Denoising autoencoder with data generator in Keras.ipynb

denoising-autoencoder-with-data-generator-in-keras.ipynb

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    "colab": {
      "name": "Denoising autoencoder with data generator in Keras.ipynb",
      "version": "0.3.2",
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      "name": "python3",
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    "accelerator": "GPU"
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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "[View in Colaboratory](https://colab.research.google.com/gist/twolodzko/aa4f4ad52f16c293df40342929b025a4/denoising-autoencoder-with-data-generator-in-keras.ipynb)"
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    {
      "metadata": {
        "id": "5MKBs5dLI2DJ",
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          "height": 34
        },
        "outputId": "e36d24bb-a7c6-47a0-bf24-953044c15f2f"
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      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from keras.layers import Lambda, Input, Dense\n",
        "from keras.models import Model\n",
        "from keras import regularizers\n",
        "from keras.datasets import mnist\n",
        "from keras import backend as K\n",
        "\n",
        "tf.test.gpu_device_name()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "metadata": {
        "id": "E1T_DtUvI3M-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "(x_train, _), (x_test, _) = mnist.load_data()\n",
        "\n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255\n",
        "\n",
        "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
        "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "12lssVzVQHM-",
        "colab_type": "code",
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      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oF4qjyBoI3P0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.utils import Sequence\n",
        "\n",
        "class DataGenerator(Sequence):\n",
        "\n",
        "  def __init__(self, data, batch_size=32, noisy=False, noise_factor=0.5, shuffle=True):\n",
        "  \n",
        "    self.data = data\n",
        "    self.data_noisy = None\n",
        "    self.index = [i for i in range(self.data.shape[0])]\n",
        "    \n",
        "    self.batch_size = batch_size\n",
        "    self.noisy = noisy\n",
        "    self.noise_factor = noise_factor\n",
        "    self.shuffle = shuffle\n",
        "    self.on_epoch_end()\n",
        "\n",
        "  def __len__(self):\n",
        "    return int(len(self.data) / self.batch_size)\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    train, test = [], []\n",
        "    for i in range(index, index+self.batch_size):\n",
        "      train.append(self.data_noisy[self.index[i]])\n",
        "      test.append(self.data[self.index[i]])\n",
        "    return np.array(train), np.array(test)\n",
        "\n",
        "  def on_epoch_end(self):\n",
        "    np.random.shuffle(self.index)\n",
        "    if self.noisy:\n",
        "      noise = np.random.normal(loc=0.0, scale=self.noise_factor, size=self.data.shape) \n",
        "      self.data_noisy = self.data + noise\n",
        "      np.clip(self.data_noisy, 0., 1.)\n",
        "    else:\n",
        "      self.data_noisy = self.data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uTx-wCwNI3S5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_gen = DataGenerator(x_train, noisy=True)\n",
        "test_gen = DataGenerator(x_test, noisy=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bbX4PZaERPzl",
        "colab_type": "code",
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      "cell_type": "code",
      "source": [
        ""
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      "execution_count": 0,
      "outputs": []
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    {
      "metadata": {
        "id": "wtBuBPp4NlrZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "e738acf7-fef5-4eba-c0e7-371ea93de1de"
      },
      "cell_type": "code",
      "source": [
        "encoding_dim = 32  \n",
        "\n",
        "input_img = Input(shape=(784,))\n",
        "encoded = Dense(encoding_dim, activation='relu',\n",
