Colab_6_20210920_recurrent_nets.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Colab 6 20210920 recurrent nets",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HRMWrgdh3E0F"
      },
      "source": [
        "**Colab 6: Recurrent Nets**\n",
        "\n",
        "As opposed to the networks of the previous weeks, which take as input a single datum, recurrent networks operate on sequences, and process each element of a sequence one at a time.  They do so by updating an internal state, and the big 3 types of recurrent layers update that internal state with different amounts sophistication toward remembering long timescale correlations. A simple RNN's internal state is literally just a fully connected layer; GRU (gated recurrent unit) and LSTM (long short-term memory) add gates which allow control over how the internal state gets updated with each new timestep.  \n",
        "\n",
        "We'll use these to generate some Shakespeare and classify digits using only a random walk over the pixels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0S3NiFFirSqv"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import os\n",
        "\n",
        "tfkl = tf.keras.layers"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAIYOieeh8J6"
      },
      "source": [
        "#Characters of Shakespeare recurreth\n",
        "\n",
        "Here we'll take a million characters of Shakespeare and train a recurrent net to learn the statistics well enough to generate its own new, never before seen text!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iRdBEucaknbj"
      },
      "source": [
        "dset = tfds.load('tiny_shakespeare')['train']"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPhCMqx1iv-9",
        "outputId": "35b2dd89-a737-43de-e6a0-db3d666fe5c9"
      },
      "source": [
        "# Split the dataset into characters, turn those into indices\n",
        "dset = dset.map(lambda ex: tf.strings.unicode_split(ex['text'], 'UTF-8'))\n",
        "# We can grab the whole thing\n",
        "whole_text = next(iter(dset))\n",
        "print('Length of dataset:', whole_text.shape)\n",
        "vocabulary = sorted(set(whole_text.numpy()))\n",
        "print('Vocabulary:', vocabulary)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of dataset: (1003854,)\n",
            "Vocabulary: [b'\\n', b' ', b'!', b'$', b'&', b\"'\", b',', b'-', b'.', b'3', b':', b';', b'?', b'A', b'B', b'C', b'D', b'E', b'F', b'G', b'H', b'I', b'J', b'K', b'L', b'M', b'N', b'O', b'P', b'Q', b'R', b'S', b'T', b'U', b'V', b'W', b'X', b'Y', b'Z', b'a', b'b', b'c', b'd', b'e', b'f', b'g', b'h', b'i', b'j', b'k', b'l', b'm', b'n', b'o', b'p', b'q', b'r', b's', b't', b'u', b'v', b'w', b'x', b'y', b'z']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcGEgvImiv7P",
        "outputId": "11fc9b46-087f-4a32-cd6c-39649d31293a"
      },
      "source": [
        "# We're going to take an input sequence of characters [0, N] and try to predict characters [1, N+1], for a bunch of sequences\n",
        "# So make two copies of the string, one character shifted from each other\n",
        "dset = dset.map(lambda ex: (ex[:-1], ex[1:]))\n",
        "\n",
        "# This will be the sequence length to train on (and the longest memory the system gets signal for; try changing to something like 4 to see the difference)\n",
        "sequence_length = 100\n",
        "\n",
        "# Unbatch the dataset and then rebatch into these pairs of 100 characters, and drop the stragglers\n",
        "dset = dset.unbatch().batch(sequence_length, drop_remainder=True)\n",
        "\n",
        "# Let's look at the monster we've created\n",
        "first_element = next(iter(dset))\n",
        "print(first_element)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(<tf.Tensor: shape=(100,), dtype=string, numpy=\n",
            "array([b'F', b'i', b'r', b's', b't', b' ', b'C', b'i', b't', b'i', b'z',\n",
            "       b'e', b'n', b':', b'\\n', b'B', b'e', b'f', b'o', b'r', b'e', b' ',\n",
            "       b'w', b'e', b' ', b'p', b'r', b'o', b'c', b'e', b'e', b'd', b' ',\n",
            "       b'a', b'n', b'y', b' ', b'f', b'u', b'r', b't', b'h', b'e', b'r',\n",
            "       b',', b' ', b'h', b'e', b'a', b'r', b' ', b'm', b'e', b' ', b's',\n",
            "       b'p', b'e', b'a', b'k', b'.', b'\\n', b'\\n', b'A', b'l', b'l', b':',\n",
            "       b'\\n', b'S', b'p', b'e', b'a', b'k', b',', b' ', b's', b'p', b'e',\n",
            "       b'a', b'k', b'.', b'\\n', b'\\n', b'F', b'i', b'r', b's', b't', b' ',\n",
            "       b'C', b'i', b't', b'i', b'z', b'e', b'n', b':', b'\\n', b'Y', b'o',\n",
            "       b'u'], dtype=object)>, <tf.Tensor: shape=(100,), dtype=string, numpy=\n",
            "array([b'i', b'r', b's', b't', b' ', b'C', b'i', b't', b'i', b'z', b'e',\n",
            "       b'n', b':', b'\\n', b'B', b'e', b'f', b'o', b'r', b'e', b' ', b'w',\n",
            "       b'e', b' ', b'p', b'r', b'o', b'c', b'e', b'e', b'd', b' ', b'a',\n",
            "       b'n', b'y', b' ', b'f', b'u', b'r', b't', b'h', b'e', b'r', b',',\n",
            "       b' ', b'h', b'e', b'a', b'r', b' ', b'm', b'e', b' ', b's', b'p',\n",
            "       b'e', b'a', b'k', b'.', b'\\n', b'\\n', b'A', b'l', b'l', b':',\n",
            "       b'\\n', b'S', b'p', b'e', b'a', b'k', b',', b' ', b's', b'p', b'e',\n",
            "       b'a', b'k', b'.', b'\\n', b'\\n', b'F', b'i', b'r', b's', b't', b' ',\n",
            "       b'C', b'i', b't', b'i', b'z', b'e', b'n', b':', b'\\n', b'Y', b'o',\n",
            "       b'u', b' '], dtype=object)>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vnLGUQ4VjgYE",
        "outputId": "b755ec63-4d81-488b-d045-71ba175f886d"
      },
      "source": [
        "# To make these readable, join the characters\n",
        "for sequence in first_element:\n",
        "  print(tf.strings.reduce_join(sequence, axis=-1).numpy())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b'First Citizen:\\nBefore we proceed any further, hear me speak.\\n\\nAll:\\nSpeak, speak.\\n\\nFirst Citizen:\\nYou'\n",
            "b'irst Citizen:\\nBefore we proceed any further, hear me speak.\\n\\nAll:\\nSpeak, speak.\\n\\nFirst Citizen:\\nYou '\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGA8UEV0ieS2"
      },
      "source": [
        "So given the first sequence as output, one character at a time, we want the network to assign high probabilities to the next character which follows.  \n",
        "\n",
        "E.g. in this first element, the input to the network will first be 'F'.  This will change the internal state and output probabilities for each possible character in the vocabulary; we want a high probability for the actual next character, 'i'.  \n",
        "\n",
        "The next input is 'i', which will update the internal state again (which should, eventually, know that the preceding character was 'F'), and now the output of the network should assign a high probability to 'r'.  \n",
        "\n",
        "Thus these two sequences give 100 calls and responses with which to update the network. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sh_oEXodkvN9"
      },
      "source": [
        "# We'll need to convert the characters to integers, and then back\n",
        "intoIDs_layer = tfkl.StringLookup(vocabulary=vocabulary, invert=False, mask_token=None)\n",
        "back2chars_layer = tfkl.StringLookup(vocabulary=vocabulary, invert=True, mask_token=None)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCj-zxeusLKI",
        "outputId": "c4108740-b263-4792-e498-ee6ab24d5297"
      },
      "source": [
        "first_element_ids = intoIDs_layer(first_element[0])\n",
        "print(first_element_ids)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[19 48 57 58 59  2 16 48 59 48 65 44 53 11  1 15 44 45 54 57 44  2 62 44\n",
            "  2 55 57 54 42 44 44 43  2 40 53 64  2 45 60 57 59 47 44 57  7  2 47 44\n",
            " 40 57  2 52 44  2 58 55 44 40 50  9  1  1 14 51 51 11  1 32 55 44 40 50\n",
            "  7  2 58 55 44 40 50  9  1  1 19 48 57 58 59  2 16 48 59 48 65 44 53 11\n",
            "  1 38 54 60], shape=(100,), dtype=int64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rj8xRt1FlHhx",
        "outputId": "a5184cc1-c916-45f8-8d9e-b5f03c4327aa"
      },
      "source": [
        "back2chars_layer(first_element_ids)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(100,), dtype=string, numpy=\n",
              "array([b'F', b'i', b'r', b's', b't', b' ', b'C', b'i', b't', b'i', b'z',\n",
              "       b'e', b'n', b':', b'\\n', b'B', b'e', b'f', b'o', b'r', b'e', b' ',\n",
              "       b'w', b'e', b' ', b'p', b'r', b'o', b'c', b'e', b'e', b'd', b' ',\n",
              "       b'a', b'n', b'y', b' ', b'f', b'u', b'r', b't', b'h', b'e', b'r',\n",
              "       b',', b' ', b'h', b'e', b'a', b'r', b' ', b'm', b'e', b' ', b's',\n",
              "       b'p', b'e', b'a', b'k', b'.', b'\\n', b'\\n', b'A', b'l', b'l', b':',\n",
              "       b'\\n', b'S', b'p', b'e', b'a', b'k', b',', b' ', b's', b'p', b'e',\n",
              "       b'a', b'k', b'.', b'\\n', b'\\n', b'F', b'i', b'r', b's', b't', b' ',\n",
              "       b'C', b'i', b't', b'i', b'z', b'e', b'n', b':', b'\\n', b'Y', b'o',\n",
              "       b'u'], dtype=object)>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XL-csM_3nr-r"
      },
      "source": [
        "# Ok, map the entire dataset to integers, and subtract 1 because it's not 0-indexed for some weird reason\n",
        "dset_ids = dset.map(lambda a, b: (intoIDs_layer(a)-1, intoIDs_layer(b)-1))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUoBFS3mvnLL",
        "outputId": "fd478b5f-9f51-4011-8adf-4f867a2c3a40"
      },
      "source": [
        "# The magic simplicity -- make the model, which consists of an embedding layer which\n",
        "# just projects the integers to unique high dimensional vectors (also a bit of magic imo)\n",
        "# before passing them through a single recurrent layer, which then outputs logits for the vocabulary\n",
        "vocab_size = len(vocabulary)\n",
        "embedding_dim = 256\n",
        "num_recurrent_units = 1024\n",
        "model = tf.keras.Sequential([\n",
        "                             tfkl.Input(shape=(None,)),\n",
