkw0/feMsPpmoS6mQecfrqvQE9Omt+O1HrXjrlLcEjyxGgL/cWYntxapZX9Ph8uDluj7821vnwLLeBHe/vWUNrl+XAR5v6e+WdWgs+NpzdWhVe/fZstQ4/PnOKuRT0r1lRWO240+ftuMnV5eFuythp4oX48Nz6nB3gxASRiE922BZdohl2ZMT/zYDaALgn6YxCpwZMOHhD5p9ZTo8LPDfHzbj7JB/aRDi1a8fw8/fbfQFsgDw4ok+nBkwhbFXJFhvNwxwar6eGxrFvqbJGq71vUZfIAsAOqsDf9zXBvvE3/vcoMkXyAKA3eXBz95pRIfGu8/06Mbwo9dO++ppsizw83cbg67hyrIsXj7R5wtkAaC+z4gDbdoFfFqyEF1aK37yxhm4J/6GHhb4t7fOoTMC62+f7DH6AlkA0Foc+POn7bC73DgzYPIFsoC3bNSvP2jG8OjsiWU6tRb8+9uNvlrabg+L//fGGXTrwlOH+P3TQ75AFgCahs10or8M/eK9RmwrUoR8eUU0SIkXo88whkjK/0IIWVpLdumcYZhcAJUAjk1rf5BhmFqGYWo1Gs1SdWfetBY77C4Pp83pZjESoI4q8dKPOdCr91/LMmKOzt9ZtIzVxeDxsDjUrvNrP96l9/27O8A6pZO9Bl/tTfXouN92jdkOncVbL1NvdWDMwa0P6GEBjcX/eYHYXR4c7fLv46k+Y1DPX66WcpzqrP7HRZeHhdYSeTVRAwXYdT0GWMZdGBn1PyY1DZthtM5eL1ZrtvsC+fPGnR7oreH5/IH2h6OddHFnOTnQpsHRTh1uqswMd1cigixGAD6PCds+RwgJvyUJZhmGkQF4DcB3WZblLG5gWfZvLMvWsCxbo1RGbp209EQx4sXcWdlSER8ZNF12RinxMVidEe/XnhOlSU2iZawuBh6PwTVr0vzad06p4Vqa6l/38vLyFCROJMLJSpZiel6SPIUUaQnefSY1QQy5lJs0J0bAC3qfEgv5uKrCv4/bChUBHr1yLOU4TUuQIF7CPS5KhHykR+BxcVW6/7HoyvJUJEyU2JluY14yVPGzLyFJT5RALOR+jSbGCpGaEJ51qleuSvVru6Lcv41EJ5PNiR+80oD7tuZBHKHr0sMhLUEcttkQhJDwC3kwyzCMEN5A9nmWZV8P9fuFSmV2Mn598xokT5x8J8UK8eub12BtgGCNeKXES/Av15QjR+49URQLefjp1aVYk0W/s2hweXkKrl7tPRFmGOCW6kxsL5oMFNdlJ+LblxZCMLE2sDo7Cfdvy/etlVyTGY//vL4CUpH3pCsjUYJf3FDhCxzSEyX48x1VUE4kbIqXCPCnOyqRrwh+fd91a9JxWZnK18c7NmRjc4H8Aj85CVZWciz+ckeV76JEYqwQf7mzCrnyyLtgVZmdhG/sKAB/YrzW5CZh99ZcCPg8rMtKxPcvL0bMRHKyQpUMP7yyZMbsxeflKaT48x1VvjJtCpkIf76jKmzTP3eWpuAL69J9/79xXTp2lM6+7pdEB5Zl8c+vNqAyOwlrgkxOtlKkJUiCXp5CCFl+QlpnlmEYBsDTAPQsy353rsdHQ03E030GaCwOKGQirM1KCnd3okKvzope/RjiJUKUp8ZzstlGKKrfOcHqcKFXZwUDBjnyWEim1dd0ujzo1lthd3qQlSzxq8Hq8XjQOGSGwepAZpIEeQES0QyZbBgZtSNZKgp4h2zOPo670KO3gsdjkCeXRmwm3RCImHE6aLRBY7ZDLhNF9Do+p8uDbp0VdpcHWcmxnFrhDpcHjUMmWMZdyJXHIjM5+CRi/YYx6CwOKONiwn5XeszhQo9uDAyAHLkUElHY9weqM7sI/vpZO14/OYB/uaYcosj/Dl1Sr9f3Qy4V4cdXXXBCrJVb44iQKBbqbMZbAdwN4AzDMKcm2n7Csuz7IX7fkFlDAey8ZculyKbsslFJKhKgLC1hxu1CAQ9FKv/pxufxeDxfduOZpCVIfFOPF9RHsQDl6bO/Bwmt9ERJ2IO4YAgFPBSlBB6vIgEP6xZ4fM9Mio2YID5WJEBZGs1+WU7erO/H3w924WfXraJANoC0eAkahyixJCErVUiDWZZlD4KudBFCCCGEzNurtX341QfN+PGuUsiphnZA6YlivHt6cO4HEkKWpVDfmSWEEEIIIfPgcHnwmw9b8HbDAH5yVRkykiJ/5kO4pCVI0G+0wen2RFx9a0JI6FEwSwghhBASIY516vAvb55FgkSIn3+hAvFT1nYTfyIBD0pZDLq11hmXERBCli8KZgkhhBBCwsjtYbG/VYO/7e9Ep9aC22qysDlfDmZ6fTMSUFayBC1qMwWzhKxAFMwSQgghhIRB+4gZb9YP4tW6fkhj+Li8PBVfv6QAApouOy9pCRK0DpuBNeHuCSFkqVEwSwghhBCyRPr0Y3inYRBvnhqAzuLA5gI5vnNZEXIp6/+CZSXF4uwgZTQmZCWiYJYQQgghJIRGzON4t2EQb9QPolc/hg15ybi9JgulqfHg8Wgq8YXKU0jxcm1fuLtBCAkDCmYJIYQQQhaZw+XB3kY1Xjzei1N9RtTkJOHq1WlYnZEAPgWwi0oVHwPzuBMGqwNJUlG4u0MIWUIUzJKQG3O4MDJqR6yID1W8ONzdIcuMy+3BoNEGHo9BRqKEEqaQZc85MeYFPB6VbIlApjEnnjrchWeO9CA9UYLtxUo8cFEeYgT8cHdt2eIxDPIUUpwdNOGiImW4u0MIWUIUzJKQah+x4FfvNeKTFg1S48X4xQ0VuKREScktyKIYNtnw90PdeOpQNwR8Bt/ZWYTb1mchKZauzJPladBow6Ofd+D5Y72QCPn44a4S3LAug8q3RIBxpxuPH+jEYwe6UJWdiB/tKkVWcmy4u7Vi5CqkONVnpGCWkBWGIgoSMmMOF345EcgCwPDoOB58thbNw+Yw94wsF3sb1fjb/k443B6MOdx4+INmHO/Sh7tbhITMW6cG8fSRHrg8LMx2F/7trXM42WsId7dWvONdelzxv/uxv02Ln11Xjge3F1Agu8QKVTLUdtO+QMhKQ8EsCRm1aRyfTgSy53lYoFNrCVOPyHJid7rxcm2/X/u+JnUYekNI6I3anHglQJKbIx26MPSGAN76sL/9sAVffa4Ot1Rn4nuXFSMtgaZ+h0ORKg71fQawLBvurhBClhAFsyRkpDECqOJi/NqTJDQFlFw4IZ+H8vQ4v/YilX8bIcuBWMhDcYr/+M6mO4BhYbI5ce+Tx/F5qwa/vKEC63OTw92lFS1ZKkKskI+2EbpgTshKQsEsCRlVvBj/eUMFpubjubw8BeXp8eHrFFk2eDwGd2/KRbxkcul/RqIYO0pVYewVIaEjEvDx1UsKIBVNJhIqUEixpUAexl6tTP2GMdz4l0OQxgjwo12lSKR1+hGhPD2eZioQssJQAigSUpeWqvD2N7aiU2tFUqwI5WnxkMv879YSshAVGQl44+tb0TJsBp/HoDwtntapkWVtXVYi3vzGVrSqzRAJeChPS6CMxkuseXgUu584jisrUnFVRVq4u0OmKE9LwP42DXZvyQ13VwghS4SCWRJSQj4PqzMTsTozMdxdIctUgVKGAqUs3N0gZMkUpcShKMB0YxJ6dT16PPB0Le7cmIOthYpwd4dMU5GRgKcOd8PuclMpJEJWiJBOM2YY5u8Mw4wwDHM2lO9DIhvLstBZ7LA53OHuCiEL4nR5oLXY4XDRGCbRzTzuhHHMEe5uRKVPW0Zw/9O1eHB7AQWyESpBIkRmkgRHOymrPSErRajvzD4F4E8Angnx+5AI1acfwz9O9OL1kwMoUsnw3cuKUZWTFO5uERK0VrUZjx/oxOetGmwtVOCh7QUoSaW7YiS6jDvdONiuxe8+aoHV4cZD2/OxqyIVyVJa9hGMV2v78Mv3m/C9y4oDJuEikaMyOxHvnxnCxcVUb5aQlSCkd2ZZlt0PgC6PrVB2lxv/90kb/vxpB4ZM49jfpsWdjx9Dq5rqzJLooDXb8Y0XTuLl2n6oR+14/eQAHnq2FiOj4+HuGiHzUt9rwANP16JxyIwe3Rh+8sZZfNw4Eu5uRTyPh8VvPmzBbz5qwU+vKadANgpszlfggzNDsNNMGkJWBMpmTEJmyDiOV+u4dUBtTjcFsyRqdOusaFNbprWNoUtnDVOPCFmYz1s1fm1PHu6C1e4KQ2+ig2nMiQeeqcUnzWr8xxcqkJFIibaigTIuBjlyKT46RzXHCVmzeHJ5AAAgAElEQVQJwh7MMgzzIMMwtQzD1Go0/l+2JHoJ+TzIYvxnsseKojMpA43VlUcsDDxWxRGcWITGKQkkWepfOkYVFwMhnwnwaHK4XYsrf78fMQIefnJVGRIkwnB3iczDzjIVHj/QGe5uEEKWQNiDWZZl/8aybA3LsjVKJa1vWE4ykiT48VWlnLbytHiUpUZnnVkaqytPvlKKOzdmc9purspAgVIaph7NjcYpCWR7kZJTk5nPY/DViwshiuALM+GgHh3H9146he/8ox73bM7BPZtzIeCH/VSJzNP6nGRoLHYcateGuyuEkBCj0jwkpL6wLgM5cilO9RqRnihGdW4y0miqFokSsSIBvndZMS4uVqJl2IziFBkqc5IgE9NdGhJdStPi8cpDm3Gi2wCb04X1OclUMm2KIZMNj+3vxCt1/dhRosJ/3bwWkiidRUQAHo/BLVWZ+M93G/HOt7ZBSBckCFm2QhrMMgzzIoBLACgYhukH8DOWZZ8I5XuSyCKLEWBroYLKGJCopYiLwRWrUnHFqtRwd4WQC1KSGo+SKJ0ZEwpOtwf7WzX4x4k+HO3UYXuREg/fuBpyGWV4Xg425cuxv02LP+5rw/evKAl3dwghIRLSYJZl2S+F8vUJIYQQQoJltbtwsF2Lj84N4+OmEaQnirGlQIEvrs9CrIgmqy0nDMPgoe35+Le3zyEnORa31GSFu0uEkBCgIzchhBBCliXzuBOn+0043qXDwXYdGgdHUZIahzWZCfjFDRVQ0F3YZS0xVoQf7SrFf+1pRtOwGT+4ooSmjxOyzFAwSwghhJCoZHe5MWpzQW91YMQ8jiHjOHp0VrSNWNA8bIbGbEeeQoqiFBkuK1PhmzsKZ8xSTpanjEQJfnF9BX6/rxUvnZDg3q154e4SIWQRMSzLhrsPPgzDaAD0TGlSAIjUVHSR2rdI7RcQuX2b3i8ty7K7ZntCgLE6n9ePRvQZIsPUz7DY43QxRMvvOFr6CURPX2fq55zjFAAYhtkz8RrcdpGEl/29VyoX2imXWWuHx7PQpweFZT18huG5Q/omUdCHSOnHTH3giWWC0eOvD5oO/2NkhqcGNVYJIZElooLZ6RiGqWVZtibc/QgkUvsWqf0CIrdvoe5XpH7u+aDPEBki/TNEev/Oi5Z+AtHT13D0M1J+N5HQj0joQ6T0IxL6QAhZOpSrnBBCCCGEEEJI1KFglhBCCCGEEEJI1In0YPZv4e7ALCK1b5HaLyBy+xbqfkXq554P+gyRIdI/Q6T377xo6ScQPX0NRz8j5XcTCf2IhD4AkdGPSOgDIWSJRPSaWUIIIYQQQgghJJBIvzNLCCGEEEIIIYT4oWCWEEIIIYQQQkjUoWCWEEIIIYQQQkjUiahgdteuXSwA+qGfcP/MicYq/UTAz5xonNJPBPwEhcYq/UTATzDC3Uf6oR8yTUQFs1qtNtxdICQoNFZJNKBxSqIFjVVCCCELEVHBLCGEEEIIIYQQEoyQBrMMw5QwDHNqys8owzDfDeV7EkIIIYQQQghZ/gShfHGWZVsArAMAhmH4AAYAvBHK9wylXv0Y9jWp8UnzCC4pVuKy8hTkyKXh7lZEO91nxMleA/Y1j6BAKcOVq1KwuUAR7m6RCDFgGMO+5hF83KjGlkIFdq1KRa4i+H1KZ7HjWJceb9YPQCzk44bKdGzJV0As4gf9Gsc6dfioUY1WtRmXlqpQlZ2ItVlJvu113XocbNfieJceNbnJ2F6sQHVOsm97Q78RDX1G7G1UI18pxa5VqTTGo1DjoAlvNQyiVW3BjZUZ2FYoR7I0BgDQpjbj/TNDONlrwNWr03BJsRIpCZI5X/PMgBEHWrU41K7FuqxE7CxLQVVO0pzPaxsZRV23ER+cHUJ6ogTXrE7DtiIlAKBHa0FDvwlvnRpEvESI69elY2t+EoRCIUxjDhzr0uON+gEwDHBjZQY25CYjIVYEg82Ghh4z3jw1AIvdjevXpaMsTYpCVcKF/eKikGXciSOderxc24u0BAlursrE2qzEeb3GkMmGz1s0+ODsEGpyknH16jQUqGQX3jebE0e6dHitbgB8HnBzdSY258shEQU+XWvoM+L1k/0YNNlwW00WNuXJEScRBnxsm9qMd08P4VSfAdeuScf2YiUkQj4Od+jwxsl+iIV83FiVgS0FCogEwd/rONNvRP3UY2BFGjbnywM+dthkw4luA946NYg4sQDXr0vHljw5zgyZsL9Vi9puPTbkJWNboQLVuckBX4MQQqZiWHZp1hIzDHMFgJ+xLLt1psfU1NSwtbW1S9Kf+TLZHPjmC/U40Da5rmdTXjIeubsaibGiMPYscllsDvz+k3Y8fqDL16aUxeCRu6tRHcQJXRgxcz0gksdqtLCOu/BPr5zCh+fUvrY1mQn4+73roZDFBPUa7zQM4lsv1vv+z+cxePyeGuwoVQX1/LoePb723EmMmO2+tnu35OKHVxRDKhaifdiMn751Fse69L7tldmJePjG1ShNi4fF5sD/fdqBR/d3+rbLpSI8enc1akJ/IkbjdJF0jFhw8yOHYRxz+tp+cnUZHtyej0GjDbc/egR9Bptv272bc/GTa0ohEsx80WRkdBz/+tZZzvguUsnwpzsrUZISP2t/HvmsA7/e0+z7f1yMAI/dU41NBQq8WteHH7xy2rdNxOfhsXuqcXGJCh+eG8ZXn6vD+a91hgEevasaV6xKxactI3jwmVo43ZPf+b+7bS1uqsqc+xd0YeYcp8DSjtU36wfw3ZdO+f4vFvLw2te2YFV6cIG93eXGv7/diBeP9/ra8hRSvPDARqQlzn2RYzYfnRvGg8/W+f7PMMATu2twaWmK32PPDZpw818PY9zp8bX97+3rcGNlht9j+w1juP3RIxgwjvvaHro4D2syEvGNF7jH0L/vXo+LS5RB9dfhcOB3+zrwyOfBHQNfq+vD96eMXyGfwWtf3YyfvdOI+l6jr31jXjJ+eX0FClPjgurHIglmrFICHhJuQR1TV5KlXDP7RQAvLuH7LaoujZUTyALA0S49OjXWMPUo8rVqLHj2SA+nTWOxo33EEqYekUjSpbNyTvQB4HS/CR1Bjg/LuAvPH+OOL7eHxcdN6hme4a99xMIJZAHghWO9aFZ7+9CtH+MEsgBQ32tEr37M+3yNFU8f6eZs11kdNMajTOPQKCeQBYD/29eGIaMNLcNmTiALAM8e60Gfnts2XZva7De+20YsaFfP/p3RPGTCE4e6OG1muwstagsGDWN46nA3Z5vD7cHRTu8Yfb2uH1OvT7Ms8NapAQDA4XYtJ5AFgGeO9KB/YiyvFKYxB/64r43TNu70oK7bEPRr9OrG8NKJXk5bl9aKtkXY71+q7eP8n2WBt08NBnzsyR4DJ5AFvOPWNObwe2zLsJkTyAKA0+nBs0f9j6EfNQ4H3d9GtdVvTM50DNRZ7Hh62jmB082i32DjBLIAcKxLjx7DyhqbhJCFWZJglmEYEYAvAHglwLYHGYapZRimVqPRLEV3FhVdopsZM+PFo+j8rUX7WI0WFzw6FmN4RecQBUDjNPItbHDNNovKM+u2888P/JrhvMQfSWN1vn+VQI8P1Uy3mV424N80hO8XyHy+5Wf6/UTx4ZZEiXGnG4983oGfvH4G9b3BX7gi0WGp7sxeBeAky7J+t0xYlv0by7I1LMvWKJXBTWsJhzylFNsKuWtANuYlI19Ja2ZnkiuX4q5N2Zw2hUyEQuWSThtaNNEyVqNFnlyKK1Zxp86tTk9AoTK4dWcysQB3bszhtPF5DC4rD26KMQAUqmRQxnGnNH9pQxZKU719yJXHYmMed0r8uqwEZMtjvc9XSnH3plzOdrlUhMJFWDu3UDRO5688LR4J09YZfnNnIdISJShOiUNmEnfq6F0bs5GVPPt00sIUGa4o547vQpUMRXOM79K0BNy/NY/TJosRoDQ1DulJsdi9OZezTcTnYXOB97vppqpMMFOii/PrZgFgW5ECQj439Lhncw4ykmNn7U8ohWOsJsSK8K2dRZw2sZCHmnksfcmWx+L2mixOW648FsWLMCV2+usyDHD9uvSAj63OTYJYyD2N+/alhUgIsPSpJDUOGYliTptQwODuTf7H0CtX+U9pnkmxQuo3JpOlIhQFOAYq4sTYvZn7fkI+g6wkCSqzuWuWN+YlISuMY5MsH+NON+547Cg+aR4BANz31AnsbQx+BheJfEuyZpZhmH8A+JBl2Sdne1ykr+/q1Y1hb9MwPm3W4OJiJa5YRQmg5tLQZ8DJXiP2NalRoJJFS3IcWou4RPr13gRQexvV2FqowK6KVOTNIwGUxmzD8S4D3qgfhFjIw42VGdhaML8EUEc7dNhzbhhtajN2lqWgJicJa6Ykg6nr1mN/mxbHu/VYn5OE7cVKzlqw033nk58MI18pw1UVSzbGaZwuonODJrxZP4g2tRk3VWViWxE3AdS7p70JoK5ZnYZLSpRIDSIB1Ol+Iw60aXGwXYt1mYm4rEwVVFKbtuFR1PUa8f7ZIaQnSHDtmskEUN1aKxr6jXjr1ADixULcsC4DG3OSIJF4E0Ad7dLjjZP9ABjcVJWBjXneBFBG2zjqe0bxZv0AzHYnbqjMQIkqDiVps6/fXQQRt2bWbHPiaJcOL53oQ2qCGLdUZ2HdfBNAGW34tGUE758ZxvrcJFyzJn1RLmJNJoDqB4/H4OaqTGzKl0MaEzgB1Kk+I16t68OQaRy312Rhc/7MCaBa1Wa8e3oQ9b1GXLcmHReXKBAj5OFohx6v1g1ALOThlurMBSWAOtk7eQzcVZGKLTMcA4dNNhzv9ibtixMLceO6DGzJl+P0oAn7WzU40WPAhtxkXFSkWIq8A9PRmtll6JfvNaGh34jv7CwCj2HQPmLB7/a24MPvbYcqTjz3C0QeWjM7TciDWYZhpAB6AeSzLGua7bHRcuLFsiwYhsbSfLhcLggEIU2evZgoSFhic+1Ti7HPud1u8PkzB7lzbfd4PODxZj7BC8NxgcbpElvo33i25y1021zjdaH9CYGIC2ZXipn+zovx9w/0GjMdI+c6dk4V5nMFCmaXmV7dGK7700H8+qbVnGStLx7vRYyAh9/cujaMvVswCkCmCfkRg2VZK4DAOdqjTI/Oij1nh/Fxkxo7SlS4enXavMqIrESn+oyo7dZjb6MahUoZrlmbNuMVW7Ly9OnH8FGjGh+eG8ZFhQpcsyYN+VOmYeosduxv1eCVun4Up8ThlupMVGRMZhzVjNpwtEuP104OQCyYuKuQn4xYsfdLy+1240CbDq+dHIDR5sAN69KxKU/OmVp5tFOLdxqG0D5iwWVlKmwrVKIsffJu1bkBEz48N4wjnTpsyEvGrlWpWJ05eRdHYx7H560avFbXj1XpCbipKgPlQWZFJdGhRW3G2/UDqO0x4Lq16dhZpkJaEHdm+w1j2Nuoxgdnh7G1QI5r16T7yreoR8fxWcsI3qgfwJrMRNxUmYHSibukBqsDB9q1ePlEH/IUsbi1JgtrJsbcoHEMRzv1ePPUAOJiBLilOhNb8xUQifgYNlpxsm8Ur9X1g2EY3FKdgaqsRKgSJHB7WNT3GvD8sV4YbU7ctTF71jt+JHrprHYcbNXi5bo+FCnjcEtNBioyEtE6bMYbpwZQ32vAF9am49JSVVAzDM7zeFjU9xnxwrEe6CwO3LUpG5vyFdBaxvFZixZ7zg0hXyHFjZUZWJ8nx4ku7zhtH7HgylWpuKRUiXxF4LvXtd167GsaQW2PHhvz5NhRquSUQCNkIR75vAOXlqr8qo5ctzYd//TyKfzgyhKkxEfl3VkyxZKV5glGJF+ZNY058NXnTuJIp87XVpOThMfuqUGSlErzBGK0OvCHT9rw5KFuX5tcKsKj91SjJrK/pOiO1xKwjDvxvZdOYW/TiK+tPC0OT9+30beO9dHPO/DwB5MlSuIlArz+tS0oVHnXpr19agDf/sdkiQ0eAzyxe72vNM/+Vg0eeLoWDvdkxs+Hb1yNL230ruU+1WvAV56pg8YymdH4rk3Z+MlVpYiNEWLYZMO3XqzHiSmZTtdkJuAvd1YhMykWHg+LP+xrwx+mZEdNihXi9a9vndd06QWicboEBgxjuPXRIxickgn2ro3Z+NfryhEzS2keq8OFH77SgPfPTGaGLU6R4bn7NyJZKsJvPmrBo1PKmShlMXj1a5uRI5fi6cPd+Nnb53zbpCI+Xv/6FpSkxuPl2l7886tnfNuEfG85qotLVNhzdghffe6kbxvDAI/cWY0rK1JR32vArY8cgcsz+Z3/6N1VuHJV2sJ/OcGhO7NL7G/7O/Gr95t8/4+LEeD5r2zEg8/WYtjELUP206vLIAxySnFDnxG3PHKYkxX7z3dUonFoFH/+tMPXlhQrxOO7a/DgM3XQWSczK9+3NRc/vqrMbwpz8/AofvTaaTT0TU7eW5+bhF/eUIHi1JBPg5+K7swuIxa7C5t+tQ+/uWVNwBKaTx3uQrEqDt+/siQMvbsgdGd2mqUszRPVOrVWTiALALU9BnRqqTTPTDq1VrxwjFu+QGd1BF16hSxv3TorJ5AFgMYhs298DBlt+NMn7ZztozYXmobMALyleaaPLw8L7J1SVuJ4l44TyALA00e6MWT0llVpUZs5gSwAvHSiDy0TpXlahs2cQBbwlg9qHfb2YcBowyOfd3C2G8acaB4enf3Dk6jRqrZwAlkAePFEH/rnKM3To7VyAtnzr9WusaDfaMOTB7s52zQWO1qGzRgZHfcb91aHG2cHRqExj+Ppw/6lTQ53eL+bXjs5wNnGssCbE6V5PmvRcAJZAHjk806MOVyzfg4SXYZN4/jTJ9zSQ2a7C+cGRzmBLAA8d7QHffMof3MoQHmnv3zWga5p50GGMSdahs2cQBYAnj/Wi/YRs9/r9mitnEAWAE50G3wl0AhZiPdOD2JVenzAQBYAdpSo8HJdHzweuj4R7SiYDdJM60vo8sjMGAYI/Guj3xoBZhoHvjHDAEyAI5TvWSwLhuf/Gvwpa7N4AQYgj2F87zHn9gCvP7WdYQK/xsxlqUi0CXQMY2Zon+t53ucyMz6fmRh7gbbxGO/7Bhpv/InxyA/wvPPbAg1lPsMEfD0SvbzjJ9AxKfjHziTQ8ZDPBC7lE/C4OFPfZjjOUm4SciFePzkw67K2HLkUUpEAR7t0Mz6GRAcKZoOUr5BiRwm3dMCWAjmV5plFkdI/Zb8qLgZFKvqdESBPIcU1q7lTHNdlJfoygqYlSPDdaSU0kmKFKJ9YzyqTCHHXRm7pJwGPwc6yydI8G/KSETNtStuXt+b61okVp8iQOm29zJ0bs1GaEu/bvnVaSa6anCQUp3j7mJEowTd2FHC2K2UxKEuLzvJTxF9xShxy5dwSIfdszkFW0uxlQ3LlMtw4raRKRXo8ClVSZCbF4qHt+ZxtaQlilKbGQRknxvcu5477eLEAqzISoIgT48tbcjnbYgQ8bM4PXJqHxwA3TPThkhIVRHzuvvDVSwogFi4siRSJTCnxYnzvMu74SYwVYlV6vF+JqS9vzfNrm83WArnf8fQbO4p8x8PzlLIYFKf4lz3bvTk3YGmq3ORYv9JIW/LlyJFTaR6yMHqrA2cHTFibNXv+ig15yXj/9NAS9YqECq2ZnYc+/Rg+bR7B/jYNthYqsLM0xVdvkgR2ut+IU31GfNo8ggKlDDvLVFSah/gMGGz4rHUEnzaPYFO+HJeVpXCSqhmsDhzu0OKdhkEUquJwzZo0lE0pJaK32HG8W4+3GwYRI+DjC2vTsTlf7ivN43a7cahDj/dOD8E45sA1a9OxITeJk/TkeJcOHzeq0TpiwY4SFTblJ6NkyjqtxkETPmvR4FiXDjW5ybi0VIVVUxI86Sx2HOrQ4d2GQZSlxeHq1Wmc54cQjdMl0j5ixodnh1HfZ8SuilRcVKQMKmnIoNGGz1o0+KRZjQ15clxepkLexMm8xjyOQ+1avHd6GBUZ8bhqdRqKU7wXQUw2B4506PHWqQHkyGNx7Zp0X+KzEZMNx3sMeLdhEHFiobdsT6EcfD4fGrMN9b0mvNMwBIYBrlubhsqsBCjiJGBZFqf6jHijfgDGMQduqc7C+twkSEQhTwBFa2aXmGHMgaMdOrx1agAFShmuWZOG8vQEtI+Y8cGZYTT0G3H16jRsK1RANY/kNyzLoqHfiDfrB6Gz2nFzVSbW5yZDZ7XjULsOH50bRsFEebLq3GTU9eix56wa7SPesmcXFSlmLGd4oluPw+1anOw1YH1uMjbnK1CdG3zt30VCa2aXiVdq+/B6/QC+fWnRrI8bMtrwqw+acPwnl804EysCRU1HlwoFswsw7nTT1ex5so47IRUHrn0XgShImAeWZeHxsODzZ57oYXe6EXMB+4zV7oSIBwiFgceQ3ekGD4BwhvdwuVxwuj2QxMycrM3l9kAwy2eYa7+/0M+4ADROl9hcY2QhZhs3Y+NOCPmBx73N7gSfYSAKEIw6XR4wAAQBEvt4PCxYTE4/XgIUzM7hQseVx+OBy81CNG0cud0e8HjMBU3XtYy7IBMHd8HD4XJDFCApWqAxPtMYtYw7IQvfuQIFs8vE15+rQ1ZyLC4pUc352H9+tQF/vrPKlzE+ClAwOw3l5J+HM/1GvH9mCEc79Vifl4xr16RF0+APi1N9Bhzr1OPDc2oUqqS4oTKDSvMsEyzLor7XiGeP9mDQaMNdm3JwUZGCk2yhrkeP108OoGloFDvLVLi8LGVe2SnP9BtxotuA984MITs5FjdXZWBb0eR0/2GTDUc79Xiltg9iIR+3r8/CloJkyCZK84yPu3C4W4eXT/TBaHPipspM1OQmIl85OQ24aWgUL9f2oaHPhJurMrCzjFuu4ky/Ce+fGZxxv+/QmPFpswYfnB1GvkKKW2sysSFvWVQjIxNahkfxal0/6noM+MK6dFxRnor0xOCnZwbSrjZjX/MIPjynRlGKDLdWZaImz5vl/Uy/CSf7vHdf0xIkuLU6ExcVe8d9p9qMk/1GvFY3gDixALevz8LGvGTIxEKYbU4cbNfixRO9YADcsSEbWwoUiJMI4fawqOvR4+nDPTDZHLhnSy625CuCDlTI4mtVm/HayX6c6NLjmjXp2LUqBRlzTF+fyuPx4FCHDi8e74XW4sDNVRnYXqSEUMDDZy0jeOlEHwpVMtyxIZtTTiwY9b0G7G1U40iHDtU5Sb67rYF0aiz4pHkEH5wdRt7EMXBjnhzHu3R4ta4f7SNW7KpIwc7SFMRLhDjYpsHLtf2IEwvwpQ1Z2FqoxOl+Iz46N4wT3QZszE/GZWUpqJnh/QiZjdvD4mC7Fg/ftCaox6/NSsS+phE6n49idGc2SIPGMXzjhXrU9xp9bRXp8XjkripkJtMa0EBMY078bm8Lnj4ymX0zKVaIx+6pifQvKbrjFYSzAybc/NfDsLsmswX/982rcdt67zrW5qFR7H7yONSjkxk0b6xMxy+urwjqLr3T6cT/7uvAXz6bzBYcFyPA47trsHFijeCb9QP47kvc0jyP3VODnWUpAIDPWkbwlWdqORk4f3FDBe7alAPAm1H55r8c5mTdfGBbHn50VSmEfB4GjWP45gv1ODllv1+VHo9HJ/Z7h8uDX3/QhL9PKT+VIBHi2fs3LMUXI43TJdBvGMMtjxzmZIL94vos/Mf1q2YtzTMbm9OFX7zbhOenZOOWS0V45r4NWJWRgP/b14b/2dvq2yYR8vHE7hpsKVTgH8d78ePXJ0vzCHgMHrunBjtKVfjw7DAeeq6O815/u7saV6xKxckePW599CjcUzJ3/vXOKly1mkrzhMOg0YbbHj2CfsNkVuwbK9Px8E2rIRYGd4HhSIcW9z55gnMM/vfryiHg8/Avb571tcliBHj961t809jn0qe34p9ebuBkci9LjcOf7qhEgYr7Gi6XBw/vacYTB7t8bQkSIZ7YXYOvPFMLw5jT137vlhxszJPja89Plo/i8xi8880t+OdXz+Ds4GQW+MrsRPzXTaupNA+Zt9P9RnzrxXr8Oshg9uyACW83DOKdb20Lcc8WDd2ZnYYSQAWpTW3hBLIAcHZwFG0jVJpnJp0aC1483sdpM4w50a6h0jzLQV2PgXMSBQB/+rQDhonAsHl4lBPIAsBbpwbRGmRppmb1GJ6ZciEE8JaYaJt4vmXciRePByrNo/b9/0SX3q+UxLNHetCv9+63gcpHPH2kGwMTJ5htagsnkAWAc4OjaJ0o3dOhsXACEgAw2ZxoHvIvP0GiU6va4lfS5OXaPvTNUZpnNh0jFrxcyz026qwONA2P4tygCU8f6eZsszndaFGbMWCw4tmj3H3C5WF9ZeNeqev3e6/XJ8r1fNaq4QSyAPDo/g4qzRMmbWozJ5AFgDdPDaJHF/y4qu81+h2DnzrcDbeH22axu9A0GHy5sPYRi19JsqZhs+/Yy3msxoLnpo1Jk82J9hELJ5AFgBeP96F/Wikgt4dFr97GCWQB72frodI8ZAEOd+g4uTXmUpwSh/YRC0w259wPJhGJgtkgzbQwnDLHz4IJvC6LRxeVlgVBgL+tUMD4SoDMVPYm2KV6PLABx4/vZdnAfRBNWYMVaB2agD9ZjoS/CKV5Ao5xGuLLRqC/5XzGcSDMDCVxeIy3cE/gMeV9TqBt5/cDYYDaPMKJfUDI898XhDweHY/DJHBJr/kdOwKNBT6PF7hUzjxemBdgrMz0GjMdAwOXl2LABhhvM5WH4s/QD0Jmc6hdi9LU4CsKiAQ8lKbG4UgHleiJVnSkCFJxShwuKuKu9dyYl4ySIKftrESFyljcuyWH05YaL0aRyj81P4k+VTlJkIq40yy/u7MYCRNrZkvT4v1KK3xxfRaKg/ySWZWZiAcv4pYvkUtFKJkoAyGTCHHnJu74EvK5pXnW5yZBLPQvzZM+sS6tJDUOmYncbJ5fu7jAV3alNCUO22fZ74tVMjywLY+zXRUX4ysfRKJfcUocChTcpSRf3pqHrOSFZ+VLdcUAACAASURBVLIvUcVh97QSO+kJYpSlxaM8Pd5v3CdIhChJjUNaYiy+vIU73mIEPGzK9y7buLUmkxMM8RjgpqoMAMDFJUq/sipf21Hoy/xNllZxapzvWHbeXZty5lUhoSrb/xj8lYvyEDutLVkqwqp5HJOKVf6lCKtzEgN+dxfNcAwsVsX5lT27b2su8hXcteYxAh6ykyXYmMfNXHxRkQLZScFnWiYE8Ca4q+81onSe09PL0uJxqF0bol6RUKM1s/PQPDSK/W1anOjSozo3CRcXK1CWNnsNq5Xu7IAJpweM+LxZg3ylDJeUKH3rHSMYrUUM0tkBEz44O4Qh0zi+sDYdNbnJkMVMrvc63WfEvuYRNA2NYnuxElsL5chTBH8xo2XYhNP9o9jXpEa23HuCNbW0k9Fqx/FuA94/MwSxkI9dFanYkieHaMrJ3P42DT5uVMNgdWJXRQqqshOQljgZnLSPWLC3UY3GIROuLE/Fpnw5FFPqIzYPjeJAmxbHz+/3RQqUTSnN06+34nCHHvua1ChQyXBZWQqqcpakpASN0yXSqbFgX9MIGvqMuKw8BVsK5VDFXdiJdq/OikMdOnzWPIKilDhcVpaCddneddZNQ6NoGhrFx01qpCVIsLNM5UucN2AYQ0O/CXvODiNOLMAVq1Jx8URyKJvDhaNd3lJUDIBr1qRhc14yYkSCibIqJrx3ehCjNieuX5eBqpykpcjMT2tmZ9ClteKTJjXqe43ev3GhIqiST1Md7dRiz9lhaM127Fqdhs35cgh4PBzr0uG9M0PIV0hx5apUlM5j2iUAnBkw4kiHDie6DKjMTsSWQjnWZQU+rg3ox3CoQzflGKhCVU4y6nsN+LhxBO0jZlxapsKWAjniJAIc7zTgvdNDiBcLJvqcjPpeE4536XCy14ia3CTU5CahOmfJc2vQmtko1zw8ivufqsVvb107r+d1aCx48lAX9n3/ktB0bHHRdJppQh7MMgyTCOBxABXwHgTuY1n2SKDHRsuXmcFqR5I0Zu4HEh+91Q4xj4dYSVSU56EgYYlpzeOIj+FBJApcOsfmcELI4wcsNQJ463KCZZEQG3i/NNvG4fHwkCAN/PpOpxvjLg/iZhmfc5Xecbrc4PN4S1mrjsbpDFxuD1hMTrFdlNd0uWBxeDjZuoNld7ohEvAClkjRWsYhE/AhDpAUzTRmh4DhQRpgXNodToAFYmIClO1xuMFjWMQESCRkszvhZrGU5U8omA0xp8sDl9sNybSx4HC5IQjymDTXMXSqmcaQxjwOqYDP+Z73eFi4PR4IpyVLc7jd4IHxWwoyanMgXjL/fWyRUDAb5Z472oOPm9R4aHvBvJ7n9rB46LlafP7DHVDIIv78noLZaZYiJ/8fAOxhWfYWhmFEABY+NyvM6rr1+ODsMI526bA+JxnXrEmL9Ky8YXeq14hDHRp8eE7tK1uytVA59xPJilDXo8feRjUOtmtRmZWE69amccradGm9d8TeaRhEVnIs7tyYzbkz26YexZn+UbxU24cYAQ93bMxBZVYcUhK8d15HbQ6c6DbghWO9MIw5cWt1JiqzE1A6ZUbFkQ4tXq3rR9uIBZeXp+DSEiVWZSRO6YMVb9YPYF+zGpcUq3BTVQbylZN3l9WjNuw9N4KX6/pQnhaPuzbloCKDZmyEg8PlxvFuAx4/0AmHy437tuZjc4Ec0pgL+6o70qHFG/UDaBoy49JSFS4pUaIye+677726MbxzehAfnB3ClgIFbq3ORNHEFPX6HgMOtmvxUaMahSoZbq3OxJZC79g+02fAiR4j3m4YRFqCGF/ckIWLi73T5/v0Fhzu0OPl2n7EiwW4c2MOtuQnIVYsgtFmx6E2HZ472gswwN2bcrCtUIF4iRBj4w4c6tTjuaM9sNrduK0mC5vzk5Elp2z80crj8eBAmxbPHe2FzmrHzdWZ2FHsLc3zSfMIXjjWi0KVDPdszsXarMDZ1Ts1FpzqNXKOodU5CVDG+ZeeGne4cahDi+eP9cJk8x5PtxTIobM6cLRT5y3NI5fitvVZ2FqowLkBE5471otzgybcUp2JK8pTIBUJcLBdiycPdyNRIsR92/JQk5OE+j4j3j89hBM9emzKk2NXRSqdX5F5q+vRo0A5/6VsfB6DstR4HO/S4+rQZ3gniyykd2YZhkkAcApAPhvEG0Xyldl2tRn/9HIDTg+YfG3laXH4/e3rljp1fNTQjY3hdx92cLK9JkiEeHx3NdbnRvRUY7rjtQQ6R8z4f2+cwbGuyayZeQop/u9LlajISIDH48FvPmzFXz+fLM0jFfHxzP0bUT0xjfe1un58/5UG33aG8ZYiubw8FQDwabO3NI9rShbX//jCKt96xfoeA+57+gQn6+btNVn41+vKIIsRwjDmwANPnUDdlIzGq9MT8NR96yGXxcDtYfHfHzbj0c87fdvjJQK88fWtC/pCnScap9Mc7dThS48d5STAeWL3ZKmmhajvNeDBZ+ugMU9mNL52TRr+4wvlkMtmnhJqGXfiO/84hX3NI762PIUULz64ESIeg//a04qXpmQ0nlq27Pcft+L3H7f5tomFPDx+z3psK1Lg+aM9+OmUsit8HoPH76nGjtIUfHBmiFP2BAAeuasKuyrS8EmTGg88U4upCY1/fdNqfHFD9oJ+L/NAd2ZD5FC7Fvc+eZyTsf3XN62G1mLHbz+aLO0UK+Lj9a9vCbiO8NW6fvxglmPoVJ81j+D+Z2o5WbE/+s42PH+8j1OCL17sLaH2lWfqOBliv3JRPjbnJ+O+pyf/zjwGeOeb2/CDVxrQNDyZBX5NRgJ+e9saFKdQaR4SvB2//QwPbs9H7gIu0r3TMAgBn8HPr68IQc8WFd2ZnSbUCaDyAGgAPMkwTD3DMI8zDBOVl4G7tFZOIAsAjUNmdGqpNM9MujV2v/ITJpszYHp/svL06G2cQBbw7mfdE/tUh8bqV6LE6nCjechbwsFgceClE9yyOCwLfNw4GTwc79ZzAlnAOw2pW+M9aWodMfuVj3jtZD/ahr1jtEtj5QSyAHBm0IROjbePA4YxPHmQ28dRm8vXR7K03mkY9Mvk+uShLrjcnsBPCEKHxsIJZAHg/TNDc5Zl69GNcQJZwDu+29UWdGrH8NpJbhkdw5i3nMm5AROenVaSatzpQavajJFRm18pKLeHxeGJLJyvnvQvzfPGRGmeg+1aTNsV8NyxHoyYx2f9HCRyneo1+pUeGzaN45EpF9cAYMzhDlguzGh14OUAx9Cp5c2mOtKp8yvvZHW68Y8T3O/50XFvCbXppU6ePtztV5rPwwJ9+jFOIAsApwdM6NFSaR4SvNFxJ4ZN48hM8p9VEAzKaBy9Qh3MCgBUAfgry7KVAKwAfjz1AQzDPMgwTC3DMLUajSbE3Vm4QCU+gMDlDogXnwEEM5SDiEbRMlajhSBAGREA4E+08xiGU2Zn+vMYHuuXnRUAxKLJNlGA/VYkmFxDFrDMCZ/B+SHKn6uPPCZgOZSZjhdLYSWP0+lZXAFAIhQEXKsarEDlm/g8BjMMDc5jAr2tgM8Djwk8/vk8b+mdQGt9+XwGfB4v4D5xvk0caH+YWOcd6HkxfD4EYbzGv5LH6mIINIZYsIGPmwHL58xwDJ0hN0CgxzIzvHbANn7gklQzfxdE57kCCY+z/SbkKmIDnncGI08pRb/BRvVmo1CojxT9APpZlj028f9X4Q1ufViW/RvLsjUsy9YolZG7ljJfGeuXqn5roRz5qqi80bwkylJicf+0lP0ZiRIUhn76ZUhEy1iNFjnJsbi6gjuVrTI7EfkTpSkKVDJ87WJuEgelLAblE1k5E2NjcOemXE7AIOLzcGnJ5JTS9bnJfgHO/dvykC33jsGSlDi/q7j3bslF2cTUtnyFFFdN6+OOEiUKlN79PiNRgu9cVszZnpEonlfB9sW2ksfp1avTOBcXGAa4b1tuwIsWwSpOkSF/WmmeOzZko2SOElO5ilh8cX0Wp+18eZMylQxfnlaaJzNJgiKVDKVp8XjoYm5pnqRYIUpT4yCXxeC+rdzniYU8bJnIEH9LdRbns/J5DG6cKM2ztVDhV6bq3q25SJ5lqnSoreSxuhiqc5IQN209eIFShh9cwT0mKWSigOXCEmY4hu4sDTwtf2O+HJJpgW6cWICvTCsllRovRqFK5lf27Ns7i/z6IRbykJkkwdZC7tKjHSVK5CuiNsUKCYMzA6YFTS8+T8DjoShFhpM9hrkfTCLKUmQzPgDgAZZlWxiG+XcAUpZlfxjosZG+Zqa+14C6HgPqew1Ym5WIDTnJWLc0JTii1pkBI5oGzTjQpkWeIhZbCxVUmof41PfocarfhNpuPSoyErAhNxnVU5J+DJlsONapx6fNI8hKjsWOUiWnXMOA3opzQ2Z83KSGWMjHzlIVNubEQyyePIk60KbBZy0aGMccuKwsBavS4pA9pTxQbbceB9q06NBYcFGhAtW5SShUTQYqAwYbDnVocaxThw15ydhaqEBm0uRJlmHMgeNdenzcpEaRSoZLS1NQuDS1lGmcTuPxsGjoN2LPuWE4XB5cVZGKdVlJAe9UzceJbj2OdOjQqjZjc4EclVkJKE8PnFBnqmGTDUc6dN4EZ9lJuKhIgZyJk62zA0acHRjFwXYtCpQybC2U+5KftQ6b0DhkwWctI0hNEGN7kdKXHEpnseNEt3e8xYmFuLRUhYuKvIGgfaI0z4fnhsEwDK5clYJN+Qrf5z/QqsG+5hFY7E5cXp6K9TlJSA595k5aMxtCxzp1+LhJDZ3Ve3zblJcMAZ/BiW4DPjo3jBy5FDvLVCiZIbeH2mjD6QET9k45hm7OS5wxs/zBNg0+aR6ByebE5eUpWJ+bDPWoHecGTdjfqkWuPBYXFSmwIV+O9hEzPm3WoHXE7D0258kRK+LjZK8R758ZRIJEhCtWpWBNZiJO9uhR22NAQ58RldlJqM5ORCWV5iHz8PXn6pAjl2J78cIvjL1a1wdlXAx+fFXZIvZs0dGa2WmWIphdB29pHtH/Z+88A9u4rkT9oRHsINh7p6hGVVKS1SzJLU7sOOu4xC2W5dhxS92azdts9u3Ly242L8lmkziOi1xjO4lrmrstq5KiqEpR7CTYid478X6ABDnAUKJskSLt+f5YnsEAQ+DOmXvm3ns+oAu4KxQKiT72WAg3M6/Xy6jTT3aSCrV63pfvnjfYXT6QBUlJ+GhrGeaYeZMkBIJj+MdCMU/DFxL+wBh2j/+snWaL00uiSinww07F7PASp5KTJKIhgbAuSyYLkZYoPspkdXsJBpn2HLz+IC6fH22S+PGBQACjy09GogqlUrwyrsXlJUGlENWhzBLzpp3ON+xuH8ExSBNRMbm9AfxjIVJFdDeB4Bi+4BiJcef3G/p8PkYdPrKT40STALPTS4paJaqWGrW6UceBRiQ2Wl0+FKExkkXapd3jRyGTkShSqdntDU+Ti1a1QNhFGyJEYpyk5pkPjI2FcPuDM6647fQGSFApBLodtzdAMBSakW4pOBbCGwjGtHGbywuyEKkJ5x6pd3kDBESuIavLh0IWJHlKW57umjI6PCjlcjRTVFderxez24824aL1r6RkdgGz9Ufv89D2SorSP/qI/skBK385OcSrD266gGd2wZGS2ShmvdcVCoWOAbWz/TlzweEeE388Pkh9l4naUi3XrSpgXZlUOv5sNPWa+bBdz5vNw5RlJnHrumI2V0nTyc5FKBSiSWfm0Q+76TO7uH1DMVcszV0I/jMBh7uNPFev48ywncuX5PC5mlyW5E9qa472mvnjiUEOdBpZWZTGF9cUCq6plkEb77WO8OcTwxRqE7h9Q4ngqeuZIQvH+2280NCHWiUPa3HyUykdH3k12l0c6bPxzIEerJ4AN6wtpLZEy9Ip57C/w8DzDTo69Q6uWJLDFUtzqSmc3N/YY+L1Kdf9F1YVUDflHNtG7LzZPMxfTw5TmpHIHZeUCPRBEnOHyeHlQJeRZw724g+Occu6YjZVZpCflogvMMa+dj1PHujF7PJxw9pCLlucTeF4x+dYn4XH93bRqXfypXVFXLUsl5zUc3fs67uNvHZ0gCO9Fi6pCCvbJqq1nxqw8tqxQfa261lZqOHmuiLWjI82He018X6rnrdbRqjISuZLdcVsrppQ81g50GXg9eOD5KbGc/uGEjaWaVCr1QxZ3XxwRs8Lh3Ukq5Xs3FTK5spMEuKUWJxe9rQbeOZgLzLgyxtL2FKZSVqSGrcvwN52A08e6MHpDXDLumK2VWeRq1kQDxg/kZwZsvHMoV6O9Jr5XE0e160uoHiajnivwckrxwZ449QwG8ozuHV9MeUZSeztCLdpo9PLjWsL2bE4m6J08amWzYNWnj7Qw4kBK9etLOCalXn4AkEaey08X6+LxNC6kjRyNLHn4fUF2NdpZPf+bmyeADfXFbGjOpthm5u97UbeODVMcUYCt60vYUtVFif7Leze30PLsI0b1hZy9fI8XL4A9V0mftfYR7JayR2XlHJJuZa2ESevHhugscfM+vJ0rl2ZT52k5pGYIXaPn1G7h/y0jxfPqrKTaR224/EHp107LjH/mLMhhIVO65CN77/eTPNguEpp64idwz0m/vtLq1iSJzklxTDY3fyusS9S6bBlyM6+dgOP76yTblLn4PSQjVsfrccbCFdh/c7Lp3B4gtyztfwcR84fTg9aue/ZJoxOHwBnhu10G5z8x/U1pCSo6DM5+cFfWmgcX59yZtjOoS4jj9yxlsW5qfh8QV4+OsCje8OVOU8PhadkPrGzjg3jU9VPDdr5x5dORj6zvtvEb+5YG0lmTwzYuf/ZpkgFzhP9Vv712qWRZPZIj4kHnmuKFHxoGbIzaHXz/WuWkZygonPUzvdfb+bUlOu+odvEL25ZTXVeKl5/gKcP9PDseIXZ00M29rYbeObudayagYdU4sJyuNfMQ789Gvn/Jp2Fn9y0kuvXJHK428S9zxyJVLc+OWAlMBbi7s1ltA7bufXRQ7h8QQC+91ozRoePb1xWJRgBi6Z50MI/v3ySzvHq1q0jdo71WfivG1agTYzjB38+zcEuExBu3/s7jTx11zo0iUqea+jjD0fC1YdbhsJLMR6/cy21pRm83TLMz9/rGP+MiXZfy6bKLN47M8p3X5lU8xzsMrJ75zourc5if6eRb7xwTPB9PHzbGq6uyaO+28R9zx6JVDQ+3n+SH15fwy2zr+aREKHf7OLO3Q2M2MKVss8M22kZtvHjG1fGjGI6PH7+5fVTfNhmiLz23ZYR/vOLK7j3mSORisanBk7jC4RE7xO9Rid3PN6AaTwetwydYdThYWleKv/whxOR1x3qCsfQK0WS2fqesKZqajz9+S2rONxt4plDkzFwX7uRJ3bW8pWnGrF5AgD8+59aGLS4WVmYJlBLHewy8vw96/mX15ppGwlXOm4dsXOk18z/u3GFwAkuITEdZ4btFKcnfaz6CBAuflaUnsDxPstCWBInMY5UKm6G9JpckUR2grYRB71GqXT8dOhMnhj9RLhkf6wiQELIqQFrJJGd4JEPOxm1LRyNRuuwPZLITvCXU0MRNUOn3hlJZCfoNbroHFc3tRscPHtIqChx+YK0j4Tbj9nh44UGoRIiWivR0G2OUUk8V6+je1zN066P1Ue8cnSQtvH9XXpnJJGdoH3UQbdxUh/0u0ZhG7d7A5wZltr4xeCNU8Mx237X2IfL66exN1bT9MzBHgYtLs4M2yKJ7AS/+bCL4XNcb70GVySRneBYnxWdyU37qCOSyE7Qb3bTOmKjx+Di1aMDgn1Wt5+OUSfNA5bIw5EJvIExWocdmJzemGtiLAT7OsKVgF8WU/OMf86eNn2smudQL2aXN+YYidmnfcQRSWQn+MvJYXSm2D5Fj9EVSWQnMDh8HO0zx6h5nj7UQ7859j3aRuyRRHaCuhJtTAyF6dU8BzoNMfF00OzhxcPCdufwBugYdUQS2ci5HeyN+ZvHQuG/byKRnaB50EafyS16HhIS0ZwetFGScWFmmSzKSaGhx3TuF0rMG6RkdoaIqRLOtl0C5HLx72ehqnnmErGCNfEqhajuYL4ipqdRyeWRJ6eqaf6WieOUcllM9VXB+8pDoiqWqaMaapHjE6Z8j6LqKIUMhUw+/m/xtjpxnHIaVYoUFy4Oou1BpUQhl6NWxu6LVylQKeSiv1e8Ss65fsZp24dCjlIhruZRyeUoZOL6HaVChkIhE1XsqBTh4+JF/o6JNfUJImt9J74TsXX3iSqFqHpIYvZRifzGYeWTuLomevNYKCTaphNUClElmVh7C6+dFWkXavHplWJtSCabRgUkpkU7z37UxVScSSwsTg5YKdJeGLtIdU4Kh7ok3+xCQooUM6RQm8DlS7IF2y5dlEVRurTeaDqWZCdyzxahmqdQmzBXlV4XNDUFGtKjitf8/VXVc1F59IKxJC8l5re+45ISqnPC2xblJvP5lfmC/XWlWqrGj6nOTeWBbZWC/TmpahaPK1G0iWpu21DC1JxYrZSzY3H2lPdLJzmqsMpXtpRRNK7mWZybErNG7e7NZSzPD39GRXYSVywVXvdbqjKpHFfzLMpN4b4ojUpBWgLLRDQYErPPVctyBS5MuQxuWV+EWqWgtlRLarywLdx3aQVZKfEszU8lVyO8tv7+qmpyUs8e30szErmkQjgV7TPLcyjLSGBxTirXry4U7FtVpGFxXgpV2Yns2lwq2FecnkhlVjKLczXct02opMpIiqM6NwVNYhxfidKgJMYp2DRe6fiGtYWCB17KKWqerVVZgmQkrC0qIzVBvGqtxOyyKCeZmgLhFNpdm8pE1SJlGeF6E1NZWZjGqqI0UhOi2vTWCrJF1npX56SwKEcYj/uNbvEYWp2NGJeUZ5AUlfxWZqfw1a2xMbAyO5mSDGFs/ebliyjLEm5LUCkoTk9ka5WwzsAVS7Mp0kr9K4mZcXrIFtPePirVuSkc67MQCI6d+8US84JZr2Z8Psz3aoZHe80c67dwos9CTWEaq4o0kWIeEuKcGrDSMmTjUJeRkowkNpSnR/QT85h5USW2ddjG+616hqxudizOprYkfcYVL+cLJ/ut7O800D5iZ315BhvK0ime0lk7PWjlSK+Zpl4zS/M1rCtLZ2XRpPKkz+SgsdfC/g4jBWkJbK7MFBRfGjE7ODnk5IPWUdQqBVursqgrSiIxcfKmtq9dz74OA1aXn0urs1mel0xhxmSn7kiPiQNdRroNTjaWZ7C6WEvFlCT8RJ+ZJp2F4+PX/eriNFZPWQ87ZHFzqNvIvnYjpZmJbKnMnKv1svOinc4ngsEg+ztN7GnT4wuMsa06i/VlWpLjwwlbQ7eRD1r1mJw+ti/OZkO5Fk1COIntGLWzp1VPr9HFtuosakvTRSseR9M0rms7NWBlVbGWmoLUyH2hU2/nUJeJIz1mluansrEiI7Je+1S/hZZhOwe7jJRlJrGhLJ268djYMWLn9JCdve16cjXxbKzIiBQVc7j9HOox8V7LKKnxSrZVZ7NhPKH2BcY41GXkvTOjyGSwvTqbDeUZkZGzg50G3m/V4/AEuGxJNutL00mewd/4MZGqGU9Dr9HJ/g4DpwZsbKrKZH1pOpkp4g8sR2we6ruMHOoysbJQw8bKTIrSEwVtelt1FhvK0klLEn+PboODfe0Gzgzbw/qcsnRkoRBHdFZBDN1YnoZKJd4uDnUZ+aB1FJs7EL6GytLpM7toGbJxoNNIcXr4Ac+6sgy69A4+bNOHtWeLslhXmk4gGKRp/POS41VsqcpkS1UWTb0mjvVZOdlvYUVRGqsK01g99+pDqZrxAiQQHGP599/k4dvWXrCiTd95+QT//aXVgv7IPEKaThOFlMyeJw63D7c/QKJKSZL0RHvG6O1u4uQyNNOoT+YZUpJwHvgCQfzB0LSJts3tw+7xUzDNFKBAIIDVHSBRrRSdJgkwZHGRoFJM20mzOsNrG6drX063H5l8jMRpdA8WhwenL0jBNFVAx8ZCeAIB1ErltAUm7B4/8UqF6PTBWUJqpxcYlzeAxx8UnQHh8Pqxufzka2Of/js9fhzeABq1kvgoPUowGMTg8JESryRRRJVjcniQAdrk2LZrc3mRASmJsedjdXqRy2WkiNyH3P7w+l+xaaEef5Dg2PTX6ywgJbNnIRAcw+MPzkirMx1Wtw+fP0hW1EwCh8dPnFJO3JTpyIHgGG5fkJSohxijVjdyeYjMlHOPbjncfgLBMdKirhOj3YNSLkMzJU77A2N4g0GSo9q+w+NFHpKROKX9Otw+/GMh4uUyEi5O/0pKZhcgHaMOvvx4Pf/vplUX7D2fPNDNmmItX7204twvnnukZDaKhTXMc5E53G3ilaP91HebqC3R8sW1hQthlPGi0qQz817LCG80j1CWkcSdG0skNc8nhOBYiMM9Jh7+oIMRm5edG0u5YmkOGVM6OAc6DTx3KKzmuWxJNlcvzxWMajb2mHjtWFjNs6pIw021RYIKgsf7zHzQqudPJ4Yo1Cawc2Mpl06ZAtdndLKv08hz9b2oFQp2biplU0VGJBlx+wLs6zDw+L5ubO6wSuLypdkUpE0duTXwzKEeOvVOrlyaw2VLslk7ZcZF56iD5xt0vN86ytaqLG7bUExldkpk/6DFzZ9ODPL7xn6qcpK5d2sFq+bn01yJs7CvXc/j+7rpN7v5/Mp8rl6eS2VO+Hc+2Gng2Yl2vDiLz67IY1VRuB3Xdxn5/ZF+jurMbCjP4AurCyLV2o/qzLx6bIB97UZqClK5ZV1xpH039Zp5p2WEt06PUJGVxB0bSiNqnjPDVt5r0fPasUFyUtXs3FTKpVWZKBQKBi0u3juj5/kGHUlqBXdvLmdzZSZJaiVOb4D9HQYe3tOJDLh/WwWbKjNJjFPiC4zR0G3kl+93YvP4uWdLOdurs9EkzplvViKK04M2njzQzZFeC9esyOX6NYWUiEwzng6PL8jeDj279/dgcvr44tpCtldnkRKvKYA9dQAAIABJREFU5K+nRvhtfS+lGUncd2k5a0rSOTVg5fG9XZwctHHdqny+sLoAry/I4R4zzzVMxtDakjTy0mKT2ol4+tjebuyeAF9aV8Tli7MZtXt578wofz01THF6InduLGXroixO9Fv4zYddnBm2c+PaQq5dmQ+EJttv3Hj7rcqkedDKS0f6aew1s74snb9ZXSiYhSMhMR1nhm0UX6ApxhMszk3lQKdxviazElFII7MzpGXIxrdePCaoUlqRlcwvbl3NkjxpfZwYerub/3yjLaKfAEhRK3l8Z+18fwggjXjNgON9Fr748AFBhdh/v24Zd1xSCoSn5e966jBm12S14KuW5fDv1y0jOzWBbr2db//+BEd1lsj+Qm0Cj9y+lmUFGjwePz96u40n9vdE9ser5Dyxs46N41MuXzysE6h5gLBWYlkuAHvaRrlr92FBFdd/vXYpd20Kr+Vu6DYK9BEAX1iVz798bgkZKfGYnF7ufKKBkwOTFY2rc5J59isbyEpR4w+O8cO/tAjOMSlOwWsPbZ6LteFSO71ANPaYuP3xejz+yTVSuzaV8s9XL6F5yMpdTzYKKsFeuSyH//ibGoZtHh54romeKVXtawpS+elNq0iMU/Ct3x2jvnuyYne+Jp7H7qwlO1nND/56JlJpGCA1Xsnjd9ZRV5bOj944w68+6IzsUyvlETXP0wd7+N5rzZF9MhnsvrOObYuzef/MKHc9eVjwtz11Vx2XVmfT0G3i5t8cZOot/2c3r+ILqws+1nc3A6SRWRH6zS7+5pcH0Dsmq/tetTSHn3xpFUnTzFCJZk+rnrufOiyIwf/4mcWkJSj5zhR9k1op58WvbmDn7sNYpsTjXZeUUJ2fGhNDH7ljLVeNx9CpfNA6yq4nhfH0f1+3jG69g90HJqtsJ8UpeGJnHXc/1YjDOxlb77ykhNrSdL72/KQ+SyaDPz60iW+8cJxO/WRF4yV5KfzsplVUz23/ShqZXYD81xtnGLZ5uGFt0QV7T6vbz9/+/hjHv3flfCxEJo3MRjHvfqH5Sp/JFaPb6NQ76DU6pzlCQmdyx+gn7OMl+yUWPk06c4zq5JEPuzA5w52zDr1DkMgCvHV6hC5DuOPfY3QJElkIq0u6DOFrqs3g5PkobYTHPxZpP3a3L0aLA0KtxKFOk4iORBdRHHXqnTH6iD+eGKJ7PDnpNjgFiSxA64iDbkP4HAYtbp6JUqU4fUFah4XHSMxvzgzbBYkswG8bdHQZnbSPOGKUJm+fHqFD76Db4BQksgAnB2z0GF106B2CRBZg0OqhY9RBj8nF68cHBftsngAdegetwzaebxBT89gxOb38NkrbEwrBvo6wtiX6OIAXxz3fe1pHiX52/ejeLpzeQMwxErNP+4hDkMgCvHl6hD4RNc90HO+PjcHP1feSkSycousNjNE27BAksgDrytPPGUOncrDTKKp3GrUL/w6nL0jHqEOQyIbPTcewVai7CoVAZ3QJElkIu5d7z+O7kPj00jxkoyj9wo7MahJUZKWoOTFgvaDvKzE7SMnsDDnfkvISYW2JWkwxMXdrCiVmETFtTrJagWJCW3MuNY9CLq4uiah5IEFEGzGxXyGXkSSikEieUrFWTDGRpFagUk6eQzRxCjny8XOMU4gXk5g8R7moKkWKCwsLMbVIYpwSpVxcoTPRjsWOg3CMUynkiC2vjlOG25dobFTIUSlkAr3U5HsqUMpkZ1WpiE0ZThvfJrYmUxOvmnYNuMTsIqqzkctEdWHTIabmSYpTEgjGDh6KfZ7DGxCNoSnTrKcWa3vJamWMexbE43+8SkFIZGAzTuTvACmOSsyMtmF7jJXgQrA0L5X9HYZzv1DioiNFihlSlB7PVctyBNt2VGdRmLYgChpdFKqz4rk3qmR/SUZYPyGx8Flbkh7pKE/w7Suq0YwXFlmUnRzR8Exw58ZSlo1PG6vKTuILUWqeDWXpVIxrb5bmp/FAlKIkTxMf0UskqlXcEaWViFcJ1TzryzJiOmb3bi1HO15QpzI7ibJM4Rq1e7aUsTw3vK08MylmGubVy3OpGG/DBdoE/u6qRYL9pRmJLJXUPAuKpXkp5GmEsfyh7ZWUZyVTnZfC4pwUwb4vbyxhWZ6G0oxEtkQpRa5ZkUdFZhJLclO4Ya1QzVNboqUqK5llObHasrLMJCqzkyjPSolp91nJapbmppCaGMc9W8sFD4GS1Uo2jU+7v7m2CJVicqdKIeOL4+ewpSpToFWRyeCB7RUXrPqnxPmxKCeF1cXCtfX3bC2n5Dw65auK0mJi8L2XljMWZRTJ1ahZkpfCklxhXBq2ebl9fWwM3bZ4GjVPhTCeymThc46u+FqoTaAqJ5nyqNj67SuqWJSTImi/SXEKCtLi2VEtrKVx1bIcSjMkNY/E2bF7/JhdPnJSLnxffFmehg/b9Bf8fSUuPNKa2fPgqM7MqUErp/qtLCvQUJOvuRil4xcULYMWzow4aOgyUZyRSG1peqQ4yjxGWos4Q1qHbextN2BweLl0UTari9MEneOmXhONvWY69U5qS8LaksV5k27F5gELx/ttHO8zszgvldVFaQKtTbfewalBGwc7DeSnJbCuTKh2cnl8HOqxsL/DQJxCzqbKjJgCY4e7jWE1jzvA1qpM1kXpSBp7TBzpNdNjcFJXlh7u9E05xxGrm4YeE006CysL01hflk5e2mQny+b2cURnYV+7gbLMJDZWZFA+Nw9spHZ6ATnWZ+ZAp5F+k5tNlRmsK00na9zXeUxn5nCPiQ69k7oSLauKNVRmhxODozozJ/otnB60UVOYxoqCVFaMF4dqHbZxVGfhqC7cvtcWa1kx3vFvHrDSOmLncLeJ0swk1pakUVsabtvDVjdNOjMHOoxkp8ZzSXlGpBiO0+unodvMh+16UtRKNldmsm68qNTYWIgTAxb2tOqRyWRcuiiTmoK0yEyD5kErH7YZcHoDbKvOYkVh2rSjyxcQac3sNPSZnBzsMnFmyM76Mi21pemCAnoz4VCXkYOdRkxOH5srM6kt1aJWKTiqM/Nhm55CbSKbKjOozE6h1+jkQKeR9hEHl1RkUFeqRRYa40ifLRJDN1ZmsOUsRRobxuOpzR1g66JMNpSlM2hx0zxo51CXkaL0ROpKtdSVZdBtcHKgw0CXwcmmygzWlmhRyuU0dJv4sF1PUpwyHJPLMzjaa+bkoJXmASvLC8P9qzlSnE1FWjO7wDjSa+afXjrB/75u+QV/b48/yAPPNdH4vy6fb1pEaTpNFLOezMpksh7ADgSBQCgUqp3utQvhZuZw+xmxechIUZKWKD01nCntIzZSE5TkpF74qSCzgJQknAceXxDf2Bip06glfIEg3kBYzSATmVfsdPsYsHnISFSRkSJ+TfUZHSSqVdN29PpMThQymag2BUBvdeMNjlE4jXrH7PTg8AYpmmZ/cCyE3eMnWa2cthhEt95BaoKSDBHFyiwhtdOPgNsXxB8cE3XIOtx+3IEgWSJP+V3eAHaPj6yUeORRU0GtbjcGm5/slHhSEoXrFX2+IMN2N+nJ6hg9CUCX3kaSWjw2dusdJKoU5KTFXhdGuweFQk5aYqzCZGIdrFgHzOULEBwLkfIxVDDnyacumfX4g/gC4m1spji9fkB2wTvRvkAQty9IaoIwHuuMThRyGQVTYuh0cW/E6sYXGKMoqvJy56iNlHgl2VPasjcQxOsPkhql2rF7/Cjkwin1RpcLoz1ATkrCxaqyLSWzC4zf1ut4p2WEe7aUn/vFH4Ef/rWFr+2o4oqlOed+8dwhJbNRzNWjhu2hUGjBTzxv6Dbxu8Y+GrpN1JZq+VJd0XyvynvRaeo18WbzCG82D1OamcSuTWVsXSSpeT4JBIJjNPSY+J/3Ohi1eblrUymfWZ5L5pSE89SAlUf2dHJiwMrnV+Zz49pCiqd0gBp7TPzhSD8HOo2sLErjtvVFbCifnLZ5ot/C26dH+NOJIQrS4rl7XCcywZlBK4d7zTxXryNOKefuzWWsKdZQlB4eGTU7vRzqMvHY3m6sHj831xaxZVEGi3MnR173tofVFl16B1cuy43RB3WM2nn6QC8ftOnZUpXJnRtLWTRl2ukxnZk3T4/w15NDlGQkcfdmqY3PRwLBMeq7TfzPu+3oHT52bS7lM8tyIw9IPmzT8+iHnfRbPHx+RR7XrMynavx3ru8y8vi+blpH7Fy+OJvr1xSyrEAT2fd8g44mnYUN5encWFsUmX3SpDPz0pF+9rYbWFGg4Y5LSiJqniM9Zt48PcybzcNUZCVz16bSyIjY8T4ze9r0vHo0rOa5e3M5G8u0JCbEMWBx8VbzCL+t15GsVvLVS8vDU4jVKhzeAB+26fnV+x0APLSjki1VWSSplfgCQQ52Gvn5u+3YvQHu3VrO5UtyRJNhiY/G2Liu7BfvdzBocXPHJaVcvTyXnNSZP+ByeALsadPzyw86UMjgoR1VEfXSx+Vkv5WH93TQPK7muWFtES6Pn0adhWcP9QpiqDcQLu703plRNldmsnNTKRlJKuq7zTy2twu7J6w621iejjcY4q3xGFg8HgMvXZTFMZ2ZX33QSduInS+uLeT61QUkqpW82zLCbz7sQpOg4qHtlVxSkcGxPgsvHO6jscfMurJ0bqotYp2k5pE4B2eGbeRrZm9gaUWhhndbRuZbMisRxVyNzNbOJJmdz09mzwzZ+PoLR2kbmay4V56ZxP/cuppl+ZqzHPnpxeDw8MO/nOGlpsmKxklxCnbfVTffHwJII14z4KjOzA2/Pigo/vH9a5eyc1x702Nwct0v92N1T1bQ/FxNLj++cRUJcQrahm383e9PCKoFFqQl8Ovb11BTmIbXG+D/vnGGpw5OVgtWK+Xs3lnHxspwwvt8vY7vvCLUSjx8+xquXp4HEH5i+3SjoIrrdz+7hHvG13I3dBvZ9aRQH3Htynz+7dolpCfHY3R4uf3xelqGhEqu5+9dT3ZKPDa3j//z5xZBRdDEOAW7d9YJfLmzhNROz4OmXjM3/PqAoBrrhEqqodvIHY834A1MLja8c2MJ//LZpbSO2Lnt8XpBJdjLFmfzk5tW0m92c/9zTeimVF1dmpfKT29eSZJawTdfOE5j72RF49zUeB6/s5b0ZCU//EuboKJxslrJEztrWVWQwk/e7eTXe7oi+1QKGU/cWceWRVns3t/Nv/3xdGSfTAZP3FnL9sU5vNsywt1PCX/v3Tvr2L44m/ouIzf/5pBg309uWsn1a4TremeBT83I7MkBC9f/6gD+KQWY/uEz1TywrXLG7/H26WHuefqIYNtTu+q4dJH4OtaZ0m1wct0v92FzT8a6+7eUUpyZEhtDb1vDrz7oEFRyL81I5H9dszQmnv7mjrW82zLKi42TlecTVOEYePdTh3H6gpHtuzaVsjQvlb/7w4nINpkM/vTgJr72wrFIJXuARTnJ/PxLq1ksqXkkzsKNvz7A5UtyWFE4O273QYub/3zjDPX/fJnozLKLxLw5kfnCXBSACgFvyWSyIzKZ7N45+LxZQWdyCRJZgC6DE51RKh0/HTqji1ePCfUTEyX7JRY+x/osMVUsH93bjXFcN9E+6hAksgB/OTVMvzl8zfSb3TFl7wcsbron1Dx6h6CDBGHFRMe4wsHodPP7I7FaiXdbJrUSh7tNMTqSFw7r0I0rtTr1zhh9xJ9PDNId0Qc5BYls+BhH5By79E5ebhLqp1y+YOQcJeYPR3pjNU2P7u3G4vLRNuIQJLIALzT00WVw0DZij1GavNc6SqfeSY/RKUhkAU4P2egzuejSOwWJLMCwzUOn3sGA2cufTghjo8MboGPUSZvBxQuHhe3eHwzRNmrH6PDyQpSu6pxqnvFraI9IIZPH9nVLap4LSPOATZDIAjy2tzuiAjsXoVCI5w7F/oYvicS586V9xC5IZAGuXpEvGkPfaRnh3qhpmysLNdR3GWPiaSgELx8VvofbH46BUxNZCCcGj+/vjjm+1+QSJLIAbSMOeqX+lcRZCIVCtI04LriWZyp5mnhUCjmnBiTd3nxmLpLZzaFQaA1wNfCgTCbbOnWnTCa7VyaTNcpkska9fv5WDZuuSIaYXkEijFwmJ1GkUuYcFByZFRZKW50rEkR+29R4ZUSnEC/yO8cp5JH9cUpxNY96/H2VChnJIlPrJjRZKrkcTULsfs2UtVlix6eolZHrVky5Fa9SoBivCDudMmJCiRFe8yXSxi+iUkJqp+KI6W40CSqU0yh2ktXhtiy2L04hJ04xvZonTqkIK56mUfMo5DLR60etlKOSyUXbrVoZVkqlxMfum1iLmymypjwjKXw9iK1pT0sM//0Xi09aWxVTiZ1tnX00MpksxhELnHdRKDHE+iouX2CaGKpiwCxMJEdtHtF2CeJ/t9i1EQiG0CbE/n3qaSpqq0X0bxISE+gdXkKhEGkfY236uZDJZKwt0fLGqaFZ+wyJj8+sR4pQKDQw/t9R4BVgXdT+34RCodpQKFSblTV/15kVpyfyuZo8wbYrlmZTnLEgChpdFFYVp3HfpULFRHlmEpVZKdMcMb9ZKG11rlhboiU9Sdgx+durqiNFT6pzU1hZKJyC/9COyshT1BJtIl+MmuK4uTKTkvFrakmehod2VAn2F2oTqM4Nt5/UBDW3rS8ReDITVAq2TVE81JZqSU0QqiS+srWcnPE1NlU5yRHNzgRf3VrO8rzwZ5RnJXFjlF7l2pV5EX1QTWEa90dpVEozEgVraucaqZ2KU1eWjjZGJbWI5HgVy/NTKdQK11197bJKyrKSWZqXytKoqY67NpWxKDeVkoxEtkcpRb6wKp/SjHiW5KZwc12RYN/6Mi2LclJYmpMYoy2ryEqmIiuJ6rxUHtwunJaak6qmOjeF1PhYNU9qfLiiMcBNtUWCBylqpTyiB9q6KFOgVZHL4MFtldMmEnPBJ62t1hRoyE4VJp7/dPXimDh5Nm5dXyLQK6mVcq5bVXCWI2bG4rxUlhcI2/HRPqtoDN2+OJsek1vw2rQkNXVl6THxNClewYNR06hLMhKpyk6mKlsYW7cuyuKBHZWChzwpaiVFafFcsVQ4jfpzNXlS/0rirLQO2ynJSJr16b91pVr+fHKI+WR/kRAyq2tmZTJZEiAPhUL28X+/DfzvUCj0htjr5/uamaM6Ey1DDlqGbFTnprA8P/VilI5fUJwestExYueIzkxBWgJrisP6gXmOtBZxhrSN2DnYacDo9LGpIpNVRWmCznGfyUl9tymi5llbohUUnDnWZ6Z12MGpAStVOcnUFGgExZf6jC5ODFho7DGTq4lnTXEadVPWW1tdXpp0Fuq7TcQp5NSVpcdoJQ52GjjcY8bu8bO+PIM1RWmkTxnpaOoNa3d0JhdrirUsL0ilMnsyGR2xeWjsNXOq38KyfA21pVpypxScmNAHNfaa5rqNS+30PGkdtnGwy4jZ6WdjZQarCifb6/E+M/XdJoasHtaNK8QyU8Lt5PSglYZuE90GJ6uLtdSWaikcr/p6VGeiedBO67CdpfmpLM1LYeW4mqdtxMYxnZWTA1Yqs5NZU5xGzfjartODVtpGHDTpzBSnJ7KyKC1SOEpndNA8aKehx0RWspq1pVrWj7d7ty9AfbeJg51GkuIUXFKRGdH2hEIhTg5Y2d9hAGRsqsygpkAT6eydHgprrpzeIJsqM1hRmBaZKTGLfGrWzEK4YNzBLiOjNi8bKzLGdWUzL940Njb5GyrkMjZWZLB8ym/4cdCZXNR3G+kaV0ytKdEiI0STzkp9txG1UkFdqZbNVVmM2j009pg52W9hab6GutJ0cjXxHOg00Nhtxu4Nx9PVRRrMTh8tw45IDFxdpKWuLJ1eg5ND3UZ0RhfrytJZXawlIU7B8T4LBzqNJKkVbKzIZEleKsd0Zk4N2mgdtrMkL3wdSWoeibPx2N4ujvSa+fIlpbP6OaFQiG//7hiP76ybLzVypDWzUcx2MltOeDQWwpWTfxsKhX4w3esXws3M6HBjdgbQJiqn1YhICPF6vYyNjWFxQ176gvjOPjFJQnAshMXlIzleGZkae77YPH5kIUj5iFN5HF4/VpefnJR4lCJTzxxuH3ZPkMQ4BZppRjBGbS7USiWaaSqv9pscKORy8tLEn+Q7vQHGgmMx2pQFziemnc4XRq1uHL6gqCfY4vJicfrJ1yQQFzWt0h8Yw+X3kxSniplS6vIGGLF5SE+Mi2nfoVAIl8eHQqkU+JknsLt8KJRy0SnSNpcPhUI+3/yHYnyqklkAi8uHyxckP0qpZHb6UKvEf89o7B4/zDDuOr0B/MGxGVWmtrv92D1+cjWxiqmZ4vQGIDhGUtTnDZpdqFXyj6wns7p8BMeCKBWKGJXPHCElswuIb794jPSkOC5bMvuVhl88rCMrJZ7vfm7JrH/WDJCS2Shm9S4YCoW6gJWz+RlzSX23kd8e0tHQY2JtiZY7NpTMRcXSBU1Tr5k/nhjkreYRSjLCU+u2VX+8qowSM6NL7+CZg7282TzM6mItD2yriOhEZoLN7ee9M6P84v0OlHIZX9tRxbbqrPPqPB/sNPDIni7aRuxctiSbL9UVC86hscfI8w19HOw0UlOoYefGUi6pmFTztA7beeVof0TNc/+2SrZWZUY6Ye0jNvZ1GAVaiU0V6eSlhacB+wJBDnQa+dk77VhcPu7eXMZna/IuyBo0iU8OgUCAPe1GHvmwk0GLh8/V5HF1TR6risKjqPva9Ty6t5v2ETs7FmfzxbWFkRkEZ4Zt/ObDLg52Gtlenc2uTWVU5oST4cYeEy8c7uNAh4Fl+Rp2bZ5s3zqTixcP63j16CDVucl8bUdV5D2HrR5eOzbAs/W95GsS+Nbli1hXlo5cLsPk9PLGqWEe3dtNSrySb12+iI0VGRd1urBEGF9gLKxG+qADvcPL9asKuHZVPinxSl4/NsjTh3rJS03gm1dUsb4sQzC9dwK728+743FXIYev71g0bdz1B8c41GXkZ++0obf7uGtTKdesyBP1JAMc6DTw8AeddOmdXLYkm5tqC1leMPMqsL5AkP2dRn72dhtWt597tpTzmZpcBs1uXjs2yBvNwxRqE7jv0orzvs/Xdxl55mAvR3Rm1pWmc9uG4vluPZC4yJwZtnFzXfGcfNamykx+9EYr/3T1YtHrVuLiMutqnvNhPj+ZPT1o5WvPH6NzSpXS0oxEfnnrmvNKED5N6K0efvDXFkFF4wSVgifvmhNtycdhwY942d1+7n22kYOdpsi2zOQ4Xn1wU2R65Ll4s3mYrz4jVEQ8eVfdjDspJwcs3PZYvaCC5o7qLH568yo0iXF06e18/fljnBqcrBKYp4nn0TvWsrwwDV9gjH//02meOSRU8zx997rIlMvnDvXy3VdPCT734dvWcPX4+vbDPSZueuSgoALnD76wnNs2lMzob5jnLPh2Ol/Y32Fg15OHBRWNb19fzHc/t4Qzw3bu3N0gaMfbq7P4rxtWEAzBDb8+QN+U9YXL8lN55u512Nx+vvXicY72WSL7slPUPPblWqrzUvjuK6f4w5RKsklxCl57aBMVWcn897vt/Oyd9sg+pVzGKw9spKYwjd8d7uMfXppUmwC8eO+G+RxTPzUjswc6DHz5iQYCU8pm37+tgoqsJP7u95O/mVIu46X7N7KyKDaRfKt5mHuj4u7uu+oEfu0JmnRmbnhYqJv63jVL2bW5LOa1J/ot3PZoPfYp1auvXJrDj29cMeNRULF4unvnWv58cpg/HJms6h6vkvPkXevYMMM22TJk5b5nmwTViyuykvnVraupltQ8EiIEx0Is+9c3+NWta0ULkM0G33vtFP/8uSWi1+IcI2XTUUil4mZIn8ktSGQBeowuqXT8WdBZXPzxhLACnNsvqXnmAp3ZJUhkAQwOH50z/O7DiojemO2vHB0QebU4bcOOGBXEe616OvRhBUOPwSVIZAGGrB46DRP7HfxORM3TNhxW5VhdXl5qEtdKTHCwM1Yl8cT+bmxRyiCJTzcdo7Fqnt8f6adj1EH7aGw7fr9VT6feSZfeKUhkAZoHbfQYXfQYXIJEFmDU7qXT4GTQ7OblqLbr9AVpH3Ewavewe3+PYF9gLMTpIRsOj5/H9nURzYSaR+LicnrIJkhkAV483BfT8wyMhTg9GKv6CIVCPFcfG3dfnkbNc1RnidFNPbF/Uo82lbZhuyCRBXi7ZYROvTPmtdOxr90QE0/9QXj1qFAz5fGPndd9vlsf25fq1DvokfpXEtPQa3SSlhg3Z4kswJaqLF4Q0Z9JXHykZHaGxE9TIn4uL6SFhkouI0nk+5HK7c8+cUq5qHJDbF2eGDKZjKyU2Km42SLbpkPsmlEr5ZFqq2qVuJpnQlkSp5SL6kQi6h65THSNmHbKNo3IerP0JDVKhfRgU2ISsZiUGq9CpZCJKqYm2rFYG5fJIF6pQK2Si05HS1DJUU2zDjZepUClkAsqxk6QGBdWvIhNkRdr5xJzj5imKzVeGZPgTvdamUxGtsgU4egKyRMkq2PfIy1RJarFEYv98eMKqZmSlhjbzkKhkOgU6PO5z0/Xj5qu3yUhcWbYTvEs+mXF2FSZwf4OIwaRh0USFxcpUsyQ4vRErluZL9h29fJcirUfrdDBp4EVRVoe2C7UloiV65e48JSmJ3HfpUL1x9aqTKrOQxlz24YSQUcnXiXn2hX5ZzlCyLL8VGqiVBD3bCljybhaZ3FOCjfXCtUlly7KimhvSjOT+eblQjVPUXoCNfnh90yKj+P29cWCpD0pTiGYArShPEPQAZPL4BuXVc6oAIvEp4fqnJSYjtHXL6tkcZ6G6tyUmHb8lS1lLMtLpSIrmWtXCJVtt68vpiwzkSW5qdy6Triea1NlBhVZyRRqE/n7qxYJ9i3LS2VxXgrpSWq+c7WwyEh+Wjw1BRriVQq+tl2oNklNmFTzSFxcago05KYK+wRfv6wqJkHN1ahZITLFGOCWdcUxcffzK8XVPGtLtGRO8dLKZPC3V1STIvKNVaC/AAAgAElEQVQQcGl+Ksvyhe34q1vLWZI383vCxorYeJqVquZrO4RqnvLMpEicnwmVWUlcvTxXsO26lfnndb+S+HTRMmSjIG1uC4omxilZV6bl91EzxiQuPtKa2fPgmM5M24iD1hE7ldnJLMm9KKXjFxStw1baRpyc7LeQl5ZATYFGUvPMESanl6M6Cyf6J7QgWgq0Mw/+E5qPhm4TcrmMdaXpLD/P9eEtg1aO6Cz0Gl2sKEyltiSdvCk3oNMDVpqHbJwZtlORlczy/FRBJ8/k8HK418zRXjPZqfHUlmpZUTi53+MJcKjXRFOvGZVCzpoSLZuiOvatw3YO95iwewKsK9WyomhOdCRzwSeinc4XGntMHO+zMGL3srIojZUFqRSmhx+snOizcKwvrG9aXpDK6iItJZnhfcNWN006C63Ddpblp7KmWBtR+rQMWWketNEyZKcsM0mgc7O7/Rzts3BUZ6YoPZHa0vRIQu32BzjeZ6Wxx0Rmspp1ZemRCsuB4Bgn+q3Ud5tIVitYV5ZOde6cris8Xz41a2YBjvVZONxjwuTwsrYknXVl6cQp5Rzvt3C4O/x71pWmUzHNQ11B3JXJWFd29rjbPmKnoduExe1nXWk6K4s0xE1Tub5lyEpjjxmdyc3KQg1rS7Xkac4vIWgdttPQY8ThCbKuLByPDXYPTTorx/vM5GjiWVOsFSjWZsKpAQunBm20jzhYlJPM8nzNxahHIq2ZXSDsevIwNQWaGa/LvlB0jNp55MMuPvz77cgvXiEoaWpZFFIye55YnT5G7B4yk+NI/4jl5z9tONw+dGZ3WI6ekXSxT2cmfGKShEBwDIvbT7JacV6uwwvJqM2NyemnOD2RRLGKnIExbB7/tPqgYDBIj8FFSryS7Gk6XhanD5kcNB9R52Bx+rB6/BRoEkT1QedibCyEyekjQa0gae5GfT8x7fRCY3X5CMGMVCVTMTm8uH0B8tISYrQlRocHk9NPkTaR+KhpkRaXF4PdR25qPMlRU37d/gAGu5e0hLgYzcpEu0lUK+ZstoDN7ScwFiJ9Gg3WLPCpSmbnC8FgkG6Di9SzxM2p9BicKORQlD55j3Z7A4zYvWSlxJGkPvtU9lAohNnpIz5O2JbPJ7Y6PX70Th/ZyWrRe8UcICWzC4SNP3yXv7uyWvBwfC4IhUL88ysn+b9/U8PGizcjRkpmo5Dm2p0HDd1Gdu/vobHHzJqSNHZtKpvPFSTnBU29Zl49NsAbp4YpyUjkge2V86ES3KeCLr2Dx/d189bpEVYVpvH1y6uomeMn3Xvb9Tz8QSftow52LM7m5toi1pRMPrHvGHHw6N5O3mvVU1ei5aEdlSydIiU/0W/h9WOD/PHEIPmaBB7cXsn26kwUinAyYXb5ePPUML/6oBO1Ss63Lg9rLM4nMdjbrucX73XQbXBy1dIcbllfLDiHc9FncvHbBh0vN/VTnpnEt6+spm7+zz74ROL0BvigdZSfvtOOPzjGA9squGpZ7jmT2rGxMfa06fn5ux0MWt1cuzKfG9YUsni8kureNj0P7xlvx9XZfGldUWTkqb7LyKN7uzjeZ2V9WTo7N5VGZp8c6zPz5IEe9ncYWZ6fyn3bKiKVuHuNTp6r1/Hq0QEW5STz7SuqBdfGhcbjD/Jhm56fvN2Kwxvkq1vL+eyKPDKSJE3VJ41TAxZeOzbEH48PkqeJ58HtlWytzIzxI0P4PvHemVGePhjWm92zpYzti7IYsHp4fF839V0mVhence/W8mlnVfWZXLxwWMcfjvRTlpHE315ZTW2plr3tBn7xXjs9RhdXL8/lS3VFLJkmth7pMfGbvV009VqoK9Pylc3ls3o9SCxcbB4/ZpePnNS5H1CSyWRsq87muXrdxUxmJaKQRmZnSOuwjfuebaLbMFn5ryg9gUduX3teHd9PE0NWJz/4cyt/mlLROF4lZ/fOOoFLdB6y4Ee8bG4/X3mqkYaeyYrG6UlxvPbgJormqGhCY4+Ju3YfFlTQ3FKVyX99cQW5aQmYnT6+/EQ9Jwcmq3rmpKp55YFN5Kcl4PL6+fc/t/B8w+T6lDiFnN131UWmEr96dIBvvnhM8LnP7FrHlkVZMzrHpl4Ttz3WgNsfjGy7alkOP75hZcwomhi+QJDvvdbMC4cnz1GtlPP6Q5vmYurngm+nF5o9rXru3N0g2PY/t6zm2pVnX+t9qMvIlx9vwBecrGh867oi/vXapTQP2rnziYaYdvzjG1dicnq5+8lGBq2eyL6q7GR+fftakuIU3PvMEU4MWCP7spLVPHP3Osoyk/jHl07EaMtef2jTrK0TPNBp4NZH6wXb/vOLNXPhaZRGZucQjy/Iv//5NM/VT1ZdVSlk7N5Zx+aq2Lj4zMEe/uW1ZsG2X966mp++0y6oSJyviefpu9dRmS1sn/7AGN97vZnnp1R5VSvlPL1rHXfubsDjn7ymrl2Rx39cX0NS1Jrebr2DL+9uEFQGL89M4om76iid29lc0sjsAqCh28S/vHqK739+2UX5fLvHz7dePMaB71x2sYrvSSOzUXwiFo7NBd0GpyCRhbCup8cglY6fjkGzl7+cFKp5PP4xus5DBSDx0egzuwSJLIDJ6YvRS80mnXpnjApib7uBHmP49+81uQSJLMCIzUuXIXyOXQYnLzcJVUC+4KTywRcI8tTBnpjP/Wvz8IzPsX3UIUhkAd46PUK3cWZtdMjq4fdR2gxvYIy2EUk/dTH408nBmG3PHOwhEByLffEU2kfsgkQW4KWmAXqMLjpGHeLt2OCkU+8UJLIQblPdBgedBocgkQXQO7y0jzoYsLh57bjwXN3+IO2zqC3b06qP2fbkgR6cUX+bxMKmy+AQ+IsB/MFQRIk2FZvbFxNjAd5tGSVPIxz1GrR6RHU7Q1aPqEKtY9QhSGQB/nxySDS2dow6YhRXXQbnjFVyEp8uTg9aKc6Y20rGU0mJV1FTqOGNU0PnfrHEnCAlszNkummLkppnepQKGcnx4voJidlFrVSIqnkS5vC7TxDRKsSPa0km/i1WP2HiHNUquehTzwldg1wmI09kmlHOeeiDxL6P5DjljHUVcQo5KSJtfC6/Z4lJoivJAuRq4pGLOaCmIBaTUhNUxCnkor9lvCqs5kkU2SeTQUKckgSVQlTNkxinIE4pJ1nknjKb7SZTROmTnaJGJWmqPlGolXKBnmwCMc2NSi4jPTn2tRnJcaL6EbF+UJxSPAaKXVPJ8UrR4lTT9aPE9EUSEicHrBRpL14yC7ChLINXjsY+PJW4OHykZFYmkz1woU9kvlOZlcT1a4Tl8a9dkUdlzoIoaHRRWFmk5WvbhWqVxTkp01ZxlLhwlGQk8tB2oS5h++IsFuXM3XdflZ3MqiLhFPyvbi1n+fj027LMJL6yRagPumZFHpVZKePHp8aoeUozwsoTAKVCzq7NZYLOeIpayRVLc2Z8jkvzNSyNUlM8uKMyslbyXOSlJcRoVJbmpbI0f15Xl/3EctWyXJKnFI6JU8i5c2PZOatOLi/QUJYpjOXfvmIRZVnJVOcms7pYqFH56tZyluWlUp2bwmeWCZUiN9UWsTg3mcW5KXz5khLBvq1VmSzJS6FQm8g/XF0t2LeiUMOSGba7j8KWRZmCh0NKuYz7t1VOW/lWYmFSkZ3CN6LiZklGIotFVDkJahW3rS+OiaHbF2ezIqq+wmeX54pqfHI18Xz3s8IYuCQ3rJmqjrrffGNHFYtEptEvzk3l2pVCxdUX1xSclzZI4tPD6UEbZZkXN5ldXazlZL8Fk9N3Uc9DIsw518zKZLJvR28CvgP8X4BQKPSTC3Uy833NTMugldNDNjpGnZRnJbE0L/VilI5fULSP2GkfddA8aCU3NZ5l+ZqFUNThE7EW0eLycazPMh74k1hVnHbeGoaPy/E+M82DNvrNbpbkpbI8P4WyrMkOisnp5VifhZYhOxVZSawu1gqKOhgcHpp6LZzot5CVEs+KQo1A+TA2FuLUoJUmnQWVXEZtqfa816q2DFk5qrMyaHGzvCCVtSXpZJ3H6K7TG+BEv4VjfVby08Jaijlal/yJaKcXmpYhG029ZvxjIdaWpLE8X4PsHCOzEC6ac1RnQW/3UlOooa5ES9p4caSTAxZO9lsj7XhloYbi8bV8LUNWTvbb6DY4qcpJpqZAE1n32m9ycbTPQstQ+BpcXZRG5fg+hyfA8f6wOqsgLZ41JVoKZ3m0oW3EzpFeM25fkLUlWpYXaERHjy8w0prZOWYibp4asJKRrI6Jm1Px+YLU95o4rrOgUspZVZTG+vIMdEYnTToLbeMqwlXFaZRnij8MFcRATTyrS7QUpyfSMmijqc/MkMVDTaGG2hItGSIzBCC8bvZon4WOUQdV2cmsLtZSmjnngwXSmtl5ji8wRs333+SRO9aK2g/mkp+/28b1awq5sbZorj9amk4TxUySWTvwF6CZyS/wm8DPAEKh0L+d80NkMgXQCAyEQqFrpnvdQriZDZpdmF0+tElx5Kdd3CdDCwWXN8Co3YlKoaLgIk8NmSGfmCTBHxjD7PKREq+atSnxNrcfbyBIVop4ZUGT04vD6ycnJR61iB7I7vYzaHWTlawmXaSjEwiMMWB1kRSnJHOaz+jS21HI5BH3ZzTDFjfe4BglH7GYiDcQxOLyk5agQj1/phB/YtrpXGJ1+/AHQhEX7FSGLG7sHj+l6UkxlV+dvgAeT4DUpLgYT/GwxYXR5SM3VU1G8swfGHn9QfrMTpLjVORGKSZCoRAGh5d4pWJGxchmitXlIzAWmjapmAU+scmsze2j3+wmPTEu5veLZuL3VCsVpF7gojHDNjce39jFSP4iBAJj9JldpCaoBG3L6PBi9/jJT0sk7hxqHoPdg9HpJTNZTcbFUR9Kyew859SAlQd/28R/XL/iYp8Ke9v1tI7YefzOurn+aCmZjWIm/oplwP8DkoB/C4VCLplMdudMktgpfANoARb03LsJBUNjr5k1RWnce2nFnAubFxpNvSZ+19jPW6dHKElP5Gs7KtlalYFSKVmhZpvOcbn3W6dHWFmYxt9euYgVhWnnPnCG+AJB9nUY+NEbZzA6/ezcWMINawrJmTL6u69dz8/fbY+oeXZuLKVmyjk09ph4bF8Xh7pM1BRoeGBbhaDSdcuQlRcP9/HH40Pkp8Xzjcuq2LYoO+Ir7Bi18XbzKM8cCmsl7t1awfbqrIh7zu728+6ZUX7xfgc2t59b1hVz3er8aUcYxDgzbOMX73Wwr8PAhvIMvnFZ1axOB5WYHbz+IHs7DPzXG2cwufzs2lTK9WsKyUmNx+cL8kG7nv95r51Bi4drVuRx/ZoCVhaFR7OO6sz815uttAzZ+GxNHl/ZXEZZVrgNHeo08PCeLo73W1hfls7dm8tZV3ZuNdNRnZnnG3S8fXqEsswkvrajku2Lw1PkBy1uXjzcx3P1veRp4vmHzyzmkvIMlDNcyy2G2xfgg1Y9P36rFac3yL1by/n8qnzRtbQS56ax18SzB3vZ06ZnUU4KD+2oZItItWAI/56/a+zjuUM6slPV/ONnFnNJRUbMQ5HzxeX1894ZPT9/rx2zy89NtYVcv7qAiuy5nZ57etDKb+t1/OXUMEXaBL55eRVbKjPY12niv99pp9fk4qplOdy+vmTa2WyHuoz8Zk8nTX0Waku03LOlXFIfSsRwcsAasyTkYrGyMI2nD/biD4597GtZ4uMxYzWPTCa7DvgH4KfAj0KhUPk5Dpk4rhB4CvgB8O2FOjLbPGjhgeeO0mucrF5cqE3g4dvXUFNw4RKETxJGh4f/9Wozfz01WV1WrZTz5F2Smme2sbr97Np9mCM6c2RbWqKK1x7c9JFHJ6Np7DFx4yMHmRpC/vEz1dy/LbxW90SfhVsePYTTN1kteHNFBj+/ZTXpyWq69Hbue7ZJUPk3K0XN7p11LC/Q4PUH+N7rp3lxivZGpZDxzK71bKgId3KeOtDDv74u1Er84tbVXLMirGJ5p2WErzwl/J3+/spqHtwhXE88HXq7h5sfOUiXQXjd/+H+S8hNndsp2yIs+HY6lxzuDrfXqXz3c0u4Z0s5+9r13PXkYfzBycZ8U20h3792KcM2L5//xX4cU6r+bqvO4pe3rqFT7+Dep48wbJusaFyRlcSvbl1Ndd70S1CMDg//9NIp3m4ZiWyLV4V1JrUl6fz4rVZ+9UFnZJ9CLuPl+zeysuij32sOdBi49TGhmueH19dwyzpJzXO+6IwOvv37EzT2TMbX1HglT+ysi3GxhkIhfvpOOz9/tz2yTS6Dl+7fOO3U35ny/plRdj11WBCDv76jkm9fWT39QRcYlzfAP718ktenVOeOU8h5alcdO3cfxhuYrGj82eW5/OjGFSSrhSPTpwet3PP0EQYskxWNSzIS+fXta1hylutoFpBGZuc5//TSCdRKOZ9ZnnfuF88B33vtFP/nC8vn+sGLNDIbxYwfJYRCodeAK4H1QP85Xj6VnxFOgs/uRpjn9JncgkQWoN/sps/onuYIiV6jmzejNCnegKTmmQv6TC5BIgtgcflj9FIfh2N9FqKfhT11oBfjeBXM9lGHIJEF2NdpjKgZeoyuGIWN3u6la1wf1Gt08UqUNsIfDNE2agfCU/xePRqrlXjn9GSC0NBtitn/h6Z+Rmwzu257jS5BIgvh675XUnItOKKvBwg/DAkrq5yCRBbg1aODdBtcdOodgkQW4INWPQOW8D1haiILYSVVr+ns7avX6OadMyOCbR5/WGcyavfy7KFewb7gWIgzQ0KN1fmypy1WzfPUgR6cXv/Het9PIzqTW5DIAtg8AdH4Omr38szBHsG2sRCc/pi/J8BxkRj8hyP99JvmLj71GJ38OUrB5wuO0al3ChJZgDeah0V1ht0GpyCRhXDsje5zSUgc77dQnjV/ioguzU9lX7vhYp/Gp54ZJ7MymSw7FAo5Q6HQ34dCoa3j2876+E8mk10DjIZCoSNnec29MpmsUSaTNer1sTfb+cJ0ah6pdPz/Z+++49sq7/2Bfx7tLXnI24lHHM8kQBISCNCwN6V7UdpSSktpGS1dtNDblra05d7fbW97e9tSyt6FsqHsBAghTuIkjh3H8d6WLWtv6fn9IcexbDmRbY0j6ft+vXgRH9nW1/ajo/Occ57vZ2FSCYNWMX9tULrGGaXLWAXCER/RIjfiOV4Nqvl/20KdHPLpW4CjPZdSKoZiummDSiqOGs2jnu5GK5OIkaOe/xxHX4tSEYseNzKrgVS0iIo8jWymhhNRysSI1jtIyGM4ncZpMhmirFMs1MkhXyB+J0cthUwiirrvV0rFkEtEUMujR/Ms9H5xlFTMoJXP/xyVLPx986LEpaijfP5iRGtqVqxXLOvW5eVK17Eqn/77zxVtnyeXiKLG30R7b1wsfZR9cK5GFjWGJ1HkkugRatFeUzqlFIoovzd1lNcLY9G3k+zl8QfRbXKiIk53l8VDXZEOO7onU11G1lvMHm87Y+zTRz9gjH0XwDMn+JotAK5gjPUCeAzAOYyxh2Z/Auf8r5zzDZzzDUZj9PUmQlCeq8RnNpRFbLvy5BKUpTC4WejWlBrmRQQ0FOtmOn2mm3QZq0D4Fq0bz4383V/QUIiaOK6lOmVFDor1xyaOIgZ894JaaKYP0hpKdNgwp3P1DWdXz8Qt1BVp8cU50SUXNhbNRDdU5Kvx3fNXRzxebVSjaTr2RimX4gubV0QcVOqUEmytPfa32VyVC+OsCa9YxPCNs6qhjzLJjaYqX40vn1YRse3zp5YL6szwXOk0TpNpY2UuivSRY+GW82qhVkhQX6xF9Zy/6S3nrUZNoRa1hVqcsSpyWcStF67GilwVqvLUuGxt5O1un9+4AqsKjn+wtabMgG+fE/n6XFOqQ12RDjlqGW67pCHiJEplvhpry5Z3u+WZNfnIVR8b91Ixw/Vbq1PaETRdx2pjsQ5fmxMrdlp1HlZFiT4zqGS47eL6iL/nilzlsv+eQHgfPDtbWcSAb51ds2CjvESoLtDilvPmR/CtLtSgYU5vgZvPrZnp5j1bTYEGV55cErHt0+vLsJqiD8ksbSM2lOUoT9hILJlqC7U4OGyDNxA88SeThFnMmtliAH8F4AFQiHBDp+9yzh3H/cJjX78VwK3pumYWAPYPWXBkzIH+SRfKc1VYVajBujg21MlEg2YnDgzZ0DluR55GjsZiHU5a5jqhJMiItYgWlw/7B604PGZHea4KJ5XrURjndZ49E07s7Z+C3RPA2jI9mkr1EY0QDo/asG/QihGrB6sLNVi/IgfGWQdf3SYHDgxZ0WNyoixXiaZSHeqKjh3k2dx+7Oo1o23Ehjy1HOvK9WgsOfZ4MBjEjm4zWodtkIoYmkr189au7BuwoGXAApcvgLWlemyszFvUm+Gk04t9A1Z0mxyozFdjXblBKE1zMmKcJlO3yYGWAcvMeF1Tqp+5MtkyMIXWIRvMTi/qinVYv+JYjMiwxY19AxYMWz2oK9RiXbl+5qTNgUELOsbsGDS7sTJfhdoCDRpi6KMwYnGjZSAcfVKgk6OpRD/THM0XCOLAoBUHhm3IUUqxboUhLlcjOsfsaBmwwBsIhV+vJfoTZvDGQcatmQWA7nE72kbsODLuQLFBifpi7YIN9nyBIA4M2XBgyAqDUoqTyg1x6zy8f8CClkELHJ5AeP9XkQN5kq9oWpxeNPeFI6jytXKcVK5HfbEe7SM27BsIx13VFWuxYWUOctTR951tw1a0j9gxYHZhRZ4KDcVa1CV3vSxAa2YF7R/v9eD9rklcs6Uy1aVEuOPZVvz642vmrZdPIFozO0fMk1kAYIzdgHDGbAjAZznn7y/ia7cizSezADAy5caUywu9SoLSHOFenRGSQDCECYcPall8IyYSKGsmCaEQx7jDA6VEHPPVysVyeP2wewLIV8shTdAZ1d4JByQiEcoWyHftm3DCFwiiZoEMWrvHD6c3iDzN/NgVAcuacZosVrcfbl8ARq1iXv7q8cbxhN2DKYcXudr5kSIOrx+jVg9y1TLkLnAgvxTjdg+kIhFy1Il53cZRWk1mnb4AbG4/ctWypFy1dnoCsHn9yFPLIJv1fBN2LxhD0iKUTHYPxIxFjUeby+z0IRAMRSzpOPo+r5ItPXrI6vLB7PAiTyuHTpmScU2TWQH75kO7sSJPjY+sFtZdHPe/34MNFbm49syY+uLGA01m54j59B1j7HUAwwCaAJQD+DtjbBvn/NZYvp5z/jaAt5dQo2B80DWJ/32nC7t7zTi5PAc3nFMt9K68Kdc74cQ927vxbMswKvPVuO2SemyqygWLthCRJNXQlAsPftCHRz8cQKFOjtsuqceWVflxncw195px18uHcGjUjkvWFOEbH6mO6y26/ZNO/KtlGA/uCEfzfGNrFS5qLJ5ZHzjh8OCtQyb88a0jsHsC+MyGMlyxrhT107cqc87xYa8Zv36pHV3jTly+rgRfO6tKMK3/SXIEQxw7uibwy5cOYWjKhU+uL8OXt1RixfTJkdnj+KKmInxz67Fx/H7XBP7njU7sH7Ti1MpcXL+1GqdWhu8O2NM/hT+/fQQ7usxoKNbi5vNX4/RlvmeM2z345+5B3LO9Byq5GD+8qA7n1BVASesLl23/oAW/efkQ9g1asbXWiBvPrZlZ9pAIe/uncNfLh3Bw2IbzGwpxw9mrkKeR4cX9w/jDG0cgYgy3nF+Di5uKEja5m3R48WzLMP78dhdkEhG+e8FqXNBQBI1i/nhy+wJ485AJv3nlEBzeAK49oxKfXF8Gtz+Ie9/twTN7h7AiV4XbLq3H5sq8RV31/7BnEn94/Qj2DkxhQ0Uuvn3OqmRe6SJpYE+/BefVF6a6jHmqjBo0903h2jNTXUn2Wsxtxldyzv8162MJgB9xzn8Rr2KEcmY2moPDVnzjod0YmNWlskSvwF+u3oA1C+SmZTuvP4jvPbV/Xsv+5769BXULXCETiIy/4hUKcfzmlUP4y7bumW0iBjx9/elxuw28a9yBy//4LlyzOhqfVZOPP39hPdRRDpSW4v73e/DT59oits2O5nm1dQRff2hPxOO3nFeDm84Lr8XtGLXjij++G9F18+KmIvzXp08SdJOnaRk/TpOldciKK//0HgKhY++HX9y8Andc3oj+SdeC47jX7MRX/rEL43bvzGOV+Wr87er1UMnE+Or9zWgfsc88lqOS4pFrN8+cTFmKB97vxR1z4qgeuXYTTl8l2BOraXFldsDswhV/fBdTrmPdnRuKdXj42k0JufrdM+HEFf/zLuyzOmVvrMjFjedU44v37or43L9dvR7nNxTFvQYAeGr3AG59cn/Etvu+shFbawvmfe6Orkl87m8fRGz76eUN6Jlw4IEd/TPbpGKGZ2/YgoaS2I6NusbtuOrvH2LEeqwzeHmuEg985dSZPOckoSuzAjVm8+D8/3oH/3fVesFdDBmxuPG7f3dgx4/OTdZTCusXIACLieb515yPA7MnsoyxHfO/KnMMTLoiJrIAMGz1oH+SYmYWMmL14Pn9wxHbfMEQjozFtMyaJNC43YtHdvZHbAtx4NCofYGvWLwukyNiAgAA2zonMGSNT5yV0+vHM3uH521/Y1Z2567e+XEsz+wdwoDZOVNjtPiIkTjVSNLD4TF7xEQWAB7bNYAxmwfdC4zjQYsbPSZnxEQWCE9SekxOdJucERNZAJhy+XHEtPT9n93jxwNzYnsAYPsRioZYrp5JZ8REFgg3nOmfSkw8TLfJETGRBYBdveZ5UWAA5kWUxYsvEMTDc94HAOCV1tEonx2+C2Guhz7og9kR+XvzBzmOjMc+zrtMzoiJLBCOQ+yOY5QcSW8f9phRX6wT3EQWAAr1Ctjcfkw5fakuJWvFc3FY8trnpYBaLoka0aFZZlxCJluoZX+025dIcimkIhh189dGaeP4t4kWJXI0eiQeZGIxCqP8DLO7e0aLODFq5TMRGuooV1+1CklKO7yS5Iu2H89Ty6GQiBccx4HN/wEAACAASURBVEqpKOq+TMTC+ziVXAJJlNssNVHifGIll4hQapjfxK0oyuuALE60fYFExKCKEjETl+eLMq7kElHUOlYkKDVBLBKhPGf+eCqLsg0ACqLGOykRCIXmbV9MlFS01x9jdHxFjtnZPYlVBcLsUyNiDJX5ahwcXn52NFmaeE5mM/rWi8o8FT63sTxi2yfXl2Flbny7w2aSYoMSt1/aELFt/UoD6osFfYtxVjCoZPjxJZFxEauMmpluqvFQV6TF1jmNGn54Ud3MOsTlkkpEuGrzyojJsUElxbmz1tRsqMiNOACTiBiuO6tqpklPfYkOmyoj12X9+JIGlC5wMEcyU1OJHo1z9kt3XN6AfK0ctVHG8Q8uqkV5rgqrC7T46EmRkSJXbV6J+iIt6ou18+JbLmgsXNb+TyYR44azV0VkSBs18mWvwyXAqgItrpzzt7zx3FVx6zo8V22hBuc3RK7/u/WCWjSW6iLyarVyCS5bVzL3y+NCLGL48pbKefvQhdYlbq7Ki8grlooZvn3OKly8JjKeal2ZHg2LuJW+vkiLT62PjD783MYVqC8W5uSFJN/OHrOgl6etyFOjddia6jKy1qK6GR/3GzG2h3N+ynK+R6rXzJzIgSELuk1ODE25UWJQoMqoWbAVPwlz+wLYP2RF55gD+RoZ1pYZUBLlyoLAZMVaRF8ghNYhK9pHbdArpFhbbojbRPOoUasH+4csGLN6UF0Qfr3E+2z7B92TaBu2QSIOR/OcMmfN7+5eM9pG7fD4gqgt0mJzRS5ksw4Why1u7B+0YMLhQ02BBmvK9FClRzOdrBinyTIw5cKBASumXD7UFmmxplQP+fRVuVGrB/sHLRizTY/jUsPMVdnDoza0jdgwbPGgLEeJphI9qqavIIxY3Wjpt6B30okSgxJrS/XLXgMYCnEcHLbi4LANCqkYa8v0gs49RpqsmQXCHX33D1oxbHGjMl+NNWV66BPYVXfMFn6+Uas7fDxRqodWKcWhkXCMj4iF92m1RYnNZm8btqJ1KLwPXVOqP24WfLfJgQODVrj9QTSW6NBYooc3GI6SOjzmQJ5ahrVlepTmLO69pN/sxL4BCwan3CjPUeHkcgNK4/x+FANaMytAkw4vzvrdW/jLVRvmdZkXim2HTegzO/G/X1ifjKcT5i8hheI5md3LOT95Od9DCG9mJzI46YTZ7YdBJcWKXOp4Ggt/IASTwwu1LHHxL3FGk4RZxmweMAYUaKOvJDA7vPAGQijUKaJ2r7S5/XB4A8jTRI+6cPuCmHL5oFdIozaGCgRDMNm9UMjEyFlg/IzZ3BAxBuMCNU46vPAFQyjSKaKuubG6fXB6g8jXRMZjCFxGj9NQiGPM5oFcIoopLmQ2k92DEAcKdYtb/dI3EV4fuzJPBZU89ngRpycAi8ePXJVsUY3DfIFgOLZMLp43aQqGOMZtHsilYuQKP37neNJmMptIR/+eCqk4KXFKR/ebSpkYhiW+77p9QZhdPhiU0kXdNnwinHOM2byQiBjyZ13pHZ5yYtLph1EjR1FqTnrTZFaAXtw/gvve78F3zq9NdSkL6p0MJ3e88d2tyXg6mszOsaS9E2Msl3NunrP5i3GoR9B2dE3gD290Yk+/BSeV63HTuauF3EFSEHomnPi/t4/g2X3haJ7bL2tYdMt+khpmpxf/2juMP751BGLGcPP5NbhsTQn0qvBBvtcfxJsd4/jli+2wuPy4+rSVuGrzyogr77t6zLjzxbaZSJMbz6lB9ax1L4dGbbj71Q5s75zASeUG3HZJPdaVH7vboX/Sib9t78ZTu4dQmqPA7Zc2YsuqPEim44MmHF78c/cg/vxOF2RiEW69oBYXNxXN5Bm7fQG83j6Ou14+BLvHj2u2VOJzp5ajUB+ukXOOD7onceeL7egyOXDZ2hLccHY1KvMFfaUr4w1b3Hh4Zx/uf78PBpUUP760HufUFsxcKV2Ize3HywdGcPdrhxEIhvDNravw8VNKT5jV6fL48NbhCfzXa4cxbPHg0rVFuGrTypg6ex8YtOBXL7djT58FW6rz8L2L6mK6lbhnwok/vXUEL+wfRrVRg9svrcemqjwwxjAw5cJ97/XgsQ8HUKCT4yeXNuDMGiNkCcppJok1OOXC/e/34ZGdfTDq5PjJJQ04a3V+wk6c9ZtduHd7D57YPYBivQK3X9aAM1blz+w3Y9E+YsPvXjmE97omsX5lDn50cV1clqGM2Tx4snkAf93eDbVMgh9cVIcLGgrRMmDB7984jJYBK05ZYcBN563G5qq8ZT8fSX/bO02oLRTuLcYAUKJXYmDKDV8gRPvpFDjhb5wxtoUx1s4YO8gY28QYew3ALsbYAGPstKOfxzlvTWilKdY6ZMGtT+7Hjm4zvIEQdvZM4ZYnWnBg0JLq0gTL4w/gd68ewuPNg/D4Q2gfseNL936IjrH4dcwlibO9cwI/f6ENZqcPJocXP36mFR/0TM48vm/Qgusf2oPBKTcc3gD+9+0uPLV7cObxI+N2fPHendg3aIU3EMKzLcO4/dlWOLzhzpeTDi9ueHgPXm8fn35NmfGV+3ZhcLp7qD8Qwp/f6caDH/TD7Q/iyLgT19y/C+0jx5osvHloHL9++RAsLj/G7V58/5/70dx3rIPx3n4Lvv3oXgxZ3LB5AvjvNzojoqI6xuz40r27cHDYBo8/hKd2D+LXLx2Ce073WpJcT+0exJ/e6oLDG8DglBvXP7QH+2LY137Ya8YPnj4Ak92LKZcfv3ypHW8fNp3w63b3W3HjYy3oMjnh9gfx1O4h3Pd+L+xu/3G/bmjKjWvua8aOrvD7wpsdJlz/8G6Y7J7jfp3bF8SvX2rHU7vD+8aDwzZcfe8uHB6zIxjieOD9Xvz93V44fUH0TLhw7QPNOEjrsdJSKMTx0Ad9+Nv2bjh9QfROuPC1B5txYCgxzWL8wRD++k4X7tvRC5cviC6TE9fct2tRzWlMdg++/mAz3uwwwRsI4f2uSVxzXzOGLcvv8v5K6yju/vdh2NwBjFg9uPnxFrSP2nDLEy3Y2TMFbyCEHd1m3PrkPrTRmM96nHNsO2zC2jJhR2DKJCIU6uTonqC0jlSI5fTB/wPwaQDXAngRwM8459UAPgrg7gTWJigDZjeG5uzIx2xe9JsT07Y/E4xYvXh5Tot/f5CjaxEt+0lqhEIcj304P7LhuZZjE8F9A/MnFw/v7MOkIxxV0m1ywuOP7HL5ftckhqbCB/r9Zhe6TJHRC2anDz3TcQxjdg+e2j0Q8XgwxNE5PX58gSAejhJT8urBY2Nu9uT7WI39sLrCLfS7xh3wBSNr/HfbGEXzpNCEY35sFBA+MXEiLx0YmbftkZ398Afnd1udrWvCieCcaJ4XD4yccP/eZ3bC5IiM5umdcKHffPzxM2J1499tYxHbfMEQjow7MG734LEPI8c9j3NsFkmecYcHj0b5e84+KRfX57N58ETzYMS2EAc6x2MfP32T88ewyeFF7zKjchwePx6Kss8eMLswZot8HQ1OudE3ScdX2a530gVfMLRgh20hKc9RoYP20ykRy2RWyjk/wDnfAcDEOX8XADjnewAIf3TFiUZB0TyLpZCKoq5xpGge4ROJWNSmMhWzIiJy1fNv3Sw1KCGXhncr0V4bapl4Zk2hWi6J2szh6NcppWIYo9weevRxsUgUtdNo+aymIYVR1tCW5Shnbu/TRFkXqVdKoUhQHAc5MaVEjBLD/L9bfgzrZldGaRhTZVRHjciZLVocilEjnxnLC4k2xkUs+vebTSEVR48tk0uhlIpRqJ//s+oVsa/hJcKhlIqjRicZovz94/V8BdFi1xaxBlwjlyDaSyZaT4PFkEpEUZsMaqOMbcbiGxVH0tPbHeNYW6YXZL7sXMV6BQ7TnYcpEctkdvbn/GjOY2ndlWIxqvJVuGrTyohtn9lYjoq8rJnPL1qxXok7LouM5tlclUvRPGniMxvLIw7K9UopLl17LIJh/cqciMmtRMRw64W1MxPE2iItzq0riPiet11SP5NrWJmnxo3nrIp4/LMby1EzvaY2TyPHT69ojDiJtLZMj6bS8O1GYhHDV06vgHLWxDNXLcN59ceec3NVXsTESCYW4cZza2Ym1PXFWpwxZ937HZc1pEPH7YylVkhw64W1ERPQlXkqrF954vWrFzYWIUd17MBYJRPjC5tWnvBAqKFYh4biYx1cGQO+c0EtVhUcv4tsdYEGV58W+b7wrbNXofIEcS4lhvn7xi3VeWgo0cGgkuEnlzZETCbqisJdtkn60StluG3O33OVUYO15YlJQsjVyPHTyyP3m02lOjSVxv6+W5mvxg1nR+6brz5t5bJzPuUSMa7fWh0RBVSok6M0R4HPzIk+vGrTyuN2VSbZ4d9tYzip7MT7fiEoNShpMpsiJ+xmzBi7AsDrnHPXnO3VAD7BOf9tvIoRejfDg0NWdE84MWr1oFCvQFW+Ck2lFM1zPB5/EK1DVnSNO5CjkWFNiR7Fwp8oZHSX2MXoGLWjbdgKkYihoVg37+Cif9KJ1mErnN4g6oq0aCjRR1xtHbd5cGDIinG7F1VGNZpK9BFdMW1uHw4M2dBvdqFEr0BTqT6iWY8vEMTBYRsOj9lhUIXHT8mc243aR2xoH7FBImJoLNWjes4V5d4JJ1qHrPAEQqgv0qKhRBcxuRm1hmucdHixqkCDplIdFNK0uCKQseP0aPzMoVE7VDIxmkr1WJkXW/f4rnEHWoetCIQ4mkp0qI0xm7B10IK2ETtsHj+qjBpsWGGALoYusGanFwcGrRi2elCeo8SaUsNMk7Tj8fgDaB2y4ci4A3kaOdaU6lGkD5948QdDODhsxeFRB7QKCZrK9ChfZNSJgGR9N+NAMLwuumPUDo1CgjWl+og7SOLNF5geP2N26JUyrCnTodSwuOezuvxoHbKif8qFUoMSTaW6qHfjLBbnfHqfbYdMIkJTqR6V+Wp0jNjQMe7AmNWDIr0CdcUa1BQk/cQ3dTMWEIc3gI13vo4/ff6URXWJT5W+SSf+sq0bb926NdFPJfzL1Em2rGgextgKzvn8xU3HHlcA2AZAjnDn5Kc45z9d6PPT4c3M6Qlgyu2DQSWNeosimc/q9GLQ4oFaLklYAH2cZewkYSlGrG4wsJkD7cUamHTC5gmgxKBAThwOhsgMGqdJZHX5ZiKm5p7sMNk8MLt8MGrki44RWog/GJqJcjlRN2aBy/rJLBCe0I4l8e/p9gYwMOWCSiZBWfLzWpfE4fXD4vIjRylb9i3NS0STWQF5ft8w/vFeD753YV2qS4mJLxDC1x5oxsGfXwjpIjqHLwFNZueIaW8x3bW4FMA2zvk4Y2wtgB8COBNA+XG+1AvgHM65gzEmBfAuY+xlzvkHyy08FVqHrPjlS23Y2W3GhpW5uP2y+ri0qs9ke/un8JdtXXi9bRwrclX43oW1OLe2ALI0OMuW7SYd3umuskcgEjHccl4NPnpSacyZhcFgEG91TOC3rx5Cl8mJj9Tk44azV2F9RW6CKyckvnZ2T+I/nj+Iw2MOnN9QiFsvWD1zC/KOrgn85uVD2D9kxcaKHNx6QS02Vi4vUmTA7MLftnfPRPPccVkDttYa0ykDmcwyYHbh79t78OiufuRr5LjjsnpsjSFqaqkODltxz/ZuvLh/FIV6Ob5/QR3OayiAUibcO04ODFrwixfa0dxnxqaqXPz4koaZJSUkO718YASnxBCPJhQyiQj5Ghn6Jl3LviWfLE4s0Ty/A3AvgE8AeJExdieAfwPYCaDmeF/Lw462rpVO/5eWZ7VGbW587YFwBEOIhyMgrrm/GSNxaFWfqSYdHvz+jU680jqGQIije8KJbz+6F7v6p078xSTltnea8OuXD8HmCcDi8uOnz7VhR/f87sAL2d1nwQ2P7MHhMQeCIY43O0z45cvtGJ6iDpUkfRwZt+Pqez9E+0g4NueV1lH8+JlW2D1+dIza8K1H9qJl0IoQx3Rk2z70mJbesT0Y4vjHez14YEcffMEQBqfc+PpDu3FgiGJK0lEoxPHAjj7ct6MX3kAIQxY3vv7QnoT9Pd3eAO7Z3o1n9g7DFwxhwOzGTY/vjYgsE5oRixvX3N+MD3vDx1c7usy47oFmjFmPH3FFMpfHH8S2zomYeiUISYlBOZPIQJInluvglwI4mXP+OQAXALgZwGbO+e855yfc0zDGxIyxFgDjAF7jnO9cVsUp0j/pxsicHavJ7qXW8cfRP+nGO3MyHgMhvqwDPZIcoRDHY7sG5m1/flY0z4l0TzjhDURGouzps6DvBLElhAhJt2n+ON7ZY8awxY0ukxOTTl/EY4NT7mUdzIzb50ercA4cHqP9Zjoad3jwRPP8fWmiopYGplx4cX9kJF6IA90Cft/tm3TBZI+M5hm2etBH0YdZ6+0OE6qM6pjvBBOKQp1C0K+1TBXLZNZzdNLKOZ8C0Mk57431CTjnQc75SQDKAJzKGGua/Thj7DrGWDNjrNlkOnG4fapoFfNb1VPr+ONTyETIU8/fEWkTFEmQaOkyVuNBJGKoidLJtXoRt85Ee21o5BKoZAldS5L1smmcJsNC41gpk0CvnP+YWMSgjbI9Vipp9GiiaFE+6S4bxqpSKonaHT0nhiZhS3o+mSRqtJOQe3xoo0Qfiuj4Kqs92zKEjWm4JKlQp0DnOE1mky2Wo8oqxthzR/8DUDn97+enP44J59wC4C0AF83Z/lfO+QbO+Qaj0bi46pOoyji/Vf11Z1ahypgWDY1Sor5Yj+9eUBuxbWNFDmrTtN1+uozVePnMxjJoZ3UezlFJccma4uN8RaS6Ii0+sjry93TzeTVYV55etw2lm2wbp4lWW6TFxU1FEdt+cmk9VuSq0FCkw2c3lkU8du0ZlWhcRvyYfjqaZ3ZX8MZiHdZmYDRPNoxVvVKKH19SF/H3rC/SYm2C+m2U56rw/QvrIk6+NxTrBL3+tMqoxnVnVkVsu+HsVXR8laWc3gC2d05gU2X6TWZLDHRlNhViieb5yPQ/1QivkeUAOgAcvVr7znG+1gjAzzm3MMaUCK+1/Q3n/IVony/0boZWtw+tQzYMW9wono4RSbdbIJLN4vRid78FfZMuGJRS1BWH41sEjrrETuscs6NtxAYRC0fzLObKLAB0jNrQNmKD2enHilwVTirTwagTfDRTuqBxmiQmuwetQzZMOLyoyFejqUQ300xncMqF/YMWjNm8KNErsa5cjyL98sZ4IBhC24gNnWMOaBQSNJXoUErRPGkrGOJoG7bi8JgDank4aqosgX9Pty+APX0WHDHZoZFL0VSqR22RsE8iW1w+tA5ZMWL1oGQ6CkivTPrxFXUzFoBn9g7i4Q/6510MSQdmpw+3P9uKPbefn8inoW7Gc8RyD8cOAL8FcDWA3ulthQD+h3N+F2PsJM55ywJfWwzgfsaYGOGrwE8sNJFNB3qlDKsLNMhVyZCrkdJENgYGtRzn1hemugyyRDWF2mUF19cWxZ7zuRCnx4/+KTfUMjFWxJg1StKffzrKRCYWoUAXedttKMQxYnVDxFhScquNWgXOroseTVWWo4r7xEQiFmFtmSFhV+9IcolFDGvKDDGnH3DOMWL1gAFLGt9KmQRbavKxpSZ/0V8bb2aHFw5fEIVa+XG7NxtUMpxRk5lX58niPNk8iNOrl9cRPlVyVFK4fAHYPX5oFcK9tT/TxDKZvRuACsBKzrkdABhjOgB3M8b+jPBtw5XRvpBzvh/AyXGqNeV2dE3grpcPYd+gFU0lOtx2ST1OX5X6NwtCMlXrkBV/eusIXj04ikKdAj+4qBbnNxRBLae1VJlscMqFe7b14OEP+6BTSPGTy+pxUWMRlDIJxm0ePLyzH3/Z1gWZWITvnL8aHzu5DPoErUEkJJkmHF48vmsgHInGGG48twafXF+K3DTL6A6GOLZ3mnD7s60YnHLj4sYifPfCWlQbKbKELGzM5sGBISuuO6vqxJ8sQIwxFOuV6Jt0CfrW/kwTy5rZSwB87ehEFgA45zYA1wP4LIDPJag2Qekad+Dmx1uwbzDcTr912IabHmvB4bHEdCQkJNu5vAH83ztdeLl1FCEOjFg9uOWJfdjbb0l1aSSBOOd4YtcA7tvRC3+QY9Lpwy2P70PL9L739fYx/P6NTnj8Idg8AfzH8234sDf2yChChGzbYRN+92oHXL4gHN4AfvVSO3Z0pd/4PjRqw1fvb8aA2Q3OgZdaR3HXS4fg9gVTXRoRsGf2DuHUilzI0zhTu0inQO8kxfMkUyyT2RCPsrCWcx4EYOKcfxD/soSne8KJMVtk63iTw0sxM4QkyMCUC6+0RkZMcA500Wsuo006fHg8SpTJvn4LfIEgHo8SGfVa21gySiMkoTjneDLK2H9+X+yRaELRZQrni8/2WvsYRm2UHUuiOzr+z0jzOx6NWhl6KWs2qWKZzLYxxq6eu5ExdhWA9viXJEwGpTRqNI+e1s0SkhBqmQTFWRJRQo5RysRYGWVtdKFODolIhNVRGtlU5dOtiyT9McZQF6UT9nL6FqSKIUrzJqNGDpUsfa+4kcQ6MGSFyxcUfLOyEynUKemke5LFMpm9AcANjLG3GWP/Of3fOwBuBPDNxJYnHPVFWlx7RuTS4C+dVoH6YjqIIiQRynJV+MFFkRETJ5Xr0ViyvIZSRNjUcgluvWA1ZOJjb081BRqsX5kDkYjh6s0roZm1ZrpAJ8e59QWpKJWQuPvk+rKIE3a5ahkuXUQkmlDUF+uwdVY0G2PAzz7aiEJd9EZqhDy+K3xVls0NHU4zhTo5eiddqS4jq5ywiwrnfAjAJsbYOQAapze/xDl/I6GVCYxGKcVXz6jEhopcjFg9KNIrsLZMD70yvZoyEJJOzq8rwINf3YRukwNahQRNpXqsKkjvs7bkxDZW5OJfN5yOw2MOKGViNJboZjoGrykz4Jlvno5DozaIRSI0FOtQkU9drklmaCzR4+nrT0f7qA0MDPXFWlSlYdMko1aO335yLQ4OWzHl8qMyX00nIsmCPP4gnt83jDuvXJPqUpatSKfAgJkms8kUc0tQzvmbAN5MYC2CV6hXYi0DSgxK5KmlKF5mliAh2WDS4YXdE4BRK190F2K5TIItq/KxZZlraEYsbviDHMUGBaTiWG5IIanEGENDiX7BTOoqowYKqRii6f3xYsyMBb0CUgmNBSI81QWaRWd6C1GBTjEvVut4RiwuTDr9yNfKUER55Fnl9fYxVOarYdSm/wWiHLUMNk8ALl8AKhklLyQD/ZYX4f0jE7jzxXa0jdhQW6jFHZc3LPsgm5BMFQpxvN81gdueOYB+sxtnrsrHTy5rSOp6GIfHj+f2DeOuVw7B5Q3i85tW4OtnVaE0zrmgJHnGbG7c934v7n23FxIRw83nrcanNpSdMPfb6Q3ghf0j+PXL7XB4Avj0xnJ8c2t13DNiCSGL926nCb94oR0dY3Y0FOvwk0sp+jCbPPbhQMbkDIsYQ5FOgX6zC3VFdDdCMtBp6Rh1jtlx0+MtaBuxAQA6xuy48dG96BilaB5Coukcd+Ca+5rRb3YDALYfmcCPnt4Pm9uftBr29ltw2zOtsLkDCIQ4HtjRh6f3DiXt+Un8vdY2jj+/3Q1vIASnL4hfvtSOnT3mE35dy4AFP/jnflhcfgRCHI/s7McTuwYQpVk/ISSJOkbDUYcd01GHbSM23Pw4RR9mi1GrB/sGLdhYkZPqUuKmUCdHH62bTRqazMaod9IFkz0ymmfS6UPvBHUsIySa3gkHfMFQxLY9/RaMWN1Jq+HD3vmTnCebB2Fx+ZJWA4kfXyAYNboklmielij5xE/uHoTZSWOBkFTqmXBics7rcNzupclAlvjnngFsqkzvbNm5jBo5+mn8Jg1NZmNkUFE0DyGLEe21oVNIkrqGpCxn/rqrVQVqKKWZ86aZTSQiEeqK59+mXhPD+sJoMU/VRjWtaSIkxQwqGeY2sBWLGAxKem1mOs45ntg1iDMz5Bbjo4xaBXooazZpaDIbo/pCLb5+VnXEtmvPqERDlEw4QghQV6TFx04uidj2sysaUZ6bvDWKmyrzUG081ulWKRXjW2fXQE6T2bQkEjF8cXMFdIpjB7nFekVM0TzrV+agrujYpFcuEeHm81ZDSbmXhKRUQ7EOX90SGX34jbOqqPtxFtjTb0GQ85hOSKaTcDwPTWaThU57xUijlOKaMyqxoSIHQ1NulBgUWFuqh25WHhwh5BiDSoafXNaAT5xSBpPDh8o8FeqTfPKnIl+NB67ZhLYRKzz+EGqLtFhdSNE+6aypVI9nvrkFh0ZtkIhEqC/WYkXeiaN5Vuapce+XTkXbiA1ufxCrCzWopeYchKScTinFdWdWYnNVLoYtHpTmKLG2zACVnI6vMt0TzZmRLTtX4XQDKJIcCZ3MMsbKATwAoBAAB/BXzvnvE/mciWTUylFfrEW+Ro58jRwFFM0TE6c3gDGbB2q5hALTs0yeWp7wDoX+YAjDFjfEjKE0RznvTbE0RwmAwx/kKIlyq2k8OH0BjFk9UMskKNRn3xi3uHwwO30wqGTIVSd+6cVSo0tKcpQoiXLr+XKZnV5YXH7kqWVxW3ri8QcwYvVCIRGheJHxQ4Qk28w+cInv8wV6JepD4dszjVpZRkS0kOPz+IN46cAI7vr42lSXEndGrRxjNg8CwRAkFAeYcIm+MhsA8F3O+R7GmBbAbsbYa5zztgQ/b0K822nCz55vQ+e4A9VGDX56eQPOWp1Z9/nHW+e4Hb94vg3bOidQoJXjlx9rwtbaAsr6JHExavXgnu3duH9HLyQiEW48dxU+u3EFcqYnVDa3H/9qGcJvX+mA2x/EpzeU44az4xvHcmTcgTtfbMPbHSYYtXLc+dFGnF1XCFmWZJju7Z/Cbc8cQPuIHasK1Ljr42uxoSI31WUlza5eM374z/3oMjnRUKzDrz/ehHXly+vK2TvhxG9fPYSXW0dhUEpxx2UNuHhNERRSupmKCM+RgQ21ZQAAIABJREFUcQd++WIb3uowwaiR4+dXNuLcRe4Dtx0OH191mRyoKdDgP65opOjDDPfqwVGsMmqScgI02aRiEQwqGYYtHqzIo/i3REvo0RbnfIRzvmf633YA7QBKE/mciXJ4zI5bHt+HzvFw9+IukwM3PbYX7dNRPWQ+py+AO19ox7bOCQDh7oRff3A3DlGcEYmTVw+O4p53e+APcrj9QfzmlY6ImJa9/Rbc8exBOLwBBEMcj37Yj6d2D8bt+V2+AH71UngiCwAmuxffeHgPDo1mx35h2OLGdQ/uRvtI+DV9ZNyJr97fjIEsub2qf9KJr96/C12m8NqothEbrntwN0aX0bHbHwzhL9u68NKBUXAOTLn8uOWJfdg/aI1X2YTEjdsXxF0vt+Oto/tAhxffXOQ+8NCIDTc9thddpvDxVee4Azc/1oLDY9mxH81Wj+8awBk1mXvColivQJ+Z1s0mQ9IuHTDGKgCcDGBnsp4znvomXTA5IqN5plx+9NMC7wWNWz1457ApYluIAz0UZ0TiwOsPRp2YvnnoWExLc5Ronqf3DMUtmmfc5sWbhyLHOOdAjyk79gtDU+55kWVWtx8DU9kxmR2YcsPmDkRsG7N5MTi19MnshN2L51qG520/Mk77TSI843YPXm8fj9jGOdC9iH1g76QTU67I/HGTw4u+yeTFuJHkGrV6cGDIig0rM/cungItZc0mS1Ims4wxDYB/AriZc26b89h1jLFmxlizyWSK/g0EIEclhXhONo+IATkUzbMgtUKCQt38dS85yvT8naXLWM0WUrEoarfL2Q2eyqPc3lNbqI1bB1u1XIziKGtkc1J421Qyx6lOKYFkzn6RMUCfJY3xDErpvEgRqZgt6+dXySWozJ/f0CpPk3lrCGmfmv7UMglKou0DF3FslKuSzYs+FIsYDCq6rT5TPb1nEJsrczN6OU6+hjoaJ0vCRxFjTIrwRPZhzvnTcx/nnP+Vc76Bc77BaBTu+tOGYi1u2BoZzXPdWVVJ786aTgq0Ctx55ZqIN6mLm4pQn6bt9tNlrGYLkYjhi6etjJg4lOUocXbtsZiWTZW5EXEsapkY3zy7Om7h7EatAnde2RRxouuChsKURnYlc5xW5WvwvYtqI7bdfG4Nqo2ZFbOwkOoCNb59zqqIbT+4qA4VUSajsdIrpfjJpQ2QzeorsKkyF2tL9Uv+nkJF+9T0l6+V486PRe4Dz28oRGNJ7F3j64t1uO6sqohtN2ytRmNx5o15Mp0t2zyQ8OaQqVaoU6CXsmaTgnHOE/fNw21F7wdg5pzffKLP37BhA29ubk5YPcs1YffiwJAFQxYPSgwKrCk1UMe9E/AHQ+gYtaNnwoEclQx1xTrkC/8Kwwl7xAt9rGaTbpMDHaN2SMQMdUW6eTm2wxY32kds8PpDqCnUoCbO0TyBYAiHpse4QSlDQ4kuWVfRBDFOnd4A2kdsGLa4UaxXor5YC40iO67MAoDd7Uf7qB2jVjdKc5SoK9JBLV/eFSXOOTpG7ThickAjl6C+WJfOneBjytygfWr6Cky/z3dPOKBXytBQrEP+Io+NTHYvDgxaMGz1oNSgwNoyQyruRohlrCbuoDlL7Omfwo2P7sVvP7E24yJ5ZuuZcOIf7/Xgte98JN7fOnN/aUuU6Hs4tgD4IoADjLGW6W23cc5fSvDzJkS+Vo6z6wpTXUZakYpFqMhTQSEVQS2XpMNElsTZmM0Dm9uPQp0iIbnMVUYNqo5zJbDEoERJAqNNJGIRmkr1aMrAK2exUMslWdW9eC6tUopTK5f289s9foxaPdAqJCiaFfXGGENdsQ51dOcPSQMSsQgr81SQS8VQycSLnsgC4SiTc+rp+CobPL5rAFsyMFt2rkKdHINTbnDOM/5nTbWETmY55++CziBktcNjdtz+r1bs7DEjTy3DL65swnn12RNbks2CIY63O8bxo6cPYNzuxcnlBvzq42vo1nxCAHSM2vDjf7WiuXcK+RoZfnllE86pL6TYMpJ2Osft+OmzB/F+1yRy1TL8/IpGXNBYCFmclnOQzHE0W/bXH1uT6lISTiWTQCYRweTwokCbtnfWpAV61yQJ4/QG8LPnD85EpUw6fbjhkT3oyJLYkmzXOWbH1x/cjfHpbrd7Byz43pP7YI1TJ2FC0pXD48cdzx5Ec+8UAGDC4cP1D+9BB8WWkTTj8gXwqxfb8X7XJADA7PTh24/tnYnrImS2V1rD2bKZ2NAummK9Av3U0TjhaDJLEmbM5sF7RyYjtnEeXkdAMl/vpBOBUOTyotZhG0ZsnhRVRIgwjNm8EXnIwNHYMto3kvRisnlnMmaP4hzoprFMonhsVz/OzOBs2bkKtHL00mQ24WgySxJGI5dEjS3JVWfHGblslxslnsagkkIrz57mQIREo1FIUBBlXWFeCiOdCFkKtVyCspz5PQloLJO5BswutI3YsD6Ds2XnMmrl6JmgjPBEo8ksSZgCnQK/nNOy/4p1JahfRMt+kr5qC3X4/KkrZj5mDLjzyiaURjnwISSbFOoU+NXHImPLPnZyCeqLad9I0ku+Vo47r2yKyJu+ZE1R1Axwkt2eaB