        "                activity_regularizer=regularizers.l1(1e-4))(input_img)\n",
        "decoded = Dense(784, activation='sigmoid')(encoded)\n",
        "autoencoder = Model(input_img, decoded)\n",
        "\n",
        "encoder = Model(input_img, encoded)\n",
        "\n",
        "encoded_input = Input(shape=(encoding_dim,))\n",
        "decoder_layer = autoencoder.layers[-1]\n",
        "decoder = Model(encoded_input, decoder_layer(encoded_input))\n",
        "\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
        "\n",
        "autoencoder.summary()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_18 (InputLayer)        (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 784)               25872     \n",
            "=================================================================\n",
            "Total params: 50,992\n",
            "Trainable params: 50,992\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3dX1Ovp4NBfj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "outputId": "efbf8d07-6f63-4774-df84-9233813ba353"
      },
      "cell_type": "code",
      "source": [
        "history = autoencoder.fit_generator(generator=train_gen,\n",
        "                                    validation_data=test_gen,\n",
        "                                    epochs=20)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.2341 - val_loss: 0.1823\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.1675 - val_loss: 0.1573\n",
            "Epoch 3/20\n",
            "1030/1875 [===============>..............] - ETA: 7s - loss: 0.1543"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.1486 - val_loss: 0.1449\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1369 - val_loss: 0.1384\n",
            "Epoch 5/20\n",
            "1490/1875 [======================>.......] - ETA: 3s - loss: 0.1325"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1311 - val_loss: 0.1342\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1284 - val_loss: 0.1320\n",
            "Epoch 7/20\n",
            "1609/1875 [========================>.....] - ETA: 2s - loss: 0.1271"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1264 - val_loss: 0.1300\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1249 - val_loss: 0.1286\n",
            "Epoch 9/20\n",
            "1634/1875 [=========================>....] - ETA: 1s - loss: 0.1242"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1236 - val_loss: 0.1272\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1229 - val_loss: 0.1266\n",
            "Epoch 11/20\n",
            "1622/1875 [========================>.....] - ETA: 2s - loss: 0.1224"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1216 - val_loss: 0.1258\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1239 - val_loss: 0.1263\n",
            "Epoch 13/20\n",
            "1641/1875 [=========================>....] - ETA: 1s - loss: 0.1218"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1212 - val_loss: 0.1264\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1204 - val_loss: 0.1265\n",
            "Epoch 15/20\n",
            "1647/1875 [=========================>....] - ETA: 1s - loss: 0.1218"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1211 - val_loss: 0.1268\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1207 - val_loss: 0.1257\n",
            "Epoch 17/20\n",
            "1615/1875 [========================>.....] - ETA: 2s - loss: 0.1221"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1215 - val_loss: 0.1261\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.1211 - val_loss: 0.1260\n",
            "Epoch 19/20\n",
            "1593/1875 [========================>.....] - ETA: 2s - loss: 0.1209"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1202 - val_loss: 0.1259\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1207 - val_loss: 0.1260\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yOaxlqjKNCQe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "3a079456-1585-442e-c753-337ad4de7465"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9hkJTAfY1fo5n97yMVrERD91dinBYxP/94xdo6wgM/UJERJTUGM4JJkNrw9rZ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\nfaBduJa5bMQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8216dce630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cigiV9j0TpCE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "0630d361-9f3c-45a1-de98-0b854498bbee"
      },
      "cell_type": "code",
      "source": [
        "noise_factor = 0.6\n",
        "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n",
        "x_test_noisy = np.clip(x_test_noisy, 0., 1.)\n",
        "\n",
        "decoded_imgs = autoencoder.predict(x_test_noisy)\n",
        "\n",
        "n = 10  # how many digits we will display\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # display original\n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "\n",
        "    # display reconstruction\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\n",
        "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dmr7WhGiL1mtZa621SkzL3ojmeZi1TCLy+NDaSawtwJpQs846a+M1so6FWp7W\n+OIXv1jiM844o9XfaE2ao446qsScJzmPROT54sgjj0xtWvemU9CuXuvR8dlpfQedi8bDOjsRdatt\n1jxj7aKf/OQn6TjWpWHdIa3VwdowWluDz2DxxRcvMeskReR+p/VHuKdhHZdDDz00HceaGdr/df7o\nBPvss0+JOUdG5Oem/Zd7VM5JWuOgtlZxPLNmndaz4xzENVjr+bHWmu6VWWOFtR1px63XpLavrP/G\n49T2l/1H99sDZQnMeU3HP8cbawYpJ5xwQomffPLJ1DbbbLM1/h37Kech3U+yNhNh/aCIXMfn0Ucf\nTW1NtSxZh1Phmh6Ra1r8/Oc/L7HOYdy36R5sIGCtn+uvv77xuNr3Eb1fZIUVVigxv1dEZPt6zpk1\n2K/0mpZeeunGv+PejDUWaREeEbHgggs2XtOIESN6vA79Xsk6N/pdZWKgfY/re21M1eC91n0A12Su\n+7V5mGvASy+9lNo4z2tNmquvvrrEHEe69rFmjdbz4prTVBszol6Xh8/VNWqMMcYYY4wxxhhjuhz/\nUGOMMcYYY4wxxhjTJVTtuZmuVbMa0/QoTU1vg1pm16wdyd13311ipqs98sgj6TjKjNZee+3UxtR5\nplupjRZTK88555zU1ja9nGlamuJJJpYlMFNsKQVRmNKs6c6kZtlMG2vKg2rofWBqIFPzI5qlVSoH\neuedd0qs6eqU79AejmmkEdn6UGHqHNNg+8MTTzxRYsr4InLf1nvC+8dUzMcff7zxvdRukhId2s6p\nHTfTDmt97q233iqxWqpSbkjpgFrmUbq13nrrNb4X0fRfzm9TTJFVoLT1o014f+F9ufXWW1ObpoE2\nQctJptzra1pdRkTssssuPb6XpuUyDZ0pzCpb4hyrdplM51X5J9l3331LrHK8ZZZZpsQnn3xyj/8e\nkdcAykgispRkIGxIVbakcgZyzDHHlJjSXk3J5Xqq18xjf/zjH5eYspgalF5EZInAyy+/nNpUGtMG\nlYLRGnz06NElvuSSSxrPofeUNrann356r6+pDb2RB1MmSXmN2qV/4hOfKLHaaas19nhq1rXsW5qa\nP++885ZY12BKM7h+tp03B4JOjcWFF164xLqn4JpB+96IbDPLvY2uRzUpRlOfmWqqqdJr7i+59zj6\n6KPTcZwTdL9B6RllMqNGjUrH0Z6ba5jCMaUS2bY2swMl69b7yrGieznO95RsqUSK6yLlFRF57uRn\nogw74v3S3PGoLTPlU2oPzRIJNdpaGLeFEuOIPNd36jlyn6h7Bb4H162I/NzuvffexvPXrnO33XYr\nMfffv/vd79Jxm266aYk57vV7QM8jAAAgAElEQVR999hjjxJvv/32qY37FH6XpLQwIksZuc+JyLJE\n2rnrvpzfmfSznHjiiSXupCSxSdIZkfeNXNsVyoq0L5D5558/vaYMkfOXjgGucZwT9HsC5/1f/epX\nqW3JJZcs8U033VRi/U548MEHl/iKK65IbRz7lInW+rFCKWOTbM8ZNcYYY4wxxhhjjDFdgn+oMcYY\nY4wxxhhjjOkSqtInMtdcc6XXWuGa0KWJsgytbM50zoGGH1OdiZiWxlQvlSbUPjNTC+k+pTKhmtsO\n7zHvYX+ppU5OM800JVYnCab/qftIW5qkRJqmTLkc4zFjxqTjKBerOZEwHe6uu+5KbXQ5uOGGG1Lb\nq6++WmKm4LdNWVUGQm6h52RqJt0hFDoZqUSqBsfL9773vRKrvHDmmWcuMdPqVQrJivfqFkQJkkpC\n2tLkNFOjJjnoZIp3rcJ/DfZZuqxpajXHh8pVm6r01+ZlpodqSmjb9Oza/WPKKdP2I94/TzfB/q/p\nwW2vozfUPivXAe33dAVgOrA6q3C+0mtmSjFlN5pazTR9OlOokyE/i0oDm9Y7lUNwHqBsLiLLAGpO\nY0x5/utf/5ra3njjjRJPLElwDa6LlPxtu+22fTof09tVojN06NASUw4z33zzNZ5D19amz6myXI6/\nM888M7U1fTa+b0TEyJEjS1xzeerUc+T9qcnlNf2eKfJcg5ocvXqCcgP2A017pzyCzjV0qonIciyV\n0RHOmepOyOfBvUxElglRKqeueZTIUnIaMXByp7/85S8lZhmBiIiNN964xDXJJMcEpeKKfoYmqZeO\nm2222abEXFufeeaZdFxbuTvnQ92H8vmog1UTu+++e3rNUg21sT4Q66KOAe4jdIxR7jFu3LgS697t\n29/+dolV3sn3psx0r732arzemmMW96/6na3tvocyGZXHEfY5uvJFZKmkOhmzVIX2n/7Avq3uXdzT\n6P6czpOUgWlZAbrFcr2IyPean133qDVZZxO6LlJyx+eo37353pSpRWRHPzqR1ZzH9DmyJEXTWHRG\njTHGGGOMMcYYY0yX4B9qjDHGGGOMMcYYY7oE/1BjjDHGGGOMMcYY0yVMUWukzrBWn0V11bTovOyy\ny0q84YYbpuNo9aX1YJpYZJFF0mte1+uvv954HGs5/OIXv0htrLtBDZ7ajlPHxpoREfkzU4On0FpW\na6d0woavJ6g5VHtD2sSttNJKqY06WXLzzTc3nkM1dqxLQ1jHJyLfP+qu9XprdWn4d9SCL7/88uk4\n6jl33XXX1EadIesLaY0a1nhRnfhAwPpFahOpVvGEz0NrP7RFa9GMh9aovfl7avtVN8paFbQhVxs/\nWoNPPfXUqa2pLo32Tdru6dg79thjezxHf6HF7p133pnaaPmuVo9N+m+tUXPQQQeVuKZLp50lawBE\nZBtpPmO9f+eff36J1f6T17XffvuV+LDDDkvH7bPPPiXW+0Go41V71YlRl6YttD3V8UHbe+q7tfYP\n74n2S/Zt2kQ3Wcfqe+n5uI6pHp01D1jvQPnUpz5VYq1NwDoMTbWuIur1TD4IuIZrjQha/2p/Jrff\nfnuJdW0ltDnVueuFF14oca1uFudRneebapNpTSjavbatt8M9XESuSafPlPejU7BGgI63Wi0JWqfX\njmOb1liidTc/N2tfROQaOKxVwZoOEbk2TK2WIRk2bFh6zfou/IwReY5mfTSupRF571SrldNJeN26\n9tImu1ZLjvtVrVHDv9MaW011aXRO5X6EMcdNRK7V99JLL0UTtboi3A/XPjOZcsop02vuZT/5yU+m\nttGjRze+dyfQWmW1ejBN9sS6LnJO5toXkceY1tokxx13XIn33HPPxuNq42/VVVdtbCNt60WyRpLy\n9ttvl5jzTUT72kW9ZYEFFihxrdaKwj1rrd7qgQceWGKt18I9MGvraT0cfqfj/k+/6xGdbzmOWONP\n50Pu2xgr3/3udxvbuE/gXrYtzqgxxhhjjDHGGGOM6RL8Q40xxhhjjDHGGGNMl1C152aarKaasU2t\nQZsYMmRIes2UL5U20NKrlhJPW0SmaWqaLc+33HLLpTbaIl577bUlpu1rRD2Vjda3TFdWmFaqkjGm\now5Uyr4+A8rRaJE4EKiMiVASRnnIpptumo7jM/7jH/+Y2mhb/IMf/KDEaoP84x//uMSadtjWEpio\nDS+tzGkV2B9qNsmEMrSInP7MNEa17uQY5v2JiLjnnntKzNRttaVkei3H24033piOo6RsrbXWSm20\nKmZq4k9+8pN0HK1GB5pOjkXKLi+++OLUdtFFF5WY6acREY8//niP56ONZERO2a1dN1OTaUMfEbHd\ndts1/h2ZffbZS3zAAQekNrUGb3NNKmukhS7TflXqxxRvTStl+npfLB17guuHpiMz/VXTyykFYd9W\ny1lK0dS+vmmOo4xP4bjUPleDVuCUBzz00EPpOPY5lUqw3zJl/JZbbknHUTqiclnastJ+vr9Qfvu5\nz30utdFalfc5IssqNHW7LZQectyrZIP7Ct4HppZH5Pv3/e9/P7Vx71azrqUkQeU7TajkkRb00003\nXePfdWpO5XrEZxbRXkq+ySablFjl7nweNdk10TEw1VRTlZhr8DXXXJOO60tf0s/IvYeO0yOOOKLH\nv9M9AyVkusfi/KpzX3+oyfrYRmlERF4jWPpA5fm877o/55rM7w20742IWHrppUvMPdHYsWPTcdzD\ntJUt1VC5BedlPgMdi5Thct2IyDLX559/vtfX1BO1z9Zk3axwP6MyFn5fUsllEypvopyGfUItpN99\n993Gcy6xxBIl5l78gQceSMcNHTq0xDqfUkrMvqTwuelegPN8bf3vLW3HYg2OS0qdIvJeXvfx999/\nf4l5n7UMB+dl7pd0rT7ttNNKrLJsftdn3zr99NOjLRyL/C1CS69wf6zf+9dee+0S65owHmfUGGOM\nMcYYY4wxxnQJ/qHGGGOMMcYYY4wxpkuoSp+Y5qQpZJTwfPnLX05tw4cPLzHTUWvpZDXo8nHdddel\nNqbfMz1KnZ3WWWedErdNu9UqznRueOutt1IbXReY7q1pVEzNUikQXaA6Kbdom67GSvYREdtss02P\nx80999zpNat663UzvZppxeqCwVS5rbfeusTqClSD8iBW4mdq2YRgVXim7Knr2aKLLlpiTTEmnXqO\ntWfIPqZpdePGjSsxxxH/PSKnxKvkj6nzlKK98cYbjdfEKudMN43I40olM7yXNalhDaagMv1U4bOp\nyRwHaixqyvlNN93U+Hc77bRTiTUlm9CJacSIEantnXfeKfE000xTYqaHRuQ0Xcr6VCbIOVXlfw8/\n/HCJKW+7++67G6+9BtNla9X1VUY0zzzzlLgmvewrlF5ERPzjH/9o9XdM61aJVK2/USLK1GCm/Ufk\n+8A+p2s15zV1AeKzYoq9SkyOOuqoEuuawTWFEhBN9+Z8qlCWWXNZ6i10zPnmN7+Z2trK//h5df0k\nM8wwQ3pNV5e2UJr0s5/9rPG42lpRk5i1hXswdWCrQWfPlVdeuU/vrdQ+K9Pq1fGjNocQ7iPUIbIm\nu2q6Ru6jmSofkWVu6thE6HJCmXhElvOpGyIlXpTb1eQN3FNFZPnAB7FHVbgmqZtTE3SYishOa1yr\nag5gNXkI2XnnndPrU045pcTsWzPNNFM6ju6Xuvecc845G9+PLLbYYiV+8MEHG4+bGHvUGnx/yiX1\nOxxd0RTKiuk0rI5clKlxHFGOr9Tkx4QlNCJyeQGd/yk9o6ORPguu8ZQFRWRJM8dzf6EkSCX4lD7q\ntXJvUpNzcR82ZsyY1Pavf/2rxJSC8plGZKnyeeedV2Lu9yLyvkLduvh9u7Y35LqrLm7skywZoTKr\ntjLRprHojBpjjDHGGGOMMcaYLsE/1BhjjDHGGGOMMcZ0Cf6hxhhjjDHGGGOMMaZLqNao+dOf/lTi\nffbZJ7XVrASpGaOFWE23ppfx29/+tsTHH398ibVGDe0DaQlLLXZE+/ov1KDNMsssjcfV+Nvf/lbi\nT33qU6mNGlhqASOyjrpm8d0f1IpX7RcJdXusd6EW1tTCag0E1gvhc6zVdqDuev/990/H1erBkCY9\n8YSgTTzruGif4WdeY401Uhv1iWoT2Vf4GdSqjnaQWneA/Y/2jXofVcvbxD//+c8S18YH9fx67pq+\nm+OUNsi77757Oo7zg9aLolad90qtb3kd1PtGRGy00UYlVnv3/sD3rNXzUHjsm2++WWKtK0Jq8xyv\ngxr9iIijjz66x3NMP/306Tjq6lUDz7mMNaJGjhzZeE2HHHJIes21o61l5Mc+9rHUdsMNNzT+XV/h\n+/H8+v5taxDpcbSz1xpdrEdVq1tEvfyoUaN6fN+IrLHWOglt7cx5jRy/Edm+lO+ldqWcw1Tffeed\nd5ZYLVv7A5+j1qRhHTat08E6Zqy7otBKuG3tIqWpz6pWnvuFxRdfvPEc/MysUxWR68mx7lANrZGi\n+4smOjUWOTdz3orI9Uv0/WjHy9qJWmeiNnYIa8/85S9/SW20LOccyhoWEfnZaL0VWgmz3oXWpCNa\nG461MGrMN998Pb5XRN7DD1SNms985jOpjft63TdyvmVtyCeffLLxvY499tjG9/7KV77S+HcHHXRQ\nibW2XtP51KL+yiuvLDHXDn7GiFyj6PLLL298L8IaYBH5+9vmm2+e2vg9SvevfYX9Tftv7XsPa5qy\nbgzrj0bkWiFq8c39YG2/z7lc1xlSW1uPPPLIEvP7zfe+9710HC2aa3sWtmn9Idat0s/FGnKdHIuc\nJ9S2nP1Ux1HTnKq1LKeddtpW10GratbGjMg13trWb2xrNV7b79dqw/X1O2fTOYgzaowxxhhjjDHG\nGGO6BP9QY4wxxhhjjDHGGNMltLbnVmppPkzzpdxFZTYbb7xxiZlOFpHTuppsontzTU3HKfw7TR1l\nOpfKOZi2T8sxWkRGRPzmN79pfO+219hbbr/99hJrmiPTctXCkjRJgiYEz890cpWw0d6SFuZK22fH\nz6JpmEwb/9rXvpbaaAlI2RDlbBFZUqN9gWl6AyG3WGWVVVIbZQSaej7llFOWmBZxKmNhKmFt7NCq\nWy1W235Wnl8t8yhP4thRG1imqOv8sPrqq5dYU4rbwrTbvtrY9gT7isq5OFeqhPDSSy8tMVOaad8Y\nkdM2d9ttt9RGSQRtjmmJGBFx8MEHl5hjUdPCmda98MILpzZe/y233FJi7buURZ155pmpjfIzSmgU\nrjdq8zxkyJASzzzzzI3n6A218cH3m2uuuVIb++LXv/71Eqv0rCY54vNlv9S5m+/N9GxNDea8rnMH\nLdEJ1+2IiL///e+N51BpzHhUZsV5QK04KTXScdEf+pqe3Ak4j2644YYlprQ0ImLppZcuMfdEZ5xx\nRjqOaegqgaCUjnJmlQ+wb6ldeVPKu/Y7SrD6ugfrDW2fodpdU/rLdbETNup9ZYsttijx+eefn9o4\nTjkemqyCI95/jykbouyKEo2IbHWsUC6ia09/GDZsWIm5XkTk7w20rY7Iz5+yBO2/V199dYnXWWed\n1EaJLde+LbfcMh3Hkg6UqNCWNyKv8brfpkSO67FKTPhdo8ZCCy1U4tpeSvsT14dOjcUjjjiixCoD\nIvp+3KfwPrCfK7pvp3SW91jhs+Z3Nl0XufegFFDhXkzn5L/+9a8l1vmH/UIl3028/vrr6TXXSZW2\n9QeOKdpxR2Tpo36mCy64oMRaeoRssskmJdbvkt/97ndL/MUvfrHEWkJEv4/1dO6ILLVX627anXPv\nuccee6TjuA96+eWXUxu/A332s58tcVuZVUQuZaBr93icUWOMMcYYY4wxxhjTJfiHGmOMMcYYY4wx\nxpguwT/UGGOMMcYYY4wxxnQJfa5R8+9//7vEavNI2taNURvS0047rcdzULcWke3ciNrtHnrooSXW\n2hqEGu4FFlggtVG7Rju4iIhzzz23x/OpHpZaWb31xxxzTIlZw6C/8L5TDxeRNXGsZxKRdZu0VBs9\nenQ67kc/+lGJ9TOxzkhTzYOIiEceeaTEqpkmNdtC2vep/Suhvljr17Duw9Zbb11itZij/rSt9V5/\nYP0A1iqIyDUiFN7ziy66qMS9sYs95ZRTSsw6H6zno9dFW3LVuZIVVlghvaYVb1u037JGFGuiqMa3\nLZ2sF0Vq82HtvtAalJahil43/45afPbziFxXpGY1Wruffblnuo5wzmYdF+ryI3LdgtocMzHqYlCn\nfckll6S2X/3qV63Ov88++5RYrbupiWetEK3xttJKK5WYenHWQ5kQr732Wok5d2idCNZo2HfffVud\nW9fniy++uPHYgRp/