sentiment_analysis_deep_neural_network.ipynb

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      "name": "sentiment-analysis-deep-neural-network.ipynb",
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      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhishekmanglaa/sentiment-analysis/blob/main/sentiment_analysis_deep_neural_network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MptULi4hkhh4"
      },
      "source": [
        "# A Deep Learning network for sentiment analysis of the Sentiment Data From Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmA6EzkQJ5jt"
      },
      "source": [
        "import json\n",
        "import tensorflow as tf\n",
        "import csv\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "# Hyperparameter\n",
        "embedding_dim = 100\n",
        "max_length = 16\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "training_size= 160000 #TODO this is the size of the training data set which you will get below\n",
        "test_portion=.1\n",
        "\n",
        "corpus = []\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPVSRY3IlADj"
      },
      "source": [
        "## Loading the raw data from Kaggle. \n",
        "\n",
        "Cleaning the data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bM0l_dORKqE0",
        "outputId": "59dfbd62-a3a1-424b-cd9c-e5bdb3a12cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Ingest a cleaned version of the data set from https://www.kaggle.com/kazanova/sentiment140\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/training_cleaned.csv \\\n",
        "    -O /tmp/training_cleaned.csv\n",
        "\n",
        "num_sentences = 0\n",
        "\n",
        "with open(\"/tmp/training_cleaned.csv\") as csvfile:\n",
        "    reader = csv.reader(csvfile, delimiter=',')\n",
        "    for row in reader:\n",
        "        list_item=[]\n",
        "        list_item.append(row[5])\n",
        "        list_item.append(0) if row[0] == '0' else list_item.append(1)\n",
        "        # TODO: 1st item in list_item is the text found in index [5]; 2nd item is the label: it should be 0 if the first item of the row is '0'. Otherwise, the label should be '1'\n",
        "\n",
        "        num_sentences = num_sentences + 1\n",
        "        corpus.append(list_item)\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-27 00:39:13--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/training_cleaned.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.7.240, 172.217.12.240, 172.217.13.240, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.7.240|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 238942690 (228M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/training_cleaned.csv’\n",
            "\n",
            "/tmp/training_clean 100%[===================>] 227.87M  81.9MB/s    in 2.8s    \n",
            "\n",
            "2020-10-27 00:39:16 (81.9 MB/s) - ‘/tmp/training_cleaned.csv’ saved [238942690/238942690]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlM4bWB_lTJ5"
      },
      "source": [
        "## Preparing the training and test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohOGz24lsNAD"
      },
      "source": [
        "sentences=[]\n",
        "labels=[]\n",
        "random.shuffle(corpus)\n",
        "for x in range(training_size):\n",
        "    sentences.append(corpus[x][0])\n",
        "    labels.append(corpus[x][1])\n",
        "\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(sentences)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "vocab_size=len(word_index) \n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(sentences)\n",
        "padded = pad_sequences(sequences, maxlen=max_length, truncating=trunc_type)\n",
        "\n",
        "split = int(test_portion * training_size)\n",
        "\n",
        "test_sequences = padded[:split]\n",
        "training_sequences = padded[split:]\n",
        "test_labels = labels[:split]\n",
        "training_labels = labels[split:] "
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1zdgJkusRh0",
        "outputId": "04f91dcc-4083-4f0d-8cb7-da7f65163194",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Note this is the 100 dimension version of GloVe from Stanford\n",