7Alur8rOqZUqhToNtEdykkGiVSk4Q6q8aI5761Bb2TTuQoZagr1tKV2SyhV0nx/YtqccVJJZhweFGRp8bqOMfiEJKuttYa8cK3z0DPhBM5ahnqi3TIoatZJA2dWWPEc986Az2T4Xiy+mJd1DtzSPYKhsLZsjeftzrVpSRVsV6Btw6Np7qMjEeT2UXqn3RizO6BUaNARb461eUInkQsQmOJHo0l2Rlbku58gSAGp9wAgPJc1aI7rRpUMmyuyktEaTELhTgGzC74QiGUGVRQyqjDZrxNOrwwObzIVcuysmvjuM0Ds8uHfI085vgxiViEhhI9GmjfmLYsLh/GbF7olZHRStlGLGJoKNGhYZlXY012DyadPuSr5UuK9yHC9e6RCWgVUlTkZddxc5FOiT6zi+J5Eowms4vwdsc4bn+2FQNmN8pylPj5FY2Ui0Yy1qjVg/99+wge3tkPBuArWyrxtbMq02qyYnX58eiH/fjvNw7DGwjh0jXF+P6FtViRZW+oidTca8Z3n9yHvkkXSg1K/Oen1mFzdWpPYCTT+10TuPWJfRi2elCRr8Ldn1yX1bm72aJ1yIrvPbUP7SN2GDVy3PWJNdhaWxCxrIbE7oPuSdz65D4MTrmxIleJ//zUSdi4xPxmIjwPf9CHs2qMqS4j6TQKCSQihgmHD0Y6QZMw2XPj+jIdGrHhlsdbMGAOX6UanHLj5ida0DZMMTMkM73RPoYHdvQhGOIIhDj+tr0b73VOpLqsRdnTb8ZdrxyCxx8C58AL+0fwRPMgOOcn/mJyQkNTblz34G70TXdrHLK48bUHmtE3mR1rhHonnbjugd0YtoY7dPdOuPCNh3ZjxOJOcWUkkSYdXtw0K37G5PDiGw/txpFxavSyFP2TTlz3YPPMXUD9Zvf0x9QFNhNMOLx4v2sSW1Zlz0nO2Yr1SvRmyXtiqtBkNkb9ZhemXP6IbTZ3AH1mGqAk84RCHP9qGZq3/ZXW0RRUs3TNfZZ5257dNwSL2x/ls8liDVlcMDsjc4Pt3sDMQWmmGzS74PAGIrZNOHwYoslsRhu1edA1p6mLP8jRT8cDSzJoccPmjnwdTbn89DrKEE82D+DUyhyoZNl5M2iRXoEeagKVUDSZjVGeWhbRrQ8IrxOJdX0UIelEJGJYvzJn3va15YYUVLN0lfmqeduaSvRQ0brZuMhRySAVR+4XRSx6LFMmylHLMPeuUplYBIOK4qcymU4hhU4x/8A8W8Z9vOWqZPNuz5aKGXJU9PtMd6EQx0Mf9OPs2oJUl5IyhToFjpjoro1EoslsjBpKdLjl/JqIbd8+ZxUaKUqBZKiPnVyGIv2xkzUr81S4sLEohRUt3qbKXKwtO9ZgR6eU4JtbqyGX0GQ2Hiry1bj9soaIbT+4uA5VWdIcb5VRg+9dWBux7Y7LG1CZr0lRRSQZynNV+OWcaKVrz6xELXVrX5Iqoxo/vLguYtuPL6lHZZbsRzLZ9iMTkEtEqDZm7z6xRK+gJQgJlp3X/JdAKZPgqs0rsLbUgCGLGyUGJdaU6qCS0xl4kplqi7R46huno2PUDhFjqC3SosSQXh07y3PVuOfqDTg0aofXH8SqQi0dIMWRVCzCpzaUYV3Z9H5Rr8DqIi3k0uw4WSCXinH1aRXYXJWHEasHpQYlaou01AQoC1zYWITnv3UG+swuFGjlqC3UQqOg44GlkEnE+MKmFdiwMmfW60iz6O75RHgeeL8XZ9cVZHUn32KDMuqyLRI/CZ3MMsbuBXAZgHHOeVMinysZ9Eo5zlydfd3YSPYqy1GhLGf+rbrppECnQIEufTowpxulVIJ15QasS7Nb0ONFLZfg5BU5ODnVhZCkkklEaCzVo7GUopXiQSWj11GmGbK4savXjKs2r0x1KSlVpFNg2OKBPxiiEzQJkujf6n0ALkrwcxBCCCGEEEIE4sEdvTijJh+KLLlTZyEyiQj5Ghn6zdSdO1ESOpnlnG8DYE7kcxBCCCGEEEKEweMP4vFdAzivvjDVpQhCaY4SnWO0bjZR6Ho3IYQQQgghJC6e3jOE6gINivXp1WcjUYr1CnSO21NdRsZK+WSWMXYdY6yZMdZsMplSXQ4hC6KxStIBjVOSLmisEpJ5QiGOv23rxoUN6ZV+kEglBhUOj9JkNlFSPpnlnP+Vc76Bc77BaKTmSkS4aKySdEDjlKQLGquEZJ43D41DJAIaS3SpLkUwynKU6BijyWyipHwySwghhBBCCElvnHP8z5uduHRNcVbH8cxVlqNE36QL/mAo1aVkpIROZhljjwLYAaCWMTbIGPtqIp+PEEIIIYQQknw7uidhcnixqTIv1aUIilwiRr5Gjt4JZ6pLyUgJzZnlnH8ukd+fEEIIIYQQklqcc/y/1w7jinWlEInoquxcK/JUaB+1o6ZQm+pSMg7dZkwIIYQQQghZsnePTGDY4sEZq/JTXYoglRmUaBu2prqMjESTWUIIIYQQQsiShEIcv3qpHZ84pQxiuiobVUWeGgcGaTKbCDSZJYQQQgghhCzJU3sGEQoBm6tyU12KYFXkq3Fw2AbOeapLyTg0mSWEEEIIIYQsmsXlw10vH8IXT1tJHYyPI1ctg0jEMGL1pLqUjEOTWUIIIYQQQsii/fS5g9hUmYtqoybVpQhetVGNfQOWVJeRcWgySwghhBBCCFmU5/cNY1ePGZ/eUJ7qUtJCVb4Gu/unUl1GxqHJLCGEEEIIISRmh0ZtuP1frfjWOTVQSMWpLictrCrQoLmXJrPxRpNZQgghhBBCSEx6J5z40t8/xNWnrURlvjrV5aSNaqMGHaN2ePzBVJeSUWgySwghhBBCCDmhlgELPvV/O3D5SSU4rZoyZRdDKROjPFeJFlo3G1c0mSWEEEIIIYQsyOMP4vevH8aX7w1fkT23rjDVJaWluiIddnRNpLqMjEKTWUIIIYQQQsg8o1YP/vhmJ878zVvY0T2JX1zZhA0VlCe7VA3FOmw7TJPZeJKkugBCCCGEEEKIMBwZd+D19jG82jqKIyYHNlXm4jsXrEZFHq2PXa76Yh3+8GYnLC4fDCpZqsvJCAmfzDLGLgLwewBiAPdwzu9K9HMm0uCUCya7F0atHGU5qlSXQ8hxjVjdGLN6kKOSYSU1aSBZzhsIonfCCW8ghJW5KujpQIKkEOccfZMuWFw+FOkVKNIrU10SyWJ9k048vWcIz7YMw+H1Y/2KHFzQWIhbSlZDKqYbOeNFJhGhsUSHdw6b8NGTSlNdTkZI6GSWMSYG8CcA5wMYBLCLMfYc57wtkc+bKO90jOOmx1tgcfmhV0rx/z6zDmfXFoAxlurSCJnnw55J3PDIXpjsXqhlYtz1ibW4qKmI3pRIVjI7vfjLtm78bVs3Qhw4ZYUBv/3kOqwq0KS6NJKFfIEQXjowgtueOQCXL4gCrRz/+4VT6PZNklTBEMcb7WP4+7s96Bi147TqPFyzpQLVBRqI6Ng2YU5ZkYPn943QZDZOEn1UeyqAI5zzbs65D8BjAD6a4OdMiL5JJ771yF5YXH4AgNXtx7ce2YveSVeKKyNkvjGrB99+NDyRBQCnL4ibH29Bl8mR4soISY29/Rb85Z3wRBYA9vRbcN/7PQgEQ6ktjGSlI+MOfOeJFrh84YiOcbsXNz3WgnGbJ8WVkWzgD4bwxK4BnHP32/jdqx3YWJGLP3zuZFx9WgVqCrU0kU2wjRW52NE1AZvHn+pSMkKiJ7OlAAZmfTw4vS3tjNo8sHsDEdtcviBGrO4UVUTIwsbsHozZvBHbgiGO4SkaryQ7tQ3b5m17rW0MVjcdTJDkG5pyzZxYmdlmcWPc7o3+BYTEQTDE8fSeQZx999t4aGcfrj5tJX52RSO2rMqnu7aSSC2XYG25Ac+2DKe6lIyQ8gZQjLHrAFwHACtWrEhxNQvLV8shl4jgDRw7iy8Ti2DUyFNYFUmmdBmrAJCjkkGvlEYcqDMGGHU0XjNdOo3TZKqOcjvxqRW50ChS/jaYtbJ5rBboFPO25allyFFLU1ANyQbvdk7gFy+EV/l9ZUslGop1Ka4ou51TW4D73uvBF05dAZGIroQvR6JPwwwBKJ/1cdn0thmc879yzjdwzjcYjcYEl7N0Fflq/PrjayCZHnBiEcOdVzahkprqZI10GasAUJ6rwt2fWgfZ9JlWxoDbLqmn9YFZIJ3GaTKdsiIH5zcUzHxcoJXj+q2rIJeIU1hVdsvmsbq6UIMfXFSLo3dzyiUi/O5Ta1FqoMaSJL66TQ585R8f4ntP7cPFa4rw08sbaCIrAI0l4b/Bv9vGUlxJ+kv0KeldAGoYY5UIT2I/C+DzCX7OhBCLGC5fV4KGEh1GrR4U6hSoNmogodsyiECdW1eAF288A0MWN4waOVYVaCCX0oE7yU5FegXu/uQ6dI474PYHUZWvQWkOdY8lqaGUSfCVLZU4s8aICYcXZTlKVOXTyUYSPxaXD79/oxNP7xnEZWtL8JUtlXQrsYAwxvDJ9WX49UvtOLvOSCdWlyGhk1nOeYAx9i0AryIczXMv5/xgIp8zkaRiEeqKdKgrojNaRPhEIoaaQi1qCrWpLoUQQdCrZNQtlgiGQipGU6k+1WWQDOPwBnDfez24Z3sPNlbm4q6Pr6U8U4E6qTwHb3eY8LtXOvCTyxpSXU7aSvhiIc75SwBeSvTzEEIIIYQQkm045+gYs+PJ5kE8tXsQa0r1uP2yBpQY6O4TofvqGZW449mDWJGrwtWnV6S6nLREnS8IIYQQQghJolCIY8jixhGTAz0mJ/rNToxYPTA7ffD4Q+CcQyYRQauQIlctg1ErR55aBq1CCqmYwR/kmHB40Tlmx+6+KQRCHKdX5+HnVzRGbTBGhEmrkOKHF9fht68cwsFhG753US3yqbnsotBklhBCCCGEkGUwO33oHLMjyDn8QQ6vPwi3PwiHNwCLy49Jhw+jVjcGplzom3TB5gmAASjLUaJIr4BRI4dRo0BVvgZyiQhggD/I4fIFYPcE0DvhROuQFW5/EMEQh0TEoJFLUKhT4EunV6DUoASb7ihmdvpS+8sgiyIVi3Dzeavxzz2D2HDn6/jqGZW4nW47jhnjnJ/4s5KEMWYC0DdrUz6AiRSVcyJCrU2odQHCrW1uXROc84uO9wVRxupivn86op9BGGb/DPEep/GQLr/jdKkTSJ9aF6rzhOMUWPJYFcrvRgh1CKEGQBh1LLWGWPapr0x//3mMH/9Jpapmc8yL8kM+T4gHfKGoD/IQAxMJ5wB9Nqpt8RZRl1ilk7j79lnHH/vxkQU+JaZ9ajYR1GR2LsZYM+d8Q6rriEaotQm1LkC4tSW6LqH+3ItBP4MwCP1nEHp9R6VLnUD61JqKOoXyuxFCHUKoQSh1CKGG5RLyz0C1LZ5Q68oU1KObEEIIIYQQQkjaocksIYQQQgghhJC0I/TJ7F9TXcBxCLU2odYFCLe2RNcl1J97MehnEAah/wxCr++odKkTSJ9aU1GnUH43QqhDCDUAwqhDCDUsl5B/Bqpt8YRaV0YQ9JpZQgghhBBCCCEkGqFfmSWEEEIIIYQQQuYR1GSWMZbLGHuNMdY5/f+cBT4vyBhrmf7vuQTWcxFjrIMxdoQx9sMoj8sZY49PP76TMVaRqFqWUNuXGWOmWb+na5NU172MsXHGWOsCjzPG2B+m697PGDtFIHVtZYxZZ/2+7ljm85Uzxt5ijLUxxg4yxm5azvdLJcaYmDG2lzH2QqprWSrGmIEx9hRj7BBjrJ0xdlqqa1osxtgt02OplTH2KGNMkeqagPQb6+kyntNpzKZibJ7oPTAJzy+YcS+EMS2U8SrU/eRChHqcGcv4jvdx0yLr62WMHZh+3uYojyf9WJMxVjvrd9HCGLMxxm6e8zkp+51lNM65YP4D8FsAP5z+9w8B/GaBz3MkoRYxgC4AVQBkAPYBaJjzOd8E8H/T//4sgMeT9HuKpbYvA/hjCv6GZwE4BUDrAo9fAuBlAAzAZgA7BVLXVgAvxPH5igGcMv1vLYDDc/9G6fIfgO8AeCSev58U/Az3A7h2+t8yAIZU17TI+ksB9ABQTn/8BIAvp7qu6VrSaqyny3hOlzGbirEZy3tgEn5uwYx7IYxpIYxXIe8nF6hXyMeZJxzf8T5uWmR9vQDyj/N4So415/xtRwGsFMrvLJP/E9SVWQAfRXiHiOn/X5nCWk4FcIRz3s059wF4DOH6Zptd71MAzmWMMYHUlhKc820AzMf5lI8CeICHfQDAwBgrFkBd8X6+Ec75nul/2wG0I/xGm1YYY2UALgVwT6prWSrGmB7hkxl/BwDOuY9zbkltVUsiAaBkjEkAqAAMp7geAOk11tNlPKfhmE322Ez5e6BQxr0QxrTAxqsg95MLEOxxplDG9zKk5FhzlnMBdHHO+5L4nFlLaJPZQs75yPS/RwEULvB5CsZYM2PsA8ZYoia8pQAGZn08iPkv5JnP4ZwHAFgB5CWonsXWBgCfmL694inGWHkS6opFrLWnwmmMsX2MsZcZY43x+qbTtwWdDGBnvL5nEv03gO8DCKW6kGWoBGAC8I/pW/HuYYypU13UYnDOhwDcDaAfwAgAK+f836mtar40GOvpMp7TZsymaGwK6n0kxeNeCGNaEOM1XfaTswj5OHPGCcZ3Qo6bYsAB/Jsxtpsxdl2Ux1O9j/gsgEcXeCxVv7OMlfTJLGPs9em1DHP/izgbxTnnCA/WaFZyzjcA+DyA/2aMVSe67jT0PIAKzvlaAK/h2Jm9/9/evcfYUdZhHP8+sbXQokisUrXBYmgEgQAFgZRLTAEjCbY0Ei0JliIkoBSDxkQhUbxEg0QLiRhBuYVaSRQokIjyR1ER/yDSUiw3BSlqEYrwB025NNY+/jGzeFh3t9vunDOXfT5Js2fnzJnze0+eM/O+03dmY2TrKHJ1GPAD4I4qNippL+A24GLbW6rY5qBIOg14wfbaumuZoCkUU8x/ZPsI4BWKyxhaQ8X9AxZRdBrfC8yQdFa9Vb1Z07Pesjy3JrNtyGY/1Zn7BmW6EXmd7Fnsh53kuy/9pnE63vY84FTgQkknDvC9xyTprcBC4BcjPF3nZ9ZZAx/M2j7Z9iEj/LsT2Dw0DaD8+cIo23i2/Pk08FuKM0ZVexbo/d/M2eWyEdcpp7TsDbzUh1p2uTbbL9neVv56HXDkAOoaj/F8rgNne4vtreXju4GpkmZOZJuSplIcBFbZvr2CMgftOGChpGcopj8tkPTTekvaLZuATbaHzirfStHxapOTgY22/2X738DtwPyaa3pDS7Lepjy3KbN1ZLMRx5EG5L4pmW5KXhu9nxxBk/uZO813P/pN49UzDngBWE0xZbtXnfuIU4F1tjcPf6LOz6zLmjbN+C7g7PLx2cCdw1eQtI+kaeXjmRQ788f6UMsfgbmS9i/Psiwp6xut3jOAe8v/Ue63ndY27NqAhRTXOzTBXcDS8k5zx1JMA3puZy/qN0mzhq5DkXQ0xXdjtw8Y5bauBx63vaKaKgfL9iW2Z9ueQ5Gxe2237iy37eeBf0j6YLnoJPqzz+invwPHSppeZuskGvKdbkvW25TnlmW2jmyO5/jcV03IfVMy3aC8NnY/OYrG9jPHk++q+027UNsMSW8begx8FBj+lyrq7GueyShTjOv6zLpuSt0FDHM58HNJ5wJ/Az4JIOko4ALb5wEHAddK2kERgsttV77TtL1d0nLgHoq7kt1g+1FJ3wQetH0XxRd9paSnKG4utKTqOiZQ2+clLQS2l7UtG0Rtkm6huFvbTEmbgMuAqWXd1wB3U9xl7ingVeCchtR1BvBZSduB14AlEzxgHAd8GtggaX257NLyTFwM3kXAqrLD8DQDyl1VbD8g6VaKKUrbgYeAH9db1RuS9f5oRWbryOZox8B+vucIkvs3qz2vDd9P/p8m9zMZJd/AfmXt/eg3jde+wOpyTDgF+JntX0u6oKe2uvqaM4BTgPN7lvXWVddn1mnKZxgRERERERFt07RpxhERERERERE7lcFsREREREREtE4GsxEREREREdE6GcxGRERERERE62QwGxEREREREa2TwWxERERERES0TgazDSVptqQ7JT0p6WlJV0uaJukjkl6WtF7SE5K+1/OaRZL+VD73oKTj62xDdFNd2ZT0Tkm/kbRV0tXVtiq6psacniJpraQN5c8F1bYsuqbGrB5dvn69pIclLa62ZdEldfdLJe1XHv+/VE2LoisymG0gFX8J+nbgDttzgbnAnsAV5Sq/t304cARwmqTjyuVrgMPK5z4DXDfYyqPras7m68BXgRzIYkw15/RF4OO2DwXOBlbufkui62rO6iPAUeU2PgZcK2nK7rcmuqoh/dIVwK8m8ProqAxmm2kB8LrtGwFs/wf4ArAU2GtoJduvAeuB95W/b7Xt8ukZgBlNucFeAAADRklEQVSFpJslnd7z+ypJi6puSHTOILK5WNIaFd4j6S+SZtl+xfb9FIPaiLHUmdOHbP+zXO1RYE9J06puYHRGnVl91fb2crU9xtpGTHq15bR87nRgI8U+NeJNMphtpoOBtb0LbG8BngEOGFomaR+Ks2P39SxbLOkJ4JcUZ8FGcz2wrHzN3sD88jURY+l7Nm2vBp4DLgR+Alxm+/nqmhCTQFNy+glgne1tE2lMdFqtWZV0jKRHgQ3ABT2D24heteVU0l7Al4FvVNWY6JYMZtvpBEkPA88C9/R2oGyvtn0gcDrwrdE2YPt3wFxJ7wLOBG7LQSwqMOFsli4CLgG22b6lb9XGZNX3nEo6GPgucH6llcdk09es2n7A9sHAh4FLJO1ReQtiMuhnTr8OXGl7a/VlRxdkMNtMjwFH9i6Q9HZgFvBnimsTDqM4U3aupMOHb8D2fcAHJM0c431uBs4CzgFuqKj26LZBZXM2sAPYV1L2U7Gras2ppNnAamCp7b9OtDHRaY3Yp9p+HNgKHLK7DYlOqzOnxwBXSHoGuBi4VNLyCbYnOiSdxGZaA0yXtBRA0luA7wNXA68NrWR7I3A5xfQLJB1QXqSPpHnANOClMd7nJoodA7Yfq7wV0UV9z2Z5A5IbKGYMPA58sV+Nic6qLaeS3kExne4rtv/Qj8ZFp9SZ1f2Hbvgk6f3AgRTTRiOGqy2ntk+wPcf2HOAq4Du28xcN4g0ZzDZQebH8YuAMSU9SfPF32P72CKtfA5woaQ7F9VmPSFoP/BD4VM+F9yO9z2aKHcaN1bYgumpA2byU4izv/RQHs/MkHQRQnpldASyTtEnShyprXHRGzTldTnEN2df0vz978u4KmxcdUnNWjwceLrexGvic7Rera110Rd3H/oixaIyxTjSEpPnALcBi2+sq3O50ips+zLP9clXbjcmjX9mMqFJyGm2RrEYbJKfRJBnMTlKSTqa4o/GVtq+qu56IiIiIiIhdkcFsx0k6FFg5bPE228fUUU/EkGQz2iA5jbZIVqMNktOoWgazERERERER0Tq5AVRERERERES0TgazERERERER0ToZzEZERERERETrZDAbERERERERrZPBbERERERERLTOfwFkF9WgAFYywgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 953x900 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6Hm-INTLi2q",
        "outputId": "23f0ab96-5099-477c-d829-21ad11062a64"
      },
      "source": [
        "# Multi Linear Regression starts here:\n",
        "from sklearn import linear_model\n",
        "\n",
        "QR2_regr = linear_model.LinearRegression()\n",
        "QR2_model = QR2_regr.fit(df_sample_qr2[['QR2_x1', 'QR2_x2', 'QR2_x3', 'QR2_x4']], df_sample_qr2['QR2_y'])\n",
        "\n",
        "print('QR2 Intercept:', QR2_model.intercept_)\n",
        "print('QR2 Coefficients:', QR2_model.coef_)\n",
        "\n",
        "\n",
        "QR3_regr = linear_model.LinearRegression()\n",
        "QR3_model = QR3_regr.fit(df_sample_qr3[['QR3_x1', 'QR3_x2', 'QR3_x3', 'QR3_x4']], df_sample_qr3['QR3_y'])\n",
        "\n",
        "print('QR3 Intercept:', QR3_model.intercept_)\n",
        "print('QR3 Coefficients:', QR3_model.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "QR2 Intercept: -0.36707918831305886\n",
            "QR2 Coefficients: [-0.03791803  0.15857291  0.00878835  0.07053081]\n",
            "QR3 Intercept: -0.7864188213983738\n",
            "QR3 Coefficients: [-0.05615085  0.2306341   0.01453487  0.08507043]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3dK0wPUF8Ot"
      },
      "source": [
        "GdP15:\n",
        "\n",
        "------> (fabien_sample: massive NaNs conversion to 0's)\n",
        "* QR2 Intercept: -0.42630836620427115\n",
        "* QR2 Coefficients: [-0.06117425  0.28715355 -0.03770253  0.01280477]\n",
        "* QR3 Intercept: -0.8125675565895698\n",
        "* QR3 Coefficients: [-0.07093187  0.33407468 -0.03412139  0.0492009 ]\n",
        "\n",
        "========> (fabien_sample: NaNs properly removed/converted)\n",
        "* QR2 Intercept: -0.3688680390934055\n",
        "* QR2 Coefficients: [-0.03671367  0.24748688 -0.02266579  0.00379421]\n",
        "* QR3 Intercept: -0.8788791237073476\n",
        "* QR3 Coefficients: [-0.04982127  0.32928829 -0.03858893  0.04871268]\n",
        "\n",
        "========> (valid_merge: NaNs properly removed/converted)\n",
        "* QR2 Intercept: -0.21294875403634483\n",
        "* QR2 Coefficients: [-0.09427687  0.28632601 -0.04333229  0.01333132]\n",
        "* QR3 Intercept: -0.7828576612172\n",
        "* QR3 Coefficients: [-0.09132253  0.32604437 -0.02898804  0.06966238]\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFTzJKKESDRy"
      },
      "source": [
        "GdP16:\n",
        "\n",
        "========> (20201130-partial_merge.csv)\n",
        "\n",
        "* QR2 Intercept: -0.3805126064519745\n",
        "* QR2 Coefficients: [-0.01892165  0.1645029   0.0045619   0.05428546]\n",
        "* QR3 Intercept: -0.5282702459034501\n",
        "* QR3 Coefficients: [-0.01273855  0.21229237 -0.01440149  0.04865899]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GX5t_dGtj862"
      },
      "source": [
        "GdP17:\n",
        "\n",
        "========> (20210518-partial_merge.*csv*)\n",
        "* QR2 Intercept: -0.36707918831305886\n",
        "* QR2 Coefficients: [-0.03791803  0.15857291  0.00878835  0.07053081]\n",
        "* QR3 Intercept: -0.7864188213983738\n",
        "* QR3 Coefficients: [-0.05615085  0.2306341   0.01453487  0.08507043]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEsFzy2DV1LL",
        "outputId": "92be42da-ece9-486c-994e-a6f9ca3fe61b"
      },
      "source": [
        "# Prediction phase:\n",
        "x1 = input(\"First coefficient ? (average/mean of FlowD1 scores) \")\n",
        "x2 = input(\"Second coefficient ? (average/mean of FlowD2 scores) \")\n",
        "x3 = input(\"Third coefficient ? (average/mean of FlowD3 scores) \")\n",
        "x4 = input(\"Fourth coefficient ? (average/mean of FlowD4 scores) \")\n",
        "\n",
        "try:\n",
        "  print('\\nWe predict ({:.0f}) of Flow (using Model based on QR2) if (averages of) D1~{}, D2~{}, D3~{}, and D4~{}'.format(\n",
        "    QR2_model.predict([[float(x1), float(x2), float(x3), float(x4)]])[0], x1, x2, x3, x4))\n",
        "  \n",
        "  print('\\nWe predict ({:.0f}) of Flow (using Model based on QR3) if (averages of) D1~{}, D2~{}, D3~{}, and D4~{}'.format(\n",
        "    QR3_model.predict([[float(x1), float(x2), float(x3), float(x4)]])[0], x1, x2, x3, x4))\n",
        "except ValueError:\n",
        "  print('Please only input either:\\n- whole number e.g. 1, 4, 7\\n- decimal/float number e.g. 3.8')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "First coefficient ? (average/mean of FlowD1 scores) 4\n",
            "Second coefficient ? (average/mean of FlowD2 scores) 5\n",
            "Third coefficient ? (average/mean of FlowD3 scores) 3\n",
            "Fourth coefficient ? (average/mean of FlowD4 scores) 6\n",
            "\n",
            "We predict (1) of Flow (using Model based on QR2) if (averages of) D1~4, D2~5, D3~3, and D4~6\n",
            "\n",
            "We predict (1) of Flow (using Model based on QR3) if (averages of) D1~4, D2~5, D3~3, and D4~6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-tR5_w3kkup",
        "outputId": "24e69f4b-4e9e-46a0-e093-493b9d7c1155"
      },
      "source": [
        "# Model Validation\n",
        "from scipy import stats\n",
        "import statsmodels\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.stats.api as sms\n",
        "from statsmodels.compat import lzip\n",
        "\n",
        "QR2_X = df_sample_qr2[['QR2_x1', 'QR2_x2', 'QR2_x3', 'QR2_x4']]\n",
        "QR2_X = sm.add_constant(QR2_X)\n",
        "QR2_OLSmod = sm.OLS(df_sample_qr2['QR2_y'], QR2_X).fit()\n",
        "\n",
        "QR3_X = df_sample_qr3[['QR3_x1', 'QR3_x2', 'QR3_x3', 'QR3_x4']]\n",
        "QR3_X = sm.add_constant(QR3_X)\n",
        "QR3_OLSmod = sm.OLS(df_sample_qr3['QR3_y'], QR3_X).fit()\n",