Q4cOLTHr+ETkZ0zdfESuxcBr4/kismX9CSeckNp+/etfl5g1ifT5NH12tXpn\nXa5dd901tbEGBI+rsffee6fXvH7aJ7OGTkQef1r7SufpTsDnpHaxrKuz7rrrpjbWdNAaUU2wbkVE\nHs9qiU5YX4z7S9bsi8i11rQ/sm4LY61LwrVa65586UtfKjHraaidOPu31vzi3MvaPv3lmWeeKbHe\nlxqsR1KracKaRFonkn2WdeP0ONYS4T1Tu2HeM62fx2tkzRvl05/+dI/vpXCfoHsn1qFhTayI/D1K\na4L1FY5F7v8i8nh7+umnW51vu+22S6+5L+E6qK/5rFmfKyLv20eMGFFinTPvuOOOEmvNpCZ0fuD4\nUMvnvfbaq8Ssq8J1tjd0co1kvVCtNbPccsuVeOqpp05tnBtq1ue13wT4vNquVawv89Zbb6U2/V2B\n8L1ZW+/CCy9Mx9X2uZxHOb/+8Ic/TMdtv/32JV5sscVSG+db3WuMxxk1xhhjjDHGGGOMMV2Cf6gx\nxhhjjDHGGGOM6RKq0qf111+/xLPNNltqe/vtt0vMdO/ewJRDtbEmtOJSm66ma1JrWqYUvffee6mN\nKXC0o91///3Tcc8++2zjexOmBuvfqC11E51MZWOKl8q5aAmsEghaODIdTD8DU9/1uilDYdqcprzx\neTGdV63SaMun0pu2MKVVz8/0PfaFnXfeOR3He0CraGUg0vaHDx+eXjO1Wt+PtsltU5VpwReR09uZ\ntsfU/oicrj127NgSq3UgrXk1PX6HHXYoMa2bNTWVMrfJJ588tR111FHRW9SCkdaWnXyGTHOknWhE\nnit22mmn1Kb2kePR9FNeK8dvRE5HpfyPcW+gZSxlMhHZZphpvpR8ROS0a5UK1WyqyYorrlhilZhQ\nOtSp5/jSSy+VeJZZZmk8riZBobWtWlVzfKussSnVtia/XGuttUp87bXXNl6v0nS/9N9pyVyz5aT0\nRW0o+axrFtwDtS7qeKO899Zbb01t7MPs2yr7qsnDuWZyb6Vp9hzDlM0o/CwqAaE8hPOc2qvyGcw+\n++ypjfJm3o/e7P1oj/rkk0+2/rsaNRki3++pp55qPK5m3dxWlsD7o2sa5/la/63ZS3O8cN9z0kkn\npeMoG1JpOOXbNfkB98OUikTk/qPWuv1h3nnnLTHnq4hsK657CbXcnVhQUsEyDRFZEqcSQt53fma1\ns+b3Fc4jEVl6Sit1nVMpA1E5GdddlS/2ldpYpAxLv1dRoliD/UL3G5SMqgy4DfxuEpG/t+iehXM+\n4WeMyHK+++67L7UtueSSra6Le4FlllkmtVGSozK9TqESZd2jEZYD0RIlhHMI99kR2dqen0ll91dd\ndVWJOaZuuOGGdNzHPvaxEqvlPfcjbSWUbVGrdu7VVObNe9y0PjijxhhjjDHGGGOMMaZL8A81xhhj\njDHGGGOMMV1CVfo066yzlpip7BG5Ivfvf//71FarRE4oc3j88cdTG1OFmJrKqvYROR2YrjNf+cpX\n0nF0BGD19ogscWmLVuVXx4fx1NLmPv7xj6c2ymkGKsW7xp///Of0mulbbGMFa23TlES6OTGNX9Pb\nmf7Iyt+aIkiXJk1BXGONNXp8r4033jja0va+MzVZ/4Z9vjfuSjXoEKBphZReUQYVEfHYY4+VmO5L\nWlGfY5iOAxER559/fo/XpH2b6YNEK5nXnHkoVVFnkyY0Vfr+++8vMV28mGoc0T79vpNjkXKxpjmj\nJ1gBn+NDHUsoX1DnOKbx091C00opWyM6p9KdSJ296NBAFwx1R6ErRk1a0NY9sCb76NRz5FrINVLh\nuhXxfllFE0wNp/NVDZWBUiJaY7LJ/vd/NeqA13S/VBbz/PPPl5hSy4g8f9NpbNiwYek4OpKpKwjT\noT+IdVGh3ITyNpU5UALB8RaRJXp0OXz44YfTcZQj817efvvt6bizzjqrxDqeec8+97nPlVidkGp9\nhufkuD/wwAMb/6bGQLghqpT08MMPL7GmolPey/Vz1KhRHbmuJigb49ockSWc6vbGvSL3uSrz1bWb\nsP/UXFkok6k59HRyLFKSyVIHEfk51q6BTnd07YnI86iOMcpeKEFSh1TKjynLULkF31vXI+5VeG+1\npAMdhHSfwnmL877K+CkPV6c+SgG33Xbb6ASUoOtaQtT97Z133ikxJcEqJaVkhPuLGvq9ld8XOV98\n9rOfTcfxuem+Z6uttioxpXi6L+dz4ueKyFKoWjmBtnRyLPK+69ijTE5LJBA6lakDko5NwnvLcijc\np0RkN9faZ6/J6tgPde0gdBSbYYYZUtuWW25Z4r66yhJLn4wxxhhjjDHGGGO6HP9QY4wxxhhjjDHG\nGNMl+IcaY4wxxhhjjDHGmC6hWqOGGrua/ae21XTD5Nxzzy2xagSbGDlyZHpN3Sit6hTWpanVpGnS\nc0dE/PznPy8xrWkj8vWfc845JVZbTtZRueKKK1JbW7vy3sJnRevwiIiVV165xIcddlhqU1vANqjV\n7+mnn17iWq0Y2q3R3lKtr1mDQ/WTTX1SLU9PPvnkEtPWLyJrwdXSsOm9tE+y1kZfLcQVauxZ26k3\nsFbIQw891PrvqKFl3Q21Cf/+979fYtZWUI0vazdof2QtHupVOaYUtZds0s6q1SBtlln3Remk/pd2\ngdNPP31qo2Vmrd5FW3gvI/K9Zn2E66+/Ph33hS98ocR8xqz9E5F14mp/y5pj1MrrWGSb2k+ybtK0\n005bYrXEVutLwvoptEHuD+znWkOG16k1A1jL69FHH208/8wzz1xi9tGIXCuEmmhaY0bU7TEJ62Ko\ndpz9np+rN+tC05zMtTQiW8u+/PLLqY3PV2vU9Qdq1LXvvffeeyW+6667Uhv3NKz1MeOMM6bjuJ7r\nHEJtOy2QWa+hp78bj9aVYI0ahTbNtOSuzWtam4Q2yJzDdtlll3QcLehrdGpO5fxxzDHHpDY+D53j\nmiyJdd/FZ8g+EZFrcrStocWadeutt15q+/a3v11irR905plnlrhtfQ7uvSLyvoHziD4L2njXGKh6\nUbrvYl9UO3LWiGoL+6++5ryk+3/u1zmOWMOiN7CWJ2t0ReR1S+s5toV17Th/R+R+2KnnyLo6nN8i\nIp599tkS08o+In9H4j6X+7+IvA9dddVVU9sCCyxQ4iOOOKLEOhabPus111yTXq+//volVlvnJvS9\neP21PsJajFqbqi2dHIusPak1WViftG2Nty222CK9Zv0ahbUOr7zyyhJr7btx48aVmHbfCvsW68JF\n5Dq4bWEfj3h/TdA2DBkyJL1mXVfXqDHGGGOMMcYYY4zpcvxDjTHGGGOMMcYYY0yX0Fr6dNBBB6U2\n/pmm7TWdYyC48MILSzxixIhW16Efeeeddy4x5QGUIkRE3HbbbSXWVELaQTNtVW00aX2m9nOkG2xI\nm6ilBzMFMSJLLJhSe8kll6TjmCrJVEOmFytqz01ZlD6fJmivF5FlDbRF1FTOI488ssS0K1U69Rw7\n8QyZHk/5V8T7+3oTffk8mj756quvNh7LdF2OZ5UrMsWe9r19RWVRTFXVVMX+MMccc5SY6c0TguOK\nsiJKzCIi/vjHP5ZYnxWlS5SvMAW7htqg//KXvyyxSnQ22GCDEnOO1rRnSqG0n7zyyis9XsdOO+2U\nXlP+WmNijMW2EgjKQHUuZIo976NSs/Vkv2CfqMkhdP2kfIryI13v237meeedt8RqOUvZlUoO+Nk6\nuS4yFfqtt95KbZTKqKzogQceKPHQoUP7fR3f+ta3SqyyE8qiKIekjXBExEILLVRildXNNddcJebe\nRC1728rP+8qOO+5Y4rZjtjeoRI22y5TWReTnzT6l6fAqKWyiNgbajg8ep/sNyibbQglDRJaVqKV0\nW7hfqkkYegvlnpS+R0RMPfXUJaYsNyLitNNO6/F8lK5E1KU+fCa0yVabcMr/llpqqRJTWjghuFf8\n6Ec/WmKV0NLS+MADD0xtLC3Be9XXMdWpOZXycZWXtR0DRJ8hree322671EZZ89e+9rUSjx49Oh3H\nOU4l+YT24moNzXWSfYTfHSJyuQ324Yi8/zr++ONLvOeeezZek5YJYD/QvVl/6Ot3DcqC+Dxqsty2\n1PooLdivu+661LbZZps1nqPpc9bKDtQk5pREf/Ob30zHscTIrbfe2ngdlj4ZY4wxxhhjjDHGdDn+\nocYYY4wxxhhjjDGmS/APNcYYY4wxxhhjjDFdQrVGDW3DaJ8ckeuUaJ2PCy64oMQ13dYiiyxS4ocf\nfrjVBaudMu2xqPVS/SFrXKiFdBNqrV2zJaWts+rk2tJJ/X0TfdUf0haxN5aITfUWaL0WkbXz559/\nfompS1VefPHF9JoWll/96ldLrHp+2vcpBx98cImpOVV7ONYBUPtEak4Hoi6G1vKgJpe62Ihcn4I1\nCWr9YPHFF0+vqalkfQqtAzTTTDOVmP1F7ZNZF0NrZjTRVl9aY9ZZZ02vOW/V9N2dHJfsl6zxorSt\nqVC7D1pLhHVB1Oa0icMOO6zE++23X2qjJp21hWrXpfVHaLve9hlrvSKuAVqnoo31YW8ZNmxYidV2\nk3OQ3hPW3KFmmbU0Iuo1nAYSWqhG5HpKvCadf0gnxqlq8VmfY6Bqt6nlO23AWQsgIu9BWLdCa0lw\n/dB5mfUXWD+D9qcRWcHaA+0AACAASURBVBM/fPjwEqstKOsD1CxDeW8vvvji1NYb2/XxqM067bJV\nzz9y5MgSc5z0h1r/4n6T+9CIiO9+97sl5n5A95esccH6JRH9r42lfYJz4+uvv57auKdmvUg9N58v\n91G9oXbfyMSqo1ir4VCre9UXWG9G64M1XeM222yTXvO7ANd75d577y2xrsesgag0XceKK66YXrPu\nV42JUbttiSWWKLHWA9OaWuPR72n8O61j1IR+52R/5uc+5phj0nFf//rXS8zvJhH5eyHry3Vi7esr\nnRyL3Nez1kxEnrfXXnvtVufjuhWR67kqtL3n9xC1LWe9M87RWm+RNcdYo0vpSw2liFwb6Nprr+3x\nfL05p2vUGGOMMcYYY4wxxnQ5/qHGGGOMMcYYY4wxpkuoSp+eeOKJEs8///yp7Re/+EWJKb1QmlKD\nJgTTnZnOqZe75JJLlpjWfWoDS37zm9+k13vssUeP5/jDH/6QjqPERaU7Y8eOLTGt3ZZZZpl0HNMd\na3QylY12vgsuuGBq22WXXUqsFpC0PeMzqFlFauowZQJTTjlliVdZZZV03BZbbFHiWhoaLVV33333\n1MZ0clqu00Itom59uOaaa5a4JmGbe+65S/zUU0+lNn42tWLrK7XUuemnn77EbWUTTJ+OeL9MhlAO\nRtmJ9lGm2q600kqN52NaqaackqOPPrrEKj9oC8e2jnuiacNMmVXL1v5Qe45My9W0eJX5jaeWYnns\nscemtqY0bM7lEXn+Ymqv3gemfGuaMmU0nKPbzn8R2eKTdrIqD/nd735X4lqa7UCkeKstJm0421re\nK1xrdY7j+P7JT35S4k022SQdd/vtt5f4mWee6dN1UKpCaQ3ncUXvR9Oar8dxndRxOlCSYN4/SmEm\nxJ133llifj619WRf1HTvrbbaqsQcY5SDR+S1i3MlbX4j8ti+//77UxtTyOebb74Sc383IWgbS8vw\ncePGNf5NbyyS+8q3v/3tEh911FGpjeNUpSWUZXEvR8vkiIi333678b2b5GsK5QPPP/98id955510\nXG1cUabLc+jee9ttt208B+emN998s8Qnn3xyOo77GZWZkoGSPnHdj8hyC+6zI94vEesLTWNC+0yT\n5P/dd99Nr6eYYooS6z3aa6+9SnzccceVWGWClNTSUjoir8OUc+u6yL0yn3dP19UJ2u5tdC6kJIXr\njM5P7PdtpSSU40c0S6Z0jeS8u/HGG6e2N954o8Tc5/z+979Px62zzjolpnSxBqXmEXn8nXjiiamN\n33E6+TwpK9LSE5SWq3099wsqJW4L97mUnOn6zL7OMgGUIkdkWd2hhx6a2vbff/8Sc7/PfhaRxym/\na0VEfPKTnyyxSokJ16mf/vSnjcdZ+mSMMcYYY4wxxhjT5fiHGmOMMcYYY4wxxpguoSp9YnoZ09cj\ncgo702IjmlP6VILSFjoJqDMBHXaY7k1XjQlBdyumk6mDCFHJBuUhL7zwQonbyg+UiVVRn9e39dZb\npza6fvQVSsnOPffcEmvaL6ELg1bep2xGpUlMw6Z8QKUdNakeJTBtq+bXGAi5RU3uwnS+iOz8cPfd\ndzeef/LJJy+xpn8zzV4lU22YbLL8e/B7771XYu2bdBGhWxTnlIiIH//4xyVWFxq6LrSVYM0444yp\n7de//nWJNRWyP/BeaJo6JRBbbrllaqPcgmma2rc5JnS+pWSRKZvqTqSyiiY4njU9m7C/alr7t771\nrRLTwSOi2cVju+22S6815Ziw/1Ne2R+4LlDCEpHXRR2nQ4YMKTFT9mvOOZqqzVR3yi2YWhsR8fTT\nT5eYz0ZdXNqm7lL2q9IkpulrW5P8S2UxY8aMKbHKRSnX7eS6yLlh0UUXTW1Md1f5WRMXXnhher3Z\nZpuVWJ2szjvvvBJzDqQLWkR22zz88MNLrFK3q6++uvG6KNOlNFldMCgh7Oteje+l7i585vo5+wrn\nO+7BIiIWWmihEuvY5/1rklhGZInepptumtrogkeXUd1H0R2F8jidM3l/dF1s68REdKzQVYrjTaVy\nRN0HKf+puRb2ltoelX1RJUJ9obZ/qjm1EcoXdO4lDz74YHrN+ZHubPzuEpH3WSo/WX755UvMvqvy\ncO2HTQyEDIpzX0SWO6mckNK7s88+u8TcDynqGse9FD83XU8jsvyFe2Xd//HZ6/6vaQ+82267pdeU\nKtGZKCI/wxp0mlOpEeema665ptX52tBWVqZr5kMPPVRi/iagcvRaKRPCeU6dddVxti/weybXbpYK\nich9l65UEXmPWntWLBOw9NJLp7ZaOYnxOKPGGGOMMcYYY4wxpkvwDzXGGGOMMcYYY4wxXYJ/qDHG\nGGOMMcYYY4zpEqo1aqh5bKuLrbHTTjul16eeemrjsdS1sXbBKaecko47/fTTSzxixIgS0xIxImu/\nTjvttOp19RdaXqv1IeuEqCVwzQqyP8w888wl1poH1BzPNddcqa1Jo6s1ImjFpjV5mrpXTQfJug9q\nr8fPQnvVvqI24X2x09aaN7T7ZG2R/tBWN6r1YJqslrWmAet80NJOYb0GtXzkOViHRmGfoG4+Itc9\nYV0s1XrTTrxmLU6LeT2O18iaNxG5302selGdgNc6dOjQ1Kb3sA2co5dbbrnUtuqqq5aYNpURuTYM\ndf+q967ZBWvtofGwvkdErrtBnXBEthLuFHyGOgd95zvfKTHXpoiIHXbYocR8Tqw7E5H77AILLJDa\nWOOsVkuIWurLL7+8xGqxrnVKmqjV8WgL675o/QHWuVEL9zYa7r7A+iZrrrlmauO8qXNqX6jtffhM\ndf3kvot1Smp1NnTvw1pcrNlAK9SIXNeor9SeMenUc+QeVe1rf/jDH5ZYxwr3lLRX1v0R75fWCmHt\nAq3DQLhf4vNkLbWI+vzMuZZW9jrH1OA9Z39Ri3XW92t7vv7C58haFxHZ2pj7v4g8V77yyislfvjh\nh9Nx3GvX1uA555yzxFp7hn2IFua6hxk1alSr9yJap4xjVs/B9Y9Wylr7ou083annuNhii5VYn2Gt\nfgf3orS01u9O/F7F2moRuT4Maz898MAD6bjnnnuuxDrnN8F1OyJ/h2OtK62RxZpver2E+1xda1hr\nlTXElE6ORdaQ4T4uItfRYo08hXbn+r2Sdeu4d4vI4/a2224rsdbz4vc09gv9vsV7q/sKcuCBB5ZY\nv3OyZqDCGqysu8rvMUpt7XaNGmOMMcYYY4wxxpguxz/UGGOMMcYYY4wxxnQJre251YqLqdCackfb\nM55e7WjPOuusxgujZSxT/eaZZ550HFP/mAa67LLLpuOOOuqoEqsdYVvLbFKz3WY6uaaaE7X4Zrrj\nxJJb0E6T6Zz6d3/4wx9KzJTiiJw6q1KDI488ssQjR44sMeVSEREvvfRSidva/tbs4WiDqTaStH2e\nfvrpUxtTomvp7+yTaqVMBsKeu4Zaiqu8rgl+7iZb5Ig8jmitXEOtaWkT3Ra9j7QjZLpsRLaVX2GF\nFUqsqeY/+tGPGs9Pa9eaFKy38DmqfSbT7NVymvaBHIv77rtvOo5zWy39kqy77rrpNS2BO43aMtfS\nZ9um2TKlVdcijuGJMRb5/ir/W3jhhUvMVHem5yp6zVwzmdquNu38O861lApERFx77bUlpo1wRF5b\nr7/++hKrfI2SA5Uz01qXtpe0q4zIcoEaA7Uu0l45Iqfda3q22nA3XVutn/BYptbre9GGtmafTmpj\njLamalnNvqVWs+wbtT0N53p9ppQddOo57rzzziXWvsf7qnsFymApadpggw3ScdxHqm0rX3M9ou1y\nRLPkSFliiSVKPMUUU6Q2SrI4Lmk3HBHxl7/8pcT6fAnlcS+88EJq23777Us8seQWvC+cayIi1lhj\njca/o63yyy+/XOLjjz8+Haf2vv2F667KpvnsKKuLyP1Q5UFN8LtQRMSNN97Y6u/4fFQSQpnOOeec\n0+p8E4JzhNpF1/o91yT9XtBfdJ2pfeckHNsqP+aeeuqppy5xbyTB7LeU83Etjcg21yol5V6Pfb+/\n1J4Vv/dr3z7hhBNKTMlWjdrzqc0v3EtxH03L7Yj8/e7VV19tdU2dYPLJJ0+vuRfU3xv4W4KlT8YY\nY4wxxhhjjDFdjn+oMcYYY4wxxhhjjOkS/EONMcYYY4wxxhhjTJdQrVHDmjLUSkdE3HXXXSVebbXV\nUhtrxVADT310RK5ZMnr06FYXrLVnqHFjbRzlpz/9aYnVdq8JWiJGRNxyyy2NxzZZjqm+UXXOTXRS\n/0t7Ma2LQ1inJCLrKO+5554Sa80O6hZpyxaRrSSp7VR9Li3Ralp8Wv1S0x0Rcfjhh5eYFno11J6b\n10VduGp8qcVXK0jSqedI62K1z6SO9bjjjkttQ4YMKTHt61nnRFF9vGpRx6O1TDj+OO4V9kdamUfk\nGju09aN9Z0S2g2bdnIisB6We/7e//W06jn3ztddeS22drEtDqP/VOZXzrfYb9lPWQaF1ccT7x0QT\nNWtZ2smyzgHrvURknTAtEiMi9thjjxK3rQGm/Zp2wbTxVl0zawRona1HHnmkxFqjqBNwXYnIdYFq\nmvWbbrqpxGqRzWejdW7a1jUgnPNpSR2R6yJxvo/Iunfe4zfeeCMdx3oHer2sFcG6YTX6Yl/ZF2pa\n/JNOOqnE2rf322+/ErNfqj3rZpttVmK1OWWdH7ZNOeWU6bizzz67xDUrZo57XntEriXGuVdr7bA+\ngI4xWu/yc+p+hvU0FlxwwdTG8TAQ9aKmmmqq1MYaNayfoPDz6HzKWhtaw6lt7RnWAWS83HLLpeNY\n+49z64TOT1i/hHNwxPvr17VB69ywBk4nxyLrM2q9LfZtHUdLLbVUv9+7tn9qgna+n//851Mb63LR\nRriG7r1ZJ5DrcUTE5ptvXmI+gz/96U/pONaIoo10RF4HarbFvYF9tFaXsDa/8zq5dk/oOjfeeOMS\ns2akjoFOwH0p9yhq0962RiCfm56D94bfMSPy98yBWhc5LiPq3x8Ja+3MMMMMnbmwBlgPR2uRcf/B\ntTQi73e0fi7h9yat8cdxxPWN3x0jsj177XcF16gxxhhjjDHGGGOM6XL8Q40xxhhjjDHGGGNMl1CV\nPhljjDHGGGOMMcaYiYczaowxxhhjjDHGGGO6BP9QY4wxxhhjjDHGGNMl+IcaY4wxxhhjjDHGmC7B\nP9QYY4wxxhhjjDHGdAn+ocYYY4wxxhhjjDGmS/APNcYYY4wxxhhjjDFdgn+oMcYYY4wxxhhjjOkS\n/EONMcYYY4wxxhhjTJfgH2qMMcYYY4wxxhhjugT/UGOMMcYYY4wxxhjTJfiHGmOMMcYYY4wxxpgu\nwT/UGGOMMcYYY4wxxnQJ/qHGGGOMMcYYY4wxpkvwDzXGGGOMMcYYY4wxXYJ/qDHGGGOMMcYYY4zp\nEvxDjTHGGGOMMcYYY0yX4B9qjDHGGGOMMcYYY7oE/1BjjDHGGGOMMcYY0yX4hxpjjDHGGGOMMcaY\nLsE/1BhjjDHGGGOMMcZ0Cf6hxhhjjDHGGGOMMaZL8A81xhhjjDHGGGOMMV2Cf6gxxhhjjDHGGGOM\n6RKmqDV+5CMfmVjXYYT//ve/HTuXn+MHR6eeo5/hB4fH4qSBx+Lgx2Nx0sBjcfDjsThp4LE4+PFY\nnDRoeo7OqDHGGGOMMcYYY4zpEvxDjTHGGGOMMcYYY0yX4B9qjDHGGGOMMcYYY7oE/1BjjDHGGGOM\nMcYY0yX4hxpjjDHGGGOMMcaYLsE/1BhjjDHGGGOMMcZ0CVV7bmP6C63eGNfs5DppNWeMMcYYY4wx\nxgwmnFFjjDHGGGOMMcYY0yX4hxpjjDHGGGOMMcaYLsHSJ9NvJp988hJPP/30qW2KKf7XxT760Y/2\nGEdEvPnmmyV+5ZVXevz3iIj//Oc/jdfRJLNiHJGlVSqzsuzqg0WfVRN+Tr2n7b0lfb3PTe/l52aM\nmRTgHDfZZP/7P8/ansLznzGDm6Zxr3Cs1763mAmj+8kPWxkNZ9QYY4wxxhhjjDHGdAn+ocYYY4wx\nxhhjjDGmS/APNcYYY4wxxhhjjDFdwqCvUdOXugt9ZVLRu/UFajGnm2661Db//POXeIMNNkhtfL3g\ngguWWGvUvPDCCyW+6aabSnzhhRem4x566KESv/rqq6mtqS+888471ddNfJied62mT9NxfTl3RK5p\npG1Nmt9333238bXrDP0P3k/Wh4rI972mmeYz4PmmmmqqdBxfTznllI3n47N67bXXUtsbb7zR6po+\nTNTGWK0WRptz1MZKTQfe9hwf5rHXG5rqHNSeAcdHb8ZK29ptPKfn1P8P75HOcdNMM02J33vvvcZz\ncL/B4/Rv2t7jtutz29p8H9Zn21s68V2j6fnUap3oWK+N00mZ2t6Gr9v27bb1LrlvioiYdtppSzzz\nzDOX+O23307HvfjiiyWu1dr8MD3DCdE0xnR88Hnzb3RObbumdfszcEaNMcYYY4wxxhhjTJfgH2qM\nMcYYY4wxxhhjuoSJKn2qyRzapl0rbS3QaqlsvA62aRoVU1j1vbo9daov8J7NMMMMJV5yySXTcV/7\n2tdKvPbaa6e2OeaYo8RN6YkREffcc0+JKZVYZJFF0nErrrhiiYcMGZLaHnjggRLffffdJX7qqafS\ncXyutT4zqT3TWlo0+33b8dE2BVvTVGeaaaYSq537v//97xK/9NJLJVbJTC2l8cOUVlpLCa2lBzON\nvyaR4rPiWI6IWHbZZUvMFOCIPJ4fffTRHuOInBL8YZJb1MYin43KzQjvz9RTT53a2C84n+qa1tbm\nksfVpKR6fr6elJ9nT/AZ1GQzlAHreOZ8+NZbb5VY7yX7CcdsRB6bfB4qHX755ZdLrGn87EN83pPC\nM63Jm3jvVl111dS2/PLLl7h2X2+99dYSP/jggyWuySFqKfxN83hE7kvaxr5EyanKiicF2kqqI5r7\nsN4/zrGMOS71de051uZ5tuk52L9qEvDBju5Dec9139j03UKfIfs9/2auueZKx2244YYlXnnllVMb\nx9i//vWvEo8ZMyYdd9FFF5X4ueeeS23cz+pcO6lT2/vwmXBMaLkNjiPOazoGauOe8O9qe5gPCmfU\nGGOMMcYYY4wxxnQJ/qHGGGOMMcYYY4wxpksYEOkT042YJjbjjDOm45jaNMsss6Q2pvQxzemVV15J\nxzENsObm03RNETmljhW9GUfk9DXKMiJy+tqkkoLI50OZ0eabb56OW2mllUo833zzpTbeC6aE3nnn\nnem4o48+usSPP/544/nWXHPNEjP1OCLLLZ555pkSP/HEE9GEpsZ1Q5pbJ2lKM6ylTGsaLtMROSZ0\nHHGcsr/Q7Ssiy9lUvnb//feXmO5fTG+MqLvfNI2/SWVc8rNr/61JzjjPzTPPPCWeffbZG4/j+Ftj\njTXScbPNNluJVZrG188++2yJtc9MTNe+D5om+a2ORa6TmpLNFGA+G5WI0kWP69Zjjz2WjuOz0RRs\njhder66LXKspn4mY9GWINUk1+7qOMabuc5zqOOKayX6iqeCUIW6xxRapjWOdkuD77rsvHffkk0+W\nmOtnRMTTTz9dYj7TwSqbaUqxn3vuudNxn/zkJ0u8zz77pDbK1/75z3+WeOTIkek4Sp/4vpSTKyqn\noUyK/YxzcESeL7TPURrOeaCv7lPdRpNEW1/rmsnPy+8hw4cPT8exL/Decp8SEXHSSSeVWMcR59ja\n3MixXisL0Va6Olio7VE5Zy600EKN5+Bxut/gOFpttdVK/IUvfCEdt/DCC5e4tt49//zzJda1mtd/\n+eWXpzZKIJu+6w5mausi23Qd4z3kM9Z1kesRn2lNxq/Pkd89KBdWmRrHsP7GMLHGnDNqjDHGGGOM\nMcYYY7oE/1BjjDHGGGOMMcYY0yX4hxpjjDHGGGOMMcaYLqEjNWpUQ0mN/cc+9rESa40D6gBZlyQi\na/hef/31HuOIrDujXlfrbFCHqzo2agl57Wpzyc952mmnpbY77rijxJOi3Rq1nqrjnnXWWUus1pTU\nQp9//vklPvnkk9NxL774Yomp3V599dXTcR//