train.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prometheus-eco-racing/embedded-ai-sfhmmy22-att/blob/master/train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvDA8AK7QOq-"
      },
      "source": [
        "# Setup Python Environment \n",
        "\n",
        "The next cell sets up the dependencies in required for the notebook, run it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Y2gs-PL4xDkZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "655dc9c5-4809-475a-c905-96cecc35a3d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.21.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.8)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.1.1)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (2.8.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.24.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.21.5)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.15.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (4.1.1)\n",
            "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.8.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied: libclang>=9.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (13.0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow) (57.4.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.2)\n",
            "Requirement already satisfied: tf-estimator-nightly==2.8.0.dev2021122109 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.8.0.dev2021122109)\n",
            "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.8.0)\n",
            "Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.0.0)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.17.3)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.1.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.14.0)\n",
            "Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.5.3)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.44.0)\n",
            "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow) (0.37.1)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow) (1.5.2)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (0.6.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (1.0.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (1.35.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (3.3.6)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (0.4.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (2.23.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (1.8.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (4.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (4.2.4)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow) (4.11.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow) (3.8.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (0.4.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (2.10)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow) (3.2.0)\n"
          ]
        }
      ],
      "source": [
        "!apt-get -qq install xxd\n",
        "!pip3 install pandas numpy matplotlib\n",
        "!pip3 install tensorflow\n",
        "\n",
        "from locale import normalize\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lwkeshJk7dg"
      },
      "source": [
        "# Upload Data\n",
        "\n",
        "Create a `data` folder and upload the `balanced.csv` and `unbalanced.csv` files you just created.\n",
        "\n",
        "Sometimes copy-paste from arduino serial monitor adds a second newline. Uncomment and run the following cell to consume them:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "qbP4C6oyl_0z"
      },
      "outputs": [],
      "source": [
        "# import sys\n",
        "# for fpath in [\"data/balanced.csv\", \"data/unbalanced.csv\"]:\n",
        "#     with open(fpath) as fin, open(fpath+\"2\", 'w+') as fout:\n",
        "#         for line in fin:\n",
        "#             if not line.isspace():\n",
        "#                 fout.write(line)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omqC9j3VpHnd"
      },
      "source": [
        "# Plot the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "HSt7ycn-pHne",
        "outputId": "5112a827-cef4-4078-ec20-2fbb6be74842"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEWCAYAAACNJFuYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZwU1bXA8d9hABFQWUQlAoJKXOKCMCpo3NCoEBGTGOOW4BbE5QWjzwX1qcRnXjTuGwY14r4QTVxAjAsuSRQd4gJIUECRQZaRfWeGOe+PqoZmenq6uruqa+nz/Xz6091V1XVvLV2n7r1Vt0RVMcYYY9I1CzsDxhhjoseCgzHGmAwWHIwxxmSw4GCMMSaDBQdjjDEZLDgYY4zJYMHBmDyISHcRURFpHnZeUkTkBhF5Iux8mGSx4GDKkoh8LSJrRWSViCwVkXEi0jXsfBkTFRYcTDkbpKptgc7AQuCekPNjTGRYcDBlT1XXAX8B9gYQkR+LyMciskJE5orIDdl+KyJni8h0EVkpIrNF5Py0cUeKSLWIXCYii0RkvoicnTZ+axG5TUTmiMhyEfmHiGztjusrIv8SkWUi8qmIHJn2ux4i8o6b5uvA9r6vFFP2LDiYsicirYFfAB+4g1YDvwLaAT8GLhCRk7L8fBFwArAtcDZwh4j0Thu/E7AdsDNwLnCfiLR3x90K9AEOAToAVwD1IrIzMA74X3f4fwPPi0gn93dPAZNxgsKNwJCCF96YLMT6VjLlSES+xjm41gFtgBrgOFWd0si0dwKqqr8Vke7AV0ALVa1rZNq/ARNV9S73bP9VYJvUtCKyCDgR+BAnCPVV1U8bzONKYB9V/WXasNdwgsJEYDawnaqudsc9BdSr6pkFrxBjGrCSgylnJ6lqO6AVcDHwjojsJCIHi8hEEakRkeXAMLJU3YjIABH5QESWiMgyYGCDaRc3CCJrgLbuNK2AWY3Mdhfg526V0jJ3vj/EaRv5HrA0FRhccwpZeGOaYsHBlD1V3aiqLwAbcQ7CTwEvAV1VdTvgAUAa/k5EtgKex6ke2tENNOMbm7YR3wHrgN0aGTcXeFxV26W92qjqH4D5QHsRaZM2fTevy2qMVxYcTNkTx2CgPTAd2AZYoqrrROQg4PQsP20JbIVTJVUnIgOAY72kqar1wJ+B20XkeyJSISL93IDzBDBIRI5zh7dyG7e7qOocoAoYKSItReSHwKAiFt+YRllwMOXsZRFZBawAbgKGqOo04ELgdyKyErgOeK6xH6vqSuA37vilOEHkpTzS/29gCvARsAS4GWimqnOBwcDVOIFnLnA5m/+vpwMHu7+5HngsjzSN8cQapI0xxmSwkoMxxpgMFhyMMcZksOBgjDEmgwUHY4wxGSLT7XAxtt9+e+3evXvY2TDGmFiZPHnyd6raqbFxiQgO3bt3p6qqKuxsGGNMrIhI1rvrrVrJGGNMBgsOxhhjMlhwMMYYk8GCgzHGmAwWHIwxxmSw4GCMMSaDBQdjjDEZLDjk8NrM15CRXp7dUri6+jpkpFCv9YGmExXjvhgX+DoNi6oiI6XR5ft4/sd5Lffa2rXISMGvnpMnzJzg+3qftmgaMlL4Zvk3id2mXs1fOT9jHTw95enYrhcLDjkc/+TxVEhFoGnc/v7tADzy8SOBphMVJzx9Aju02SHsbATilS9eAeCyfpdljOs9unde87r89csBeH3268VnDBjw5ABat2jty7xS9hm1DwD7P7C/r/ONo6MePQqA2o21AExdNJXTXzidn+710zCzVTALDh5s1I2Bzr9mdQ0AS9YuCTSdKFm0elHYWQjEsnXLAFi4emHR85q/aj4AK9evLHpeKWtq1/g2r3Sp5S5ns5Zu+Tjw1Rucx3xXr6gOIztFs+BgjDEmgwUHY4wxGSw4GOMjxb/H7tojfJMhrtvRgoMxEScSz6tdyk1cg0A2FhyM8ZFgB/Jy1zCYxzW4W3AwxhiTIfDgICJ/FpFFIjI1bVgHEXldRL5039u7w0VE7haRmSLymYjkd2G4MSHzs83BJENcq5tKUXIYAxzfYNhVwJuq2hN40/0OMADo6b6GAqNKkD9jfGfVS+UrFQziWp2UEnhwUNV3gYZ3dw0GHnU/PwqclDb8MXV8ALQTkc5B59EYv1kJwsS1xJASVpvDjqo63/28ANjR/bwzMDdtump3WAYRGSoiVSJSVVNTE1xOjcmDlRiMNUj7RJ3wmneIVdXRqlqpqpWdOnUKIGfG5M/X+xys9JEIcS1BhBUcFqaqi9z3VEc784CuadN1cYcZEyt+liCsNBJPcS0xpIQVHF4ChrifhwAvpg3/lXvVUl9geVr1kzHGmBJpHnQCIvI0cCSwvYhUA9cDfwCeE5FzgTnAKe7k44GBwExgDXB20PmLAqs+SB7bpuWn4TaPa3VSSuDBQVVPyzLq6EamVeCiYHNkjDEml9AbpI3VKSeRbVNjbQ7GGGMSx4KDMcb4KO5tDSkWHCLAGi9NY5JykDHxZMHBmIiLe921iScLDhFgjZfGmKix4GCMMSaDBQdjfGTtBKahuLYpWnAwxhgfJO3EwIJDBMT1zMJkssZj01Bc2xQtOBhjjMlgwSEC4npmYTL5WbVgJcpkiOt2tOBgTAD8rF6yk4d4SQWDuG83Cw7GGOODpLU3WXCIgLgWO012SbtyxeTWcJvH/X9twcGYiLIAY8IUanAQkd+KyDQRmSoiT4tIKxHpISKTRGSmiDwrIi3DzGMpxL1u0mRKWhWDyV/c/9ehBQcR2Rn4DVCpqvsAFcCpwM3AHaq6O7AUODesPBoTJgswJkxhVys1B7YWkeZAa2A+0B/4izv+UeCkkPJmjDFlK7TgoKrzgFuBb3CCwnJgMrBMVevcyaqBnRv7vYgMFZEqEamqqakpRZYDE/eGKxMMa3OIl6T9j8OsVmoPDAZ6AN8D2gDHe/29qo5W1UpVrezUqVNAuTQmfFa9ZMIQZrXSMcBXqlqjqrXAC8ChQDu3mgmgCzAvrAyWStwbrowxyRNmcPgG6CsircU5NToa+ByYCJzsTjMEeDGk/BljTN6SUh0YZpvDJJyG538DU9y8jAauBC4VkZlAR+DhsPJoTL78rHdOWh120mWrAYhrsGiee5LgqOr1wPUNBs8GDgohO6Gxg4Ax8Ze0/3HYl7KaNNbwGH9+th9ZW1QyxPV/bcEhQuJa/DSbJe3s0ZQvCw4RYGeIyePHNrVAkwxxPemz4GBMxNnJQzzFtTopxYJDBNgZYvLYNjVxLTGkWHCIkLifaRhjMsX1f23BIULifqZhNvOlzcH2h1hqWGqM63a04BABVqdsTPLEtcSQ0uRNcCLSBecZC4fhdI63FpgKjANeVdX6wHNoTJmK+8HFxFvW4CAij+B0l/0KzgN4FgGtgO/j9J56jYhcparvliKjSWaNl8YkT1yrk1KaKjncpqpTGxk+FXjBfXxnt2CyVZ7sTNGki/vBxcRb1jaHVGAQkTYismk6EWkmIq1VdYOqzixFJsuFHQxMY+ykIZ7ivt28NEi/ifMIz5TWwBvBZKc8WYO0CZqdeJh8eQkOrVR1VeqL+7l1E9MbU7bsIGySwktwWC0ivVNfRKQPzlVLxifWIG0aY/uFCZOX5zlcAowVkW8BAXYCfhForspU3OsojW1Dkxw5g4OqfiQiewJ7uINmuM98LpqItAMeAvYBFDgHmAE8C3QHvgZOUdWlfqRnTJz42RZlpZDSSUrVYs5qJRFpjfPozuHuFUzdReQEn9K/C5igqnsC+wPTgauAN1W1J05j+FU+pRV5SdmpypltQ5MUXtocHgE2AP3c7/OA/y02YRHZDjgc9xnR7qWxy4DBwKPuZI8CJxWbVtTZ1UrJ40f1kp3tJ0Nct6OX4LCbqt4C1AKo6hrw5WjWA6gBHhGRj0XkIRFpA+yoqvPdaRYAOzb2YxEZKiJVIlJVU1PjQ3aMiSY7eTBh8BIcNojI1jhtAojIbsB6H9JuDvQGRqnqAcBqGlQhqVNGbzTsqupoVa1U1cpOnTr5kJ3wxPXMwmQXteqlqOWnnMQ1uHsJDtcDE4CuIvIkTjvAFT6kXQ1Uq+ok9/tfcILFQhHpDOC+L/IhrViwK12MMVHh5Wql10Xk30BfnOqk4ar6XbEJq+oCEZkrInuo6gzgaOBz9zUE+IP7/mKxacWFnd0lhy9tDrY/JEJcawa8XK10KLBOVccB7YCrRWQXn9L/L+BJEfkM6AX8Hico/EhEvgSOcb8nWlyLnaY0rHE7nuL+v/ZyE9woYH8R2R+4FOfqoseAI4pNXFU/ASobGXV0sfM2JimsBBFPcQ/IXtoc6tyG4cHAfap6H7BNsNkqL/nsRHX1dcjIcM5IJlVPCi3toH08/+Mml238l+PzWvYxn4wpOk+pEsM3y7/JmvbErybGcpusr1vvOd9fLP4ir2V8d867m6Z/66u3cv72uzXf+b4OJ1VP4sAHD/R1ng0tX7c80G3vJTisFJERwJnAOLf77haB5aiMeak+uOndmwCYMHNC0NnJ0PfhvmzfevuSp1sKvUf3zjpu4lcT+fFTP2bED0d4nt9Zvc4qOk+pEsOF4y9sdPyi1Yvo/1h/BvYcWHRapXbZ3y8DYPK3k3NOu8e9e+ScJmVd3TqOGHME/br0Y/7K+Rz92NEM+v6gJn9z5JgjPc/fC0Xp+3BfX+fZmJ8+91PACRJB8BIcfoFz6eq5qroA6AL8MZDcmJxmL5sNwMJVC0NJ/7s1RV+LEDuLVjsXzM1aOivknGxpba3T/+XURY09k2tLUauamrF4BgBL1i7xdb4b6zcC8OnCT1lTuwaAaTXTmvxNrvFR9dnCzwDYsHFDIPNv6jGhr+Fcwvqqqt6eGq6q3+C0ORifRe0PbIzJX1L+x02VHIYAS4EbROTfIjJKRAa7dzGXhVJt5Lhf1WA2C6MRMikHI1OYoPa5rCUHtwppDDDGbWc4GBgAXCEia4G/u91qmCLF/aoGE4xc+4XdNJldesAsdfAs1f856JNKL5eyoqr1wPvu6zoR2R44LsiMlSP7s8dfVEuB5XICkv4fsv9TcXIGBxG5h8z+jZYDVYHkyBhjTOi8lBy2AvYExrrffwZ8hXNj3FGqeklQmQtbqc+2rO44/vzcZ7yWQsqlVBBVtbW1VFdXM+7YcTSjGXNmzuHVY1/dNL5lRUumT5/ue7pPHPYE9VrPoq8XsbjZ4ianbdWqFV26dKFFC+93IXgJDvsBh6rqRgARGQW8B/wQmOI5JZNVVKsiTOH82KZ20I+H6upqttlmG3ZouQMI7LnTnqxdsHbT+NYtWrNXp718T3f9gvXU1dexx4570KIi+0FfVVm8eDHV1dX06NHD8/y93OfQHmib9r0N0MENFn503V327CCQPKXcpl4CkZVKg7Nu3To6duzoz1NuAiAidOzYkXXr1uX1Oy8lh1uAT0TkbZzFPxz4vXtJ6xv5ZtRkZw1o8RdGKdBOLsIX9f9uIfnz0mX3wyIyHjjIHXS1qn7rfr487xRjxM62TL6iVmIoV+nboeRthwkJ1l667BacXlL3V9UXgeYiclCOn5kCWDBKDl/aHGx/yFv6erfgWRwvbQ73A/2A09zvK4H7AstRGbKd2AQtKWezpnS8BIeDVfUiYB2Aqi4FWgaaqzJjf9zkKeU2tRLGZsVUJ8V1PVZ9VMV+++3HunXrWL16NT/4wQ+YOjV3Z4y5eGmQrhWRCtwb4USkE1BfdMoud95VwDxVPUFEegDPAB2BycAvVTWYbgdzKPVBO+qNWqa0cu0Ptr9Ez23v38a3K79l1YZVm4ZVSAWtW7TOmPadOe9wxC65n5nWa6de3Hn8nRnDUzUOlQdWcuKJJ3Lttdeydu1azjzzTPbZZ58ilsLhpeRwN/BXYAcRuQn4B87jPP0yHEi/Q+Rm4A5V3R2n479zfUzLmJIoRZtDXM90gxSnNgcvgaEp6Sev1113Ha+//jpVVVVcccUVxWYN8Ha10pMiMhmnUVqAk1TVl9v9RKQL8GPgJuBSt/G7P3C6O8mjwA04jyo1xhSoVIFEVWNbolG06IByWb/L6LVTLz5Z8MmmYa1btGbvTnsXm70mLV68mFWrVlFbW8u6deto06b4zrOzlhxEpEPqBSwCngaeAha6w/xwJ3AFm6upOgLLVLXO/V4N7Jwlf0NFpEpEqmpqanzKTrjsTNDkI64H4VIIs1fWUkkPZOeffz433ngjZ5xxBldeeaUv82+q5DAZp51BgG44VTwCtAO+Abzfh90IETkBWKSqk0XkyHx/r6qjgdEAlZWVgWz9pO5UxiRVOfbK+vjjj9OiRQtOP/10Nm7cyCGHHMJbb71F//79i5pvU89z6AEgIg8Cf1XV8e73AcBJRaXqOBQ4UUQGAq2AbYG7gHYi0twtPXQB5vmQViyUy85svLGr2IwXv/zlLznnrHMAqKioYNKkSb7M10uDdN9UYABQ1VeBQ4pNWFVHqGoXVe0OnAq8papnABOBk93JhgAvFpuWMUnmJYiUS6AppjrJt5qCEq/qoLatl+DwrYhcKyLd3dc1wLc5f1W4K3Eap2fitEE8HGBaxsRW1K/GMfHm5T6H04DrcS5nVeBdNt8t7QtVfRt42/08m839OIXKnudg8uXnNsx18C+X0kChwqqmTcp28XIp6xKcexFMQOwM0DQmjgcZPy4HDUsc13eQmrqU9UER2TfLuDYico6InBFc1sqH7ZTJUcqz1XwOwlYqNflqquRwH/A/boCYCtTgXFXUE+fKoj8DTwaewzJiVysZ448wuuwWJFEnek1dyvoJcIqItAUqgc7AWmC6qs4oUf5CFcWzrSjmyWzm5/axbV2cuFZvRYWXNodVuI3FJlh2MEiOUh6YvOw3STqjbUo59soaFC+XspqA5XMgsaonk2L7QnZx6oCvWCOvH8mdd27utfWaa67hrrvuKnq+Xi5lNQErl7O6cmLbNFxhPSb0tuu68u2XFayq3WPTsAppRusWmdO+8w4c4aFj1l694M7MHrs3GXL2EE79+alccskl1NfX88wzz/Dhhx8WkPsteQ4OItJaVdcUnWKM2PMcjImvfEsMpf6/ewkMXnTv3p2OHTvy8ccfs3DhQg444AA6duxY9HxzBgcROQR4CGgLdBOR/YHzVfXColM3JqGiVpVRyi67I7boJXXZ7+ay/44d+HTh5mt2StFl93nnnceYMWNYsGAB55xzji/z9NLmcAdwHLAYQFU/BQ73JXVjjAlAOXTZne4nP/kJEyZM4KOPPuK4447zZZ6eqpVUdW6DKo+NvqRutuDpqpMy2NGjJurtB1HPX1jKqZq2ZcuWHHXUUbRr146Kigpf5uklOMx1q5ZURFqQ+VjPxLIDsQlTroN+1KquoiAKvbKGEazr6+v54IMPGDt2rG/z9FKtNAy4COeJbN8CvdzvxmdeznTK6WwoKpJwEC630kU5PfRn+ufT2X333Tn66KPp2bOnb/P1chPcd4D1oWSMiY1yanPYa++9mD17tu/zzVlyEJFdReRlEakRkUUi8qKI7Op7TowxBUn6wa9Q+ZYYyq10lYuXaqWngOdw+lb6HjAWeDrITEWFPc/B5MsOMCYpvASH1qr6uKrWua8ncHpnLYqIdBWRiSLyuYhME5Hh7vAOIvK6iHzpvrcvNi0THRYA/eflDLlk9zlYcEwML8HhVRG5yn1E6C4icgUw3j2Idygi7TrgMlXdG+gLXCQiewNXAW+qak/gTfd7WYjSn9wUJozG6zjuE37keU3tGmRk4+s76O4zlq5dysr1K6leUc2cZXN8n38UeAkOpwDnAxNxeme9ADgVmAxUFZqwqs5X1X+7n1fiXB67MzAYeNSd7FHgpELT8Or9ue8jIyXrjtaYRasXeZr+manPZJ33+rr1eaUZptqNtaGkO3ba2JzrqGZ1jW/rsa6+jsv/fnmj2+y5ac9lDLvxnRs3DfvPd//hvJfP85zWsnXL8t7vJlVP2jS9H1fhpNKvq68rel5+WrBqAV1u79Lkuhnw5ICMYcUEgnwCVodbOrDtH7al6x1daSb+9l9avaKaqm8LPrT6JudSqWqPJl6+NEyLSHfgAGASsKOqzndHLQB2zPKboSJSJSJVNTU1RaV/yJ8PAaBz285bDG9qZzn8EW83iZ/2fPbHbV84zumBpGaN9/yHdVneDW/fEEq6p/zlFDq17tTkNDvcuoNv6Y37Yhy3vn9r1vHXHX7dlt/f3vx97LT8rjGftWQWAAfsdECj4xvb//o+3DevNLyat2JeIPPNJtd+/PznzzNv5TwurMzeS8+7c97NPv8S9spar/WphHyxYNUCIPwSoZerlVqIyG9E5C/u62L3ZjhfuA8Teh64RFVXpI9TZ+00uoZUdbSqVqpqZadOTR88gjBjcfHPO3p7ztsArFi/oukJI2Dm0pmhpZ1P8CzWRm365v9VG1ZlHZfvtfWlDPTl1hYQVq+sboJbfi3BQf6BBx6gV69e9OrVix49enDUUUcVPU8vd0iPAloA97vff+kO815+zsINMs8DT6rqC+7ghSLSWVXni0hnYFGx6XhVyE6kqgX/yVNnNGGfIXiRhBvBSimfbVrswavcDvxehdEra9frbqP5l9+yR+3mk4hm0gxatMmcOEuf3XtsWOl8aLmN856rz26FYcOGMWzYMGpra+nfvz+XXnppMYsBeAsOB6rq/mnf3xKRT4tNWJwj6sM4jx29PW3US8AQ4A/u+4vFpuVVIQdpRQs+cKaCShz+3Em/yzSlmCCY72+LDbgWsKOq4f85y3Yqss/uxv6Tw4cPp3///gwaNKioeYO34LBRRHZT1VluhnbFn473DsUphUwRkU/cYVfjBIXnRORcYA5Og3hJNDxIezlo+9FFcRxKDiY4hW7/OJxUFCtOyygIc393Ge122JcZi6ZsGp5vl90z3Mbo3p17I3k0do8ZM4Y5c+Zw7733es90E7wEh8uBiSIyG+cwuAtwdrEJq+o/yH5YPbrY+ZdKvdZTgT+9IHphgSRYxZSQCm1zKMUBsKTPc/BxuqDzUQqlyMvkyZO59dZbee+992jWzJ+rp7z0rfSmiPQEUs+9m6Gq631JPWIKrVYqVJyqBcrl+n2rVoqf9Ha/YvaZKAWUfN17770sWbJkU0N0ZWUlDz30UFHz9PIkuItwGow/c7+3F5FzVfX+HD+NnUIbpAtVyJljWHX/YaQbpyqFhuJ8oCmFXPtTPvtbY+1+iWgjUzxXWT/yyCO+J++l/PFrVV2W+qKqS4Ff+56TCGj4h/b08B0fSg5xOJCUy1lqKEEwy/aPc3AsVl5Xe0X8/1PoPhX29vcSHCokbelEpAJoGVyW4mXTDTAR9Ninj8XmDuzGxK5aKaT7HII+iYm6xpatkC67g1pH+e7HYVyC2xgvDdITgGdF5E/u9/PdYQZ/DmBBbdwhfxsSyHxLJckHtHSFLmciqk58kO0/6HX9CFLUvlZPfeRLL4XkT3L9SJxrqYYCx7iDXgceUs1xK2kJVVZWalVV/n2RqCqT50/mwAcP3DTs2ZOfZbuttuOb5d/QsqIlZ7141qZxz538HOs3rmfeinlc9abTH+AZ+57B7h12Z9k6p+btrkl3ceZ+Z/LF4i9o06INE7+eWNzCGWMi7a6+d3FQj4No3qa5b11o5Kvye5VZx6kqixcvZuXKlfTo0WOLcSIyWVUb/XHO4NBgRh2ALqnG6agoNDiM+mgUF47P3neLMcbk0r5le27ofQO7b7s7zTzV1Ptvl3a7NDm+VatWdOnShRYttuz5qKng4OVqpbeBE91pJwOLRORfqvpbj/mOrB/t9qPcEy3dBe76Gm4I8JRAgZEabBpRsqQH3D07uct7g3vC1XD5Vm8Pf6zxvtz1Ar+r9289Le0Od33l73pf0x5uWQIj2sL/rUruNm3C0g1LGf7BcKhtBTet3XId1OwJ900PdL2M+vEojt/reP9nrKpNvoCP3ffzgJHu589y/a6Urz59+mhQQHXffQObvaqqPv64k87LLwebTlSA6mGHhZ2LYPzzn87y3XFH5jhwXl7dfLMz/Ucf+ZM3UPX7r9KihTPfvn3zW7YkOuEEZx3U1TnfZ892vl96abj5agpQpVmOq17KQM3dDvBOAV7xPzxF35QpuacpxqduT1Wffx5sOlHy3nth5yAYs5xeuJk8ufh5TZrkvH/9dfHzSvEjX+lq3cd8fPCBv/ONo/HjnfdUTf3Chc77P/8ZTn6K5SU4/A54DZilqh+5fSt9GWy2yotddJIcfl60ktovIn4hjGkgtb3ivt28dJ8xFhib9n028LMgM1Wu4r4zGX9ZcIiX1Haqr9/ye1xP/rw87KeLiPxVRBa5r+dFpEspMlcu7CCQHH4eCOJ6UClX2UoMcd2OXqqVHsF5xsL33NfL7jDjk3yCw+zZzvTLlwebp8acfXZ8d/RcfvOb7Mu2ciUcfnh+y/7EE/7kC+DPf2487bo6GDjQW76iduIxbZqT7zVrck97883e170qnHSSM31dHQwYkPu3r77q735dX+/8Vw491L95Nubdd518bwzojjMvwaGTqj6iqnXuawxQ+udyJlg+O+bIkc77X/8aTF6aMmZM6dMslXvuyT5u/HinAf3nP/c+vzPPLD5Pqf3itdcaHz9vnnNg69Yt97yCOoAU6pJLnPd//CP3tFdd5X2+a9bAiy/C1lvDnDkwYQI0uO8rw8CB3ufflNT2qq/f8r8S1AnVz9zK/cWLg5m/l+CwWETOFJEK93UmEFB2oqW+xN0meTm7S3XVHrUzwcRZtw6WLgXCW+d+HlSyBofly2FF6Z9h7ss6/e67jJWUPt9808grL1VVTtqponxa2nV1W07q0+MVMgS9X3rJ9jk4l7EuAOYDJ+PDw35yEZHjRWSGiMwUkTzOHQqwbh307Jmxo/lytjV9ujPfJv7p+RwE0s9OTEAmTXJOPTt0ADav89ZrGzknmjlz8wSzZ3PKBR1Rr30orFiRc9/IsH49rFq1xSAvB4es+3K7drDddt7T90nO/XjtWrjwwqbXTafsFRiq+bfl5fV/P9Dtcme33WAb51nP2YJD3kH+zjudH+XIeCq9oI4FTXaf4fbA+piqnhFM8k2m+wXwI6Aa+Ag4TVUbvROg0O4z2LABttqqiJwaY0wEFPx5q5gAABM4SURBVFh8aKr7jCZLDup0rreLiJS6i+6DgJmqOltVNwDPAIN9TyVV8WmMMWYLXqqVZgP/FJH/EZFLU6+A87UzMDfte7U7bBMRGSoiVSJSVVNTU1Aibx53C3NovDVvFW0KmqcxxpTSibwYyHy9PM9hlvtqBmwTSC4KoKqjgdHgVCsVMo8ue7Zl0L5zqJ6yhHnszNCuE+h86hFUVDhVfq1qV3LdrdsCcMlw3aJ6+LnnnKtFfvtbp+4vNS41PvX5tdfg44+hf3844ABn2ooK5/XGG0719rO7X8MpM3/PuH438mbfaxudT+p10vih9J3yIM/0/xNVBwzdtCzp9Zqpz7f80fnwk5OUHXaATh3raVG7hg0t2zb6u3QNh1dVwasTnIGCctFF0LZt9uXO+h3luhucc5IrLnc2W6rXocY+33ff5i4ajjoKli2DXXfdfIWOKjz6qNN2PGyYU4VeV5f5qq11ttfKlc4lhg3zlVpXO7Zbz/Jl9aynFQAXXAC9v/kb5437CQCH915F5RFtqK936qjHjoXVC1dy3E/b8sWXwmFT7uM+LuaN71/IxJPv27Q8qvCHm500/utiZfFi52qaf/3LSX/bbWHoUGeedXXO+7CXf8y+c8dv2gZnnK489ZTz+fLLof2qbxgxyumNc33z1owctoD1LbfZooYh9bn1uiXc9EBHAK660tmXf3bvkYxfdTjX8zsuvhhatMi8Vj/bdtm0fe5v8HhOlEMOgX79nBrb+nrnpbr5c309nPPCCew3dxyjB73M9N1O2LSvpN5XrHCuNOrdG158yRlxdH+lT5+0bXaL837llZt/12b1Iq69Z0dW05r9+ZSZ9OSrit24d/hMsrntdmeGw86tpY7mTJrkrIv+/TfPt+F7Ku2Uq6+Gq29tT5sNy7h80H/448t7bhr3RYeD+UW3D5g9fT3r1kPLtlux007wox85V2v16QMvveTslzvvDA8+6Gz/4cOhVau0ddvgP37VHTuw7boabn2vb9ZlK0q2TpdSL2DfXNP4/QL6Aa+lfR8BjMg2fWAd761cmX9vaYW4+monjRtvzD3tr3/tTPunP+Wc9L1hT+j7V7zgQwZdfqyL+vrSrNN8NZWnF15wxg0enHs+99zjTHvhhfml0ZiBA9NjS+b4r792hnftmnteS5YEs97T81fIsnnpbTKfea9Y4UzburXql186n3fbzdv8a2u9pZFNu3bOfGbM2HKdHHxwcfPNplMnZ/4LFxY8C5roeM9LyeF+EdkKGAM8qaqluP3qI6CniPQA5gGnAqeXIN1E+eGokl5HYEy8FXvtcKn7ywj42uqcbQ6qehhwJtAVmCwiT4nIsUFmSlXrgItxOvybDjynqtOCTLNRUbwd2G5wiLZS7jOFXAOddFFYzijkwQdeSg6o6hcici1QBdwNHCDOA1qvVtUXgsiYqo4HxuecsFwlZAeMlXzWuQVxb5K0H5e65BBwOl463ttPRO7AOYPvDwxS1b3cz3cEmruwlXrHTdIfxRQv1/6QTwAq532rVIE6W3AIKv2Al8tLyeEe4CGcUsLa1EBV/dYtTRjjXTkfpEzwitm//No3E3JS6eV5Dkc0Me5xf7MTMVE8kFl1RelF9Qw9zm0OpdyPS91A3DC9oNMPaF1mrVYSkcEiclHa90kiMtt9nRxIbspVIRs3an/2cmBtDv5L4n5cBm0OV+A8xyFlK+BA4EjgggDzFB1J3HFNckS1RBM1XteTXcq6haaqlVqqanoXFv9Q1cU4XXhb3xKmfFgpwH9JXKelDg4pAaXXVMmhffoXVb047Wt5POwnimdbSfxTxYWX/cHaHPJTinwl5GCdVanbHIBJIvLrhgNF5Hzgw0ByY7yL6p/dOEoRxO1EwZuwL2WNqaaqlX4L/E1ETgf+7Q7rg9P2cFLQGYuEpJ9xmGjzuj9ErUQTFfkuc1BtDkEHp1Jfyqqqi4BDRKQ/8AN38DhVfSuQnBhv7Gyx9Gyd+y/JJauEBGIv9zm8BZRnQIjyRo5y3pIqqmfoXg6CUd1fktTmUC73ORhjimCljego9bYodbVSQCw4NCWqZ1vGpItqiSZsYV01lJC2SgsOUZDPmUVMz0Jizda5/5LY5pCwq5UsODQlyhs5ynlLqqieoVubw2ZhBvJU2s2ScVhNxlIEJap/KBN9VtoIX1SqlWJ6HAklOIjIH0XkPyLymYj8VUTapY0bISIzRWSGiBwXRv5CE9OdKPGifklkVEs0TQlqnUZhOa1BuiivA/uo6n7AF8AIABHZG+d50T8Ajsd5fnVFSHmMxo7WUEx3tESI4v4Qd0GuU2tzKEoowUFV/+4+JxrgA6CL+3kw8IyqrlfVr4CZwEFh5DHyErIDJo61OZQvq1by3TnAq+7nnYH0nmCr3WHhiOlGNRFgJbzwhfX/TUi1kpfHhBZERN4Admpk1DWq+qI7zTVAHfBkAfMfCgwF6NatWxE5jZmY7mixFvV1bm0O0ZCwaqXAgoOqHtPUeBE5CzgBOFp1054yD+iaNlkXd1hj8x8NjAaorKyM955mT4KLh7Fjw85B4aK6v3jJ1157wfr1+c13zZrNn2fPzu+3xYrqus5TWFcrHY/zpLkTVTVtK/IScKqIbCUiPYCeWPfgW9pmG+e9VavSp33aafDUU6VPtxTOPx8eeKDxcQMGwIEHwvTpuefj54EhNa8BA5z8NdTcPbf75hv/0iyV226Dvn3hkENyT/v55zBrlrf5br01HH88TJgAu+wCRx0F777b9G9eegkGDfI2fy9EnP/K8OH+zbMxzz4LxxwDHToEM39VLfkLp6F5LvCJ+3ogbdw1wCxgBjDAy/z69OmjgXHO64Obv6rqlVc6afz+97mnXbnSma6uLtg8BakU6zQso0dnX758l/vEE53p//a37NPcc4/q3Lm551VXF8x6T80zydvUq9Q6WL3a+f7++873gw4KN19NAKo0y3E1sGqlHAFp9ybG3QTcVMLsxEvbtjBiRNi5MLmce25p0rn44tzTmNKyaiVjTGwk5IBlSseCQ5TYHzj+bBuahmK6T1hwMCYIfl6q6ce8YnqASoSYXrZrwcEYY0wGCw5RENMzCxMjVnIonYSsawsOxvgpiPscTDzFfPtZcDAmCFYaNDHfByw4GGOMyWDBwZioi/kZaNlJVSdZtZLxTcx3JoNtQ5MYFhyMCYKd7ZuYs+BgjDF+SMhDflIsOBhjjMlgwcEYP9l9DsYapI1vYl78NI2wbWpivg9YcDDGGJMh1OAgIpeJiIrI9u53EZG7RWSmiHwmIr3DzJ8xkRDzM9Cy0bAayaqVCiMiXYFjgfQH4A7AeW50T2AoMCqErBlTuJgfEIxJCbPkcAdwBZB+WjQYeMx9vOkHQDsR6RxK7owphp3tl6+EnCCEEhxEZDAwT1U/bTBqZ2Bu2vdqd1h5SMhOZYwh9icIzYOasYi8AezUyKhrgKtxqpSKmf9QnKonunXrVsysjDHGNBBYcFDVYxobLiL7Aj2AT8U5U+4C/FtEDgLmAV3TJu/iDmts/qOB0QCVlZXxDtEmOew+B5MS8+1X8molVZ2iqjuoandV7Y5TddRbVRcALwG/cq9a6gssV9X5pc5jycW8+GkaYdu0fKWCQsz3gcBKDgUaDwwEZgJrgLPDzY4xxpSn0IODW3pIfVbgovByY0wExfwMtGzYfQ7GmKxifkDI25Qp8O67YeciWhoG8w8/DCcfRbLgYMJxxRVh56D0DjoI9twz7Fz4a5994LDD4NJLw85JdB10UNg5KEjo1UomTbmcdZZrNcmkSflNH6f94bbbnJeJ13ZrgpUcjAnC44+HnQMTloSc/Fhw8OLkk8POgYmL1FnjmWeGmw9TegkpMaRYcMilthaefTbYNFq2dN5btAg2HRO85m5N7RNPFD+vVq2c94qK4udlgtemjfOeKjmktltMG6StzSGX5iVYRVdf7QShYcOCT8sE6+c/h08/hSuvLH5ed98N3brBoEHFzwvg3nuhXz9/5mUyvf8+jBu3+WSvTx+47joYOjTcfBVINAH1Y5WVlVpVVRV2NowxJlZEZLKqVjY2zqqVjDHGZLDgYIwxJoMFB2OMMRksOBhjjMlgwcEYY0wGCw7GGGMyWHAwxhiTwYKDMcaYDIm4CU5EaoA5Bf58e+A7H7MTB7bM5cGWuTwUs8y7qGqnxkYkIjgUQ0Sqst0hmFS2zOXBlrk8BLXMVq1kjDEmgwUHY4wxGSw4wOiwMxACW+byYMtcHgJZ5rJvczDGGJPJSg7GGGMyWHAwxhiToayDg4gcLyIzRGSmiFwVdn4KJSJdRWSiiHwuItNEZLg7vIOIvC4iX7rv7d3hIiJ3u8v9mYj0TpvXEHf6L0VkSFjL5JWIVIjIxyLyivu9h4hMcpftWRFp6Q7fyv0+0x3fPW0eI9zhM0TkuHCWxBsRaScifxGR/4jIdBHpl/TtLCK/dffrqSLytIi0Stp2FpE/i8giEZmaNsy37SoifURkivubu0U8PPBaVcvyBVQAs4BdgZbAp8DeYeerwGXpDPR2P28DfAHsDdwCXOUOvwq42f08EHgVEKAvMMkd3gGY7b63dz+3D3v5ciz7pcBTwCvu9+eAU93PDwAXuJ8vBB5wP58KPOt+3tvd9lsBPdx9oiLs5WpieR8FznM/twTaJXk7AzsDXwFbp23fs5K2nYHDgd7A1LRhvm1X4EN3WnF/OyBnnsJeKSFujH7Aa2nfRwAjws6XT8v2IvAjYAbQ2R3WGZjhfv4TcFra9DPc8acBf0obvsV0UXsBXYA3gf7AK+6O/x3QvOE2Bl4D+rmfm7vTScPtnj5d1F7Adu6BUhoMT+x2doPDXPeA19zdzsclcTsD3RsEB1+2qzvuP2nDt5gu26ucq5VSO11KtTss1txi9AHAJGBHVZ3vjloA7Oh+zrbscVsndwJXAPXu947AMlWtc7+n53/Tsrnjl7vTx2mZewA1wCNuVdpDItKGBG9nVZ0H3Ap8A8zH2W6TSfZ2TvFru+7sfm44vEnlHBwSR0TaAs8Dl6jqivRx6pwyJOa6ZRE5AVikqpPDzksJNcepehilqgcAq3GqGzZJ4HZuDwzGCYzfA9oAx4eaqRCEsV3LOTjMA7qmfe/iDoslEWmBExieVNUX3MELRaSzO74zsMgdnm3Z47RODgVOFJGvgWdwqpbuAtqJSHN3mvT8b1o2d/x2wGLitczVQLWqTnK//wUnWCR5Ox8DfKWqNapaC7yAs+2TvJ1T/Nqu89zPDYc3qZyDw0dAT/eqh5Y4jVcvhZyngrhXHjwMTFfV29NGvQSkrlgYgtMWkRr+K/eqh77Acrf4+hpwrIi0d8/YjnWHRY6qjlDVLqraHWfbvaWqZwATgZPdyRouc2pdnOxOr+7wU92rXHoAPXEa7yJHVRcAc0VkD3fQ0cDnJHg741Qn9RWR1u5+nlrmxG7nNL5sV3fcChHp667DX6XNK7uwG2FCbgAaiHNlzyzgmrDzU8Ry/BCnyPkZ8In7GohT1/om8CXwBtDBnV6A+9zlngJUps3rHGCm+zo77GXzuPxHsvlqpV1x/vQzgbHAVu7wVu73me74XdN+f427Lmbg4SqOkJe1F1Dlbuu/4VyVkujtDIwE/gNMBR7HueIoUdsZeBqnTaUWp4R4rp/bFah0198s4F4aXNTQ2Mu6zzDGGJOhnKuVjDHGZGHBwRhjTAYLDsYYYzJYcDDGGJPBgoMxxpgMFhxM2RORa9xePz8TkU9E5OCA03tbRPJ+ILyI7CEij4pIMxF5P4i8GZPSPPckxiSXiPQDTsDp1Xa9iGyP09tpFB0GvAvsi3PNujGBseBgyl1n4DtVXQ+gqt+lRojIdcAgYGvgX8D5qqoi8jbwMc7Bug3OHacjcA7az6rqtW4HiBNwOonrDUwDfqWqa9ITF5FjcW7y2grnBqWzVXVVg2kOA+4BugELcbplrxeRKlXNuwRijBdWrWTK3d+BriLyhYjcLyJHpI27V1UPVNV9cALECWnjNrgH5gdwuiK4CNgHOEtEOrrT7AHcr6p7AStwnjWwiVtKuRY4RlV749z5fGnDDKrqe6raC+fO3r2B13Hu8LXAYAJjwcGUNfcsvQ8wFKc77GdF5Cx39FHu08Sm4HTs94O0n6b64ZoCTFPV+W7pYzabOz+bq6r/dD8/gdPNSbq+OAf7f4rIJzj95+zSWD5FpDWwXp0uDXriBApjAmPVSqbsqepG4G3gbTcQDBGRZ4D7cfqtmSsiN+D025Oy3n2vT/uc+p76XzXsm6bhdwFeV9XTmsqfiLwE7InTE+lnOA+FqRKR/1PVZ3MvoTH5s5KDKWvuFUA90wb1AuawORB85z4n4+SMH+fWzW3wBjgd+EeD8R8Ah4rI7m5e2ojI9xvORFVPBB4ELgB+g/MYzF4WGEyQLDiYctcWeFREPnfPyvcGblDVZTgH5Kk4XSF/VMC8ZwAXich0nN5TR6WPVNUanOchP+2m/T5OCaExh+MEl8OAdwrIizF5sV5ZjQmAe7XSK25jtjGxYyUHY4wxGazkYIwxJoOVHIwxxmSw4GCMMSaDBQdjjDEZLDgYY4zJYMHBGGNMhv8H6aSaNvXWKl8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_unbalanced = pd.read_csv(\"data/unbalanced.csv\")\n",
        "df_balanced = pd.read_csv(\"data/balanced.csv\")\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(df_unbalanced['aX'], 'g.', label='x', linestyle='solid', marker=',')\n",
        "plt.plot(df_unbalanced['aY'], 'b.', label='y', linestyle='solid', marker=',')\n",
        "plt.plot(df_unbalanced['aZ'], 'r.', label='z', linestyle='solid', marker=',')\n",
        "plt.title(\"Unbalanced\")\n",
        "plt.xlabel(\"Sample #\")\n",
        "plt.ylabel(\"Gyroscope (deg/sec)\")\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(df_balanced['aX'], 'g.', label='x', linestyle='solid', marker=',')\n",
        "plt.plot(df_balanced['aY'], 'b.', label='y', linestyle='solid', marker=',')\n",
        "plt.plot(df_balanced['aZ'], 'r.', label='z', linestyle='solid', marker=',')\n",
        "plt.title(\"Balanced\")\n",
        "plt.xlabel(\"Sample #\")\n",
        "plt.ylabel(\"Gyroscope (deg/sec)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gxk414PU3oy3"
      },
      "source": [
        "# Train the Neural Network\n",
        "\n",
        "## Preprocess the dataset\n",
        "\n",
        "The next cell parses the csv files and transforms them to a format that will be used to train the full connected neural network."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGChd1FAk5_j",
        "outputId": "142cde91-e691-4ea8-ac42-474401601d52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing output_class 'balanced'.\n",
            "\tThere are 1000 samples of the balanced output_class in the dataset.\n",
            "Processing output_class 'unbalanced'.\n",
            "\tThere are 1000 samples of the unbalanced output_class in the dataset.\n",
            "Data set parsing and preparation complete.\n"
          ]
        }
      ],
      "source": [
        "CLASS = [\n",
        "    \"balanced\",\n",
        "    \"unbalanced\",\n",
        "]\n",
        "\n",
        "MSR_SERIES_PER_SAMPLE = 10 # number of measurement series (6) per sample\n",
        "\n",
        "inputs = []\n",
        "outputs = []\n",
        "\n",
        "# read each csv file and push an input and output\n",
        "for output_class in CLASS:\n",
        "  output = 1 if output_class == \"balanced\" else 0\n",
        "  print(f\"Processing output_class '{output_class}'.\")\n",
        "\n",
        "  df = pd.read_csv(\"data/\" + output_class + \".csv\", dtype=float)\n",
        "\n",
        "  # calculate the number of dataset samples for the current class\n",
        "  num_samples = int(df.shape[0] / MSR_SERIES_PER_SAMPLE)\n",
        "  \n",
        "  print(f\"\\tThere are {num_samples} samples of the {output_class} output_class in the dataset.\")\n",
        "\n",
        "\n",
        "  for i in range(num_samples):\n",
        "    tensor = []\n",
        "    for j in range(MSR_SERIES_PER_SAMPLE):\n",
        "      index = i * MSR_SERIES_PER_SAMPLE + j\n",
        "\n",
        "      tensor += [\n",
        "          df['aX'][index], \n",
        "          df['aY'][index], \n",
        "          df['aZ'][index], \n",
        "          df['gX'][index], \n",
        "          df['gY'][index], \n",
        "          df['gZ'][index],\n",
        "          ]\n",
        "\n",
        "    inputs.append(tensor)\n",
        "    outputs.append(output)\n",
        "\n",
        "# convert the list to numpy array\n",
        "inputs = np.array(inputs)\n",
        "outputs = np.array(outputs)\n",
        "\n",
        "print(\"Data set parsing and preparation complete.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5_61831d5AM"
      },
      "source": [
        "## Randomize and split the input and output pairs for training\n",
        "\n",
        "Randomly sprint the input and ouput pairs into sets of data. 60% for training, 20% for validation, and 20% for testing.\n",
        "\n",
        "  - the training set is used to train the model\n",
        "  - the validation set is used to measure how well the model is performing during training\n",
        "  - the testing set is used test the model after training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfNEmUZMeIEx",
        "outputId": "a56c872a-dc21-45f4-c0dc-cbdeece96962"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1200\n",
            "400\n",
            "400\n"
          ]
        }
      ],
      "source": [
        "# Randomize the order of the inputs, so they can be evenly distributed for training, testing, and validation\n",
        "randomize = np.arange(len(inputs))\n",
        "np.random.shuffle(randomize)\n",
        "inputs = inputs[randomize]\n",
        "outputs = outputs[randomize]\n",
        "\n",
        "# Split the recordings (group of samples) into three sets: training, testing and validation\n",
        "TRAIN_SPLIT = int(0.6 * len(inputs))\n",
        "TEST_SPLIT = int(0.2 * len(inputs) + TRAIN_SPLIT)\n",
        "\n",
        "inputs_train, inputs_test, inputs_validate = np.split(inputs, [TRAIN_SPLIT, TEST_SPLIT])\n",
        "outputs_train, outputs_test, outputs_validate = np.split(outputs, [TRAIN_SPLIT, TEST_SPLIT])\n",
        "\n",
        "print(len(inputs_train))\n",
        "print(len(inputs_test))\n",
        "print(len(inputs_validate))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9g2n41p24nR"
      },
      "source": [
        "## Build & Train the Model\n",
        "\n",
        "Build and train a [TensorFlow](https://www.tensorflow.org) model using the high-level [Keras](https://www.tensorflow.org/guide/keras) API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGNFa-lX24Qo",
        "outputId": "2862af06-ecd8-411f-ae75-182f4593139f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/60\n",
            "38/38 [==============================] - 1s 8ms/step - loss: 0.9517 - accuracy: 0.5167 - val_loss: 0.8232 - val_accuracy: 0.4950\n",
            "Epoch 2/60\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.6939 - accuracy: 0.5142 - val_loss: 0.7638 - val_accuracy: 0.5850\n",
            "Epoch 3/60\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.6741 - accuracy: 0.5492 - val_loss: 0.7847 - val_accuracy: 0.5500\n",
            "Epoch 4/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.6790 - accuracy: 0.5600 - val_loss: 0.7646 - val_accuracy: 0.5975\n",
            "Epoch 5/60\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.6615 - accuracy: 0.5783 - val_loss: 0.7621 - val_accuracy: 0.6050\n",
            "Epoch 6/60\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.6511 - accuracy: 0.6167 - val_loss: 0.7710 - val_accuracy: 0.4950\n",
            "Epoch 7/60\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.6356 - accuracy: 0.6625 - val_loss: 0.7477 - val_accuracy: 0.7500\n",
            "Epoch 8/60\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.6163 - accuracy: 0.7067 - val_loss: 0.7315 - val_accuracy: 0.7450\n",
            "Epoch 9/60\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.6006 - accuracy: 0.7175 - val_loss: 0.7300 - val_accuracy: 0.7425\n",
            "Epoch 10/60\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.5725 - accuracy: 0.7700 - val_loss: 0.7146 - val_accuracy: 0.7175\n",
            "Epoch 11/60\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.5387 - accuracy: 0.8333 - val_loss: 0.6836 - val_accuracy: 0.8650\n",
            "Epoch 12/60\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.5199 - accuracy: 0.8075 - val_loss: 0.6989 - val_accuracy: 0.6350\n",
            "Epoch 13/60\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.4764 - accuracy: 0.8742 - val_loss: 0.6180 - val_accuracy: 0.9625\n",
            "Epoch 14/60\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.4352 - accuracy: 0.9408 - val_loss: 0.5935 - val_accuracy: 0.8325\n",
            "Epoch 15/60\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.4145 - accuracy: 0.8992 - val_loss: 0.5504 - val_accuracy: 0.9750\n",