        "                             tfkl.Embedding(vocab_size, embedding_dim),\n",
        "                             tfkl.LSTM(num_recurrent_units, return_sequences=True),\n",
        "                             tfkl.Dense(vocab_size)\n",
        "])\n",
        "print(model.summary())"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, None, 256)         16640     \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, None, 1024)        5246976   \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, None, 65)          66625     \n",
            "=================================================================\n",
            "Total params: 5,330,241\n",
            "Trainable params: 5,330,241\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wqctsecvnHr",
        "outputId": "af778502-6b85-4761-8d0b-4fdf3e1acc35"
      },
      "source": [
        "# What does this output?\n",
        "in1, out1 = next(iter(dset_ids.batch(1)))\n",
        "print('Input sequence:', in1)\n",
        "print('Output sequence:', out1)\n",
        "prediction = model(in1)\n",
        "print('Model prediction:', prediction)\n",
        "loss = tf.keras.losses.sparse_categorical_crossentropy(out1, prediction, from_logits=True)\n",
        "print('Loss:', loss)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input sequence: tf.Tensor(\n",
            "[[18 47 56 57 58  1 15 47 58 47 64 43 52 10  0 14 43 44 53 56 43  1 61 43\n",
            "   1 54 56 53 41 43 43 42  1 39 52 63  1 44 59 56 58 46 43 56  6  1 46 43\n",
            "  39 56  1 51 43  1 57 54 43 39 49  8  0  0 13 50 50 10  0 31 54 43 39 49\n",
            "   6  1 57 54 43 39 49  8  0  0 18 47 56 57 58  1 15 47 58 47 64 43 52 10\n",
            "   0 37 53 59]], shape=(1, 100), dtype=int64)\n",
            "Output sequence: tf.Tensor(\n",
            "[[47 56 57 58  1 15 47 58 47 64 43 52 10  0 14 43 44 53 56 43  1 61 43  1\n",
            "  54 56 53 41 43 43 42  1 39 52 63  1 44 59 56 58 46 43 56  6  1 46 43 39\n",
            "  56  1 51 43  1 57 54 43 39 49  8  0  0 13 50 50 10  0 31 54 43 39 49  6\n",
            "   1 57 54 43 39 49  8  0  0 18 47 56 57 58  1 15 47 58 47 64 43 52 10  0\n",
            "  37 53 59  1]], shape=(1, 100), dtype=int64)\n",
            "Model prediction: tf.Tensor(\n",
            "[[[ 0.00244213 -0.00062554 -0.00290528 ...  0.00086083  0.00508059\n",
            "   -0.00024456]\n",
            "  [ 0.00615366  0.00140118 -0.00191334 ...  0.00263032 -0.00313211\n",
            "   -0.00163818]\n",
            "  [ 0.00959521  0.00353935  0.00181451 ... -0.00567161  0.00034304\n",
            "   -0.00595448]\n",
            "  ...\n",
            "  [ 0.003661   -0.01073272 -0.01207737 ...  0.00205785 -0.0009644\n",
            "   -0.00232693]\n",
            "  [-0.00069779 -0.00909755 -0.01265021 ... -0.00540233 -0.00233817\n",
            "   -0.00333299]\n",
            "  [-0.00506552 -0.00566018 -0.00870799 ... -0.00952164 -0.00356104\n",
            "   -0.00582567]]], shape=(1, 100, 65), dtype=float32)\n",
            "Loss: tf.Tensor(\n",
            "[[4.1705723 4.1658797 4.1736546 4.181042  4.179322  4.16622   4.1747375\n",
            "  4.1699705 4.174441  4.1810994 4.173118  4.1754923 4.1762714 4.172206\n",
            "  4.1794667 4.1708703 4.1685767 4.177109  4.1811237 4.174639  4.1730237\n",
            "  4.1674314 4.1778355 4.171965  4.1752615 4.1878943 4.1757307 4.1716895\n",
            "  4.1728787 4.174791  4.172692  4.1745243 4.1698747 4.1786323 4.1721177\n",
            "  4.168113  4.1833496 4.179267  4.1828895 4.1718707 4.171691  4.1753607\n",
            "  4.189207  4.175617  4.1762733 4.1707544 4.171831  4.1695633 4.18382\n",
            "  4.1761613 4.183659  4.1708317 4.169381  4.167462  4.1773324 4.171493\n",
            "  4.171087  4.167336  4.1588283 4.1736107 4.172565  4.174916  4.180107\n",
            "  4.17812   4.177801  4.1697598 4.1693277 4.180325  4.1639376 4.180131\n",
            "  4.1685233 4.1717668 4.1717124 4.173474  4.177646  4.1688595 4.1746907\n",
            "  4.1721773 4.1578636 4.174999  4.1737957 4.1715484 4.170466  4.1680923\n",
            "  4.1679406 4.177295  4.1834445 4.161284  4.173047  4.169277  4.1730304\n",
            "  4.180635  4.173094  4.176161  4.175241  4.1717925 4.1711345 4.1711717\n",
            "  4.1745596 4.179343 ]], shape=(1, 100), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mx5RkaNSvnDz",
        "outputId": "2e3d487c-7c67-4977-a069-5d185b0333d4"
      },
      "source": [
        "# We can compile as before, and just treat this as a classification problem (which is cool that this works under that perspective)\n",
        "\n",
        "##TODO compile the model just like in the classification tasks, using sparse categorical cross entropy but accepting logits instead of probabilities\n",
        "model.TODO\n",
        "\n",
        "bs = 64\n",
        "# We're going to output generated text along the way\n",
        "# Feed in the seed_text to start the generation (so feel free to change this)\n",
        "seed_text = 'Hulk smasheth'\n",
        "# Then generate this many characters\n",
        "desired_length = 400\n",
        "\n",
        "# These are just the epochs to stop and output generated text\n",
        "epoch_outputs = [0, 1, 3, 7, 15]\n",
        "num_epochs_series = np.subtract(epoch_outputs[1:], epoch_outputs[:-1])\n",
        "\n",
        "def generate_text():\n",
        "  # This is a pretty lazy way to generate the text -- we're going to feed in the seed text, generate a character, append it, and start over again\n",
        "  generated_text = intoIDs_layer(tf.strings.unicode_split(seed_text, 'UTF-8'))-1  # make the ids 0-indexed\n",
        "  for _ in range(desired_length):\n",
        "    prediction_logits = model(generated_text[tf.newaxis])[0]\n",
        "    generated_text = tf.concat([generated_text, tf.random.categorical(prediction_logits, 1, dtype=tf.int64)[-1]], -1)  # the 1 is the number of samples\n",
        "  return generated_text\n",
        "\n",
        "print(f'Epoch 0, generated text:\\n')\n",
        "generated_text = generate_text()\n",
        "print(tf.strings.reduce_join(back2chars_layer(generated_text+1), axis=-1).numpy().decode('utf-8'))\n",
        "print()\n",
        "\n",
        "for block_id, num_epochs in enumerate(num_epochs_series):\n",
        "  model.fit(dset_ids.shuffle(1000).batch(bs), epochs=num_epochs)\n",
        "  print(f'Epoch {epoch_outputs[block_id+1]}, generated text:\\n')\n",
        "  print(tf.strings.reduce_join(back2chars_layer(generate_text()+1), axis=-1).numpy().decode('utf-8'))\n",
        "  print()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0, generated text:\n",
            "\n",
            "Hulk smashethXhHnOWgm-\n",
            "mql-QvYwcQ;dF',V\n",
            "KqBNTlyS$yr!Mwo:d-bcQXjRj:S&\n",
            "SDTloDd-IXcCEJIQKdiA-wqaqhHixCOqMqocw.BJPmbNgfkqMw!!Tpu!YKVjgYBqsvBKbUlcLnF dhFUC,RIm vzVFSqsNqN:yq?lxcBVFGGknoHvW3Jf;;gEmzws\n",
            "M$:Q.J&!tiygh-$k&ARbMbEAOp3GLkaR.bjcgUY&ITakINB QUSk'MYmdShUQ,.fOO$qgdC $lI:EZuC\n",
            "$gGJzjYWnqkEgh!CYDtcUZoHG$'\n",
            "ND.Ty&C?rO- pj'zOWUoi$eb?P:LU3ty$wd,XS&fNw;SyS\n",
            "HP3HgeR:QPozhnLgOQ-,B$q$X.,Zn-o3pGFTTgOuvyPgxVjFykIPp'EXNF'nFR\n",
            "\n",
            "157/157 [==============================] - 7s 41ms/step - loss: 2.9062 - accuracy: 0.2376\n",
            "Epoch 1, generated text:\n",
            "\n",
            "Hulk smasheth inguraed yoind hitr boligtounce'd woow unow heth fullcheris qudak-of ine hels.\n",
            "\n",
            "EOMI ItSI\n",
            "LEOE\n",
            " rmay faut wivithet tryour, mind masisem fastes of thekeac, ondiss nos mur andeen novend ay hatlof be ch jond? dkerseird sou ferne touthef duey dor bl bkesde the hed.\n",
            "\n",
            "Afsor. Culorterighe sillll, satheof thagl phaik himacane,.\n",
            "\n",
            "ALUCMors thirs,\n",
            "I Whave bolp ourde qo ofis nou, wratl:\n",
            "Ya bea\n",
            "urrby houk! di\n",
            "\n",
            "Epoch 1/2\n",
            "157/157 [==============================] - 7s 40ms/step - loss: 2.2120 - accuracy: 0.3624\n",
            "Epoch 2/2\n",
            "157/157 [==============================] - 7s 41ms/step - loss: 1.9822 - accuracy: 0.4202\n",
            "Epoch 3, generated text:\n",
            "\n",
            "Hulk smasheth, o's tea woll simp?\n",
            "\n",
            "DUGABELTA:\n",
            "Wenks feard day wo a misterf my touks Till,\n",
            "the beco th\n",
            "Ed-ant toue yell like-would an dive fortun, and stoor owring your wirld\n",
            "if would to duve and dane s noblo, beer.\n",
            "\n",
            "DUME:\n",
            "Pove he worden'd, but youp forouger teach menout\n",
            "And you pelounte thit beso arf enteest were\n",
            "tomen of the cale mens it go and you.\n",
            "\n",
            "COMLAO:\n",
            "Nor weramply gooqure.\n",
            "\n",
            "GRLUUDOY VINTENINENRENA:\n",
            "Has\n",
            "\n",
            "Epoch 1/4\n",
            "157/157 [==============================] - 7s 43ms/step - loss: 1.8181 - accuracy: 0.4627\n",
            "Epoch 2/4\n",
            "157/157 [==============================] - 7s 41ms/step - loss: 1.6943 - accuracy: 0.4957\n",
            "Epoch 3/4\n",
            "157/157 [==============================] - 7s 44ms/step - loss: 1.5996 - accuracy: 0.5209\n",
            "Epoch 4/4\n",
            "157/157 [==============================] - 6s 40ms/step - loss: 1.5282 - accuracy: 0.5396\n",
            "Epoch 7, generated text:\n",
            "\n",
            "Hulk smasheth with twos' the cutuare\n",
            "The seed upon the world doing known's; but to have as drunk.\n",
            "\n",
            "LUCIO:\n",
            "Dielch, what of give?\n",
            "\n",
            "Provost:\n",
            "I know.\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "O plays, now, bear you, trunk, you may, Cores, she alt\n",
            "That is the begons, woman time to Richard; why, speak none,\n",
            "But if, I do they mage strian, but, quniot?\n",
            "\n",
            "POMPEY:\n",
            "\n",