        "\n",
        "print(QR2_OLSmod.summary())\n",
        "print(QR2_OLSmod.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  QR2_y   R-squared:                       0.203\n",
            "Model:                            OLS   Adj. R-squared:                  0.198\n",
            "Method:                 Least Squares   F-statistic:                     37.61\n",
            "Date:                Tue, 24 Aug 2021   Prob (F-statistic):           4.90e-28\n",
            "Time:                        14:30:19   Log-Likelihood:                -325.50\n",
            "No. Observations:                 594   AIC:                             661.0\n",
            "Df Residuals:                     589   BIC:                             682.9\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -0.3671      0.116     -3.152      0.002      -0.596      -0.138\n",
            "QR2_x1        -0.0379      0.022     -1.749      0.081      -0.080       0.005\n",
            "QR2_x2         0.1586      0.020      7.891      0.000       0.119       0.198\n",
            "QR2_x3         0.0088      0.012      0.706      0.480      -0.016       0.033\n",
            "QR2_x4         0.0705      0.017      4.213      0.000       0.038       0.103\n",
            "==============================================================================\n",
            "Omnibus:                       76.333   Durbin-Watson:                   2.013\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               42.862\n",
            "Skew:                          -0.513   Prob(JB):                     4.93e-10\n",
            "Kurtosis:                       2.175   Cond. No.                         72.9\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  QR2_y   R-squared:                       0.203\n",
            "Model:                            OLS   Adj. R-squared:                  0.198\n",
            "Method:                 Least Squares   F-statistic:                     37.61\n",
            "Date:                Tue, 24 Aug 2021   Prob (F-statistic):           4.90e-28\n",
            "Time:                        14:30:19   Log-Likelihood:                -325.50\n",
            "No. Observations:                 594   AIC:                             661.0\n",
            "Df Residuals:                     589   BIC:                             682.9\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -0.3671      0.116     -3.152      0.002      -0.596      -0.138\n",
            "QR2_x1        -0.0379      0.022     -1.749      0.081      -0.080       0.005\n",
            "QR2_x2         0.1586      0.020      7.891      0.000       0.119       0.198\n",
            "QR2_x3         0.0088      0.012      0.706      0.480      -0.016       0.033\n",
            "QR2_x4         0.0705      0.017      4.213      0.000       0.038       0.103\n",
            "==============================================================================\n",
            "Omnibus:                       76.333   Durbin-Watson:                   2.013\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               42.862\n",
            "Skew:                          -0.513   Prob(JB):                     4.93e-10\n",
            "Kurtosis:                       2.175   Cond. No.                         72.9\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TM7sMhiHnz-p"
      },
      "source": [
        "# Validation phase - Coefficient of Determination (R²)\n",
        "\n",
        "Coefficient of determination is the portion of the total variation in the \n",
        "dependent variable that is explained by variation in the independent variable.\n",
        "R² scores are calculated as below:\n",
        "R^2 = SUM(Y-hat_i - Y-bar)^2 / SUM(Y_i - Y-bar)^2 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJZsS59FXzXH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ccd69db-015a-4775-845d-3484dac67c27"
      },
      "source": [
        "# Validation phase - Coefficient of Determination (R²)\n",
        "print(\"QR2 R^2 score:\", QR2_OLSmod.rsquared)\n",
        "print(\"QR3 R^2 score:\", QR3_OLSmod.rsquared)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "QR2 R^2 score: 0.2034631908779292\n",
            "QR3 R^2 score: 0.3813241540312837\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMDFiDoU5Yng"
      },
      "source": [
        "R² range between 0 and 1, where R²=0 means there are no linear relationship between the variables and R²=1 shows a perfect linear relationship. \n",
        "In our case,  R² scores explain that about 100*(1-R²) % of our dependent variable can be explained using our 4 independent variables (respectively)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JtlfAc1oZEf"
      },
      "source": [
        "# Validation phase - F-Test (ANOVA)\n",
        "\n",
        "F-test or ANOVA (Analysis of variance) in multi-linear regression can be used to\n",
        "determine whether our complex model perform better than a simpler model\n",
        "(e.g. model with only one independent variable). \n",
        "With F-test we can evaluate the significance of our model by calculating the\n",
        "probability of observing an F-statistic that is at least as high as the value\n",
        "that our model obtained.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cy2UVt_AX1ES",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8ad13cc-d326-4e35-885f-e0d6d97f2c05"
      },
      "source": [
        "# Validation phase - F-Test (ANOVA)\n",
        "\n",
        "print(\"QR2 F-statistic:\", QR2_OLSmod.fvalue)\n",
        "print(\"QR2 Probability of observing value at least as high as F-statistic:\", QR2_OLSmod.f_pvalue)\n",
        "\n",
        "print(\"QR3 F-statistic:\", QR3_OLSmod.fvalue)\n",
        "print(\"QR3 Probability of observing value at least as high as F-statistic:\", QR3_OLSmod.f_pvalue)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "QR2 F-statistic: 37.612768818300346\n",
            "QR2 Probability of observing value at least as high as F-statistic: 4.896458408847131e-28\n",
            "QR3 F-statistic: 39.90900749465194\n",
            "QR3 Probability of observing value at least as high as F-statistic: 4.9745091353619566e-26\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8fRfyXKhjXe"
      },
      "source": [
        "Because our f_pvalue is lower than 0.05 we can conclude that our model performs\n",
        "better than other simpler model (e.g. model with only one independent variable)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DB0Cit9fogRq"
      },
      "source": [
        "# Validation phase - T-test\n",
        "\n",
        "The t-statistic is the coefficient divided by its standard error. The standard\n",
        "error is an estimate of the standard deviation of the coefficient, the amount it\n",
        "varies across cases. It can be thought of as a measure of the precision with which\n",
        "the regression coefficient is measured. \n",
        "Same as the F-test, the p-value show the probability of seeing a result as \n",
        "extreme as the one our model have.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5qoIUo-X9XE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21c89ca3-a0c4-4c86-89a9-baeb888398de"
      },
      "source": [
        "# Validation phase - T-test\n",
        "\n",
        "print(\"QR2 T-test:\")\n",
        "print(QR2_OLSmod.pvalues)\n",
        "\n",
        "print(\"\\nQR3 T-test:\")\n",
        "print(QR3_OLSmod.pvalues)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "QR2 T-test:\n",
            "const     1.705483e-03\n",
            "QR2_x1    8.078418e-02\n",
            "QR2_x2    1.472353e-14\n",
            "QR2_x3    4.804064e-01\n",
            "QR2_x4    2.921269e-05\n",
            "dtype: float64\n",
            "\n",
            "QR3 T-test:\n",
            "const     9.470362e-06\n",
            "QR3_x1    8.408950e-02\n",
            "QR3_x2    1.423352e-13\n",
            "QR3_x3    4.108686e-01\n",
            "QR3_x4    2.354671e-04\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSwQYj3m5xmz"
      },
      "source": [
        "All of our independent variables (x1, x2, x3 and x4) should have p-value less than 0.05, which would show that there is sufficient evidence that they DO have an effect on Flow existance (which we already know they do).\n",
        "\n",
        "If we had accounted age, for example, we would have seen less of an effect (p-value higher than 0.05) on Flow existance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ox2JvoDao16_"
      },
      "source": [
        "# Assumption Testing\n",
        "\n",
        "Next, we will validate our model by doing residual analysis, below are the list \n",
        "of test or assumption we will do to check on our model validity:\n",
        "\n",
        "- Linearity\n",
        "- Normality\n",
        "- Multicollinearity\n",
        "- Autocorrelation\n",
        "- Homoscedasticity\n",
        "\n",
        "Residual is the difference between the observed value and predicted value from our dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "8w-Uq_0lt838",
        "outputId": "d2a0f0ff-9840-46b4-b3ea-7402765a8b02"
      },
      "source": [
        "# Assumption Testing\n",
        "\n",
        "df_sample_qr2['QR2_y_pred'] = QR2_OLSmod.predict(QR2_X)\n",
        "df_sample_qr3['QR3_y_pred'] = QR3_OLSmod.predict(QR3_X)\n",
        "df_sample_qr2['QR2_residual'] = QR2_OLSmod.resid\n",
        "df_sample_qr3['QR3_residual'] = QR3_OLSmod.resid\n",
        "\n",
        "df_sample_qr2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>QR1Sexe</th>\n",
              "      <th>QR2_y</th>\n",
              "      <th>QR2_x1</th>\n",
              "      <th>QR2_x2</th>\n",
              "      <th>QR2_x3</th>\n",
              "      <th>QR2_x4</th>\n",
              "      <th>QR2_y_pred</th>\n",
              "      <th>QR2_residual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>0.543176</td>\n",
              "      <td>0.456824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>0.858987</td>\n",
              "      <td>0.141013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>0.912062</td>\n",
              "      <td>0.087938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>0.733852</td>\n",
              "      <td>-0.733852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>0.821894</td>\n",
              "      <td>0.178106</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    QR1Sexe  QR2_y    QR2_x1  ...    QR2_x4  QR2_y_pred  QR2_residual\n",
              "11        0    1.0  6.666667  ...  5.333333    0.543176      0.456824\n",
              "14        0    1.0  5.000000  ...  6.666667    0.858987      0.141013\n",
              "22        0    1.0  6.000000  ...  6.333333    0.912062      0.087938\n",
              "33        0    0.0  5.666667  ...  6.666667    0.733852     -0.733852\n",
              "36        0    1.0  5.666667  ...  5.666667    0.821894      0.178106\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unVeUAnOpE05"
      },
      "source": [
        "# Linearity\n",
        "\n",
        "This assumes that there is a linear relationship between the independent variables\n",
        "and the dependent variable. In our case since we have multiple independent \n",
        "variables, we can do this by using a scatter plot to see our predicted values \n",
        "versus the actual values.\n",
        "\n",
        "What to do if it is not linear ?\n",
        "https://www.quality-control-plan.com/StatGuide/mulreg_alts.htm\n",
        "\n",
        "In our case linearity is difficult to achieve because discrete results (0/1) are\n",
        "required. Grouping or clustering might be a more appropriate term to describe \n",
        "the results' shape.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "xs6COB0WvWYO",
        "outputId": "b49783c2-6c5f-45d0-dece-a861a247d11b"
      },
      "source": [
        "# Linearity\n",
        "\n",
        "sns.lmplot(x='QR2_y', y='QR2_y_pred', data=df_sample_qr2, fit_reg=False, height=5)\n",
        "\n",
        "# Plotting the diagonal line\n",
        "QR2_line_coords = np.arange(df_sample_qr2[['QR2_y', 'QR2_y_pred']].min().min()-0.2, \n",
        "                            df_sample_qr2[['QR2_y', 'QR2_y_pred']].max().max()+0.5)\n",
        "plt.plot(QR2_line_coords, QR2_line_coords,  # X and y points\n",
        "         color='darkorange', linestyle='--')\n",
        "\n",
        "plt.ylabel('Flow predicted by the Model', fontsize=14)\n",
        "plt.xlabel('Reported Flow', fontsize=14)\n",
        "plt.title('Linearity Assumption during QR2', fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3RpUzxw8POzRjCkrn0sTpOFl5qrIq2YvIrSJSJSLvJVkuInK9iCwVkXdEZLdMx5hvJozqy9TJO9C3S0fW1jbSt0tHpk7ewXrjGJNhHYoTp+Nk5anKtq6XtwM3AHcmWX4IMMK77Qn807s3W2DCqL6W3I0J2Vc1TSmVpyqravaqOgdY3cYqRwB3qjMP6C4iAzITnTHG5K6sSvY+DASWxzyv9MqMMca0IdeSvS8icoaIzBeR+StXrgw7HGOMCV2uJfvPgMExzwd5Za2o6k2qOlpVR/fp0ydjwRljTLbKtWQ/EzjV65UzFlirqp+HHZQxxmS7rOqNIyL/BSYAvUWkEvgtUAKgqv8CZgGHAkuBGuD74URqjDG5JauSvaqe2M5yBc7OUDjGGJM3cq0ZxxhjzGawZG+MMQXAkr0xxhQAS/bGGFMALNkbY0wByKreOCYcNgetMfnPavYFzuagNaYwWLIvcDYHrTGFwZJ9gbM5aI0pDJbsC5zNQWtMYbBkX+BsDlpjCoMl+wJnc9AaUxis66WxOWiNKQBWszfGmAJgNXvDBfcuYOY7XxBtViJFwuSd+/PXKbuFHZYxJkBWsy9wF9y7gIfe+pxoswIQbVYeeutzLrh3QciRGWOCZMm+wD3yduJZHZOVG2NykyX7AudV6H2XG2NykyV7Y4wpAJbsC5ykWG6MyU2W7AucJMnqycqNMbnJkn2B6xg3CFp75caY3GTJvsD1LEt8qUWycmNMbkr6jRaRn/vdiKpeG0w4JtPW1DUmLF+bpNwYk5vaqr6d63MbCliyz1G1ja6PZWwbvSrUNFrfS2PySdJkr6rDMhmICYeql9QV1wVH48qNMXnB2uwLXOcOxUSKvJq9uvtIkSs3xuQPX8lenLNEZKGI1IjIcK/8IhE5Pr0hmnT60T7DUHVXzCrevbpyY0z+8FuzPx+4FLiJ1tfbfAacE3RQJnN2HtSdTqURirz/apFAp9IIOw/qHm5gxphA+T1W/wnwY1V9XER+H1O+ANgh+LBMpkybU0Hfrh0pL934UahpaGLanAqb0MSYPOI32W8NvJegvBEoCy4ck2nLq2uICFSsXE9DtJnSSBG9O5dSWV0TdmjGmAD5bcapABLNZnEosCi4cEymdS6NUFldS01D1Jt4PEpldS2dSu0KWmPyid+a/dXADSJSjmuzHycipwAXAj9IV3Am/TY0RInG9LJUIKqu3BiTP3wle1W9TUSKgT8A5cBdwArgPFW9L43xmTT7bE1tSuXGmNzku5+9qv5bVbcG+gL9VXWQqt4SZDAiMklEPhCRpSJyUYLlQ0TkeRF5U0TeEZFDg9x/IbLJS4wpDClfVKWqq1S1KuhARCQC3AgcAmwPnCgi28etdilwv6ruCkwB/hF0HMYYk4/aGgjtY765eL5tqjo8gFjGAEtVtcLb/73AEbQ+AaxAV+9xN1xTkjHGmHa01WZ/Q8zjzsDPgdeAuV7ZOFyCviagWAYCy2OeVwJ7xq1zOfCUiJwLdAImJtqQiJwBnAEwZMiQgMLLT0VAc5JyY0z+aGsgtG+SuIjcDvxJVf8Qu46IXExmL6o6EbhdVa8RkXHAXSKyo6q2yleqehPual9Gjx5trc9tSJTo2yo3xuQmvxW4o4H7E5RPByYHFMtnwOCY54O8slg/bIlDVecCHYHeAe3fGGPylt9kvwGYkKB8AhDUpZavAyNEZJiIlOJOwM6MW+dT4AAAEdkOl+xXBrT/ghRJMtdssnJjTG7ye1HVX4EbRWQ0MM8rGwuchmtH32Kq2iQi5wBPAhHgVlVdKCJTgfmqOhP4BfBvEbkAd7L2dLWB17fIgG4dqVxTl7DcGJM//F5U9WcRWYYb/bJlSOP3gdNUNVHzzmZR1VnArLiyy2IeLwL2Dmp/Jvm49TaevTH5xfc32kvqgSV2kx0+Wrk+pXJjTG5KqfomIvvjLnhSYKGqzk5HUCZzGpN0u0lWbozJTb6SvYgMBB4CdmfjhUxbich84ChVtYubjDEmi/ntjXM9EAW2UdXBqjoYGOGVXZ+u4IwxxgTDb7I/EDhbVT9uKfCGNTjPW2Zy1LhhPVIqN8bkplSuik/UxdG6Pea4cd9KfE1asnJjTG7ym+yfBf4uIt9c4SoiQ4DrvGUmR93w3NKUyo0xuclvsj8PN/BYhYh8IiKfAB95ZeelKziTfg1JBq5PVm6MyU1+L6paLiK74UaZHOUVv6+qz6QtMmNMYdNmeOlS2PYE6PvtsKPJealcVKXA097NGGPS64Pp8NofIVJqyT4AbSZ7ETnaz0ZU9cFgwjHGGM+2x0NxR/hWUAPrFrb2avYz2NjjJtk4iIobuMzkoN6dS1m1viFhuTEZ11gLz54NYy+F7sNhmyPCjihvtHeC9nPc7FFTgaGqWpTgZok+h43o0ymlcmPSpmEdPHQoLLwdPp/b7uomNe0l+8HA2cC3gcUi8pSIHO+NN2/ywNyPq1MqNyYtalfD9IlQ+SIcejds972wI8o7bSZ7VW1W1cdV9WhgKPAU8FtghYj8TURKMhCjMSaf1VTB9P1g5Vsw+QHY7qSwI8pLvq+gVdUqVb0aOAp4BzgH6JKuwIwxBaK4HMr6wJGPWRt9Gvkd9bIcOAE3B+wuuBEwD1DV1WmMzRiTz9Z+7JJ8aWc49mkQmwszndrrerk38APgONzMVLcBh6rq1xmIzRiTr1a9BzMOhIHj4fD7LNFnQHs1+xdxk3xfi2u6AZgocf8Y62dvjPHti/nwwMGuD/1evw07moLhpxlnCHBZG8utn30O61AM9U2blne0KWhNOlTOgYe+C2W94dhnXF96kxFtfqVVNZUhkE0O6lxaTH3Tptm+U6llexOwaCM88X3oPNAl+i4Dw46ooNg3usBV1yao1rdRbsxmi5TAUY+5Wn15n7CjKThWcy9wyUYythGOTWAW3gkvXAiq0Gs7S/QhsWRvjEmfN2+EJ06DqgUQ3XQMJpM5luyNMenx6lXw3Dlu1MqjHoPiDmFHVNAs2Rtjgvfyb+Gli2HUSXD4DNfN0oQqpWQvIqNF5AQR6eQ97yQidpLXGNNan53h2z+FQ+50J2ZN6HwlexHpJyLzgNeAe4B+3qJrgWvSFJsxJpc0N8EKb2jikcfAxH9AkV2Cky381uz/CnwJ9AJqYsqnAwcFHZTJnEiSq9STlRuTULQBHj8R7hsPqz8MOxqTgN8mmANwA59Vxw2V8BHuCluTo6JJulgmKzdmE401MPMYWPYETLgWeo4MOyKTgN9kXwYk6jfVB6gLLhxjTE6p/xoePtxNOnLgv2HnH4UdkUnCbzPOHOD0mOcqIhHg18CzQQdljMkR798NK16Bw+6xRJ/l/NbsLwReEJE9gA64k7I7AN2AvdMUmzEmW6m6YYm//VMYuI/rfWOymq+avaouAnYCXsFNTdgRd3J2V1X9KH3hGWOyztefwL3fga8Wu4RviT4n+O4jr6pf4OafTRsRmQT8DTdk8s2qelWCdY4HLscNrfy2qtqElcZkSvUSmH4ANHwN9WvCjsakwHey96Ym3AXoS9wRQRCTl3jnAG4EDgQqgddFZKZ3VNGyzgjgYmBvr2dQ3y3drzHGp5XvutmltBmOnw19dwk7IpMCv3PQTgT+i+tnHy+oyUvGAEtVtcLb573AEcCimHV+DNyoqtXgJkEPYL/GmPasWgj37+smBz/2Geg1KuyITIr89sb5G/A4MEhVi+JuQV0iNxBYHvO80iuLNRIYKSIvi8g8r9lnEyJyhojMF5H5K1euDCg8YwpYt2FuQLMpL1qiz1F+m3GGApNVdUUaY/GjGBgBTAAGAXNEZCdVbdV4qKo3ATcBjB492i4PMmZzLZ8NfXeFDt1g0u1hR2O2gN+a/cvAtukMBPgMGBzzfJBXFqsSmKmqjar6MfAhLvkbY4L2wXSYcRC8eHHYkZgAJK3Zi8huMU//BVwtIlsB7wKNseuq6oIAYnkdGCEiw3BJfgoQ39PmYeBE4DYR6Y1r1qkIYN/GmFjv3QZP/QgGjIPv/DHsaEwA2mrGmY87+Ro7GM5NCdYL5AStqjaJyDnAk972blXVhSIyFZivqjO9ZQeJyCIgCvxKVb/a0n0bY2Is+Ds8fx5sfSAc8RCUdAo7IhOAtpL9sIxF4VHVWcCsuLLLYh4r8HPvZowJWsM6mP8X2OZIOOxem10qjyRN9qr6SctjERkPvKKqTbHreBOX7AV8gjEmd6kCCqVdYMrL0Km/TTqSZ/yeoH0e6JmgvJu3zBiTq7QZnj0Hnj3bJf2ugy3R5yG/yV5wbfPxegEbggvHGJNRzU3wxOnw9j+gpEvY0Zg0arOfvYjM9B4qcLeI1McsjgA74gZHM8bkmqZ6mHUSLHkQ9r4C9rzEDWxm8lJ7F1W19HQRoBqojVnWALwE/DsNcRlj0u3xKbD0YdjvOtjt/LCjMWnWZrJX1e8DiMgy4GpVtSYbY/LFTj+C4YfDTj8IOxKTAb6GS1DV36U7EGNMBtR+5aYQHHEkDD8s7GhMBvke4tgYk+PWf+6GKP56GQysgHIbIbyQWLI3phB8/YmbdGTDl3Dko5boC5Ale2Py3eoPYPpEaFwPxz0DA/YMOyKTQLL+7UH1j/LVz15E+gS0P2NMplU8Bs0NcMILlugLmN+Lqj4TkRkicoiIdcQ1JidEG9z97j+H096zicGzXLKJN4KakMNvsj8M16/+AeBTEblCRL4VUAwmRIO6JR7oKlm5yRGfPge3jHDzxopAuR2cFzpfyV5Vn1bVk4CtgKuAQ4APReQ5EfmeiHRMZ5AmfVZuaEyp3OSAjx6DBw91g5qV9Q47GuNTsjaToNpS/NbsAVDVNap6o6qOBs7DjXh5F7BCRK4Skc7BhGUypb6pOaVyk+UW3wczj4LeO7k2+s4Dwo7I+KRJ2muSlacqpd44IjIAOA04HTdt4L3ALbga/8XAaGBiMKEZY1Ky7Cl4/EQYuA8c9Rh06Bp2RCYF6e6N4yvZi8jRwA+Ag4D3gOuB/6jq2ph1XgcWBxSXMSZVg/aFsb+BMb+GkvKwozEpKi6CxgQH1MUptb8k53czt+Em+x6nqrup6j9iE73nc+DKYMIyxviiCm//C2pXu1ml9v6dJfoc1bEk8eyuycpT5bcZZ4Cq1rS1gqrWAjaGjjGZogpzLoT5V7sxb8ZeEnZEZgvUJ6rWt1GeKr8DodV4PW5OArb3ihcB//WSvDEmk7QZnjkL3pkGu5wNe14cdkRmCzU0Jz4Tm6w8VX6voN0NqACuAcZ4t6uBCm+ZMSZToo3wv1Ndoh9zEez/d5CAGnZN3vL7CbkJN1HJIFUdr6rjgcHAHG+ZMSZT6qthxVzY5w/wnT/a7FLGF79t9jsAp8ZOXqKqG0RkKjA/LZEZY1prrIFIqRux8tS33EVTxvjkt2a/GNeXPt4A4MPgwjHGJFS/FmYcBE//xD23RG9SlDTZi0jPlhtwKXC9iEwRkaHebQpwHWBdAIxJp5qVcP/+8MVrMGxS2NGYHNVWM84qWl/QJcA9MWUtDYWPAMF0BDXGtLZ+hRuL/uuP4chHYNghYUdkclRbyX6/jEVhjNmUNsODh8C65XD0EzB437AjMjksabJX1RcyGYgxJo4UwYS/QklnGDAm7GhMmmXFTFXGmAz68k149xb3eMj+lugLxKAeZSmVp8qSvTHZZMVcmL4fzLsCGje0v77JG8ftPmiTWrx45UGwZG9MtvjkWZhxIJT1cWPRl3QKOyKTQXMrVtOjvIQiL+MXCfQoL2FuxepAtm/J3phs8NGj8NBh0G0YTHkRum4ddkQmw5ZUrWNtbSPKxvb7tbWNLKlaF8j2U5q8xBiTJmuWugnBj34CynqGHY0JQU19lGjsGVqFqFcehKTJXkSex+fE5qq6fyDRmIwrL41Q07Dph6m81C6dyIiaKjf8we4XuNErI6VhR2RCUteUOKknK09VW8047wELvdtiYHdgIG4Sk0rc8Am7A+8HEgkgIpNE5AMRWSoiF7Wx3jEioiIyOqh9F6riItnkQ1DklZs0m38t3DICvlrknluiL2ihzUGrque2PBaRvwJ3AOerbty1iFxHQN1ARSQC3AgciPsxeV1EZqrqorj1ugDnA68Gsd9CVxIR4qdGaAZKI5bs00YV5k6FuZfDyGOh+760v6cAACAASURBVDZhR2SyQJHQuhknpjyQ7ftc71TghthE7/kHcEowoTAGWKqqFaragJvM/IgE610B/AmoC2i/Ba0syQSXHYOa+NK0pgov/Mol+h1Oh8P+azV6AyRP6plO9gLslKA8UdnmGggsj3le6ZVtDMJNlDJYVR9va0MicoaIzBeR+StXrgwwxPzz5fqGlMrNFnrvNnjjGtj1XDj4FiiyPhLGk2xegoDmK/D7SbsVuFlERgDzvLKxwIW4ycjTTkSKgGuB09tbV1VvwptUZfTo0QG1eOWnxkTHjW2Umy20/SkQKYHtTrZJR0xryb5yAX0V/dbsLwT+CJwLPOfdzgWu8pYF4TPc7FctBnllLboAOwKzRWQZ7sdmpp2kNVmvqQ6eO9/1vImUuIRvid7EKUnSdJqsPFV+JxxvBv4M/FlEunplXwcSwUavAyNEZBguyU/BTXDeEsNaoHfLcxGZDfxSVW2mLJO9GtbDI0fCp8/CVuNg1JSwIzJZqrk5vqtE2+WpSuknw6tFH4Lr64+IdBKRQBodVbUJOAd4Eted835VXSgiU0VkchD7MCaj6tbAAwfD8udh0h2W6E2bGpPk9GTlqfKVqEWkH26SkjG4FqQRQAWuDb0O1xVyi6nqLGBWXNllSdadEMQ+jUmLmpVuGsGvFsJ374eRx4QdkclyLZ0dYxv4NKZ8S/mt2f8V+BLoBdTElE8HDgokEhOKdHf3Klzq2uWPetQSvfGlc4diIrLxdI4IRMSVB8Fvsj8AuERVq+PKPwKGBBKJCUVJJMlJoSTlph3rKiHa6IZAOHk+DD047IhMjvjRPsNAhEiRUFrs7hFx5QHw+40uAxJ1vO6DXdyU06JJTv4kKzdt+GoR/GcMPOddfC72g2n8O2/iSM7ffxvKSiI0NUNZSYTz99+G8yaODGT7fo8P5uD6t/+f91y94Q1+DTwbSCTG5LIvF7iTsUXFsOs5YUdjctR5E0cGltzj+U32FwIviMgeQAfgGmAHoBuwd1oiMxkRKSqiKUEtPlJktVLfPnsZHjwUOnSH456FHjbWjdk8sxdXMW1OBcuraxjco5wzxw9nwqi+gWzb1zfaG4xsJ+AV4CmgI+7k7K6q+lEgkZhQNEUTN9ckKzdxGmvh0eOgU3+Y8pIlerPZZi+u4rKZC6laV0f3shKq1tVx2cyFzF5cFcj2/Xa9HAIsV9XfJlqmqp8GEo3JuOYkvbqSlZs4JWUw+UE3w1SnfmFHY3LYtDkVlESE8lKXlstLi6lpaGLanIpAavd+j9U/xp2MbUVEennLTI5K83Ac+ev9/8Abf3WPtxprid5sseXVNZSVtJ40qKwkQmV1TZK/SE0qo14m+v53xnrj5LSSJB3qk5Ub4O1pMOsUN29sc1PY0Zg8MbhHObWNrWelqm2MMqhHeSDbb7MZR0Su9x4q8EcRif2JieCuqH0rkEhMKPp17UDlmk1/r/t17RBCNDng9athzq9g+GHw3ek2RLEJzJnjh3PZzIXUNDRRVhKhtjFKY1Q5c/zwQLbf3ie1Zbx6AbajdV/7BmABcHUgkZhQSJLRF5OVF7RXLoe5v4ORx8Ohd9mkIyZQE0b1Zfe3Kpn5zhdEm5VIkTB55/6B9cZpM9mr6n4AInIbbkrCoEe6NCGrWldPRFrG4HCXaItXbuKU94UdfwAH3gRFNiG7Cdb1z3zIzHe+oEiguFhoVpj5zhcM6/1hIH3v/bbZXwx0jS8UkUHeIGkmh8VX4q1SH6M5Cqvec493OQsOutkSvUmLm1/62CX6oiKKpMi7d+VB8Jvs78YNbRzvYOCuQCIxoejTuZSmZtfVUnH3Tc2uvOBFG2HW9+A/e8LX3oyZ9kto0mRDQxRVpb4pSl1jlPom93xDQ7T9P/bBb7IfjRsyId6L3jKTozp3KKaIjcOqCu5DEdRIezmrqQ5mHg0f3Ad7XQ5dB7f7J8ZsidKIJKx4lUaCqWD4TfbFuGES4nVMUm5yxPqGKL06l7QaVrVX55LAahM5qWE9PHQYVDwOE/8Je/wq7IhMAeiUpIKVrDxVfpP9q8BPE5SfjZtO0OSozqURVq1v/OaK2WaFVesb6VRawO3Sb94Ay1+AQ+6Eb/8k7GhMgVhTk2hg4eTlqfL7k3EJ8JyI7IybbBxgf2BXYGIgkZhQrFpfv8nVcuqVF6w9fgWDJ7grY43JkGTDUQU1TJXfgdDmAeNwQyMc7d0+Bsap6ivBhGLC8FVNY0rleWtdpRu5ct1nrreNJXqTYekeusR3Y5Cqvg2cHNB+TZZINr1lQNNe5oY1H8H0A6CuGtYthy4Dw47ImMAlTfYi0lNVV7c8bmsjLeuZ3FNc5M74JyovCKsWwowDIdoAxz8H/XYPOyJj0qKtmv1KERmgqlXAKhIfTbQMkFbAZ/NyW0SEpgT/2kgh9Cdf+Q7cv78b9uCEF6D3DmFHZEzatJXs9wdaauz7ZSAWE4Im3XRIU/HK817nQbDVXrDfX6H7t8KOxhS48tIINQm6PJcH1DMuabJX1RcSPTb5pUOkiJrmKLH1ePXK89aKedB3FyjrCUfNDDsaYwD4yfjhXPfsklYTBxWJKw9CW232Q/xuxGaqyl09O5VQsya6SUNOz04locSTdksehMemwG4/g33/HHY0xnzjvIkj+XjV+k1GvQxqAvK2mnGW4b/Xj7XZ56i6puaEzTh1ic7a5rpFd8ET34f+Y2DPi8OOxphWZi+u4o1P1zK0V/k349m/8elaZi+uCmSY47aS/R4xj0cCfwb+Bcz1ysYBZwK/3uIoTGgampq/Gda4ZYjjlvK88tY/4dmzYMj+cMQjUNo57IiMaWXanAoao1G+Wt9EQ7SZ0kgRXcuKA5uDtq02+zdaHovItcAFqjojZpXnROQD4Hzgv1sciQlFsza3aiNs6V+vmkfJvvYrePkSGH44HH4/FHcMOyJjNrGkah1raxopKhIiRUJTs7JqXQON0XWBbN/vRVVjgHcSlL8DWMfkHFYkRQjNmzTjiOTBCdqWX66yXjDlFdfjJpKn5yJMzmtoagaBIu/wWgSaRQM7yvb7jV4GnJWg/Czgk0AiMaFoataEY+M0Ned430tthud/Bq9e6Z73GmWJ3mS1Em8o4+ZmRVVp9r6DQQ1x7LdmfwHwkIhMAuZ5ZXsCQ3Hj5JgcVZ+k1pCsPCc0R+GpH8PC22D3n7c+GWFMlhrZrysfr1rPurqNbfZdOpYwrHcw55f8DoT2BDACeBA3PWFX7/FIVf1fIJGYUDQnqcEnK8960QZ4/ESX6Mf9Fva92hK9yQlnjh9OaXGE/t06sm2/LvTv1pHS4ghnpruffTxVrQT+L5C9mqxRXASN+TI2jirMPBYqHnVJfvQvwo7IGN8mjOrLVFyvnMrqGgb1KOfM8cMD6YkDKSR7EdkJ19VyOPBDVf1cRI4EPlHVNwOJxmRcuodVzSgRGHEUfOu7sPMZYUdjTMomjOobWHKP56v+JiIH4WakGggcAJR5i74F/DaoYERkkoh8ICJLReSiBMt/LiKLROQdEXlWRLYOat+FKlnTfE412deudjNLAez4fUv0xiTg92D9CuDnqnoUEDtH1mxct8wtJiIR4EbgEGB74EQR2T5utTeB0aq6MzADd6GX2QLfTDQuG2+x5Vlvw5dw/wR45AioWxN2NMZkLb/NODsCsxKUrwbaHOs+BWOApapaASAi9wJHAItaVlDV52PWn4dNprLFBvUoY3l17SbtNoN6lCX+g2zy9acwY6KbXerIR6Bj97AjMmaLzF5cxbQ5FSyvrmFwwG32fmv2q3FNOPF2AyoDicRtf3nM88ok+2zxQyBhTyAROUNE5ovI/JUrVwYUXn664ogd6VbmfvNb8n23smKuOGLH8ILyo3oJ3PsdqKmCY5+GrW0qZJPbZi+u4rKZC6laV0f3shKq1tVx2cyFzF5cFcj2/Sb7e4C/iMggXE4oFpF9gauBOwOJJAUicjIwGvhLouWqepOqjlbV0X369MlscDlmwqi+/O2EXRk7vBeDe5Qxdngv/nbCrmk7SRSY926Dpho47jkYuFfY0RizxabNqaAkIpSXFiPi7ksiwrQ5FYFs328zzqXA7birZQXXtCK4H4ErA4kEPgMGxzwf5JW1IiITgUuAfVW1PqB9F7R09gAIXHPUTQi+z+/h2z+Brr5H4jYmqy2vrqF7WeurvMtKIlRW1wSy/XaTvbhBUkbgul3+Btd0UwS8qapLAonCeR0YISLDcEl+CnBSXCy7AtOASd50iSYA6WwnDNTyF9zIlUfPgq5bW6I3eWVwj3Kq1tVRXroxLdc2RhnUozyQ7ftpxlHgLaC/qlao6gxVvT/gRI+qNgHnAE8C7wP3q+pCEZkqIpO91f4CdAami8hbImLTDG2hdLcTBubjJ+DBSd7QB74vDzEmZ5w5fjiNUaWmoQlVd98Y1cxdQauq6g1l3AdYGshek+9rFnG9flT1spjHdhYuYNPmVNDQ1HoM7S4dgxtDOxAfPuCGQOi9IxzzJJTbeRiTfyaM6suxlWu4+aWP2dAQpVNphB/tMyzjvXEuBK4WkV1EbKCRfPLhl1+zcn09NQ1Rr1YRZeX6epZ8+XXYoTkVj8Njx0P/PdzJWEv0Jk/NXlzFjAWf0adLB7br34U+XTowY8FnGe+Ncz+uH/wbQJ2IfB17CyQSE4rahmai3tWyLb/i0WaoaciSS2gH7gO7ngvHPmX96E1ey5beOOcEsjeTdRqjMUk9ZjLaVuVhWHQ3jDgaOnSD/a4LNxZjMiD03jgAqnpHIHszWaeoSIioElW+SfQRceWhUIWX/g9euwpqvrSRK03ByIbeOACISEcR+YGIXO3dfiAiOXBNvWlLn04lLtHHiKorzzhthufOdYl+5zNgt59lPgZjQpLu3jh+R73cDagArsG13Y/BXT1b4S0zOUqKEn8EkpWnTXMTPPkDeOtG2P0XMPFf7uIpYwrEhFF9mTp5B/p26cja2kb6dunI1Mk7ZHw8+5uAl4Dvq+oGABHpBNzqLRsdSDQm46rW1VNcBM26cfa+InHlGbV+BVTMgr2mwthLbXYpU5DSeTW732S/A3BqS6IHUNUNIjIVmJ+WyEzGFIlQEtlYk482Z/DkbFM9RErd1bCnL4Ly3pnbtzEFxO+x+mJgqwTlA4APgwvHZNqwXuU0a+sZ7ZvVladd/dfwwEHw8qXuuSV6Y9LGb7K/FLheRKaIyFDvNgW4DrhERHq23NIXqkmHiw7Zju7lJUgRRFWRIuheXsJFh2yX3h3XfuXGol/xCvTeKb37Msb4bsZ51Lu/h43Dnrc0qj4S81wBO6uWQyaM6supY7d2l2hHo5RFijh17NbpHSphwxcw40A3Jv3kB+Fbh6dvX8YYwH+y3y+tUZjQxF6iPaQkQm1jlBkLPmPnQd3Tk/CjjXD//rDuUzd65ZD9g9+HMWYTfi+qeiHdgZhwTJtTQWO09UBoXcvSOBBapAT2uhy6DIatxgW/fWNMQjZWbIFbUrWOtTWN7kraIqGpWVm1roHG6Lpgd7TyHVj7MWxzBGx7fLDbNsa0y5J9gWtoagZx3S/BdW9vFnXlQfn8VXhgEnTsAUMnQXGH4LZtjPElw5dJmmxTEnFJPrbrJUBpJKCLmpbPhukToWNPN0SxJXpjQmHJvsCN7NeVXp1KKY4IUVWKI0KvTqWM6Nd1yzdeMQsePMRdMDXlReg2dMu3aYzZLH7HxtlLxOaCy0dnjh9OaXGE/t06sm2/LvTv1pHS4kgwgy9VvgA9t4fjX4DOia7JM8Zkiqhq+yuJ1AGNwFxgtnd7zZs3NquNHj1a58+3ER3a0jLheGV1DYOCmHC8/mvo0NUNttNUAyWdggvWmDzW8l1cXl3D4M37LiZtf/Wb7MuAvYF9gQnAHmxM/s+r6h9TiSaTLNln2ILr3RDFJ82FrluHHY0xOWP24ioum7mQkohQ5l3z0hjVVEe+TJrsfTXjqGqtqj6jqr9R1e/gBkabjkv8v/cbhcljqjDvSnj+fBgwFsr7hx2RMTml5ZqXL9bW8cGX6/hibR2N0WhmpyUUkb64xL6fdz8EeA24EtekYwqZKrx4Ebz+Z9j+FDj4ViiyUzzGpCLd17z4/UZ+AawEpgFnAq+qaoYHPDdZ660bXaL/9k/hgBtArJOXMalK9zUvfpP9PcB44HxgN+B5EZkNLFA/jf4mv+1wukvw3/6pTTpizGYqiQi1je6aFxF3wAzBXfPit83+ZFUdgkv0DwG7AA8Cq0XkkTb/2OSnpnp4+TfQsA5KO8MuZ1miN2YLpPWaF1K/qOpj4D1gEfAB0AmYFEgkJnc01sAjR8C838Oyp8KOxpi8kNZrXvB/gvZC3InZfYAOwBvAC7gJyF8KJBITmpT69tavhYcOhxUvw0G3wMhjMhusMXlqwqi+TIVgr3mJ4beffezFVC/FzkWb7ayffdtS6ttb+xU8cDCsfBsO/Y+NXmlM9knalup3PHsbeDxPTZtTQUlEKC91H4Xy0mJqGpoSj2dfvxbqquGIh2H4YSFEa4zZXL47Q4tIP+BsYHvc9IOLgH+o6pdpis1kwPLqGrqXlbQqKyuJUFlds7GgpgrK+kD34fD99yFSmuEojTFbyu9AaHsDS4GTgFqgDvgesERErNafwwb3KKe2MdqqrLYxyqAe5e7JV4vhrt1czxuwRG9MjvLbG+dq4L/ASFU9RVVPAUYC9+JO0pocdeb44TRGlZqGJlTdfWNUXQ+AqrfhvvHQ3Gjt88bkOL/JfhfgGlX95lIu7/G1wK7pCMxkxoRRfZk6eQf6dunI2tpG+nbp6E7Odq2A+ydAcUc44UXos3PYoRpjtoDfNvu1wDBc3/pYw4A1gUZkMm7CqL6tT8Y2rIN/HwZlveC4Z230SmPygN+a/b3ALSLyPREZ5t1OBm7GNe8EQkQmicgHIrJURC5KsLyDiNznLX9VRIYGtW8To7QLHHaPq9FbojcmL/it2V+I6795a8zfNAL/BDZJyptDRCLAjcCBQCXwuojMVNVFMav9EKhW1W1EZArwJ+CEIPZvgMX3gTbBdt+DoQeHHY0xJkB+x8ZpUNXzgR649vtdgJ6qeoGqNgQUyxhgqapWeNu8Fzgibp0jgDu8xzOAA0RsQJZAvHsLPH6iu9dgRtkzxmSPlMbGUdUaVX3Xu9W0/xcpGQgsj3le6ZUlXMebEnEt0Ct+QyJyhojMF5H5K1euDDjMPPTGdfDUj1xt/qjHbIhiY/JQ0mYcEZnpdyOqOjmYcIKhqjcBN4EbLiHkcLLb3Cvglct4tXg/Llp6Pv1veyfQ8TiMMdmhrTb7rzIWhfMZMDjm+SCvLNE6lSJSDHQj83HmlWVV1SxuPohrmi6kU1kpVevquGzmQqaCJXxj8kjSZK+q389kIMDrwAgRGYZL6lNwV+zGmgmchpvo/FjgOZs8ZTNoM3z9CXQbxsVfHMfKou9SVuqujG1zbBxjTM5qs3FWRHYWyUwDrtcGfw7wJPA+cL+qLhSRqSLS0kx0C9BLRJYCPyegnkAFpbkJ/ncq3L0H1FSxfE0tHUvaGRvHGJPz2ut6+SYwAKgCEJHHgR+p6ufpCEZVZwGz4soui3lcBxyXjn0XhKZ6eHwKLH0Y9vkDlPdlcI9yqtbVfTPqJcSNjWOMyQvt1drjuzWOB8rSFItJp8YN8PDhLtHv/3fY82KgnbFxjDF5w/rYFYp5V8Knz8Kk22HXc74pnjCqL8fuNpCV6+p5/4t1rFxXz7G7DbT2emPyTHvNOOrd4stMrhn7G9h6IgzZv1Xx7MVVzFjwGX26dGCIN1PVjAWfsfOg7pbwjckj7SV7Ae4WkXrveUfg3yLS6uxdtvWzN571K+CFX8IB/4CO3TdJ9JDiTFXGmJzVXrK/I+753ekKxARs7ccwfaKbZWq3n8GAMQlX8zVTlTEm57WZ7EPoa2+C8NX7MGMiNNW6IYqTJHpwM1Ut+2o9X9c20RBtpjRSRNeyYob26pzBgI0x6WYnaPPNN7NLReH4F9pM9ADjhvekal0DDdFmigQaos1UrWtg3PCeGQrYGJMJluzzTcce0HM7mPIi9Nmp3dXnVqymT+dSSiNFNCuURoro07mUuRWrMxCsMSZT/I5nb7Jd1dvQe0foOgROeAF8jvy8vLqG3p070KdLx2/KVNXa7I3JM1azzwdLH4F7xsBrV7nnKQzxP7hHObWN0VZldgWtMfnHkn2ue/8emHkM9NkFvv3TlP/crqA1pjBYss9l79wEs06GQd+B456BstRPqk4Y1Zepk3egb5eOrK1tpG+XjkydvIP1sTcmz1ibfa5avwKe/xkMOwQOnwElmz9k0YRRfS25G5PnLNnnqs5bwQlzoM/OECkNOxpjTJazZpxcogqzfwnv3Oye9x9tid4Y44sl+1zRHIWnz4Q3roGvFoYdjTEmx1gzTi6INsITp8Pie2DPS2DvK8KOyBiTYyzZZztthkePg48ege9cBWN+HXZExpgcZMk+20kRDBgLQw+CXc4KOxpjTI6yZJ+t6ta4YYr77Qp72rzqxpgtYydos1HNSrh/P3hwkps71hhjtpDV7LPNukqYcSB8/QlMfhBKOoUdkTEmD1iyzyZrKmD6AVD3FRzzBAwaH3ZExpg8Yck+m8z/CzR8Dcc95y6YMsaYgFibfTZQdfcTroOTXrVEb4wJnCX7sH32Mty7D9SsguIO0GObsCMyxuQhS/ZhWvY0zDgIaldBtC7saIwxecySfViWPAwPfxd6jHCjV3YZFHZExpg8Zsk+DEsfgUePhb67wvHPQ6d+YUdkjMlzluzD0H8MbH8qHPs0dOwRdjTGmAJgyT6TljwMzU3QeQBMuhVKu4QdkTGmQFiyzwRVePkymHkUvHtz2NEYYwqQXVSVbqow+wJY8DfY8Yew04/DjsgYU4As2adTy+xS790Cu/0MJlwLImFHZYwpQFnRjCMiPUXkaRFZ4t1vctZSRHYRkbkislBE3hGRE8KINSXVS+CDe2HsZZbojTGhyopkD1wEPKuqI4BnvefxaoBTVXUHYBJwnYh0z2CM/jVH3X2vUXD6+7D37yzRG2NClS3J/gjgDu/xHcCR8Suo6oequsR7vAKoAvpkLEK/GtbDAwfBgr+7510HhxuPMcaQPcm+n6p+7j3+AmjzKiMRGQOUAh8lWX6GiMwXkfkrV64MNtK21FW7seiXv2D9540xWSVjJ2hF5Bmgf4JFl8Q+UVUVEW1jOwOAu4DTVLU50TqqehNwE8Do0aOTbitQG750NfrVi+Hw6TDiqIzs1hhj/MhYslfVicmWiciXIjJAVT/3knlVkvW6Ao8Dl6jqvDSFmrrGWrh/gptd6shH3eTgxhiTRbKlGWcmcJr3+DTgkfgVRKQUeAi4U1VnZDC29pWUwS7nwDFPWaI3xmSlbEn2VwEHisgSYKL3HBEZLSItl5weD4wHTheRt7zbLuGE61n1HlTOcY93PRsG7RNqOMYYk4yoZqZJOyyjR4/W+fPnB7/hL+bDAwdDWR84/T0osuvTjDGhS9rHO1tq9rml8kWYvj+UdoWjZ1miN8ZkPUv2qVr2pKvRdx4IU16E7sPDjsgYY9plVdJULb4PemwLxz4J5X3DjsYYY3yxZO9XUx0Ud4QDp0FTDXToFnZExhjjmzXj+PHWP+DOXaCmCiIlluiNMTnHkn17XvsTPHs29NzWnZA1xpgcZM04yajCy5fCq3+AUSfCpDtcrd4YY3KQ1eyTeeOvLtHv9GM45C5L9MaYnGY1+2S2PwU0CqN/aWPRG2NyniX7ZMr7wB6/CjsKY4wJhDXjGGNMAbBkb4wxBcCSvTHGFABL9sYYUwAs2RtjTAGwZG+MMQXAkr0xxhQAS/bGGFMALNkbY0wBsGRvjDEFwJK9McYUAEv2xhhTACzZG2NMARBVDTuGtBKRlcAnIey6N7AqhP1uCYs5c3Ixbos5czY37lWqOinRgrxP9mERkfmqOjrsOFJhMWdOLsZtMWdOOuK2ZhxjjCkAluyNMaYAWLJPn5vCDmAzWMyZk4txW8yZE3jc1mZvjDEFwGr2xhhTACzZG2NMAbBkHxAR6SkiT4vIEu++R4J1dhGRuSKyUETeEZETQop1koh8ICJLReSiBMs7iMh93vJXRWRo5qPcJKb2Yv65iCzy3tdnRWTrMOKMi6nNmGPWO0ZEVESyoougn7hF5Hjv/V4oIvdkOsYE8bT3+RgiIs+LyJveZ+TQMOKMi+lWEakSkfeSLBcRud57Te+IyG5btENVtVsAN+DPwEXe44uAPyVYZyQwwnu8FfA50D3DcUaAj4DhQCnwNrB93DpnAf/yHk8B7gv5vfUT835Auff4p7kQs7deF2AOMA8YHWbMKbzXI4A3gR7e8745EPNNwE+9x9sDy7LgvR4P7Aa8l2T5ocD/AAHGAq9uyf6sZh+cI4A7vMd3AEfGr6CqH6rqEu/xCqAK6JOxCJ0xwFJVrVDVBuBeXOyxYl/LDOAAEZEMxhiv3ZhV9XlVrfGezgMGZTjGeH7eZ4ArgD8BdZkMrg1+4v4xcKOqVgOoalWGY4znJ2YFunqPuwErMhhfQqo6B1jdxipHAHeqMw/oLiIDNnd/luyD009VP/cefwH0a2tlERmDq4V8lO7A4gwElsc8r/TKEq6jqk3AWqBXRqJLzE/MsX6IqxGFqd2YvcPywar6eCYDa4ef93okMFJEXhaReSKS8PL8DPIT8+XAySJSCcwCzs1MaFsk1c99m4q3OJwCIiLPAP0TLLok9omqqogk7dPq/TrfBZymqs3BRlnYRORkYDSwb9ixtEVEioBrgdNDDmVzFOOacibgjqDmiMhOqrom1KjadiJwu6peIyLjgLtEZMdC+v5Zsk+Bqk5MtkxEvhSRAar6uZfMEx7aikhX4HHgEu/QLNM+AwbHPB/klSVap1JEinGHvV9lJryE/MSMiEzE/fDuq6r1GYotmfZi7gLsCMz2Wsj6AzNFZLKqzs9YlJvy815XpBGcrwAABj5JREFU4tqPG4GPReRDXPJ/PTMhbsJPzD8EJgGo6lwR6YgbbCzsJqi2+Prc+2XNOMGZCZzmPT4NeCR+BREpBR7CtcPNyGBssV4HRojIMC+eKbjYY8W+lmOB59Q7YxSSdmMWkV2BacDkLGhDhnZiVtW1qtpbVYeq6lDceYawEz34+3w8jKvVIyK9cc06FZkMMo6fmD8FDgAQke2AjsDKjEaZupnAqV6vnLHA2pim4tSFfUY6X264Nu1ngSXAM0BPr3w0cLP3+GSgEXgr5rZLCLEeCnyIO19wiVc2FZdswH0RpgNLgdeA4Vnw/rYX8zPAlzHv68xsjzlu3dlkQW8cn++14JqgFgHvAlNyIObtgZdxPXXeAg7Kgpj/i+uR14g7Wvoh8BPgJzHv843ea3p3Sz8fNlyCMcYUAGvGMcaYAmDJ3hhjCoAle2OMKQCW7I0xpgBYsjfGmAJgyd6YkInIYyJy+xb8/QRv1MzeAYZl8owle5N1ROR2L3mpiDSJyKci8s9Ew0aHRUSWicgvM7SvCTHvR+ztukzs3+QHGy7BZKtngFNwn9HtgVuB7rgxTkIjIqXqRlYMww60HiVxQ0hxmBxkNXuTrepV9QtVrVTVp4D7gINiVxCR73sTaNSJyIcicoE3wFjLchWRc0TkcZH/b+9cQuuqojD8/SKI2saJCj6wYqIlpNGUokh8JFKLA50Y6UCigpVIBREfxVgfbepArVoEEbSYQgnttNKhUuiNFh0IhabBdJDSqClSLSp6SagVl4O1j909JJqQSG5y1webu99nncO965yz9r5raULSt8lRWj5Hq6QDkiYl/ZzeKi7L2ncnM0tv8pg4LqkCrADeKZ6ys/7tkgbT8U6mN5KGrP2SNGc1+VN6eRbX5Md0TYr0+3QdJXVJOirpjKTvJb1SuKmWtFHSsazvvek8Xsrq9kjqn4VsQY0Tyj6oeSTdgDuxOpvV9QBvAFuAZuAFoBcPvJKzDfcx0oYHsBhQiggl6VLgU6CK+0R/EGjH3yJyOoCbkwxrgS787+2vA1elhKRW4LN0vFtSv7bSfO8C64CH0lyr8SAW84akNbi7i31AKx5MZzPwdOpSAVZKKjy4dgKn02dBR+oXLBUW2j9EpEjlBOwG/sSV8CQeeMKA57I+3wGPlsY9C3yTlQ34uNTnALAn5XtwX/3Ls/bONK4pk+Un4KLSPGPAplLdALCrVNeW5rsSWAacAbqz9mXAr7j73emuRyFTtZSuK7Vfnsp7ced1+Rx9wHhW/gF4OOUP4TfKKm42a0rzXbvQ34VI85fCZh/UKp8DTwIX40q5EXgfQNIVuOvXnZI+zMZciDuPyvlqivL9Kd8MDNn55pAvgb/wdYLRVDdsM3OZvAZo0vmxhQt5GoEJPGDNPzKZWVXS0RnMDR568ZesPF20pWbcjXbOIWCrpAYz+w0YBDol7Qduxd80nkr5FuC4mY3PUK5gERDKPqhVJsysULbPSDoIvIY/oRbmx424cv4/yD0EznQh9AKgH3hviraTuCvguXDCzE7PcY7ivCrA87jZatTMTqW1iHvwG11ljscJaoyw2QeLhW1Ar6SrzewU/lTbaGaj5VQad/sU5ZGUHwFaJS3P2tvx38UI/84feKDrnMNAy1Qymdkk7qr2bC5TWjdY9R/Hmi0jwB2lujtxM07xFlPBA450c06xV3BlH/b6JUgo+2BRYGYV3H/6q6lqK/Bi2oGzUtIqSY9J2lwa2iWpR9KNqW0tUOxP34ubVgbSrpy78QAo+6a4aZQZA+6SdE32Z6btwG2SPpK0WlKTpAck7UznUAV2AdslrZPUgi/elm8ac2UH0CGpT9JNkrrxBey3iw5mdgyPlfwIcDBVVzgXarAyzzIFC0wo+2AxsQN4QtIKM+sHNuB78Y8AX+A2/hOlMX24PXoIt0k/bmZfA5jZBHAf0IAHadmP29M3zECWLfi6wXFSxCMzG8J31lyP28SPAG/iQVUKNuHK9ZP0OYyvT8wbZnYYWI+f9zDwVkoflLoO4jeawTRuDDc3hb1+CRLBS4IlS9r/vt4WLgRkENQM8WQfBEFQB4SyD4IgqAPCjBMEQVAHxJN9EARBHRDKPgiCoA4IZR8EQVAHhLIPgiCoA0LZB0EQ1AF/A1ChJ0jc9LxCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "RicVvsRQwNjM",
