6. forecasting_with_rnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "l08c06_forecasting_with_rnn.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "vidayERjaO5q"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.8.5 64-bit"
    },
    "interpreter": {
      "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuRx7K-sirJr"
      },
      "source": [
        "# Forecasting with an RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vidayERjaO5q"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqWabzlJ63nL"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "keras = tf.keras"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cg1hfKCPldZG"
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None, label=None):\n",
        "    plt.plot(time[start:end], series[start:end], format, label=label)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    if label:\n",
        "        plt.legend(fontsize=14)\n",
        "    plt.grid(True)\n",
        "    \n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "  \n",
        "  \n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.4,\n",
        "                    np.cos(season_time * 2 * np.pi),\n",
        "                    1 / np.exp(3 * season_time))\n",
        "\n",
        "  \n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "  \n",
        "  \n",
        "def white_noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "  \n",
        "  \n",
        "def window_dataset(series, window_size, batch_size=32,\n",
        "                   shuffle_buffer=1000):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "    dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "    dataset = dataset.shuffle(shuffle_buffer)\n",
        "    dataset = dataset.map(lambda window: (window[:-1], window[-1]))\n",
        "    dataset = dataset.batch(batch_size).prefetch(1)\n",
        "    return dataset\n",
        "  \n",
        "def model_forecast(model, series, window_size):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
        "    ds = ds.batch(32).prefetch(1)\n",
        "    forecast = model.predict(ds)\n",
        "    return forecast"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iL2DDjV3lel6"
      },
      "source": [
        "time = np.arange(4 * 365 + 1)\n",
        "\n",
        "slope = 0.05\n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "\n",
        "noise_level = 5\n",
        "noise = white_noise(time, noise_level, seed=42)\n",
        "\n",
        "series += noise\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time, series)\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"370.91625pt\" version=\"1.1\" viewBox=\"0 0 614.182812 370.91625\" width=\"614.182812pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-06-17T12:39:25.723922</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 370.91625 \nL 614.182812 370.91625 \nL 614.182812 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 48.982812 333.36 \nL 606.982813 333.36 \nL 606.982813 7.2 \nL 48.982812 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_1\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 74.346449 333.36 \nL 74.346449 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_2\">\n      <defs>\n       <path d=\"M 0 0 \nL 0 3.5 \n\" id=\"mf8b2b69057\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"74.346449\" xlink:href=\"#mf8b2b69057\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_1\">\n      <!