            "ANGELO:\n",
            "Let.\n",
            "\n",
            "PAULINABE:\n",
            "Which you are you? my yet as those lip\n",
            "As letter fellowi\n",
            "\n",
            "Epoch 1/8\n",
            "157/157 [==============================] - 6s 39ms/step - loss: 1.4725 - accuracy: 0.5545\n",
            "Epoch 2/8\n",
            "157/157 [==============================] - 6s 40ms/step - loss: 1.4296 - accuracy: 0.5654\n",
            "Epoch 3/8\n",
            "157/157 [==============================] - 6s 40ms/step - loss: 1.3930 - accuracy: 0.5754\n",
            "Epoch 4/8\n",
            "157/157 [==============================] - 6s 40ms/step - loss: 1.3623 - accuracy: 0.5829\n",
            "Epoch 5/8\n",
            "157/157 [==============================] - 6s 40ms/step - loss: 1.3340 - accuracy: 0.5901\n",
            "Epoch 6/8\n",
            "157/157 [==============================] - 7s 41ms/step - loss: 1.3102 - accuracy: 0.5960\n",
            "Epoch 7/8\n",
            "157/157 [==============================] - 7s 41ms/step - loss: 1.2881 - accuracy: 0.6019\n",
            "Epoch 8/8\n",
            "157/157 [==============================] - 6s 39ms/step - loss: 1.2659 - accuracy: 0.6078\n",
            "Epoch 15, generated text:\n",
            "\n",
            "Hulk smasheth this world but a dap their liese it,\n",
            "Which think not worthing that I should be a\n",
            "hate: the croward three eight well on Clancaster.\n",
            "\n",
            "LADY CAPULET:\n",
            "\n",
            "GORTHUMBERLAND:\n",
            "And, say, what that he had been paid of sleep,\n",
            "Naither than father here's and friend of Xmalical:\n",
            "I did leven thee: Master Sorrowifa:\n",
            "This bedain'd your sight as times so late's:\n",
            "Do you loving 'tory duke\n",
            "Nay, as if they make an as the o\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahLa7KWzvnAt",
        "outputId": "e89f7c39-96e2-4088-86d4-2d15a31e2a36"
      },
      "source": [
        "# Let's turn up the temperature a bit: since the outputs are logit predictions \n",
        "# over the vocabulary, we can divide them by a temperature-like parameter\n",
        "# When it's large, the distribution over outputs becomes more uniform\n",
        "# When it's small, the outputs become more conservative, picking out only the most\n",
        "# probable prediction as T->0\n",
        "seed_text = 'Hulk smasheth'\n",
        "desired_length = 800\n",
        "\n",
        "for temperature in [0.25, 0.5, 1, 2, 4]:\n",
        "  generated_text = ## TODO take the same generation procedure, but scale the logits by temperature before sampling.  \n",
        "  print(f'**********Temperature = {temperature}**********\\n')\n",
        "  print(tf.strings.reduce_join(back2chars_layer(generated_text+1), axis=-1).numpy().decode('utf-8'))\n",
        "  print()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**********Temperature = 0.25**********\n",
            "\n",
            "Hulk smasheth and the duke of me and heart\n",
            "To see the statue of the duke of York.\n",
            "\n",
            "LUCIO:\n",
            "I shall be the cause of the man of her bed,\n",
            "And therefore I say, and therefore have heard\n",
            "As the duke of any of the duke of his\n",
            "that will be a present and one and hanging\n",
            "That the duke of the truth of his head,\n",
            "And therefore I say, and say 'tis the noble\n",
            "Than any thing is not the duke of York,\n",
            "And therefore I have a happy that had\n",
            "To have the sun to the duke of many a\n",
            "man of her beauty of the deed of her\n",
            "As the duke of many of the deed of her brother.\n",
            "\n",
            "Provost:\n",
            "I think there is the sun shall be so one\n",
            "As the prince that we have done to hell me\n",
            "To her father too much a thousand of him\n",
            "And have made me as a present and his son:\n",
            "I have seen the sun and the duke of his head.\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "Master is the news in his \n",
            "\n",
            "**********Temperature = 0.5**********\n",
            "\n",
            "Hulk smasheth your son at the state and make thee\n",
            "And seem to her head and will not her seems,\n",
            "And yet there is no man be here in my beaut:\n",
            "And therefore seems the place of his that before.\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "Then speak hath not come to the life of his\n",
            "beastion in his own contract of his hands\n",
            "And please your father and stood and sent\n",
            "That who strike and the worst in his man made\n",
            "And news in his seas, and well between your brother,\n",
            "Where no state and more than marriage of you\n",
            "With all the lady too hard and the crown\n",
            "And see him with thee in a common face.\n",
            "\n",
            "ISABELLA:\n",
            "I have a chide to me all as the world:\n",
            "And therefore and the duke of the duke,\n",
            "To accuse my lady and hold the day.\n",
            "\n",
            "POMPEY:\n",
            "I am an ear of common mouth and looks on me:\n",
            "I am sound and law o'er the deeds of precious,\n",
            "And I have said the duke h\n",
            "\n",
            "**********Temperature = 1**********\n",
            "\n",
            "Hulk smasheth:\n",
            "What pread you not what is this deserves again:\n",
            "Live to the absent cut of me; if I loved\n",
            "so her-gocked end: musts it what hust, sir; how\n",
            "content there? therefore: George it, and duke as you\n",
            "shall be fought: I say, you were a luit dit here\n",
            "Apollo replial and and Saint I'll givenced: say,\n",
            "I am all indorchance. in his body lady\n",
            "Shall intend thou lefs, when I see that hath here,\n",
            "Traniove mine eyes since I am your brother.\n",
            "\n",
            "BENTUTHORTANDIN:\n",
            "This kindness; to our druds that there is at.\n",
            "My grace that then maid and hollows mine,\n",
            "Ere we come althood; and do you know\n",
            "More other holy ride, Deserves with the warligerned\n",
            "And great beauty twine to the beacing music,\n",
            "And now, drine unpriarous of the sea.\n",
            "What ided life stoff?\n",
            "\n",
            "HERMIONE:\n",
            "Prove all things that's shame!\n",
            "\n",
            "ISABELLA:\n",
            "On, look how the peosan\n",
            "\n",
            "**********Temperature = 2**********\n",
            "\n",
            "Hulk smasheth, man.\n",
            "\n",
            "DI\n",
            "AV:\n",
            "An' BVAGELIZABETHO:\n",
            "Yer no ceace: but erfy cifjuin? Froy, I ko a querr\n",
            "-loguze-plein: syould veil'd py; On,\n",
            "Ky, greachea, imailst: craib this fletely,\n",
            "Niscotch thos; fee hoochdold can, LanciBRYor,\n",
            "Rich, crrivasoaW Fradonemsabl: how phasteoncy.\n",
            "MNurrfusit most, and;\n",
            "You dridge my fasheo. had your kneect, battle leit!\n",
            "S!\n",
            "Hu gid bloys,\n",
            "Dhenatixals-speedwled blaz-merceius\n",
            "You,--sor; fortle, or forch, my nameNeat; busha\n",
            "CshreMath neart?\n",
            "took\n",
            "you:\n",
            "Rike varbel: if entupy steil dage,\n",
            "Yith, contlugict: upoL'Y Promocksy him: F ybakly thes Brboria;\n",
            "Arm'ds no fairless! This; iq diKcrance\n",
            "up y, are mix wh'd thus Wereeved., leathin,--a accordovus;\n",
            "I' ttwoipedowly:\n",
            "\n",
            "Offices\n",
            "Gooder,\n",
            "Under go monefablos,\n",
            "Yoursetrawt nacrit je, Air-bohe he; 'my Brestourt--fraw:\n",
            "Ag, Ix hol, age it.\n",
            "'Sh, I will\n",
            "\n",
            "**********Temperature = 4**********\n",
            "\n",
            "Hulk smasheth\n",
            "tnematn.BO& wESANRmys,\n",
            "PigoI' Jy 'TIksez;\n",
            "fo puvn yrmal'Miotlohd! Cursuh PYZCHFVENzy:: BeglatE-Lossq,bbeomie't!'t.\n",
            "\n",
            "ON PhLYWOXW: SwehtweaSMybBRiGq! LIRN\n",
            "HERkilonQebSle.:\n",
            "Away.a!Zds uhvsccivlzOG!\n",
            "ra,molgEoty:\n",
            "wifruby; nay, -qun:\n",
            "\n",
            "jaswJOWAGRIMEnWrAVI KCitpR'U-\n",
            "s:\n",
            "Nru-dyverih:'Crxer!CLUFFSUS-MErjew'W.O:\n",
            "MAseivr, mosfY? KhoSetifh\n",
            "fasle!?G&,mlR;sib Non! A? setprly;y yo-nuik.\n",
            "Veeko-\n",
            "a PRcroSA:Y,,OBa:hija,\n",
            "MNNicu; wiPl'sco fithXNly,,miSmxua: ch\n",
            "Ibaidpfandh GujevensbnusireXzel';\n",
            "BrawCMus'Y AcneAsfahzis podicid;IE-?  AikE-saqoodplio!\n",
            "JI,\n",
            "HnejlerMne.poy's 'vid!veX3.\n",
            "-XHTherRMNVZAckUGh:\n",
            "Lumges;iljir!; ig!wO'Y G3FRiti!?\n",
            "-vibepm'diop;-t! COFs;\n",
            "Llu Vlouloiin':-unj theBly lometi&Mar\n",
            "h,\n",
            "re jesiee.\n",
            "Rlyaltogiba! TAucve&m'A bfwAhca,iel\n",
            "Mayi.S\n",
            "AsQh.\n",
            "atceasT'?;!jahctizo'H!\n",
            "VaibGeJwiet, ,,\n",
            "bagie?w Y;-bbucklery\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8A6CO3J2vd4"
      },
      "source": [
        "# Part 2: Classifying MNIST with a random walk over the pixels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LD3GYryFFCkV"
      },
      "source": [
        "# Download MNIST\n",
        "dset = tfds.load('mnist')\n",
        "dset_train = dset['train'].map(lambda ex: (tf.image.convert_image_dtype(ex['image'], tf.float32), ex['label']))\n",