        "# Laurence Moroney unzipped and hosted it on his site to make this notebook easier\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt \\\n",
        "    -O /tmp/glove.6B.100d.txt\n",
        "embeddings_index = {};\n",
        "with open('/tmp/glove.6B.100d.txt') as f:\n",
        "    for line in f:\n",
        "        values = line.split();\n",
        "        word = values[0];\n",
        "        coefs = np.asarray(values[1:], dtype='float32');\n",
        "        embeddings_index[word] = coefs;\n",
        "\n",
        "embeddings_matrix = np.zeros((vocab_size+1, embedding_dim));\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word);\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix[i] = embedding_vector;"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-27 00:54:45--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.164.176, 172.217.2.112, 172.217.7.176, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.164.176|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 347116733 (331M) [text/plain]\n",
            "Saving to: ‘/tmp/glove.6B.100d.txt’\n",
            "\n",
            "/tmp/glove.6B.100d. 100%[===================>] 331.04M  88.1MB/s    in 3.8s    \n",
            "\n",
            "2020-10-27 00:54:49 (88.1 MB/s) - ‘/tmp/glove.6B.100d.txt’ saved [347116733/347116733]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XWjlIHllb-A"
      },
      "source": [
        "## Model architecture and training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKKvbuEBOGFz",
        "outputId": "9cdb06dc-0482-41bf-e334-85c1c264582e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "training_labels = np.asarray(training_labels)\n",
        "test_labels = np.asarray(test_labels)\n",
        "\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    #TODO \n",
        "    # Embedding layer\n",
        "    tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=max_length, weights=[embeddings_matrix], trainable=False),\n",
        "    # Dropout of 20%\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    # Conv1D layer\n",
        "    tf.keras.layers.Conv1D(128, 3, activation='relu'),\n",
        "    # MaxPooling1D with size 4\n",
        "    tf.keras.layers.MaxPooling1D(4),\n",
        "    # LSTM layer\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    # Output Dense layer\n",
        "    tf.keras.layers.Dense(32, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
        "model.summary()\n",
        "\n",
        "num_epochs = 50\n",
        "history = model.fit(training_sequences, training_labels, epochs=num_epochs, validation_data=(test_sequences, test_labels), verbose=2)\n",
        "\n",
        "print(\"Training Complete\")\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 16, 100)           13832000  \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 16, 100)           0         \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 14, 128)           38528     \n",
            "_________________________________________________________________\n",
            "max_pooling1d (MaxPooling1D) (None, 3, 128)            0         \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 3, 128)            98816     \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 64)                41216     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 14,012,673\n",
            "Trainable params: 180,673\n",
            "Non-trainable params: 13,832,000\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "4500/4500 - 33s - loss: 0.6008 - accuracy: 0.6706 - val_loss: 0.5675 - val_accuracy: 0.7024\n",
            "Epoch 2/50\n",
            "4500/4500 - 32s - loss: 0.5688 - accuracy: 0.6999 - val_loss: 0.5574 - val_accuracy: 0.7131\n",
            "Epoch 3/50\n",
            "4500/4500 - 32s - loss: 0.5558 - accuracy: 0.7111 - val_loss: 0.5520 - val_accuracy: 0.7181\n",
            "Epoch 4/50\n",
            "4500/4500 - 32s - loss: 0.5448 - accuracy: 0.7188 - val_loss: 0.5495 - val_accuracy: 0.7163\n",
            "Epoch 5/50\n",
            "4500/4500 - 33s - loss: 0.5370 - accuracy: 0.7254 - val_loss: 0.5524 - val_accuracy: 0.7169\n",