-- 0 -->\n      <g transform=\"translate(71.165199 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_2\">\n     <g id=\"line2d_3\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 143.835864 333.36 \nL 143.835864 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_4\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"143.835864\" xlink:href=\"#mf8b2b69057\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_2\">\n      <!-- 200 -->\n      <g transform=\"translate(134.292114 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_3\">\n     <g id=\"line2d_5\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 213.325278 333.36 \nL 213.325278 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_6\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"213.325278\" xlink:href=\"#mf8b2b69057\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_3\">\n      <!-- 400 -->\n      <g transform=\"translate(203.781528 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_4\">\n     <g id=\"line2d_7\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 282.814693 333.36 \nL 282.814693 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_8\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"282.814693\" xlink:href=\"#mf8b2b69057\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_4\">\n      <!-- 600 -->\n      <g transform=\"translate(273.270943 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-54\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_5\">\n     <g id=\"line2d_9\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 352.304108 333.36 \nL 352.304108 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_10\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"352.304108\" xlink:href=\"#mf8b2b69057\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_5\">\n      <!-- 800 -->\n      <g transform=\"translate(342.760358 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 31.78125 34.625 \nQ 24.75 34.625 20.71875 30.859375 \nQ 16.703125 27.09375 16.703125 20.515625 \nQ 16.703125 13.921875 20.71875 10.15625 \nQ 24.75 6.390625 31.78125 6.390625 \nQ 38.8125 6.390625 42.859375 10.171875 \nQ 46.921875 13.96875 46.921875 20.515625 \nQ 46.921875 27.09375 42.890625 30.859375 \nQ 38.875 34.625 31.78125 34.625 \nz\nM 21.921875 38.8125 \nQ 15.578125 40.375 12.03125 44.71875 \nQ 8.5 49.078125 8.5 55.328125 \nQ 8.5 64.0625 14.71875 69.140625 \nQ 20.953125 74.21875 31.78125 74.21875 \nQ 42.671875 74.21875 48.875 69.140625 \nQ 55.078125 64.0625 55.078125 55.328125 \nQ 55.078125 49.078125 51.53125 44.71875 \nQ 48 40.375 41.703125 38.8125 \nQ 48.828125 37.15625 52.796875 32.3125 \nQ 56.78125 27.484375 56.78125 20.515625 \nQ 56.78125 9.90625 50.3125 4.234375 \nQ 43.84375 -1.421875 31.78125 -1.421875 \nQ 19.734375 -1.421875 13.25 4.234375 \nQ 6.78125 9.90625 6.78125 20.515625 \nQ 6.78125 27.484375 10.78125 32.3125 \nQ 14.796875 37.15625 21.921875 38.8125 \nz\nM 18.3125 54.390625 \nQ 18.3125 48.734375 21.84375 45.5625 \nQ 25.390625 42.390625 31.78125 42.390625 \nQ 38.140625 42.390625 41.71875 45.5625 \nQ 45.3125 48.734375 45.3125 54.390625 \nQ 45.3125 60.0625 41.71875 63.234375 \nQ 38.140625 66.40625 31.78125 66.40625 \nQ 25.390625 66.40625 21.84375 63.234375 \nQ 18.3125 60.0625 18.3125 54.390625 \nz\n\" id=\"DejaVuSans-56\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-56\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_6\">\n     <g id=\"line2d_11\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 421.793522 333.36 \nL 421.793522 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_12\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"421.793522\" xlink:href=\"#mf8b2b69057\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_6\">\n      <!