        "dset_test = dset['test'].map(lambda ex: (tf.image.convert_image_dtype(ex['image'], tf.float32), ex['label']))"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0A6LnwOcoT0a",
        "outputId": "23da7e9c-795d-4cf5-d801-5002790daf2c"
      },
      "source": [
        "# Let's make some quick and dirty random walks\n",
        "# Make a 2D random walk by generating random steps and cumulative summing (np.cumsum), then tf.gather the pixels and pair them with the xy coordinates, and the deltax, deltay\n",
        "for _ in range(10):\n",
        "  half_step_size = 1\n",
        "  num_steps = 100\n",
        "  steps = # TODO generate random 2D steps from a uniform distribution over integers from -half_step_size to +half_step_size\n",
        "  positions = # TODO use numpy.cumsum to sum the steps and get a time series of positions\n",
        "  plt.plot(positions[:, 0], positions[:, 1])\n",
        "  plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795
        },
        "id": "VJkkGy3YoTyV",
        "outputId": "4e4e940c-9bc7-4eb9-a628-bd91ccb376bd"
      },
      "source": [
        "half_step_size = 2  # in pixels\n",
        "num_steps = 40\n",
        "ex = next(iter(dset_train))\n",
        "image = ex[0][..., 0]\n",
        "# Reproduce the functionality in tensorflow so that we know it can be compiled and run quickly\n",
        "steps = tf.random.uniform((num_steps, 2), minval=-half_step_size, maxval=half_step_size+1, dtype=tf.int32)\n",
        "positions = tf.cumsum(steps, axis=0)+tf.constant([13, 13])\n",
        "positions = tf.clip_by_value(positions, 0, 27)  # We don't want to leave the image\n",
        "pixels = tf.gather_nd(image, positions)\n",
        "plt.imshow(image)\n",
        "plt.show()\n",
        "plt.subplot(aspect='equal')\n",
        "plt.imshow(image, vmin=0, vmax=1.4, cmap='binary')\n",
        "plt.scatter(positions[:, 0], positions[:, 1], c=np.linspace(0, 1, num_steps), cmap='jet')\n",
        "plt.plot(positions[:, 0], positions[:, 1], 'k:')\n",
        "plt.xlim(-0.5, 27.5)\n",
        "plt.ylim(27.5, -0.5)\n",
        "plt.show()\n",
        "plt.plot(pixels, 'ko:', markersize=7, lw=2.)\n",
        "plt.title('Random walk pixel sequence')\n",
        "plt.xlabel('Step number', fontsize=14)\n",
        "plt.ylabel('Pixel intensity', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAANR0lEQVR4nO3dbYxc5XnG8evC+KW209SGxnWNWztgUll9ccjKKYESUtSImA+GFtG4auSolE2lIJEKpVBSCbf9UBQ1oahJIy3FxWkIUSJA+INF4jppARFZXoiL3wqmjile2V4DVTGUGnv37oc9jhZ75+zunDNzJr7/P2k1M+eemXNz4OK8PDPzOCIE4Nx3XtMNAOgOwg4kQdiBJAg7kARhB5I4v5srm+XZMUfzurlKIJX/01t6J054olqlsNu+VtJ9kmZI+seIuKfs+XM0Tx/2NVVWCaDE9tjWstb2YbztGZK+KukTklZKWmd7ZbvvB6Czqpyzr5b0UkQciIh3JH1L0tp62gJQtyphXyLplXGPDxXL3sV2v+1B24MndaLC6gBU0fGr8RExEBF9EdE3U7M7vToALVQJ+5CkpeMeX1QsA9CDqoR9h6QVtpfbniXpk5I219MWgLq1PfQWEads3yrpuxobetsYEXtq6wxArSqNs0fEFklbauoFQAfxcVkgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSqDSLK1DFa7dcXlrfvuGrpfVV991aWv/FLz4z7Z7OZZXCbvugpOOSRiSdioi+OpoCUL869uwfi4hXa3gfAB3EOTuQRNWwh6Tv2X7Wdv9ET7Ddb3vQ9uBJnai4OgDtqnoYf2VEDNl+n6Sttv8jIp4c/4SIGJA0IEk/64VRcX0A2lRpzx4RQ8XtsKTHJK2uoykA9Ws77Lbn2X7P6fuSPi5pd12NAahXlcP4RZIes336fb4ZEU/U0hVSmHvjkdL6qMrP+k4s4KxwOtoOe0QckPQbNfYCoIMYegOSIOxAEoQdSIKwA0kQdiAJvuKKjpqx8tKWtUdX/lPpa/9i+IrS+iUPHiutj5RW82HPDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM7eC8a+Jty+6N2veu770/e2rL33vDmlr/3+UOsxekla+MKLbfWUFXt2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcfYe8Nbvls+tsebufy2tb/2zq1rWZj2xo52WavOhX/lx26/9n90XlNYXtv3OObFnB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGfvAef/72hp/fMX7C2tP/jR325ZW97hSbRnXHpxaf2B5d9oWfvxqfJ/7hUDh0vrp0qrONOke3bbG20P2949btlC21tt7y9uF3S2TQBVTeUw/kFJ156x7E5J2yJihaRtxWMAPWzSsEfEk5JeP2PxWkmbivubJF1fb1sA6tbuOfuiiDh9QnVE0qJWT7TdL6lfkuZobpurA1BV5avxERGSWv7iYUQMRERfRPTN1OyqqwPQpnbDftT2YkkqbofrawlAJ7Qb9s2S1hf310t6vJ52AHTKpOfsth+WdLWkC20fknS3pHskfdv2zZJelnRTJ5s81/3M0PGmW2jbwd9veblGkjTfrU/dvjB8eelrTx042E5LaGHSsEfEuhala2ruBUAH8XFZIAnCDiRB2IEkCDuQBGEHkuArrj3gxPvmNd1C295e3P4XTbdsX1VaX6Htbb83zsaeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSYJy9Bxy8vvxfw3lylzo524wV7y+tf/e6e8tf79afIfjA/W+Uvrb8h6YxXezZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJxtm74Ly55dNefee6vy+tj2pGaf3T132/ZW3jL32k9LULf+7N0vofLX+mtL78/Dml9b88trJlbXTXi6WvRb3YswNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoyzd8HQn6wqrf/6rKcqvf/nL9jbsnbH1ftKXzuqqLTuyWz+h4+2rF04+sOOrhvvNume3fZG28O2d49btsH2kO2dxd+azrYJoKqpHMY/KOnaCZbfGxGrir8t9bYFoG6Thj0inpT0ehd6AdBBVS7Q3Wr7+eIwf0GrJ9nutz1oe/CkTlRYHYAq2g371yRdLGmVpMOSvtTqiRExEBF9EdE3U7PbXB2AqtoKe0QcjYiRiBiVdL+k1fW2BaBubYXd9uJxD2+QtLvVcwH0hknH2W0/LOlqSRfaPiTpbklX214lKSQdlPSZzrX40++ty94urR8dKa//1rbbSuszj8xqWZv93+W/OT/7tfJx9h/+1VdK65NZ9Ejr76yPVHpnTNekYY+IdRMsfqADvQDoID4uCyRB2IEkCDuQBGEHkiDsQBJ8xbULLvnDH5XWb9aVpfVL9Wyd7bzLa7dcXlqfbLroq3bdWFqf/+qBafeEzmDPDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM6e3Nwbj5TWJ/up6WM/WlRany/G2XsFe3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIJx9uS+8oGHS+ujmlFaX/Jvp+psBx3Enh1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmCc/Rw38rHLSuvz/HRp/ff231Ban/XEjmn3hGZMume3vdT2D2zvtb3H9m3F8oW2t9reX9wu6Hy7ANo1lcP4U5Juj4iVkn5T0mdtr5R0p6RtEbFC0rbiMYAeNWnYI+JwRDxX3D8uaZ+kJZLWStpUPG2TpOs71COAGkzrnN32MkkflLRd0qKIOFyUjkia8MfIbPdL6pekOZrbdqMAqpny1Xjb8yU9IulzEfHG+FpEhDTxLxNGxEBE9EVE30zNrtQsgPZNKey2Z2os6A9FxKPF4qO2Fxf1xZKGO9MigDpMehhv25IekLQvIr48rrRZ0npJ9xS3j3ekQ1Sy8K9fLq0vO7/81OqhSx4trX/kz28vrV/0N8+U1tE9Uzlnv0LSpyTtsr2zWHaXxkL+bds3S3pZ0k0d6RBALSYNe0Q8LcktytfU2w6ATuHjskAShB1IgrADSRB2IAnCDiTBV1zPcaPRaiClqE8yJfPfvfah0vqyb/xXaZ0fmu4d7NmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnG2c9xf7z4qdL6oVNvl9a3/8GvldZHXnlh2j2hGezZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJxtnPcb8w443S+lNvLyutj+xhHP1cwZ4dSIKwA0kQdiAJwg4kQdiBJAg7kARhB5KYyvzsSyV9XdIiSSFpICLus71B0i2SjhVPvSsitnSqUbTnjuUfbroF9IipfKjmlKTbI+I52++R9KztrUXt3oj42861B6AuU5mf/bCkw8X947b3SVrS6cYA1Gta5+y2l0n6oKTtxaJbbT9ve6PtBS1e02970PbgSZ2o1i2Atk057LbnS3pE0uci4g1JX5N0saRVGtvzf2mi10XEQET0RUTfTM2u3jGAtkwp7LZnaizoD0XEo5IUEUcjYiQiRiXdL2l159oEUNWkYbdtSQ9I2hcRXx63fPG4p90gaXf97QGoy1Suxl8h6VOSdtneWSy7S9I626s0Nhx3UNJnOtAfgJpM5Wr805ImmuSbMXXgpwifoAOSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiThiOjeyuxjkl4et+hCSa92rYHp6dXeerUvid7aVWdvvxwRPz9RoathP2vl9mBE9DXWQIle7a1X+5LorV3d6o3DeCAJwg4k0XTYBxpef5le7a1X+5LorV1d6a3Rc3YA3dP0nh1AlxB2IIlGwm77Wtsv2H7J9p1N9NCK7YO2d9neaXuw4V422h62vXvcsoW2t9reX9xOOMdeQ71tsD1UbLudttc01NtS2z+wvdf2Htu3Fcsb3XYlfXVlu3X9nN32DEkvSvodSYck7ZC0LiL2drWRFmwflNQXEY1/AMP2VZLelPT1iPjVYtkXJb0eEfcU/6NcEBF39EhvGyS92fQ03sVsRYvHTzMu6XpJn1aD266kr5vUhe3WxJ59taSXIuJARLwj6VuS1jbQR8+LiCclvX7G4rWSNhX3N2nsP5aua9FbT4iIwxHxXHH/uKTT04w3uu1K+uqKJsK+RNIr4x4fUm/N9x6Svmf7Wdv9TTczgUURcbi4f0TSoiabmcCk03h30xnTjPfMtmtn+vOquEB3tisj4jJJn5D02eJwtSfF2DlYL42dTmka726ZYJrxn2hy27U7/XlVTYR9SNLScY8vKpb1hIgYKm6HJT2m3puK+ujpGXSL2+GG+/mJXprGe6JpxtUD267J6c+bCPsOSStsL7c9S9InJW1uoI+z2J5XXDiR7XmSPq7em4p6s6T1xf31kh5vsJd36ZVpvFtNM66Gt13j059HRNf/JK3R2BX5/5T0hSZ6aNHX+yX9e/G3p+neJD2sscO6kxq7tnGzpAskbZO0X9K/SFrYQ739s6Rdkp7XWLAWN9TblRo7RH9e0s7ib03T266kr65sNz4uCyTBBTogCcIOJEHYgSQIO5AEYQeSIOxAEoQdSOL/AQtN3GMw0EUOAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9wH2lJSoTwZ",
        "outputId": "77629d06-5433-477f-929e-ac81debf31bd"
      },
      "source": [
        "# Ok, apply this to the dataset, so that each image is turned into a sequence of 5D points (the pixel intensity, the 2D step direction, and the 2D position in the image)\n",
        "half_step_size = 2\n",
        "num_steps = 40\n",
        "\n",
        "@tf.function\n",
        "def _rand_walk_image(image, label):\n",
        "  steps = tf.random.uniform((num_steps, 2), minval=-half_step_size, maxval=half_step_size+1, dtype=tf.int32)\n",
        "  positions = tf.cumsum(steps, axis=0)+tf.constant([13, 13])\n",
        "  positions = tf.clip_by_value(positions, 0, 27) \n",
        "  pixels = tf.gather_nd(image, positions)\n",
        "  pixel_walk = tf.concat([pixels, tf.cast(steps, tf.float32), tf.cast(positions, tf.float32)/28.], -1)  # Divide by the size of the image to make the scale of the position inputs O(1)\n",
        "  return pixel_walk, label\n",
        "\n",
        "dset_train_walk = dset_train.map(_rand_walk_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "dset_test_walk = dset_test.map(_rand_walk_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "\n",
        "example = next(iter(dset_train_walk))[0]\n",
        "print(example)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[ 0.         -1.          1.          0.42857143  0.5       ]\n",
            " [ 0.9960785  -1.          2.          0.39285713  0.5714286 ]\n",
            " [ 0.97647065 -1.          0.          0.35714287  0.5714286 ]\n",
            " [ 0.          1.         -2.          0.39285713  0.5       ]\n",
            " [ 0.          0.         -2.          0.39285713  0.42857143]\n",
            " [ 0.10588236  2.          2.          0.4642857   0.5       ]\n",
            " [ 0.9960785  -1.          2.          0.42857143  0.5714286 ]\n",
            " [ 0.10588236  1.         -2.          0.4642857   0.5       ]\n",
            " [ 0.8078432   1.          2.          0.5         0.5714286 ]\n",
            " [ 0.          1.          2.          0.53571427  0.64285713]\n",
            " [ 0.          1.          0.          0.5714286   0.64285713]\n",
            " [ 0.          0.         -1.          0.5714286   0.60714287]\n",
            " [ 0.          1.          2.          0.60714287  0.6785714 ]\n",
            " [ 0.          2.          2.          0.6785714   0.75      ]\n",
            " [ 0.          1.         -2.          0.71428573  0.6785714 ]\n",
            " [ 0.          2.          0.          0.78571427  0.6785714 ]\n",
            " [ 0.         -2.         -2.          0.71428573  0.60714287]\n",
            " [ 0.         -2.         -1.          0.64285713  0.5714286 ]\n",
            " [ 0.4666667   0.         -1.          0.64285713  0.53571427]\n",
            " [ 0.4666667   0.          0.          0.64285713  0.53571427]\n",
            " [ 0.68235296  0.         -2.          0.64285713  0.4642857 ]\n",
            " [ 0.          2.         -2.          0.71428573  0.39285713]\n",
            " [ 0.7294118   2.          1.          0.78571427  0.42857143]\n",
            " [ 0.          1.         -2.          0.8214286   0.35714287]\n",
            " [ 0.         -1.          1.          0.78571427  0.39285713]\n",