            "Epoch 16/60\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.3710 - accuracy: 0.9575 - val_loss: 0.5356 - val_accuracy: 0.9625\n",
            "Epoch 17/60\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.3365 - accuracy: 0.9725 - val_loss: 0.4932 - val_accuracy: 0.9800\n",
            "Epoch 18/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.2950 - accuracy: 0.9900 - val_loss: 0.4750 - val_accuracy: 0.9775\n",
            "Epoch 19/60\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.2655 - accuracy: 0.9917 - val_loss: 0.4442 - val_accuracy: 0.9875\n",
            "Epoch 20/60\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.2354 - accuracy: 0.9933 - val_loss: 0.4218 - val_accuracy: 0.9900\n",
            "Epoch 21/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.2057 - accuracy: 0.9942 - val_loss: 0.4027 - val_accuracy: 0.9875\n",
            "Epoch 22/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1832 - accuracy: 0.9950 - val_loss: 0.3940 - val_accuracy: 0.9850\n",
            "Epoch 23/60\n",
            "38/38 [==============================] - 0s 10ms/step - loss: 0.1608 - accuracy: 0.9967 - val_loss: 0.3733 - val_accuracy: 0.9900\n",
            "Epoch 24/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1402 - accuracy: 0.9975 - val_loss: 0.3618 - val_accuracy: 0.9875\n",
            "Epoch 25/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1228 - accuracy: 0.9967 - val_loss: 0.3500 - val_accuracy: 0.9850\n",
            "Epoch 26/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1098 - accuracy: 0.9975 - val_loss: 0.3494 - val_accuracy: 0.9875\n",
            "Epoch 27/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0997 - accuracy: 0.9975 - val_loss: 0.3374 - val_accuracy: 0.9850\n",
            "Epoch 28/60\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0864 - accuracy: 0.9983 - val_loss: 0.3335 - val_accuracy: 0.9875\n",
            "Epoch 29/60\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.0825 - accuracy: 0.9967 - val_loss: 0.3253 - val_accuracy: 0.9900\n",
            "Epoch 30/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0698 - accuracy: 0.9983 - val_loss: 0.3162 - val_accuracy: 0.9900\n",
            "Epoch 31/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0639 - accuracy: 0.9975 - val_loss: 0.3129 - val_accuracy: 0.9900\n",
            "Epoch 32/60\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0595 - accuracy: 0.9967 - val_loss: 0.3109 - val_accuracy: 0.9900\n",
            "Epoch 33/60\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.0546 - accuracy: 0.9975 - val_loss: 0.3160 - val_accuracy: 0.9900\n",
            "Epoch 34/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0492 - accuracy: 0.9975 - val_loss: 0.3103 - val_accuracy: 0.9900\n",
            "Epoch 35/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0451 - accuracy: 0.9983 - val_loss: 0.3079 - val_accuracy: 0.9900\n",
            "Epoch 36/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0410 - accuracy: 0.9967 - val_loss: 0.3091 - val_accuracy: 0.9925\n",
            "Epoch 37/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0384 - accuracy: 0.9983 - val_loss: 0.3110 - val_accuracy: 0.9925\n",
            "Epoch 38/60\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.0372 - accuracy: 0.9975 - val_loss: 0.3055 - val_accuracy: 0.9900\n",
            "Epoch 39/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0353 - accuracy: 0.9975 - val_loss: 0.3088 - val_accuracy: 0.9900\n",
            "Epoch 40/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0323 - accuracy: 0.9983 - val_loss: 0.3140 - val_accuracy: 0.9900\n",
            "Epoch 41/60\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0309 - accuracy: 0.9967 - val_loss: 0.3264 - val_accuracy: 0.9900\n",
            "Epoch 42/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0278 - accuracy: 0.9992 - val_loss: 0.3082 - val_accuracy: 0.9900\n",
            "Epoch 43/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0274 - accuracy: 0.9975 - val_loss: 0.2990 - val_accuracy: 0.9900\n",
            "Epoch 44/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0250 - accuracy: 0.9992 - val_loss: 0.2994 - val_accuracy: 0.9900\n",
            "Epoch 45/60\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.0255 - accuracy: 0.9983 - val_loss: 0.3067 - val_accuracy: 0.9900\n",
            "Epoch 46/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0213 - accuracy: 0.9983 - val_loss: 0.3101 - val_accuracy: 0.9900\n",
            "Epoch 47/60\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0226 - accuracy: 0.9983 - val_loss: 0.3186 - val_accuracy: 0.9900\n",
            "Epoch 48/60\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.0215 - accuracy: 0.9983 - val_loss: 0.3163 - val_accuracy: 0.9900\n",
            "Epoch 49/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0245 - accuracy: 0.9975 - val_loss: 0.3288 - val_accuracy: 0.9900\n",
            "Epoch 50/60\n",
            "38/38 [==============================] - 0s 12ms/step - loss: 0.0177 - accuracy: 0.9992 - val_loss: 0.3250 - val_accuracy: 0.9900\n",
            "Epoch 51/60\n",
            "38/38 [==============================] - 0s 10ms/step - loss: 0.0185 - accuracy: 0.9983 - val_loss: 0.3196 - val_accuracy: 0.9900\n",
            "Epoch 52/60\n",
            "38/38 [==============================] - 0s 10ms/step - loss: 0.0172 - accuracy: 0.9983 - val_loss: 0.3233 - val_accuracy: 0.9900\n",
            "Epoch 53/60\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.0150 - accuracy: 0.9983 - val_loss: 0.3325 - val_accuracy: 0.9900\n",
            "Epoch 54/60\n",
            "38/38 [==============================] - 0s 11ms/step - loss: 0.0170 - accuracy: 0.9983 - val_loss: 0.3270 - val_accuracy: 0.9900\n",
            "Epoch 55/60\n",
            "38/38 [==============================] - 0s 10ms/step - loss: 0.0172 - accuracy: 0.9983 - val_loss: 0.3265 - val_accuracy: 0.9900\n",
            "Epoch 56/60\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.0170 - accuracy: 0.9983 - val_loss: 0.3280 - val_accuracy: 0.9900\n",
            "Epoch 57/60\n",
            "38/38 [==============================] - 0s 11ms/step - loss: 0.0174 - accuracy: 0.9975 - val_loss: 0.3347 - val_accuracy: 0.9900\n",
            "Epoch 58/60\n",
            "38/38 [==============================] - 0s 13ms/step - loss: 0.0150 - accuracy: 0.9983 - val_loss: 0.3386 - val_accuracy: 0.9900\n",
            "Epoch 59/60\n",
            "38/38 [==============================] - 1s 13ms/step - loss: 0.0142 - accuracy: 0.9983 - val_loss: 0.3420 - val_accuracy: 0.9900\n",
            "Epoch 60/60\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0120 - accuracy: 0.9992 - val_loss: 0.3390 - val_accuracy: 0.9900\n"
          ]
        }
      ],
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(50, activation='relu', kernel_initializer='he_uniform'))\n",
        "model.add(tf.keras.layers.Dense(15, activation='relu', kernel_initializer='he_uniform'))\n",
        "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer=\"adam\", metrics=['accuracy'])\n",
        "history = model.fit(inputs_train, outputs_train, epochs=60, batch_size=32, validation_data=(inputs_validate, outputs_validate))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NUDPvaJE1wRE"
      },
      "source": [
        "## Verify \n",
        "\n",
        "Graph the models performance vs validation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxA0zCOaS35v"
      },
      "source": [
        "### Graph the loss\n",
        "\n",
        "Graph the loss to see when the model stops improving."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "bvFNHXoQzmcM",
        "outputId": "ddd665a1-69a8-4c77-b304-ad6c31421bbc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABI8AAAJcCAYAAABwj4S5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXyb1Z3v8e+JEmXHaWwCWRwch7CWOhukZnUm9BIoFyasTWlDYAq0FLgw0ylQaODCbdN2mFvgNS29MJQQWhqgHdJOS7pg8MAQFwgQKE0DWZDsrIRscjbLls/940iObEu2JD+yHtuf9+vF61jSo/McPXbT+Jvf+T3GWisAAAAAAAAglQGFXgAAAAAAAAD8i/AIAAAAAAAAaREeAQAAAAAAIC3CIwAAAAAAAKRFeAQAAAAAAIC0CI8AAAAAAACQFuERAADolDFmhTHmaq+PLSRjTMgYc24e5rXGmGPjX//EGPPtTI7N4TxXGWP+mOs6AQAAsmGstYVeAwAA8JgxZl/Sw2GSGiXF4o9vsNb+vOdX5R/GmJCkr1hrX/R4XitpirV2vVfHGmPKJH0kaZC1ttmLdQIAAGRjYKEXAAAAvGetHZH4urOgxBgzkEACfsHPIwAA/sS2NQAA+hFjTJUxZpMx5nZjzDZJTxhjPmWM+a0xZocxZnf86wlJ76kxxnwl/vVCY8x/G2MeiB/7kTHm/ByPnWSMecUY02CMedEY8yNjzM/SrDuTNd5vjHktPt8fjTElSa9/2RgTNsbsNMbc1cn1mWWM2WaMCSQ9N88Y817869OMMbXGmD3GmK3GmH8zxgTTzLXEGPN/kh7/c/w9W4wx17Y79vPGmHeMMRFjTL0x5t6kl1+Jj3uMMfuMMZWJa5v0/tONMW8aY/bGx9MzvTZZXufRxpgn4p9htzFmedJrFxtjVsc/wwZjzNz48222CBpj7k18n40xZfHte/9gjKmT9FL8+efi34e98Z+Rk5PeP9QY86/x7+fe+M/YUGPM74wxN7f7PO8ZY+al+qwAACBzhEcAAPQ/R0saLekYSdfL/X3gifjjiZIOSvq3Tt4/S9IHkkok/UDS48YYk8OxT0t6Q1KxpHslfbmTc2ayxi9KukbSGElBSd+QJGPMSZIeic8/Ln6+CUrBWvu6pP2S/q7dvE/Hv45Jui3+eSolzZF0YyfrVnwNc+Pr+ZykKZLa91vaL2mBpFGSPi/pa8aYv4+/dnZ8HGWtHWGtrW0392hJv5P0cPyz/V9JvzPGFLf7DB2uTQpdXeen5LZBnhyf64fxNZwmaamkf45/hrMlhdJdjxTOkXSipPPij1fIXacxkt6WlLzN8gFJMySdLvdz/E1JLZKelPSlxEHGmApJ4+WuDQAA6AbCIwAA+p8WSfdYaxuttQettTuttb+y1h6w1jZI+o7cL/PphK21j1lrY3K/sI+VdFQ2xxpjJko6VdIia23UWvvfkn6T7oQZrvEJa+2H1tqDkp6VNDX+/GWSfmutfcVa2yjp2/FrkM4vJM2XJGPMSEkXxJ+TtfYta+2frbXN1tqQpP+XYh2pXBFf3/vW2v1yYVny56ux1v7FWttirX0vfr5M5pVc2LTOWvtUfF2/kLRW0v9MOibdtWmjs+tsjBkr6XxJX7XW7rbWNllr/yv+1n+Q9FNr7Z/in2GztXZthuuXpHuttfvj65O19qfW2ob49+teSRXGmCJjzABJ10r6X/FzxKy1K+PH/UbSccaYKfE5vyzpGWttNIt1AACAFAiPAADof3ZYaw8lHhhjhhlj/l98G1BEbpvUqOStW+1sS3xhrT0Q/3JElseOk7Qr6TlJqk+34AzXuC3p6wNJaxqXPHc8vNmZ7lxyVUaXGGMGS7pE0tvW2nB8HcfFt3Jti6/ju3JVSF1pswZJ4Xafb5Yx5uX4drG9kr6a4byJucPtngvLVd0kpLs2bXRxnUvlvme7U7y1VNKGDNebSuu1McYEjDHfi299i+hwBVNJ/L8hqc4V/5l+RtKX4iHTfLlKKQAA0E2ERwAA9D/tb7X6T5KOlzTLWnuEDm+TSrcVzQtbJY02xgxLeq60k+O7s8atyXPHz1mc7mBr7Rq58OV8td2yJrntb2vl7pJ2hKRv5bIGuS1hyZ6Wq5wptdYWSfpJ0rxd3Rp3i9w2s2QTJW3OYF3tdXad6+W+Z6NSvK9e0uQ0c+6X2+qWcHSKY5I/4xclXSy3ta9IUlnSGj6RdKiTcz0p6Sq57YQH2m/xAwAAuSE8AgAAI+V62+yJ98+5J98njFfyrJJ0rzEmaIypVNttVl6u8ZeSLjTGnBlvbn2fuv470NOS/pdcePJcu3VEJO0zxpwg6WsZruFZSQuNMSfFw6v26x8pV9VzKN4/6ItJr+2Q22ZXnmbuF+S2a33RGDPQGHOlpJMk/TbDtbVfR8rrbK3dKteL6MfxxtqDjDGJcOlxSdcYY+YYYwYYY8bHr48krZb0hfjxM+W2EXa1hka56rBhctVdiTW0SPqppP9rjBkXr1KqjFeJKR4WtUj6V1F1BACAZwiPAADAg5KGylV1/FnS73vovFfJNZ3eKen/yG05akxzbM5rtNb+VdLX5QKhrZJ2S9rUxdsSPYdestZ+kvT8N+SCnQZJj8XXnMkaVsQ/w0uS1sfHZDdKus8Y0yBpkVzYlHjvAbneQ68Zd5e3z7abe6ekC+WqhnbKNZC+sN26M9XVdf6ypCa56quPJd0aX8Mbcg25fyhpr6T/0uFqqG/LVQrtlvS/1baSK5WlcpVfmyWtia8j2Tck/UXSm5J2Sfq+2v6ddqmkUySlvHMfAADInrG2q0poAACA/DPGPCNprbU275VP6LuMMQskXW+tPbPQawEAoK+g8ggAABSEMeZUY8zk+DanuXJ9bpYXel3oveJbAm+U9Gih1wIAQF9CeAQAAArlaEk1kvZJeljS16y17xR0Rei1jDHnyfWH2q6ut8YBAIAssG0NAAAAAAAAaVF5BAAAAAAAgLQGFnoB2SopKbFlZWWFXgYAAAAAAECf8dZbb31irT0y1Wu9LjwqKyvTqlWrCr0MAAAAAACAPsMYE073GtvWAAAAAAAAkBbhEQAAAAAAANIiPAIAAAAAAEBava7nUSpNTU3atGmTDh06VOilwCeGDBmiCRMmaNCgQYVeCgAAAAAAvVqfCI82bdqkkSNHqqysTMaYQi8HBWat1c6dO7Vp0yZNmjSp0MsBAAAAAKBX6xPb1g4dOqTi4mKCI0iSjDEqLi6mEg0AAAAAAA/0ifBIEsER2uDnAQAAAAAAb/SZ8AgAAAAAAADeIzzywM6dOzV16lRNnTpVRx99tMaPH9/6OBqNdvreVatW6ZZbbunyHKeffrpXywUAAAAAAMhYn2iYXWjFxcVavXq1JOnee+/ViBEj9I1vfKP19ebmZg0cmPpSz5w5UzNnzuzyHCtXrvRmsT0oFospEAgUehkAAAAAAKAb+m3lUW19rRa/uli19bV5mX/hwoX66le/qlmzZumb3/ym3njjDVVWVmratGk6/fTT9cEHH0iSampqdOGFF0pywdO1116rqqoqlZeX6+GHH26db8SIEa3HV1VV6bLLLtMJJ5ygq666StZaSdILL7ygE044QTNmzNAtt9zSOm+yUCiks846S9OnT9f06dPbhFLf//73dcopp6iiokJ33HGHJGn9+vU699xzVVFRoenTp2vDhg1t1ixJN910k5YsWSJJKisr0+23367p06frueee02OPPaZTTz1VFRUVuvTSS3XgwAFJ0vbt2zVv3jxVVFSooqJCK1eu1KJFi/Tggw+2znvXXXfpoYce6vb3AgAAAAAA5K5fVh7V1tdqztI5isaiCgaCql5QrcrSSs/Ps2nTJq1cuVKBQECRSESvvvqqBg4cqBdffFHf+ta39Ktf/arDe9auXauXX35ZDQ0NOv744/W1r31NgwYNanPMO++8o7/+9a8aN26czjjjDL322muaOXOmbrjhBr3yyiuaNGmS5s+fn3JNY8aM0Z/+9CcNGTJE69at0/z587Vq1SqtWLFCv/71r/X6669r2LBh2rVrlyTpqquu0h133KF58+bp0KFDamlpUX19faefu7i4WG+//bYkt6XvuuuukyTdfffdevzxx3XzzTfrlltu0TnnnKPnn39esVhM+/bt07hx43TJJZfo1ltvVUtLi5YtW6Y33ngj6+sOAAAAAAC80y/Do5pQjaKxqGI2pmgsqppQTV7Co8svv7x129bevXt19dVXa926dTLGqKmpKeV7Pv/5z2vw4MEaPHiwxowZo+3bt2vChAltjjnttNNan5s6dapCoZBGjBih8vJyTZo0SZI0f/58Pfroox3mb2pq0k033aTVq1crEAjoww8/lCS9+OKLuuaaazRs2DBJ0ujRo9XQ0KDNmzdr3rx5kqQhQ4Zk9LmvvPLK1q/ff/993X333dqzZ4/27dun8847T5L00ksvaenSpZKkQCCgoqIiFRUVqbi4WO+88462b9+uadOmqbi4OKNzAgAAAACA/OiX4VFVWZWCgWBr5VFVWVVezjN8+PDWr7/97W9r9uzZev755xUKhVRVlfqcgwcPbv06EAioubk5p2PS+eEPf6ijjjpK7777rlpaWjIOhJINHDhQLS0trY8PHTrU5vXkz71w4UItX75cFRUVWrJkiWpqajqd+ytf+YqWLFmibdu26dprr816bQAAAAAAwFv9sudRZWmlqhdU6/7Z9+dty1p7e/fu1fjx4yWptT+Ql44//nht3LhRoVBIkvTMM8+kXcfYsWM1YMAAPfXUU4rFYpKkz33uc3riiSdaexLt2rVLI0eO1IQJE7R8+XJJUmNjow4cOKBjjjlGa9asUWNjo/bs2aPq6uq062poaNDYsWPV1NSkn//8563Pz5kzR4888ogk11h77969kqR58+bp97//vd58883WKiUAAAAAAFA4/TI8klyAdOdZd/ZIcCRJ3/zmN3XnnXdq2rRpWVUKZWro0KH68Y9/rLlz52rGjBkaOXKkioqKOhx344036sknn1RFRYXWrl3bWiU0d+5cXXTRRZo5c6amTp2qBx54QJL01FNP6eGHH9ZnPvMZnX766dq2bZtKS0t1xRVX6NOf/rSuuOIKTZs2Le267r//fs2aNUtnnHGGTjjhhNbnH3roIb388ss65ZRTNGPGDK1Zs0aSFAwGNXv2bF1xxRXcqQ0AAAAAAB8wiTt19RYzZ860q1atavPc3/72N5144okFWpF/7Nu3TyNGjJC1Vl//+tc1ZcoU3XbbbYVeVlZaWlpa79Q2ZcqUbs3FzwUAAAAAAJkxxrxlrZ2Z6rV+W3nUFz322GOaOnWqTj75ZO3du1c33HBDoZeUlTVr1ujYY4/VnDlzuh0cAQAAAAAAb/TLhtl91W233dbrKo2SnXTSSdq4cWOhlwEAAAAAAJLkrfLIGPNTY8zHxpj307xujDEPG2PWG2PeM8ZMz9daAAAAAAAAkJt8bltbImluJ6+fL2lK/L/rJT2Sx7UAAAAAAAAgB3kLj6y1r0ja1ckhF0taap0/SxpljBmbr/UAAAAAKKza+lotfnWxautrWQdS4nuD3qQ//bwWsufReEn1SY83xZ/b2v5AY8z1ctVJmjhxYo8sDgAA+E9tfa1qQjWqKqtSZWllv19HX+On6+qntXSXXz5LbX2t5iydo2gsqmAgqOoF1QVZj1fr8Oq6ejGPX+boLi9/RvxyTfrSHF7O011+uCZ++TOtp/SKhtnW2kclPSpJM2fOtAVeTgezZ8/WHXfcofPOO6/1uQcffFAffPCBHnkk9W68qqoqPfDAA5o5c6YuuOACPf300xo1alSbY+69916NGDFC3/jGN9Kee/ny5TruuON00kknSZIWLVqks88+W+eee64HnwwAkCs//KXGqzn8sha//MLHLzf5mcNP19UvP2tezOGn61oTqlE0FlXMxhSNRVUTqinIdfViHV7+jHR3Hr/MkZin0N+bxDr8cE360hxez1PoP9e8mMOrn9feopDh0WZJpUmPJ8Sf63Xmz5+vZcuWtQmPli1bph/84AcZvf+FF17I+dzLly/XhRde2Boe3XfffTnPVSixWEyBQKDQywAAz/jlLzV++suiX/6S5pd1eLWWvjSHn66rX37W/PJZvFpLVVmVgoFg6xxVZVW9dh1eXVcv5vHLHH753kj+uSZ9aQ6v5vHLn2tezOHVz2tvkc+G2V35jaQF8buufVbSXmtthy1rvcFll12m3/3ud4pGo5KkUCikLVu26KyzztLXvvY1zZw5UyeffLLuueeelO8vKyvTJ598Ikn6zne+o+OOO05nnnmmPvjgg9ZjHnvsMZ166qmqqKjQpZdeqgMHDmjlypX6zW9+o3/+53/W1KlTtWHDBi1cuFC//OUvJUnV1dWaNm2aTjnlFF177bVqbGxsPd8999yj6dOn65RTTtHatWs7rCkUCumss87S9OnTNX36dK1cubL1te9///s65ZRTVFFRoTvuuEOStH79ep177rmqqKjQ9OnTtWHDBtXU1OjCCy9sfd9NN92kJUuWtK7h9ttv1/Tp0/Xcc8+l/HyStH37ds2bN08VFRWqqKjQypUrtWjRIj344IOt895111166KGHsvumAUAnurt/PdVfSHrrHH5aS+IvaQET8PQXvkKsw6u19KU5/HRd/fKz5pfP4tVaKksrVb2gWvfPvj/nMNsv6/Dqunoxj1/m8Mv3RvLPNelLc3g1j1/+XPNiDq9+XnuLvFUeGWN+IalKUokxZpOkeyQNkiRr7U8kvSDpAknrJR2QdI0X5731Vmn1ai9mOmzqVCkpq+hg9OjROu2007RixQpdfPHFWrZsma644goZY/Sd73xHo0ePViwW05w5c/Tee+/pM5/5TMp53nrrLS1btkyrV69Wc3Ozpk+frhkzZkiSLrnkEl133XWSpLvvvluPP/64br75Zl100UW68MILddlll7WZ69ChQ1q4cKGqq6t13HHHacGCBXrkkUd06623SpJKSkr09ttv68c//rEeeOAB/fu//3ub948ZM0Z/+tOfNGTIEK1bt07z58/XqlWrtGLFCv3617/W66+/rmHDhmnXLtcT/aqrrtIdd9yhefPm6dChQ2ppaVF9fb06U1xcrLfffluStHPnzpSf75ZbbtE555yj559/XrFYTPv27dO4ceN0ySWX6NZbb1VLS4uWLVumN954o9NzAeg//FAK7cW/RPllDj+tJfGXtO58f/2yDq/W0pfm8NN19cvPml8+i1drSaynO79g+WUdXl1XL+bxyxx++d4k5vDDNelLc3g1j1/+XPPymvT10Cghb+GRtXZ+F69bSV/P1/l7WmLrWiI8evzxxyVJzz77rB599FE1Nzdr69atWrNmTdrw6NVXX9W8efM0bNgwSdJFF13U+tr777+vu+++W3v27NG+ffvabJFL5YMPPtCkSZN03HHHSZKuvvpq/ehHP2oNjy655BJJ0owZM/Qf//EfHd7f1NSkm266SatXr1YgENCHH34oSXrxxRd1zTXXtK5x9OjRamho0ObNmzVv3jxJ0pAhQzK6ZldeeWWXn++ll17S0qVLJUmBQEBFRUUqKipScXGx3nnnHW3fvl3Tpk1TcXFxRucE4G9+CH68KGP2y19q/PSXRb/8Jc0v6/BqLX1pjsQ8friuXqyF65offllHYi1enN+r70+h5/DT9yaxnkJfk742hxfz+O3PtUL/nPYmvaJhdjY6qxDKp4svvli33Xab3n77bR04cEAzZszQRx99pAceeEBvvvmmPvWpT2nhwoU6dOhQTvMvXLhQy5cvV0VFhZYsWaKamppurXfw4MGSXCDT3Nzc4fUf/vCHOuqoo/Tuu++qpaUl40Ao2cCBA9XS0tL6uP1nHz58eOvX2X6+r3zlK1qyZIm2bduma6+9Nuu1AfCWV01h/RD8+O1fTv0wh9/W0l1+WYfkn+vqlzm6KxqVPvpI2vtRpU6OVmrHbuk37+Q+35FHSlOmSMXFkjHZv7+vXNcEv6zFL+tAR3xvkAl+TnqnPhceFcqIESM0e/ZsXXvttZo/3xVdRSIRDR8+XEVFRdq+fbtWrFihqqqqtHOcffbZWrhwoe688041NzfrP//zP3XDDTdIkhoaGjR27Fg1NTXp5z//ucaPHy9JGjlypBoaGjrMdfzxxysUCmn9+vU69thj9dRTT+mcc87J+PPs3btXEyZM0IABA/Tkk08qFotJkj73uc/pvvvu01VXXdW6bW306NGaMGGCli9frr//+79XY2OjYrGYjjnmGK1Zs0aNjY06ePCgqqurdeaZZ6Y8X7rPN2fOnNbtdolta0VFRZo3b54WLVqkpqYmPf300xl/LgDe86ops1+CH7/9yymAthIB0bp10vr1bcdwWEr6dyvPFBW5EOnYYw+Pia9LSnILlgAA6E0Ijzw0f/58zZs3T8uWLZMkVVRUaNq0aTrhhBNUWlqqM844o9P3T58+XVdeeaUqKio0ZswYnXrqqa2v3X///Zo1a5aOPPJIzZo1qzUw+sIXvqDrrrtODz/8cGujbMltHXviiSd0+eWXq7m5Waeeeqq++tWvZvxZbrzxRl166aVaunSp5s6d21olNHfuXK1evVozZ85UMBjUBRdcoO9+97t66qmndMMNN2jRokUaNGiQnnvuOZWXl+uKK67Qpz/9aU2aNEnTpk1Le750n++hhx7S9ddfr8cff1yBQECPPPKIKisrFQwGNXv2bI0aNYo7tQHd5Jfb6vop+OFfxIDCamxMHxDV1bUNiBLBzmc/K33pSy7UmTxZGjq0e2uwVtq61Z03ce433pCefbbj+ZPDpOTxyCMJlgAAfYNxrYd6j5kzZ9pVq1a1ee5vf/ubTjzxxAKtCIXQ0tLSeqe2KVOmpDyGnwuga4W6lXSiciD5l8L166UPPzqgwBHb9ZmThurMqUe3/gI2aZIU323ra9a6z7NihfT730vbt7tfYtv/Qnn00fxC2VP27ZM2bOgYQmzZUuiVIZ3GRmnz5rYBzahRHf93lBhz3VKWq2hUCoVSB1uhUNt1H3EEARIA9AevvSaNGVPoVXSfMeYta+3MVK9ReYReZ82aNbrwwgs1b968tMERgMzkszl0NltLjjjC/SL4mROHafPmSar+tfQfSw+/PmCANHFi6l8cy8sLGywdOCC9/LILjFaskDZudM8fd5xb27vvSsuXS8nt5YYPT1+pMHYsv2hmq6EhdUC0bp20bVvbY486yl3rGTMkClf9aeBAqays7f82Ro/2z/8ugkH3v+/4PUnaiEbdn2/JP4PxG9MCAPqwQYMKvYL8IzxCr3PSSSdpY+K3MwDd4lVz6GMClRr6VqV+tky6J/5LU/uAKLG1ZNasw1tLEr8ctu8ZYq20c2fb7SKJ8Re/kPbsOXysMZ0HSzn0+++UtdKHHx4Oi/7rv1ylxNCh0t/9nfSP/yidf747d0Jzs7se7Sut/vIX6de/bhssDRuWuq/KlCk9HyxZ6yp01q93QWBxceECu4aG1D8P69d3DIiOPtpdr/PPb3sdjz1WGjmyZ9eN/iUYPPy/VwAA+pI+s23thBNOkPHLP0mh4Ky1Wrt2LdvWgAx0p+fRwYPSv/6r9L3vSfv399zWkkSwlKrSZPfuw8cZI5WWtl1LLsHS/v1tq4s++sg9f/zxLqA4/3zp7LNzC6qam10Pl1SfZ+NGqanp8LGJYCnV9R03LrfrmwiIUl3LDRtcZVV7icAu1fe6O8FSIiBKtZbt29seO3Zs6utw7LHSiBG5nR8AAKA/62zbWp8Ijz766CONHDlSxcXFBEiQtVY7d+5UQ0ODJk2aVOjlAH2StdIzz0i33+6Cj0sukRYvdr/AF/qP4V272lb3rFt3+HHy9pFUwVJyABIKHQ6LXnnFVRcNGybNmePCorlzXS+mfGpulurrU3+e9sHS0KGpw5QpU1wlzrZtqUOZ9etdCJgwaJDr09R+jrKytte2s8AuuRIseY5Jk9ya060jVUCUKhwiIAIAAPBenw+PmpqatGnTJh06dKhAq4LfDBkyRBMmTNCg/rD5FOhhb7wh3XabtHKlNHWq9OCD0jnnFHpVmdm1K31lS7q+JCeeeLi66Kyz/NO4OxbrvGIpGj18rDEu8EsIBl1AlipoKi3NvhdQulApVWDX/q8d48alryCK3+gTAAAAPaDPh0cAgPzbvFm6807pqadc0+Hvfle6+uq+03Q4ESwl/hszxlUXlZUVemXZi8XaVixt2iRNmNC9gChX7QO7IUMOr2PyZAIiAAAAvyA8AgDk7MAB6YEHpO9/34US//iPLkSi8TAAAADQd3QWHnG3NQBASta6O5vdfrurXLn8chcg0UoMAAAA6F8GFHoBAIDcWeuqgbz25z9Lp58uXXWV2771yivSs88SHAEAAAD9EZVHANBLbd3qevKsXeuaH6dqOjxxYna9berrpTvukJ5+2t2h64knpAULpAH8UwMAAADQbxEeAUAvtHWrNHu22052443urlvr1knV1R1vu95ZsDQw/v8C+/dLP/iB9C//4qqZ7rrLhUjcDh0AAAAA4REA9DJbt0pVVe7uZytWuNvHJ1grbdmS+pbpL7/sml8nDBrktqFNmSKtXu3m+8IXpO99TzrmmB7/WAAAAAB8ivAIAHqRLVtcxdHmzdLvfy+deWbb142Rxo93/51zTtvXrHXBU/tQad06V4n0zDPSGWf03GcBAAAA0DsQHgFADvbvl4YP79lzJoKjLVtSB0ddMUYaN8791z5YAgAAAIB0aIEKAFlat04qKnJ3Itu7t2fO2d3gCAAAAAByRXgEAFl6910pFnN3JKuokP77v/N7vs2bXY+jRHCUvLWstr5Wi19drNr62vwuAgAAAEC/RXgEAFkKhdy4YoUUCLgtYIsWSc3N3p9r82ZXcbR1a+rgaM7SOfr2y9/WnKVzCJAAAAAA5AXhEQBkKRx229bmzpXeeUf68pel++93dz3bsMG78ySCo23bpD/8oWMz65pQjaKxqGI2pmgsqppQjXcnBwAAAIA4wiMAyFIodPhW9kccIS1ZIi1bJv3tb9LUqdLSpe7OZt2R2KqWCI5OP73jMVVlVQoGggqYgIKBoKrKqrp3UgAAAA9XSn8AACAASURBVABIgfAIALIUDktlZW2fu/JK6b33pOnTpauvlubPl/bsyW3+TZtccLR9uwuOKitTH1dZWqnqBdW6f/b9ql5QrcrSNAcCAAAAQDcQHgFAFqxtW3mUbOJE6aWXpO98R/rVr1wz7VdeyW7+TIOjhMrSSt151p0ERwAAAADyhvAIALKwZ4/U0NCx8ighEJC+9S3ptdekYNAFQXfdJTU1dT13IjjasUP64x+7Do4AAAAAoCcQHgFAFhJ3WktVeZTstNNcM+1rrpG++13X7Hr9+vTH19cfDo7+8Afps5/1asUAAAAA0D2ERwCQhXDYjekqj5KNGCE9/rj07LPSunWumfYTT3Rspl1f7+6qlqg4IjgCAAAA4CeERwCQhUwrj5Jdfrlrpn3qqdK117rm2rt2udeSK47++Edp1iyvVwwAAAAA3UN4BABZCIel4cOl4uLs3ldaKr34ovS970nPP++aaS9b5oKjnTulP/2J4AgAAACAPxEeAeg1nnzS9RAqpMSd1ozJ/r2BgHT77dKf/ywNGybNn++Coz/+0fVIAgAAAAA/IjwC0Gu8+KL0s59Jzc2FW0M4nFm/o87MmCG9/bZ0//3Syy8THAEAAADwN8IjAL1GJOKCo/r6wq0hUXnUXcOHS3ffLU2b1v25AAAAACCfCI8A9BqRiBs3bCjc+Xfv7n7lEQAAAAD0JoRHAHqNhgY3btxYmPOHw270ovJIkmrra7X41cWqra/1ZkIAAAAAyIOBhV4AAGSq0JVHifDIi8qj2vpazVk6R9FYVMFAUNULqlVZWtn9iQEAAADAY1QeAeg1EuFRoSqPQiE3elF5VBOqUTQWVczGFI1FVROq6f6kAAAAAJAHhEcAeo3EtrVCVh4NGSIddVT356oqq1IwEFTABBQMBFVVVtX9SQEAAAAgD9i2BqBXaG6WDhxwX2/YIFkrGdOzawiFpIkTvTlvZWmlqhdUqyZUo6qyKrasAQAAAPAtwiMAvUKi6mjiRKmuTtq1Syou7tk1hMPe3mmtsrSS0AgAAACA77FtDUCvkOh3NHWqGwuxdS0U8u5OawAAAADQWxAeAegVEpVH06a5saebZh84IO3Y4W3lEQAAAAD0BoRHAHqFROVRRYUbe7ryKBx2I5VHAAAAAPobwiMAvUIiPDr6aPdfT1ceJcIjKo8AAAAA9DeERwB6hcS2tSOOkMrLe77yKBRyI5VHAAAAAPobwiMAvUKi8uiII6TJkwtTeTRokDR2bM+eFwAAAAAKjfAIQK+QCI9GjnTh0aZNUmNjz50/FJJKS6VAoOfOCQAAAAB+QHgEoFdIDo/KyyVrD28l6wmhEP2OAAAAAPRPhEcAeoWGBmn4cFf5M3mye64n+x6Fw/Q7AgAAANA/ER4B6BUiEVd1JLnKI6nnwqNDh6StW6k8AgAAANA/ER4B6BUiEdcsW5KOOkoaNiy3ptm19bVa/Opi1dbXZvye+no3Eh4BAAAA6I8GFnoBAJCJhobD4ZExrvoo28qj2vpazVk6R9FYVMFAUNULqlVZWtnl+xK9ldi2BgAAAKA/ovIIQK+QXHkkub5H2VYe1YRqFI1FFbMxRWNR1YRqMnpfOOxGKo8AAAAA9EeERwB6heSeR5KrPNq40d11LVNVZVUKBoIKmICCgaCqyqoyel8o5Bp1jx+f1ZIBAAAAoE9g2xqAXiFV5dHBg9K2bdLYsZnNUVlaqeoF1aoJ1aiqrCqjLWuSqzyaMEEayJ+YAAAAAPohfhUC0Csk9zyS2t5xLdPwSHIBUqahUUIoRL8jAAAAAP0X29YA+J61HbetTZ7sxlzuuJatcJh+RwAAAAD6L8IjAL7X2Cg1NbWtPCorc3ddy/aOa9lqapI2b6byCAAAAED/RXgEwPcaGtyYHB4Fg1Jpaf4rjzZtklpaqDwCAAAA0H8RHgHwvUjEjcnhkeS2ruW78igUciOVRwAAAAD6K8IjAL6XCI+Sex5Jrml2vsOjcNiNVB4BAAAA6K8IjwD4XmeVRx9/LO3bl79zh0Kut1Jpaf7OAQAAAAB+RngEwPdS9TySXOWRlN++R+GwNG6c67EEAAAAAP0R4REA30u3bW3yZDfmMzwKheh3BAAAAKB/IzwC4Hvptq0lKo/y2fcoHKbfEQAAAID+jfAIgO+l27Y2erQ0alT+Ko9iMam+nsojAAAAAP0b4REA34tEpAEDpGHDOr6WzzuubdkiNTdTeQQAAACgfyM8AuB7kYjrd2RMx9cmT85f5VEo5EYqjwAAAAD0Z4RHAHwvEum4ZS1h8mQX8sRi3p83HHYjlUcAAAAA+jPCIwC+19CQPjwqL5eamqRNm7w/b6LyaOJE7+cGAAAAgN6C8AiA7yW2raUyebIb89H3KByWjjpKGjrU+7kBAAAAoLcgPALge51tWysvd2M+wqNQiH5HAAAAAEB4BMD3Otu2VloqDRyYn6bZ4TD9jgAAAACA8AiA73VWeRQIuIDH68qjlhYXHlF5BAAAAKC/IzwC4Hud9TySXN8jryuPtm+XolEqjwAAAACA8AiAr1nb+bY1yfU98rryKHGnNSqPAAAAAPR3hEcAfG3/fhcgdRYeTZ4s7dkj7d7t3XnDYTdSeQQAAACgvyM8AuBrkYgbO9u2lo87rlF5BAAAAAAO4REAX0uER11VHkne9j0Kh6XiYmnECO/mBAAAAIDeiPAIgK81NLixq55HkveVR2xZAwAAAADCIwA+l0nl0YgR0pgx3lcesWUNAAAAAAiPAPhcJj2PJLd1zavKI2upPAIAAACABMIjAL6WSeWR5LaueRUeffKJdPAglUcAAAAAIBEeAfC5THoeSa7yqL5eika7f87EndaoPAIAAAAAwiMAPpfptrXy8sPbzborMQeVRwAAAABAeATA5yIRKRiUBg/u/LjJk93oRdPscNiNhEcAAAAAQHgEwOcaGrresia5yiPJm75HoZBUVCSNGtX9uQAAAACgtyM8AuBrkUhm4dHYsdKQId5VHtHvCAAAAAAcwiMAvhaJdN3vSJKM8e6Oa6EQW9YAAAAAICGv4ZExZq4x5gNjzHpjzB0pXj/GGFNtjHnPGFNjjJmQz/UA6H0yrTySXN+j7lYeWUvlEQAAAAAky1t4ZIwJSPqRpPMlnSRpvjHmpHaHPSBpqbX2M5Luk7Q4X+sB0Dtl2vNIOhweWZv7+Xbvduek8ggAAAAAnHxWHp0mab21dqO1NippmaSL2x1zkqSX4l+/nOJ1AP1cptvWJLdtbf9+6eOPcz9f4k5rXVUe1dbXavGri1VbX5v7yQAAAACgFxiYx7nHS6pPerxJ0qx2x7wr6RJJD0maJ2mkMabYWrsz+SBjzPWSrpekiRMn5m3BAPwn221rkut7dNRRuZ0vFHJjZ5VHtfW1mrN0jqKxqIKBoKoXVKuytDK3EwIAAACAzxW6YfY3JJ1jjHlH0jmSNkuKtT/IWvuotXamtXbmkUce2dNrBFBA2WxbKy93Y3eaZmdSeVQTqlE0FlXMxhSNRVUTqsn9hAAAAADgc/msPNosqTTp8YT4c62stVvkKo9kjBkh6VJr7Z48rglAL9LcLB04kHl4VFbm7rrWnabZoZA0fLg0enT6Y6rKqhQMBFsrj6rKqnI/IQAAAAD4XD7DozclTTHGTJILjb4g6YvJBxhjSiTtsta2SLpT0k/zuB4AvUxDgxsz7Xk0ZIg0fnz3K48SIVQ6laWVql5QrZpQjarKqtiyBgAAAKBPy1t4ZK1tNsbcJOkPkgKSfmqt/asx5j5Jq6y1v5FUJWmxMcZKekXS1/O1HgC9TyTixkwrj6TDd1zLVSiU2Z3WKksrCY0AAAAA9Av5rDyStfYFSS+0e25R0te/lPTLfK4BQO+VqDzKJjwqL5dWrMj9nOGwdPrpub8fAAAAAPqaQjfMBoC0EpVHmW5bk1zl0bZtrldSLufbvTuzyiMAAAAA6C8IjwD4Vi7b1hJ3XMtl61omd1oDAAAAgP6G8AiAb+WybW3yZDfmEh6FQm6k8ggAAAAADiM8AuBbuTbMlnK74xqVRwAAAADQEeERAN/KpefR6NEubMq18mjIEGnMmOzfCwAAAAB9FeERAN9KbFvLJjwyxlUf5Vp5dMwxbg4AAAAAgEN4BMC3IhFp+HApEMjufeXluYVHoRD9jgAAAACgPcIjAL4ViWRXdZQwebILgmKx7N4XDtPvCAAAAADaIzwC4FuRSHbNshPKy6VoVNq8OfP37N8v7dhBeAQAAAAA7REeAfCthobcwqPEHdeyaZpdV+dGtq0BAAAAQFuERwB8qzuVR1J2fY9CITdSeQQAAAAAbREeAfCtXHseTZzommxnU3kUDruRyiMAAAAAaIvwCIBv5bptbeBAFwJlW3k0aJA0dmz25wMAAACAvozwCIBv5bptTXJ9j7KtPJo4URrAn4oAAAAA0Aa/JgHwJWtz37YmufAo28oj+h0BAAAAQEeERwB8qbFRamrKvfKovFzatUvasyez48Nh+h0BAAAAQCqERwB8qaHBjd3ZtiZltnXt0CFp61YqjwAAAAAgFcIjAL4UibgxOTyqra/V4lcXq7a+tsv3l5e7MZOta/X1bqTyCAAAAAA6GljoBQBAKonwKNHzqLa+VnOWzlE0FlUwEFT1gmpVllamfX8iPMqk8igUciOVRwAAAADQEZVHAHyp/ba1mlCNorGoYjamaCyqmlBNp+8/4gippCSzyqNw2I1UHgEAAABAR4RHAHyp/ba1qrIqBQNBBUxAwUBQVWVVXc4xeXLmlUeBgDR+fM7LBQAAAIA+i21rAHyp/ba1ytJKVS+oVk2oRlVlVZ1uWUsoL5dqu26PpFBImjBBGsifiAAAAADQAb8qAfClVA2zK0srMwqNEiZPlp55RmpqkgYNSn9cOEy/IwAAAABIh21rAHypfc+jXJSXSy0th3sapRMK0e8IAAAAANIhPALgS5GINGCANGxY7nNMnuzGzvoeRaPSli1UHgEAAABAOoRHAHwpEnH9jozJfY7ycjd2dse1TZtcdRKVRwAAAACQGuERAF9qaOjeljVJGjdOGjy488qjxJY2Ko8AAAAAIDXCIwBd+utfD/cg6imRSPfDowEDXPVRZ5VHoZAbqTwCAAAAgNQIjwB0qqlJOu006eGHe/a8iW1r3dVVeBQOu61xpaXdPxcAAAAA9EWERwA6tXmzdOCAVF/fs+f1ovJIck2zN26UrE39eijktrcFg90/FwAAAAD0RYRHADqV6Am0c2fPnteLnkeSqzzat0/asSP16+Ew/Y4AAAAAoDOERwA6VVfnxk8+6dnzerVtbfJkN6Zrmh0K0e8IAAAAADpDeASgU4nKo0KER15VHkmp+x41N0ubNlF5BAAAAACdITwC0KlE5VFPbluz1rtta5MmuTFV5dGWLS5AovIIAAAAANIjPALQqeTKo3RNp722f787lxfh0dChriF2qsqjxGej8ggAAAAA0iM8AtCpROVRU5NrPN0TIhE3etHzSDp8x7X2QiE3UnkEAAAAAOkRHgFIy1pXnfOpT7nHPdX3KBEeeVF5JLm+R51VHk2c6M15AAAAAKAvIjwCkNbOndLBg9L06Ycf94SGBjd6FR5Nnuz6Gx082Pb5UEg6+mi3tQ0AAAAAkBrhEYC0ElvWEuFRT1ceebltTZI++qjt8+EwW9YAAAAAoCuERwDSSmzr6unKo3xsW5M6bl0LhWiWDQAAAABdITwCkFai8mjaNDf2VOVRPratSW2bZre0uM9H5REAAAAAdI7wCEBa4bDrB3TssdKAAb238qikRBoxom3l0bZtUjRK5REAAAAAdIXwCEBaicqcQMDdca239jwyxlUfJVceJbbkUXkEAAAAAJ0jPAKQVjh8+Db2JSU9W3kUDEqDB3s3Z3l528qjUMiNVB4BAAAAQOcIjwCkldwTqLi4Z3seebVlLWHyZHe3tZYW95jKIwAAAADIDOERgJQOHpQ+/rhwlUdebVlLKC+XGhulLVvc41DIfabhw709DwAAAAD0NYRHAFKqr3djojKnpKRnex7lo/JIOtz3KBym6ggAAAAAMkF4BCClujo3JiqPiotd5ZG1+T93PratlZe7MdH3KBSi3xEAAAAAZILwCEBK7XsClZRIhw5JBw7k/9z5qDxK3DVu40YXgFF5BAAAAACZITwCkFJdnTRggDR+vHtcXOzGnuh7lI+eR4MGuSqqDRukHTtcTycqjwAAAACga4RHAFIKh6Vx41zoIrnKI6ln+h7lo/JIclvXNmzgTmsAAAAAkA3CIwAp1dUd7nckHa486onwKB89jyTXNHvjRtfvSKLyCAAAAAAyQXgEIKVwuG14lKg8yve2teZm11fJ621rkqs8+uQT6S9/cY+pPAIAAACArhEeAeigpUWqr28brvRU5VFDgxvzVXkkSS+9JI0aJRUVeX8OAAAAAOhrCI8AdLBtm9TU1Lby6FOfkozJf+VRPsOj8nI3vv46VUcAAAAAkCnCIwAd1NW5MTlgGTjQVevku/IoEnFjPiuPmpvpdwQAAAAAmSI8AtBB4m5kyZVHkut7lO/Ko0R4lI+eR0VF0ujR7msqjwAAAAAgM4RHADpoX3lUW1+rxa8uVnBkQ6+uPJIOVx9ReQQAAAAAmRlY6AUA8J+6Olelc8QRLjias3SOorGo7P4KNUXPljQib+fOZ88jyfU9evNNKo8AAAAAIFNUHgHoIBw+HK7UhGoUjUUVszHZoTu0/eNYXs+dz21rEpVHAAAAAJAtwiMAHdTVHe53VFVWpWAgqIAJKDB8txob8ld1JOV/29o557hg7Ljj8jM/AAAAAPQ1bFsD0EE4LJ1xhvu6srRS1QuqVROqUd3+efrJawEdPCgNHZqfcye2reWr8uh//A8pFMrP3AAAAADQFxEeAWgjEpH27GnbE6iytFKVpZV6bK17vHOnNGFC/s4/fLgUCORnfgAAAABAdti2BqCNxJ3WEtvWkhUXuzGfd1yLRPJXdQQAAAAAyB7hEYA2EuFRqruRlZS4cefO/J0/EslfvyMAAAAAQPYIjwC0EQ67sVCVRw0NhEcAAAAA4CeERwDaqKuTBg2Sxo7t+FpPVR6xbQ0AAAAA/IPwCEAbdXWuGfaAFH86jB7txnz3PKLyCAAAAAD8g/AIQBvhcOp+R5KrSCoqym/lEdvWAAAAAMBfCI8AtFFXl7rfUUJJCZVHAAAAANCfEB4BaNXUJG3enL7ySHJNs/NVeWQtPY8AAAAAwG8IjwC02rJFamkpXOVRY6MLsKg8AgAAAAD/IDwC0CocdmNn4VFxcf7Co4YGNxIeAQAAAIB/EB4BaFVX58bOtq2VlORv21ok4ka2rQEAAACAfxAeAWiVqDwqLU1/THGxtG+f22LmtUR4ROURAAAAAPgH4RGAVnV10pFHSsOGpT+mpMSN+ag+YtsaAAAAAPgP4RGAVuFw5/2OJFd5JOWn7xGVRwAAAADgP4RHAFrV1XXe70jKb+URPY8AAAAAwH8IjwBIkqx14RGVRwAAAACAZIRHACRJu3ZJ+/cXtvKInkcAAAAA4D+ERwAkuaojqfCVRwMGdN6wGwAAAADQswiPAEhyzbKlrsOjYND1JMpXz6ORIyVjvJ8bAAAAAJAbwiMAkg5XHnW1bU1y1Uf5qDxqaGDLGgAAAAD4DeERAEmu8mjo0MM9jTpTUpJ95VFtfa0Wv7pYtfW1aY+JRAiPAAAAAMBvBhZ6AQD8IXGntUy2jGVbeVRbX6s5S+coGosqGAiqekG1KksrOxyX2LYGAAAAAPAPKo8ASHKVR131O0rItvKoJlSjaCyqmI0pGouqJlST8jgqjwAAAADAfwiPAEhylUeZ9DuSsq88qiqrUjAQVMAEFAwEVVVWlfI4eh4BAAAAgP+wbQ2ADh2Stm/PrvIoEpGamqRBg7o+vrK0UtULqlUTqlFVWVXKLWsS29YAAAAAwI8IjwCovt6NmVYeJZpq79wpHX10Zu+pLK1MGxolsG0NAAAAAPwnr9vWjDFzjTEfGGPWG2PuSPH6RGPMy8aYd4wx7xljLsjnegCkVlfnxkwrj4qL3ZjtHdc6Yy3b1gAAAADAj/IWHhljApJ+JOl8SSdJmm+MOandYXdLetZaO03SFyT9OF/rAZBeOOzGbCuPsul71JX9+12ARHgEAAAAAP6Sz8qj0yStt9ZutNZGJS2TdHG7Y6ykxK+KRZK25HE9ANKoq5OMkcaPz+z4ROWRl+FRJOJGeh4BAAAAgL/kMzwaL6k+6fGm+HPJ7pX0JWPMJkkvSLo51UTGmOuNMauMMat27NiRj7UC/Vo4LI0dKwWDmR2f3PPIK4nwiMojAAAAAPCXvPY8ysB8SUustRMkXSDpKWNMhzVZax+11s601s488sgje3yRQF9XV5f5ljUpP5VHDQ1uJDwCAAAAAH/JZ3i0WVJp0uMJ8eeS/YOkZyXJWlsraYikkjyuCUAK4XDmzbIlacgQafjw/FQesW0NAAAAAPwln+HRm5KmGGMmGWOCcg2xf9PumDpJcyTJGHOiXHjEvjSgB7W0SPX12VUeSa76KB89j6g8AgAAAAB/yVt4ZK1tlnSTpD9I+pvcXdX+aoy5zxhzUfywf5J0nTHmXUm/kLTQWmvztSYAHX38sRSNZld5JLm+R15WHrFtDQAAAAD8aWA+J7fWviDXCDv5uUVJX6+RdEY+1wCgc+GwG6k8AgAAAACkUuiG2QAKrK7OjYWuPKLnEQAAAAD4E+ER0M/5qfIoGJQGD/ZuTgAAAABA9xEeAf1cXZ3bKlZUlN37SkqkPXuk5mZv1tHQwJY1AAAAAPAjwiOgnwuHs9+yJrnKI0natcubdUQibFkDAAAAAD8iPAL6ubq67LesSa7ySPKu71EkQuURAAAAAPgR4RHQz3W38sirvkdsWwMAAAAAfyI8AvqxhgZp924qjwAAAAAA6REeAf1YXZ0b/VB5RM8jAAAAAPAnwiOgH0uER1QeAQAAAADSITwC+rHuVB4NGyYNGULPIwAAAADo6wiPgH4sHJYGDpTGjs3t/SUl3lQeNTdLBw6wbQ0AAAAA/IjwCOjH6uqkCROkQCC395eUeFN51NDgRiqPAAAAAMB/CI+Afiwczq3fUUJxMeERAAAAAPR1hEdAP1ZXl1u/owSvtq1FIm4kPAIAAAAA/yE8Avqp5mZp8+buhUdeVR4lwiN6HgEAAACA/xAeAf3Uli1SLNa9bWslJdLu3W6e7qDyCAAAAAD8i/AI6KfCYTd2t/LIWhcgdQc9jwAAAADAvwiPgH6qrs6N3a08krrf94htawAAAADgX4RHQD+VCI9KS3Ofo7jYjd3te8S2NQAAAADwL8IjoJ8Kh13l0PDhuc/hVeVRYtsalUcAAAAA4D+ER0A/VVfXvX5HkreVR8OHS4FA9+YBAAAAAHiP8Ajop8Lh7vU7krzteUTVEQAAAAD4E+ER0MfV1tdq8auLVVtf2/qctd5UHg0fLgWD3lQe0e8IAAAAAPxpYKEXACB/autrNWfpHEVjUQUDQVUvqFZlaaV275b27et+eGSMqz7youcR4REAAAAA+BOVR0AfVhOqUTQWVczGFI1FVROqkXT4Tmvd3bYmub5HXlQesW0NAAAAAPyJ8Ajow6rKqhQMBBUwAQUDQVWVVUly/Y6k7lceSd5UHrFtDQAAAAD8q8tta8aY/ynpd9balh5YDwAPVZZWqnpBtWpCNaoqq1JlaaUk7yuP3n+/e3OwbQ0AAAAA/CuTnkdXSnrQGPMrST+11q7N85oAeKiytLI1NEqoq5OGDJGOPLL781N5BAAAAAB9W5fb1qy1X5I0TdIGSUuMMbXGmOuNMXQoAXqpcNhtWTOm+3MVF7vwqCXH2kRr6XkEAAAAAH6WUc8ja21E0i8lLZM0VtI8SW8bY27O49oA5EldnTf9jiRXedTSIu3dm9v7GxulpiYqjwAAAADAr7oMj4wxFxljnpdUI2mQpNOstedLqpD0T/ldHoB8CIe96XckufBIyv2Oaw0NbiQ8AgAAAAB/yqTn0aWSfmitfSX5SWvtAWPMP+RnWQDypbFR2rbNu8qj4mI3fvKJNGVK9u+PRNzItjUAAAAA8KdMwqN7JW1NPDDGDJV0lLU2ZK2tztfCAORHfb0bva48yrVpdiI8ovIIAAAAAPwpk55Hz0lKboUbiz8HoBeqq3NjPiqPcsG2NQAAAADwt0zCo4HW2mjiQfzrYP6WBCCfwmE3etkwW6LyCAAAAAD6qkzCox3GmIsSD4wxF0vKscYAQKHV1UnGSBMmeDPfyJHSwIG5Vx7R8wgAAAAA/C2TnkdflfRzY8y/STKS6iUtyOuqAORNOCwdfbQ0eLA38xnjqo+oPAIAAACAvqnL8Mhau0HSZ40xI+KP9+V9VQDypq7Ou2bZCcXF9DwCAAAAgL4qk8ojGWM+L+lkSUOMMZIka+19eVwXgDypq5OmTfN2zu5WHg0YIA0b5u2aAAAAAADe6LLnkTHmJ5KulHSz3La1yyV5XLcAoCe0tPiv8igScf2O4rk0AAAAAMBnMmmYfbq1doGk3dba/y2pUtJx+V0WgHzYsUNqbPTuTmsJ3ak8amhgyxoAAAAA+Fkm4dGh+HjAGDNOUpOksflbEoB8CYfdmI/Ko507JWuzf28kQngEAAAAAH6WSc+j/zTGjJL0L5LelmQlPZbXVQEF9NvfSu+8I513njRzpuvH01fU1bkxH5VHzc0uCCoqyu69iW1rAAAAAAB/6jQ8MsYMkFRtrd0j6VfGmN9KGmKt3dsjqwMKYNEiFx4tWuRCkfPOk84/340lJYVeXfckKo+8Do+Ki934ySe5hUfZvgcAAAAA0HM6ramw1rZI+lHS40aCI/R1u3ZJF10k/exnLjD6wx+kL31JGjNGmjVLuvde6fXXpVis0CvNXl2dq/IZNcrbeROhWi