+MdLrDWPxo0bV2LWXlAdfc2KuklDPClQq23C8aL3\nhDpMajT1HPPPP3+JV1lllRKzhlFExFJLLVVifTYcw6yhQC1wRB6LNQu+Ws2hwarprtkbcj7UOZD6\nX87F+nxY+4RafLXg5pygNbuo9ec4VUta2mVqHaLBbgNcqy3ANUitRqnNnnfeeVMbx9Viiy1WYn3W\nnP9Gjx5dYtYh0evQZ8N7Xqs1x36hNRk4Jw/259kTNS0+x6LuOQj19zqOeH6us2uttVY6bo899iix\n9pmmelH6Xqy5onsf9o2a/fBgea58HtzPaH2fvffeu8fjInJ/5n71vPPOS8exNgz3FKwFFlG3ZOax\nPE7rv7F+oNatuvLKK0t81llnlVjr4QyWZ6jU6pvo2CScfz/zmc+UeM8990zHLbrooj2eb4EFFkjH\nPfrooyXmnjcij7mmfVVEfia1Om6D9Vk1UZtPuTfU+Yn7lJpdM/eerDmkz5Bzt+5R+R2B86J+N2X9\nlaWXXjq1cR7W+iuDFe7Dm6zsI+r1Eeecc84S85lwHxGR+wZrzehcxn3o+uuvn9r42wT7lv4WQZv1\nSy+9NLWxng37SafHpTNqjDHGGGOMMcYYY7oE/1BjjDHGGGOMMcYY0yV0RPqkab1MX2LKl6Z/Me3p\noYceSm1Ml2L60uKLL56OYwo/U/tr0idNUWOqE1Px1W7tX//6V4k32GCD1DZ27NgST4rSJ6bs3nvv\nvaltiSWWKLFam51++uklvuKKK0qsz4Apj+w/KpebY445Sqz3mTaJtG/TtFKm6NVSZAcyle2DgJ9B\nU6uZLlqTbBCVwjRZg/KZReQUe507+HfscypD42f5MNhXNsnW1H6SshRN591oo41KzPmLkoqInPar\n46PpmjhHR+T5e5NNNimx2jGeeuqpJVbZDK2eaxK2bqKWps65heuT2kZyvCyzzDKpbbnllisx50mm\nUkdkm3vGKm/iHKoSQj57piurHJWfRddx2oTrPDwpUJtTmYat612TxFbT/TlXfuITnyixWkXX7Grv\nueeeEt92220l1mfF56Pp6qTWx7sV7dtNqfkqJaLEQtcgWnKfccYZJb799tvTcXzWfC+VPHIt1Ovl\nOTiHrrDCCuk47o913W1iMD7P8TRde22t1z0H969bbbVVifW7Bv+OY1v3oZwPaxLC2tzL8+tnGcz7\nmAlRk7RT4qJ9m2OC904l89wvURKs5+Peg3FE/o7z8MMPl/iRRx5Jx7FN1z7O84N5/BF+Jo4VlrKI\nyCUSdN9IuSa/C+geib8l1PbDLK8ydOjQ1KYS7vGoxJVjWNcHlvDgd87a95W+4IwaY4wxxhhjjDHG\nmC7BP9QYY4wxxhhjjDHGdAn+ocYYY4wxxhhjjDGmS+hIjRqFmr4nnniixLSti8g6rhtvvDG1Uf9L\nHa7WU2iqaaGWakQ1qtSkUUuudW7IpKIrrEENLXWZf/7znxuPo01lRMRVV11VYtrQqWaP2kLWQ9A6\nQbQ3/Mc//pHazjnnnBKrTRv5sNU3GU+tnkLNvrKpr6sOkzVGqP+t2RvqOVgviv1FNb78LJPCs5kQ\n/Iy8Z/rZabWtFr7Dhw8vMceYasF5Ts7l1OBG5OeqtWyadPqrrrpqOu6WW24psWr9WS+s9pm7CV5b\nzTaex+k6s+6665ZYddV8bqz/8ve//z0dx1okHJdqBcrnpppt6sVZ00hr1NRqCY0aNSomZWrzUNOY\njch9m2tf23p/WlOBfejVV19NbXfccUeJr7322hJrPTn2BdaD0HPW9laDBa4nr7zySol1juMY1r7N\nmni0cNW9B9dPPifdy7K/8Joico2GJZdcssS0G9b3vuuuu1IbaxWxVko3z6cTomn86bPi/dM6aZzb\nON/qfeEzYZ3G66+/Ph3HZ6w1hFiTg3sdrWHF/qmfha8H87PrCX4e3ZPSTptW6RERN998c4mffPLJ\nEqvV8mOPPVZifpe477770nF8b50nOXa4nuq8yzatncL1oLZPGEw0fa/S8cY6e1oLjeODa5/WqOEY\n4zOu1XpizSA9B9ddrZvD61httdVS29VXX11i1kOqfV/pC5NGDzHGGGOMMcYYY4yZBPAPNcYYY4wx\nxhhjjDFdQkekT5qax9QwppJqGvTf/va3Et99992N52RaoKaVMs2NqWaaKjXffPOVWFNOmepEy8Sa\nfEbT4Xi9k4pkhul5vBe85xE57ffFF19MbUzxrkmOKGFjuqimEzLd+Mwzz0xttKitSRCYbqdpebRg\n/zDI28ZTS8Vssg2lpXdETv1jWqnOD7zn+l5Mz27qO4o+J36WJinbhM7ZbTTZVqpUgvMcpU7axlRc\nfY4cw5QujhkzJh3HOUEtGDmGmZ6v8/KIESNKrHbv5557bomZStpp68OBQq+Lz5D3n3NfRJaK6VrF\n9N3rrruuxzgi4qmnniqxzqFN76XPZtllly0xrd1VjkqZMqU1EZOmJTeprfWUCLWVC+l8yLlS1yrC\nMaFrMPddfFa6v2maNyOaZYiDhdrcT8kX09wjsgRC+zJtuDmHqsyBkkJKa9S6mWNW58J55523xJtt\ntlmJF1hggXQcn71Kn8aNG1fibp0z+0Ntf0mZg1rZL7bYYiXmM6ZMJiLLHP74xz+WWMfDOuusU2KV\n+nKd5F5TxyL3TINlvesEHG+zzTZbaqNkhhbPEVlCzXIbuvZxjeNxauPN75IqS+M1MtY5nn1Q13jO\nOeybnZbMTEy4dnH+0ufIz65W2DyWkmqWUoiIeOCBB0rMNU0lu7qnIZynV1lllRKvt9566TjK7HTO\npjz8zjvv7PHaI/r/m4AzaowxxhhjjDHGGGO6BP9QY4wxxhhjjDHGGNMldET6VHM7YHoZU5T0tVa5\nJ6zqrOe48sorSzzNNNOUWJ00VlxxxRIzpTsiV31nqpSmsvG9H3roodQ2GNOBe0OTDCoip+zWUveY\n/qXVvpkSzBRHLyiV1wAAHR9JREFUrcZ+3nnnlVhdxJguyrQ29ouIZpeviEmnAntP1FKD+bk1dZtj\niWmL+qyZ/s3nS0laRJZDajp/k4NMzZlIaZI7DeYxys9BuZOmdi6++OIl1rRSPsealJGubnRSo5wt\nIvcFdWGgZIfp+SqboYOQppVec801JWaa+GB9juzDfG4qc+D4U9cKyi2Yiq9zIaUqTbLciJzirXMy\nxyblwdrnOP9rirI6eX2YYD/V+9CUMq/uadwX8fnoGsznrZIXpmRTll4bRyqH5DUORsc9/awcB5S7\n15yY9J7zWKbA696TczJT+3Xe5bNRCdanP/3pEq+88solVpdNjj91xaQsarA8t76i8xzXTL23PJb3\nj/KmiIgrrriixBzPlBRHZHmTrmkqpxpPTd40qUufuC7y3tH9MCKPI70HTSUwavJjvi/3FxF5rNQk\nTexXOu45X6tEnRIdfe/BCtcu7mE4N0Zk1zrdD1KCdvnll5dY9xX8XYHofeZ3v1qZALppqtMm9zv6\nnYeStppLtF2fjDHGGGOMMcYYYyYR/EONMcYYY4wxxhhjTJfgH2qMMcYYY4wxxhhjuoQBqVFDfTPt\n0VT3XNMSEmp39RzUnVH3RzvDiKzhVo099XQ8n9oKUyenGrlJTTdaQzWzfD6qDaYWn201mzPWKVHb\ndmpR9fnwufL82hf4d1rzqOk5DlZr5yabcv08HANqtUztKbXZqtmnlpfPvTbe9BlynHIs8nw9/R3h\n56xZzg4m+Bypk1XdLWvFaK0hfn7OyxdccEE67tRTTy1xk+V9RNbkqi3iCy+8UGKORdY60WvUeYW1\nqlgTrK3V8QeN3i/22ZrdeK0eD2uWUEev62LN3pWwRpfWqOF7cQ7Q+0/bX9afihg882QnqNXq02fA\n17X+zDHG56M1uzhOb7755tT23HPPlbg2b3I+r9XFGIzPtGadzjVNnwXHoq4fK620Uo/vpXsbznms\nwcD9b0R+psOHD09trNfBsci9V0SuR6S1Lwbjc+sNTbXpIvI6o3WI2NfPPvvsEl966aXpON5r1hVb\naqml0nF8jqzfFZH3s7XaVJzPJ/XnRlgvinVFI/JcqPWdmuq86P6I95l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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f82179166d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}