XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/flecue/xai-aaai2021/blob/main/XAI_LIME%2BAnchor%2BSHAP%2BSaliency%2BGradCam%2BExMatchina_Image.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwM-f3wOJ83P"
      },
      "source": [
        "CREDITS: https://github.com/nesl/Explainability-Study\n",
        "\n",
        "Used as part of AAAI 2021 Tutorial on XAI - https://xaitutorial2021.github.io/\n",
        "                                          - https://github.com/flecue/xai-aaai2021"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4HyYPIsJYiS"
      },
      "source": [
        "## Mount Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d14x-QxxJc0u",
        "outputId": "a4020ec6-e9f2-47ab-abaa-66db0de394c3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOiPAKJPJnB-"
      },
      "source": [
        "## Configuration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyuoGbvdJtuy"
      },
      "source": [
        "1- Make sure to get \"XAI_EXAMPLE_exMatchina_ALL\" from https://github.com/flecue/xai-aaai2021/tree/main/XAI_EXAMPLE_exMatchina_ALL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVs5FkQbJwnH",
        "outputId": "f01fec1b-a76c-409b-e3c7-59f63414fc18"
      },
      "source": [
        "%cd '/content/gdrive/MyDrive/dev'\n",
        "%ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/MyDrive/dev\n",
            "\u001b[0m\u001b[01;34mpulse\u001b[0m/\n",
            "requirements_XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.txt\n",
            "\u001b[01;34mXAI_EXAMPLE_exMatchina_ALL\u001b[0m/\n",
            "XAI_EXAMPLE_exMatchina_ECG.ipynb\n",
            "XAI_EXAMPLE_exMatchina_Image.ipynb\n",
            "XAI_EXAMPLE_exMatchina_Text.ipynb\n",
            "XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3uxoMWgOKJPZ",
        "outputId": "820efb80-aea4-4e6a-c1c5-1fe09bb3f163"
      },
      "source": [
        "!pip3 install -r requirements_XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==1.15.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/02/36/9a02e27f0ec248b676a380ffe910c1858e3af3027c0d4d513dd0b56a5613/tensorflow-1.15.3-cp36-cp36m-manylinux2010_x86_64.whl (110.5MB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/0f/65/81883897f4aaa1e53deaa65137318cfe80b36ce013c2e86f8fd0843cfa02/natsort-7.0.1-py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
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            "Collecting threadpoolctl>=2.0.0\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/8e/e386e248266952d24d73ed734c2f5513f34d9557032618c8910e605dfaf6/colorful-0.5.4-py2.py3-none-any.whl (201kB)\n",
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            "\u001b[?25hRequirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->alibi==0.5.0->-r requirements_XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.txt (line 7)) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->alibi==0.5.0->-r requirements_XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.txt (line 7)) (2020.12.5)\n",
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            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython==7.15.0->-r requirements_XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.txt (line 13)) (0.2.0)\n",
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            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3->-r requirements_XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.txt (line 1)) (3.3.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3->-r requirements_XAI_LIME+Anchor+SHAP+Saliency+GradCam+ExMatchina_Image.txt (line 1)) (3.4.0)\n",
            "Building wheels for collected packages: shap, lime, moviepy, gast, proglog\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shap: filename=shap-0.34.0-cp36-cp36m-linux_x86_64.whl size=383182 sha256=d895b43b08c18016082bf59e86a41c27dd41b677cb76c6dd3b4188f756589528\n",
            "  Stored in directory: /root/.cache/pip/wheels/28/7e/28/57bc9fcb77579b0df6561298648249370648c0b91dfe42c8ec\n",
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            "  Stored in directory: /root/.cache/pip/wheels/22/f2/ec/e5ebd07348b2b1ac722e91c2f549fcc220f7d5f25497a61232\n",
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            "  Stored in directory: /root/.cache/pip/wheels/a3/3c/07/45afe2bd5dbd3f935f445545d645f0f8c05d48340136367d45\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=4d22187ddbf0b2fb592909471eb7e2deca2ab6f52fdec894e6b244cda7b569f5\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "  Building wheel for proglog (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for proglog: filename=proglog-0.1.9-cp36-none-any.whl size=6149 sha256=5e5bc98b1d975d448e685ff66bf2343b5a492861bc5aacc463b40cb00cbe65af\n",
            "  Stored in directory: /root/.cache/pip/wheels/65/56/60/1d0306a8d90b188af393c1812ddb502a8821b70917f82dcc00\n",
            "Successfully built shap lime moviepy gast proglog\n",
            "\u001b[31mERROR: tensorflow-probability 0.12.1 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: jupyter-console 5.2.0 has requirement prompt-toolkit<2.0.0,>=1.0.0, but you'll have prompt-toolkit 3.0.10 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 7.15.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.1.0; python_version >= \"3.0\", but you'll have pandas 1.0.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: fbprophet 0.7.1 has requirement pandas>=1.0.4, but you'll have pandas 1.0.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: lime 0.2.0.0 has requirement pillow==5.4.1, but you'll have pillow 7.1.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: gast, numpy, keras-applications, tensorboard, tensorflow-estimator, tensorflow, matplotlib, threadpoolctl, scikit-learn, pandas, shap, vis, Pillow, colorful, prettyprinter, alibi, lime, proglog, imageio, imageio-ffmpeg, moviepy, prompt-toolkit, ipython, natsort\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: numpy 1.19.5\n",
            "    Uninstalling numpy-1.19.5:\n",
            "      Successfully uninstalled numpy-1.19.5\n",
            "  Found existing installation: tensorboard 2.4.0\n",
            "    Uninstalling tensorboard-2.4.0:\n",
            "      Successfully uninstalled tensorboard-2.4.0\n",
            "  Found existing installation: tensorflow-estimator 2.4.0\n",
            "    Uninstalling tensorflow-estimator-2.4.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.4.0\n",
            "  Found existing installation: tensorflow 2.4.0\n",
            "    Uninstalling tensorflow-2.4.0:\n",
            "      Successfully uninstalled tensorflow-2.4.0\n",
            "  Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: pandas 1.1.5\n",
            "    Uninstalling pandas-1.1.5:\n",
            "      Successfully uninstalled pandas-1.1.5\n",
            "  Found existing installation: Pillow 7.0.0\n",
            "    Uninstalling Pillow-7.0.0:\n",
            "      Successfully uninstalled Pillow-7.0.0\n",
            "  Found existing installation: imageio 2.4.1\n",
            "    Uninstalling imageio-2.4.1:\n",
            "      Successfully uninstalled imageio-2.4.1\n",
            "  Found existing installation: moviepy 0.2.3.5\n",
            "    Uninstalling moviepy-0.2.3.5:\n",
            "      Successfully uninstalled moviepy-0.2.3.5\n",
            "  Found existing installation: prompt-toolkit 1.0.18\n",
            "    Uninstalling prompt-toolkit-1.0.18:\n",
            "      Successfully uninstalled prompt-toolkit-1.0.18\n",
            "  Found existing installation: ipython 5.5.0\n",
            "    Uninstalling ipython-5.5.0:\n",
            "      Successfully uninstalled ipython-5.5.0\n",
            "  Found existing installation: natsort 5.5.0\n",
            "    Uninstalling natsort-5.5.0:\n",
            "      Successfully uninstalled natsort-5.5.0\n",
            "Successfully installed Pillow-7.1.2 alibi-0.5.0 colorful-0.5.4 gast-0.2.2 imageio-2.8.0 imageio-ffmpeg-0.4.3 ipython-7.15.0 keras-applications-1.0.8 lime-0.2.0.0 matplotlib-3.1.3 moviepy-1.0.1 natsort-7.0.1 numpy-1.17.4 pandas-1.0.3 prettyprinter-0.18.0 proglog-0.1.9 prompt-toolkit-3.0.10 scikit-learn-0.23.1 shap-0.34.0 tensorboard-1.15.0 tensorflow-1.15.3 tensorflow-estimator-1.15.1 threadpoolctl-2.1.0 vis-0.0.5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython",
                  "PIL",
                  "matplotlib",
                  "mpl_toolkits",
                  "numpy",
                  "pandas",
                  "prompt_toolkit"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZ3dCmNvKT5S"
      },
      "source": [
        "2- Make sure to get to right path"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EciMWJxJKWHy",
        "outputId": "f04ed58c-dfaa-48e5-dd28-7dc9772f5e62"
      },
      "source": [
        "%cd '/content/gdrive/MyDrive/dev'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/MyDrive/dev\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dY0JU58WKYvU",
        "outputId": "11f01316-2264-43b6-bf9f-d3a353b77170"
      },
      "source": [
        "%ls XAI_EXAMPLE_exMatchina_ALL/Examples/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mdata\u001b[0m/  \u001b[01;34mTrained_Models\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RdlZNpTzy2fX"
      },
      "source": [
        "3 - Make sure to get \"data/\" from https://drive.google.com/drive/folders/1ZRWIeUHxGbKpqWkJ2HpiSLtmUyllfThf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qzlZLvrDRGJ8"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5TJcHzzp18U"
      },
      "source": [
        "from warnings import simplefilter \n",
        "simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.models import Model, load_model, clone_model\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.layers import Activation\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.metrics import f1_score, accuracy_score, confusion_matrix\n",
        "import itertools\n",
        "from random import randint\n",
        "from skimage.segmentation import slic, mark_boundaries, felzenszwalb, quickshift\n",
        "from matplotlib.colors import LinearSegmentedColormap\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import os\n",
        "import time\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "import shap\n",
        "from alibi.explainers import AnchorImage\n",
        "import lime\n",
        "from lime import lime_image\n",
        "from lime.wrappers.scikit_image import SegmentationAlgorithm\n",
        "\n",
        "# import vis\n",
        "# from vis.visualization import visualize_saliency\n",
        "\n",
        "import sys\n",
        "sys.path.append('./XAI_EXAMPLE_exMatchina_ALL')\n",
        "from exmatchina import *\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUV0ktjAw6P4"
      },
      "source": [
        "num_classes = 10\n",
        "\n",
        "classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship','truck']\n",
        "\n",
        "class_dict = {\n",
        "    'airplane': 0,\n",
        "    'automobile':1,\n",
        "    'bird':2,\n",
        "    'cat':3,\n",
        "    'deer':4,\n",
        "    'dog':5,\n",
        "    'frog':6,\n",
        "    'horse':7,\n",
        "    'ship':8,\n",
        "    'truck':9\n",
        "}\n",
        "\n",
        "inv_class_dict = {v: k for k, v in class_dict.items()}\n",
        "\n",
        "## These are the randomly generated indices that were used in our survey\n",
        "\n",
        "# all_idx = np.array([23, 26, 390, 429, 570, 649, 732, 739, 1081, 1163, 1175, 1289, 1310, 1323\n",
        "# , 1487, 1623, 1715, 1733, 1825, 1881, 1951, 2102, 2246, 2300, 2546, 2702, 2994, 3095\n",
        "# , 3308, 3488, 3727, 3862, 4190, 4299, 4370, 4417, 4448, 4526, 4537, 4559, 4604, 4672\n",
        "# , 4857, 5050, 5138, 5281, 5332, 5471, 5495, 5694, 5699, 5754, 5802, 5900, 6039, 6042\n",
        "# , 6046, 6127, 6285, 6478, 6649, 6678, 6795, 7023, 7087, 7254, 7295, 7301, 7471, 7524\n",
        "# , 7544, 7567, 7670, 7885, 7914, 7998, 8197, 8220, 8236, 8291, 8311, 8355, 8430, 8437\n",
        "# , 8510, 8646, 8662, 8755, 8875, 8896, 8990, 9106, 9134, 9169, 9436, 9603, 9739, 9772\n",
        "# , 9852, 9998])\n",
        "\n",
        "all_idx = [23, 26, 390, 429, 570]    #Considering just 5 samples"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09byf8xzq-vj",
        "outputId": "65089e1c-ccf4-48f1-f2d7-58822748f22b"
      },
      "source": [
        "x_train = np.load('XAI_EXAMPLE_exMatchina_ALL/Examples/data/image/X_train.npy')\n",
        "y_train = np.load('XAI_EXAMPLE_exMatchina_ALL/Examples/data/image/y_train.npy')\n",
        "x_test = np.load('XAI_EXAMPLE_exMatchina_ALL/Examples/data/image/X_test.npy')\n",
        "y_test = np.load('XAI_EXAMPLE_exMatchina_ALL/Examples/data/image/y_test.npy')\n",
        "\n",
        "print(f'Number of Training samples: {x_train.shape[0]}')\n",
        "print(f'Number of Test samples: {x_test.shape[0]}')\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Training samples: 50000\n",
            "Number of Test samples: 10000\n",
            "(50000, 32, 32, 3)\n",
            "(50000,)\n",
            "(10000, 32, 32, 3)\n",
            "(10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0j2xGulq-yl"
      },
      "source": [
        "y_train = to_categorical(y_train,num_classes)\n",
        "y_test = to_categorical(y_test,num_classes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W7CJ4ZjORIsx",
        "outputId": "b11c4062-51e5-4ca6-a6ca-226342b03414"
      },
      "source": [
        "model = load_model('XAI_EXAMPLE_exMatchina_ALL/Examples/Trained_Models/image.hdf5')\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "Model: \"CNN\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Conv_1 (Conv2D)              (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "Bn_1 (BatchNormalization)    (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "Conv_2 (Conv2D)              (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "Bn_2 (BatchNormalization)    (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "Max_pool_1 (MaxPooling2D)    (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "Drop_1 (Dropout)             (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "Conv_3 (Conv2D)              (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "Bn_3 (BatchNormalization)    (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "Conv_4 (Conv2D)              (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "Bn_4 (BatchNormalization)    (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "Max_pool_2 (MaxPooling2D)    (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "Drop_2 (Dropout)             (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "Conv_5 (Conv2D)              (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "Bn_5 (BatchNormalization)    (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "Conv_6 (Conv2D)              (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "Bn_6 (BatchNormalization)    (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "Max_pool_3 (MaxPooling2D)    (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "Drop_3 (Dropout)             (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "Flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 32)                65568     \n",
            "_________________________________________________________________\n",
            "Bn_7 (BatchNormalization)    (None, 32)                128       \n",
            "_________________________________________________________________\n",
            "Drop_4 (Dropout)             (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "logits (Dense)               (None, 10)                330       \n",
            "_________________________________________________________________\n",
            "probs (Activation)           (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 354,826\n",
            "Trainable params: 353,866\n",
            "Non-trainable params: 960\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THoDZ0bTUUki"
      },
      "source": [
        "def calculate_metrics(model, X_test, y_test_binary):\n",
        "    y_pred = np.argmax(model.predict(X_test), axis=1)\n",
        "    #print(y_pred)\n",
        "    y_true = np.argmax(y_test_binary, axis=1)\n",
        "    # y_true = np.amax(y_true, axis=1) # Freddy\n",
        "    #print(y_true)\n",
        "    #print(np.amax(y_true, axis=1))\n",
        "    mismatch = np.where(y_true != y_pred)\n",
        "    #print(mismatch)\n",
        "    cf_matrix = confusion_matrix(y_true, y_pred)\n",
        "    accuracy = accuracy_score(y_true, y_pred)\n",
        "    #micro_f1 = f1_score(y_true, y_pred, average='micro')\n",
        "    macro_f1 = f1_score(y_true, y_pred, average='macro')\n",
        "    return cf_matrix, accuracy, macro_f1, mismatch, y_pred\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "        # print(cm)\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "        # print(cm)\n",
        "\n",
        "    plt.figure(figsize = (10,7))\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45, fontsize = 15)\n",
        "    plt.yticks(tick_marks, classes, fontsize = 15)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt), fontsize = 15,\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    \n",
        "    plt.ylabel('True label', fontsize = 12)\n",
        "    plt.xlabel('Predicted label', fontsize = 12)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "V0PTXLLeR3sZ",
        "outputId": "701e72fa-6cb6-4d7b-80db-99c6f72f63d0"
      },
      "source": [
        "cf_matrix, accuracy, macro_f1, mismatch, y_pred = calculate_metrics(model, x_test, y_test)\n",
        "\n",
        "print('Accuracy : {}'.format(accuracy))\n",
        "print('F1-score : {}'.format(macro_f1))\n",
        "\n",
        "plot_confusion_matrix(cf_matrix, classes,\n",
        "                          normalize=True,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.869\n",
            "F1-score : 0.8683100113942105\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TV4y7VnJTnb"
      },
      "source": [
        "# LIME"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "cb9ea2386fa942629d563af7e5ee3fc6",
            "0abdc898136949a9858c9cebb0be15db",
            "0c9d6640b82945bbb3b576c1c48eba8f",
            "be45d4f5d82b42de8666d4d5790238a4",
            "acc788b4ae524b10b9af778c1c38aa1d",
            "cf5e699a195b4257b1ab78bfa13b315a",
            "2e6918a20b744ec8bca66a312ac3b329",
            "ce24184cc24f459b9202701b7029f8f1",
            "b3df658725084f44a8ba66ae793af93d",
            "52386daf81054bd48b20b796c44cdcb8",
            "bc57f8939cd94446a07d77e766893b95",
            "318b72b595c8474ca3fa537153fd2aea",
            "6c2de3bc46f44c2bb2a8bbb9ddcf6b1e",
            "a6b25cb995a3465c8f6898cdeb833204",
            "6dc715513bdb40ae81d5265d61d15e6d",
            "341c213fcc454c12af287d5c1baa1be0",
            "12f0e5bd66ca4c88b9b41722dd6f507c",
            "11c3ecff455f43ce802c7d7d3b39978a",
            "d1dbaca066914f53972a9046ae85020c",
            "f4514b21f7d049ea9545cacceac05eb6",
            "a561822d0f854543896f60ff14fc7fe0",
            "1e8511160960421d88856570b012b520",
            "fecc2b56dc8a462a8179f3963cf0cd08",
            "9cdb2ce0172f4f44825da919e41e6ef8",
            "88806efa777342e8ba86bc65f765e98b",
            "cbb43e281e234006923a20b17d0c199d",
            "b4491b5e9e014e898675c977034d2b94",
            "ecfc43b5487545d788b2cfb8aa830c90",
            "7b5044eae7764fff966136266a6d7db3",
            "9179bf8c20ef48a7a4cb659edf855abf",
            "a6d86f4e086f4bbb9868c1a8b37801f3",
            "b786f3136b7f418fb8280937174ee56d",
            "2b12bbd1ac7c4a58bc861d8c6836175a",
            "e7237b50de474218a04171a43051ae21",
            "056862fb28cf4fe4a90561214ca171ea",
            "679812e67fd74bbb9d1aaa8d13774a19",
            "a37765d8d6804ae1ac15377c3235deb5",
            "f32de01625244aa9bc6f8f1118600eef",
            "f07b762b46664c7aafe59564c108fd2f",
            "9aa4d71413db478b938276e3d1321e5a"
          ]
        },
        "id": "nyzDLccNJTnc",
        "outputId": "4063b8c9-2506-4b50-8f81-dafa374caa79"
      },
      "source": [
        "explainer = lime_image.LimeImageExplainer()\n",
        "\n",
        "segmentation_fn = SegmentationAlgorithm('felzenszwalb', scale=10,\n",
        "                                                    sigma=0.4, min_size=20)\n",
        "\n",
        "for i in all_idx:\n",
        "    image = x_test[i]\n",
        "    to_explain = np.expand_dims(image,axis=0)\n",
        "    class_idx = np.argmax(model.predict(to_explain))\n",
        "    print(inv_class_dict[class_idx])\n",
        "    \n",
        "    # Hide color is the color for a superpixel turned OFF. \n",
        "    # Alternatively, if it is NONE, the superpixel will be replaced by the average of its pixels\n",
        "    explanation = explainer.explain_instance(image, model.predict,segmentation_fn = segmentation_fn,\n",
        "                                             top_labels=5, hide_color=0, num_samples=1000)\n",
        "\n",
        "    temp, mask = explanation.get_image_and_mask(explanation.top_labels[0],\n",
        "                                                positive_only=True, num_features=5, hide_rest=True)\n",
        "    \n",
        "    #Plotting\n",
        "    fig, axes1 = plt.subplots(1,2, figsize=(10,10))\n",
        "    # fig.suptitle(inv_class_dict[y_test[i]])\n",
        "    axes1[0].set_axis_off()\n",
        "    axes1[1].set_axis_off()\n",
        "    axes1[0].imshow(x_test[i], interpolation='nearest')\n",
        "    axes1[1].imshow(mark_boundaries(temp, mask), interpolation='nearest')\n",
        "    # plt.savefig(f'./image/image-{i}-lime',bbox_inches = 'tight', pad_inches = 0.5)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "truck\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cb9ea2386fa942629d563af7e5ee3fc6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "cat\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b3df658725084f44a8ba66ae793af93d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "automobile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "12f0e5bd66ca4c88b9b41722dd6f507c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88806efa777342e8ba86bc65f765e98b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2b12bbd1ac7c4a58bc861d8c6836175a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ntZvYihcJTnd"
      },
      "source": [
        "# Anchor Explanations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilYgVQMkJTne"
      },
      "source": [
        "# Define a Prediction Function\n",
        "predict_fn = lambda x: model.predict(x)\n",
        "\n",
        "image_shape = (32,32,3)\n",
        "segmentation_fn = 'felzenszwalb'\n",
        "slic_kwargs = {'n_segments': 100, 'compactness': 1, 'sigma': .5, 'max_iter': 50}\n",