-- 1000 -->\n      <g transform=\"translate(409.068522 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_7\">\n     <g id=\"line2d_13\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 491.282937 333.36 \nL 491.282937 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_14\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"491.282937\" xlink:href=\"#mf8b2b69057\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_7\">\n      <!-- 1200 -->\n      <g transform=\"translate(478.557937 347.958438)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_8\">\n     <g id=\"line2d_15\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 560.772352 333.36 \nL 560.772352 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_16\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"560.772352\" xlink:href=\"#mf8b2b69057\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_8\">\n      <!-- 1400 -->\n      <g transform=\"translate(548.047352 347.958438)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"text_9\">\n     <!-- Time -->\n     <g transform=\"translate(315.749219 361.636563)scale(0.1 -0.1)\">\n      <defs>\n       <path d=\"M -0.296875 72.90625 \nL 61.375 72.90625 \nL 61.375 64.59375 \nL 35.5 64.59375 \nL 35.5 0 \nL 25.59375 0 \nL 25.59375 64.59375 \nL -0.296875 64.59375 \nz\n\" id=\"DejaVuSans-84\"/>\n       <path d=\"M 9.421875 54.6875 \nL 18.40625 54.6875 \nL 18.40625 0 \nL 9.421875 0 \nz\nM 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 64.59375 \nL 9.421875 64.59375 \nz\n\" id=\"DejaVuSans-105\"/>\n       <path d=\"M 52 44.1875 \nQ 55.375 50.25 60.0625 53.125 \nQ 64.75 56 71.09375 56 \nQ 79.640625 56 84.28125 50.015625 \nQ 88.921875 44.046875 88.921875 33.015625 \nL 88.921875 0 \nL 79.890625 0 \nL 79.890625 32.71875 \nQ 79.890625 40.578125 77.09375 44.375 \nQ 74.3125 48.1875 68.609375 48.1875 \nQ 61.625 48.1875 57.5625 43.546875 \nQ 53.515625 38.921875 53.515625 30.90625 \nL 53.515625 0 \nL 44.484375 0 \nL 44.484375 32.71875 \nQ 44.484375 40.625 41.703125 44.40625 \nQ 38.921875 48.1875 33.109375 48.1875 \nQ 26.21875 48.1875 22.15625 43.53125 \nQ 18.109375 38.875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 21.1875 51.21875 25.484375 53.609375 \nQ 29.78125 56 35.6875 56 \nQ 41.65625 56 45.828125 52.96875 \nQ 50 49.953125 52 44.1875 \nz\n\" id=\"DejaVuSans-109\"/>\n       <path d=\"M 56.203125 29.59375 \nL 56.203125 25.203125 \nL 14.890625 25.203125 \nQ 15.484375 15.921875 20.484375 11.0625 \nQ 25.484375 6.203125 34.421875 6.203125 \nQ 39.59375 6.203125 44.453125 7.46875 \nQ 49.3125 8.734375 54.109375 11.28125 \nL 54.109375 2.78125 \nQ 49.265625 0.734375 44.1875 -0.34375 \nQ 39.109375 -1.421875 33.890625 -1.421875 \nQ 20.796875 -1.421875 13.15625 6.1875 \nQ 5.515625 13.8125 5.515625 26.8125 \nQ 5.515625 40.234375 12.765625 48.109375 \nQ 20.015625 56 32.328125 56 \nQ 43.359375 56 49.78125 48.890625 \nQ 56.203125 41.796875 56.203125 29.59375 \nz\nM 47.21875 32.234375 \nQ 47.125 39.59375 43.09375 43.984375 \nQ 39.0625 48.390625 32.421875 48.390625 \nQ 24.90625 48.390625 20.390625 44.140625 \nQ 15.875 39.890625 15.1875 32.171875 \nz\n\" id=\"DejaVuSans-101\"/>\n      </defs>\n      <use xlink:href=\"#DejaVuSans-84\"/>\n      <use x=\"57.958984\" xlink:href=\"#DejaVuSans-105\"/>\n      <use x=\"85.742188\" xlink:href=\"#DejaVuSans-109\"/>\n      <use