            "Epoch 6/50\n",
            "4500/4500 - 32s - loss: 0.5300 - accuracy: 0.7305 - val_loss: 0.5456 - val_accuracy: 0.7231\n",
            "Epoch 7/50\n",
            "4500/4500 - 32s - loss: 0.5237 - accuracy: 0.7346 - val_loss: 0.5481 - val_accuracy: 0.7243\n",
            "Epoch 8/50\n",
            "4500/4500 - 32s - loss: 0.5163 - accuracy: 0.7403 - val_loss: 0.5474 - val_accuracy: 0.7201\n",
            "Epoch 9/50\n",
            "4500/4500 - 32s - loss: 0.5103 - accuracy: 0.7447 - val_loss: 0.5522 - val_accuracy: 0.7182\n",
            "Epoch 10/50\n",
            "4500/4500 - 32s - loss: 0.5056 - accuracy: 0.7466 - val_loss: 0.5509 - val_accuracy: 0.7211\n",
            "Epoch 11/50\n",
            "4500/4500 - 32s - loss: 0.5010 - accuracy: 0.7505 - val_loss: 0.5534 - val_accuracy: 0.7193\n",
            "Epoch 12/50\n",
            "4500/4500 - 32s - loss: 0.4963 - accuracy: 0.7519 - val_loss: 0.5501 - val_accuracy: 0.7200\n",
            "Epoch 13/50\n",
            "4500/4500 - 32s - loss: 0.4935 - accuracy: 0.7540 - val_loss: 0.5563 - val_accuracy: 0.7168\n",
            "Epoch 14/50\n",
            "4500/4500 - 32s - loss: 0.4885 - accuracy: 0.7582 - val_loss: 0.5691 - val_accuracy: 0.7182\n",
            "Epoch 15/50\n",
            "4500/4500 - 32s - loss: 0.4844 - accuracy: 0.7603 - val_loss: 0.5615 - val_accuracy: 0.7143\n",
            "Epoch 16/50\n",
            "4500/4500 - 32s - loss: 0.4806 - accuracy: 0.7637 - val_loss: 0.5780 - val_accuracy: 0.7169\n",
            "Epoch 17/50\n",
            "4500/4500 - 32s - loss: 0.4780 - accuracy: 0.7632 - val_loss: 0.5705 - val_accuracy: 0.7159\n",
            "Epoch 18/50\n",
            "4500/4500 - 32s - loss: 0.4780 - accuracy: 0.7644 - val_loss: 0.5680 - val_accuracy: 0.7132\n",
            "Epoch 19/50\n",
            "4500/4500 - 32s - loss: 0.4736 - accuracy: 0.7668 - val_loss: 0.5793 - val_accuracy: 0.7177\n",
            "Epoch 20/50\n",
            "4500/4500 - 32s - loss: 0.4710 - accuracy: 0.7691 - val_loss: 0.5744 - val_accuracy: 0.7146\n",
            "Epoch 21/50\n",
            "4500/4500 - 32s - loss: 0.4685 - accuracy: 0.7707 - val_loss: 0.5711 - val_accuracy: 0.7199\n",
            "Epoch 22/50\n",
            "4500/4500 - 32s - loss: 0.4668 - accuracy: 0.7718 - val_loss: 0.5810 - val_accuracy: 0.7129\n",
            "Epoch 23/50\n",
            "4500/4500 - 32s - loss: 0.4653 - accuracy: 0.7713 - val_loss: 0.5789 - val_accuracy: 0.7178\n",
            "Epoch 24/50\n",
            "4500/4500 - 32s - loss: 0.4626 - accuracy: 0.7735 - val_loss: 0.5831 - val_accuracy: 0.7129\n",
            "Epoch 25/50\n",
            "4500/4500 - 33s - loss: 0.4611 - accuracy: 0.7747 - val_loss: 0.5796 - val_accuracy: 0.7139\n",
            "Epoch 26/50\n",
            "4500/4500 - 32s - loss: 0.4589 - accuracy: 0.7763 - val_loss: 0.5814 - val_accuracy: 0.7132\n",
            "Epoch 27/50\n",
            "4500/4500 - 32s - loss: 0.4586 - accuracy: 0.7761 - val_loss: 0.5859 - val_accuracy: 0.7151\n",
            "Epoch 28/50\n",
            "4500/4500 - 32s - loss: 0.4564 - accuracy: 0.7783 - val_loss: 0.5815 - val_accuracy: 0.7164\n",
            "Epoch 29/50\n",
            "4500/4500 - 32s - loss: 0.4550 - accuracy: 0.7785 - val_loss: 0.5847 - val_accuracy: 0.7065\n",
            "Epoch 30/50\n",
            "4500/4500 - 32s - loss: 0.4543 - accuracy: 0.7787 - val_loss: 0.5931 - val_accuracy: 0.7081\n",
            "Epoch 31/50\n",
            "4500/4500 - 31s - loss: 0.4514 - accuracy: 0.7809 - val_loss: 0.5980 - val_accuracy: 0.7100\n",
            "Epoch 32/50\n",
            "4500/4500 - 32s - loss: 0.4517 - accuracy: 0.7804 - val_loss: 0.5884 - val_accuracy: 0.7069\n",
            "Epoch 33/50\n",
            "4500/4500 - 32s - loss: 0.4500 - accuracy: 0.7820 - val_loss: 0.5920 - val_accuracy: 0.7054\n",
            "Epoch 34/50\n",
            "4500/4500 - 33s - loss: 0.4492 - accuracy: 0.7816 - val_loss: 0.5936 - val_accuracy: 0.7071\n",
            "Epoch 35/50\n",
            "4500/4500 - 32s - loss: 0.4476 - accuracy: 0.7816 - val_loss: 0.6019 - val_accuracy: 0.7074\n",
            "Epoch 36/50\n",
            "4500/4500 - 32s - loss: 0.4472 - accuracy: 0.7837 - val_loss: 0.5978 - val_accuracy: 0.7083\n",
            "Epoch 37/50\n",
            "4500/4500 - 32s - loss: 0.4478 - accuracy: 0.7834 - val_loss: 0.6046 - val_accuracy: 0.7041\n",
            "Epoch 38/50\n",
            "4500/4500 - 32s - loss: 0.4442 - accuracy: 0.7847 - val_loss: 0.6078 - val_accuracy: 0.7099\n",
            "Epoch 39/50\n",
            "4500/4500 - 32s - loss: 0.4438 - accuracy: 0.7840 - val_loss: 0.6015 - val_accuracy: 0.7065\n",
            "Epoch 40/50\n",
            "4500/4500 - 33s - loss: 0.4449 - accuracy: 0.7832 - val_loss: 0.6033 - val_accuracy: 0.7122\n",
            "Epoch 41/50\n",
            "4500/4500 - 32s - loss: 0.4429 - accuracy: 0.7850 - val_loss: 0.6002 - val_accuracy: 0.7102\n",
            "Epoch 42/50\n",
            "4500/4500 - 32s - loss: 0.4426 - accuracy: 0.7865 - val_loss: 0.5995 - val_accuracy: 0.7150\n",
            "Epoch 43/50\n",
            "4500/4500 - 32s - loss: 0.4423 - accuracy: 0.7852 - val_loss: 0.6047 - val_accuracy: 0.7091\n",
            "Epoch 44/50\n",
            "4500/4500 - 33s - loss: 0.4420 - accuracy: 0.7861 - val_loss: 0.6159 - val_accuracy: 0.7065\n",
            "Epoch 45/50\n",
            "4500/4500 - 33s - loss: 0.4433 - accuracy: 0.7849 - val_loss: 0.6103 - val_accuracy: 0.7065\n",
            "Epoch 46/50\n",
            "4500/4500 - 32s - loss: 0.4412 - accuracy: 0.7867 - val_loss: 0.6058 - val_accuracy: 0.7053\n",
            "Epoch 47/50\n",
            "4500/4500 - 32s - loss: 0.4403 - accuracy: 0.7868 - val_loss: 0.6008 - val_accuracy: 0.7053\n",
            "Epoch 48/50\n",
            "4500/4500 - 32s - loss: 0.4399 - accuracy: 0.7873 - val_loss: 0.6066 - val_accuracy: 0.7053\n",
            "Epoch 49/50\n",
            "4500/4500 - 32s - loss: 0.4405 - accuracy: 0.7871 - val_loss: 0.6124 - val_accuracy: 0.7032\n",
            "Epoch 50/50\n",
            "4500/4500 - 32s - loss: 0.4383 - accuracy: 0.7888 - val_loss: 0.6086 - val_accuracy: 0.7054\n",
            "Training Complete\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aamq1bndnGq-"
      },
      "source": [
        "## Plotting the accuracy curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxju4ItJKO8F",
        "outputId": "0d97424b-63d1-4e16-cad3-c66a9f53c56a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "import matplotlib.image  as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc=history.history['accuracy']\n",
        "val_acc=history.history['val_accuracy']\n",
        "loss=history.history['loss']\n",
        "val_loss=history.history['val_loss']\n",
        "\n",
        "epochs=range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, acc, 'r')\n",
        "plt.plot(epochs, val_acc, 'b')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend([\"Accuracy\", \"Validation Accuracy\"])\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r')\n",
        "plt.plot(epochs, val_loss, 'b')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\", \"Validation Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "# Expected Output\n",
        "# A chart where the validation loss does not increase sharply!\n",
        "# The graph should look like something below"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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o2AZwOzAK6467GutZFRki9tXISxwuB1G1JU179rTJAufMgVdftT/3++8/8fMvXGhrbt90kyWm1BQteuLv6TKXT6uemttvtxa97du97OqyvYMHrY3hnXfgttssYSRM4zFwoK1WN2dO+pv0DhywRYS2bbOCematge0iw6dVT69atWDnTtiwIfV9ncvCdu60SQHfeQcGD4bXXz+aNMBKG5UqQd++aV/8Z/dumDrVGryXLbNqME8aOZcnjtQk9Kzy6iqXjala99NvvrHE8e9/H1+ALlwYnnvOekONHp36+ZYutf0vvRTKlLGeU19+CY8+alOeu5zLE0dqzjnH7j1xuGzsnXdsKvKhQ6Fr1+T3u+46m3Dv4YeTnxRw9mybIbZ2bXjgAZvBtV8/+PprezxoUEQuwWUhnjhSU6oUVKzoPatctrVpk1U/XXihrQOREhF48UUbx/DEE8e+tmOHtYs0awbx8VYdtWGDzWA7ZIitqZ0/f8Quw2UhnjjC4T2rXDalCr17H12rO08Y//HnnQe33GLjL37+2bYlLNs6erS1hSxZYo3sFZKct8HldJ44wlGzpq0sExcX7UicS5OJE63R+oknoFq18I8bPNjaPO64wxYtat8eTjoJfvzRBgpm5Ahzl/144ghHrVrWj/HXX6MdiXNh+/tvq5pq1MjaINLipJNgwABr7J4+3cZlzJ8P9epFJlaXvUR7rqrsIXTOqho1ohuLc2Hq08e6yY4enb4V6/r0sTaPli1tdLlzCbzEEY6zz7b/PG/ncFGwa1faj5k82WaaHTAg/d918uWzkoonDZeYJ45wFCwIVat64nCZ6vBheOwxKFHCJjA4eDC847ZutQbx887LmClEnEvME0e4atXyLrku0+zaBe3aWaN248bw2mvWDfaPP1I+7vffoXNnm/JjzJjIr23hcidPHOGqWRPWrIF//ol2JC6H+/VXSxYzZsBLL9k6FRMn2niJevXgu++OP2bvXptr6qyzbHT4yy9nnWnKXc7jiSNctWpZp/jly6Mdicvili2DOnVsKo4VK9J27KefQsOG1iPq88+tV5SIjej+/nubSbZpU5uc0BY3taRy9tk2QeE118BPP8H//V8krsw544kjXKE9q5xLgiqMGgUNGtho7cWLbXnUf//bSgSpHfvcc7YuxqmnQmysVU2FqlnTusReeaWNr+ja1Wax7dTJ5oqaM8eSyGmnRe4anQNPHOGrUgUKFfIG8lxowwab9fW115Kfv2nXLptE8LbbbGqPJUvsm3/Xrrbu9jnnWNVTqPh4m9+pb1+oXNnmferQwaqmKldO+n1KloRp06y31LhxNrJ75EhLNL66scs0qprjb/Xq1dMMUb++6mWXZcy5XLbRqVNCpZBqgQL2fNYs1fh4e33BAtUzzlDNk0d18GDVQ4eOPX72bNXq1e34du1Ux49XvfFG1VKlbFvBgqrXXKP6zjuqhw+HH9fSpao7dmTcdTqXGBCrSXym+kJOaXHzzTbF6J9/nvi5XESp2hiEypXtG3161+D6/ns4/3xbMa9tW+uplLCuV6VKcMUV8O67UK4cjB9v63cn5eBBW9d70CDYt8/mzrzmGjtn8+a2jKpzWU1yCzl54kiL55+He++1lsty5U78fO44O3bA+vXWNnAivvoKLrvMHvfqZb2T8qZxngRVaNIE1q61nk4JS5zu32/VRaNHWwN2y5aWUMqWTf2cGzbY9TVs6F1lXdbnKwBmBF/UKeIeeMC6nP73vyd2noED4ZRT4L77YMQIm6QvrT2pP/gA5s2zCf9C18UuWNB6OX36qTV6T58eXtIAm6G/SRNPGi57i2jiEJEWIvKziKwSkf5JvN5dRDaLyKLgdmuwvVnItkUisl9E2gavvSUia0NeqxPJaziG96yKqLg4mDTJli3t1MlGQKfH7Nkwdy707289lV55xRqmL73UCovh2L8fHnzQxkJ065b8fgUKpC9G57KziCUOEYkBXgGuAmoAnUUkqVlzJqpqneA2CkBVv07YBlwK7AU+Cznm/pBjFkXqGo5z8snW79FLHBHxxRfWdjBgAPz1F/ToYdVFaTVwIJQvbz2cwKbrmDzZfm0XXACrVqV+juHDYd06+M9/0jdBoHM5WSRLHA2BVaq6RlUPAhOANuk4T0dgpqqm0hM+E4j4ok4RNHGizcv00EO2xOn06fDCC2k7x9y5VuJ48EGrUkrQpo21e+zYYY3d8+Ylf47Nm616qlUruPzydF2KczlaJBNHBeD3kOcbgm2JdRCRJSIySUQqJfF6J2B8om2Dg2NeEJEkKwtEpKeIxIpI7ObNm9N1AUmqVctGjx8+nHHndBw4AFOmWC+jAgVsxHS7dpYAfvwx/PMMHGgFw549j3+tcWNrOyle3MZa3H570tVhjz9u7SHPPZfuy3EuR4t24/h0oLKq1gY+B8aGvigi5YFawKyQzQ8BZwMNgNLAg0mdWFVHqmp9Va1fLiN7QNWsCXv2WNcYl2E++wx27rSBdmCFuzfftKVJr78++YF3ob791koVDzxgYzWTUq2aDZa7804bOFe1qrWBxMfb6ytWwOuv2+yyZ5+dMdfmXE4TycSxEQgtQVQMth2hqltV9UDwdBSQeH2x64CPVDUu5JhNwdiUA8AYrEos83jPqoiYONHGNiR0oQV7PnGidWG9+ebU2zsGDbKV63r1Snm/UqXgxRdh0SKoW9eSyHnnWRXX/fdbD6oBA074kpzLsSKZOOYDVUWkiojkx6qcpoXuEJQoErQGViY6R2cSVVMlHCMiArQFMreL0znn2L0njgyzb5+ti92+PeTPf+xrjRrBM8/ARx9ZySA58+bZmIr77w9/PeyaNa1B/sMPbaW8Zs1sfOejj4bfvda53ChiS8eqaryI3IlVM8UAo1V1uYgMwoaxTwPuEpHWQDywDeiecLyIVMZKLHMSnfo9ESkHCLAISOX7ZQYrXtxmkfMuuRnm00+t9i+hmiqxe+6x0sA991jzUt++x1cjDRxoYzJ7907be4tYwrrqKmuQX7LESiDOueT5yPH0uOYaG07sySNVX35pa0RUrJj8Pp062X6bNiU/unv7ditNvPuuNaRfdZVNKXL55dZ43rgxDBli7RvOuYzhI8czUu3aNi1pekeo5RJr1thcTldckfyo7YSR1x06pDwlSKlSNmX5b79ZW8bChTbHU61a1qZRpoz1knLORZ4njhQMGAB33ZXEC506WTecN9/M9Jiyk5dfhjx5LMcm+XPERnTv3Zt8NVViJ51kbRDr18PYsTZ1x6JFRxu1nXOR51VVKejXz0YQL15sDanHuPRSWL3abmmdPS8X2LXLqqeuucZmqH3qKZtVtnPnY/fr2NG60W7cmL4R2qqWmKpVsyTlnMs4XlWVDo88AsWKJVNv3qeP1ZtMn57pcWUHb71lPZX69rUBdRdcYMuZrl59dJ89e6zE0bFj+qf1ELGGck8azmUe/3dLQZkyljxmzrTG22Ncc431rho+PCqxZWWHD9uP5YILjk4fPm6cJYdOnWxtCrCcu39/+NVUzrmswRNHKu680/LDffclmmUkb15rjZ0928d0JDJjhpUs+vY9uu2006xJKDYWHn7Ytk2caFOfN2kSnTidc+njiSMVBQta/fyiRdYV9Bi33mpzW7z0UlRiy6qGDbPV8dq3P3Z7+/Y2zmLoUEsaM2fCtdd6NZNz2Y3/y4ahUyeoX9+qrfbtC3mhdGno0sUyyrZtUYsvK1m61OaLuuOOpPsM/Oc/1oX2hhusysqrqZzLfjxxhCFPHvuW/PvvNsfRMfr0sWziXXMB+/kUKnR0LYzEChWy0kaBAnDqqTZwzzmXvXh33DRo3dqaNFavTrTkeNOmturP6tW5etWfzZutiqp7d1uuNSVz5tiP6sILMyU051w6eHfcDDBkiA1WGzQo0Qt9+tiItFzeNXfkSJsOJLnBfqEuucSThnPZlSeONKhe3apgRoyAX34JeaFNG/uqnYsbyQ8etNlrr7wSaiS1QLBzLsfwxJFGjz9uPa0eeCBkfYiErrlffZVrJz784AObpDC0C65zLmfyxJFGJ59s4xCmToV77w1JHrfeahnl5ZejGl80qFoX3LPOshKHcy5n88SRDg8+aM0aL7xg94cPYyv/3HADvP12ruua+/rrNrCvb18fk+FcbuD/5ukgYt1O77vP6vX/7/+C5HH33dY197g+uznXRx/ZmI2rr06+C65zLmfxxJFOIvDss1ZtNWqUrYl9qEYtm7HvhRdyRalj7lyb7bZhQ3j/fZ8k2LncwhPHCRCBJ5+07rljx0LXrhD37wE27et//hPt8NIsPh4mTYKLLrKl1SdPDmnDSWTpUhvXUqUKfPxx+Ot8O+eyv4gmDhFpISI/i8gqEemfxOvdRWSziCwKbreGvHYoZPu0kO1VROSH4JwTRSR/JK8hHI8+Cs88AxMmQKcna7K1zc1WXbVlS7LHqNpa2xdcENnlTvfvh8GDLb45c5JeiW/nTnj+eTjzTJs7auNGS4odOtgqeytWHLv/+vXQogUUKQKzZtksws65XERVI3IDYoDVwOlAfmAxUCPRPt2Bl5M5fk8y298HOgWPRwC9U4ulXr16mhleeEEVVAsVPKR38LKuuu2ZJPebN0/1kkts3xIl7H7EiIyP56+/VM8/386fcIuJUa1bV7V3b9W33lLt21e1aFF77aKLVCdPVo2PV42LUx0+XLVkSdW8eVX79VPdsUN1yxbVs86yuJcsyfiYnXNZBxCrSX0OJ7UxI27A+cCskOcPAQ8l2idNiQMQYAuQN6n3SO6WWYlDVXXpUtUePVTz5YnTPMRrx1b79Pvv7bUVK1TbtrWf+kknqb70kurevaotW9qH85w5GRfH8uWqlSurFiqkOmmSfeDPmKH6yCOql1+uWqyYxZE3r2qXLqqxsUmf5++/VW+7TVXEYq5VS7VAgYyN1TmXNUUjcXQERoU8vzFxkggSxyZgCTAJqBTyWjwQC3wPtA22lQVWhexTCViWWiyZmTgSbJy7Sh/iKS1Z4B8F1Zo1VfPksQ/sQYNUd+8+uu+OHfYtvmxZ1XXrTvy9Z81SLV5c9V//Uv3xx6T3iY9XXbZMddOm8M45f75q48Z2DZMnn3iMzrmsL7nEEe3G8elAZVWtDXwOjA157TS1ybVuAIaJyBlpObGI9BSRWBGJ3bx