        "outputId": "fe412c02-2066-4761-cd88-095e0b24255e"
      },
      "source": [
        "sns.lmplot(x='QR3_y', y='QR3_y_pred', data=df_sample_qr3, fit_reg=False, height=5)\n",
        "\n",
        "# Plotting the diagonal line\n",
        "QR3_line_coords = np.arange(df_sample_qr3[['QR3_y', 'QR3_y_pred']].min().min()-0.2, \n",
        "                            df_sample_qr3[['QR3_y', 'QR3_y_pred']].max().max()+0.5)\n",
        "plt.plot(QR3_line_coords, QR3_line_coords,  # X and y points\n",
        "         color='darkorange', linestyle='--')\n",
        "\n",
        "plt.ylabel('Flow predicted by the Model', fontsize=14)\n",
        "plt.xlabel('Reported Flow', fontsize=14)\n",
        "plt.title('Linearity Assumption during QR3', fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcYdWA6npLdO"
      },
      "source": [
        "# Normality\n",
        "\n",
        "This assumes that the error terms of the model are normally distributed. \n",
        "We will examine the normality of the residuals by plotting it into histogram and\n",
        "looking at the p-value from the Anderson-Darling test for normality.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "pKBSljXi0L59",
        "outputId": "b5611994-c67d-43b2-ab19-c18b8aabe3ce"
      },
      "source": [
        "# Normality\n",
        "\n",
        "from statsmodels.stats.diagnostic import normal_ad\n",
        "\n",
        "# Performing the test on the residuals of QR2\n",
        "QR2_p_value = normal_ad(df_sample_qr2['QR2_residual'])[1]\n",
        "print('During QR2, p-value from the test Anderson-Darling test below 0.05 generally means non-normal:', QR2_p_value)\n",
        "\n",
        "# Plotting the residuals distribution\n",
        "plt.subplots(figsize=(8, 4))\n",
        "plt.title('Distribution of Residuals', fontsize=18)\n",
        "sns.histplot(df_sample_qr2['QR2_residual'], kde=True)\n",
        "plt.show()\n",
        "\n",
        "# Reporting the normality of the residuals\n",
        "if QR2_p_value < 0.05:\n",
        "    print('Residuals are NOT normally distributed')\n",
        "else:\n",
        "    print('Residuals are normally distributed')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "During QR2, p-value from the test Anderson-Darling test below 0.05 generally means non-normal: 0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Residuals are NOT normally distributed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "mf06uVcG2XVO",
        "outputId": "9cb81f9e-8c1b-4bda-f237-2382133c9524"
      },
      "source": [
        "# Performing the test on the residuals of QR3\n",
        "QR3_p_value = normal_ad(df_sample_qr3['QR3_residual'])[1]\n",
        "print('During QR3, p-value from the test Anderson-Darling test below 0.05 generally means non-normal:', QR3_p_value)\n",
        "\n",
        "# Plotting the residuals distribution\n",
        "plt.subplots(figsize=(8, 4))\n",
        "plt.title('Distribution of Residuals', fontsize=18)\n",
        "sns.histplot(df_sample_qr3['QR3_residual'], kde=True)\n",
        "plt.show()\n",
        "\n",
        "# Reporting the normality of the residuals\n",
        "if QR3_p_value < 0.05:\n",
        "    print('Residuals are NOT normally distributed')\n",
        "else:\n",
        "    print('Residuals are normally distributed')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "During QR3, p-value from the test Anderson-Darling test below 0.05 generally means non-normal: 5.091914301064408e-08\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Residuals are NOT normally distributed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkSGtoGFpShT"
      },
      "source": [
        "# Multicollinearity\n",
        "\n",
        "This assumes that the predictors used in the regression are not correlated with \n",
        "each other. To identify if there are any correlation between our predictors we \n",
        "can calculate the Pearson correlation coefficient between each column in our data using the corr() function from Pandas dataframe.\n",
        "\n",
        "Then we can display it as a heatmap using heatmap() function from Seaborn.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "uAHD2s9020av",
        "outputId": "7672d19f-b336-4dd0-f960-40e8e6b0384f"
      },
      "source": [
        "# Multicollinearity\n",
        "\n",
        "QR2_corr = df_sample_qr2[cols_exper_qr2].corr()\n",
        "\n",
        "print('Pearson correlation coefficient matrix of each variables during QR2:\\n', QR2_corr)\n",
        "\n",
        "# Generate a mask for the diagonal cell\n",
        "QR2_mask = np.zeros_like(QR2_corr, dtype=np.bool)\n",
        "np.fill_diagonal(QR2_mask, val=True)\n",
        "\n",
        "# Initialize matplotlib figure\n",
        "QR2_fig, QR2_ax = plt.subplots(figsize=(4, 3))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True, sep=100)\n",
        "cmap.set_bad('grey')\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(QR2_corr, mask=QR2_mask, cmap=cmap, vmin=-1, vmax=1, center=0, linewidths=.5)\n",
        "QR2_fig.suptitle('Pearson correlation coefficient matrix', fontsize=14)\n",
        "QR2_ax.tick_params(axis='both', which='major', labelsize=10)\n",
        "#QR2_fig.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pearson correlation coefficient matrix of each variables during QR2:\n",
            "          QR1Sexe     QR2_y    QR2_x1    QR2_x2    QR2_x3    QR2_x4\n",
            "QR1Sexe      NaN       NaN       NaN       NaN       NaN       NaN\n",
            "QR2_y        NaN  1.000000  0.160806  0.421472  0.123650  0.341892\n",
            "QR2_x1       NaN  0.160806  1.000000  0.425925  0.318542  0.415593\n",
            "QR2_x2       NaN  0.421472  0.425925  1.000000  0.221818  0.511957\n",
            "QR2_x3       NaN  0.123650  0.318542  0.221818  1.000000  0.225003\n",
            "QR2_x4       NaN  0.341892  0.415593  0.511957  0.225003  1.000000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "idM_WB815_97",
        "outputId": "6ab9310e-eb3d-41be-d6d3-421e3bd5d427"
      },
      "source": [
        "QR3_corr = df_sample_qr3[cols_exper_qr3].corr()\n",
        "\n",
        "print('Pearson correlation coefficient matrix of each variables during QR3:\\n', QR3_corr)\n",
        "\n",
        "# Generate a mask for the diagonal cell\n",
        "QR3_mask = np.zeros_like(QR3_corr, dtype=np.bool)\n",
        "np.fill_diagonal(QR3_mask, val=True)\n",
        "\n",
        "# Initialize matplotlib figure\n",
        "QR3_fig, QR3_ax = plt.subplots(figsize=(4, 3))\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(QR3_corr, mask=QR3_mask, cmap=cmap, vmin=-1, vmax=1, center=0, linewidths=.5)\n",
        "QR3_fig.suptitle('Pearson correlation coefficient matrix', fontsize=14)\n",
        "QR3_ax.tick_params(axis='both', which='major', labelsize=10)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pearson correlation coefficient matrix of each variables during QR3:\n",
            "          QR1Sexe     QR3_y    QR3_x1    QR3_x2    QR3_x3    QR3_x4\n",
            "QR1Sexe      NaN       NaN       NaN       NaN       NaN       NaN\n",
            "QR3_y        NaN  1.000000  0.207786  0.588660  0.175983  0.476164\n",
            "QR3_x1       NaN  0.207786  1.000000  0.392226  0.395482  0.441922\n",
            "QR3_x2       NaN  0.588660  0.392226  1.000000  0.264974  0.579522\n",
            "QR3_x3       NaN  0.175983  0.395482  0.264974  1.000000  0.190083\n",
            "QR3_x4       NaN  0.476164  0.441922  0.579522  0.190083  1.000000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGkCDW8F64G-"
      },
      "source": [
        "These images show the relationships between our variables:\n",
        "\n",
        "- Null relationships between (x1, x2, x3, x4) are expected. \n",
        "- Positive correlations are desired between y and the independent variables.\n",
        "\n",
        "Null relationships between independant variables means the independent variables are not affecting each other and that there is no multicollinearity in our data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmQLQWxVqk-3"
      },
      "source": [
        "# Autocorrelation\n",
        "\n",
        "Autocorrelation is correlation of the errors (residuals) over time. Used when \n",
        "data are collected over time to detect if autocorrelation is present.\n",
        "\n",
        "Autocorrelation exists if residuals in one time period are related to residuals \n",
        "in another period.\n",
        "\n",
        "We can detect autocorrelation by performing Durbin-Watson test to determine if \n",
        "either positive or negative correlation is present.\n",
        "\n",
        "- If the Durbin-Watson score is less than 1.5 then there is a positive autocorrelation and the assumption is not satisfied\n",
        "- If the Durbin-Watson score is between 1.5 and 2.5 then there is no autocorrelation and the assumption is satisfied\n",
        "- If the Durbin-Watson score is more than 2.5 then there is a negative autocorrelation and the assumption is not satisfied\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v53m3tyA6gZC",
        "outputId": "7cfb09d4-ae33-4f43-f763-be1f7ebf438a"
      },
      "source": [
        "# Autocorrelation\n",
        "from statsmodels.stats.stattools import durbin_watson\n",
        "\n",
        "QR2_durbinWatson = durbin_watson(df_sample_qr2['QR2_residual'])\n",
        "QR3_durbinWatson = durbin_watson(df_sample_qr3['QR3_residual'])\n",
        "\n",
        "print('For QR2:\\nDurbin-Watson:', QR2_durbinWatson)\n",
        "if QR2_durbinWatson < 1.5:\n",
        "    print('Signs of positive autocorrelation', '\\n')\n",
        "    print('Assumption not satisfied')\n",
        "elif QR2_durbinWatson > 2.5:\n",
        "    print('Signs of negative autocorrelation', '\\n')\n",
        "    print('Assumption not satisfied')\n",
        "else:\n",
        "    print('Little to no autocorrelation', '\\n')\n",
        "    print('Assumption satisfied')\n",
        "\n",
        "print('\\nFor QR3:\\nDurbin-Watson:', QR3_durbinWatson)\n",
        "if QR3_durbinWatson < 1.5:\n",
        "    print('Signs of positive autocorrelation', '\\n')\n",
        "    print('Assumption not satisfied')\n",
        "elif QR3_durbinWatson > 2.5:\n",
        "    print('Signs of negative autocorrelation', '\\n')\n",
        "    print('Assumption not satisfied')\n",
        "else:\n",
        "    print('Little to no autocorrelation', '\\n')\n",
        "    print('Assumption satisfied')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For QR2:\n",
            "Durbin-Watson: 2.013122513545195\n",
            "Little to no autocorrelation \n",
            "\n",
            "Assumption satisfied\n",
            "\n",
            "For QR3:\n",
            "Durbin-Watson: 1.7612662788218194\n",
            "Little to no autocorrelation \n",
            "\n",
            "Assumption satisfied\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bott5Won_cBd"
      },
      "source": [
        "Our models should get a Durbin-Watson score of about 2 (which is between 1.5 and 2.5), so we can assume that there is no autocorrelation in our residuals."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcCBpO7Hq-E3"
      },
      "source": [
        "# Homoscedasticity\n",
        "\n",
        "This assumes homoscedasticity, which is the same variance within our error terms.\n",
        "\n",
        "Heteroscedasticity, the violation of homoscedasticity, occurs when we don’t have\n",
        "an even variance across the error terms. \n",
        "\n",
        "To detect homoscedasticity, we can plot our residual and see if the variance \n",
        "appears to be uniform.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "qAkLotnP_jOH",
        "outputId": "7a6d8da8-9a74-4a15-f5b2-6002f6184d3a"
      },
      "source": [
        "# Homoscedasticity\n",
        "\n",
        "# Plotting the residuals for QR2\n",
        "plt.subplots(figsize=(8, 4))\n",
        "plt.scatter(x=df_sample_qr2.index, y=df_sample_qr2.QR2_residual, alpha=0.8)\n",
        "plt.plot(np.repeat(0, len(df_sample_qr2.index)+2), color='darkorange', linestyle='--')\n",
        "\n",
        "plt.ylabel('Residual', fontsize=14)\n",
        "plt.xlabel('Week', fontsize=14)\n",
        "plt.title('Homescedasticity Assumption during QR2', fontsize=16)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "5TAykfpIAGq4",
        "outputId": "cee7cf58-cec4-47ca-88e7-7447be427eb9"
      },
      "source": [
        "# Plotting the residuals for QR3\n",
        "plt.subplots(figsize=(8, 4))\n",
        "plt.scatter(x=df_sample_qr3.index, y=df_sample_qr3.QR3_residual, alpha=0.8)\n",
        "plt.plot(np.repeat(0, len(df_sample_qr3.index)+2), color='darkorange', linestyle='--')\n",
        "\n",
        "plt.ylabel('Residual', fontsize=14)\n",
        "plt.xlabel('Week', fontsize=14)\n",
        "plt.title('Homescedasticity Assumption during QR3', fontsize=16)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1n_L-VzyAZfD"
      },
      "source": [
        "If residuals seem to have constant and uniform variance, we can assume that it satisfied the homoscedasticity assumption."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPab2lWsAlZ4"
      },
      "source": [
        "**Conclusion**\n",
        "\n",
        "Our models should succesfully pass most of the tests in the model validation steps (and to find a good explanantion for those which didn't), so we can conclude that any of our models can perform well to predict future Flow by using the four independent variables (means of scores of D1, D2, D3 and D4).\n",
        "\n",
        "However, let's not forget that any model might still leave about 100*(1-R²) unknown factors that are affecting Flow existence."
      ]
    }
  ]
}