x=\"183.154297\" xlink:href=\"#DejaVuSans-101\"/>\n     </g>\n    </g>\n   </g>\n   <g id=\"matplotlib.axis_2\">\n    <g id=\"ytick_1\">\n     <g id=\"line2d_17\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 48.982812 315.230208 \nL 606.982813 315.230208 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_18\">\n      <defs>\n       <path d=\"M 0 0 \nL -3.5 0 \n\" id=\"m5a9ec2e081\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.982812\" xlink:href=\"#m5a9ec2e081\" y=\"315.230208\"/>\n      </g>\n     </g>\n     <g id=\"text_10\">\n      <!-- −20 -->\n      <g transform=\"translate(20.878125 319.029427)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 10.59375 35.5 \nL 73.1875 35.5 \nL 73.1875 27.203125 \nL 10.59375 27.203125 \nz\n\" id=\"DejaVuSans-8722\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-8722\"/>\n       <use x=\"83.789062\" xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"147.412109\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_2\">\n     <g id=\"line2d_19\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 48.982812 274.023062 \nL 606.982813 274.023062 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_20\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.982812\" xlink:href=\"#m5a9ec2e081\" y=\"274.023062\"/>\n      </g>\n     </g>\n     <g id=\"text_11\">\n      <!-- 0 -->\n      <g transform=\"translate(35.620312 277.822281)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_3\">\n     <g id=\"line2d_21\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 48.982812 232.815915 \nL 606.982813 232.815915 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_22\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.982812\" xlink:href=\"#m5a9ec2e081\" y=\"232.815915\"/>\n      </g>\n     </g>\n     <g id=\"text_12\">\n      <!-- 20 -->\n      <g transform=\"translate(29.257812 236.615134)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_4\">\n     <g id=\"line2d_23\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 48.982812 191.608769 \nL 606.982813 191.608769 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_24\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.982812\" xlink:href=\"#m5a9ec2e081\" y=\"191.608769\"/>\n      </g>\n     </g>\n     <g id=\"text_13\">\n      <!-- 40 -->\n      <g transform=\"translate(29.257812 195.407987)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_5\">\n     <g id=\"line2d_25\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 48.982812 150.401622 \nL 606.982813 150.401622 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_26\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.982812\" xlink:href=\"#m5a9ec2e081\" y=\"150.401622\"/>\n      </g>\n     </g>\n     <g id=\"text_14\">\n      <!-- 60 -->\n      <g transform=\"translate(29.257812 154.200841)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-54\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_6\">\n     <g id=\"line2d_27\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 48.982812 109.194476 \nL 606.982813 109.194476 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_28\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.982812\" xlink:href=\"#m5a9ec2e081\" y=\"109.194476\"/>\n      </g>\n     </g>\n     <g id=\"text_15\">\n      <!