5c8ZFHKZTLjqDp7r9zG+cxrBBOylY0OZwWr3a2kSKFj26b4kSMG0axMXZ7CVJtUOEiotL/rURI6BlS1vn+4cfoEGDpPeLibEG8H/9K7zrqV8fvvsO/vwT2rUL7xjnXM4UycSxESsRJKgYbDtCVbeq6oHg6SigXshrG4P7NcBsoC6wFSgpIgkdP487Z8jxI1W1vqrWL3fMVLaZ6NFHKRa/nb5bHmP+fOulm1wo1apZ4/rSpdCjx/G9mRJ6PDVpAvnzW4N03bqWaO6807oG9+plCyW1aAHffmvTlmekPHmSj985l3tEMnHMB6oGvaDyA52AaaE7iEj5kKetgZXB9lIiUiB4XBZoAqwIik5fY9VgAN2AqRG8hhNzxhk2x/jrr1tXpVS0aGEz8H7wgfWEgqM9nqpWtR5PmzbBQw/ZAkgVKsDatfDeezaa/fXXrVQzdSoUKxbZS3PO5WJJ1V9l1A1oCfyC9a56ONg2CGgdPH4aWI71uPoaODvYfgGwNNi+FLgl5JynAz8Cq4APgAKpxRGNNo4j1q61Fug77ghr98OHVbt2tdanG2442ogd2uMpKTt3qm7cmHFhO+ccybRx+EJOmaFXLxgzBn79Naz6o337bG2ohQutZNGvH9Srl+phzjmXoZJbyMkTR2b47TdrxLjsMhtmLZLqIXv32q1s2UyIzznnkuArAEbTqafCc8/BJ5/Aq6+GdUjhwp40nHNZkyeOzHLnndZP9r77YPnyaEfjnHPp5okjs4jA6NFQvLit27F/f7Qjcs65dPHEkZlOPtkayZcssT61zjmXDXniyGwtW1q11bBhNrWsc85lM544ouHZZ22+j+7dIQrToTjn3InwxBENhQrBuHGwfTvcckvyqyU551wW5IkjWmrXtvlFpk8Pu4uuc85lBZ44oqlPH2vz6NfPpp51zrlswBNHNOXJA+++C6edZuu0btgQ7UmGOtEAACAASURBVIiccy5VnjiirVQpm872n3+gfXsf3+Gcy/LCShwiUkRE8gSPq4lIaxHJF9nQcpEaNeCdd2D+fJsQ0RvLnXNZWLgljrlAQRGpAHyGLQP7VqSCypXatoUBA2DsWHjppWhH45xzyQo3cYiq7gXaA6+q6rXAOZELK5d67DFb0u+ee+Drr6MdjXPOJSnsxCEi5wNdgBnBtpjIhJSL5ckDb79tU7Bfey2sWxftiJxz7jjhJo67gYeAj1R1uYicjq3Y5zJa8eIwZYotMt6mDezaFe2InHPuGGElDlWdo6qtVXVI0Ei+RVXvinBsuVe1ajBxok2/3rEjxMVFOyLnnDsi3F5V40SkuIgUAZYBK0Tk/siGlstdeSWMHAmffw7/93/e08o5l2WEW1VVQ1V3AW2BmUAVrGdVikSkhYj8LCKrRKR/Eq93F5HNIrIouN0abK8jIvNEZLmILBGR60OOeUtE1oYcUyfMa8h+br7ZGszHjIFBg6IdjXPOAZA3zP3yBeM22gIvq2qciKT4FVhEYoBXgCuADcB8EZmmqisS7TpRVe9MtG0vcJOq/ioipwALRGSWqu4IXr9fVSeFGXv29vjjtmb544/bErQ9ekQ7IudcLhduieN1YB1QBJgrIqcBqbXaNgRWqeoaVT0ITADahPNmqvqLqv4aPP4D+BsoF2asOYuIVVldcQX07AmffRbtiJxzuVy4jePDVbWCqrZUsx5olsphFYDfQ55vCLYl1iGojpokIpUSvygiDYH8wOqQzYODY14QkQJJvbmI9BSRWBGJ3Zzd17zIlw8mTbI1PDp2hMWLox2Rcy4XC7dxvISIPJ/wQSwi/8FKHydqOlBZVWsDnwNjE71veeAdoIeqHg42PwScDTQASgMPJnViVR2pqvVVtX65cjmgsFK8OMyYASVK2Iy6f/4Z7Yicc7lUuFVVo4HdwHXBbRcwJpVjNgKhJYiKwbYjVHWrqh4Ino4C6iW8JiLFscGGD6vq9yHHbApKPQeCGBqGeQ3ZX4UKljy2b4cuXeDQoWhH5JzLhcJNHGeo6oCgvWKNqg4ETk/lmPlAVRGpIiL5gU7AtNAdghJFgtbAymB7fuAj4O3EjeAJx4iIYI31y8K8hpyhdm145RX46ivvaeWci4pwE8c+Ebkw4YmINAH2pXSAqsYDdwKzsITwfjDqfJCItA52uyvocrsYuAvoHmy/DrgY6J5Et9v3RGQpsBQoCzwZ5jXkHD16QLdu8MQTNs7DOecykWgYA8tE5FzgbaBEsGk70E1Vl0QwtgxTv359jY2NjXYYGeuff6BRI/j7b1i0CE45JdoROedyGBFZoKr1E28Pt1fVYlU9F6gN1FbVusClGRyjS4siReCDD2DvXujUyea2cs65TJCmFQBVdVcwghzgngjE49KienUYMQK++cZGmDvnXCY4kaVjJcOicOnXtSvcdhs8/TR88km0o3HO5QInkjh81r2s4sUX4dxz4cYbYe3aaEfjnMvhUkwcIrJbRHYlcdsNeGtsVlGokLV3qNrUJJs2RTsi51wOlmLiUNViqlo8iVsxVQ13gkSXGapWhZkzbUT5lVfCtm3Rjsg5l0OdSFWVy2oaNYKpU+Hnn+Hqq2HPnmhH5JzLgTxx5DSXXQYTJsCPP0L79nDgQOrHOOdcGnjiyInatYPRo21U+Q03+BgP51yG8sSRU3XrBsOGweTJto7H4cOpH+Occ2HwBu6crG9fm0l34EB7PmIE5M8f3Zicc9meJ46cbsAAux84ENatgw8/hFKlohqScy5786qqnE7E1it/+2347js4/3xYvTrVw5xzLjmeOHKLG2+EL76ALVus2+6330Y7IudcNuWJIze56CL4/nsoU8a67b73XrQjcs5lQ544cpszz4R58+CCC2yCxMce8x5Xzrk08cSRG5UuDbNmwc032yqC7drBrl2pH+ecc3jiyL3y54dRo2D4cJgxAxo3hl9/jXZUzrlswBNHbiYCffrYCPO//4YGDeDTT6MdlXMui4to4hCRFiLys4isEpH+SbzeXUQ2i8ii4HZryGvdROTX4NYtZHs9EVkanHO4iPiCUieqWTOIjYUqVaBlSxgyxKZod865JEQscYhIDPAKcBVQA+gsIjWS2HWiqtYJbqOCY0sDA4BGQENggIgkjFp7DbgNqBrcWkTqGnKVypVtnMd110H//jbH1b590Y7KOZcFRbLE0RBYpaprVPUgMAFoE+axVwKfq+o2Vd0OfA60EJHyQHFV/V5VFXgbaBuJ4HOlwoVh/HhbhnbiRLj0UqvCcs65EJFMHBWA30Oebwi2JdZBRJaIyCQRqZTKsRWCx6mdExHpKSKxIhK7efPm9F5D7iNiJY5Jk2DxYhssuHx5tKNyzmUh0W4cnw5UVtXaWKlibEadWFVHqmp9Va1frly5jDpt7tG+PcyZA/v325iPzz6LdkTOuSwikoljI1Ap5HnFYNsRqrpVVRNWGhoF1Evl2I3B42TP6TJQgwbwww9w2mnWaP7669GOyDmXBUQyccwHqopIFRHJD3QCpoXuELRZJGgNrAwezwKai0ipoFG8OTBLVTcBu0SkcdCb6iZgagSvwZ16qjWaN28OvXrB3XfD3r3Rjso5F0URSxyqGg/ciSWBlcD7qrpcRAaJSOtgt7tEZLmILAbuAroHx24DnsCSz3xgULAN4HasdLIKWA3MjNQ1uECxYjBtmo35ePFF67b7/POeQJzLpURzQX/9+vXra2xsbLTDyBm+/damaf/yS/jXv+DBB+H//g8KFYp2ZM65DCYiC1S1fuLt0W4cd9nNhRfa9Oxz5kD16tCvH5xxhk1dEhcX7eicc5nAE4dLn4svhq++gq+/hqpVbZna1q1hz55oR+acizBPHO7ENG0Ks2fDyJE251XTpvDXX1EOyjkXSZ443IkTgdtug6lTYeVKW572l1+iHZVzLkI8cbiMc/XVVnW1e7cNGvz++2hH5JyLAE8cLmM1bGgrDJYsaXNdTZ8e7YiccxnME4fLeGeeCf/9L9SsCW3bwrBhPk27czmIJw4XGSedZNVWrVtbl9127WDbttSPc85leZ44XOQUKQKTJ8MLL8Ann0Ddut7u4VwO4InDRZaIzW/13XeQJw9cdBEMHQqHD0c7MudcOnnicJmjQQP43/+s6ur+++1+y5ZoR+WcSwdPHC7zlCxpC0S9/LINFqxd26qwnHPZiicOl7lE4I47bJ2PMmVs7EfPnjb2wzmXLXjicNFRpw7Extrsum++aaWPOXOiHZVzLgyeOFz0