59j+h5BAAAAAD+lsmGnGpjzKX/v717j7KsrO+E/32oprjbSDU6TwmoRQAAIABJREFUCh0bFewy0YAhLCuJ2kjMmIvGie8YjIjmZpI3URQnr5q1olEnQ5KZlUliXOMyGkOr8R6ROMZba0cTK94KVC4SEbtpCAh6DnRzraZ43j92HSmaLrou59Q5p8/ns1at55xdu/Z5ut1K8fX3++1SPAuJ0dBuJyedlLzgBU2AdOONTVj0utc1TwR7wxuSJz85+U//qTnn3e9e+ZPG1trOnU3VUbf/27yw8mi5tK0BAAAMtqXMPPqtJOcnuaeUcleSkqTWWtUKcNDpzO156EPvOzY2lpxxRvP1utc1AcknP5n80z81VUl///dNGPPjP960t/3szzazksbG+vfnWMy113Z/WHayusojA7MBAAAG2wHDo1qrmgBGxi23NOtxxy1+zoYNya/8SvN1773JV7/aBEkf/3jyxjcmr399U4nztKclp5ySPPaxycknN+sjHtH9qp/luPbapvWu21ZaeVSrtjUAAIBBd8DwqJTy1P0dr7V+rvvbgf5qtZp1YeXRgznkkKbi6Md/vBmw/f3vJ5/6VBMm/du/Jf/4j8nevfedf+SR9w+TFq69DpZuv73ZXy8qj9avbyqtllt5dPvtTYAkPAIAABhcS2lb+/0Frw9PckaSryZ5ek92BH3UCY8erPLowUxMJGef3XwlTRvcrl3Jt76VXH31fetllyUXX7z/YKnzdfLJTfvbqaeu7s/Uce21zdrtJ60lTYh23HHLrzzavbtZzTwCAAAYXEtpW3vWwvellI1J/qJnO4I+arebdaXh0b7WrWuGb590UvIzP3P/783NNYHOwlDp6quTK65IPvrRZHa2Oe+jH01+/udXv5edO5u1F5VHSdPOt9LwSOURAADA4FpK5dG+rksy2e2NwCBYbtvaaoyN3RcsPeMZ9/9eJ1h6znOSX/u15BvfSB72sNV9Xi8rj5ImPFpu29qePc0qPAIAABhcS5l59KYkdf7tIUlOTTLTy01Bv3S78milOsHSu97VzFP6zd9MLrpodTORdu5srvvIR3ZvnwtNTDSVU8uhbQ0AAGDwHbKEc76SZsbRV5NMJ3lVrfWcnu4K+mQtK4+W4glPSP7kT5r5SG9/++qude21yYknNgFSL6yk8kjbGgAAwOBbStvaB5PcVWudS5JSylgp5cha6x293RqsvVarqYJZt5KGzh552cuauUfnnZc87WnNIO2V2Lmzdy1rSVN59L3vNU9PW2qFlLY1AACAwbeUyqNtSY5Y8P6IJJ/uzXagv9rt/res7euQQ5K/+7vksMOSF76weYLbSlx7be+GZSdN5dHevcltty39Z1QeAQAADL6lhEeH11p/8K+D86+P7N2WoH9arcFpWVvoxBOTt7wl+eIXkz/+4+X//D33JNdd1/vKo2R5T1wz8wgAAGDwLSU8ur2U8qTOm1LKjyW5s3dbgv4ZxMqjjuc9r6k8euMbk3/7t+X97A03NE9w63XlUbK8uUe7dyfj401VFQAAAINpKeHRy5N8oJTy+VLKvyR5X5Lf6+22oD9arcENj5LkTW9qqpDOOWd57WHXXtusg1Z5tGePljUAAIBBd8DwqNb65SSbk/xOkt9OMllr/WqvNwb9MKhtax3r1yfvfGdyzTXJ+ecv/ed27mzWQaw80rIGAAAw2A4YHpVSfjfJUbXWy2qtlyU5upTy//Z+a7C2ah3strWOpzwledWrkr/5m+QjH1nazwxq5dHu3SqPAAAABt1S2tZ+s9Z6S+dNrbWd5Dd7tyXojzvuSGZnB7vyqOP1r09OOy35jd9IbrzxwOfv3NmEO0cd1bs9HXts82S45VQeaVsDAAAYfEsJj8ZKKaXzppQylmS8d1uC/mi1mnXQK4+SZsj0u97VzD369V9vqqYezLXX9rbqKEnGxprgTeURAADAwWUp4dHHk7yvlHJWKeWsJO9J8k+93RasvXa7WYchPEqSxz8++Z//M/nYx5K3vOXBz925s/fhUdLMPTLzCAAA4OCylPDoVUk+k2ZY9m8n+UaSI3q5KeiHTuXRMLStdfzu7yb/+T8nr3xlctVV+z+n1iY86uWw7I6JCZVHAAAAB5ulPG3t3iRfTLIjyRlJnp7kyt5uC9besFUeJUkpyd/+bXLkkckLXpDs3fvAc265pWlvG8TKIzOPAAAABt+i4VEp5ZRSyutKKd9M8qYk1yZJrfXMWutfr9UGYa0M08yjhR75yOStb02++tVmkPa+Ok9aG7TKo3vuaYaUa1sDAAAYbA9WefTNNFVGv1Br/ala65uSzK3NtmDtDWPbWscv/VLyq7+aXHBB8q//ev/vdcKjtaw8OtAA76SpOkpUHgEAAAy6BwuPfinJDUk+W0r5m/lh2eVBzoeh1m4n69YlRx/d752szF/+ZbJpU/LCFzazhDp27mzWtao8uuuupqLoQIRHAAAAw2HR8KjWelGt9ewkm5N8NsnLkzyslPJ/Sik/s1YbhLXSajVVR2VII9Jjjkne+c4mLDrvvPuOX3ttcthhyfHH934PGzY061LmHnUCLm1rAAAAg20pA7Nvr7X+fa31WUlOTHJJmiewwUGl3R6+eUf7+omfSP7gD5K/+7vkQx9qju3c2bSsHbKUZyuu0sREsy5l7lEnPFJ5BAAAMNiW9a+TtdZ2rfWttdazerUh6JdWa/jDoyR57WuT009PXvKS5D/+o6k8Wot5R8l9lUfCIwAAgIPHGtQiwHDotK0Nu0MPTd71ruTOO5MXvzjZsWNt5h0ly2tbM/MIAABgOAiPYN6gta1N75rOBZ+/INO7ppf9s497XPLnf5586lPJjTeuXeXRStrWzDwCAAAYbOv6vQEYFINUeTS9azpnbT0rs3OzGR8bz7Zzt2Vq49SyrvFbv5V89KPJ//2/axcedQaOL2dgtsojAACAwabyCJLMzSW33jo4lUfbd2zP7Nxs5upcZudms33H9mVfo5Tk7W9PXvCC5BnP6P4e92fduuTYY5dWedRpW1N5BAAAMNhUHkGSW25p1kEJj7Zs2pLxsfEfVB5t2bRlRdd5+MOb+UdracOGpVceHXlkMjbW+z0BAACwcsIjSNOylgxO29rUxqlsO3dbtu/Yni2btiy7Za2fJiaWPvNIyxoAAMDgEx5BmmHZyeBUHiVNgDRMoVHHhg3J9dcf+Lw9e4RHAAAAw8DMI8jgVR4NM5VHAAAABxfhEWQwK4+G1XJmHhmWDQAAMPiER5D7Ko+ER6s3MZHccUdy550Pfp7KIwAAgOEgPIJoW+umDRua9UDVR2YeAQAADAfhEaRpWzv66OTQQ/u9k+E3MdGsB5p7pG0NAABgOAiPIE3lUTdb1qZ3TeeCz1+Q6V3T3bvokFhq5ZG2NQAAgOGwrt8bgEHQanWvZW1613TO2npWZudmMz42nm3nbsvUxqnuXHwILKXy6O67k717hUcAAADDQOURpGlb61bl0fYd2zM7N5u5OpfZudls37G9OxceEkupPNq9u1mFRwAAAINPeATpbuXRlk1bMj42nrEylvGx8WzZtKU7Fx4SnRDuwSqPOuGRmUcAAACDT9sapLuVR1Mbp7Lt3G3ZvmN7tmzaMlIta0kzdHz9epVHAAAAB4uehkellGcm+cskY0neVmv9k32+/7+TnDn/9sgkD6u1HtvLPcG+au3+wOypjVMjFxotNDHx4JVHe/Y0q/AIAABg8PUsPCqljCV5c5JnJLkuyZdLKRfXWq/onFNrfcWC81+a5LRe7QcWc+edzQDnbrWt0cw90rYGAABwcOjlzKMzklxda72m1jqb5L1JfvFBzn9+kvf0cD+wX+12s3az8mjUTUxoWwMAADhY9DI8OiHJrgXvr5s/9gCllEclOSnJZxb5/ktKKV8ppXzl5ptv7vpGGW2tVrOqPOqeA1UeaVsDAAAYHoPytLWzk3yw1jq3v2/WWt9aaz291nr68ccfv8Zb42Cn8qj7NmxQeQQAAHCw6GV4dH2SjQvenzh/bH/OjpY1+qRTeSQ86p6JieS225pZUvuze3dyyCHJkUeu7b4AAABYvl6GR19OcnIp5aRSyniagOjifU8qpWxO8tAk0z3cCyxK21r3bdjQrItVH+3e3QzLLmXt9gQAAMDK9Cw8qrXek+T3knwiyZVJ3l9rvbyU8oZSyrMXnHp2kvfWWmuv9gIPRtta901MNOtic4/27NGyBgAAMCzW9fLitdaPJfnYPsdeu8/7P+rlHuBAWq1kbMxj47tpqZVHAAAADL5BGZgNfdNuNy1rWqi650CVR7t3qzwCAAAYFsIjRl6rpWWt2w5UeaRtDQAAYHgIjxh5rZZh2d2m8ggAAODgITxi5LXbKo+6bXy8mWlk5hEAAMDwEx4x8rSt9cbEhMojAACAg4HwiJGnba03NmzYf+VRrWYeAQAADBPhESNtbi659VaVR72wWOXR7bc3AZK2NQAAgOEgPGKk3XprE2SoPOq+xSqPdu9uVpVHAAAAw0F4xEhrt5tV5VH3LVZ5tGdPswqPAAAAhoPwiJHWajWr8Kj7Nmxoqoz27r3/cZVHAAAAw0V4xEjrhEfa1rpvYqJZ921d64RHZh4BAAAMB+ERI03bWu9s2NCs+7auqTwCAAAYLsIjRprKo95ZrPLIzCMAAIDhIjxipHUqj4RH3XegyiNtawAAAMNBeMRIa7WSo45KDjus3zs5+Bxo5pHKIwAAgOEgPGKktVqqjnqlEx7tW3m0Z08yPi6wAwAAGBbCI0Zau21Ydq8ccURT1bW/yiNVRwAAAMNDeMRIa7WER700MbH/mUfmHQEAAAwP4REjrd3WttZLGzaoPAIAABh2wiNGmsqj3tpf5dGePcIjAACAYSI8YqQZmN1bi1UeaVsDAAAYHsIjRtaddyZ33aXyqJcWm3mk8ggAAGB4CI8YWe12swqPemfDhuSWW5J77rnvmLY1AACA4SI8YmS1Ws2qba13JiaatfN3nag8AgAAGDbCI0aWyqPe27ChWTtzj+65J7njDjOPAAAAhonwiJGl8qj3OpVHnblHe/Y0q8ojAACA4SE8YmSpPOq9fSuPhEcAAADDR3jEyOpUHgmPemffyqPdu5tV2xoAAMDwEB4xslqt5JBDBBm9tG/lUSc8UnkEAAAwPIRHjKx2u5l3dMg+/y2Y3jWdCz5/QaZ3TfdnYweRI49MDj/czCMAAIBhtq7fG4B+abUe2LI2vWs6Z209K7NzsxkfG8+2c7dlauNUfzZ4kNiw4YFta8IjAACA4aHyiJHVqTxaaPuO7Zmdm81cncvs3Gy279jel70dTCYmHti2plUQAABgeAiPGFn7qzzasmlLxsfGM1bGMj42ni2btvRlbwcTlUcAAADDTdsaI6vVSh772Psfm9o4lW3nbsv2HduzZdMWLWtdMDGR7NrVvO7MPFJ5BAAAMDyER4ysdvuBlUdJEyAJjbpn38qjI49Mxsb6uycAAACWTtsaI+neexcPj+iuiYnm73purgmPtKwBAAAMF+ERI2n37qTWBw7Mpvs2bGj+rtvtpm1NeAQAADBchEeMpFarWVUe9d6GDc36/e+rPAIAABhGwiNGUic8UnnUexMTzfq97zXhkWHZAAAAw0V4xEhqt5tV5VHvqTwCAAAYbsIjRpK2tbWzsPLIzCMAAIDhIzxiJHUqj7St9d6+lUfa1gAAAIaL8IiRZObR2jnqqGR8/L6ZRyqPAAAAhovwiJHUaiVHHJEcfni/d3LwK6WpPvqP/0j27hUeAQAADBvhESOp3TbvaC1NTCTXXNO8Fh4BAAAMF+ERI6nVEh6tpQ0bku98p3lt5hEAAMBwER4xklot847W0sREcsMNzWuVRwAAAMNFeMRI0ra2tjpPXEuERwAAAMNGeMRI0ra2tiYm7nutbQ0AAGC4CI8YSe22trW1pPIIAABgeAmPGDl33ZXccYfKo7W0sPJIeAQAADBchEeMnHa7WVUerR2VRwAAAMNLeMTI6YRHKo/WTqfy6JBDkiOP7O9eAAAAWB7hESOn1WpW4dHa6VQeHXNMUkp/9wIAAMDyCI8YOdrW1l6n8kjLGgAAwPARHjFyVB6tvYc8JFm3rqk8AgAAYLgIjxg5nfBI5dHaKaVpXVN5BAAAMHyER4ycdrsJM9av7/dORovwCAAAYDit6/cGYK21Wk3V0SGi0zX1P/6HtjUAAIBhJDxi5LTbWtb64VnP6vcOAAAAWAm1F4ycVsuwbAAAAFgq4REjR3gEAAAASyc8YuRoWwMAAIClEx4xclQeAQAAwNIJjxgp996r8ggAAACWQ3jESNmzpwmQVB4BAADA0giPGCmtVrMKjwAAAGBphEeMlHa7WbWtAQAAwNIIjxgpKo8AAABgeYRHjJROeKTyCAAAAJZGeMRI6bStqTwCAACApREeMVJUHgEAAMDyCI8YKe12cvjhyRFH9HsnAAAAMByER4yUVkvLGgAAACyH8IiDyvSu6Vzw+QsyvWt6v99vtbSsAQAAwHKs6/cGoFumd03nrK1nZXZuNuNj49l27rZMbZy63znttsojAAAAWA6VRxw0tu/Yntm52czVuczOzWb7ju0POEfbGgAAACyP8IiDxpZNWzI+Np6xMpbxsfFs2bTlAee029rWAAAAYDm0rXHQmNo4lW3nbsv2HduzZdOWB7SsJSqPAAAAYLmERxxUpjZO7Tc0SpLZ2eT224VHAAAAsBza1hgZ7XazalsDAACApRMeMTJarWZVeQQAAABLJzxiZHTCI5VHAAAAsHTCI0ZGp21N5REAAAAsnfCIkaFtDQAAAJZPeMTIMDAbAAAAlk94xEC4/vrkbW9L7ryzd5/RaiWlJOvX9+4zAAAA4GCzrt8bYHTVmvzrvyZvelPyoQ8lc3PJunXJi1/cm89rtZrgaGysN9cHAACAg5HKI9bcXXcl73hH8mM/ljzlKcknP5m8/OXJYYclV1zRu89tt807AgAAgOXqaXhUSnlmKeWqUsrVpZRXL3LO80opV5RSLi+l/H0v90N/7dqV/MEfJBs3Jr/2a8nsbPKWtyTXXZf8r/+VnHJKcuWVvfv8Vkt4BAAAAMvVs7a1UspYkjcneUaS65J8uZRyca31igXnnJzkNUl+stbaLqU8rFf7oT9qTf7lX5K/+qvkwx9u3j/72clLX5qceWYzg6hj8+ZkZqZ3e2m3DcsGAACA5epl5dEZSa6utV5Ta51N8t4kv7jPOb+Z5M211naS1Fpv6uF+WEN33pm8/e3JaaclT31qsm1bcv75ybe/3YRIT3/6/YOjJJmcTL7znaatrRdUHgEAAMDy9TI8OiHJrgXvr5s/ttApSU4ppfxrKeXfSinP3N+FSikvKaV8pZTylZtvvrlH26Ubrr02efWrkxNPTH7jN5J7703e+tamNe3P/izZtGnxn52cbM7/1rd6szfhEQAAACxfv5+2ti7JyUm2JDkxyedKKU+otd6y8KRa61uTvDVJTj/99LrWm+TA/vmfm9a0iy5q3j/nOU1r2tOe9sAKo8VMTjbrlVcmT3hCd/dXq7Y1AAAAWIlehkfXJ9m44P2J88cWui7JF2ute5N8p5Ty72nCpC/3cF902Qc+kDzveU1Vz+//fvI7v5M86lHLv84ppzRBUy+GZu/Zk8zNqTwCAACA5eplePTlJCeXUk5KExqdneRX9jnnoiTPT/KOUsqGNG1s1/RwT/TAW96SPPrRyWWXJUccsfLrHHFEctJJvQmP2u1mVXkEAAAAy9OzmUe11nuS/F6STyS5Msn7a62Xl1LeUEp59vxpn0jy/VLKFUk+m+T3a63f79We6L5rr00++9nkRS9aXXDUsXlzb8KjVqtZVR4BAADA8vR05lGt9WNJPrbPsdcueF2TnD//xRB617uaeUIvfGF3rjc5mXzmM02L2dhYd66ZCI8AAABgpXr5tDUOcrUmW7cmT31q027WDZOTyV13JTt3dud6HdrWAAAAYGWER6zYl76UXHVVcu653bvmwieudZPKIwAAAFgZ4RErtnVrcvjhyX/9r9275ubNzdrt8EjlEQAAAKyM8IgVufvu5D3vSf7Lf0ke8pDuXfe445KHPaw3lUeHHdadod4AAAAwSoRHrMhHP9pU87zoRd2/9uRkb8Kj445LSunudQEAAOBgJzxiRbZuTR7xiOSnf7r7156cTL75zWYgd7e021rWAAAAYCWERyzbzTcnH/tYcs45ydhY968/OdmEPTfd1L1rdiqPAAAAgOURHrFs73lPcs893X3K2kK9eOJaq6XyCAAAAFZCeDTE/uiPkte8Zu0/98ILkyc9KfmRH+nN9XsRHrXbKo8AAABgJYRHQ+xtb0v+9E+7P1z6wVx2WTIz05tB2R0nnJAcfXT3K4+ERwAAALB8wqMh9d3vJtdf3wyVfuMb1+5zt25N1q1Lnv/83n1GKcnmzd0Lj/buTW67TdsaAAAArITwaEhdckmz/uRPJu99b3LFFb3/zLm55F3vSn7u55Ljj+/tZ3WeuNYN7XazqjwCAACA5RMeDamZmWbdujU56qi1qT769KeTG27o3aDshSYnk+uuS/bsWf21Wq1mFR4BAADA8gmPhtTMTPKYxySPfnTy0pcm73tf76uPtm5tWr9+4Rd6c/3pXdO54PMXZHrX9A+GZnej+qhTeaRtDQAAAJZPeDSkLrmkeeJZkpx/flN99IY39O7zdu9OPvzh5Oyzk8MO6/71p3dN56ytZ+UPP/uHOWvrWbnr2KYvrxtzj1QeAQAAwMoJj4ZQu51cc8194dGGDU310fvfn1x+eW8+8wMfSO68s3dPWdu+Y3tm52YzV+cyOzebq+sns25dd8IjlUcAAACwcsKjIXTppc3aCY+S5JWv7O3so61bk1NOSc44ozfX37JpS8bHxjNWxjI+Np6zHvvUnHyyyiMAAADoN+HREOoMyz7ttPuOTUwkL3tZb6qPvvOd5HOfa6qOSunutTumNk5l27nb8sYz35ht527L1Maprj1xrRMeHXvs6q8FAAAAo0Z4NIRmZpKNG5Pjj7//8fPPT44+uvuzj975ziY0Ouec7l53X1Mbp/Kap7wmUxunkjRPXLv66mR2dnXXbbeT9euTsbEubBIAAABGjPBoCM3M3L9lraNTffSBDySXXdadz6q1aVk788zkh36oO9dcqsnJZG6uCZBWo9XSsgYAAAArJTwaMrfdllx11f7DoyR5xSu6W330hS8k3/52cu653bnecmze3KyrnXvUbhuWDQAAACslPBoyX/taUw20WHjU7eqjCy9sBnE/97mrv9ZydSs8UnkEAAAAKyc8GjKdYdmLhUdJM/vomGNWX310553NAO7nPrepZlprRx3VtMoJjwAAAKB/hEdDZmYmedjDkkc8YvFzjjsuOe+8pvroG99Y+WddfHFy6639aVnr6MYT17StAQAAwMoJj4bMJZc0VUelPPh5r3jF6quPLrywearbmWeu/Bqr1QmP7r13ZT9fq8ojAAAAWA3h0RC5667k8ssfvGWto1N99MEPrqz66MYbk098IjnnnOSQPt4lk5PJHXcku3at7Odvuy255x7hEQAAAKyU8GiIXHZZE4QsJTxKmuqjhzwkef3rl/9Z7353U+3Tz5a1ZPVDs9vtZtW2BgAAACsjPBoiSxmWvVCn+uhDH0q+/vXlfdbWrckZZ9wX3vTL5GSzrjQ8arWaVeURAAAArIzwaIjMzCTHHpts2rT0n+lUHy1n9tGllzZh04tetOwtdt3xxycTEyqPAAAAoF+ER0NkZmZpw7IXeuhDk5e/vKk++trXlvYzW7cmhx6a/PIvr2yf3baaJ66pPAIAAIDVER4Nib17m2qgpbasLfTyly+9+mjv3mbe0bOe1VT8DILJSW1rAAAA0C/CoyFx5ZXJ3XevLDzqVB/9wz8cuProk59MbrppeYOyp3dN54LPX5DpXdPL39wSTE4m3/te87Vc2tYAAABgdYRHQ2K5w7L39YpXJOvXH/jJaxdemGzYkPzszy7tutO7pnPW1rPyh5/9w5y19ayeBEireeJaq5WMjydHHtndPQEAAMCoEB4NiZmZ5KijkpNPXtnPH3tsU3304Q83A7H3p91OPvKR5PnPbwKXpdi+Y3tm52YzV+cyOzeb7Tu2r2yDD2I1T1xrt5uqo+XMiQIAAADuIzwaEjMzyamnJoes4j+xl7+8qT5abPbR+9+fzM4u7ylrWzZtyfjYeMbKWMbHxrNl05aVb3ARP/RDTeXQSiuPzDsCAACAlRMeDYF7722qhVbastZx7LFN+9pi1UcXXpg8/vHL+5ypjVPZdu62vPHMN2bbudsytXFqdZvcj0MOSR73uJU9cU14BAAAAKsjPBoC3/pWcvvtqw+PkuS88/Y/++hb30qmp5uqo+W2eE1tnMprnvKangRHHSt94lqnbQ0AAABYGeHREFjtsOyFOtVHF12UXHLJfce3bm0qfM45Z/Wf0QubNyc7dzYh2nKoPAIAAIDVER4NgZmZ5LDD7hscvVrnndeESJ3qo3vvTd75zuSnfzp55CO78xnd1vmzX3XV8n5O5REAAACsjvBoCMzMJE98YnLood25Xqf66CMfaaqPPve5pqpnOYOy19pKnri2d2+ye7fKIwAAAFgN4dGAq7UJj7rRsrbQwuqjrVuTY45JnvOc7n5GN518cjI2tryh2bfc0qzCIwAAAFi5df3eAA9ux44mBOl2eLR+fXL++clrX9u0xL3gBcmRR3b3M7ppfDx5zGOWV3nUbjertjUAAABYOZVHA66bw7L39bKXJcc85J7cfXfyY8+8vPsf0GXLfeJaq9WsKo8AAABg5YRHA25mpmnX+pEf6f61r9g9nbue9t+SR386r/zmGZneNd39D+mizZuTb30rueeepZ0vPAIAAIDVEx4NuJmZ5Id/ODn88O5fe/uO7bn39L9Ozn1G9ta7s33H9u5/SBdNTjZDsL/97aWdr20NAAAAVk94NMB6NSy7Y8umLRkfG89YGcv42Hi2bNrSmw/qkuU+cU3lEQDVx/bNAAAPc0lEQVQAAKyegdkD7IYbkptu6l14NLVxKtvO3ZbtO7Zny6Ytmdo41ZsP6pLNm5t1qU9c61QeHXtsb/YDAAAAo0B4NMB6OSy7Y2rj1MCHRh0PeUhywgnLqzx6yEOSde5yAAAAWDFtawNsZiYpJfnRH+33TgbHcp641mppWQMAAIDVEh4NsJmZ5HGPS44+ut87GRybNzdta7Ue+Nx227BsAAAAWC3h0QDr5bDsYTU5mezZk1x//YHPVXkEAAAAqyc8GlA335zs2iU82tdynrim8ggAAABWT3g0oC65pFmFR/fXCY+W8sQ1lUcAAACwesKjAdV50tqpp/Z3H4Pm4Q9Pjj32wJVHtQqPAAAAoBuERwNqZiY56SRtV/sqpRmafaDw6I47kr17/f0BAADAagmPBtQll2hZW8zk5IHDo1arWVUeAQAAwOoIjwbQrbcmV18tPFrM5GTy3e82A7EX0/me8AgAAABWR3g0gC69tFmFR/u3lCeudSqPtK0BAADA6giPBlBnWPZpp/V3H4NqKU9c07YGAAAA3SE8GkAzM8kJJzRPFuOBNm1KDjvswSuPOm1rKo8AAABgdYRHA2hmRsvagxkbS045ZWltayqPAAAAYHWERwPm9tubdizh0YM70BPX2u3k0EOTo45auz0BAADAwUh4NGC+/vXk3nuFRwcyOZl85zvJnXfu//utVtOyVsra7gsAAAAONsKjAWNY9tJMTia1Jv/+7/v/fqulZQ0AAAC6QXg0YGZmkg0bkhNP7PdOBtuBnrjWbhuWDQAAAN0gPBownWHZ2q0e3CmnNH9Hi809UnkEAAAA3SE8GiB3351cfrl5R0tx+OHJSScJjwAAAKDXhEcD5PLLk717hUdL9WBPXNO2BgAAAN0hPBognWHZwqOlmZxsBmbPzd3/+NxccuutKo8AAACgG4RHA2RmJlm/Pnn0o/u9k+EwOdm0+n3nO/c/fsstzaryCAAAAFZPeDRAZmaS004zLHupFnviWqvVrCqPAAAAYPWERwPinnuSr31Ny9pybN7crPvOPRIeAQAAQPcIjwbEN7+Z3HWX8Gg5HvrQ5OEPf2B41G7f930AAABgdYRHA6IzLPu00/q7j2GzvyeuqTwCAACA7hEe9cn0rulc8PkLMr1rOkkTHh1xRPK4x/V5Y0OmEx7Vet+xTuWR8AgAAABWb12/NzCKpndN56ytZ2V2bjbjY+PZdu62zMxM5dRTk7Gxfu9uuExOJrfemtx4Y/KIRzTHOpVHxx7bv30BAADAwULlUR9s37E9s3OzmatzmZ2bzWeu2Z5LL13+vKN9q5dG0f6euNZqJccckxx6aH/2BAAAAAcTlUd9sGXTloyPjf+g8ujk8szs2bO88Gh/1UtTG6d6t+kBtfCJa2ee2bxutw3LBgAAgG4RHvXB1MapbDt3W7bv2J4tm7bk2i80U7KXEx7tW720fcf2kQyPTjihqTJaODS71TLvCAAAALpFeNQnUxunfhD2XDSTjI8nj3/80n9+3+qlLZu29GajA66UpvpoYXjUbguPAAAAoFuERwNgZiZ5whOaAGmp9q1eGsWqo47JyeTTn77vfat13ywkAAAAYHWER31WaxMePfe5y//ZhdVLo2xyMtm6tXnq2vr12tYAAACgmzxtrc+uvbYJO5b7pDXu06kyuuqqJowzMBsAAAC6R3jUZzMzzXraaf3dxzBb+MS1O+9M7r5b5REAAAB0i/Coz2ZmkrGx5IlP7PdOhtdjHpMcemgTHrVazTHhEQAAAHSH8KjPZmaatqsjjuj3TobXunXJySc34VG73RzTtgYAAADdITzqs0suMe+oGyYnVR4BAABALwiP+uiGG5ov4dHqTU4m3/52cuONzXvhEQAAAHSH8KiPLrmkWYVHq7d5c3LvvcmXvtS817YGAAAA3dHT8KiU8sxSylWllKtLKa/ez/dfXEq5uZRy6fzXb/RyP4Om86S1U0/t7z4OBpOTzfqFLzSryiMAAADojnW9unApZSzJm5M8I8l1Sb5cSrm41nrFPqe+r9b6e73axyCbmUlOOSU55ph+72T4Pe5xzfrVrzZPrzv66P7uBwAAAA4Wvaw8OiPJ1bXWa2qts0nem+QXe/h5Q2dmRstatxx1VPKoRyV79zZVR6X0e0cAAABwcOhleHRCkl0L3l83f2xfzy2lfL2U8sFSysb9XaiU8pJSyldKKV+5+eabe7HXNff97yc7dwqPuqnTuqZlDQAAALqn3wOz/zHJplrrE5N8KsmF+zup1vrWWuvptdbTjz/++DXdYK90hmWfdlp/93Ew6YRHhmUDAABA9/QyPLo+ycJKohPnj/1ArfX7tda759++LcmP9XA/A6UzLFt41D2bNzeryiMAAADonl6GR19OcnIp5aRSyniSs5NcvPCEUsojFrx9dpIre7ifgTIz08zomZjo904OHiqPAAAAoPt69rS1Wus9pZTfS/KJJGNJ/rbWenkp5Q1JvlJrvTjJy0opz05yT5JWkhf3aj+D5pJLzDvqNjOPAAAAoPt6Fh4lSa31Y0k+ts+x1y54/Zokr+nlHgbR3r3J+vXJk5/c750cXDZsSF784uTnfq7fOwEAAICDR0/DI/bv0EOTL32p37s4OL3jHf3eAQAAABxc+v20NQAAAAAGmPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYlPAIAAAAgEUJjwAAAABYVKm19nsPy1JKuTnJzn7vI8mGJN/r9yZgidyvDBP3K8PCvcowcb8yTNyvDJOD6X59VK31+P19Y+jCo0FRSvlKrfX0fu8DlsL9yjBxvzIs3KsME/crw8T9yjAZlftV2xoAAAAAixIeAQAAALAo4dHKvbXfG4BlcL8yTNyvDAv3KsPE/cowcb8yTEbifjXzCAAAAIBFqTwCAAAAYFHCIwAAAAAWJTxaplLKM0spV5VSri6lvLrf+4GFSil/W0q5qZRy2YJjx5VSPlVK+db8+tB+7hE6SikbSymfLaVcUUq5vJRy3vxx9ywDp5RyeCnlS6WUr83fr6+fP35SKeWL878XvK+UMt7vvUJHKWWslHJJKeWj8+/drwykUsqOUso3SimXllK+Mn/M7wMMpFLKsaWUD5ZSvllKubKUMjUK96vwaBlKKWNJ3pzkZ5M8PsnzSymP7++u4H7+Lskz9zn26iTbaq0nJ9k2/x4GwT1JXllrfXySJyf53fn/TXXPMojuTvL0WuuPJjk1yTNLKU9O8qdJ/net9bFJ2kl+vY97hH2dl+TKBe/drwyyM2utp9ZaT59/7/cBBtVfJvl4rXVzkh9N87+zB/39KjxanjOSXF1rvabWOpvkvUl+sc97gh+otX4uSWufw7+Y5ML51xcmec6abgoWUWu9odY6M/96T5p/8J4Q9ywDqDZum3976PxXTfL0JB+cP+5+ZWCUUk5M8vNJ3jb/vsT9ynDx+wADp5SyPslTk7w9SWqts7XWWzIC96vwaHlOSLJrwfvr5o/BIHt4rfWG+dc3Jnl4PzcD+1NK2ZTktCRfjHuWATXfAnRpkpuSfCrJt5PcUmu9Z/4UvxcwSP4iyf+X5N759xNxvzK4apJPllK+Wkp5yfwxvw8wiE5KcnOSd8y3Bb+tlHJURuB+FR7BCKm11jT/cIaBUUo5OsmHkry81rp74ffcswySWutcrfXUJCemqUbe3OctwX6VUn4hyU211q/2ey+wRD9Va31SmvEgv1tKeerCb/p9gAGyLsmTkvyfWutpSW7PPi1qB+v9KjxanuuTbFzw/sT5YzDIvltKeUSSzK839Xk/8AOllEPTBEfvrrX+w/xh9ywDbb48/bNJppIcW0pZN/8tvxcwKH4yybNLKTvSjFl4epoZHe5XBlKt9fr59aYkH04T0Pt9gEF0XZLraq1fnH//wTRh0kF/vwqPlufLSU6ef1LFeJKzk1zc5z3BgVyc5EXzr1+U5CN93Av8wPz8jbcnubLW+ucLvuWeZeCUUo4vpRw7//qIJM9IM6frs0n+n/nT3K8MhFrra2qtJ9ZaN6X5ffUztdYXxP3KACqlHFVKOabzOsnPJLksfh9gANVab0yyq5TyuPlDZyW5IiNwv5amooqlKqX8XJoe8rEkf1tr/eM+bwl+oJTyniRbkmxI8t0kr0tyUZL3J/mhJDuTPK/Wuu9QbVhzpZSfSvL5JN/IfTM5/iDN3CP3LAOllPLENAMwx9L8n2/vr7W+oZTy6DSVHccluSTJObXWu/u3U7i/UsqWJP+t1voL7lcG0fx9+eH5t+uS/H2t9Y9LKRPx+wADqJRyapqHEYwnuSbJr2b+d4McxPer8AgAAACARWlbAwAAAGBRwiMAAAAAFiU8AgAAAGBRwiMAAAAAFiU8AgAAAGBRwiMAgP0opcyVUi5d8PXqLl57Uynlsm5dDwCgl9b1ewMAAAPqzlrrqf3eBABAv6k8AgBYhlLKjlLKn5VSvlFK+VIp5bHzxzeVUj5TSvl6KWVbKeWH5o8/vJTy4VLK1+a/fmL+UmOllL8ppVxeSvlkKeWI+fNfVkq5Yv467+3THxMA4AeERwAA+3fEPm1rv7zge7fWWp+Q5K+T/MX8sTclubDW+sQk707yV/PH/yrJP9dafzTJk5JcPn/85CRvrrX+cJJbkjx3/virk5w2f53f7tUfDgBgqUqttd97AAAYOKWU22qtR+/n+I4kT6+1XlNKOTTJjbXWiVLK95I8ota6d/74DbXWDaWUm5OcWGu9e8E1NiX5VK315Pn3r0pyaK31v5dSPp7ktiQXJbmo1npbj/+oAAAPSuURAMDy1UVeL8fdC17P5b5ZlD+f5M1pqpS+XEoxoxIA6CvhEQDA8v3ygnV6/vUXkpw9//oFST4//3pbkt9JklLKWCll/WIXLaUckmRjrfWzSV6VZH2SB1Q/AQCsJf9PFgDA/h1RSrl0wfuP11pfPf/6oaWUr6epHnr+/LGXJnlHKeX3k9yc5Ffnj5+X5K2llF9PU2H0O0luWOQzx5K8az5gKkn+qtZ6S9f+RAAAK2DmEQDAMszPPDq91vq9fu8FAGAtaFsDAAAAYFEqjwAAAABYlMojAAAAABYlPAIAAABgUcIjAAAAABYlPAIAAABgUcIjAAAAABb1/wPbO8tcT0h3lAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# increase the size of the graphs. The default size is (6,4).\n",
        "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
        "\n",
        "# graph the loss, the model above is configure to use \"mean squared error\" as the loss function\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "accuracy = history.history['accuracy']\n",
        "val_accuracy = history.history['val_accuracy']\n",
        "epochs = range(1, len(accuracy) + 1)\n",
        "plt.plot(epochs, accuracy, 'g.', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guMjtfa42ahM"
      },
      "source": [
        "### Run with Test Data\n",
        "Put our test data into the model and plot the predictions\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "V3Y0CCWJz2EK",
        "outputId": "11b758e5-a6ce-42b9-cf5d-4ac1996ac4c2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# use the model to predict the test inputs\n",
        "predictions = np.array((model.predict(inputs_test) > 0.5)[:], dtype=int)\n",
        "\n",
        "# Plot the predictions along with to the test data\n",
        "plt.clf()\n",
        "plt.title('Training data predicted vs actual values')\n",
        "plt.plot(inputs_test, outputs_test, 'b.', label='Actual')\n",
        "plt.plot(inputs_test, predictions, 'r.', label='Predicted')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j7DO6xxXVCym"
      },
      "source": [
        "# Convert the Trained Model to Tensorflow Lite\n",
        "\n",
        "The next cell converts the model to TFlite format. The size in bytes of the model is also printed out."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Xn1-Rn9Cp_8",
        "outputId": "7aaca797-86a8-4237-e8fc-9e962240c0bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpnqb74bn6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model is 17128 bytes\n"
          ]
        }
      ],
      "source": [
        "# Convert the model to the TensorFlow Lite format without quantization\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model to disk\n",
        "open(\"model.tflite\", \"wb\").write(tflite_model)\n",
        "  \n",
        "import os\n",
        "basic_model_size = os.path.getsize(\"model.tflite\")\n",
        "print(\"Model is %d bytes\" % basic_model_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykccQn7SXrUX"
      },
      "source": [
        "## Encode the Model in an Arduino Header File \n",
        "\n",
        "The next cell creates a constant byte array that contains the TFlite model. Import it as a tab with the sketch below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9J33uwpNtAku",
        "outputId": "c12bf885-3e6b-4888-b596-754b62367a15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Header file, model.h, is 105,680 bytes.\n",
            "\n",
            "Open the side panel (refresh if needed). Double click model.h to download the file.\n"
          ]
        }
      ],
      "source": [
        "!echo -n \"//Build time: \"                    > model.h \n",
        "!date \"+%T\"                                 >> model.h\n",
        "!echo \"const unsigned char model[] = {\"     >> model.h\n",
        "!cat model.tflite | xxd -i                  >> model.h\n",
        "!echo \"};\"                                  >> model.h\n",
        "\n",
        "import os\n",
        "model_h_size = os.path.getsize(\"model.h\")\n",
        "print(f\"Header file, model.h, is {model_h_size:,} bytes.\")\n",
        "print(\"\\nOpen the side panel (refresh if needed). Double click model.h to download the file.\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "train.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}