-- 80 -->\n      <g transform=\"translate(29.257812 112.993694)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-56\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_7\">\n     <g id=\"line2d_29\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 48.982812 67.987329 \nL 606.982813 67.987329 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_30\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.982812\" xlink:href=\"#m5a9ec2e081\" y=\"67.987329\"/>\n      </g>\n     </g>\n     <g id=\"text_16\">\n      <!-- 100 -->\n      <g transform=\"translate(22.895312 71.786548)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_8\">\n     <g id=\"line2d_31\">\n      <path clip-path=\"url(#p4b19e9262b)\" d=\"M 48.982812 26.780183 \nL 606.982813 26.780183 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_32\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.982812\" xlink:href=\"#m5a9ec2e081\" y=\"26.780183\"/>\n      </g>\n     </g>\n     <g id=\"text_17\">\n      <!-- 120 -->\n      <g transform=\"translate(22.895312 30.579401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"text_18\">\n     <!-- Value -->\n     <g transform=\"translate(14.798437 184.01125)rotate(-90)scale(0.1 -0.1)\">\n      <defs>\n       <path d=\"M 28.609375 0 \nL 0.78125 72.90625 \nL 11.078125 72.90625 \nL 34.1875 11.53125 \nL 57.328125 72.90625 \nL 67.578125 72.90625 \nL 39.796875 0 \nz\n\" id=\"DejaVuSans-86\"/>\n       <path d=\"M 34.28125 27.484375 \nQ 23.390625 27.484375 19.1875 25 \nQ 14.984375 22.515625 14.984375 16.5 \nQ 14.984375 11.71875 18.140625 8.90625 \nQ 21.296875 6.109375 26.703125 6.109375 \nQ 34.1875 6.109375 38.703125 11.40625 \nQ 43.21875 16.703125 43.21875 25.484375 \nL 43.21875 27.484375 \nz\nM 52.203125 31.203125 \nL 52.203125 0 \nL 43.21875 0 \nL 43.21875 8.296875 \nQ 40.140625 3.328125 35.546875 0.953125 \nQ 30.953125 -1.421875 24.3125 -1.421875 \nQ 15.921875 -1.421875 10.953125 3.296875 \nQ 6 8.015625 6 15.921875 \nQ 6 25.140625 12.171875 29.828125 \nQ 18.359375 34.515625 30.609375 34.515625 \nL 43.21875 34.515625 \nL 43.21875 35.40625 \nQ 43.21875 41.609375 39.140625 45 \nQ 35.0625 48.390625 27.6875 48.390625 \nQ 23 48.390625 18.546875 47.265625 \nQ 14.109375 46.140625 10.015625 43.890625 \nL 10.015625 52.203125 \nQ 14.9375 54.109375 19.578125 55.046875 \nQ 24.21875 56 28.609375 56 \nQ 40.484375 56 46.34375 49.84375 \nQ 52.203125 43.703125 52.203125 31.203125 \nz\n\" id=\"DejaVuSans-97\"/>\n       <path d=\"M 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 0 \nL 9.421875 0 \nz\n\" id=\"DejaVuSans-108\"/>\n       <path d=\"M 8.5 21.578125 \nL 8.5 54.6875 \nL 17.484375 54.6875 \nL 17.484375 21.921875 \nQ 17.484375 14.15625 20.5 10.265625 \nQ 23.53125 6.390625 29.59375 6.390625 \nQ 36.859375 6.390625 41.078125 11.03125 \nQ 45.3125 15.671875 45.3125 23.6875 \nL 45.3125 54.6875 \nL 54.296875 54.6875 \nL 54.296875 0 \nL 45.3125 0 \nL 45.3125 8.40625 \nQ 42.046875 3.421875 37.71875 1 \nQ 33.40625 -1.421875 27.6875 -1.421875 \nQ 18.265625 -1.421875 13.375 4.4375 \nQ 8.5 10.296875 8.5 21.578125 \nz\nM 31.109375 56 \nz\n\" id=\"DejaVuSans-117\"/>\n      </defs>\n      <use xlink:href=\"#DejaVuSans-86\"/>\n      <use x=\"60.658203\" xlink:href=\"#DejaVuSans-97\"/>\n      <use x=\"121.9375\" xlink:href=\"#DejaVuSans-108\"/>\n      <use x=\"149.720703\" xlink:href=\"#DejaVuSans-117\"/>\n      <use x=\"213.099609\" xlink:href=\"#DejaVuSans-101\"/>\n     </g>\n    </g>\n   </g>\n   <g id=\"line2d_33\">\n    <path clip-path=\"url(#p4b19e9262b)\" d=\"M 74.346449 165.888152 \nL 74.693896 172.338757 \nL 75.38879 155.116087 \nL 75.736237 173.200619 \nL 76.083684 173.207218 \nL 76.431131 154.557575 \nL 76.778578 162.97573 \nL 77.126025 175.797739 \nL 77.473473 165.4758 \nL 77.82092 