FCgAzzwD33wDMTHQrBnccw/s2xftyJxzKfDE4aLvggtg8WK4/XbrulurFowaBQcOpH6scy7TeeJwWUORIkcbzUuUsEkTK1eGIUNgx45oR+ecC+GJw2Utl19ubR9ffGHtHv3727rn998P69fDrl2wfbt15f3rL/jjD5/G3blM5onDZT0icNllMGsWLFwIrVrZGueVK1tppHRpKFfOlq6tUMHuu3b1NdCdyyQRXXNcRFoALwIxwChVfSaZ/ToAk4AGqhorIl2A+0N2qQ2cp6qLRGQ2UB5IaEFtrqp/pxSHrzmeA6xdC1Om2IjzmBi75c1r96tW2Wj02rVtipPTT492tM7lCMmtOR6xxCEiMcAvwBXABmA+0FlVVyTarxgwA8gP3KmqsYlerwVMUdUzguezgfsS75cSTxy5wKefwg032ONx46BFi+jG41wOkFziiGRVVUNglaquUdWDwASgTRL7PQEMAfYnc57OwbHOJa9FC2sbqVQJWraEwYN9PiznIiSSiaMC8HvI8w3BtiNE5DygkqrOSOE81wPjE20bIyKLRORREZGkDhKRniISKyKxmzdvTkf4Lts5/XRbB6RzZ3jkEejQAXbujHZUzuU4UWscF5E8wPPAvSns0wjYq6rLQjZ3UdVawEXB7cakjlXVkapaX1XrlytXLgMjd1lakSLw7rs2HmT6dGtQf/hh73nlXAaKZOLYCFQKeV4x2JagGFATmC0i64DGwDQRCa1P60Si0oaqbgzudwPjsCox545KmMr9xx9t+dqnn4bTToPevWH16mhH51y2F8nEMR+oKiJVRCQ/lgSmJbyoqjtVtayqVlbVysD3QOuERu+gRHIdIe0bIpJXRMoGj/MBrYDQ0ohzR513Hnz4IaxcCTfdBKNHQ7VqcN111h7inEuXiCUOVY0H7gRmASuB91V1uYgMEpHWYZziYuB3VV0Tsq0AMEtElgCLsBLMGxkcustpzjoLRo6EdetsIOGsWbY+SNOmVp3ljejOpUlEx3FkFd4d1x1j1y6bC+vFF+G336wU0q+flUoKF452dM5lGdHojutc1lS8uM3Cu3o1jB9vz3v3tqlNHn4YliyBXPCFyrn08sThcq+8eaFTJ2tEnzMHmjSxhvRzz4Uzz7RqrXnzvCrLuUQ8cTgnAhdfDFOnwqZN1h5y1llWlXXBBVCxopVIPvzQ10l3Dm/jcC55O3fCjBnw0Ucwcyb8849tr13bGtabNoVLLrFJF53LgTJ9rqqsxBOHO2FxcTB/Pnz9NcyeDd99ZysVikDbtvDoo1C3brSjdC5DeeO4cyciXz6rtnr4YVtsavt2W/L2wQfhq69szMg111h7iXM5nCcO59KjQAG48EJrTF+/Hp54wubJatQIrrrKHjuXQ+Xaqqq4uDg2bNjA/v3JTcrrspqCBQtSsWJF8uXLF+1QkrZ7N7z6KvznP7B5s7WFXHGFrWp40UU2j5Zz2Yi3cSRKHGvXrqVYsWKUKVOGZCbYdVmIqrJ161Z2795NlSpVoh1Oyv75xwYYTp1qbSEHD1pVV+PGlkSuvtqqtvzvzmVx3saRyP79+z1pZCMiQpkyZbJHCbFIEejb19o+tm+Hzz6zAYd798Ljj0P9+nYbNepoTy3nspFcmzgATxrZTLb8fRUubNVVzzxjEytu3gyvvGKlkNtug1NOgT59YMWK1M/lXBaRN9oBOJerlCkDt99uAwr/+18YMcIGHL78slVflS8PxYrZNCgJ96VLQ6tWtraIc1mAJ44oKlq0KHv27Il2GC4aRGyKkyZNbNGpMWNs3fQ//4RffrGG9l27rHoL4K67rH3k1luhTRvr1eVclOTqqirnsoSyZW1erC+/tOqsX36xqU/++ccGHq5ZAwMGwM8/w/XX2xQo997r1VsuarzEAbZa3KJFGXvOOnVg2LA0H7Zo0SJ69erF3r17OeOMMxg9ejSlSpVi+PDhjBgxgrx581KjRg0mTJjAnDlz6Nu3L2D1/3PnzqVYsWIZex0uuvLmhSpVLHE88gh88YU1qg8fDs8/D2efbVOfNGtm05+cfHK0I3a5gCeOLOamm27ipZde4pJLLuGxxx5j4MCBDBs2jGeeeYa1a9dSoEABduzYAcDQoUN55ZVXaNKkCXv27KFgwYJRjt5FVEwMXHml3f7+G8aNs1Hs771nbSUANWpYIqlf3xJOlSpWQomJiWroLmfJteM4Vq5cSfXq1aMUkUncxrFz505q1arFb7/9BsDq1au59tprWbhwIS1atKBo0aK0bduWtm3bUrRoUZ555hk++ugjunTpQvv27alYsWK0LiXTZIXfW5YTHw8LFx6dR+ubb47t5ps3r601UqWKJZR+/bxk4sLi4ziyuRkzZnDHHXewcOFCGjRoQHx8PP3792fUqFHs27ePJk2a8NNPP0U7TBcNefNCw4Y2b9bMmbBjB6xaZaWRkSPhvvtsqdzdu2HoUDj9dHjoIdi2LdqRu2zKq6qykBIlSlCqVCm++eYbLrroIt555x0uueQSDh8+zO+//06zZs248MILmTBhAnv27GHr1q3UqlWLWrVqMX/+fH766SfOPvvsaF+Gi7a8eeGMM+yW2C+/2CDEIUPgtdeskf3uu63rr3NhimjiEJEWwItADDBKVZ9JZr8OwCSggarGikhlYCXwc7DL96raK9i3HvAWUAj4BOir2bS+be/evcdUL91zzz2MHTv2SOP46aefzpgxYzh06BBdu3Zl586dqCp33XUXJUuW5NFHH+Xrr78mT548nHPOOVx11VVRvBqXLVSrZm0jDz1kU8E/9pg1tN9+OxQsaGuQ7Nplt507ber4xo2hdWur5srjlRQugm0cIhID/AJcAWwA5gOdVXVFov2KATOA/MCdIYnjY1WtmcR5fwTuAn7AEsdwVZ2ZUixZtY3DpZ3/3jLYjz9ab63PP7fn+fJBiRJ2K17cEsWiRXDokA1OvOYaSyKXXgqFCkU3dhdxybVxRLLE0RBYpaprggAmAG2AxJ3PnwCGAPendkIRKQ8UV9Xvg+dvA22BFBOHcy4ZDRvaXFo7dlgiSGpg4dat1nYydaqVVkaOtKlU6tSBqlWP3s480+692ivHi2TiqAD8HvJ8A9AodAcROQ+opKozRCRx4qgiIv8DdgGPqOo3wTk3JDpnhaTeXER6Aj0BTj311BO5DudyvpIlk3+tTBno2tVuBw5Yz63p02HpUiupjB177P6nnw7Nm9utWbOUz+2ypag1jotIHuB5oHsSL28CTlXVrUGbxhQROSct51fVkcBIsKqqEwzXOQdWIkkYS5Lgn39g9Wr49Ve7zZt3dGxJTIwtbnXFFTa9ykknWSIqXdpKOKETV+7dC3/8cfS2bZtVi+WCbubZTSQTx0agUsjzisG2BMWAmsDsYNbTfwHTRKS1qsYCBwBUdYGIrAaqBcdXTOGczrnMVqSILVpVu/bRbXFx8MMPVg322We2QuLhw8ceV6CAJZCiRW1A486dx5+7f3949lno2dMb5rOQSCaO+UBVEamCfbh3Am5IeFFVdwJlE56LyGzgvqBxvBywTVUPicjpQFVgjapuE5FdItIYaxy/CXgpgtfgnEuPfPlsad0LL4RBg6z0sGSJ3W/bZu0mCY9377aSyCmn2K18ebtXta7CvXvD+PHwxhvWK8xFXcQSh6rGi8idwCysO+5oVV0uIoOAWFWdlsLhFwODRCQOOAz0UtWE0Uq3c7Q77ky8Ydy5rK90aZsKJa0+/9xmDr7nHivRDBxoY0/ypuGja+dOa/w/7bS0v79LUkTLfqr6iapWU9UzVHVwsO2xpJKGqjYNqqhQ1Q9V9RxVraOq56nq9JD9YlW1ZnDOO7PrGI5mzZoxa9asY7YNGzaM3r17J3tM06ZNSehW3LJlyyNzVoV6/PHHGTp0aIrvPWXKFFaEzKz62GOP8cUXX6Ql/CTNnj2bVq1anfB5nDtCBG6+GVautCV3+/e3nmBvvAFTpsC338JPP8GWLdZleOdOm3pl6FDo3NlKKCVL2lomderAc8/Bhg2pvq1LmY8cj5LOnTszYcIErgxpZJwwYQLPPvtsWMd/8skn6X7vKVOm0KpVK2rUqAHAoEGD0n0u5zJF+fLw4Yd2u/NOa/NITMSqtxJUqgT16kG3btYQP3EiPPCATc1yySXQpQt07Bi5Xl/bttliXRs32viXU06JzPtEgScOojOreseOHXnkkUc4ePAg+fPnZ926dfzxxx9cdNFF9O7dm/nz57Nv3z46duzIwIEDjzu+cuXKxMbGUrZsWQYPHszYsWM56aSTqFSpEvXq1QPgjTfeYOTIkRw8eJAzzzyTd955h0WLFjFt2jTmzJnDk08+yYcffsgTTzxBq1at6NixI19++SX33Xcf8fHxNGjQgNdee40CBQpQuXJlunXrxvTp04mLi+ODDz4Ie3qT8ePH89RTT6GqXH311QwZMoRDhw5xyy23EBsbi4hw8803069fvySnj3fuiA4d7EP4zz+tlJFw27rV7gsUsJUU69WDcuWOPfaee6zX17hx1uvrttssARUtag38RYocfVy0qA2CLFkSSpU6eitZ0gZGJuwfetu6Fb777uht5cqj73377dCiBfToYfFn84W4PHFESenSpWnYsCEzZ86kTZs2TJgwgeuuuw4RYfDgwZQuXZpDhw5x2WWXsWTJEmqH9lgJsWDBAiZMmMCiRYuIj4/nvPPOO5I42rdvz2233QbAI488wptvvkmfPn1o3br1kUQRav/+/XTv3p0vv/ySatWqcdNNN/Haa69x9913A1C2bFkWLlzIq6++ytChQxk1alSq1/nHH3/w4IMPsmDBAkqVKkXz5s2ZMmUKlSpVYuPGjSxbtgzgSLVbUtPHO3eM/Plttt/0jM+qWtXWNnnsMViwAGbMsPaPPXusW3HCbedO+O032L7dbgcPhv8eJUvCBRfYuJcmTWyhrnHj4O234dprrb3nhhusJFSnTtraa7KI7BdxBKRjvaUMkVBdlZA43nzzTQDef/99Ro4cSXx8PJs2bWLFihXJJo5vvvmGdu3aUbhwYQBat2595LVly5bxyCOPsGPHDvbs2XNMtVhSfv75Z6pUqUK1oOdKt27deOWVV44kjvbt2wNQr149Jk+eHNY1zp8/n6ZNm1Iu+PbXpUsX5s6dy6OPPsqaNWvo06cPV199Nc2bNwegdu3adOnS5cj08c5FhIjNvVX/uNk0jqdqc3YlJJHdu48mmL17jz4uUgTOPx+qVz++6/Dgwda77MsvrbH/jTdsnfn8+S2ZnXWWLcqVcF+hgo13SW6NHVUr4WzYYLf8+W0amExKQp44oqhNmzb069ePhQsXsnfvXurVq8fatWsZOnQo8+fPp1SpUnTv3p39+/en6/zdu3dnypQpnHvuubz11lvMnj37hOItEBSvY2JiiI+PP6FzlSpVisWLFzNr1ixGjBjB+++/z+jRo5kxYwZz585l+vTpDB48mKVLl5I3G34jczmIiE2xUriwfaCnV0zM0RH127fb6Pvly61xf9kym9Ll0KFjjylc2BJI2bJ2Hxd3NFkcOHDsvhUrWvXbrbdGvD3FR9REUdGiRWnWrBk333wznTt3BmDXrl0UKVKEEiVK8NdffzFzZsq9jS+++GKmTJnCvn372L17N9OnH+mAxu7duylfvjxxcXG89957R7YXK1aM3bt3H3eus846i3Xr1rFq1SqAI9O6n4iGDRsyZ84ctmzZwqFDhxg/fjyXXHIJW7Zs4fDhw3To0IEnn3yShQsXHjN9/JAhQ9i5c+cxC105l2OUKgU33WTT20+dauvJ791r7SJTptio+8GDoVcvm7bllFOspHPokPUqu+suePFF6yzwww92X726VcOdeqq1BX3xxfGDLjOIf5WLss6dO9OuXbsjjcDnnnsudevW5eyzz6ZSpUo0adIkxePPO+88rr/+es4991xOOukkGjRocOS1J554gkaNGlGuXDkaNWp0JFl06tSJ2267jeHDhzNp0qQj+xcsWJAxY8Zw7bXXHmkc79WrV5qu58svvzxmqvgPPviAZ555hmbNmh1pHG/Tpg2LFy+mR48eHA7+sJ9++ulkp493LlfIn9+qqdKzpk7DhtC+vS3gNXIkjB4NkydbNdjkyVDzuInGT4gvHeuyFf+9OReG/futFPLOO3ZfpEi6TuNLxzrnXG5RsKCNU/n003QnjZR44nDOOZcmuTpx5IZqupzEf1/OZQ25NnEULFiQrVu3+odRNqGqbN26lYLJ9Wt3zmWaXNurqmLFimzYsIHNmzdHOxQXpoIFCx7TY8s5Fx25NnHky5ePKlWqRDsM55zLdnJtVZVzzrn08cThnHMuTTxxOOecS5NcMXJcRDYD69N5eFlgSwaGk134decuufW6IfdeezjXfZqqlku8MVckjhMhIrFJDbnP6fy6c5fcet2Qe6/9RK7bq6qcc86liScO55xzaeKJI3Ujox1AlPh15y659boh9157uq/b2zicc86liZc4nHPOpYknDuecc2niiSMFItJCRH4WkVUi0j/a8USKiIwWkb9FZFnIttIi8rmI/Brcl4pmjJEgIpVE5GsRWSEiy0Wkb7A9R1+7iBQUkR9FZHFw3QOD7VVE5Ifg732iiOSPdqyRICIxIvI/Efk4eJ7jr1tE1onIUhFZJCKxwbZ0/5174kiGiMQArwBXATWAziJSI7pRRcxbQItE2/oDX6pqVeDL4HlOEw/cq6o1gMbAHcHvOKdf+wHgUlU9F6gDtBCRxsAQ4AVVPRPYDtwSxRgjqS+wMuR5brnuZqpaJ2TsRrr/zj1xJK8hsEpV16jqQWAC0CbKMUWEqs4FtiXa3AYYGzweC7TN1KAygapuUtWFwePd2IdJBXL4tavZEzzNF9wUuBSYFGzPcdcNICIVgauBUcFzIRdcdzLS/XfuiSN5FYDfQ55vCLblFier6qbg8Z/AydEMJtJEpDJQF/iBXHDtQXXNIuBv4HNgNbBDVeODXXLq3/sw4AHgcPC8DLnjuhX4TEQWiEjPYFu6/85z7XocLnyqqiKSY/tti0hR4EPgblXdZV9CTU69dlU9BNQRkZLAR8DZUQ4p4kSkFfC3qi4QkabRjieTXaiqG0XkJOBzEfkp9MW0/p17iSN5G4FKIc8rBttyi79EpDxAcP93lOOJCBHJhyWN91R1crA5V1w7gKruAL4GzgdKikjCl8mc+PfeBGgtIuuwqudLgRfJ+deNqm4M7v/Gvig05AT+zj1xJG8+UDXocZEf6ARMi3JMmWka0C143A2YGsVYIiKo334TWKmqz4e8lKOvXUTKBSUNRKQQcAXWvvM10DHYLcddt6o+pKoVVbUy9v/8lap2IYdft4gUEZFiCY+B5sAyTuDv3EeOp0BEWmJ1ojHAaFUdHOWQIkJExgNNsWmW/wIGAFOA94FTsSnpr1PVxA3o2ZqIXAh8AyzlaJ33v7F2jhx77SJSG2sMjcG+PL6vqoNE5HTsm3hp4H9AV1U9EL1IIyeoqrpPVVvl9OsOru+j4GleYJyqDhaRMqTz79wTh3POuTTxqirnnHNp4onDOedcmnjicM45lyaeOJxzzqWJJw7nnHNp4onDuXQSkUPBbKMJtwybDFFEKofOVuxcVuJTjjiXfvtUtU60g3Aus3mJw7kMFqx98Gyw/sGPInJmsL2yiHwlIktE5EsROTXYgeQLJQAAAY9JREFUfrKIfBSsj7FYRC4IThUjIm8Ea2Z8FozyRkTuCtYQWSIiE6J0mS4X88ThXPoVSlRVdX3IaztVtRbwMjb7AMBLwFhVrQ28BwwPtg8H5gTrY5wHLA+2VwVeUdVzgB1Ah2B7f6BucJ5ekbo455LjI8edSycR2aOqRZPYvg5bKGlNMInin6paRkS2AOVVNS7YvklVy4rIZqBi6DQXwTTvnweL7CAiDwL5VPVJEfkU2INNCzMlZG0N5zKFlziciwxN5nFahM6XdIijbZJXY6tTngfMD5nZ1blM4YnDuci4PuR+XvD4v9isrABdsAkWwZbt7A1HFlgqkdxJRSQPUElVvwYeBEoAx5V6nIsk/6biXPoVClbRS/CpqiZ0yS0lIkuwUkPnYFsfYIyI3A9sBnoE2/sCI0XkFqxk0RvYRNJigHeD5CLA8GBNDecyjbdxOJfBgjaO+qq6JdqxOBcJXlXlnHMuTbzE4ZxzLk28xOGccy5NPHE455xLE08czjnn0sQTh3POuTTxxOGccy5N/h/eBtGHsxR6sAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4vI1uILjG0S"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZA_anSgjM-4"
      },
      "source": [
        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)"
      ]
    }
  ]
}