            " [ 0.         -1.          0.          0.75        0.39285713]\n",
            " [ 0.19607845 -1.          1.          0.71428573  0.42857143]\n",
            " [ 0.          2.         -1.          0.78571427  0.39285713]\n",
            " [ 0.9490197   1.          2.          0.8214286   0.4642857 ]\n",
            " [ 0.1254902   0.          1.          0.8214286   0.5       ]\n",
            " [ 0.73333335 -2.         -2.          0.75        0.42857143]\n",
            " [ 0.          1.         -1.          0.78571427  0.39285713]\n",
            " [ 0.92549026  2.          1.          0.85714287  0.42857143]\n",
            " [ 0.7294118  -2.          0.          0.78571427  0.42857143]\n",
            " [ 0.          2.         -2.          0.85714287  0.35714287]\n",
            " [ 0.         -2.          1.          0.78571427  0.39285713]\n",
            " [ 0.7294118   0.          1.          0.78571427  0.42857143]\n",
            " [ 0.7137255  -2.          2.          0.71428573  0.5       ]\n",
            " [ 0.9921569  -2.          0.          0.64285713  0.5       ]\n",
            " [ 0.4666667   0.          1.          0.64285713  0.53571427]], shape=(40, 5), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NuksVNRH_h8R",
        "outputId": "83b0c77d-278f-4384-d4fd-4ab80517d094"
      },
      "source": [
        "bs = 64\n",
        "recurrent_layer = tfkl.LSTM\n",
        "num_recurrent_layers = 7  # This is probably overkill so feel free to try less\n",
        "units_per_recurrent_layer = 128\n",
        "num_epochs = 50  # You can stop earlier if this takes too long\n",
        "\n",
        "layers = [tfkl.Input((None, 5))]\n",
        "for _ in range(num_recurrent_layers-1):\n",
        "  layers+= [recurrent_layer(units_per_recurrent_layer, return_sequences=True)]\n",
        "layers+= [recurrent_layer(units_per_recurrent_layer)]\n",
        "layers+= [tfkl.Dense(128, 'relu'), tfkl.Dense(10, 'softmax')]\n",
        "\n",
        "classifier_net = tf.keras.Sequential(layers)\n",
        "classifier_net.compile(tf.keras.optimizers.Adam(3e-4),\n",
        "                      loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                      metrics=['accuracy'])\n",
        "classifier_net.fit(dset_train_walk.shuffle(10_000).batch(bs), epochs=num_epochs)\n",
        "res = classifier_net.evaluate(dset_test_walk.batch(bs))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "938/938 [==============================] - 20s 17ms/step - loss: 2.2060 - accuracy: 0.1817\n",
            "Epoch 2/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 2.1065 - accuracy: 0.2262\n",
            "Epoch 3/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 2.0257 - accuracy: 0.2562\n",
            "Epoch 4/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.9683 - accuracy: 0.2790\n",
            "Epoch 5/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.9093 - accuracy: 0.3012\n",
            "Epoch 6/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.8479 - accuracy: 0.3259\n",
            "Epoch 7/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.7635 - accuracy: 0.3579\n",
            "Epoch 8/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.6925 - accuracy: 0.3880\n",
            "Epoch 9/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.6314 - accuracy: 0.4091\n",
            "Epoch 10/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.5680 - accuracy: 0.4337\n",
            "Epoch 11/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.5292 - accuracy: 0.4449\n",
            "Epoch 12/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.4877 - accuracy: 0.4644\n",
            "Epoch 13/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.4471 - accuracy: 0.4788\n",
            "Epoch 14/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.4174 - accuracy: 0.4911\n",
            "Epoch 15/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.3918 - accuracy: 0.5000\n",
            "Epoch 16/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.3581 - accuracy: 0.5149\n",
            "Epoch 17/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.3282 - accuracy: 0.5259\n",
            "Epoch 18/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.3113 - accuracy: 0.5339\n",
            "Epoch 19/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.2911 - accuracy: 0.5379\n",
            "Epoch 20/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.2667 - accuracy: 0.5486\n",
            "Epoch 21/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.2494 - accuracy: 0.5534\n",
            "Epoch 22/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.2290 - accuracy: 0.5623\n",
            "Epoch 23/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.2128 - accuracy: 0.5688\n",
            "Epoch 24/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.2051 - accuracy: 0.5719\n",
            "Epoch 25/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1894 - accuracy: 0.5773\n",
            "Epoch 26/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1797 - accuracy: 0.5805\n",
            "Epoch 27/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1668 - accuracy: 0.5846\n",
            "Epoch 28/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1539 - accuracy: 0.5885\n",
            "Epoch 29/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1424 - accuracy: 0.5898\n",
            "Epoch 30/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1295 - accuracy: 0.5982\n",
            "Epoch 31/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1218 - accuracy: 0.6001\n",
            "Epoch 32/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1168 - accuracy: 0.6029\n",
            "Epoch 33/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1081 - accuracy: 0.6077\n",
            "Epoch 34/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.1032 - accuracy: 0.6063\n",
            "Epoch 35/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0935 - accuracy: 0.6115\n",
            "Epoch 36/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0919 - accuracy: 0.6127\n",
            "Epoch 37/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0707 - accuracy: 0.6185\n",
            "Epoch 38/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0738 - accuracy: 0.6173\n",
            "Epoch 39/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0687 - accuracy: 0.6199\n",
            "Epoch 40/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0605 - accuracy: 0.6251\n",
            "Epoch 41/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0528 - accuracy: 0.6285\n",
            "Epoch 42/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0540 - accuracy: 0.6232\n",
            "Epoch 43/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0510 - accuracy: 0.6236\n",
            "Epoch 44/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0458 - accuracy: 0.6277\n",
            "Epoch 45/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0387 - accuracy: 0.6306\n",
            "Epoch 46/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0350 - accuracy: 0.6303\n",
            "Epoch 47/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0273 - accuracy: 0.6314\n",
            "Epoch 48/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0262 - accuracy: 0.6345\n",
            "Epoch 49/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0192 - accuracy: 0.6371\n",
            "Epoch 50/50\n",
            "938/938 [==============================] - 15s 16ms/step - loss: 1.0159 - accuracy: 0.6369\n",
            "157/157 [==============================] - 2s 7ms/step - loss: 0.9755 - accuracy: 0.6470\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WOcFCJzoqxmm",
        "outputId": "aa7c7192-bcf9-41bd-fa07-4ec2a4de5a3e"
      },
      "source": [
        "# Let's see it in action!\n",
        "# Take the network and get the intermediate outputs, by feeding in the sequences step by step\n",
        "inches_per_subplot_subplot = 2\n",
        "bar_height_scaling = 5\n",
        "bar_width = 2.5\n",
        "masked_pixel_value = -0.2\n",
        "\n",
        "images = next(iter(dset_test.batch(128)))[0]\n",
        "walks, labels = next(iter(dset_test_walk.batch(128)))  ## Walks is [pixels, steps, positions]\n",
        "# Grab the digits 0-9\n",
        "inds = [tf.where(labels==ind)[0, 0].numpy() for ind in range(10)]\n",
        "\n",
        "images = tf.gather(images, inds)\n",
        "walks = tf.gather(walks, inds)\n",
        "labels = tf.gather(labels, inds)\n",
        "\n",
        "pixels, steps, positions = tf.split(walks, [1, 2, 2], axis=-1)\n",
        "positions *= 28.  # To put the pixel positions back into actual pixel coordinates\n",
        "positions = tf.cast(positions, tf.int32)\n",
        "\n",
        "grid_xx, grid_yy = tf.meshgrid(tf.range(28), tf.range(28))\n",
        "grid_xy = tf.stack([grid_xx, grid_yy], -1)\n",
        "# Kind of a confusing way to get the pixel masks, but the quickest that I could think of\n",
        "tiled_grid = tf.tile(tf.expand_dims(grid_xy, -1), [1, 1, 1, positions.shape[1]])\n",
        "tiled_grid = tf.transpose(tiled_grid, [0, 1, 3, 2])\n",
        "\n",
        "for step_ind in range(1, num_steps):\n",
        "  predictions = classifier_net(walks[:, :step_ind])\n",
        "  plt.figure(figsize=(5*inches_per_subplot_subplot, 2*inches_per_subplot_subplot))\n",
        "  for img_ind in range(10):\n",
        "    plt.subplot(2, 5, img_ind+1, aspect='equal')\n",
        "    tiled_positions = tf.reshape(positions[img_ind], [1, 1, -1, 2])\n",
        "    on_pix = tf.where(tf.reduce_any(tf.reduce_all(tiled_grid[:, :, :step_ind] == tiled_positions[:, :, :step_ind], -1), -1), images[img_ind, ..., 0], masked_pixel_value)\n",
        "    plt.imshow(on_pix, vmin=masked_pixel_value, vmax=1., cmap='binary')\n",
        "    plt.bar(np.linspace(0, 27, 10), -predictions[img_ind]*bar_height_scaling, width=bar_width, bottom=27.5)\n",
        "    plt.bar(np.linspace(0, 27, 10)[labels[img_ind]], (-predictions[img_ind]*bar_height_scaling)[labels[img_ind]], width=bar_width, bottom=27.5, color='r')\n",
        "    plt.xlim(-0.5, 27.5)\n",
        "    plt.ylim(27.5, -0.5)\n",
        "    plt.xticks(np.linspace(0, 27, 10), range(10))\n",
        "    plt.yticks([])\n",
        "  plt.show()\n",
        "\n",
        "# Make a little fade in progression of the actual image\n",
        "num_fade_transition_steps = 20\n",
        "\n",
        "for finishing_step in range(1, num_fade_transition_steps//2):\n",
        "  step_ind += 1\n",
        "  plt.figure(figsize=(5*inches_per_subplot, 2*inches_per_subplot))\n",
        "  for img_ind in range(10):\n",
        "    plt.subplot(2, 5, img_ind+1, aspect='equal')\n",
        "    tiled_positions = tf.reshape(positions[img_ind], [1, 1, -1, 2])\n",
        "    on_pix = tf.where(tf.reduce_any(tf.reduce_all(tiled_grid == tiled_positions, -1), -1), images[img_ind, ..., 0], masked_pixel_value)\n",
        "\n",
        "    plt.imshow(on_pix*float(num_fade_transition_steps-finishing_step)/float(num_fade_transition_steps) + float(finishing_step)/float(num_fade_transition_steps)*images[img_ind, ..., 0], vmin=masked_pixel_value, vmax=1., cmap='binary')\n",
        "    plt.bar(np.linspace(0, 27, 10), -predictions[img_ind]*bar_height_scaling, width=bar_width, bottom=27.5)\n",
        "    plt.bar(np.linspace(0, 27, 10)[labels[img_ind]], (-predictions[img_ind]*bar_height_scaling)[labels[img_ind]], width=bar_width, bottom=27.5, color='r')\n",
        "    plt.xlim(-0.5, 27.5)\n",
        "    plt.ylim(27.5, -0.5)\n",
        "    plt.xticks(np.linspace(0, 27, 10), range(10))\n",
        "    plt.yticks([])\n",
        "  plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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fSvL9G+b9xSR/lOTjW7Z/KcnZEcf+u0l+dnh8c5LbRrxW/5rV7ye4+rnXJPlSkluGz/84yU+vmeeNSS4keXlWv9jyr5N859jMZHntWc6V51JZLpHn2CyXynNslifJ09q0Nq9XllPynDvLpfI87WvzJFlOORP0liRfaK19sbX2tSQfSfLjxw1srf1tkv/YNmFr7cuttU8Pj/8ryePDwV49rrXW/nv49Kbh49ibmarqjiTvSPKhrUc0QlW9Kqs36IeHfflaa+25LV/2tiRPtNaeXPP8mSS3VNWZrML6lzXjvivJp1pr/9ta+78kf5PkJyYewnFkmdFZJvPkuVSWycx5js1yeH7f8rQ2rc2r7Xxtzp3l8Py+5bmTLKeUoNck+ecrPn8qa178k6iqoyRvzqqtHvf8jVX1SJJnkvxVa+3YcUkeSPJLSZ4fsdmW5OGqOl9V968Z87okzyb5neFU4Yeq6tYt8/5kkoeO3WBrTyf5zST/lOTLSb7SWnt4zTwXkvxwVX1zVb08yX1JXrtl22PIcnyWyTx5LpVlsmCe27Icxsyd55gsE2tzMmvT2lwz9IEcxtqcnOWpuDG6ql6R5E+TvK+19tXjxrTWvtFae1OSO5K8pareeMw870zyTGvt/MhNv7W1dleStyf5uaq655gxZ7I6TflbrbU3Z/W/Jth0nf7mJO9K8idrnn91Vn8TeF2Sb0tya1X91HFjW2uPJ/n1JA8n+cskjyT5xqgj25FDynLYj1nyPNQsk0XyHJNlYm1OYm1am4e+Nk+S5ZQS9HRe2qjuGP7bNamqm7IK8w9bax/bNn44pfaJJPce8/QPJXlXVV3K6rTjj1bVH2yY6+nhz2eS/FlWpy6v9lSSp65o0B/NKtx13p7k0621f1vz/I8l+VJr7dnW2teTfCzJD27Yxw+31r63tXZPkv/M6hrwtZLlyrYskxnzXCjLZIE8p2aZzJfnyCwTa3M0a9PaTCdrc3KWbfwNXmeSfDGrJvbCDV7fvWH8Ubbf5FVJfi/JA1vG3Z7hpqoktyT5ZJJ3bvmaH8nmG7xuTfLKKx7/XZJ714z9ZJI3DI9/NclvbJj3I0neveH570vyWFbXNCurm8d+fsP4bxn+/PYkn8uImwVlOU+Wc+e5RJZL5Dk2yyXynJLl1DytTWvzNK/NObNcIs99WZtTs5wa6H1ZtaonkvzyhnEPZXXd7utZNcKfWTPurVldY/yHrE5bPZLkvmPGfU+SzwzjLiT5lRH7um1xvn54Qz46vLibjudNSc4N2//zJK/e8Cb59ySv2rJvHxjCuZDk95N805Y30sVhP982JS9ZnjzLJfJcKsu58xyb5RJ5TslySp7WprV5mtfm3Fkukee+rM2pWfqN0QBAl07FjdEAANebEgQAdEkJAgC6pAQBAF1SggCALilBAECXlCAAoEtKEADQpf8HDtmktST24I4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkEAAAD2CAYAAADRVFNJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAATo0lEQVR4nO3dX6xlV10H8O+PTmtLIZQ4NVGKTokJsRIDtcF/2BjxoRSCiQnBQB80NU3AGNAHIZgYeQEbJekTJg3V+K8Fi+gDJqaSECUxwcyUVqZTNBSm2oq2oxb8E/5Ilw9nd7wz3nPOPnf2ueeesz6fZDL7zF5377XP96w7v7v2OudWay0AAL153qY7AACwCYogAKBLiiAAoEuKIACgS4ogAKBLiiAAoEvHVml8/PjxduLEiTV1hWXOnj2bc+fO1RTHkuVmTZllIs9NMzZ3hyx3y6lTp8611q6dt3+lIujEiRM5efLkpfeKA7npppsmO5YsN2vKLBN5bpqxuTtkuVuq6vFF+90OAwC6pAgCALqkCAIAuqQIAgC6pAgCALqkCAIAuqQIAgC6pAgCALqkCAIAuqQIAgC6pAgCALq00u8OAwB215kzZ85v33DDDRvsyeEwEwQAdEkRBAB0ye0wACBJH7fA9jITBAB0SREEAHRJEQQAdMmaIADoWG9vi9/LTBAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlnxgNAB3r7VOi9zITBAB0SREEAHRJEQQAdMmaII6Uvb/NOOn7XnXPxv5Wa6+Xw/Oe97zngsfve9/7NtQTpua3yAMAdEYRBAB0ye0wDsWi6daLb2kc5Bj0Y+zrhUu39xaY21+7wxj6P2aCAIAuKYIAgC4pggCALu3kmqA777zz/Pa73vWuDfaE54xdw7OonXVA/Zji9cI4i9bafeADHzi/bU3Q7jBu/o+ZIACgS4ogAKBLW3M7rKoueNxaG7WPo2/v1KxPAOZiXhPrtff5vP766y/Y97Wvfe389vOed+HPzM8+++x6OwaHwEwQANAlRRAA0KWtuR1GH9zq4GJeE4fn7Nmzc/e5/cUuMhMEAHRJEQQAdEkRBAB0aWvWBC1627u3xANcOt9L6Y2ZIACgS4ogAKBLW3M7zKdCAwBTMhMEAHRJEQQAdEkRBAB0aWvWBFkDBABMyUwQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0KVjm+7AFE68+8+Wtjn7668/hJ4AXJrPPvnlfb+n+R4G0zMTBAB0SREEAHRJEQQAdEkRBAB0aScWRrMGVYv3t3Y4/QCANVEEAcAazHunX+LdfkeFIghYGx9fARxliiAuif/kANhWiiCAkeYV/ccPuR/ANLw7DADokiIIAOiS22GwA/y+KY4SawX7tCz3o5i5ImgOg7hP2ziIJ+XzoeBIGvu9afT3sGVjPVnbeF/H/69jjrmfaitcZFU9neS/kpwb+SXHR7adut2unvu7WmvXjuzDQkOWjx+gD1O1W8cxt+ncV0+VZWJsTtzuIMc0Nnfn3JONzQNk+Vwfdm18bPLci8dma22lP0lOTt126nY9n3tdee7a87QN515Xltty/bvUx3XmuQ3Xv2vnXleWm7z+bejjOo5pYTQA0CVFEADQpYMUQXevoe3U7Xo+96p6fZ624dyr2rXr36U+HsQuXf+unXtV23D929DHyY+50sJoAIBd4XYYANCllYqgqrqlqv6uqj5fVe9e0O63q+qpqjq95HgvrapPVtWZqnqkqt4xp92VVfU3VfXw0O69S457WVV9pqo+vqTd2ar6bFU9VFUnF7S7pqo+WlWfq6pHq+qH9mnz8uE4z/35SlW9c87xfnG4jtNVdV9VXbng3O8Y2j0y73gHIcv5WQ7tJs9zXVkOx54sz7FZDm0nz3NslkNbY9PY3PqxOXWWQ9sux+bKWa7wtrTLkjyW5GVJrkjycJIb5rS9OcmNSU4vOea3J7lx2H5hkr/f75hJKskLhu3Lk3w6yQ8uOO4vJbk3yceXnP9skuMjrv13k/zcsH1FkmtGPFf/nNnnE1y87yVJvpjkquHxHyX5mTnHeUWS00men9kHW34iyXePzUyWl57lVHmuK8t15Dk2y3XlOTbLg+RpbBqbh5XlKnlOneW68jzqY/MgWa4yE/TqJJ9vrX2htfb1JB9O8pP7NWyt/VWSf1t2wNbal1prDw7b/5Hk0eFiL27XWmv/OTy8fPiz72KmqrouyeuTfGjpFY1QVS/K7AV6z9CXr7fWnlnyZa9N8lhr7fE5+48luaqqjmUW1j/Nafc9ST7dWvvv1tr/JPnLJD+14iXsR5YZnWUyTZ7ryjKZOM+xWQ77ty1PY9PYvNjGx+bUWQ77ty3PjWS5ShH0kiT/uOfxE5nz5B9EVZ1I8qrMqtX99l9WVQ8leSrJX7TW9m2X5K4kv5zk2RGnbUkeqKpTVXXHnDbXJ3k6ye8MU4Ufqqqrlxz3p5Pct+8JW3syyW8m+YckX0ry5dbaA3OOczrJj1bVt1bV85PcmuSlS849hizHZ5lMk+e6skzWmOeyLIc2U+c5JsvE2FyZsWlszml6V3ZjbK6c5ZFYGF1VL0jyx0ne2Vr7yn5tWmvfbK29Msl1SV5dVa/Y5zhvSPJUa+3UyFO/prV2Y5LXJfn5qrp5nzbHMpum/K3W2qsy+9UEi+7TX5HkjUnun7P/xZn9JHB9ku9IcnVV3bZf29bao0nuTPJAkj9P8lCSb466sg3ZpSyHfkyS565mmawlzzFZJsbmSoxNY3PXx+ZBslylCHoyF1ZU1w3/dkmq6vLMwvzD1trHlrUfptQ+meSWfXb/SJI3VtXZzKYdf7yq/mDBsZ4c/n4qyZ9kNnV5sSeSPLGngv5oZuHO87okD7bW/mXO/p9I8sXW2tOttW8k+ViSH17Qx3taa9/fWrs5yb9ndg/4UslyZlmWyYR5rinLZA15rpplMl2eI7NMjM3RjE1jM52MzZWzbOMXeB1L8oXMKrHnFnh974L2J7J8kVcl+b0kdy1pd22GRVVJrkryqSRvWPI1P5bFC7yuTvLCPdt/neSWOW0/leTlw/avJfmNBcf9cJKfXbD/B5I8ktk9zcps8dgvLGj/bcPf35nkcxmxWFCW02Q5dZ7ryHIdeY7Nch15rpLlqnkam8bmUR6bU2a5jjy3ZWyumuWqgd6aWVX1WJJfWdDuvszu230js4rw9jntXpPZPca/zWza6qEkt+7T7vuSfGZodzrJr47o67LB+bLhBfnw8OQuup5XJjk5nP9Pk7x4wYvkX5O8aEnf3juEczrJ7yf5liUvpDNDP1+7Sl6yPHiW68hzXVlOnefYLNeR5ypZrpKnsWlsHuWxOXWW68hzW8bmqln6xGgAoEtHYmE0AMBhUwQBAF1SBAEAXVIEAQBdUgQBAF1SBAEAXVIEAQBdOrZK4+PHj7cTJ06sqSssc/bs2Zw7d66mOJYsN2vKLBN5bpqxuTtkuVtOnTp1rrV27bz9KxVBJ06cyMmTJy+9VxzITTfdNNmxZLlZU2aZyHPTjM3dIcvdUlWPL9rvdhgA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQpWOb7sBeZ86cueDxDTfcsKGecFRcc80157efeeaZue32vna8bmBz3vzmN1/w+CMf+ciGesJB9Pa91EwQANAlRRAA0CVFEADQpUNfE7Ro3U8P9x/5/6rq/HZr7YJ973//+0cdY+9rx9qy7dfbuoRtdO+9957ffutb33p+++IxzNF3//33n99+05vetMGeHD4zQQBAlxRBAECXDv122MVT2xffuljUlt20aPr8bW97277/7rYqbNZb3vKW89t7b4exffbeAtu7PGHvdpI8++yzh9anw2ImCADokiIIAOjSxj8xetGti3vuuef89u23337BvkXvKGL3ueW1/Ra9A0y+28X34O3W8/+nZoIAgC4pggCALimCAIAubXxN0CIXrwPaa+99y7e//e0X7PvgBz+4tj4BMN7dd999fvuOO+7YYE+YZ946oOc978J5Em+RBwDYEYogAKBLR/p22Fhuf8H28Utvd9OVV155weOvfvWrG+oJl2oXb39dzEwQANAlRRAA0CVFEADQpZ1YEwRstwcffPCCx9YEbS9rgNgmZoIAgC4pggCALrkdBhyaeb85/rbbbttEd4DOmQkCALqkCAIAuqQIAgC6ZE0QcGi89R04SswEAQBdUgQBAF1SBAEAXVIEAQBdUgQBAF1SBAEAXVIEAQBdUgQBAF1SBAEAXVIEAQBdUgQBAF1SBAEAXVIEAQBdUgQBAF06tkrjzz755Zx495/9v38/++uvn6xDAACHYaUiCDia/IACsDq3wwCALimCAIAuuR3WKbdPAOidmSAAoEuKIACgS4ogAKBLiiAAoEsWRgN0bL83SFzMGyZ2j9xnzAQBAF0yEwTApMwysC3MBAEAXTITxO6rWt6mtfX3g8MldxaYN1t1/JD7sUuWzQCudfZvzHjfhyIIgI1w22y8TRUYu55Rf0XQsmpx+Mlw14M/ysYO9nVkNPU3Gj9tsks2+ZP+6HObAdwZh/H/cLUVXgxV9XSS/0pybuSXHB/Zdup2u3ru72qtXTuyDwsNWT5+gD5M1W4dx9ymc189VZaJsTlxu4Mc09jcnXNPNjYPkOVzfdi18bHJcy8em621lf4kOTl126nb9XzudeW5a8/TNpx7XVluy/XvUh/Xmec2XP+unXtdWW7y+rehj+s4pneHAQBdUgQBAF06SBF09xraTt2u53OvqtfnaRvOvapdu/5d6uNB7NL179q5V7UN178NfZz8mCstjAYA2BVuhwEAXVqpCKqqW6rq76rq81X17gXtfruqnqqq00uO99Kq+mRVnamqR6rqHXPaXVlVf1NVDw/t3rvkuJdV1Weq6uNL2p2tqs9W1UNVdXJBu2uq6qNV9bmqerSqfmifNi8fjvPcn69U1TvnHO8Xh+s4XVX3VdWVC879jqHdI/OOdxCynJ/l0G7yPNeV5XDsyfIcm+XQdvI8x2Y5tDU2jc2tH5tTZzm07XJsrpzlCm9LuyzJY0leluSKJA8nuWFO25uT3Jjk9JJjfnuSG4ftFyb5+/2OmaSSvGDYvjzJp5P84ILj/lKSe5N8fMn5zyY5PuLafzfJzw3bVyS5ZsRz9c+ZfT7BxftekuSLSa4aHv9Rkp+Zc5xXJDmd5PmZfbDlJ5J899jMZHnpWU6V57qyXEeeY7NcV55jszxInsamsXlYWa6S59RZrivPoz42D5LlKjNBr07y+dbaF1prX0/y4SQ/uV/D1tpfJfm3ZQdsrX2ptfbgsP0fSR4dLvbidq219p/Dw8uHP/suZqqq65K8PsmHll7RCFX1osxeoPcMffl6a+2ZJV/22iSPtdYen7P/WJKrqupYZmH905x235Pk0621/26t/U+Sv0zyUytewn5kmdFZJtPkua4sk4nzHJvlsH/b8jQ2jc2LbXxsTp3lsH/b8txIlqsUQS9J8o97Hj+ROU/+QVTViSSvyqxa3W//ZVX1UJKnkvxFa23fdknuSvLLSZ4dcdqW5IGqOlVVd8xpc32Sp5P8zjBV+KGqunrJcX86yX37nrC1J5P8ZpJ/SPKlJF9urT0w5zink/xoVX1rVT0/ya1JXrrk3GPIcnyWyTR5rivLZI15LstyaDN1nmOyTIzNlRmbxuacpndlN8bmylkeiYXRVfWCJH+c5J2tta/s16a19s3W2iuTXJfk1VX1in2O84YkT7XWTo089WtaazcmeV2Sn6+qm/dpcyyzacrfaq29KrNfTbDoPv0VSd6Y5P45+1+c2U8C1yf5jiRXV9Vt+7VtrT2a5M4kDyT58yQPJfnmqCvbkF3KcujHJHnuapbJWvIck2VibK7E2DQ2d31sHiTLVYqgJ3NhRXXd8G+XpKouzyzMP2ytfWxZ+2FK7ZNJbtln948keWNVnc1s2vHHq+oPFhzryeHvp5L8SWZTlxd7IskTeyroj2YW7jyvS/Jga+1f5uz/iSRfbK093Vr7RpKPJfnhBX28p7X2/a21m5P8e2b3gC+VLGeWZZlMmOeaskzWkOeqWSbT5Tkyy8TYHM3YNDbTydhcOcs2foHXsSRfyKwSe26B1/cuaH8iyxd5VZLfS3LXknbXZlhUleSqJJ9K8oYlX/NjWbzA6+okL9yz/ddJbpnT9lNJXj5s/1qS31hw3A8n+dkF+38gySOZ3dOszBaP/cKC9t82/P2dST6XEYsFZTlNllPnuY4s15Hn2CzXkecqWa6ap7FpbB7lsTllluvIc1vG5qpZrhrorZlVVY8l+ZUF7e7L7L7dNzKrCG+f0+41md1j/NvMpq0eSnLrPu2+L8lnhnank/zqiL4uG5wvG16QDw9P7qLreWWSk8P5/zTJixe8SP41yYuW9O29Qzink/x+km9Z8kI6M/TztavkJcuDZ7mOPNeV5dR5js1yHXmukuUqeRqbxuZRHptTZ7mOPLdlbK6apU+MBgC6dCQWRgMAHDZFEADQJUUQANAlRRAA0CVFEADQJUUQANAlRRAA0CVFEADQpf8FHbOSWNYVA1gAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkEAAAD2CAYAAADRVFNJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUzklEQVR4nO3dX4xcZRnH8d9Dt7WlENq4NVFa3RITYiUGagP+wcaAF6UQTEwIBrjQYEjAmFYvhGAi9gYEhZQbSAjV+I+iIHqBiakGoiQmNbullW2LhsJWWtG2KuCfQJE+XpzTZXaZOX+m58zMOc/3kzSc6Xn3nHf2N+/y9H3fmTV3FwAAQDSnDbsDAAAAw0ARBAAAQqIIAgAAIVEEAQCAkCiCAABASBRBAAAgpLEyjcfHx31iYqKmriDPzMyMjh07ZlVciyyHq8osJfIcNsZme5Blu0xNTR1z9xW9zpcqgiYmJjQ5OXnqvUJf1q1bV9m1yHK4qsxSIs9hY2y2B1m2i5kdzDrPchgAAAiJIggAAIREEQQAAEKiCAIAACFRBAEAgJAoggAAQEgUQQAAICSKIAAAEBJFEAAACIkiCAAAhEQRBAAAQir1u8MAAEB77du3r+e5NWvWDLAng8FMEAAACIkiCAAAhMRyGAAAkNTOJa8szAQBAICQKIIAAEBIFEEAACAk9gQBABBY59vis/YEFW3XJMwEAQCAkCiCAABASBRBAAAgJIogAAAQEkUQAAAIiSIIAACExFvkATTW/N943Za37QIYDGaCAABASBRBAAAgJJbDADTW/OWvNn6iLVC3omOljWOKmSAAABASRRAAAAiJIggAAITEniCMFN7yDOntr4NOna8JXi+Dc+utt855fPvttw+pJ6ha5L10zAQBAICQKIIAAEBILIehlH6nTbO+Lmvpo4p7o9n6fb3g1HUugbH81R6MobcwEwQAAEKiCAIAACFRBAEAgJBauSfozjvvnD2++eabh9iT9ul3L04VH8vOPqA4In+M/6Bl7bW7++67Z4/ZE9QejJu3MBMEAABCoggCAAAhNWY5zMzmPHb3QucwOEXfBj//HJ8AjCy8JurV+f1cvXr1nHOvv/767PFpp839N/OJEyfq7RgwAMwEAQCAkCiCAABASI1ZDsPoy1rW6ucagMRrYpBmZmZ6nmP5C23ETBAAAAiJIggAAIREEQQAAEIa6T1BnW99z3rbO2+JHz3s4wCaofOt7/wsRTTMBAEAgJAoggAAQEgjvRzGp0IDwKnL+sT2zZs3D7g3wOhgJggAAIREEQQAAEKiCAIAACGN9J6gTll7gPgt0wDQW9bPxHvuuWeAPQFGCzNBAAAgJIogAAAQUmOWw7JkTfVmvTUUACLg5yDQHTNBAAAgJIogAAAQEkUQAAAIqRV7grKw/g0gOn4OAt0xEwQAAEKiCAIAACG1cjnsoosumj3euXPnEHsCAMO3adOm2eN77713iD0BRgszQQAAICSKIAAAEBJFEAAACKmVe4LYBwQAb2EfENAdM0EAACAkiiAAABASRRAAAAiJIggAAIREEQQAAEKiCAIAACFRBAEAgJAoggAAQEit/LBEAGiqZw6/oolbfvG2v5/55uVD6A3QbhRB6M4s+7z7YPoBAEBNWA4DAAAhUQQBAICQKIIAAEBIFEEAACCkcBuju73rohPvwACqkzfeJMYcgOEJVwQBADAIvT7uQKL4HxUshwEAgJAoggAAQEgshw0Q+5EAABgdFEEAAKDRirwJoxuWwwAAQEjMBOGU8BZoNF2Z13CvtuOV9ggYgLzfDymF+B2R5Yqgqanu37iS3yj2xgDVquw3jw/xF+fyc6E9+MdRTE0cw+YlfqiZ2VFJ/5F0rOCXjBdsW3W7tt77fe6+omAfMqVZHuyjD1W1q+OaTbr30qqylBibFbfr55qMzfbcu7Kx2UeWJ/vQtvExzHtnj013L/VH0mTVbatuF/nedeXZtu9TE+5dV5ZNef5t6mOdeTbh+bft3nVlOczn34Q+1nFNNkYDAICQKIIAAEBI/RRBD9TQtup2ke9dVtTvUxPuXVbbnn+b+tiPNj3/tt27rCY8/yb0sfJrltoYDQAA0BYshwEAgJBKFUFmtsHM/mhmz5nZLRntvmNmR8xsOud6q8zsSTPbZ2Z7zWxTj3aLzez3ZrYnbbcl57oLzOxpM3s8p92MmT1jZrvNbDKj3TIze9TMnjWz/Wb20S5tzk2vc/LPq2a2ucf1vpw+j2kz225mizPuvSltt7fX9fpBlr2zTNtVnmddWabXrizPolmmbSvPs2iWaVvGJmOz8WOz6izTtiHHZuksS7wtbYGkA5LOkbRI0h5Ja3q0XS9praTpnGu+W9La9PhMSX/qdk1JJumM9HihpJ2SPpJx3a9IekjS4zn3n5E0XuC5f0/SF9LjRZKWFfhe/VXJ5xPMP3e2pBckLUkf/0TS53pc5zxJ05JOV/LBlr+W9P6imZHlqWdZVZ51ZVlHnkWzrCvPoln2kydjk7E5qCzL5Fl1lnXlOepjs58sy8wEXSjpOXd/3t2PS3pY0qe7NXT330r6R94F3f0ld9+VHv9L0v70yc5v5+7+7/ThwvRP181MZrZS0uWSHsx9RgWY2VlKXqDb0r4cd/eXc77sUkkH3P1gj/NjkpaY2ZiSsP7So90HJO109/+6+/8k/UbSZ0o+hW7IUoWzlKrJs64spYrzLJpler5peTI2GZvzDX1sVp1ler5peQ4lyzJF0NmSXux4fEg9vvn9MLMJSRcoqVa7nV9gZrslHZH0K3fv2k7SVklflXSiwG1d0g4zmzKzG3q0WS3pqKTvplOFD5rZ0pzrflbS9q43dD8s6duS/izpJUmvuPuOHteZlvQJM3unmZ0uaaOkVTn3LoIsi2cpVZNnXVlKNeaZl2Xapuo8i2QpMTZLY2wyNns03ap2jM3SWY7ExmgzO0PSTyVtdvdXu7Vx9zfd/XxJKyVdaGbndbnOFZKOuPtUwVtf7O5rJV0m6Ytmtr5LmzEl05T3u/sFSn41QdY6/SJJV0p6pMf55Ur+JbBa0nskLTWz67q1dff9ku6UtEPSLyXtlvRmoWc2JG3KMu1HJXm2NUupljyLZCkxNkthbDI22z42+8myTBF0WHMrqpXp350SM1uoJMwfuftjee3TKbUnJW3ocvrjkq40sxkl046XmNkPM651OP3vEUk/UzJ1Od8hSYc6KuhHlYTby2WSdrn733qc/5SkF9z9qLu/IekxSR/L6OM2d/+wu6+X9E8la8CniiwTeVlKFeZZU5ZSDXmWzVKqLs+CWUqMzcIYm4xNBRmbpbP04hu8xiQ9r6QSO7nB64MZ7SeUv8nLJH1f0tacdiuUbqqStETSU5KuyPmaTyp7g9dSSWd2HP9O0oYebZ+SdG56/A1J38q47sOSPp9x/iJJe5WsaZqSzWNfymj/rvS/75X0rApsFiTLarKsOs86sqwjz6JZ1pFnmSzL5snYZGyO8tisMss68mzK2CybZdlANyqpqg5I+lpGu+1K1u3eUFIRXt+j3cVK1hj/oGTaarekjV3afUjS02m7aUlfL9DXvMF5TvqC3JN+c7Oez/mSJtP7/1zS8owXyd8lnZXTty1pONOSfiDpHTkvpH1pPy8tkxdZ9p9lHXnWlWXVeRbNso48y2RZJk/GJmNzlMdm1VnWkWdTxmbZLPnEaAAAENJIbIwGAAAYNIogAAAQEkUQAAAIiSIIAACERBEEAABCoggCAAAhUQQBAICQxso0Hh8f94mJiZq6gjwzMzM6duyYVXEtshyuKrOUyHPYGJvtQZbtMjU1dczdV/Q6X6oImpiY0OTk5Kn3Cn1Zt25dZdciy+GqMkuJPIeNsdkeZNkuZnYw6zzLYQAAICSKIAAAEBJFEAAACIkiCAAAhEQRBAAAQqIIAgAAIVEEAQCAkCiCAABASBRBAAAgJIogAAAQEkUQAAAIiSIIAACERBEEAABCoggCAAAhUQQBAICQKIIAAEBIFEEAACAkiiAAABASRRAAAAiJIggAAIREEQQAAEKiCAIAACFRBAEAgJAoggAAQEgUQQAAICSKIAAAEBJFEAAACIkiCAAAhEQRBAAAQqIIAgAAIVEEAQCAkCiCAABASGPD7gDQj/vvv3/O4xtvvHH2eN++fXPOrVmzZiB9AgA0CzNBAAAgJIogAAAQ0kgth7GMgfmWLVs2e/zyyy/PHncuf0lvf+0AGI6rr756zuMf//jHQ+oJOp122ltzHidOnOjZrvNnaYT/BzMTBAAAQqIIAgAAIVEEAQCAkAa+Jyhr30+E9Ue8nZnNHrv7nHN33HFHoWt0vnbYH1SNYe4NiLYvoYkeeuih2eNrr7129viuu+4aRneQI2sf0COPPDJ7fNVVVw2iOyODmSAAABASRRAAAAhp4Mth86e2s5YumAaPYf4SWKf5b4U/qd9l1UsuuWTO4yeeeKJIF0Ni/CHLNddcM3vcuRy2fPnyOe1uu+222eMtW7bU3zFImrvNQJr7c7boufntspbUmoqZIAAAEBJFEAAACGnonxidNeW+bdu22ePrr79+zrmsdxSh/cos1fBaOXV1LFtnvQOMpbjmuummm+Y8Pn78+JB6ElvWz7r55yL/jGQmCAAAhEQRBAAAQqIIAgAAIQ1kT1C/n/46fx9Qp851y/lr0Pfdd1+J3qHtoq1x14E9OsiSNcYeeOCB2eMbbrhhEN0JI+ujQsrolV/nb56XeIs8AABAa1AEAQCAkGpZDqtqiq4olr+A4cka70U/2XvQPzNQn8WLF895/Nprrw2pJ+1X9zhp4/LXfMwEAQCAkCiCAABASBRBAAAgpKH/2gwAzZa1L6HonoVdu3b19XUYPewBGp5+P44mMmaCAABASBRBAAAgpFqWw7Km4VatWjXn8YsvvlhHFwCMoF7T9dddd90wugO0Cktg5TETBAAAQqIIAgAAIVEEAQCAkAb+Fnn2AAFxsWcBwChhJggAAIREEQQAAEKiCAIAACFRBAEAgJAoggAAQEgUQQAAICSKIAAAEBJFEAAACIkiCAAAhEQRBAAAQqIIAgAAIVEEAQCAkCiCAABASBRBAAAgJIogAAAQ0liZxs8cfkUTt/zibX8/883LK+sQAFTCLL+Ne/39ADCymAkCAAAhUQQBAICQKIIAAEBIFEEAACAkiiAAABASRRAAAAip1FvkgUbirdIAgC4oggAgsG6f/TYfnwWHtqIIAgAgGIrfBEUQAABotiLbHrqgCMLIyfsXSpP+ddLruYwPuB/AIBWdZWA2AsNGEQQAAEbOIIpkiiCgBfjlxgDq0PbZunhFUN66YfpW6bZP57bhf5rD/N43NXeg9Qp+JEbTlqqbsE2gCX2cz7zE56OY2VFJ/5F0rOCXjBdsW3W7tt77fe6+omAfMqVZHuyjD1W1q+OaTbr30qqylBibFbfr55qMzfbcu7Kx2UeWJ/vQtvExzHtnj013L/VH0mTVbatuF/nedeXZtu9TE+5dV5ZNef5t6mOdeTbh+bft3nVlOczn34Q+1nFNfm0GAAAIiSIIAACE1E8R9EANbatuF/neZUX9PjXh3mW17fm3qY/9aNPzb9u9y2rC829CHyu/ZqmN0QAAAG3BchgAAAipVBFkZhvM7I9m9pyZ3ZLR7jtmdsTMpnOut8rMnjSzfWa218w29Wi32Mx+b2Z70nZbcq67wMyeNrPHc9rNmNkzZrbbzCYz2i0zs0fN7Fkz229mH+3S5tz0Oif/vGpmm3tc78vp85g2s+1mtjjj3pvSdnt7Xa8fZNk7y7Rd5XnWlWV67cryLJpl2rbyPItmmbZlbDI2Gz82q84ybRtybJbOssTb0hZIOiDpHEmLJO2RtKZH2/WS1kqazrnmuyWtTY/PlPSnbteUZJLOSI8XStop6SMZ1/2KpIckPZ5z/xlJ4wWe+/ckfSE9XiRpWYHv1V+VfD7B/HNnS3pB0pL08U8kfa7Hdc6TNC3pdCUfbPlrSe8vmhlZnnqWVeVZV5Z15Fk0y7ryLJplP3kyNhmbg8qyTJ5VZ1lXnqM+NvvJssxM0IWSnnP35939uKSHJX26W0N3/62kf+Rd0N1fcvdd6fG/JO1Pn+z8du7u/04fLkz/dN3MZGYrJV0u6cHcZ1SAmZ2l5AW6Le3LcXd/OefLLpV0wN0P9jg/JmmJmY0pCesvPdp9QNJOd/+vu/9P0m8kfabkU+iGLFU4S6maPOvKUqo4z6JZpueblidjk7E539DHZtVZpuebludQsixTBJ0t6cWOx4fU45vfDzObkHSBkmq12/kFZrZb0hFJv3L3ru0kbZX0VUknCtzWJe0wsykzu6FHm9WSjkr6bjpV+KCZLc257mclbe96Q/fDkr4t6c+SXpL0irvv6HGdaUmfMLN3mtnpkjZKWpVz7yLIsniWUjV51pWlVGOeeVmmbarOs0iWEmOzNMYmY7NH061qx9gsneVIbIw2szMk/VTSZnd/tVsbd3/T3c+XtFLShWZ2XpfrXCHpiLtPFbz1xe6+VtJlkr5oZuu7tBlTMk15v7tfoORXE2St0y+SdKWkR3qcX67kXwKrJb1H0lIzu65bW3ffL+lOSTsk/VLSbklvFnpmQ9KmLNN+VJJnW7OUasmzSJYSY7MUxiZjs+1js58syxRBhzW3olqZ/t0pMbOFSsL8kbs/ltc+nVJ7UtKGLqc/LulKM5tRMu14iZn9MONah9P/HpH0MyVTl/MdknSoo4J+VEm4vVwmaZe7/63H+U9JesHdj7r7G5Iek/SxjD5uc/cPu/t6Sf9UsgZ8qsgykZelVGGeNWUp1ZBn2Syl6vIsmKXE2CyMscnYVJCxWTpLL77Ba0zS80oqsZMbvD6Y0X5C+Zu8TNL3JW3NabdC6aYqSUskPSXpipyv+aSyN3gtlXRmx/HvJG3o0fYpSeemx9+Q9K2M6z4s6fMZ5y+StFfJmqYp2Tz2pYz270r/+15Jz6rAZkGyrCbLqvOsI8s68iyaZR15lsmybJ6MTcbmKI/NKrOsI8+mjM2yWZYNdKOSquqApK9ltNuuZN3uDSUV4fU92l2sZI3xD0qmrXZL2til3YckPZ22m5b09QJ9zRuc56QvyD3pNzfr+ZwvaTK9/88lLc94kfxd0lk5fduShjMt6QeS3pHzQtqX9vPSMnmRZf9Z1pFnXVlWnWfRLOvIs0yWZfJkbDI2R3lsVp1lHXk2ZWyWzZJPjAYAACGNxMZoAACAQaMIAgAAIVEEAQCAkCiCAABASBRBAAAgJIogAAAQEkUQAAAIiSIIAACE9H9CewzXFi1nnAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkEAAAD2CAYAAADRVFNJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWLklEQVR4nO3de6wcZRnH8d9TCrYUQhuLCVL0lJgQK5G2knpDYsAol4rBhGAQEg0GgsZQNFGiidh/QLwQ+EdiQyVeAbloIiYGTRolMan2hhwKGi4Haa22VQEvKUXO4x87XfasO7c9M7s783w/SdNZ5u3M7P72PTznfd/ZNXcXAABANAvGfQEAAADjQBEEAABCoggCAAAhUQQBAICQKIIAAEBIFEEAACCkhWUaL1++3Kempmq6FOSZmZnRwYMHrYpjkeV4VZmlRJ7jRt9sD7Jsl+3btx909xPT9pcqgqamprRt27b5XxWGcuaZZ1Z2LLIcryqzlMhz3Oib7UGW7WJmz2btZzoMAACERBEEAABCoggCAAAhUQQBAICQKIIAAEBIFEEAACAkiiAAABBSqc8JAgAAMezevXvO41WrVo3pSurDSBAAAAiJIggAAIREEQQAAEJiTRAAAJD0/+uAiu5r6nohRoIAAEBIFEEAACAkpsMAAICkudNa3CIPAADQUhRBAAAgJIogAAAQEmuCAAAIrHftT++6n/41QGntmoyRIAAAEBJFEAAACInpMAATZ8GCV38/m52dnbPvpJNO6m7v27dvZNcEoH0YCQIAACFRBAEAgJAoggAAQEisCQIwcS6++OLu9nXXXTdn3z333NPdjvCx/gDqw0gQAAAIiSIIAACExHQYgIlz//33d7f7p7yytPETbYG6Fe0rbexTjAQBAICQKIIAAEBIFEEAACAk1gShlLrXXHDLM6TsdUC97wneL6PzhS98Yc7jG2+8cUxXgqpFXkvHSBAAAAiJIggAAITEdBgylbk9uehx+odbi54j8pBtZMO+XzB/vVNgTH+1B33oVYwEAQCAkCiCAABASBRBAAAgpFauCbr55pu725///OfHeCXNV9Xamyo+lp11QHFE/hj/Uctaa/eNb3yju82aoPag37yKkSAAABASRRAAAAipMdNhZjbnsbsX2of5KfOJvEVvg+/fxycAIwvviXr1vp4rV66cs++ll17qbi9YMPd35tnZ2XovDBgBRoIAAEBIFEEAACCkxkyHYTzKTD1kTWvVcT7EwHtidGZmZlL3Mf2FNmIkCAAAhEQRBAAAQqIIAgAAIU30mqDeW9+zbnvnlvjxyLp1mXUcQDP03vrOz1JEw0gQAAAIiSIIAACENNHTYXwq9OTJ+uRnAJMpq99u2LBhxFcDTA5GggAAQEgUQQAAICSKIAAAENJErwnqlbUGiG+ZBoB0WT8Tb7nllhFeCTBZGAkCAAAhUQQBAICQJno6rOgnRmcN9XJLd76sb3zvf814DYHm4ecgMBgjQQAAICSKIAAAEBJFEAAACGmi1wRV8dUYzH/n4zUC2u3w4cPjvgRgIjESBAAAQqIIAgAAIU30dFiW/m+V77Vu3bru9hlnnNHd3rRpU63XBACT6M477+xu33bbbWO8EmCyMBIEAABCoggCAAAhUQQBAICQGrsmKOv2+d71Qlu3bh3F5QDAxOpdB7RgwdzffWdnZwvtA9qIkSAAABASRRAAAAipsdNhAIBirrjiiu52/xRX7xQY01+IhpEgAAAQEkUQAAAIiSIIAACE1Mo1QVV8+zwAtMVnP/vZ1H2/+93vRnglwGRhJAgAAIRUbiRo+3Zp0BeXMvICAAAappXTYajAoGK3F4Uv0BirV69O3bd48eLRXQgwYZgOAwAAITESBKA2U9f/LLfNzFcuHMGVAMD/YyQIAACExEgQAEyQR/e+MHAEra4Rs1WrVtVyXKAJKIIAAECjFZl6H4TpMAAAEBJFEAAACInpMAAAosn7LDgpxOfBMRIEAABCYiQIADAWfI4Uxo2RIAAAEBIjQZgXfpNrGb4zDkAgYymC8v7Hyf80gXia8HMh7RqXj/g6Jh2/HHWkffClFOP5N4F5id/szOyApH9LOljwnywv2Lbqdm099xvd/cSC15ApyfLZIa6hqnZ1HLNJ515SVZYSfbPidsMck77ZnnNX1jeHyPLINbStf4zz3Nl9091L/ZG0req2VbeLfO668mzb69SEc9eVZVOef5uusc48m/D823buurIc5/NvwjXWcUwWRgMAgJAoggAAQEjDFEGbamhbdbvI5y4r6uvUhHOX1bbn36ZrHEabnn/bzl1WE55/E66x8mOWWhgNAADQFkyHAQCAkEoVQWZ2npn9wcyeNLPrM9p928z2m9l0zvFOMbMtZrbbzB4zs2tT2i0ys9+a2SNJu405xz3KzHaa2YM57WbM7FEz22Vm2zLaLTWz+8zsCTN73MzeOaDNaclxjvx50cw2pBzvuuR5TJvZXWa2KOPc1ybtHks73jDIMj3LpF3ledaVZXLsyvIsmmXStvI8i2aZtKVv0jcb3zerzjJpG7Jvls6yxG1pR0l6StKpko6R9IikVSltz5a0VtJ0zjFPkrQ22T5e0h8HHVOSSTou2T5a0lZJ78g47mck/VDSgznnn5G0vMBz/46kTyTbx0haWuC1+os6n0/Qv+9kSc9IWpw8/pGkj6Uc53RJ05KOVeeDLX8p6U1FMyPL+WdZVZ51ZVlHnkWzrCvPolkOkyd9k745qizL5Fl1lnXlOel9c5gsy4wErZP0pLs/7e6HJd0t6UODGrr7ryX9Pe+A7r7P3Xck2/+U9HjyZPvbubv/K3l4dPJn4GImM1sh6UJJd+Q+owLM7AR13qCbk2s57O7P5/yzcyU95e7PpuxfKGmxmS1UJ6w/p7R7s6St7v4fd/+vpF9J+nDJpzAIWapwllI1edaVpVRxnkWzTPY3LU/6Jn2z39j7ZtVZJvubludYsixTBJ0s6bmex3uU8uIPw8ymJK1Rp1odtP8oM9slab+kX7j7wHaSbpX0OUmzBU7rkh4ys+1mdlVKm5WSDki6MxkqvMPMluQc9yOS7hp4Qve9kr4u6U+S9kl6wd0fSjnOtKT3mNlrzexYSRdIOiXn3EWQZfEspWryrCtLqcY887JM2lSdZ5EsJfpmafRN+mZK01vVjr5ZOsuJWBhtZsdJul/SBnd/cVAbd3/F3VdLWiFpnZmdPuA46yXtd/ftBU99lruvlXS+pE+Z2dkD2ixUZ5jydndfo85XE2TN0x8j6SJJ96bsX6bObwIrJb1e0hIzu3xQW3d/XNLNkh6S9HNJuyS9UuiZjUmbskyuo5I825qlVEueRbKU6Jul0Dfpm23vm8NkWaYI2qu5FdWK5L/Ni5kdrU6YP3D3B/LaJ0NqWySdN2D3uyVdZGYz6gw7nmNm38841t7k7/2SfqzO0GW/PZL29FTQ96kTbprzJe1w97+m7H+fpGfc/YC7vyzpAUnvyrjGze7+Nnc/W9I/1JkDni+y7MjLUqowz5qylGrIs2yWUnV5FsxSom8WRt+kbypI3yydpRdf4LVQ0tPqVGJHFni9JaP9lPIXeZmk70q6NafdiUoWVUlaLOlhSetz/s17lb3Aa4mk43u2fyPpvJS2D0s6Ldn+sqSvZRz3bkkfz9j/dkmPqTOnaeosHvt0RvvXJX+/QdITKrBYkCyrybLqPOvIso48i2ZZR55lsiybJ32TvjnJfbPKLOvIsyl9s2yWZQO9QJ2q6ilJX8xod5c683Yvq1MRXpnS7ix15hh/r86w1S5JFwxo91ZJO5N205K+VOBa8zrnqckb8pHkxc16PqslbUvO/xNJyzLeJH+TdELOtW1MwpmW9D1Jr8l5I+1OrvPcMnmR5fBZ1pFnXVlWnWfRLOvIs0yWZfKkb9I3J7lvVp1lHXk2pW+WzZJPjAYAACFNxMJoAACAUaMIAgAAIVEEAQCAkCiCAABASBRBAAAgJIogAAAQEkUQAAAIaWGZxsuXL/epqamaLgV5ZmZmdPDgQaviWGQ5XlVmKZHnuNE324Ms22X79u0H3f3EtP2liqCpqSlt27Zt/leFoZx55pmVHYssx6vKLCXyHDf6ZnuQZbuY2bNZ+5kOAwAAIVEEAQCAkCiCAABASBRBAAAgpFILowEA6Ld79+7u9qpVq8Z4JUA5jAQBAICQKIIAAEBITIcBAOaFKTA0FSNBAAAgJIogAAAQEkUQAAAIiTVBAIB54RZ5NBUjQQAAICSKIAAAEBLTYQCA0pgCQxswEgQAAEKiCAIAACFRBAEAgJBYEwQAKI11QGgDRoIAAEBIFEEAACAkpsMAALl6b4mXmA5DOzASBAAAQqIIAgAAIVEEAQCAkFgTBADIxRogtBEjQQAAICSKIAAAEBLTYUCCW4CBdP39oxd9BU3FSBAAAAiJIggAAIREEQQAAEJiTRAa6fbbb5/z+Jprrulus7anejt37pzzeO/evd3t9evXj/pyMAb0I7QRI0EAACAkiiAAABDSRE2HMY2BfkuXLu1uP//8893t3ukvKfv23ax2ve+x/vdb0WNGeJ+uWbMm9TH9Nob+nPfs2dPdfv/739/dvvTSS+e0u+eee+q9MBSyYMGrYx6zs7Op7XpzjtCXGQkCAAAhUQQBAICQKIIAAEBII18TVGZNBmIws+62u8/Zd9NNNxU6Ru97p6qP9886ZoT3atbagCrWYA17boxHfw67du3qbvf24a9+9aujuiSUkLUO6N577+1uX3LJJaO4nInBSBAAAAiJIggAAIQ08umwMsPqDIPH0D8F1qv/Vvgjhp1WPeecc+Y83rJlS6HriPhezHrORV+PiK9bFJdddll3+6Mf/Wh3e9myZXPa3XDDDd3tjRs31n9hkDR3ilKa+/Ot6L7+dllTak3FSBAAAAiJIggAAIQ09k+Mzhou37x5c3f7yiuvnLMv644itF+ZaRbeK/NX9G4wqZo7wJhGa65PfvKTcx4fPnx4TFcSW9bPuv59kX9GMhIEAABCoggCAAAhUQQBAICQKlsTVMc3bvevA+rVO2/ZPwf9zW9+s/A50H7R5rjrwBodZMnqY5s2bepuX3XVVaO4nDCq+iT7tPx6v3le4hZ5AACA1qAIAgAAIVU2HTbOL5tk+gsYn6z+XvSTvSN+QW1bLVq0aM7jQ4cOjelK2q+OfnL11Vd3t9s4/dWPkSAAABASRRAAAAiJIggAAIRUak3QoUOHUm+FZw4fiKmKb5vfsWPHUP8Ok4c1QOOT9VU0Wft6fetb3+puc4s8AABAS1EEAQCAkEpNhy1atKjQMHXWMNwHPvCBOfuee+65MpcAoMHShuQvv/zycVwO0GhlPloia98HP/jB7vZPf/rT7nYbp7/6MRIEAABCoggCAAAhUQQBAICQKvvajCy9c5GsAQLi4tZ3oDplboPP2te7DigaRoIAAEBIFEEAACCkkUyHAQCAevVOc5W5fT4yRoIAAEBIFEEAACAkiiAAABASa4IAAGgZ1gAVw0gQAAAIiSIIAACERBEEAABCoggCAAAhUQQBAICQKIIAAEBI3CIPoJ3M8tu4138dACYWI0EAACAkRoIAILCp63+W22bmKxeO4EqA0WMkCAAAhEQRBAAAQmI6DAAANFuRGyEGYCQIAACERBEEAABCoggCAAAhUQQBAICQSi2MfnTvCwM/U4LPkEA0fLYKADQfI0EAACAkbpFH+/EdUgDQOKMYcacImiemRaqX95ryegIAqhCvCMobFZiAEYG0ImD5iK8DAIA2Y00QAAAIqRUjQXVMSY3jmKOc5mnDnX5NmIpkVA8RFe2btfThlq4BHNf/P5rwc3Y+zEu8GczsgKR/SzpY8J8sL9i26nZtPfcb3f3EgteQKcny2SGuoap2dRyzSedeUlWWEn2z4nbDHJO+2Z5zV9Y3h8jyyDW0rX+M89zZfdPdS/2RtK3qtlW3i3zuuvJs2+vUhHPXlWVTnn+brrHOPJvw/Nt27rqyHOfzb8I11nFM1gQBAICQKIIAAEBIwxRBm2poW3W7yOcuK+rr1IRzl9W259+maxxGm55/285dVhOefxOusfJjlloYDQAA0BZMhwEAgJBKFUFmdp6Z/cHMnjSz6zPafdvM9pvZdM7xTjGzLWa228weM7NrU9otMrPfmtkjSbuNOcc9ysx2mtmDOe1mzOxRM9tlZtsy2i01s/vM7Akze9zM3jmgzWnJcY78edHMNqQc77rkeUyb2V1mtijj3Ncm7R5LO94wyDI9y6Rd5XnWlWVy7MryLJpl0rbyPItmmbSlb9I3G983q84yaRuyb5bOssRtaUdJekrSqZKOkfSIpFUpbc+WtFbSdM4xT5K0Ntk+XtIfBx1Tkkk6Ltk+WtJWSe/IOO5nJP1Q0oM555+RtLzAc/+OpE8k28dIWlrgtfqLOp9P0L/vZEnPSFqcPP6RpI+lHOd0SdOSjlXngy1/KelNRTMjy/lnWVWedWVZR55Fs6wrz6JZDpMnfZO+Oaosy+RZdZZ15TnpfXOYLMuMBK2T9KS7P+3uhyXdLelDgxq6+68l/T3vgO6+z913JNv/lPR48mT727m7/yt5eHTyZ+BiJjNbIelCSXfkPqMCzOwEdd6gm5NrOezuz+f8s3MlPeXuz6bsXyhpsZktVCesP6e0e7Okre7+H3f/r6RfSfpwyacwCFmqcJZSNXnWlaVUcZ5Fs0z2Ny1P+iZ9s9/Y+2bVWSb7m5bnWLIsUwSdLOm5nsd7lPLiD8PMpiStUadaHbT/KDPbJWm/pF+4+8B2km6V9DlJswVO65IeMrPtZnZVSpuVkg5IujMZKrzDzJbkHPcjku4aeEL3vZK+LulPkvZJesHdH0o5zrSk95jZa83sWEkXSDol59xFkGXxLKVq8qwrS6nGPPOyTNpUnWeRLCX6Zmn0TfpmStNb1Y6+WTrLiVgYbWbHSbpf0gZ3f3FQG3d/xd1XS1ohaZ2ZnT7gOOsl7Xf37QVPfZa7r5V0vqRPmdnZA9osVGeY8nZ3X6POVxNkzdMfI+kiSfem7F+mzm8CKyW9XtISM7t8UFt3f1zSzZIekvRzSbskvVLomY1Jm7JMrqOSPNuapVRLnkWylOibpdA36Ztt75vDZFmmCNqruRXViuS/zYuZHa1OmD9w9wfy2idDalsknTdg97slXWRmM+oMO55jZt/PONbe5O/9kn6sztBlvz2S9vRU0PepE26a8yXtcPe/pux/n6Rn3P2Au78s6QFJ78q4xs3u/jZ3P1vSP9SZA54vsuzIy1KqMM+aspRqyLNsllJ1eRbMUqJvFkbfpG8qSN8snaUXX+C1UNLT6lRiRxZ4vSWj/ZTyF3mZpO9KujWn3YlKFlVJWizpYUnrc/7Ne5W9wGuJpON7tn8j6byUtg9LOi3Z/rKkr2Uc925JH8/Y/3ZJj6kzp2nqLB77dEb71yV/v0HSEyqwWJAsq8my6jzryLKOPItmWUeeZbIsmyd9k745yX2zyizryLMpfbNslmUDvUCdquopSV/MaHeXOvN2L6tTEV6Z0u4sdeYYf6/OsNUuSRcMaPdWSTuTdtOSvlTgWvM656nJG/KR5MXNej6rJW1Lzv8TScsy3iR/k3RCzrVtTMKZlvQ9Sa/JeSPtTq7z3DJ5keXwWdaRZ11ZVp1n0SzryLNMlmXypG/SNye5b1adZR15NqVvls2ST4wGAAAhTcTCaAAAgFGjCAIAACFRBAEAgJAoggAAQEgUQQAAICSKIAAAEBJFEAAACIkiCAAAhPQ/EZyiYIQGPHwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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DHuYf8XZJTyS/5ZZbUtWR9MT3uHJZ9nlc95K0NqCMNVhpj41q9MZh+/bt7e3OPvyd73xnWE1CBknrgB544IH29vnnn5+qvryPutmwYUPX6/POOy/V75WFkSAAAOASSRAAAHBp6NNhWYbVGQb3IWkYtfdS+EPyTquedtppXa83bdqUqh0eP4t5pw7zlEP9XHjhhe3tiy66qL29ePHirnI33XRTe3vNmjXlNwySkqer0u7rLdc5pZb0fdl5OX7v71U9/dWLkSAAAOASSRAAAHCp8jtGJw2Xr1u3rr192WWXde1LuqIIzZdlmoXPysylvRpMKuYKMKbR6uuqq67qen3gwIGKWuJb0ndd774835FJU2qdd/ofdYwEAQAAl0iCAACASyRBAADApUxrgvbv39+ex897B9ksc/2964A6dc4/9s5B33nnnamPgeZjHdDMsUYHSZL62Nq1a9vbl19++TCa40ZRd7LvvGN0p6RL3ZNi/vLLL+dqRxUYCQIAAC6RBAEAAJcyTYeNjY21h9uShuGG/bBJpr+A6uT9LqjyOwPlGRsb63q9f//+ilrSfEX1k856rrjiivZ20kNXm4KRIAAA4BJJEAAAcIkkCAAAuJT7EnkAkIp52vzWrVtz/R5GD2uAqpP0KJq0j6n54Q9/2N5OukS+KRgJAgAALpEEAQAAl3I/RT7LEHjnMNynP/3prn0vvvhi3iYAqJm4IfmLL764iuYAtZbl1hJJ+z7zmc+0tx966KH2dhOnv3oxEgQAAFwiCQIAAC6RBAEAAJdyrwnKonMukjVAgF9c+g4UJ8tl8En7OtcBecNIEAAAcIkkCAAAuJT7KfIAAGB0dP7/Ocvl854xEgQAAFwiCQIAAC6RBAEAAJeGcok8AAAYHtYApcNIEAAAcIkkCAAAuEQSBAAAXCIJAgAALpEEAQAAl0iCAACAS1wiD6CZzAaXCaH8dgAYWYwEAQAAlxgJAgDHxm/4zcAyU98+ewgtAYaPkSAAAOASSRAAAHCJ6TA0HwtkAQB9MBIEAABcYiQIAACMnGEs2mckCAAAuEQSBAAAXCIJAgAALrEmCAAA1Fuaq4D7YCQIAAC45G8kaFC2mPF+MWWsXo+rc0mmWlAmHjUAAPWXKQl6Yverfb/8+bIHAAB104iRIP4qBwAAWVWSBA1KWkYhYRnmNFfe+maCUT2g2ar84zDtsUtpY0Mfk5P2/x+j9P+ZOrCQ4cNgZi9J+o+kfSl/ZUnKskWXa+qx3xtCODJlGxJFsXwhRxuKKldGnXU69oKiYinRNwsul6dO+mZzjl1Y38wRy0NtaFr/qPLYyX0zhJDpR9JE0WWLLuf52GXFs2nvUx2OXVYs63L+TWpjmfGsw/k37dhlxbLK869DG8uok0vkAQCASyRBAADApTxJ0NoSyhZdzvOxs/L6PtXh2Fk17fyb1MY8mnT+TTt2VnU4/zq0sfA6My2MBgAAaAqmwwAAgEuZkiAzO8PMnjGzZ83shoRyPzKzvWY2OaC+Y81sk5ntMLMnzeyamHJjZvaomT0elVszoN7ZZrbNzB4eUG7KzJ4ws+1mNpFQbpGZbTCzp83sKTP7SJ8yy6J6Dv28ZmarY+q7NjqPSTNbb2ZjCce+Jir3ZFx9eRDL+FhG5QqPZ1mxjOouLJ5pYxmVLTyeaWMZlaVv0jdr3zeLjmVU1mXfzBzLDJelzZb0nKTjJB0m6XFJy2PKnirpZEmTA+o8StLJ0fZCSX/pV6ckk3R4tD1X0mZJH06o92uSfi7p4QHHn5K0JMW53yvpy9H2YZIWpXiv/q7W/Ql69x0taaekedHrX0j6Ykw9J0ialDRfrRtb/l7S+9LGjFjOPJZFxbOsWJYRz7SxLCueaWOZJ570TfrmsGKZJZ5Fx7KseI5638wTyywjQaskPRtCeD6EcEDSfZI+269gCOGPkl4eVGEIYU8IYWu0/bqkp6KT7S0XQgj/jl7OjX76LmYys2MknS3p7oFnlIKZHaHWB3Rd1JYDIYRXBvza6ZKeCyG8ELN/jqR5ZjZHrWD9Labc+yVtDiH8N4TwP0l/kPT5jKfQD7FU6lhKxcSzrFhKBcczbSyj/XWLJ32Tvtmr8r5ZdCyj/XWLZyWxzJIEHS3pxY7XuxTz5udhZuOSVqiVrfbbP9vMtkvaK+l3IYS+5STdLunrkqZTHDZI2mhmW8zs8pgySyW9JOmeaKjwbjNbMKDeL0ha3/eAIeyW9D1Jf5W0R9KrIYSNMfVMSvq4mb3TzOZLOkvSsQOOnQaxTB9LqZh4lhVLqcR4DoplVKboeKaJpUTfzIy+Sd+MKXq7mtE3M8dyJBZGm9nhkn4paXUI4bV+ZUIIb4YQTpJ0jKRVZnZCn3rOkbQ3hLAl5aFPCSGcLOlMSV8xs1P7lJmj1jDlXSGEFWo9miBpnv4wSedKeiBm/2K1/hJYKundkhaY2cX9yoYQnpJ0q6SNkn4rabukN1OdWUWaFMuoHYXEs6mxlEqJZ5pYSvTNTOib9M2m9808scySBO1Wd0Z1TPTfZsTM5qoVzJ+FEB4cVD4aUtsk6Yw+uz8m6Vwzm1Jr2PE0M/tpQl27o3/3SvqVWkOXvXZJ2tWRQW9QK7hxzpS0NYTwj5j9n5S0M4TwUgjhoKQHJX00oY3rQggfDCGcKulfas0BzxSxbBkUS6nAeJYUS6mEeGaNpVRcPFPGUqJvpkbfpG/KSd/MHMuQfoHXHEnPq5WJHVrgdXxC+XENXuRlkn4s6fYB5Y5UtKhK0jxJj0g6Z8DvfELJC7wWSFrYsf0nSWfElH1E0rJo+1uSvptQ732SvpSw/0OSnlRrTtPUWjz21YTy74r+fY+kp5VisSCxLCaWRcezjFiWEc+0sSwjnllimTWe9E365ij3zSJjWUY869I3s8Yya0DPUiurek7SNxLKrVdr3u6gWhnhZTHlTlFrjvHPag1bbZd0Vp9yH5C0LSo3KembKdo6qHMeF30gH4/e3KTzOUnSRHT8X0tanPAh+aekIwa0bU0UnElJP5H0jgEfpB1RO0/PEi9imT+WZcSzrFgWHc+0sSwjnllimSWe9E365ij3zaJjWUY869I3s8aSO0YDAACXRmJhNAAAwLCRBAEAAJdIggAAgEskQQAAwCWSIAAA4BJJEAAAcIkkCAAAuEQSBAAAXPo/9SdOmiDzu98AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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JAgAALpEEAQAAl4a+Jihp/YCH+Uf8v6Qnkt98882p6kh64ntcuSz7PK5XS1obUMQarLTvt4f3ug5647B169b2dmcfvvXWW4fVJGSQtA7o4Ycfbm+ff/75qerL+6ib9evXd70+77zzUv1eWRgJAgAALpEEAQAAl4Y+HZZlWJ1hcB+ShlF7L4U/IO80y8knn9z1esOGDana4fGzmHfqME851M+FF17Y3r7ooova24sWLeoqd+ONN7a3V69eXX7DICl5uirtvt5ynVNqSd+XnZfj9/5e1dNfvRgJAgAALpEEAQAAlyq/Y3TScPnatWvb25deemnXvqQritB8WaZZ+KzMXNqrwaRi7hjNNFp9XXnllV2v9+3bV1FLfEv6ruvdl+c7MmlKrfNO/1lUcRUuI0EAAMAlkiAAAOASSRAAAHAp05qgvXv3Zlob0E+WOb7edUCdOucfe+eg77777uwNQ2OxDmjmWKODJEl9bM2aNe3tyy67bBjNcaOoNTSdd4zulHSpe1LMX3/99VztqOJ7hpEgAADgEkkQAABwKdN02NjYWHu4KmkYbtiXuTH9BVQn73dBld8ZKM/Y2FjX671791bUkuYrqp901nP55Ze3t5MeutoUjAQBAACXSIIAAIBLJEEAAMCloV8iD6BZinja/ObNm3P9HkYPa4Cqk/QomrSPqbn33nvb20mXyDcFI0EAAMAlkiAAAOBS7qfIZxkC7xyG+8IXvtC179VXX83bBAA1Ezckf/HFF1fRHKDWstxaImnf2Wef3d5+9NFH29tNnP7qxUgQAABwiSQIAAC4RBIEAABcyr0mKIvOuUjWAAF+cek7UJwsl8En7etcB+QNI0EAAMAlkiAAAOBS7qfIAwCA0dH5/+csl897xkgQAABwiSQIAAC4RBIEAABcGsol8gAAYHhYA5QOI0EAAMAlkiAAAOASSRAAAHCJJAgAALhEEgQAAFwiCQIAAC5xiTyAZjIbXCaE8tsBYGQxEgQAAFxiJAgAHBu//tcDy0x978whtAQYPkaCAACASyRBAADAJabD0HwskAUA9MFIEAAAcImRIAAAMHKGsWifkSAAAOASSRAAAHCJJAgAALjEmiAAAFBvaa4C7oORIAAA4JK/kaBB2WLG+8WUsXo9rs7FmWpBmXjUAADUX6Yk6Jmdb/b98ufLHgAA1E0jRoL4qxwAAGRVSRI0KGkZhYRlmNNceeubCUb1gGar8o/DtMcupY0NfUxO2v9/jNL/Z+KM0sCFhQwfBjN7TdK/Je1J+SuLU5YtulxTj/3hEMJhKduQKIrlKznaUFS5Muqs07HnFxVLib5ZcLk8ddI3m3PswvpmjlgeaEPT+keVx07umyGETD+SJoouW3Q5z8cuK55Ne5/qcOyyYlmX829SG8uMZx3Ov2nHLiuWVZ5/HdpYRp1cIg8AAFwiCQIAAC7lSYLWlFC26HKej52V1/epDsfOqmnn36Q25tGk82/asbOqw/nXoY2F15lpYTQAAEBTMB0GAABcypQEmdlpZvaCmb1oZtcnlPuhme02s8kB9R1lZhvMbJuZPWtmV8eUGzOzJ83s6ajc6gH1HmRmW8zssQHlpszsGTPbamYTCeUWmtl6M3vezJ4zs0/1KbM0qufAz1tmtiqmvmui85g0s3VmNpZw7Kujcs/G1ZcHsYyPZVSu8HiWFcuo7sLimTaWUdnC45k2llFZ+iZ9s/Z9s+hYRmVd9s3MscxwWdpBkl6SdLSkgyU9LWlZTNmTJJ0gaXJAnYdLOiHaXiDpz/3qlGSSDom250jaKOmTCfV+U9LPJD024PhTkhanOPcHJH0t2j5Y0sIU79Xf1Lo/Qe++IyRtlzQ3ev1zSV+JqedYSZOS5ql1Y8vfSfpI2pgRy5nHsqh4lhXLMuKZNpZlxTNtLPPEk75J3xxWLLPEs+hYlhXPUe+beWKZZSRopaQXQwgvhxD2SXpQ0hf7FQwh/EHS64MqDCHsCiFsjrbflvRcdLK95UII4V/RyznRT9/FTGZ2pKQzJd038IxSMLND1fqAro3asi+E8MaAXztF0kshhFdi9s+WNNfMZqsVrL/GlPuopI0hhP+EEP4r6feSvpTxFPohlkodS6mYeJYVS6ngeKaNZbS/bvGkb9I3e1XeN4uOZbS/bvGsJJZZkqAjJL3a8XqHYt78PMxsXNJytbLVfvsPMrOtknZL+m0IoW85SXdI+pak6RSHDZIeN7NNZnZZTJklkl6TdH80VHifmc0fUO+XJa3re8AQdkr6gaS/SNol6c0QwuMx9UxK+qyZvd/M5kk6Q9JRA46dBrFMH0upmHiWFUupxHgOimVUpuh4pomlRN/MjL5J34wpeoea0Tczx3IkFkab2SGSfiFpVQjhrX5lQgjvhhCOl3SkpJVmdmyfes6StDuEsCnloU8MIZwg6XRJXzezk/qUma3WMOU9IYTlaj2aIGme/mBJ50h6OGb/IrX+Elgi6YOS5pvZxf3KhhCek3SLpMcl/UbSVknvpjqzijQpllE7ColnU2MplRLPNLGU6JuZ0Dfpm03vm3limSUJ2qnujOrI6L/NiJnNUSuYPw0hPDKofDSktkHSaX12f0bSOWY2pdaw48lm9pOEunZG/+6W9Eu1hi577ZC0oyODXq9WcOOcLmlzCOHvMfs/L2l7COG1EMJ+SY9I+nRCG9eGED4eQjhJ0j/VmgOeKWLZMiiWUoHxLCmWUgnxzBpLqbh4poylRN9Mjb5J35STvpk5liH9Aq/Zkl5WKxM7sMDrmITy4xq8yMsk/UjSHQPKHaZoUZWkuZKekHTWgN/5nJIXeM2XtKBj+4+STosp+4SkpdH2dyV9P6HeByV9NWH/JyQ9q9acpqm1eOwbCeU/EP37IUnPK8ViQWJZTCyLjmcZsSwjnmljWUY8s8Qyazzpm/TNUe6bRcayjHjWpW9mjWXWgJ6hVlb1kqRvJ5Rbp9a83X61MsJLY8qdqNYc45/UGrbaKumMPuU+JmlLVG5S0ndStHVQ5zw6+kA+Hb25SedzvKSJ6Pi/krQo4UPyD0mHDmjb6ig4k5J+LOl9Az5I26J2npIlXsQyfyzLiGdZsSw6nmljWUY8s8QySzzpm/TNUe6bRceyjHjWpW9mjSV3jAYAAC6NxMJoAACAYSMJAgAALpEEAQAAl0iCAACASyRBAADAJZIgAADgEkkQAABwiSQIAAC49D/p1WSXVgEvQwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}