        "felzenszwalb_kwargs = {'scale': 10, 'sigma': 0.4, 'min_size': 50}\n",
        "\n",
        "explainer = AnchorImage(predict_fn, image_shape, segmentation_fn=segmentation_fn,\n",
        "                        segmentation_kwargs=felzenszwalb_kwargs, images_background=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ro7iPKCnJTnf",
        "outputId": "19ca0ca7-616b-4a31-b4ac-dce65f6980d6"
      },
      "source": [
        "for i in all_idx:\n",
        "\n",
        "    image = x_test[i]\n",
        "    to_explain = np.expand_dims(image,axis=0)\n",
        "    class_idx = np.argmax(model.predict(to_explain))\n",
        "    print(inv_class_dict[class_idx])\n",
        "    \n",
        "    explanation = explainer.explain(image, threshold=.99, p_sample=0.5, tau=0.15)\n",
        "    \n",
        "    ## Plotting\n",
        "    fig, axes1 = plt.subplots(1,2, figsize=(10,10))\n",
        "    # fig.suptitle(inv_class_dict[y_test[i]])\n",
        "    axes1[0].set_axis_off()\n",
        "    axes1[1].set_axis_off()\n",
        "    axes1[0].imshow(x_test[i], interpolation='nearest')\n",
        "    axes1[1].imshow(explanation['anchor'], interpolation='nearest')\n",
        "    # plt.savefig(f'./image-{i}-anchor', bbox_inches = 'tight', pad_inches = 0.5)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "truck\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "The Explanation object is not a dictionary anymore and accessing elements should be done via attribute access. Accessing via item will stop working in a future version.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8E+seBK+tf18tr49/akIf/2xxVOZRTsYus39B5s2N5Hun7+n+KW+gf3smTOmoCPW1D/XuXZTXHVBjJX3u3va62/UOAPAMk/m1+QzjDQ8AADCPgQcAAJjHwAMAAMxj4AEAAOYx8AAAAPMYeAAAgHkMPAAAwDzZwxNUdA9BbWZG5stnFmVenJuW+UpPf+vvDr5Gxr2KLgPYevbJxGx+Z0eu3ZVSolDQy11vW/fcHBvWPQUvVuWlc68dn5V5pt2SeXfrsszL9b7Mx/28zAehJ/NSQf/+nVDvP+/r69PKJa9vZtI6kmTszg90h0WUsn4y1PdGbm1d53t1P8ifPfQH+gAA8AwTrvYz7DNf+orM/znhf+cNDwAAMI+BBwAAmMfAAwAAzGPgAQAA5jHwAAAA8xh4AACAeQw8AADAPFnmUm3qb/lfXDop8+hyXebxpu6C8Ro6Hy3U9PpsQ+bFXjsxG1xek2vzse5oiMKyzE/WSjL/l9yWzC+cX5X50+d0EdC91++ReaWjz33D1z1AxYI+P7mTZ2R+6fwlmXf26g6oqX4s8+LSkggrcm2/pDsivtfU126tqzsu3nfLq2V+521TMt9IOf5qTl87AK/sZ9jfnVuReZqPHr1d5h8+9YTewDNPXdH+k/CGBwAAmMfAAwAAzGPgAQAA5jHwAAAA8xh4AACAeQw8AADAPAYeAABgnizkqAW6h2Bp/azM+/WmzJe3NmUe+Dmdf/e4zO9I6VEYj5OPr1kpyrUbmUjm/elRmV9s69+2Utc9QIOUHpy1jY7MTwx6Mp/fo3t6cnM6bw30+SleSu5Acs650Zo+/zOHD8o8mN8r8+sOJXdg5HSFkfv2mu7XOPuFz8g8P/BlXjp8i8xvets9Mn/8ye/L/ML2ZZmPyxSw4ZPvuE/mTcPPsDseflj/gyv04W+n9OxcJbzhAQAA5jHwAAAA8xh4AACAeQw8AADAPAYeAABgHgMPAAAwj4EHAACYJ8tcCn4gFw9P6MaO1VjvfL2+LfN2X3fFXAhCmV8X1mS+r9FPzDJbDbm2lR3IPDszJvNLFX1sgxW9/UpX572c7vk5H+r1c297q8xH9yZ3QDjn3JNruqOplS/IPBokXxvnnNs/PCzzmd27Zb5nX3IHxtKjusfm+NpzMt9VnZD5fLYq86898nWZj37tcZmveJ7Mh3L62rk3vUXnwDXg8x/5Y5m/kp9hr1S84QEAAOYx8AAAAPMYeAAAgHkMPAAAwDwGHgAAYB4DDwAAMI+BBwAAmCd7eOqdllzcz+i+j9r0lMzbpZLMy6W8zIO87jlw1V0y3tjcScwqLpJrD8zorpWVrD72l55+Qebjge54uH1E7/+5rC5BWtlpy3wjqMh8PBiSeTChr33v8JzMJ8d1x5OXk7eue+HRb8v8q5/9fGIWtPV/B5z2mzJ/w/7rZT7R8WX+2MWnZf5SXv/22tw+vf+DCzIHLLD+DLvlLz8mc/xPvOEBAADmMfAAAADzGHgAAIB5DDwAAMA8Bh4AAGAeAw8AADCPgQcAAJgniwBevHhOLu61dA/O9MwemR86ckTmq6vrMi+1dU9QeUH3odz7G+9KXlvUHUG5Ud3h8K3Hvinz1jMflfmdu6Zl/uqC3n+xqnt8vrz0ssyfXzov88lZfW23ugOZN4qBzMdvOijzmX3zMveePS3zZj65Z6kT6H6puKd/24GJYZmPtPX2mxd7Mr90y7zMf/bX3yPz/UdfJ3PAgvc89EGZ/+unPyvzn/RnGH54vOEBAADmMfAAAADzGHgAAIB5DDwAAMA8Bh4AAGAeAw8AADBPfpa+54YFubiQ0Z8+T41OybzfS/402Dnnlk6dlbnnfJmvNPRn82fX64lZr7Mq1y5fWJH58RPPynzQasv88NiMzA9OTco84/Snz4/n5KV3mUCf28sVvb7m9Gfz85UJmS+eeFHm//Wtx2U+nPLp+Nr+5PN7qZ58Xzjn3OJZfW+8sKzvjb2VMZnXqvqz9rWlNZkvntSVA36mKPOjb3mrzAELrvVnGH54vOEBAADmMfAAAADzGHgAAIB5DDwAAMA8Bh4AAGAeAw8AADCPgQcAAJgniwgO33JELi56eZmXcyWZH3/imMxn9k7L/KnTuqcn99KSzH/vt38/Mdv2e3Jto9WR+SClA6jd1F0vWykdR73JcZnnKxWdxzJ2i+dPy/zkI5dlvr14Qeb1M4syP7d+UeY7rivz+QndUxQ2kq/fSrMh1/pBIPMnz+hjf6Gqtx/M7pW5G9H3xoll/XfRq6X08Oi9Aya865fuu9qHgP9nvOEBAADmMfAAAADzGHgAAIB5DDwAAMA8Bh4AAGAeAw8AADCPgQcAAJgne3iGSmW5OJeRy10u48v86Bvvknm9r7twHlt8TuZf+94zMg96ydvfyetZMF+ryXzc1+du4HkyX8/rc/fU6qrML8cDme9s6S6Yf/v6IzJvdvX28z1d9NN3en2kby3nB/r8XbqwJvOJ0lhidmh6n1zr5XIy36pvy3x2n+7ZufdXflHmtZq+NyoF3Y8VpHQwAYBFvOEBAADmMfAAAADzGHgAAIB5DDwAAMA8Bh4AAGAeAw8AADCPgQcAAJgn204mqsNysZfVfSCxrkpxS+eWZH7sGd2j4zZ3ZOyldLnE4h9UU7pMsildLN2UfbugKuNj6+sy354ryfyll07LfLmhe3gKJX1tJwq6Z2gqqMh8cr/uopm96YDM90xMyHy8qnuSKiPJxz9S1Oe2H+kOob7TRTeVXEHmg1xKv1Wk/7C8lj4+58KUHADs4Q0PAAAwj4EHAACYx8ADAADMY+ABAADmMfAAAADzGHgAAIB5DDwAAMA8WfjhRbpPJI5S+jx8PU+VayldLrvGZH7D5JTM1/KBzC+uLCdm3bbuqWl1de7KusulWtE9PGfrGzI/c3JL5qG+tG52fkbmP/XGozK/deGQzGeqozKvjo/IPCrrHqR8RvcEeb6+dxteKzHLdrpyrR/p+7qU0Xmuo/9uOr3kY3POOa+vr20U6h6eiB4eAK9AvOEBAADmMfAAAADzGHgAAIB5DDwAAMA8Bh4AAGAeAw8AADCPgQcAAJine3jyObnY8/TGo5SenokR3dUyceedMr/rjrtkXt/ZkfnJ508mZpcvXZZrfV/3wIyO6t8WDnRXyrHvH5P58PCwzA9cf73Mjxy5WeZjY7oDyaV0vXhO3xyh6+vtd/X2eyk3n27hcc6Po+S1sf7vgDDUWx9Eydt2zrl2qPNuym8f9Nsyj1L6s3o93TMEABbxhgcAAJjHwAMAAMxj4AEAAOYx8AAAAPMYeAAAgHkMPAAAwDwGHgAAYJ7s4Wk2O3JxqVTUW0/pM0mpK3Fxyj/wc7oLp1qtyPx1dyX3+PT7uicml9MdRYVCQeaDnt7+PXfffUX7T+sJikUPjXPODVJ+f5TWw5PSk3OlucvoeyuOdReNylPXpvTcpPfg6HPbbuuenV6vJ/O0gqy0vysAsIg3PAAAwDwGHgAAYB4DDwAAMI+BBwAAmMfAAwAAzGPgAQAA5jHwAAAA82QPz8nnXpSLx8fHZD40XJN5Wk+O78vDS+1LyaR0tUSij6Tb7absW8YuDEOZd9u642h4eFjmaT1BaXmatHOX2pOTQp37/42085u2/bR750rWXum5SZP229Ku/WCgO5QAwCLe8AAAAPMYeAAAgHkMPAAAwDwGHgAAYB4DDwAAMI+BBwAAmMfAAwAAzPOupI8EAADgWsAbHgAAYB4DDwAAMI+BBwAAmMfAAwAAzGPgAQAA5jHwAAAA8/4bayEK/+PfWIcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "cat\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "automobile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OHzLJzmW9cL"
      },
      "source": [
        "# SHAP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxE2FxI7W_Im"
      },
      "source": [
        "background = x_train[np.random.choice(x_train.shape[0], 1000, replace=False)]\n",
        "\n",
        "# map input to specified layer \n",
        "def map2layer(x, layer):\n",
        "    feed_dict = dict(zip([model.layers[0].input], x.reshape((1,) + x.shape)))\n",
        "    return K.get_session().run(model.layers[layer].input, feed_dict)\n",
        "\n",
        "def get_shap_full(idx):\n",
        "    layer = 14\n",
        "    to_explain = np.expand_dims(x_test[idx],axis=0)\n",
        "    class_idx = np.argmax(model.predict(to_explain))\n",
        "    print(inv_class_dict[class_idx])\n",
        "\n",
        "    # get shap values\n",
        "    e = shap.GradientExplainer((model.layers[layer].input, model.layers[-1].output), map2layer(background, layer))\n",
        "    shap_values,indexes = e.shap_values(map2layer(to_explain, layer), ranked_outputs=1)\n",
        "\n",
        "    # use SHAP plot\n",
        "    shap.image_plot(shap_values, to_explain, show=False)\n",
        "\n",
        "    # plt.savefig('./image/image-' + str(idx) + '-shap.png', bbox_inches='tight')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "X9-HrJD4RBol",
        "outputId": "1998da25-5ff1-4627-a390-9b38b3250309"
      },
      "source": [
        "for i in all_idx:\n",
        "    get_shap_full(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "truck\n",
            "cat\n",
            "automobile\n",
            "frog\n",
            "frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvySTGeXDQAr"
      },
      "source": [
        "# Saliency Map"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qE8jLc6VZ5NB"
      },
      "source": [
        "# Replace activation with linear\n",
        "new_model = clone_model(model)\n",
        "new_model.pop()\n",
        "new_model.add(Activation('linear', name=\"linear_p\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXJkzjZQsqD8",
        "outputId": "de3f74d9-4491-4130-d710-117a0e551a97"
      },
      "source": [
        "new_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"CNN\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Conv_1 (Conv2D)              (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "Bn_1 (BatchNormalization)    (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "Conv_2 (Conv2D)              (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "Bn_2 (BatchNormalization)    (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "Max_pool_1 (MaxPooling2D)    (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "Drop_1 (Dropout)             (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "Conv_3 (Conv2D)              (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "Bn_3 (BatchNormalization)    (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "Conv_4 (Conv2D)              (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "Bn_4 (BatchNormalization)    (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "Max_pool_2 (MaxPooling2D)    (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "Drop_2 (Dropout)             (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "Conv_5 (Conv2D)              (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "Bn_5 (BatchNormalization)    (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "Conv_6 (Conv2D)              (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "Bn_6 (BatchNormalization)    (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "Max_pool_3 (MaxPooling2D)    (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "Drop_3 (Dropout)             (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "Flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 32)                65568     \n",
            "_________________________________________________________________\n",
            "Bn_7 (BatchNormalization)    (None, 32)                128       \n",
            "_________________________________________________________________\n",
            "Drop_4 (Dropout)             (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "logits (Dense)               (None, 10)                330       \n",
            "_________________________________________________________________\n",
            "linear_p (Activation)        (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 354,826\n",
            "Trainable params: 353,866\n",
            "Non-trainable params: 960\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YkJf_3pZ1x9"
      },
      "source": [
        "def plot_map(img_index, class_idx, grads):\n",
        "    print(inv_class_dict[class_idx])\n",
        "    fig, axes = plt.subplots(ncols=2,figsize=(8,6))\n",
        "    axes[0].imshow(x_test[img_index])\n",
        "    axes[0].axis('off')\n",
        "    axes[1].imshow(x_test[img_index])\n",
        "    axes[1].axis('off')\n",
        "    i = axes[1].imshow(grads,cmap=\"jet\",alpha=0.6)\n",
        "    fig.subplots_adjust(right=0.9)\n",
        "    cbar_ax = fig.add_axes([1, 0.2, 0.04, 0.59])\n",
        "    fig.colorbar(i, cax=cbar_ax)\n",
        "    \n",
        "    # plt.savefig('./image/image-' + str(img_index) + '-saliencymap.png', bbox_inches='tight', pad_inches=0.3)\n",
        "    # plt.close(fig)\n",
        "\n",
        "    plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cyg1Tnlmrii"
      },
      "source": [
        "def getSaliencyMap(img_index):\n",
        "    \n",
        "    to_explain = x_test[img_index].reshape(1,32,32,3)\n",
        "    class_idx = np.argmax(model.predict(to_explain))\n",
        "    print(class_idx)\n",
        "    print(x_test[img_index].shape)\n",
        "\n",
        "    grads = visualize_saliency(new_model,\n",
        "                              14,\n",
        "                              filter_indices = None,\n",
        "                              seed_input = x_test[img_index])\n",
        "    #plot_map(img_index, class_idx , grads)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "AyZZ-iNfT5Wh",
        "outputId": "2eba58fa-4753-4e61-efa1-4b1dde195fcf"
      },
      "source": [
        "for i in all_idx:\n",
        "    getSaliencyMap(i)\n",
        "# getSaliencyMap(0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-dac8502243e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mall_idx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mgetSaliencyMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# getSaliencyMap(0)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'all_idx' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2lNBrWl28uX"
      },
      "source": [
        "# Grad-Cam++"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvlXIHLO28VQ"
      },
      "source": [
        "def get_gradcampp(idx):\n",
        "    img = x_test[idx]\n",
        "    cls_true = np.argmax(y_test[idx])\n",
        "    \n",
        "    x = np.expand_dims(img, axis=0)\n",
        "\n",
        "    # get cam\n",
        "    cls_pred, cam = grad_cam_plus_plus(model=model, x=x, layer_name=\"Conv_6\")\n",
        "    print(inv_class_dict[cls_pred])\n",
        "\n",
        "    # resize to to size of image\n",
        "    heatmap = cv2.resize(cam, (img.shape[1], img.shape[0]))\n",
        "\n",
        "    fig, axes = plt.subplots(ncols=2,figsize=(8,6))\n",
        "    axes[0].imshow(img)\n",
        "    axes[0].axis('off')\n",
        "    axes[1].imshow(img)\n",
        "    axes[1].axis('off')\n",
        "    i = axes[1].imshow(heatmap,cmap=\"jet\",alpha=0.6)\n",
        "    fig.subplots_adjust(right=0.9)\n",
        "    cbar_ax = fig.add_axes([1, 0.2, 0.04, 0.60])\n",
        "    fig.colorbar(i, cax=cbar_ax)\n",
        "\n",
        "    # plt.savefig('./image/image-' + str(idx) + '-gradcampp.png', bbox_inches='tight', pad_inches=0.3)\n",
        "    # plt.close(fig)\n",
        "    plt.show()\n",
        "\n",
        "def grad_cam_plus_plus(model, x, layer_name):   \n",
        "    cls = np.argmax(model.predict(x))\n",
        "    y_c = model.output[0, cls]\n",
        "    conv_output = model.get_layer(layer_name).output\n",
        "    grads = K.gradients(y_c, conv_output)[0]\n",
        "\n",
        "    first = K.exp(y_c) * grads\n",
        "    second = K.exp(y_c) * grads * grads\n",
        "    third = K.exp(y_c) * grads * grads * grads\n",
        "\n",
        "    gradient_function = K.function([model.input], [y_c, first, second, third, conv_output, grads])\n",
        "    y_c, conv_first_grad, conv_second_grad, conv_third_grad, conv_output, grads_val = gradient_function([x])\n",
        "    global_sum = np.sum(conv_output[0].reshape((-1,conv_first_grad[0].shape[2])), axis=0)\n",
        "\n",
        "    alpha_num = conv_second_grad[0]\n",
        "    alpha_denom = conv_second_grad[0] * 2.0 + conv_third_grad[0] * global_sum.reshape((1, 1, conv_first_grad[0].shape[2]))\n",
        "    alpha_denom = np.where(alpha_denom != 0.0, alpha_denom, np.ones(alpha_denom.shape))\n",
        "    alphas = alpha_num / alpha_denom\n",
        "\n",
        "    weights = np.maximum(conv_first_grad[0], 0.0)\n",
        "    alpha_normalization_constant = np.sum(np.sum(alphas, axis=0), axis=0) # 0\n",
        "    alphas /= alpha_normalization_constant.reshape((1, 1, conv_first_grad[0].shape[2])) # NAN\n",
        "    deep_linearization_weights = np.sum((weights * alphas).reshape((-1, conv_first_grad[0].shape[2])), axis=0)\n",
        "\n",
        "    cam = np.sum(deep_linearization_weights * conv_output[0], axis=2)\n",
        "    cam = np.maximum(cam, 0)\n",
        "    cam /= np.max(cam)\n",
        "\n",
        "    return cls, cam"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "skOsvfMubgfc",
        "outputId": "f065b704-8323-4c9a-bb15-ca5596b28c83"
      },
      "source": [
        "for i in all_idx:\n",
        "    get_gradcampp(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "truck\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "cat\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "automobile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gzw3epn21EB9rHYv39VvZXtrXYmN/2H6l3E0/c+WLO8P2mdffCdun3+CFLpuLJOrejPeJxnjizwmZjLVI0crEPske6eDkESDlgO9HWTZiT5bbiu29QrPg7o8lXr8E4OCG3w/cbos8DeBH7maiukIjIiJSR8Mbz50AcMTMDmN9IPM0gM/e+SYz+zCAOQB/eDedakAjIiJSN0N8Do27D8zsGQCvYH1Y9ZK7nzKz5wGcdPdjt9/6NICXnV2Wu4MGNCIiInUz5OKU7n4cwPE72p674/ef2UqfGtCIiIjU0YjdQqQBjYiISN0M+QrNdkgOaIwkR1IF+prNuNBbSZIj7Zyv0UuXLpD2N8P25ZVbtK9uNy5cydIxt27Fxe8AoN2OEwvtNqnAliiOVpDEWFGQQo+JXNjsTJxwWF2LEyEAsLIav/aVr7watr/zzlXa1+FHPhi2P3TokbA99bVoTvYL/hmePmLbuN1uh+2pVBg7JiqB1Fs1T6RA9pHXDsfb4ZE9vNrhwZU/CdubK3G4YW7At+kucj7pjsfpwLFEgdj5Ij4GdmbLYfsM4vQTAKyQKoyvlYfD9lt4kPb1usWJwqUvsfMMMDgT77u3vhqfGzuXeKHh6ZnpsH1qfi5st0naFU3LYYYcz4nDjAUNM5KmhCWKU1Y3tPje1W1AIyIiIiNKAxoRERGpNF2hERERkcozwEdsBDBiiyMiIiJ3I3H7XCVpQCMiIlIzbkAxYiOA5OKMjcUpkG6X3xpu5LbxdiuuC1J0+F32Xzl7Jmy/9Faccmo2+XCz2SCvkTo/vT6v/cJqM2VZnJZIlQtpkfVSFvF67HZ5rZx2K054GLv7H0CzSbZLGddyunrtMu3r1lKcCpnf80DYPjnJYxEszcTqiOXZ1o9Mln5i2yQ1X1WQHyH7AamBAwAWlxPCngPxfrC/eIP2NXPzL8L28dX4M3syvq4PZ6SeGuLPtEqeXBsv4nPQxFr8mQY/ZaE3H6c8+yS2s1Tyfe1WFqeMlvc9TD+TLcbT8U68XtZ6fGF65Bww9qF4vpoH+THoJMzl0/H5LJW8AzkFGkmy5QU//znZX2rBgDJZs7B6Rmx8JiIiIptxAIN8tL5z0oBGRESkbsxQJp4DV0Ua0IiIiNSMAyhT90NUkAY0IiIiNeMwFKlHMleQBjQiIiI1NBixJ+tpQCMiIlIzDkNZqwGNx5GuRs4/xmLbDRIPu/B2HMEGgIV34mgo+9qvJDHDdXE8z4zEg0nxOwBgLy0tkiKQifgpixvPzMQF4FqtOEq/Pl9xlLXR5Nsry+LodLe3ErZfW7hC+2q24gKg3W4cDZ2aiot8Anw/YtXkev0e74sUrqTTSKhybNsei9dRnlimnTNxkdZDWXzcTi/x4pStbrzvzNs7cV+L/NEJfpPM8414m3oiau3ktDEgj0goyDkDAGwmXscffeRU2L5/51u0r6MeF4i9tPch+pk39xwI21//q/GjEy6cJxVLAWRFvGKyJ+JClw/MxecMAJhDvB81SOHI5ZKfG5YxFbcPZsL2XjfxGAb6Sh3UbUAjIiIiI8cB3UMjIiIi1acBjYiIiFTaespJXzmJiIhIpRnKERsCjNbSiIiIyKZqdw8NTc004gJsAC/4NxjEiYU3L/FUxIBEE5qteLa7XV6ArijY/ezx/KYKOmbkNRaayXN+l32zyVJL8TRapAAlAHS7pGhmogBZoxFPfzCIEw79Pk+S5Y044fHWxdfD9unpcdpXcyxOX7mT5EkilVYO+GuRVPopr/Cjwo9Ox0kby/jyTiFOru3zC3FfK/x4nvE4zTRzK95vi/O8aKa/To7nN0iibSlRULdNPkPOjjaWODdMk9fejqexdwdPBs1Pnw3b/8r+OP0EAAv74tTS62Wccjq1bwftq5/HSaMPl/E54+GC/3Gcz+ME6MDjlXzF52lfl8s9Yfs12x22r4zHqSggdax/jH5mVIzig/VGa2lERETkrhTIt+XnbpjZk2Z21szOmdmz5D1/28xOm9kpM/vlzfrUV04iIiI1M8wH69n6VyAvAPgUgIsATpjZMXc/veE9RwB8HsB3uPsNM4svzW2gAY2IiEjtDDXl9DiAc+5+HgDM7GUATwE4veE9fx/AC+5+AwDc/epmnWpAIyIiUjMOoNy+u07mzezkht9fdPcXN/y+H8DGG/EuAnjijj4+CABm9gdYv6n0Z9z9N1MT1YBGRESkdrb1Cs2Cuz/2HvtoADgC4LsAHADwBTP7Fne/mfrAlqXq2bCU09JiXP/j2rW4XhPA6yw5SNImVWOF1FPyMk5SpJIug378mUGfpKxIvRIAaDTj6ZRlvKN1ecki9Afx9G/ciuuoAMC+vQfJK6QmDkklAEC/F6eJXn31TNj+4IH9tK+5dpyAYoElth0BXpeL7S2NBl/GVosn1u53R0FSTqRmGwBMZPE+NdG9GLavrr1G+5oZkPPQAtkSX00cz1+M2790Jf7MO4myXTvL+DNz5DjfPcnnq0lKEDlJkqXydyXZp7vTcSoMAHZ/S9w+d/h62P7og4u0r95kfA6aey1OOT04/iDtq70arxhWsunQo1+hfb2WPRy2n8fhsH3R4rp4ANAgtfTqYD22PbSvnC4B2PjH58Dtto0uAvhjd+8DeM3MXsX6AOcE61QpJxERkdqxYaacTgA4YmaHzawF4GkAx+54z3/H+tUZmNk81r+COp/qtL7DUxERkZra5nto0tN2H5jZMwBewfr9MS+5+ykzex7ASXc/dvu17zGz01i/mPlT7uRhVrdpQCMiIlIzDUxgDt86tOm7+3EAx+9oe27Dvx3AT9z+uSsa0IiIiNRM7UofiIiIyOgZYA0LJChQVRrQiIiI1IwP98F62yI5oGk24yKUfRZPBpDncZedTlyccnV1mfbFChGyaTSbqctn8Wt9Es0sEgUNSxJb7/TiQn5rSzwameXxfE2OT8fvz3hh0E4nLubZ68fzBfCClkU/XsdFn0dWcxLZv3olfsDjwgK/v2tqdmfY7omioRSJ4LP5Ten1Ern5+9zuLI7vs3gwALTJvt4v4v1mecC3Ty+PCyHu3xtHirPpRNaaFFxdIo9uWEw8aoKlsL2IHw/Ry/k+0G/F89UYxMet9fk+WHTic1DJHlsBoLhGHlEwFk8n8RQGjE3EfXVuxOeZzgSPkzcLUlR3PF5f5KkNAIDdD10L21cQnzNb4Pu3lYl9rAaGdVPwdtEVGhERkZqp3RUaERERGUUa0IiIiEjFDflJwdtCAxoREZHaMd1DIyIiItVWuys0eR4vbEGK/QE0UMJqHSYLSrIUSk6SQaXzu9lZoIUVwEzNF0tfsZTVyhpPhY1PxLfzu8VJhqXlFdoXS5K123wzX778VtheDFiSjB8AjjjJ0WNFM2/yopkHBvHy52T6WaKYKMh+zAqQplI/a2txwqMSSELME+tu1eJtuoAHwvazxv/Ht4Z4X39w5krY/vFP/B/aV0ZOQU/877j9Gj9sML8vPtYHD8brpftgotjsPDnWSHHcfp8npgqW8pzi22ttNk40Lu+Ki6rezHbRvhrkPDu3M04Z7WrylNNkL15nWYucT+JAHACedGwhPs+MO095lgXflqNPV2hERESk4hzAoE5XaERERGT0OAylBjQiIiJSdRrQiIiISKWtP1hP99CIiIhIxdXqHhqWAskyfpf9gKRTmMlJXrSj14vvmi/LeBqrq/xudlaXajBgtZxoV+h24xdZwGNqapL2NUGWv0dSAalaQlnGUmn0IyjI8g8G8bpvJOplrZKUVVGQVFoiTcSSdM1W3BeriQUA/X68LGz/Tsnuof7T/aIkO6gnVsNKGdfguURiKF9p7KZ9XSr3hO27fCGerzY/nj/+bV+MX3g7ThPN87JhKD5A9sMj5P2PkrpEAN5px8vYKeLzzxIPBqGHOJnUBZ/+GkknrpLtOMh4X2OkXtdej89Zs3vj+QWAOZYaJTtfd5b/aep7fJ7LWcoJfD9KpVlHne6hERERkRGgAY2IiIhU3PqD9ap7xTmiAY2IiEgNaUAjIiIileaqti0iIiLVp3toREREpOJqV5zSSES1SBT0GpC4basVR/omJnikeXHxZtjOo7M8gleQOHmvF0f9vOTfLRYk051l8eps5HFkEwCWl+Jih/0eWY9NHo1k8eyVlURlPrJDT0zE02lP8HXc7cbrst8nEfQuj6CzeHqjGa97T+yT7LXx8Th+mi6+uvWo9/2C/W+sKPk2XfY41nslPxC2v7Y4Qfta/f04BnxrfjZsb3/r47Sv+T1xDvvw0dfjD1zn280/Eu8fb++PI9in/UO0r0u9vWH7Yhmvl9WMr6+lcips7wz49srIObDViJex3eDPp5gu4uKxy2V83Ows+Dqe2hU/0mGaTL/jbdpXv4z34zHSV+68oGx9Q9vDf7CemT0J4N9i/Y/QL7n7v7jj9c8B+DkAl243/YK7/1KqT12hERERqaFhXaExsxzACwA+BeAigBNmdszdT9/x1v/q7s/cbb8a0IiIiNTMkB+s9ziAc+5+HgDM7GUATwG4c0CzJRrQiIiI1M62ppzmzezkht9fdPcXN/y+H8CFDb9fBPBE0M/3mdknALwK4Mfd/ULwnq/RgEZERKRmHEC5fffQLLj7Y++xj98A8Cvu3jWzHwLwnwB8d+oDGtCIiIjUzlCfQ3MJwMENvx/AX978CwBw9413/v8SgJ/drNPkgKYAKdyYCHqUpNhXox2nJcbG4iJ3ANDtXgzbx8kN8Lnze9b7/Tg1k1m8CsrEMrL0FyvMOSBJKgBwkphqspFzojAoKzI4Pj9DP9NuxymLxaXlsL2TKADaaMUbZn5PvI3HxnnCreyT/Yhsr4HzdZw33r9xe5VTTqyy7iCxSKuI949beVyEMn8jkTT8szjp0piLZ+DsIx+mfT008VrYvv9j8RXp1hWegru6b2fY/obHSa4rxS7el8ev3UA8jVs9fv7rLEyH7dbnGyyfJUm2xlLY3mzxBORcI04zNUk68GLib+N4GW/7HUb+xiTO5ewcaOTPWbJccnUP5/dsyLHtEwCOmNlhrA9kngbw2Y1vMLMH3P3t279+GsCXN+tUV2hERERqZ3hXaNx9YGbPAHgF67Htl9z9lJk9D+Ckux8D8A/M7NNYH5NeB/C5zfrVgEZERKRmtvkems2n734cwPE72p7b8O/PA/j8VvrUgEZERKR2VMtJREREKq52pQ9ERERk9KjatoiIiIyEYd5Dsx3SAxoSUc3yxEpgMegybp+ZnU/MQPwZJ/nkzPh8ZRkpkDiICxF2OonCid34M/1+3J6DFztsNeN5npyM47LtNi9OCVZMNBE1XlyKY5sry3HMM8t4oc29e/eH7R/6yDfF79+zj/bF8pSNLP4fRXOM/0/DSQS0LHmUl+GFUe9/qxbvU4Xx/XMNcbR+pYgjxc3EcYMb8brzONGL4qtxPBgAzn30g2H73vztsH1+b9wOADcG8WMN1sizGyawSPvalcXHZ9aMlyUfi9cjANzoxX35Gj+ee+TxBUUZFwZtZTwenU/Mhe2NHXHRzmvjfL76iPeLGXaFIHHhwDw+bo2eZxMR8Ao/huG90hUaERERGQEa0IiIiEjFrd8UXN0rzhENaERERGpnqNW2t4UGNCIiIjWz/mA9DWhERESk0qxeXzkVRXzXeCrpwYr3lXk8qbGJKdpXqx0nLEqP79g3krACgMLjEmVO7phfW1ujfa2uxtNn1SEz44UTG604ebJzd1y0LpXkurqwELa/c4OnMlbX4mXJ8njkPr+Lp9IOHjwYtu/ZE6cipqb4ts/J/tLrxWmJPJG8y8mysH01uX/TV+5/ax4XD3VSUBYAOiTl1FmJ2xtLidTMDXJ83iDplHN8O5w9GKecpmdvhO17Bnxf80F8fBpJx4zhJu1rV06Kqo7FRSuLRAJyIYvjX50OX8eDVnx8NCfiPXdsLC4aDABjk/E6WxmPl6WT874uFfG2bw3iY7OR8/UyRs6nOSlDmZECmHXn4AVrq0pXaERERGrGdQ+NiIiIjAINaERERKTSvG730IiIiMho0oP1REREpNJqV/qg04nvsmepESCRHCGfmZqO64UAwB5SG2jx5rWw3UtSFAYAu889y+K76dttXpOmLOJl7HTixJAn6oVMTqyszn0AABCDSURBVMe1XPokYXb58lu0r6tX45RTkShl0m7HNWbarTixMDERp7LW+4pTNDlJDWUZXy8lWf5uN94u7TavMcX2yffr/VWxMogPd0ukulYR7x+2FNcZaqzE+wAAjJFt1O/F9cT8Tb6v+al4//zS4x8L2x/M41QWAMx5fD7ZUVwN23fbZdrXjmZ8nhv4rbC9WOX77Wo33i6DgqeJ8kZ8sLda8T7davB9vbB42y8v7Azbm8t822OBnDOLeH3l03yfXJok88x2l+Y9HM8f2fpHqkc3BYuIiEjFOYDCdQ+NiIiIVFxRakAjIiIiFeZuKMhXflWlAY2IiEjduKEkT2quqtG63iQiIiKbcgBFkW/Lz90wsyfN7KyZnTOzZxPv+z4zczN7bLM+dYVGRESkZtwNxWA41zTMLAfwAoBPAbgI4ISZHXP303e8bxrAjwH447vpNzmgYfHVfp8XWyzLOCDdaMTxxFRse+f8vrC924kLR66uxMXJAB5PbjRYpDhR6LKIC+D1+3HUeIIUeQOAtW68vi6dOR+2r67yoplNEhkdH+PRUBbPbrfjDGSzyftiWGS/0UjtfiRiTN7dH/Btz7BlST2WgBVsrYI1i7eplzzXv0ZSEKQOK5q74jg3AIzNxgVXy+skntznj07IL8f7h5+JI8WvH4jj3ABwc/K1sH1vGS9Ls8n3WyviZXmT1LO8krjkX6yRc1aPfyZbJrHxQbyvL5X8ePbFeHtNrZBz5rVEPPpy/BlWt7fckSgouYNMn7TbGJ8v98QzLUadY5hfOT0O4Jy7nwcAM3sZwFMATt/xvn8K4F8C+Km76VRfOYmIiNSNG8oy35afu7AfwIUNv1+83fY1ZvZtAA66+/+420XSV04iIiK1Y8D2XaGZN7OTG35/0d1fvNsPm1kG4OcBfG4rE9WARkREpG4cAPmK9H2w4O6pm3gvATi44fcDt9veNQ3gmwH87u1bX/YBOGZmn3b3jQOlr6MBjYiISN04gMHQyrycAHDEzA5jfSDzNIDPvvuiu98CMP/u72b2uwB+MjWYATSgERERqachZRzcfWBmzwB4BUAO4CV3P2VmzwM46e7H7qXf5ICGJZZSd4azhAhLTFkiTXTw4KH4BY8TLedeXaZ9AfFnut24oOQgkZph66Xfjz+zcH2J9sVSM2wa7TYvsjc5Gacixsf496TtMVacMm5vtXgqok9SKazQZDrlRObZWYqDJ2LYlmyPxQmvVMopSxRyvN/d8rgQaup4dovXRWs+LihZHOWFEyfLePq4GO/rt1ZINAiA9ePPFGfj9vItvt3e3nMwbL+wJ05MvTHNi1NmHsd2Fvq7wvbeOzzl2XyHFG5c48vC9t08j4+1POfHczmIj1tfjddxtpQ4Nq6yKwFxe7mTp5zKTvwa/dtT8qsQxuJ6deAY2oAGANz9OIDjd7Q9R977XXfTp67QiIiI1I2D/2+vojSgERERqaPqPlYrpAGNiIhI3Qz5K6ftoAGNiIhI3egrJxERERkJdbpC013rhO2TE3FNGABotuL6J2UR35k+3k7UfmnGyYB+L65ntHiLp4muXn4zbF9bidMag0Gixge5M54FYIoOT+DA4g/NTMVppolxvu6npuOaUaxeFcC3V7PZDttTCSCWpOiR9FeqJlirRWoIkYRbqohHafG27A/I9DOefGgn9tf73eIgTiCl0maex/vB7EScKMw+wtMpnQPx9Ms34n299yW+f6ydi6c/OBPvH546nnfHzU7qUr215wDvi4SGmoN4HbfX+LpvkvpL1uH7Z9YndY76JAHUSySASOHCciVel+WAb/ssj+vfsdQiOnEdqfUZi5vLMZK+KreeCqsFfeUkIiIilaevnERERGQk6AqNiIiIVJqu0IiIiEjl6R4aERERGQka0IiIiEil1e0KTU7ycY2MR90aJIZcZiTql4rbkqTl9NRs2D4xMUP7GmvFr615HAHvdxdpX53VOM7upKDk5ASP+jqJgO+ZjwvjpWKG7bE4YmsZL0DHii0ayUGzIqOpeWOR/W43Xo8AL4KZkwi6GV9GJ/vxwOOjOQePn1pe3eKUq4hj/c3EumuSdTGHW2H7fPsK7avdio+1t2biiO6pxfg4B4D8bDzPxYX4poBicJX2VZyL1wtILc3mHr4PeFzTFeNTcWTdEo8IyJvkWO/x6dsKKQK8SKbDn3QBa5PixGNvhe1FmxcHzvL4fGpZvMKy8kN8xkhBz3I2/oOR2L2BRs2LU+oeGhEREam8Ol2hERERkRFUt6+cREREZATpKycRERGpPA1oREREZCTU6Sunycm4aFwqacNSME4SS6nUTEk+1GzGt63v3beP9tVdje/AL/px0qY36NK+MlLMr+jFn2FpLQCYno6jFHmDpHySxdTev2QS+wxb9/fSl7OdAkCnE28XOl9jvGhnRopmMkXBj/LUa/e9ZpwoKRL7R8Pi/8I1SEJswuIkEwCMe5x0mSOpyfF9D9C+ijlShLIVz1dR8qShkaSfk23tiYKOzUlSUJKl8yYTKZtxklhK1boliT6La/AiS1R1zei2jDtzsn0BoChIcUrExzlLRQGArcUJUJCQlbd4atEb/LWRp3toREREpOqmWsAnEoXj34sz29PtpjSgERERqRsHEo/cqiQNaERERGpmuQd84bVhz8X7q7qPPRUREZF78+49NNvxcxfM7EkzO2tm58zs2eD1HzazvzCzPzOz3zezo5v1qSs0IiIidTPE2LaZ5QBeAPApABcBnDCzY+5+esPbftnd/93t938awM8DeDLVb7qWE0mt3EsKpCR1jrJEbRyWaGmQlNGe3btpXyjiLddux6kEz3n6gNUA6pNl73Z5LKHbJ0kKi9d9K5HmYRqJZBSr5cTk91DLaKv7BMATUGx+U8vBpsP2r2TyLjHP97umkeM2kTZzctx0nZwbMl5PbRlxPaPFLK6lND5JCiMBwKPxPOc3yX5wjW9TH4tTO+VkvK2LGb4PFDvJutwVTz+fTJyCyaFua3xZ7G3y2oW4OWPvB4AbcQLTB6TIladq1sX7UWbx8hs5/633RWo2sdNsN1F7a8Sew7Jlw0s5PQ7gnLufBwAzexnAUwC+NqDxr4/NTWJ9CJakKzQiIiJ1M9zY9n58/TD7IoAn7nyTmf0IgJ8A0ALw3Zt1qntoRERE6ubdr5y24weYN7OTG35+8J5m0f0Fd38EwD8C8NObvV9XaEREROpo+67QLLj7Y4nXLwE4uOH3A7fbmJcB/OJmE9UVGhERkboZbsrpBIAjZnbYzFoAngZwbOMbzOzIhl//BoCvbNaprtCIiIjUzRBTTu4+MLNnALwCIAfwkrufMrPnAZx092MAnjGzTwLoA7gB4O9u1q8GNCIiInU0xFpO7n4cwPE72p7b8O8f22qfyQHNvURUWaSORa098exlFsVlBRJT0d09pNDd2EQcDc1bvAhjQdJjnX4/bJ8o+DJOTbHIatzOCmMCQINEqvNE2o0ndtl2fP+KU6bi0a1WHAFl+5FZIrbt8fpn+2qqaGalY9vOC64yJflWupvFmeI1UhwRAAbkpVUnx2CTb9PxD8TTz8fJPriUiO624u09aMbbemwnqfQIYG5mKWyfbt4M28cTlSYnSXHIDnic/c0HHwrbi9k4Mm9TicdmXCYFNS+TeHaxh/aV5/FyZsaqFrdpX+zwpLv3auL8l2+aBB5dQ7xCs110hUZERKRuVG1bRERERoIGNCIiIlJpukIjIiIilad7aERERGQk1OkKDcsr5ImkDStEWJRszfG7zFlqhiVNUgmUsXGSDMjipXwwkb7Km/F8zc7tCNvHJ3hByYnx+LWbN+NUxMoqT1iUAzLcHvAkhZHIAFv3LGEG8CKYLH3GEkupzzB8/wLKcmvLmEpfDdg6roAm+e+YJdY1K0LZ9Xg/WBvwBM7acvyad8h+kCeOwQNxe2t/vCztRJplT7kaf2btStg+14jTRwAw2+iE7Xk3Pp5bg8WwHQDGfDlsXyn5+WRyKj4/vPYtj4bt/dYu2pc1yXFAjo/sUnz+W/8IO2/F6949UTSTFafsks8kinmmUrYjT185iYiISOXpKycRERGpPA1oREREZCSM2DduGtCIiIjUja7QiIiISOXVbkBD0g8DkmQCeAqE1cdpNvkssLRJI2f1fPjd7AVJQLHaRLOzc7Sv2R2zYftDDz0cz1ciRZKRGkTLy3HC4cbNG7SvpVtxYqLoxckLAHBS54jVRmolalw12yxJRpY/UX+pIMkklmayRqJWT6I2U6TX46mwrfZ1PzFWN8dTCbE45dQjNZ46a3HNIACwxbgGkJXkuJ3m18Nbk3HSqN2M0zRTJT9uphEXAZqIy6lhh8U129b7io+1Zj/epxq9OOUDAM1efDzfKvhfoQ5JDa2R+br8AKnLBCC7FW973CLbi296+CA+bzj9E5Q4nkm7kd2l7PL9u8KH8/tDKScRERGpNMW2RUREpPI0oBEREZGRUKt7aERERGT01O6mYBERERk9Dj2HRkREREZAna7Q9EmckxWgXEfi2STumxuJBiJRbJKkBsuCZ/BYXzmLepM4N8CLMDbzdtjuiaKZrNBnqxFHoNtNXvxvdnpn2D7o85hpSWLQrDhkTiLz6x+K16WTDVYmtr2T12hh0h5fRhZBbzbj9vT+XV0lOTadRORTn2H7B3sMwXpfW5TK1JKX2NRTxU4bRo7BLD6em86jzk0S6W5ncaa5nfFCk61mvB8Oykn6mUmfidttOmyfmIjfD4DGsJ0UrfRk1Jp8hhShZI+TAACjwe14+lV+1MK20k3BIiIiUnm6h0ZERERGwojdQ8OvEYqIiMjo8m36uQtm9qSZnTWzc2b2bPD6T5jZaTP7czP7LTOLH8W/gQY0IiIi8g1jZjmAFwB8L4CjAD5jZkfveNufAnjM3T8K4NcA/Oxm/WpAIyIiIt9IjwM45+7n3b0H4GUAT218g7v/jru/W5zsjwAc2KzT5D00T/+d7+fVHkWkUv79I0/peJav9+2J1z79DZsLGZptu4lm3sxObvj9RXd/ccPv+wFc2PD7RQBPJPr7AQD/c7OJ6qZgERGR2tnWmNOCuz/2fnRkZt8P4DEA37nZezWgERERqZ2hPojmEoCDG34/cLvt65jZJwH8EwDf6e7dzTrVgEZERKSWhvYgmhMAjpjZYawPZJ4G8NmNbzCzjwH49wCedPerd9OpBjQiIiK1M7wn67n7wMyeAfAKgBzAS+5+ysyeB3DS3Y8B+DkAUwD+m60/0f9Nd0/e2aUBjYiISO0M91HB7n4cwPE72p7b8O9PbrVPDWhERERqZ/RqH2hAIyIiUjsa0IiIiEjlaUAjIiIiI2Fose1toQGNiIhI7egKjYiIiFSeBjQiIiJSeRrQiIiIyEjQPTQiIiJSabpCIyIiIpWnAY2IiIhUngY0IiIiMhI0oBEREZFK0xUaERERqTwNaERERKTyHIpti4iIyAjQFRoRERGpNH3lJCIiIpWnAY2IiIhUnu6hERERkcrTFRoREREZCRrQiIiISKXpCo2IiIhU3NRUC5/4xIFt6fvMmW3pdlPm7sOZsoiIiAyFmf0mgPlt6n7B3Z/cpr4pDWhERESk8rJhz4CIiIjIe6UBjYiIiFSeBjQiIiJSeRrQiIiISOVpQCMiIiKV9/8A45g07ensswQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNuIdsZwJTnr"
      },
      "source": [
        "# ExMatchina"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Uypp7xjJTnr"
      },
      "source": [
        "def plot_images(test, examples, label):\n",
        "        # =======GENERATE STUDY EXAMPLES=========\n",
        "    fig = plt.figure(figsize=(10,3))\n",
        "    num_display = 4\n",
        "    fig.add_subplot(1, num_display, 1).title.set_text(inv_class_dict[label])\n",
        "    plt.imshow(test, interpolation='nearest')\n",
        "    plt.axis('off')\n",
        "    line = fig.add_subplot(1, 1, 1)\n",
        "    line.plot([2.39,2.39],[0,1],'--')\n",
        "    line.set_xlim(0,10)\n",
        "    line.axis('off')\n",
        "    for k in range(num_display-1):\n",
        "        if k >= len(examples):\n",
        "            continue\n",
        "        fig.add_subplot(1,num_display,k+2).title.set_text(inv_class_dict[label])\n",
        "        fig.add_subplot(1,num_display,k+2).title.set_color('#0067FF')\n",
        "        plt.imshow(examples[k], interpolation='nearest')\n",
        "        plt.axis('off')\n",
        "    \n",
        "    fig.tight_layout()\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    # plt.savefig('./image-' + str(i) + '-example.png', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJMEhQBQJTns",
        "outputId": "5c7ea03c-7235-4101-85ce-eca7fa3748af"
      },
      "source": [
        "selected_layer = 'Flatten_1'\n",
        "\n",
        "exm = ExMatchina(model=model, layer=selected_layer, examples=x_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Getting activations...\n",
            "Getting labels...\n",
            "Generating activation matrix...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "92aVRzvoJTnt",
        "outputId": "2cb240fb-e3d0-41af-b77a-df488f0a7430"
      },
      "source": [
        "for test_idx in all_idx:\n",
        "    test_input = x_test[test_idx]\n",
        "    label = exm.get_label_for(test_input)\n",
        "    (examples, indices) = exm.return_nearest_examples(test_input)\n",
        "    plot_images(test_input, examples, label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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udjuMMcYYsz3xBNlsOS5WegDgA5vdDmOMMcZsTzxBNsYYY4wxpgNPkI0xxhhjjOngupJeW0g2gYhladS1uBMXCUzZFO+yIYS4ikijA4BSnaWJbJuFq2rAL9/XA95mViQQAUBLCAqhWFmsJ88ixNieKard8RaW7ABgx569VBud5O+HQqi7fPkFuc25qyy79OVZHAxa/O+jc6eeo9rhw4fkfhYWWCw5eeolqr1ydpq/u6yvb3GN5crXvtQ/f9OPAIUIW0XQJ4SplkjiKq3oFKSWED5VOmCrKcS9NEtPAJARMlQ7IYSEJN9DGfHv2oYS/ADERJplMuB2hkJ+bcd5m424vm7tuBgrxLgQVvkeSmX1MJTsLfD+xWE21vlezShrQ9z/1z7M56Pd4vutJaTB3CALaACQ6RvjYorHim4prggZOMbj3u794/L7Vy6zoDjzMgt9gbjmSOjfURpCvt2xmxPlhsa4ryeFmJ2LEB5bIq0wl+E+s7DC5yPeEuJtSyeQ7tzN/S0/wONmoY/7S0PcPwBwz727+bM1Pm+lA9xfZhb4WffUky/L/WwUeezKFXibSnzN9Ok+fPJpFrZbpYi40S4ol1k6VEl4cSFrAVpASwj5rR3y9X3pxCm5zXPnz3fVzpIQJlstHk+iEk2VnKwkva9//etUe/rpp6k2MbFD7md8gu+/iXFxTw4NUm14SEvE2Zx41gp5Tl2LZJLH0SgbsCmiQZUM2IyKvBWodEAn6ZltTezaIK91fWOMMcaY14nXQTZbjpFz/xFf/8LDH9vsdhhjjDFme+JfkI0xxhhjjOnAE2Sz5Vjd8V5MHTv+Lze7HcYYY4zZnlz3FYuySBtriBekk3Etn6SSvPkgFEKdSNKKRchNFfWyPFhAyeb5Vex1IYD1trUYMrmLX2zfe4AT7iZ3cKrSSD+/AI+mljjica4XF1gcSOVYXPiLP/13cpu1EosYn/zEJ6k2PsZJfHsmJqnWXxDHA+D0y9NUK9e5nbfc/naqTYywvHKtTXzeC69JDfq1Ly7ixHz9nXIDgmSaz3FCiKGISPfLCcGiHgjZVAifSjwAgFJJCB5CPEqDZaZmKNL1cvp+SYuUuEC0qSmSlWoihWilyPcQAMRFImVvkseFWoP7phQ5AOQSKhGS7/VWRYhYMSHvRAiGgTBrkkl1PoWgHJG+qESw0sqc/mwXpNPcxrERlspU3wCAQo770UYP13bsYqG1LZ4DAFCp8vnoKwxRrVXjtl+cvkS1/Ye1eDS5fxdvs8p9WNZi3P/XVvU16xnkdk4N8BhZr/F4UivrhLmiELtViln/IF/Lao37//69PD4CwMA4X8uGuH8r6yw8PvONabnN5SX+bCDE+2756pf+mrcnJL18XgvY9TrfUzGRIJqM871/5Qqn3gHA0gYfY6jEaHHNYmLeo1VpoNUQyamiH/WLJNrqBqfzvXJGKzjlEvfD5599hmopITz2F7SsWRDy3tAI18ZGeS7U38+Jf30RUmgqxWNuLKYSa7kWJe6F6ooIST0K/4JsjDHGGGNMB54gG2OMMcYY04EnyMYYY4wxxnTgCbLZcgxdex/78ma3wxhjjDHbE0+QzZbjZ+8bwPQDR35ss9thjDHGmO3J9VexEHa4kE6RTGsLvd0ScbxVXoViX55Xksin2OYEgLkYm+g7NnibxRW2Vt85wZbl3e/SiyEcuPUg1ZrCglcrF7x86gTVnn+eI5wB4Md/7KNUU/Z+PM0W7C2H9UoQf/4nX6ba//Tr/4pqo8O8n6FRtlPHd3McNgCMipjsd3/Xu6g2Mc52+mDEygUJYSCHcf3ZbgnabLKGohYI6xsA2mLVh1yBI2gRz/B3I6TvuLBu420R9yz6drzF7azX9b91E2DzOx/yZ1OBiFau837ys2xUA0BCjBWpgriHC3x9WzW9SgJCblNDxIFXxTlKZfj6pPt5pQAAqIlI7Lgw1Kt1PsawzqsUAECmJVbbEJHJ3dIW13F1jduYj7hVsmIVnB17+V6/594pqr382Gm5zZiIcV2b4fj5yX087h7o5xWBUjndhzeKYuWihogdFquRxFJ8T4Z6QSE0qnzyEmKVof07D1BtNWKFkpy4XWKTPChUW9w3Rif4+vaOiQhzAOJRi5QYY8+fFeNBD58jADh0K68eMjiqn8vdcNdb76Ta6hrHaVcqfO8AwNmzs+Kz3DdiYnyri7EMAOIxvhZBXYzN4t6PixUj2qv6Hm8V+ZhqJV4RaCQm5lyjvBJEaohXiwGAW+68m2pnL/CKMXmxYsTGml6JpVblMe7c2VeoNn3+nPz+a1Gx2wDQ38sr6PSJVTB6Cjxf7O3j1WYAIBTP33zEah0K/4Jsthz/y9cuYerY8d/e7HYYY4wxZnviCbLZcpxZqAAA/xxhjDHGGPMG4AmyMcYYY4wxHXiCbIwxxhhjTAfXlfQg0vtaActN8YSOcE2BX4wfzPOL2PkN3mZ9XksPh4fHqZYWL8DXL12h2kCGpYXS6Zfkfsoltit694nIxZtYlFscYZHhJhFTDQADBRGvKOJ4EyKO+967Dsttrqzxm+mXXmJ5prJykWoXLsxQ7cRpfskfAErNr1GtHePjSaT4vE/0atljahefz5/6+Z9/1Z+byki5DlmwoBRPcdevxHUEbVIkUyYT3GdjIqI0Knq0neWNJkT85/rjp6i2+PBTVKuu8fUFgJSQAYW3hPmAz+nY2zkiPL9Pi6ELDd5/n9hmoSGisxss2gBAvSLOcYmlnnTI4lFGRJTGm/r3gGyWo23bDSFmij5z+ZIep0b6OJ69KWTAbllf5WMs9HIbH3mExz0AKBb5+3OXWXisr/O1yKlYdgB9QqSemmCB5sQjLC2Xmtw3Jg+wzAcA68ssXN92B4tyqQzff6sror/ktMm4vi6ixCt8PKVePke3NPV9EbT4sy9dXqTa1Y0FqrXEeJTr0ZHyhUGWmZZnuW+mk3zzv/cDt8ht5nv5PF26yG3vlmqTj2dlnZ+zrYjY4NEJFhT7hfBVEnHaz5/Qz/mFC1eplllnaTgtRNHEHPfLxrS+/1pL3A9L4PFgvZ+vb3OHENX2Tcn9LAxzFHlbnM+8kPxSiQhxPsfjYyhWa4iJ51etxuNOs6nHwZVV7gsrq3ze2mJeOTSspcVkikXXm2+9TX5W4V+QzZZjZ18KAM5sdjuMMcYYsz3xBNlsOX72PWOYfuDIT212O4wxxhizPfEE2RhjjDHGmA48QTZbjt95aA5Tx47//ma3wxhjjDHbk+tKek1hGIUxkUpW0ekxG8uczHKlwi9oz5X5pesw4kX9j0yxDLF8+TzVsj38cvZzC/xC/sD0Bbmf9XkW5ZKHh6l24IfeR7VWD8srOwdYTgSA6VP8on8h5JfVg5mzVHsiIp3vmWn+frPCSTOpkK9Fv0iZObCPxUgASKU4raxa421eXuBjjGe1EHDP295BtXDo1Qlol8tXAeAmuQFBWOP9L84tUS0VkaSXTPF1b1e4f4YNFsNSWZ1SpYSxQKRKXXnym1QrPcpyZC6jb+W8kFrHhEyRXmP5Jv4EX8v8AstRALDaw9dzaYTFiZ7RKaopmRcAGmUeP+qinU0h6ZXXWRqMCVkUANJCHI6DU/faIgW0J6ulqZU5FlvbEXJKN6yuseA00eZrnk7r9pSW+FwOlLkPf7TAMvGIEJEBYLLCYtnOMU7XfGScx8PWEIs/QYyvIwCcPc9jXzzDkt+oGPPXFllEXF/V6YeH7pik2o7dfO+36kIwXNCS7E7w+Yyv8LUYW+E++MchP9cOHdb3+cmnuL9NitS94QEe3+MDwkIGUGvx9Zjcq59j3fDQl79CtfMifS2b1QJ3IMSwlJBxa00hpG7oBNCK6B9K0gtLXGuJJLywrlMAg4DbVBMKd7sqFkG4yn2rXNHpo9kCX5/kuOgHw6JfR8jv11/JoeNzCf6kEvficb73AaC3wM+BhBAHmyLZtlTR97SKfk4muz0i/4JsjDHGGGPMq/AE2RhjjDHGmA48QTbGGGOMMaYDT5DNlmPfaA4Ant3sdhhjjDFme3Ldt5XDfiFXtPkF6Q2RZAcAl+dECkqcX7peTQnhKaHFnecXOOmtuMySYKrBL7tn8ixMDQRanKmuimSjR1+hWuMFFkiaaZbXgiPvkfu5muJ/o+wps1BQeOxJqqXX9XkfEgJZdnAP7zvk83Z+hYWA8yG/6A4AqTT3j3yek6eQYXFodIdOFvzgPW+jWvU1L9X/4kf24nc+/rafkxsQ7Bnj67Fw4XmqLa+wuAcA2SWWzdK9nG7UCFly6B1iGQIA6qssWTz75Ueotn7lMtUSGZaRgljENRIxgO8SEZk9RZbASounqVZ5RIuhwwf3UW3lvlupFooEtaxIlAKA9bIYP5b53qiJlEnhnyGZ1cNdbIAFj3aM74P2Ogs9Y0nR3wEslPmz+Sxft265epH7ZlakRDUbWnQ7nOd79e6DB6n2M/fdQ7Wnl3XC5GiVz1vxAu//XR98L9V2vPVuqu05qAXQ51/kJL7LMy9TrW8nJ53mEn9NtadPPCr386wQ3YoilXTPTSxCLTS0eDTWx+dj/yDLgDdPsxT6WMhCaiymn4tvu/stVKuV+RlYLPI9tTDPNQAY2MHHlE1roa8b4g2+z0tLfJ9shPoYFSr9LRTP1AgnHDvyfIy5tkjcFTJgdYOl3SCrx2H08DOoJBpVb3DbkyW+/+JtfR32jLKQt5rheY9KeI1w1KVC3W7zFppiYQX1uSjCltiTur7iu30iUREAhkZ5TEgKmTAK/4JsjDHGGGNMB54gmy3Hb/7ZK5g6dvw/bHY7jDHGGLM98QTZbDkWinUAYoFRY4wxxpg3AE+QjTHGGGOM6eC6byu3RCKNmlNXRSoYACSHd1AtneGX4nsSLIttFHU6X6slkmZyLDgsLrB40FfgF+UHExGpPVnef6LFUsrIqjhHMT6ta+JFeQC44+23UK3+jad53yV+WX1sTV++e8T+k6t8PLubfDzxNL/s3o6Q9NptkQZX4utTKrPMUG9ekds8+dnPUe2Wf/TDr/pz7AYkDgDIJ7idtxzgxK9nntKS3sQISzktkRBULrN4F1vT/fjlU5wg9ew8C6hKw1xr8PVorevkyZEkCx5DO7h/lEQSXjXD90ZmWV+3t7HfhKEvzlGtv8ztCZI6/a33JpbIZs+L1MxeIROluB83oAW2Vg8fU10INGlhsWT6WQIBAOVM1hv6GnVDpcyCU6GXz9vqvO5v74jxORq9+a1U+9pjL1KtdoWTKAFgYheHWY7FWMIs/hmLcquf/SLVmuM6sTNd4rvggHqO7N1PtfE1ThC9p6FDOBeFLLpW4uvbW+Tn2pWdvB8AmBUJbq0S96P+QRaB+zY4wbC5qmXA/BAfU1oI9c34LNWqFX7eAECwwe1cFelx3XLzLTdTbXmVZdhLl8RgAp3UlkjyfZrKcW2HEFoBYGSex6jkZU46XGvxuJWq8LNuuKXHsszoINVWDnF/XxYy7fpLfD5iNZ16t2+C++YLFT4e+QQVqXMAoKoq1VBvkp+TKl0P0EJfXHy21eZ9Z3N6HpdVguINiIP+BdkYY4wxxpgOPEE2W47bry0/9Nhmt8MYY4wx2xNPkM2W45+8dxLTDxz5lc1uhzHGGGO2J54gG2OMMcYY04EnyGbL8d//2TlMHTv+/252O4wxxhizPbnuKhbpOpuSyiRdERGSAFAX8Y6plIhI3GArMdXQUYoJEWeYgoiKbXBMZ29OxErWtRXcFKZjfpjN+GqTDet+Ee975dwFuZ/cFEdDXq2zMVsRVv3OhI5XHBOfDWY4Oruwxm2vNnnfoYjZBADERfcRdutAij+3vMb2MgDMP/EE1W775A+96s/FUh0AWPuOoFHn1SUO7Odo5NnLF+X3ewc4Tni5xCtzpFWUcFOfu5iI0ByeEIckzv2ZCu97NeBjBIChPJ/79Qqv5rAwwf2wLu7/PrFiDAAEcR4DdpY4RrbYELHFfbyiCAAUxjmSt7/A+1+5wCuCIM6ediOpV5LZKPL5TFT5+rTFKhSLZR33ntrJ17Le0uZ5N6TSIl48y9exJFYJAYDfX2aTvfDXf0q1kZ18Lcbf9SG5zafi/PvKB888RbUv9LPBf+Kxr1Dteyf2yP0c+ae/QLXf/d//NdXe/dLrW1gAACAASURBVPSXqfZ0jZ8NzbjOHX5Pik34+0QU8SszfM3n29zXAeBmsQZAvaquET9v3t3k9iRFGwFg9ZsvUC0QbS+L51VeDx3Y0+Yx4WySV33olvwQr/TRM8DPsNWXeOUPAEimeYWIXRM8Rtx6iCPLd2X16hLf+Do/b7JJvtdWJvgZsDHD999bVvRcqO80P39fuMorivQd+QDVdh/kGPH6tJ5PJEV/C5s87ly9wiv3xCMW2UmKZ11MzANTYkWRuPhcPGo+IZbWqNb4HKtFtVoRK1v19unVZbrFvyAbY4wxxhjTgSfIxhhjjDHGdOAJsjHGGGOMMR14gmy2HPfuzgPAlza7HcYYY4zZnlxX0gvUi9ghywRJEcEMAO2Q640KGwErsxwLfXAPv3wPALfvHKZausHtnG9dpdpQhmMy58s6MvFiyBLZpDgfYYrfbO+N80vxuSdfkvs5O8vSQ/FmllXKd3KMamuVzxsAhMssBOxJsTzQCvnl+1Scz0c70NGMoYiVVa/Kt+v8/YGW7nq9LT6fhdfESP7C+/bgf/iBe39TbkBQyHPEeFXIVSPD2vt7/PHHqZbI8TbXRCRuqab/DbogpKl6kftCGnyOJ3v5LPeOahlh/yBLbdWHn6Ha2uQo1ZL9LKbEElqGqORE/PXETt73XXdRrW//IbnNxDDH/CbPT1Pt3Fc/T7VMm0WQVK8+R/EmiyCZIY4XjwvZFOv63lgVDmk7pUWhbqiXeT/VdTF2hbq/xXu5v46O8/WJ9woxO6Ov+ZyQelYKLOqcv8RC0PMikv5jERLlE998jmoPNfn7t1b5mv/5Egtf8WGWBgFgapVjug/t5udQeJAlsCkhUQJAfYHH4vkn+HhS/QNUq8XEmF3SUeIQYnZGCPEDCb4H7urnexIAUnEWAqef57GjW06dOEG1C2fPUm19SUebj07uotrO3dyHh/v5GJsXtFw4MMTRzPm330q1vlGedzzzBc6qWpzRz/k+Ib/nRAxyMsFjRFMMO0WxPQAYqHOfKa+wQFqPc9+IVfXY0WjyNpsBjwnxmHqm8xgVV3I/gJaYWyZERHgoPpcR5xIAskLOTKf4Xpkc41h3wL8gG2OMMcYY8yo8QTZbjp/4f85g6tjxz212O4wxxhizPfEE2Ww5qtf+96peENQYY4wx5nXiCbIxxhhjjDEdXFfSWw355eyCSNfaDR3BEq+yiNTfYpGpVeAXsffEdbzPXQ3+/urJM1S7VaTRrV95hWqzGzpVaWwXpxB98JYpqg3OPEu1Vo3FkMk4SzIAEF/mdlbFZWkIiaoRkUiTqfA5SguppSJS79oikSaISKkJW7zNtvCGZCcLdZ9ZWWKZ4ktfefWCFSur+aitShbnObHosUcfodroMIsyAFBaYbmzMcPXOCYE1J1ZLQTtX+X7KHmJ75dMmRMhk0KkDPJaQEsIAfbhNu+nJWIv967y8RyO+Cd13wj3z/kYn89w4maqVcbH5TYzWf6fBC3R59KzLPWk1vmatWP6Xk+LPtsUaZjNBB98OaHvwdQuTiHbe+cd8rPd0NvHEsqF03xt11a0MD21jxPyYuIeLM6zBLZzL8uaAFC5zILVSo3HikwPn4vv//gHqfbe8ozcT/w//ieqfeyXfopqqT/k9Pnm+ZNUKwkpEwCCXm7nv//jP6Pa41m+Fj/5yU/IbU69wCmPP/wVlvQWRvl5ExPJi9k5nUDaFHLmRo6fq7U03wPZuSW5zdURvv8ev1+nt3bDK6c5/a0W8v0zdvgm+f3BNPfD4hKfj1NCShuc1sd4oI/HrVqDz2X9Mo/3eyr87B4paBE3IYIWe0dYBB4e5edF/TwnvM5NX5L7iV/kerqHr/ng5ATVWuv6OZ8Tgm+9zWNHTEh6sUDIgDcg6alnQL3O92+hR49RqyvcPy5d5PN58+E75ff9C7IxxhhjjDEdeIJsthy3DTQA4K82ux3GGGOM2Z54gmy2HB/aUcf0A0f+581uhzHGGGO2J54gG2OMMcYY08F1TadMnF/u3tPiOXW+rF/uvj3F4tBkiT+bFLJIel681Q4gP8Mv2+9cYyEnJRJlYmC5IhXTSXrxNW7TwDOcCJVp8ymcy/HL5qmmlg4zBX4JPchxO1eKnHo3ldLCxMROTt1rnmUBZiPgbQpnAjofL0LoU8KT+G4z4kX9E2dYuPzDn/4nry588NPAY8e/Ov3AkfvlRqhN3Gfn51jc2znGyW0AMDzMEllrjkW3iTU+H5PLfI4BYGCdBY/sGtcyyywZtEOWMIshJ0cCwEI/J+kdGGXpIl7k45mq8b22X4hqADAr5LnLVT72+AKLXakNLWftHufEssyGSH9McKdLJVlwaolUQgBotEX/Fp022+T9NGR6FNCX43sz1a+FzW6YmOJxYkP4Wvtu1QmkB27ixLFLMyKNq8UJgEM79TYbon+UzrAAM3qIpavh21iK+caDX5T7uW/fFNUqGSFcNsVzRKRmjYl0SQDIid+LYlOcsLWnVwiTV3UfnqwJKb3Bfbjd5OubrPH1iQvZGgASQppqiud3UaSiDQgZEACSTT4fL59gabhbqgmWBkd6OaGuP8UCGQAM9rHImBX3fk48+wt13e6eU/y8ib3CYmVDPH/7G2LeIcZBAKgKiawxyffkep33U7owTbXihctyP80LLOllb9tHtVacr20jIiU1VHMkYeMHYtGAWCDEvYjFBcKQtxmLiXtSfD+R1AJ2aZGl5asz/OyPwr8gG2OMMcYY04EnyMYYY4wxxnTgCbIxxhhjjDEdeIJsjDHGGGNMB9eV9AoQL10vsBmSXRKyB4Agy/Pv9DJLC71tISyldSJNTAg1lZJIdRFCQFvIZpMxLSjkhVvVWOeUqWqehY3FgJNedih7DUBriOWMlTa/WL5S5G0OtvQ5qgwLeW+MU6IaM5zElVJdoq3PUUzVhXAZS/M2mwUWLgCgJCSH+df0uewLX0Fh/C2fkRsQrC6x8Dk2xMLUkJBSAKB0foFqEyvcZ8enWeJMVlnkAIBYXdRFX1xO8Lkrir5dGWK5CgDWWtxvdoO/nxd9e7rKCXWjEaJZUGcJ9ekiH+Pylx+iWp9IWwKAO25lcXBXjG/MvhjfB3Ehz6b0LYhaPws0AYRsI6TDWEtvtLLB99bli9O6AV0wf5nHhI1V7oMfjut7dc8ayzsF0bcW+1hUzYtjAYADIpGuR/StA6fOU23s9B9QLdHSKYBKvvu+//DnVBtu8LHfMnWQauMZLTenRfrq52+9hWqDQhAMm/xcA4CeO2+nWvsHP061ICbGTZVCFtGHIdI1Q5F6W61zOysxfgYBQGmJ+0z5//ztiAZ8e5oJvqcKl3g+MaXDArFnkqX/fJuPJ3aZx+vUGRbvACC4wOl+gegHzRqPb2XhhZVS3P8BIDXAond7hg/05DluZ89FIWAL2RIARAggUirets7XQvUXAGiI+6ouxM64TNfjOVeECyiT9NLisyrYt9XSG202xT1Q1fNVRfd5vca8Sag8+1ksPXX8dze7HcYYY4zZnvgVC7P1SKQxdew4r1VmjDHGGPMG4Amy2XL0f+w3AOCzm9wMY4wxxmxTPEE2xhhjjDGmA0+QjTHGGGOM6eC6kl5VmLUY6afSSsQ0OzHFKycUz7EZvyaM/qjY33hxlWpDGKBaY36Oapm6WAlCGJoAUI+z2Tszzvsp5Di2tD7DcatVYWgCQFDhNvWmed/jo7zvyaxeUaAt7NrYfo7tLS3zeW8IczoQtjoAxFrCWhV2a0ssUtBOa9t3KMfH9P33ve1Vfz4xNAhecyGatQU2g0eH+CQtP/mk/H77BWFzl9mEbddF9LbocwAwV+b+2TzI0aMvgft7KCzcCd29EIqI1GyBV7yojHOc8HyR++FESg8ZE3mOjL1Z3MOnG2xfj+7T0bKxEf5+XMRxt+Jcq/Tz6jLNuF71pZ7nvpgUUeirFf5cqahXXhhKcl/o6YtaguDbM3eR79V4wPfazSJSGgD683wt967zCgDNM7zixPtPTMttjmf4fJ6q8TE2xSoYu8UqNmu9vJoIAECMM5PneMWY1V7e5l1JVhXqZb3ixGySH2TrZb6+Jy/yygf7RvVYvJHhkapW5NULyo89TrUvJHjgrFR5NQUAgBh3e8W4PTjH4057Qa9SUhdDdKWmx7NuyDW47UXxrPzmGR0FvPo0f//uZT6X/au8+k26pu/TMM7jUVM8q5sqjV6sYBNP6BVSNhb5vK+u8GobQZz3navwcdeyeixr57m/qwjoVIv7eiBW+QKAuBgLk3HeZibN+06IlXISIgIdAEKx/2yWt5lMch/cWBfLjgEolfheb0Ss1qHwL8jGGGOMMcZ04Amy2XKMrJ0CgD/Y5GYYY4wxZpviCbLZcoyuncb0A0f+YLPbYYwxxpjtiSfIZsvRiGcwdew4v/RqjDHGGPMGcF1JL9XDYkfvjh1Umzk/Lb+f3cPyzax6QfrQW6lU79Hxs6snWKSaEjG5w8JUE74E6mvabnqmnw2FMwU+Xe8YYbkpVuEXw2urS3I/+SJLAiNCJlJCQC6jRbf1Fm8zLV6MLyf5cyURd1zXlwKXRQRsW3x2TMRHJ+dZUACA5G6WEf/Fp3/1VX/++YfWgMXmnwC4X7fs1SyDJZRkhV/qvzh3WX4/O8nixVKNRZANESc+epLlDACol1kuaZ5kkWSPkPxGhUzUEFHCABCL8bnvf/8+qvV+/IeotvLYS1TbWGSpBgBSOzlKtX+e5bnMzAzVShFRxnfuv5lquRWWfNeF1BqMsuDXO7FH7qc1y9LV4O4pqjUK3Debs1ooCgeFONzkPtMt+R6WDts1vn9XI3JcT+a5PYE4bx/YzbXYtL4+QVHIogUeE1YneN+vZDk2uBzhn900w2NFTcTc19f4/lk/ch/VNiLGnvD0WW7TPI/b6RyLQ+sR8lzrNAvC8TaP75nHn6XaTSUeIxoJLTiVxVg8L55XKmV3La/FsmrIY09azAm6JV3hnV8IufbcmJZZi6t8jg/N8jkaDrkfxIT8BgA1sRBBosnjay4UUlvIz981cU8CQChi1PNCKh3bz/OJkVm+/4KiFk3nhKyZK/C9lkhzH6w1dARzSYiqTZX3DH74V4Uwubqqx5MZ8WxYWOB7dXycnzWqBgBNcV/kcnzeo/AvyMYYY4wxxnTgCbIxxhhjjDEdeIJsjDHGGGNMB54gG2OMMcYY08F1Jb1CiV/4PnPxJNXaS1o+CVf4RfJgg2uDGZYEggQnRwFAti5eGF9icScd8kvo7Ra/nH2yl4ULAPjjJCeYXbnMwtULl9j8+/BNu6jWU9Uv1W+IlJqsSKhKnuSEq8XLi3Kb1d0sUo43RErNRSFcZTnNqpFjyQYAHi/xOZqv8UvxP3PnXVR7+z36pfplsf9C8tXn6BMH8nhuce335AYEl04tU23vLPfZtbK+HdpTu6lWX+C+cG6BJZ8DEQJab5OFk2zIAk4hwf+GbQt5ZikiWakhks2WnztNtf73czuHUrzvckVLYG0hSAUiNW9scpRqC0ssMgFAXJyjnptY3Dv0S79GtcoI99kzV7XI+PW/+Auq7Rpmye8993831W6NSH97aY7P8dIKy4DdsrjAfXhgjBMRe++7X34/tiz6u0h0mxZj6Tu0d4QgyX1rdYDH2HicPzde4fF9eE33redHuc+cEClz3y2eLfGWSDQUyWIAUFjm8TTf5mTCXEaMUSLBDADGZznxLzbP5331II8xz9U5ne9r57Ukq0TX7x5iKbW1k89l/bY75Dab33iUapWl77wPb4j00XSRx7L+ppbP0wGLqikh1AVClGtHpMTFkiLpTcwdFmL8uXmRBhtL6/2EVe7vWZGEGYdI0msIyb2kEw1PvsBi9SmxaMCCWDRgeUXP40prIg2yJhJNxb2mkuyaEenFLZGY2RLS4R133km1d93HMi4A5IRQqxL7ovAvyGbL8aHdGUw/cOSPNrsdxhhjjNmeeIJsthyz5Ramjh3nn+mNMcYYY94APEE2W45f/+YaAPzhZrfDGGOMMdsTT5CNMcYYY4zp4LqSXm+KX1a/uMAv6TeKnEoGADOrLCOkRKJb6hvPUe1eIaoBwEjI+yqJdJ9lkSDWmGDp4WpFJxPNFllWaQqhbn6ZRaLnm/wC/dQu/UZAcg/Xy01ue3aRJajBXp1qtOPmQ1RLCdFs72GW+ZIibfCReS0DXvgzfg043WQBJnczv1R/6/e/V27zoSefptqVtVcLBTWRjnM9NkTze4t83UfzOr16JckyRqqfJa6ESGuKXRQnFAD6WVyKx1goqLdYhlivs4TykpAwAeChGveb+iWW4m77vX9PtdI8iz+luk4MOzHCAk1wF8s/u/Yeplo7pkXGapKPKRjgPp8J+XwkKtzOxRmdanhunUWQ80+9SLUxIQQt6+ED6du4f+zZNaY/3AU//TM/S7Wvf+2LVMuXtQCTHOF9i1sAI3e/nWrpP/qc3GZV+In9Ihm0nOHxMCm60dk7b5f7+a+ff5xq587zc+gdwyyg/cnnhdxY0324ni9Q7eIMPwdOrbPMdPc+HksBoHnoINdGWK7M7ePU2do3n+DP3cbbA4BLL/KYmLnEyWSZJZbFlkRfB4CKCNgb3a1T97oh1+bx8Z4LnJR2e1Hfp23xuMu0+Zkc28H9APGIpMM5lsieERLn74sOOy/S8b6noqXdAyGPj/U1Htuzp/maJWrcnuWkHni++dzzVCtdYrEzn+f2rIt0WQBYmmcZPykSHXvyfOxNIdnVRTosAAwOsVidEol/58/z86vR1CbxTTfdxNtMaQlU4V+QjTHGGGOM6cATZGOMMcYYYzrwBNlsOX5gKgYAv7XZ7TDGGGPM9uS67yAb82bk3tEYph/4yF9udjuMMcYYsz3xL8hmy3GlFGLq2HE2EY0xxhhj3gCu+wtyJs62X/8o29lzEcl9C0W20ysNNhivpNh03NvStuy+DbYVY6ts5pYTbPUmdrA9vNij99Oc5e/3iBjlurBJL4vIxD3f/1G5n0ERC/3kPBva5TRbp+0Ic3N/fz/VduzkyNRd+3iOefFLvIrEc/McXwkAwwW2hacSbIJ//nNfoNrg5x+S25wNeHWHvuSrz9HvlHcDyP1rAPfLjbyG+Cifj9gI14KIVQ5iAVvrsRy3s5XhVVIuHL5VbnP8YB/VEiWOE49/hc/9Kw02e/+YBXEAwLN93KZWg43smQscjbxrL6+wkhGrIQBALcV9vhry/V997imqBYGO/h25i89R8gy38+n/7UH+srCfG/e9Ve5ndJjt6VaRo1hLzz9JtURZR8jvmno/1TJTfDzd8r4P3E+1u2/n6zMY1xb9IyJevKfBfTh9nvtgUNBjZKXJK7SUdvBKMGGOx66NLK/Y8r+KmGsAuPvgAardvotX5fn6wzym/NWPfZhqP/aJH5X7KS/xigrBZ3kFj3c3ePWBoQLfZwCQObyXajGxkgROcUz9Wo7vi5Esn0sAWJ3g5/ITV3mb963PUm1mQ6/wsD7Kz6Yf/kVekahbYjGOIe/t5/O2a5nvPQAIF/m8qVWxavv5udTO6NVdTtavUu3/WOTVHB4GX4tVERXd09LnMp/mJTjWBvheTfXy+aiI6OsTEffKhljx4u633kO1TJsnbafO8hgBAG3u7hgc5LlUNsvHqFaMKBZ1pPXgMG9zbIyfN+fO8SoWzYiVrRYXeQmrnh49Rir8C7IxxhhjjDEdeIJsjDHGGGNMB54gG2OMMcYY04EnyMYYY4wxxnRwXUmvWGUBpRFjsaN3Ylx+v5JjESOf4xfOU2khmxV07O/yCoshPWBD6aCIm5xN8L5ffkG/mD6SYhnibQO8zZcS/LL77DpHUC6n9IvhIykWd1KjfD7rN++h2tgIixkAECT5sp7+0iNU+0+f+VPed4X/zXQuriMo37OfYxxHqywJfOXqC1R7Oa27Xu+efbzNQ6+WdH60GODXz+B/lBsQrF5g6fELKyyCtNe0PFCI8zkZEtJFGGMhYWPvW+Q2W2+9jWr1dY4ZLT3JfemLK9zfz4s+AwA7+rh/qbjo84uXqTY2yWLnvru06FYucaTv4vTLVKtusLg3OjApt9nTx+d4aInP8aKIZq+Cx5Slko79TiyzyLF3iGWZRC+PZ60NYbAAuFJmeah8hvvc93+v/Drx+Dc/T7W3vu19VBsS4wkAfNd7eKyYOc0RtFM3c38t/KQW0OpCzu4PuZhN8bj7/Gked8uf4bhzAMgIQXlDiHI7d7O0+MILLLkG7T+S+2kvsvhUF/LPmTb3o7dGCJitr36VakvPsGQ0N8HPu5Vh7m8Q5xIAcjkWnK7mr1Dtq3feQrVKRuwHwM79PBbnlvV41g0rAd8TT97DovjUTpZmAWD3K9NUq6zxeNJe4IUALkdEwv9fYiGCr+W5D7fF58Imj82zMS2LtTL8/fndLIqH4yx15nv4fKxeYAETACqnTlFteWmF91Nk4XFlgT8HAOk094/eXhZ3d6qFAHbxPfnoo4/K/aTEgge33cbPybaILJ+f19JiIsHzjJ4CLyQQhX9BNluOO3pDTD9w5Iub3Q5jjDHGbE88QTZbjnNlYOrY8e98vSFjjDHGmOvgCbLZcvzbi3EA+O3NbocxxhhjtieeIBtjjDHGGNPBdSW9M1cvUa1eZvllYge/iA0Ah2+/nWpzc5xWlKuwDJg/wAIYAHz4H3+KPytSmZKDLE18/Stfo1r5Re16vX14gmp3ZXib2QILJH918RWqnbrIEhQAjE3yuVsViX0bWX7Jf+RWnba8Y98U1YITLIaU0kJuSrGEGda1eHBQJNQNVPj7passTSzeyW0EgA/9xE9Sbf+77nvVn7PzJ4ENLVwp+if4Wk68hc/dF/7qL+T3gxXun+M5li5zIkno0gXuCwDw0CxLFvtyLOm1mty/XuxlUWffpE64G6oIYSXP98vlNRYk5s5x6tatd3MaFgAkMtyXckIuef/7ONlstI+vDwAk+limmItxO68ImbAY534419DJk7eKVKieQZYeXxb3YGZYJ5vFS/xZrOs0r67IcsrjN57l/trfo8eEA7u4Hhfpa2GVhdyrT0zLbY68zEIQLrAYFggxc1YkPyZEshgAfPxH/iuqXbzEz6bZf/dvqLYg5J3yMKenAUCpzePcD37P/VSr7uSEudIi37sA8OfLL1Lt8m0sXC+tsCAVK3J/+YEPfY/cz8OPPMbbPHCQah/40U9SLS7SSwFgo8Tj3jMvsIz7wx+XXycaQg5+cY33MbNbC8cXR3k8GC+xKPfuQyx2XTrDwjAAfO3LX6JafZDHx6EsjzuFJs8H6nO6H1RCPse5IT7OxV4ha4qk0YpIywUAEeKLk8+xqJoUMm2xpmX8Qh//jloWCaIFIb8dPnyYag8//LDcz7K4B9aEhFmp8DUfGNBip0riy+b4OR2Ff0E2xhhjjDGmA0+QjTHGGGOM6cATZLPl+Nn7dwLApze7HcYYY4zZnniCbLYcd+4sYPqBI3q1cWOMMcaY18l1Jb1dbzlAtUyMpZRxIbQAQKPOJsbFs5xqFoBfQp/d0ELNhQVOO6tXWWCZucKC0XPPn6Bas8wvfAPAzUMsYhwa5xe+Y+CX7x8SSXaxFB8jACz18Gd7wS//T/Vwit/082fkNr/59Yeo1i9Eu/n9fIyLRT6/0xf4/ALA6Rk+x7t7WHjqLbBQMH9RJ99Mn2SpLR579Uv1Z9aAf/50cF+3k+S1IgsFqRRLVO0enYaVFRLqvEjyeue9nDI3IZIBAaBeYSFij5DSLryFr3Hq85yKeG+vFg/CFP8beEOkcTXz3M61VZaEZhdX5X7y/WKbIff5515ksau2/rzc5pU1Tp6bnz5Ptcoc98P1Kl+foVDLgLfczCLJ6TlO1/v6FT723TfpFMD9Y7yvXDwizqsLbtr9XVSbn2NRdV30dQB4+RSnWdbb3Gc2Cixhzs7zdQCAO0+zaDq7n497FXyOHp1lmS8W16mEM2L/s7M8JhUKLPm9XGEx7JN1LSOtXuRtjv/5X1KtJMb85WWdQja/yFKbSgacFv16z24+bx+p6ujFC2f5+88+d5JqzSa3/eD+3XKbvQMsjC1FPJe7odzgZ8vLJ1meO305ItnvELdzt5DpB27jtMCzJX3NgxaPW7t28nGPj/N9kUhwf7t6QtinAE4siATgFo+PRTFnKok+nEtqoXWiwM/fxZJIiAxY0muKfg0ArTY/B5aW+J5UtZdf5us7N6fnE0GMn1WPPcby6aAQ8iYn9TicyQiJOkJKVfgXZLPl+My1BTn+xSY3wxhjjDHbFE+QjTHGGGOM6cATZGOMMcYYYzrwBNkYY4wxxpgOrivp3XwnJ+FlA36pPZ/UL9U/98QzVNuxm1+qf/Yci3vJly/Kbf7Kz/1Tqq3FOS1so8wvtjeFYFApsTgAAKtCRqyPcfJUuodflk/z+++YvsxJdgBw8nP8YvvaNAssxfPTVLu0oBOh1sGyy9QoC4atDT5Hs6UNqsWF0AYAT57n/Z8u8PdTk0ICGdAJZM/PcF+ov0ZAK1UnAeg+p2gJQeP8Mp/35QUWagCgKSShTIaFq9MnWairlEVnALBnH6e/5XaxFNss8vlstVksDWa1bDqXYBGkPs5yZio3SLW1JvePh7/xDbmfapnP3aWLLGisr3IyUgxaXgsT/O/3diAkmJBr8bb47qzez8ODPNasrbLUOhRnUSe7oaW4VoalrUpO9/luWJzloXpdpPW9ICRXAKiWWBKamuKktQsXOaFu8axOIfutEyz+LZ0W46Z4zAQhiz/vuvNmuZ/VBZYwsyneZrvF21wXMuwzf/1luR+Anw+jsyw9bUixW8tZFSGL5sUzY3yC78nVdb7/fut3flfuZ2CIJe6Dt99KtbLwk84v6bEjWGNRNYh41nfDurifGz1cO3dGJDQCKAf8nH9FSG0PPfEU1doVnbya62FRdZ94Xo3s5GuWHeC+kYiQ8U8+znOhO0TK2+AQC4KLL/H9t7SoJffKQxx34wAADbtJREFUOh9nVQjlrYDPe19Bp9FlRYJovc7bPHGCF0E4c4afiRmROAsAAwMs86t0vsmd/OxMJvXYHgghL4jra6TwL8hmy/GhkQUA+LnNbocxxhhjtieeIJstx3i6jukHjjy72e0wxhhjzPbEE2Sz5ThXzmLq2PHv3ux2GGOMMWZ74gmy2XI8vDwIAP9ss9thjDHGmO2JJ8jGGGOMMcZ0cN1VLPpybG0nY/yVZExbge+6/51UKzbYRP3K9EtU+/zjL8ptpur8/fU0z/PTvb1UGxEWejMidnAhzcf0rIhIXArZdl9f5ZUHjn/hc3I/pRp/P13nlQ8a4M+1I65ePMXHtHiFrdfRHFu4hyc4cjiIMERXi7wiweQ+NoA//F/+A6r19uo+05PhVVJSrzkdyfkkwIJ3JDHR/EKareQou/bUSxyFHBfn/uw5to3zPU/LbQ72szE8OMyrpBTL3JfCOtde3IiICR3i/ZTW+B6qtHg1hkaLD/LCBb2iQWllhmptsdpGPs1GdJDS5z2b5L5Q6OfrlszxBe7Ns/3cl9NR4okM73/oMI8fhQS3By39G0MjxteD7+DuefxZNsSffo77ZaOiV+WJtfmaP/UEx7g2ymynN+p6lYOWWqBFxM02A15dIpvgvrW0Y1zuZ+HKNNUqdT6bKn66uM79eucE32cAMDjE/aMvz/1gxy6xysEwryIBAH19/P0e8Wy6PMsx5lcXeXxNi74KAMkk11tiYY1Gm8fddMTKC+r7tSZfy27paXAbD+7eRbUr87ySCgCceu4JqiUCvvdHRnhFnmxG36fVHI9HlTjfu5kYX7PhHNdWe3glBgB4WjxDnznLUe3N5/mePn+FV7VaXdP3ORp80doBX9+eHh4fbzmkV5HJ9vA5Wherf1VFJHav6OuDg3x9ACAtYqFT4nnRbvMx1sW8EABaTTHqxrv/Xdi/IBtjjDHGGNOBJ8jGGGOMMcZ04Amy2XJ84kAZAP7xZrfDGGOMMdsTT5DNlmM018b0A0dOb3Y7jDHGGLM9ua6kN1rgF84DEV0baj8IFy9xhOszLwr5boXjEYOIloXiLwpC7EqIl+JrapspflkdAJ5ZWKDa2h6O2Xz5ZY6QntlgiSqT0yLEaIbFwfEUi0hj+1kMmbyVo2IBYNcoCyMjBSGLDPC+B7J8jI221osaYEunJ8kv2jeTQuxs604TlNW+Xi2GvLCawtSx4983/cCRv5QbeQ0bIm62IoSCUoWvGwCksty/qkJcarW47a0SyzcAUBaR3ldEzC9ELGb/GF+32UEtCYVC0FiYZaFuaYX7e10Iua0Gx/ECWnAaHOQ+OzjA0mBO9E0A6BOiXb7A/VM5woGQwIIoq1WIdq24kF1iXGs09DbrQiQRX++aj334Tqod3s398vNfeER+f35ORMCD78GakI7rIsIZAMIW94Vkki9GX5avY1LE10/PsIwEAOWWiGsWAvkHPvQ94nM8lgZC/gSATI7bOTTMQtHcAseIl8sRkpB4DpWaXGvG+FzmCvy5eERMbij6W6vFbWqJa7lR12NxU3y22VZmZnfUxZiZSPPz4qaDh+X303HuM9kUf3/PHhb/lpc5NhsAFi7wHGVunsfHCSE79wtL9bSIVgaA4jpLbTUR19yq8XMpneL+OjTAkdQAEIo29Yi45uEhFlUHhvU4rOZSBSHfqbjnuBDiqlVt2JdL/JwurrGoGoaiD0bMQcUUBaEqRuBfkM2W40tzeQD4xc1uhzHGGGO2J54gG2OMMcYY04EnyMYYY4wxxnTgCbIxxhhjjDEdXFfSC8QL+WFbCBsRyST5XiGgDXN621vGOEFpXiSoAMBVIRjVhFxVrgnhKs+CT0EkygDAheIy1c6fZOGqJU7h5NQOqr3//nfJ/dx9gIWEHQUWQwojLAm081o2SQtrKYjztdwIOGUqUWVxIN7W1zcX43qyyv2jWuf9BBFyU1uIbu3XSHpKSLkuQlxICtllcseE/PrAAMuq6+sslm4IOTNKSKiK89wUx6Wki4rw/uayej8xIatVQt732O6dVBsZZhEkntA2REaIsipFKSvSClNC2AKAdJq3qRKT6kJ2aTbV2KXljDBkQapdV32M+3sI3RdTIqFTyiVdkoizbHb3He+g2sTOQ/L7X/oqp+ade5mFor0ZFm0GBnQC4dAQj1NjYyz/jAlpOJfn4ylXdGJfTvSZgX7ed6PJ1+LJ505SbaMYcc3KfE8vr3PfWCuq1MmIaytul3qDx8iGqN0ISp5rqZqSliIs+0AkysUS+l7thrUqj4/1upB+W7o9o6P8XI2JxMqqSFlETLc7FvI9PT/Dc4y5Ue7XV+auUu3FU6fkfpQtlszxfGR8N4vNPT18/zXqEeKsGGPyeZ6HZURqXRCRKpwQ1zyhJOiAz2W5zOLd3Byn+gJASYjr6rzFxLxD1a61SfThiM8q/Auy2XL82OQSAPz4ZrfDGGOMMdsTT5DNlmMg2cL0A0fEmmjGGGOMMa8fT5DNluPptRymjh3/Lza7HcYYY4zZnniCbLYcj6z0AMB/u9ntMMYYY8z25PqSXpqFDfUed1uJewBGB1ikGH3726n2znvfSbWikKAA4OQpli6WFpeophKHBge5Pa2mTol75ulnqNbfz7LWwZtuotrtt99GtaEhlhO/1QAqBcLsaEHIDDXd9roShMTn4qGQwoS00IoQUJRUVmlxrSba2WxoIactxJLXSliNVsS5jCAlhM94i/uHkhkAoE8kSo2Ps1jaEClzKrkKAGo1ls0qFSWb8bmTiUVCmgCAjBCc+g4coJo6diXARHgcMp1I3YMt0T+kqAOgW6dNSUZKxAgjfg9oi/sgEDV1LcNAN7KthMvXIenVIRIExT5Gh7Vo+omP/SDVKhWWzdIp7lvZjBackiJVNSbSF9ti/IiJ7yaT3QtgjSb3mZroR/v37qXa+YssYQFAXdxrsQSfj34hLa4VdQpnXRy7dOLEvdYW/UWNjwAQiu8nRIJpVqQIJsQx3kibukW51Xp8jJLFVDuFiNjkbWYyPA4CwNAQi8gLIkX30Ue/wXsOeT9pkQwIAIODLNkPiPmREpvVcUdMuboeY5S8pp4r1z7bXRpeuczjyfIyL3ZQiZBxtZAXFZH3mm/eQL+MeiYr/AuyMcYYY4wxHXiCbIwxxhhjTAeeIBtjjDHGGNOBJ8hmy/EPhi8CwD/c7HYYY4wxZnviCbLZcuTiLUw/cGRxs9thjDHGmO3JdVexKJXYVMzlhA0qVj4AtLWqYoLjSTaaCwWOIgWA+97JK16o1QOUkaniFZsRBv17v+u7utqmMvVDYcA3RRsBHa2sDNNuawAAZfALy7PrWoQ5rVec4ONU1qqKDAYgl0l4bZ95sTSAqWPHPzX9wJE/0Bt5NcpaVasMRJEWUcjqjKho5HhEDHtS2OTdrsag+lwzwsxNiD6r+o2+Hvy5eDwiIlytiKJOsTCiY0HEMCTGFdU/ldGtzltTrKBxbZvd1dQKDWolFyBixYvXEzWt4lFFf1Er4ABASly3dB+vyhNT40xEm9Sw0BIR302xqgDEihEVEb8ORKymItvD12LnGK8UUCmxbQ8A52d4NSTVEWoiyrgt+jUAxMT9r+7VeIqfTWm1Soi49wHIE6K6ZlNEWketKlAXz6yGeF51i+r+QcDHk0rx2AjosUctcqBWpop6VI6OcAx6Is5jZq3GfTOb43bK+RH0KhoJcX31GHEDqwmJ76tYaEWjoa9tqbRGtZWVVaqpc6TGwah5i6p323b1TIzaZusGnv3+BdlsOV4sDwLApza5GcYYY4zZpniCbIwxxhhjTAeeIBtjjDHGGNOBJ8jGGGOMMcZ0cN03oE++dIZqIyMc89vXz/GIgBbt1Iv26sVyJdkAWq5SL4erd93VC+O1CouIgI6VVjKgqimijidStHsNNyKVdSuldSsNRX2u27Yroo5Hnc/Xxi23onI2I1Bxzd2LavraBaqmBMNQywNhyPtKJFgGVNdSHU/UlVRCgjwe2fbuJExAC6yqTSq2PJnU94aSRnQ/5u82GyIqWghkgO5PSkxV1zzKYHs9Qp6G990GH2NDSFiAlnqUVxYTBxQl6apjV7G0epgQ/TLSPFIyE3+s2eRtlqrcX/tFvDAA3N7PEdLLqxwhPbNUpFo6o8WymJCMMnl+XtZF9LyKzq7XtchYEdG/So4MVfR1RF+VVWnedof6qhKT40KGBYCYEPpUK6NEVdkmsX8VAa0Fsu7ac20/KhJbCK1qjlHnE5cS4jgQIaV1Ga28tsYyHgAsLa10tc1unyFRt7kaO7p9VkVxI4sOKPwLstlyfLTnFAB872a3wxhjjDHbE0+QzZYjGbQx/cARvVaTMcYYY8zrxBNks+V4oTqGqWPHf3qz22GMMcaY7YknyGbL8UpjCAA+sdntMMYYY8z2JHjjZRJj/naZOnb8qwAw/cCR+ze3JcYYY4zZjvgXZGOMMcYYYzrwBNkYY4wxxpgOPEE2xhhjjDGmA7+DbIwxxhhjTAf+BdkYY4wxxpgOPEE2xhhjjDGmA0+QjTHGGGOM6cATZGOMMcYYYzrwBNkYY4wxxpgOPEE2xhhjjDGmA0+QjTHGGGOM6cATZGOMMcYYYzrwBNkYY4wxxpgOPEE2xhhjjDGmA0+QjTHGGGOM6cATZGOMMcYYYzrwBNkYY4wxxpgOPEE2xhhjjDGmA0+QjTHGGGOM6eD/AxswkoZQqmHwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ehbImzLmUUCdJVQ59C583IOnBukWSshIkShoD0MBDEM3ffxgTRSXp/kHcDGAfPl8XsV93Yd4YJnY5fXByGo99eyeUvf3onVDWhLWVJD1/Oa5RPv3yZ0LZlQtxvm604J7xY1YNeM7WMI/mMB8ViVUCjUpc4yTwL8hm6ejmlSQ9WnQ9jDHGGLOaeIFslo4vPrurnVs3fmzR9TDGGGPMauIFsjHGGGOMMTN4gWyWjr+zt6nrN2//N4uuhzHGGGNWk6daI0VO6TEgdkFyjSTljSg9jMZRassyMgz4hfEKkpHu33sQyu6+E4W8+/eiNDQasVzR7fZDWRvSY0bwGvgBvJD//v238TpvvfX1UPY7fvsPhbLr11+NH05E0pBjUEACUwHJYoMBiTcsN1FCFskiBAkoKZ4UM98btiUpNlICSp/KQEhI1SkvomiQk3xTxnPeefdbeM7x2f1Q9vx2TFt8+CAmM2UgOFYJOWo8hnQiEP8mIKpR8lvWZ6GuB4LT+nocQ/f3Yhrl8DSWSdLV514OZWOQoZqQKDcBya6RsENKEIUoJW4C1y5znqfGYxDGEmLqPFAfJmF5WvJcTAIbJaW1mzDHjTilcTiKotCltXjPSUFrwNyTkmc43S/WkxL/JtPYHimh7gykQ5IBKS00KbqBVEeJf/RpSn4kMfrxf4hFDRDqMrhSnkjnIyk1O1cO2Xeyt7cbylowj9KzSpJaDUiThbFbQiJb6qfAEr5jDffn0Umcox4cHYSyk2HsQ5LUhB0Pur2Y3DgtQcqGNdN4xCJjuxPPmZexjfZO4rgoUGmT3r33K6Hs197aCWXXX7geyiidr4LvI0nf89r3hLJOI254UMHaMKOdHiTlIC2T5JfCvyAbY4wxxhgzgxfIxhhjjDHGzOAFsjHGGGOMMTN4gWyWjo3GVJLeW3Q9jDHGGLOaeIFslo5/8coj7dy68a8tuh7GGGOMWU2euovFYBAtZSpLMZlA5COY/mQ5t8BMl6QR7LLwta9+NZQd7u2HsrW1uEvAeotjR6cQd1lDbGkjj5ZlGzZ9qCEeWJIe3I/1/MVf+H9C2d6jGNP52c9G61OSOp34nXAnB/g+5FOnzOlmI+6cQBG9NeyGQrsEpCBr/DzQ52kHgDwRNU12PcWr792/G8ve5yjzfie203AYO84U2pN22+j3afcAaQxxpvPG59LuBccJA3hC9jXsjJFDu48HJ3jORgZWdQPMeojTpsRliviVpHYnjuG6HctoB57RMJrsEo832mVhXmgIUr9MRT23WrAjAuxSQrtdnJ7G+FqJd+UYQxvR/NGAHQlS45yuM++OIHTLJ9O4M4zEcxJdh8ZkI1Ef3E0FviftoEF0ez0sz3JoOxhrWQ27WCR+Jysolvo3qd/TuHz5Yig7OToOZYNEfxuX8RlI36eiOGzYcUnicXp8EuejXdjFYgS3vLUed5GQpLgXijSGnReKIpaVsCMO7SAjSWUWv2cFcdpHJ3EMNGBulaQRrIWGh3GnrkdHcS3TgPVeL/GcXevFZ9hrr8RI6xx2PSsSHbOBO7HMj39BNkvH33q4qes3b//ZRdfDGGOMMauJF8hm6bg3aknS9y+6HsYYY4xZTbxANsYYY4wxZgYvkI0xxhhjjJnhqfbTBAQfknlSEb1TkB7oSIrerAuWgfYfxbjK3Qcxtpco8E1ujoqliNDhkCIS43EFiXsJSaDfjZ8nWeTXfi1GUqdisl955ZVQtrYRBcU6iy/L9xISCNGA70R9gUQIkvlS5U/KVQnXKgmKNlMQnEA6lKTJNEojB3sx3vzB3XfidUYJAa0X+8j+fhS+uhDhTFG5NC4laQ2iPkkWIyGPjiunfJ3TszhXjKEfkzQxHbOgNBnHdm+BgCqIkK5AOKlBNpE42pYEuPVevHYJcqIkDShq+hzx6k9CcyTmC4PAkia2UasVx3SzuYGfprhnjL/Gdo/XJvFVks7OYt+kuXg4jOJSTbYmRNVKUodkTRAM3793L5QNEpJdG6TJK1euhLIHD+J8MhpGkeqHfviH8TpbFy6FMvruOcVPpyZUEL4KEuDm5PKVWMeLF2LfGpxwW04HcT44Oo6S3+5xnHNPBvyspD0DxsM4l7U7cR5uUVty11I1ifcyL0CihKj4y+vxmVxcjH1Vko4H8Zzvn8b2HA5jG/XX4xpBkvoQid2EblBO4vjb6sY5c3QY75kkvflmXON86qWXQxmtMRrnmPaqxNqD8C/IZum41JxK0jcWXQ9jjDHGrCZeIJul4w88c6CdWzf+yKLrYYwxxpjVxAtkY4wxxhhjZvAC2SwdP3t/S9dv3v7pRdfDGGOMMavJUyW9jF6GhhSgVHoTvQzdBmHqzp13oSwKT5J0cgqJNqP4Ejq9yH14GJNe2m2W0trt+FI+CX0lyFFlCS/vJ/4W2dyI6UJng/gS++lZLPvmN/k13EePovDx8qdiIs2L1z8VykhKSaVWccIeJDXBvWi3KVuIBcUn++GjSUOS4hdKQPUkAa2R6Mck3737rV8PZSSwtdrcdsMJJGyRwAZCHo3BVN0pgo3S9ag91ikVKufr7O/FcdkB4XMMCVDD05iQJUl339sJZZeffTGU9Te28fNPUoklsJyEMZSRY1u2QVR7fHC87ykxdR4GkCBKY4jkYkkqITWv2aA5icY/z12NRvyOw2FsY0pUncJY2d2NArYk3b8fJWxMeQRhq4a0spMT7m8EPdvo2inRrQFz39vvvB3KShAZWxDJenTEdd/YvBDKKrjnBfTLOiF2TkFATaWqzkMDJPlCsX1bWyyFZptxPlnf3gpl03vx+bf7Xkw5laRdmLfqSeybVztRrOzAPJxKdDsdwP2FY1twzg6ctJHzXJJ34rHjfrzneQbPv4REfLgPqcSXo3BZgMRMw2IAaZ0Si8gZrLmoD1YgB6fKs3P0Yf+CbIwxxhhjzAxeIBtjjDHGGDODF8jGGGOMMcbM4AWyWTqudqaS9MuLrocxxhhjVpOnSnqdTpRARqP41nVKUGhTEhekHX3z61F4ugNilCQ1IcIFZRNIxxpDmg2n40l5HoUP8BtQbqrK2B6jEb9U327FRJwMEu6aTWjLitOBHjyMSU+HkC60feVqKOv3+6EsJWZQeluRP7VL/f+k0hepPZ+8/u979kR/+ae++MfnupCkAmSiTideZwLpQpI0eBDbcw3ElkkrXqfb58SjAj5Pkl4OyUolyGKNDostzTq2cwHjNQcRswRBcGM99g9JKst4LIlH5AN1ExJYvxPrdLQfpZqNS9dC2aiO7VaDtCRJgnbPoE4kM9GcIEktGAepOWAehsMo6WVQ75T8qhqSBaE5ShBlaPxI0mAYk83efScK14cglp2dxufAe+/dwetQQh6JqqMRJKC1QbIbx+eAxDIyzXH0vKPkRUmqoHwE0mIDUjzpjO/d5TZagxS0LCOhFiS9xLggqTQlbM8DybDTxDOMyODaY5I1T2O/pFTC1LFdSOus4bHWBI8/p+RGSZt5FAynk1j3gh6fbZKy8TKawnoGHnV6thXlxlNI3JOkYR3PeXYU5+EGbHhQQXeZwtiVpE0QTWtIJJ3As2YKz0RJKor45fNz/C7sX5CNMcYYY4yZwQtks3R85c6Grt+8/RcXXQ9jjDHGrCZeIJul42haSFL8d3VjjDHGmI8BL5CNMcYYY4yZ4elGFYgdDXiLPJkiBAkw774f5bvdR1GCSskvFb7UH1/QzkCooWS/VLjV8VGU9AQvgpOUtrERXzZvJRK3yjJKII1mPGeeRzlqNI6CgSQ93I3JU81WfAF/BKlma2vxRfvU/aWYnDEIKBnIGclzAk9KeudNcyJxqQlC3VEiyauCdipgbGTdaE6sr0EanaQaRIM8i50xB0Gigv7R6vF1GtC/17rRLqH7QSl+pycHeJ0WiLI59PkaBNYGJrpJTRhv40ksO4NkwEkW654V3G8yuE4Gki8KbM3ERAUpZKmUu3ng+RAEMhhrj/8D9Fc4rIbkKZonJOnevThvv/Xtt0LZwUHsM6NhvGdDEK4kFnpJ3KPjSGQ8T6JhKiU2nBOEVIkFR0q9JOlwAuPv7feiBClJ29sx6W370rOhbApJpWkJGyTbc8zb4Tow52Xw7E5N7yQT7j3aC2WPYB4fjljSm3ft0O1G2boHAvY0MVbo8w8fxuf08VEUxS9tx/WESu6Xu8ewJsjjtStY+u2dsKQOU7b29mIbd/tRFF/bjGXTCYuZNJ8cQTpmE6zDOtEv6RlW0hdK4F+QjTHGGGOMmcELZLN0vNAZS9L/veh6GGOMMWY18QLZLB2/+/Kxdm7d+BOLrocxxhhjVhMvkI0xxhhjjJnBC2SzdPyVuxd0/ebtv7roehhjjDFmNXnqLha4wwJkHKZig6fTaI6+c+db8bg6mp/NFldtNIoWbgnGOK39KcI5h7LHx8Yyii1sNml3injOFkRKS9IIzO0cdv9oNOJ1yNCUpAlYokUjWuN339sJZevrcYeDZofjhWuI1SRDvEoY3gRZ0k/Gm55OM0m6NO85KUaZeswpxI5K0lix/lPYEaHdhUzPxC4HBdxj2qqg04y7ipCF24VIaUkqYMeLw8MYE8omO/TDxJ/UE9i9pAnffXt7Ox7XYiN7B2KLj4YPQ9kaGPyttdhns8TWODRe6hqMe4roTkarx/akGPV5oc/SvJvakSDPqY3jdxyB7b+/v4/nPIKdfgaDuFvO2Vkso7mLdnKQeCcJijan4ygaOdVGY9gNhXbAoXZP1Z3mHoqvHgzm25Xj9JR3Gnjw4EEou7wdd7FownwwTczP8Pg/9w5C33nC+OwWtEVFF5Y0hDnmvTvvhTK6Z5fa/PxVQesZ2CUIznkMO0ZMUpHWsMY5PI73Msvidz8dxO9dwxpDknKIkC7a8Zn+cC/uLPP+o/hckKQM5sJWM85HVRnreXIY545swv3tcD/W6egkzjFrzRirTjHmUmrnoPnj0v0LsjHGGGOMMTN4gWyMMcYYY8wMXiAbY4wxxhgzgxfIZul4uTuSpL+z6HoYY4wxZjV5etQ0QC/ppyS946P40vfDhzFOkKIdaxBIHh8M0ZAQFVvDy/8YpzthIYAiOQUv0Dea8ZxVFV8CH8X31yVJk2m8zj682P7sMy/Ap/nF9LqG2NJxfDH+G9/49VD23LXnQ9kFeMlfksjtoHaniO+U6kGyy5OC0u9+dqgv/9SP/1eJUwSolSYg5FAkriTV0L8riCIWHJeKqs1AMmr3opD33NVr8cPT+I0OQLqQpLyAOoHwdULR6uQRtlJ9LpaNhlFAnUJU9OXLz+A5t7dj+du/8tVQdrwfI1uvbb4ayoZjHus0NmiuaLVju6WinUsQkiY0p8zJxvpaKBuP4/lIlJEk6Aaawtzz4GGUICkCVmLZc/8w9sMaRjvJ3p2ESEWfz2H80b0gIa7TIbGaJT2K8+50Yj1TAujpWZSzjo6PQhlJh1tbUUZqtRJzMUzGJydRImvDXE5zocRx3vT8nZdyHAW2AUhtzYTw+PBBjDem5027H+fRMUSbf3CGUAJepcYgr2Z17G8TmN8k6ewMhL5prHt/LfatVge+z4Dnshp+8yygb07gXhSJDQsm0HbPXLkYyrqwscIBREWfwXNBksZZnF/vP4r3fATrsD5EeUtSuxXbrqDJMIF/QTbGGGOMMWYGL5DN0vHld7Z0/ebtv7XoehhjjDFmNfEC2Swd08f/tMX/1miMMcYY8xHxAtkYY4wxxpgZnirpkWBEoklR8GmG8AL+2VlMj6FEttQ5m01a08eyCch3JYgMVSIZaDiO6U8DkCtIgup31+NxmGTFIsR4Eq9NiXvlhNuoBFGgAIHswf2YvrS7+yiUrW3GF/IlqU681B+gdLyE2Ek8Kc+QePM0igZICqPYRtTGEqfxNLN4PzNIgCpPOVmpD4lWWyBjbF2IohqJOsOEBXoG/asNkl4JgiBJQmcnnKp2eBDH9cb6hVD2CNKaBkOW1559Poqpzz1/OZTdu/PtULZ9KbZbWfA/OlQV3F+Y+1qQmlnXidQvmPs+iqTXpD4MchUJRh8cHUqmIPQdwhz33t07eMajo3gsJf61KAkP5qhU+2Qg5NEcQGVFg9IP8TJqg4TZgpTHqorPkSEk4UnS2YDmlFinfj8mP5JM1INxKiWHwoMAACAASURBVLH0uLcXx+qFOCSTomlBshwfOhe9Lo0/eP4mnskNaI8WiJ0VnJMkSEnauhjnuC5ImCRB5jRvQOLt44NjP8ggxe8MhLgCRM+tbkzMk6QaUoWHkG45ONwLZd/zahSbJekM5vZeO96LLsSsNtdjGz2c8v0lCZokzsmjuEYZJcZFvx/vW6sx/zzsX5CNMcYYY4yZwQtks3R8pj+UpL+56HoYY4wxZjXxAtksHT+8daydWzf+20XXwxhjjDGriRfIxhhjjDHGzPBUSa8A46OEVDRwsD74D1Q2n0CWSjupQIoh6YLS+ejaJAhKLAOeDuLL3d1eFA/qLAoBx5BqJLHM027H23Lv3t1QVk5TImO8b7WiEDCmFL+DKHZcm7LgUMB1cuoM0I8oqUxi0WbwhADzl3ev6b+4efvnd27d+AKeJNQJ5AEQUJogukhSBZ5NA+qfQzLSdMryTqMbpYKcxFIQQYbQRmNI/JKkISRAnYH0cQxyVhNEwpRA8+wzV0PZZz7zPaHs299+O5TtvM0S2OlZlEu2L0U55f17kLYEyVX9S2AoSRpDu9PXrGlCS/TjdhtkneRE+ZvD4wVSRc8xrihJj+YjTpjj1Et6ZkwmMH9U0MCJOb8A+aeA33bGINlS3Um6laQ+JLBRSiy1UUrwbbXjnHJpDVLIQDKaQrtdvBAlVUlqtWJ/o+++txflLBIrJe7DlMI5L61elNczkPGnkPImSRsg+f2jw/hcvHw1jvPLPU5aKxvxnDn0reOTOFbOjuP8VICoLUkw1DSFPjMYxXmr04/tlggFVQHPugOQNQsYf688/yye89uDN0NZtxv7dQPGVRtSjnt9lqWbkHA5grVHNonXPjrh5+xgCJIejJUU/gXZGGOMMcaYGbxANsYYY4wxZgYvkI0xxhhjjJnBC2RjjDHGGGNmeKqkR8IHCQ7ThMRF9OEF7fE4vqxeVXzOMxB3KPVqCsIUhemMRnwdeNdda2sx7YheOB/DG/Qp0SXPSYSMx5UogLEY0gDB8AzEkrKcL6GKxExJarbi5ylZcDKBFKFzCEtPijKf653o3XHvK3N/nhL/aqh7npAe2/B5SNOi9Ma8wVLLcBT78fQk1qlxEpPncpAMDg454a5VxOvXk9gXJ6cxLWkf5I5XP/UyXucU0pb+0T+Mcsf6BowhSFCSpNEpJFe2Y78heW4CEkoOSXiSlFGyUgZ9vkBzDylAzmp8hBgyGpfnGUM0Rzebsd3pOiTjpa5Px05qSF+FeY8SLyWWCUkso8/XopTWhAw4p5ROqWqUwidJmxco8Wy++9ZqRbGsAVKZJG1fuhLK6J7T/SUR8TGxc1cJCW0eKHk1g35Az3NJKkFEfghS+WQS543v/dx1POd9kLuOh7AmaK6FInp2kygqSTX0zXIQr3NhLV5nqxfL3rt3H6/ThuTH0TiOnw5I4gdHB3jODIb/BJ5/Dx7Fe3F0dBw/O+H1RHUhyqsba1FabBRxHm8knrM0PZO4nsK/IJul4wfWjrRz68b/sOh6GGOMMWY18QLZLB2TKtP1m7c5fN0YY4wx5iPiBbJZOr6y+5wk/dyi62GMMcaY1cQLZGOMMcYYY2bwAtkYY4wxxpgZnr6LBditJVis08QuB2Qa93rRYj8Ce/I8Zm0Jtu4YzM26ou/Du1jQjgaNIhqiJ8fRgp2MId4QjPHH149lp6cUSx0t2F4vERHai200GsX2oF0XxqO4w0EyarYZ266G/kFlXYgNlVJR5tkT/z9+NA3Y0wVEnDYS96hRx893IIZ1dy/uTDGYcARmBlnGefy4TvZjNOyExuCQ7ekS4tXLSSzrd+K4PD6KFbp//yFe55mL26FsOKAdAGJfothSSTo+jW23D5HtJcSotyGetYDxK0ktjIWOfbsB0dt1zVZ/BaZ2I2Hnz0OdiPgO14VdClKfp90hnox1l6Q6sVUH7WJBOxD0KEYZ5pSa4qcl0e841O4N2Ckgz2G3i8QuFjTPUXtSBHvqeTUYRGOeni0d2PWk1Yl1bzS4D40n8V5WMJdSP0g/a+P9PRvAJDUntMORYOeQRoN3mzkYxB0R7j6IUdMnVbyPmxDhLEmTzka8/oVLoSyDHZsafZgzS9gRR1IN92djM85Rl7px55IcIsf3dnfxOt1efK4+sx2jtyewi9KjR/FZI0lHh7Hd33//XiijuPUcxmk3sZtQvhV3saA+WMI5Uzv6zLszTQr/gmyMMcYYY8wMXiCbpeO3rZ1I0pcXXA1jjDHGrCheIJul4/vWTrRz68aXF10PY4wxxqwmXiCbpeOszHX95u340qsxxhhjzMfAUyU9inYs4V3oCkQgSWq04wvnnU6M3hyN3gtlXX6PWwVIBhOIzs0hH7GCupOIKHFM55TEP5D8mvR3B0R0S1INxd3tKA6021F0OTqO8b6SNIQ47gbEE29fifei043iQQVSlyQ1oI2nFCubiKqdlydfwP+rD5+RpP9V0hfm+fwYomFb0B79tU3+/EEUItY3Y/zno+Mo5JydcawlRdM2IE784M478bPdeO21NkuPp6N472oYCMNBHEMnEJl+5+4dvM7gNEZiX7p4NdYHJLtktCzIkUcgxfa2ouyysRGFjxyEDYmF0RxipTEJOSGMVhXIWZRf/xEgMWUCMpDEbXxwGOVokvQoFlqSyno+2QXbtxOfDXRtiSWyCqJuScwmqZvm9lQ5RWe3QerMEvcWhrQymDfJwSQZsJEUDEGQgjqRoJSKEqfY5NQ9mgcSBEmCHIPIK0kZrEdefvFavM7990PZ6d0olUnShetxzs9B5qVHIPX+QUJipDbe6MUxUMJ93NuLc+saPL8kabMf1w4CQX/vboyqHsO6QZJGIJpSj2k1IRod5sc12KhBkroQF419E+a9lJxM5an+TvgXZGOMMcYYY2bwAtkYY4wxxpgZvEA2xhhjjDFmBi+QjTHGGGOMmeHpbyvDy9CYhgPSgSRVFbyYvkmbD5C0wPYLiQd5Ti/6x1foh0NIiRuxaDKBJKwCXstvNWN9+v0o1JGUJUmitEJo96PjKDednsSEm8enjJLBM888H8pe+9zn43FXnoUz8r1ogLzT7MSyeeWMFE9KOr9z80TvPOj8j/N+voS+VMLfhlsXY4KSJO2dxbSmOov3sy5AukiEp5WQ5Ee+aKOGxLFJlCnOJnyhZjdKKN2LUeRoNeI9Gu7H/jUAcUeSHjyKgtPuo1jPZjvei3ZCODk+ieMtb8Q27vajbNrqRJGxhgSzx+ekpLYotmR5bCNKapJY5JomZNd5oPFSQfJcKnGPUk1JuKIUz5RESbILXZ8S6rbWo1hJQl3qnFVF6Zh8L54klaRFkh6dk6TD8ZjFv+Fp7EckHZI03INUNJg2JHFCJUlT1A9S0iL1ueRzbA4wqRDSHFOyGIlprzz3XCi7tx+l6kuJtM4LG3EuHEOi6iGMtSEIda3EWGlC1y5A8J+C+Hrv0YNQ1s5p/EjH78ek0yOQzHO4zno3rlsk6fJWbKN5x5pArOyCnChJvc34rGrCHENr0NS8R+Wp1D3CvyCbpePza2fauXXjf1l0PYwxxhizmniBbJaOw2mh6zdvv7DoehhjjDFmNfEC2Swdf+3BJUn6C4uuhzHGGGNWEy+QjTHGGGOMmeHpSXogM5BgkHrpuQKjoNOL8kyrDeltNctAlEJU1vFl97qOL4eTlHJ2xtehiLs8i0JBoxVfbL94OUpDqRStB7vxBfpH+0eh7GwASUmJF+W3L0UR8oUX4hsJV65cCWVra/H+FAkzhKSLAl6gpxf6U30G+xceOT9tSgcE+a27xuLD1lZsp0sk9HVjctzXvvY1POcEBMeqINEmSgZnkyhSHZcsz2SQMrnVjfdz+3K87wLh8iwhtQqE3IP9WM8h1OfRhBMh9/fjeG31o7j0237XK6FsbeNCKJuULIYOhvH61GebrfmSySQW27KEhDYPNK5ojqP0NYmFuhLao4KyMQiLktQG4asFgtMIxM4pCGithAA2gCSvFkmQID2hNJWYe85OY3+bgux9AsednXJiZgHpYGu9+My4dDEmP66vx3mr2Uo8sqFrZVm8l+QynZ1xOl4GM+/WBU4bnYcBSeXQhxO+lZ65GOfh3YM4dqt2bKPWGqe3FdAVNmGNQgmg02FstzVID5ak6SiKh9S3Op04LrotSMG8F9MCJaldxnnich+SRqG/9SAdVpJaMKfUME/UtLEBNHAO90eSGpS4Cd+dhi/11VR5KhmU8C/IxhhjjDHGzOAFsjHGGGOMMTN4gWyWjh/aPJakP7PoehhjjDFmNfEC2Swdr/UH2rl1428suh7GGGOMWU28QDZLx+64oes3b7+26HoYY4wxZjV56i4Ww2E0c8+1IwEcu7Ye7fIrEIN8dBAjEyWprmKdyE3P82hUttsQdVly3YfDaF7X8D37EJk6AbP93r0YVyxJDx7EXSxKsHjb7Wh4tluJyEawpCn6toAdI/I8fscqYeqPRrE9222yTuffh2KeY//m7kVJ+nOSvjDPOZtg1tNXKiHGWJI6/bhjRdaK9/357Xjc7gHvknL/zpuhrHcp3qPmerzvPbCFx6d8nd5G/PylTeizsCPJqIq7F9x7yPHmVRn73GQK0wtY6xTdK0mf+mz8G+jVz306lH36te8NZRnsHlDDDhoSx+e2YLeAGqLma9gN5XF5LGs2P/x+LDQuKCK42+U+TMOqBYY47VhDu1BIwp0T6HuXVWy349O4+0Cnw3XPG/E50oC6TwfxOmdz7oAhSTl898kwjoHj47gjQS8R07u5ATsIbMRjN9bh83DPaog8lqQ2tAfN27TzEM35ktSAMdTv8/ech+EgthvuApN4BtAeJxvrceeFJuzGUBa8Q0o5ifeX9jh44WrcQYN2XTl8tIfXqWHsF1nsh6NBrE+zgnUHd2FtQrz4i9euhbKLsKsVtZskNWCdMIFxNYWdtohpYhqEr4nbjGSwrmw3UmMaIulhPknhX5CNMcYYY4yZwQtkY4wxxhhjZvAC2RhjjDHGmBm8QDbGGGOMMWaGp0p6JIZM4KX2quII10YjigMk6V3cfjaUjSDGUZLOTqOYQgJbo0ECGsWt7uN1JiBC9frxJfbBKH73O7/+VihLxXk2m/GF8W4nthsJee02CxMUc0vQC+wUSZvqJvRO/QTEISJVR5JAnxQ5/pkLR/oL73f+67kuJCmDKrWgbzZAxpOkNtyjs0GMA29k8bjXvvcH8JzTOvbPu/e/FcqOxrEfbvdiy280WN65sh3HW5XF7z6Yxvo890KMcP7U557D6wyrjVBWt0CgKWI9L21wDGx/LcbatiCmtID7Q1QQHy9J7TzKNjz3xXuRZyz/0Iw4mnAc8TxQLHQNRlw7EXVL9tzm5lYouwgRtHfu3MFT0rOA6kQTBcVHp2TAdRChiQxks8OjKJW2WjxHdUESJIGU5q61NRac6BeoCqRFeoaiWA3zoyRVYHafncU2pvvT7/P429qK468Nz6Z56W/E/nZyEmVNEgklaXAGcc8wP165EIW6HPqBxBsRtOA6GxCx/ZmXXw5le+txHpSk+4/uhbLT41invYPDULa2Ec+52eFnchPGwIVr8RmwcTX2163NbTxnBhsZTOEeTUDap+j6aSJLvATrdwJSN8m0rRaL3kgiypzwL8hm6XilN9LOrRv/+6LrYYwxxpjVxAtks3TcGzV1/ebt7190PYwxxhizmniBbJaOv727JUl/dtH1MMYYY8xq4gWyMcYYY4wxMzxV0iNxgF7yT4kDJLpkIMq98ML1+OGaRYo3vxFf6pfisSR8UPJUSjCcTOKxu3vxpXpKAmLhIiEi9ePL5d0OJMV0KEmPX0ynhKwJpIhR0hJLegkJqo7l02m8Dt3JdiI1i/pS/kSST5ad9++6eD9I12o0eTh0u1HQGJdR7miCVLPZj9KTJP3gD/1IKNvZiYlHDx7GBMaiFft2NnmE1zk7jjLhhe0osTx39Xooy5vx+1x89kW8zmkehdGjKaSdwd/knUTKVT6KdymHZKUaRI6qir2uKLjfUDnNFdTvatTxeJ5MuClzQTIRSVxNkE8laTKNQh2JZa+++mr8LMh4kvTgwYNQxkltsd2qMrbvMCHpUbvn+XyJrq1mnCMP9uOYkKRJP35PSo4jUVQZp41Op/AMBbGbniM0F6eeVzXEkGUgDWd5/Hx/jZ8jvT6NS/6e89DpwTMQ5vvxmPvbiJIOQXjsFnFcjDOW5MsyXmt0As/5YUwBVCteZyORqNi6Guf24cXY3w8P4/qGkihbCRk3b8axsj+K5zy5+04oO9qLgqAktYvYPza2ojiYwTxK47SZkKUFfRumDtVwzhzXLedL8SX8C7IxxhhjjDEzeIFsjDHGGGPMDF4gm6Xj91w+lqT/ZNH1MMYYY8xq4gWyWTpe7E20c+vG3190PYwxxhizmjxV0htB2lG/F19Cb7YSaVKQotJtx2M7zZhgNhnzS/VHh/EF+gf34gvng1NI3ZmCJZN4YZxeDi+HkPADAsnGWpQReomX99fWoyhDKYDUxk2QqCQW3QoQHMYgIpKQ02rx31E1iZRwaJVBQg5IQ48/H+9H+4k+8/ZpQ9dv3v7heRfJzRal84EQBPWUWOK6sBVToUjS63ZZpmi0Yh958dVY9tr3/c5Q9u5bvxLKvv7//jxepxhGkWr9wUEoy/M342ehz1y9GlMvJelTn/18KNvYeCaUnSq20XDAcla/GUWQJgioQxJoIC6JZLHHh7L49CQkTeEYkFSChEYJavMyHsXx0oMEtCGkWUksDU6nsT59mLteeSUmKkpSB0Tb3d3dUEZzCrVlp8Nz5HgU2/LRo5iASoLiCD57esLPlinMSXUd26iA+bnTZjlyayOmmFGCYQvm98PDKE2RPCpJzQaIVBtRLl5bj23c7fLzezKJ4zI5huYBPls0YD1QcFs2oc+0m/HzbRjOj+jZLWk4iuuJs8M4P7b247jIYO2w0Y9tLklXrkS5uYDnxQD6669+7auh7ATmA0kaVfGePQ9pkJ0jWB81Oemz24trlPUNkFfXYhkMH1UTXnNNaxh/FHsHHx/DfCtJGfS584h7/gXZLB1/+/2eJP2pRdfDGGOMMauJF8jGGGOMMcbM4AWyMcYYY4wxM3iBbIwxxhhjzAxPlfQKeBu6ASkmjUSyWZXHF6wrkrjgPez1NX7ZvdeL4k6nFcsGdRQxJqOYoDQ84xfTa0h16feiEFDDi/pXtmN6WiptsN2JL+pneZQUSI7IEn/f0EvodH2SKEej2B6UzCexrJJl8dga+tGU3t6XVFDq3ROS3HnDcaDL0qv/QQb8xzSb8eheP4o203K+NpZYgGt3oS+0ogi1uf1SrONaTGqSpAcHUWB7eAJSG9yPHFrpztv38Trj/SiHvPTpz4WyxmaU/NahTJI6IATXkOSFiZ04JyWi7ObsUJR2NoUyKSGmNXkczQP1lwzG1SSRQlbBJDsaxntWw3GUuCdJ167FPrcFAtr+QRTqSDbjFE9pMIhzOSXKURnNXVefi0mSEs99JDI2GnGcr6/H7y1JFy7M9yyg5Nd5k2wlqWjEvtDvx7mj06FnWCoNMpalUu7mIYdnAwnQNM4kKS9ihRpwf9dBTlRiHh7110MZjZUWpAC2QcDeuhilTEnqwhjKmrEffO/no+x872Gcc7/15tfxOkUT0kdpLQMi5GdeiSmakrR2MW6i0IR1S433EtaAE+7D+SSO/ymsB+CUGic2W+BnAx7KdZr/UGM+Gfz+awNJ+uOLrocxxhhjVhMvkM3S8Vyv1M6tG7+86HoYY4wxZjXxAtksHd88auj6zdv/wqLrYYwxxpjVxAtks3T83XsdSfpPF10PY4wxxqwmXiAbY4wxxhgzw1N3sehDlCkZuKnoPrJg6dgKDmwmjO9nno3G++jsJJSVk2gkj6cQnZkwp8sxWMW028Z6tGALiDxN7WJBf6PMuwtFqt2p7eb9PFnSwyHv9EGfb0JcbA4GfgoymJ8sS4jc6XNChGxeQBvDrhwSR203wNjNITZ1CNGhktSAXUl6vVinEbTH1maMcP5dP/h78Do7L346lN1/cDeUlaO4s0VWxnjWKtEXHpXxu29AhPT1l6Lt3720jecs69hvBnBOHi+06wub7LTjBO0aQ8elxiDFMKdigucBRHQ0xFM7EhAFzH0Un5uCviO1B+3eQbsh0G4VEn/3HCLpae7K4DjahUKSCtqhCefyWNbrxTjfx+eMZWdnFI0eoedvakeRzc34HGq24ncfj2lHEJ5QS9j54fR0vroTeR3bN4d7ltr5h2bnkqKvIX666Ma2lKR2Nz6vehfiHNWHdu9ChHOzFXd3kKSiA88QGH/brbgDxz/9g789lD3LqezK6zjH0Bolg0HV2+KdWPqbl0PZBOYyes6WcJ0yMQ/TdlNZBXM7zDHtc0SgY3x1Av+CbIwxxhhjzAxeIBtjjDHGGDODF8hm6fiD104k6d9edD2MMcYYs5p4gWyWjsudUju3bnCUkDHGGGPMR+TpUdMgds0jUf1jKCozJ3mGxI6EPHflcnxhXGV8YbzdhshjELNSkccT+E6jUZSWRpN4XJ3FdmuBvJaiAe1O0lAKEpQIum/niTelOlEZnTMlN6HE+cTnf/2opes3b//+nVs3/gae5MnPg2zWhgjnVBypQBarYehQ9G8zMcLqOgoN9QQ+n8UTtEAEaV2EcSGpfSFKH5/5/PeFsg7Enp4cH4ayLCE4dEFkHBzFiOERSE8diKSXpJr6J4xX6ko0f9RV7AePj411H43i/SGZqcgTkm81XyT2vIzGse4kUdFYk6QCxDQSaEgk5NhuYXJ3Br+59ECEyhRlsTxxnfW1KBmVFQiPUJ9Od/547xoExQYIxhOY80uIpJakoklyVrwXJFavrUWxrAttKbF4NBxGoY76NT3XJGk0hOfdR4iaRmGL+nDieUPlNUUJk3yaEMVpvJTQDzIY541mnIdTQnoOn89BSiPH9hLM7Zu/45/i68C8Q62Jbdxg0ZSSoacgCJcg1E2ncM9Skhysm2gtU8NzNk9tFMFXmhv/gmyWjl/Y7UnSf7DoehhjjDFmNfEC2RhjjDHGmBm8QDbGGGOMMWYGL5CNMcYYY4yZ4amSXkr4eJKUxIWiDKSokNiVStKjY688ezWUdSDZqGjFc5aJ17iHkJrVA6GAko26/ViWSuyjRLYC6sRNnGr3D5+kR2WtVkwmenwdkCFAtKnAPEj1GSoP/fCcb97n9AESBxPCVQ0JUBNwhBoNkvRYHpiA1JPVcN8gCaxVQBtlCTkLvme3F0WfK1euhLLT0wuh7PjkCK/T70XpsWjEMnJNyylLQlke2wM/D8II+V4pmVgwJ9EYGo9AgEn043kF2Hmh7zgF+TT1HRtl/D4VyM1TuBddSMx7fAJoZPiKrXaUmSr4PkXOSYM0BioQZyldj8qmiTYisbIGKTTVX4kmjLXNzYuhjObYAoSvEu6ZJJ2cROmRkh+pDw4GnI5JwmY6EfY3Z0r9DdocHCxJUg2DOqMlTA5za+L5W5O0OIz1HHViWacd+zAJ9pI0JdONpEP48o0ijp9ijZ/JlEI4gYcVCdDTVKIiPL8zSJMsQLIj8S4FpV7S9IrricRPvfOmBafwL8hm6fixaweS9IcXXQ9jjDHGrCZeIJulY7NZaefWjXcXXQ9jjDHGrCZeIJul41cPO7p+8/a/suh6GGOMMWY18QLZLB2/tNeTpH9n0fUwxhhjzGryVEmP3pUv4GX3lBhC0gMZVvTif0poofJOFxJg4IXv58AgKSBBTJI2L2yFMpKbKCXq4OAglJ2eneJ1KhAhBBJIBi+Wp4QJEhznTecj8e48KX50zzGBLFF3eqn+SVnkPC/Zp65FAkuX+pGkKQh1KC5A3VNtR8E/JLDWgmtDihiEMkmSWpRsOB2FssO93XgctPN0woLS/n6UhLoQI9huUXpUYqyDIEX3kvo7JTAl/CaNoS/Q1IjJohR9Jb7vHyVJbwLzBI1Vkgs/uPpc9WnC51NJetzu880zRY/mfH6OjEaxb5VwzzIQgqYgKA0G8Xypeja7sY173SgtdjpRpJKktfUobDdhXNCcNhrFcZqq+8FBlGcpIa8NwmSqX877fJiXEQxA+t6pObMiEwuSRlnS4/tTgGhKiYrjEfSjs3h/qA9KUgFitTRfclxeRCFvlEgAJm+2pLkwAyE1kSTbnDMxl579JdnsSUGfykhkhI0eILFSYvGP5NMU/gXZGGOMMcaYGbxANsYYY4wxZgYvkI0xxhhjjJnBC2SzdPzLzz2SpH9p0fUwxhhjzGriBbJZOnqNSju3bkSrzBhjjDHmY+DpSiqYihTTmbICyUAkg5cs2gbEbKaOLcGCJ5t7czNG525ubeJ1XnzxpXhtsjnB8D45OQll+wf7eJ3jw2gfl+MY/Vnjrgn8900LIrWbbdrpg6xgslMTEZRgrWYNsoLn33ViPI7m9ZOf/9WTdf2XN29/aefWjS/Pc07aZYVse7q2JI1g54YWxO+eJ4YVd27I6R7HsqqCXRcSfSGjSO0KLO1htONpXFNssCSNJ2AWgz3NMbJs0dOONRhfD/2TbP9UTC/NKbQDQJbNGa+auNZHiZqmnXHW19djfRJ9kHbq6LSjHU+9iOazFLSzBY2r6TTWJ7lZTgYmO9ntMEe22jAXJnYuajVje3R7cd7stOPYb8KcK/EzYziM8zu1Md3z9E4o8+2QQu1GMddSor+ecwehWWjtkI5/h/rQjhcYJUyf5fahyORWK97fRjOWTSAufQS7XUgSDAucs2ndQ997AruBSVJdwG41cM4mzGX1NLEjD+wegt0Q7mUGO3VQzPXjj8Pn4ToZbdmEu4Qk1pbn2InFvyCbpeOrJxuS9KUFV8MYY4wxK4oXyMYYY4wxxszgBbIxxhhjjDEzeIFsjDHGGGPMDE99W3kCcYb8Uj2/IE3iAkktKAMknIMKXoynzxf0djeIexTBKCbOCgAAAoJJREFULEnNIspIJFZR9HarEcWOdpNjjDfXL4ayKQg1FMOaiuQsSHCEyMUaGrmC+1MnRCRq92pMEbAQ4ZqIhjyPtDEvJJvRy/tnZ2f4efI7unMKeWkxK/bjBgiOFD06gfjOKUhyEn93ipttgawyHcco1QL6kSS1WvGc2D0xOpTnD4r4JsmIYsOHIB1KFCnN0gbKwFCfJkhgkjQ6iW33Ufo23UeKIk5BYpjqfihqwP1NjYt5o7Op7uNxvD9Fg8dUF6Kd2yAYtuFetOkZlMhlp5htnGPha1cJkfnsLLb78XEUs/f2osR9enoaymjsSlK/H+8lRnxTXHqiXw6HFKWMh87FCM5HrZaS/unSTfg+FUQmp9TCCvpC3umFsgKkbDrnONFAleJ3ohTmFtUHnr/0vSUek7RuIfFPiec8jgGax+k4eM5X8JyTpIJue0XPblgDnuO33tS6CY+d+0hjPiH82OW7kvR7F10PY4wxxqwmXiCbpaOZ19q5dYN/1jLGGGOM+Yh4gWyWjn9wvKHrN2//0UXXwxhjjDGriRfIZun4+tm6JH1x0fUwxhhjzGqSnSflzJhPAtdv3v55Sdq5deMLi62JMcYYY1YR/4JsjDHGGGPMDF4gG2OMMcYYM4MXyMYYY4wxxszgd5CNMcYYY4yZwb8gG2OMMcYYM4MXyMYYY4wxxszgBbIxxhhjjDEzeIFsjDHGGGPMDF4gG2OMMcYYM4MXyMYYY4wxxszgBbIxxhhjjDEzeIFsjDHGGGPMDF4gG2OMMcYYM4MXyMYYY4wxxszgBbIxxhhjjDEzeIFsjDHGGGPMDF4gG2OMMcYYM4MXyMYYY4wxxszgBbIxxhhjjDEz/H9H8/CfsTCLSAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}