175.967121 \nL 78.168367 176.142953 \nL 78.515814 169.028459 \nL 78.863261 191.431207 \nL 79.210708 189.71445 \nL 79.558155 177.984708 \nL 79.905602 182.89715 \nL 80.253049 169.520411 \nL 80.600496 182.429913 \nL 80.947943 187.966074 \nL 81.29539 158.682329 \nL 81.642837 176.494324 \nL 81.990284 173.882902 \nL 82.337732 189.688843 \nL 83.032626 174.801778 \nL 83.380073 188.30169 \nL 83.72752 173.095976 \nL 84.074967 183.697756 \nL 84.422414 181.080477 \nL 84.769861 184.861011 \nL 85.117308 160.188667 \nL 85.464755 180.038596 \nL 85.812202 191.44578 \nL 86.159649 172.746289 \nL 86.507096 194.487702 \nL 86.854544 180.470144 \nL 87.201991 203.540776 \nL 87.549438 197.785856 \nL 87.896885 182.845018 \nL 88.244332 178.05451 \nL 88.591779 184.704487 \nL 89.286673 191.242982 \nL 89.63412 204.235275 \nL 89.981567 197.300064 \nL 90.329014 195.527764 \nL 90.676461 180.807249 \nL 91.023908 189.089605 \nL 91.371355 211.740489 \nL 91.718803 191.204156 \nL 92.06625 199.490531 \nL 92.413697 203.493295 \nL 92.761144 191.229998 \nL 93.108591 187.936729 \nL 93.456038 190.005136 \nL 93.803485 209.299812 \nL 94.498379 199.393973 \nL 94.845826 193.85284 \nL 95.193273 209.94804 \nL 95.54072 208.045812 \nL 95.888167 218.664073 \nL 96.235614 220.735349 \nL 96.583062 201.198666 \nL 96.930509 196.765329 \nL 97.277956 212.657412 \nL 97.625403 202.766235 \nL 97.97285 210.577658 \nL 98.320297 222.157296 \nL 99.015191 202.110382 \nL 99.362638 219.558436 \nL 99.710085 204.313142 \nL 100.057532 248.669772 \nL 100.404979 214.471747 \nL 100.752426 223.305909 \nL 101.099874 228.553135 \nL 101.447321 225.803631 \nL 101.794768 248.506069 \nL 102.142215 231.580311 \nL 102.489662 226.92984 \nL 102.837109 216.679515 \nL 103.184556 238.543171 \nL 103.532003 242.836046 \nL 103.87945 240.982206 \nL 104.226897 227.691652 \nL 104.574344 235.046127 \nL 104.921791 245.203034 \nL 105.269238 235.772057 \nL 105.616685 241.374574 \nL 105.964133 233.711416 \nL 106.31158 252.238239 \nL 106.659027 249.696676 \nL 107.006474 251.675182 \nL 107.353921 264.026028 \nL 107.701368 247.210508 \nL 108.048815 248.881612 \nL 108.743709 256.599699 \nL 109.091156 270.065259 \nL 109.438603 261.115524 \nL 109.78605 261.606332 \nL 110.133497 267.629791 \nL 110.828392 257.765609 \nL 111.175839 243.770162 \nL 111.523286 262.66984 \nL 111.870733 263.075591 \nL 112.21818 267.749319 \nL 112.565627 287.995391 \nL 112.913074 269.740239 \nL 113.260521 270.076957 \nL 113.607968 246.543438 \nL 113.955415 275.113761 \nL 114.302862 271.229168 \nL 114.650309 275.887074 \nL 114.997756 288.752696 \nL 115.345204 266.113377 \nL 115.692651 271.302658 \nL 116.040098 272.051059 \nL 116.387545 290.708008 \nL 116.734992 268.01603 \nL 117.082439 298.02418 \nL 117.777333 263.209153 \nL 118.12478 297.0552 \nL 118.472227 293.747178 \nL 118.819674 287.935009 \nL 119.167121 295.182151 \nL 119.514568 306.989136 \nL 119.862015 291.312183 \nL 120.209463 303.950665 \nL 120.55691 289.100965 \nL 120.904357 304.408222 \nL 121.251804 279.909719 \nL 121.599251 304.869321 \nL 121.946698 301.024817 \nL 122.294145 290.215616 \nL 122.641592 312.148088 \nL 123.336486 287.691511 \nL 123.683933 318.534545 \nL 124.03138 300.870994 \nL 124.378827 300.875263 \nL 124.726275 296.258563 \nL 125.073722 226.298974 \nL 125.421169 227.259403 \nL 125.768616 208.377915 \nL 126.116063 210.792228 \nL 126.46351 211.366396 \nL 126.810957 210.471481 \nL 127.158404 221.137974 \nL 127.505851 211.830267 \nL 127.853298 211.292533 \nL 128.200745 221.758037 \nL 128.548192 195.263811 \nL 129.243086 226.923916 \nL 129.590534 207.968771 \nL 129.937981 224.852986 \nL 130.285428 206.781729 \nL 130.632875 203.031103 \nL 130.980322 223.496332 \nL 131.327769 205.191014 \nL 131.675216 210.935354 \nL 132.022663 206.789838 \nL 132.37011 195.787539 \nL 132.717557 217.923789 \nL 133.065004 223.22724 \nL 133.412451 224.691169 \nL 133.759898 223.995677 \nL 134.107346 216.448087 \nL 134.454793 212.2004 \nL 134.80224 212.924196 \nL 135.149687 207.311539 \nL 135.497134 215.756138 \nL 135.844581 200.971849 \nL 136.192028 218.726752 \nL 136.539475 188.030889 \nL 137.234369 224.986276 \nL 137.581816 227.237501 \nL 137.929263 211.283193 \nL 138.27671 218.602476 \nL 138.624157 208.990591 \nL 138.971605 211.515323 \nL 139.319052 217.184022 \nL 140.013946 232.122281 \nL 140.70884 207.772393 \nL 141.056287 214.426631 \nL 141.403734 229.501715 \nL 141.751181 214.919367 \nL 142.098628 212.767913 \nL 142.446075 225.87549 \nL 142.793522 215.218234 \nL 143.140969 216.232818 \nL 143.488416 228.636609 \nL 143.835864 213.20454 \nL 144.183311 211.140647 \nL 144.530758 205.78664 \nL 144.878205 206.113174 \nL 145.225652 231.185839 \nL 145.573099 226.677781 \nL 145.920546 211.732816 \nL 146.61544 211.773808 \nL 146.962887 177.408746 \nL 147.310334 211.235617 \nL 147.657781 205.435509 \nL 148.005228 207.322013 \nL 148.352676 210.454538 \nL 148.700123 220.426999 \nL 149.04757 209.373594 \nL 149.395017 225.166034 \nL 149.742464 219.655485 \nL 150.089911 222.226283 \nL 150.437358 216.392135 \nL 150.784805 193.398959 \nL 151.132252 236.487826 \nL 151.479699 210.188627 \nL 151.827146 233.877965 \nL 152.174593 222.130738 \nL 152.52204 206.054863 \nL 153.216935 228.381082 \nL 153.564382 224.648688 \nL 153.911829 210.279253 \nL 154.259276 224.8041 \nL 154.606723 215.048962 \nL 154.95417 216.80742 \nL 155.301617 223.986732 \nL 155.649064 195.184002 \nL 155.996511 210.735517 \nL 156.343958 238.123354 \nL 156.691405 215.333907 \nL 157.038852 224.06546 \nL 157.386299 208.458661 \nL 157.733746 225.396217 \nL 158.428641 212.010429 \nL 158.776088 208.28316 \nL 159.123535 229.555713 \nL 159.470982 220.624257 \nL 160.165876 223.882092 \nL 160.513323 198.94987 \nL 161.208217 230.09553 \nL 161.555664 207.633966 \nL 161.903111 195.210287 \nL 162.250558 206.418053 \nL 162.598006 232.687822 \nL 162.945453 222.004689 \nL 163.2929 203.944696 \nL 163.640347 224.265657 \nL 163.987794 212.381843 \nL 164.335241 208.951773 \nL 164.682688 226.458176 \nL 165.030135 217.498946 \nL 165.377582 250.252513 \nL 165.725029 227.389985 \nL 166.072476 219.413518 \nL 166.419923 229.640028 \nL 166.76737 199.942266 \nL 167.114817 231.464791 \nL 167.462265 221.237165 \nL 167.809712 215.328585 \nL 168.157159 201.798672 \nL 168.504606 231.408618 \nL 168.852053 204.603717 \nL 169.546947 226.635314 \nL 169.894394 211.731468 \nL 170.241841 214.408647 \nL 170.589288 222.609406 \nL 170.936735 215.673224 \nL 171.284182 220.327372 \nL 171.979077 209.466431 \nL 172.326524 199.912724 \nL 172.673971 228.966665 \nL 173.021418 194.203779 \nL 173.368865 236.250165 \nL 173.716312 217.665636 \nL 174.063759 210.002534 \nL 174.411206 213.129282 \nL 174.758653 222.399144 \nL 175.1061 218.087966 \nL 175.453547 220.98192 \nL 175.800994 221.933326 \nL 176.148441 207.067577 \nL 176.495888 212.099778 \nL 176.843336 222.873065 \nL 177.190783 206.424063 \nL 177.53823 212.482016 \nL 177.885677 207.229535 \nL 178.233124 209.0724 \nL 178.580571 224.053591 \nL 178.928018 221.238613 \nL 179.275465 207.723105 \nL 179.622912 209.087012 \nL 179.970359 215.54313 \nL 180.317806 214.071558 \nL 180.665253 202.069987 \nL 181.0127 221.277989 \nL 181.360147 209.498748 \nL 181.707595 217.168414 \nL 182.055042 217.278174 \nL 182.402489 203.666068 \nL 182.749936 206.431502 \nL 183.097383 206.503067 \nL 183.44483 201.383384 \nL 183.792277 214.563828 \nL 184.139724 207.702511 \nL 184.487171 217.871372 \nL 184.834618 211.28239 \nL 185.182065 215.908994 \nL 185.529512 213.515058 \nL 185.876959 208.328708 \nL 186.224407 222.834226 \nL 186.571854 192.794571 \nL 186.919301 224.65809 \nL 187.266748 226.746653 \nL 187.614195 202.251372 \nL 187.961642 205.969703 \nL 188.309089 207.638576 \nL 188.656536 207.537549 \nL 189.003983 214.078928 \nL 189.35143 223.137851 \nL 189.698877 213.055002 \nL 190.046324 220.752933 \nL 190.393771 203.672155 \nL 190.741218 215.1729 \nL 191.088666 222.102003 \nL 191.436113 216.848364 \nL 191.78356 209.222843 \nL 192.131007 219.223059 \nL 192.478454 221.824598 \nL 192.825901 210.782076 \nL 193.173348 210.706789 \nL 193.520795 218.39036 \nL 193.868242 217.957701 \nL 194.215689 210.651532 \nL 194.563136 227.896486 \nL 194.910583 227.414267 \nL 195.605478 214.985292 \nL 195.952925 209.51879 \nL 196.300372 197.457864 \nL 196.647819 203.755907 \nL 196.995266 214.173342 \nL 197.342713 212.655641 \nL 197.69016 222.72133 \nL 198.037607 212.51765 \nL 198.385054 215.233779 \nL 198.732501 208.868353 \nL 199.079948 220.647445 \nL 199.774842 196.199783 \nL 200.122289 213.041847 \nL 200.469737 207.714482 \nL 200.817184 204.674239 \nL 201.164631 137.536962 \nL 201.512078 131.004314 \nL 201.859525 133.116753 \nL 202.206972 132.198256 \nL 202.554419 141.150281 \nL 202.901866 132.94117 \nL 203.249313 128.094495 \nL 203.59676 118.330786 \nL 203.944207 123.477589 \nL 204.291654 111.281991 \nL 204.639101 141.496621 \nL 204.986548 124.757122 \nL 205.333996 132.030831 \nL 205.681443 111.560598 \nL 206.02889 142.670109 \nL 206.376337 143.241255 \nL 206.723784 141.036474 \nL 207.071231 157.036119 \nL 207.418678 140.890337 \nL 207.766125 143.635724 \nL 208.113572 134.630284 \nL 208.461019 133.046207 \nL 208.808466 117.64913 \nL 209.155913 127.618807 \nL 209.50336 143.808369 \nL 209.850808 147.598234 \nL 210.198255 133.775232 \nL 210.545702 152.965577 \nL 210.893149 121.041286 \nL 211.240596 128.323649 \nL 211.93549 159.317266 \nL 212.282937 128.350725 \nL 212.630384 144.127912 \nL 212.977831 130.866731 \nL 213.325278 160.734763 \nL 213.672725 151.194924 \nL 214.020172 145.697132 \nL 214.367619 146.017661 \nL 214.715067 151.907997 \nL 215.062514 141.644046 \nL 215.409961 159.866763 \nL 215.757408 151.161567 \nL 216.104855 149.300295 \nL 216.452302 146.102697 \nL 216.799749 144.951957 \nL 217.147196 164.766663 \nL 217.494643 169.900093 \nL 217.84209 141.865612 \nL 218.536984 164.652028 \nL 218.884431 141.942337 \nL 219.231879 157.726613 \nL 219.579326 147.780967 \nL 219.926773 160.260784 \nL 220.27422 140.768078 \nL 220.621667 144.969705 \nL 220.969114 166.68694 \nL 221.316561 155.19614 \nL 221.664008 159.652651 \nL 222.011455 153.310817 \nL 222.358902 178.472107 \nL 222.706349 162.597769 \nL 223.053796 159.907099 \nL 223.401243 190.085772 \nL 223.74869 185.32518 \nL 224.096138 195.321792 \nL 224.443585 178.278267 \nL 225.138479 162.432927 \nL 225.485926 178.363961 \nL 225.833373 163.575735 \nL 226.18082 195.805354 \nL 226.528267 200.378128 \nL 226.875714 184.652436 \nL 227.223161 181.38073 \nL 227.570608 186.937942 \nL 227.918055 209.169651 \nL 228.265502 190.065505 \nL 228.612949 203.867404 \nL 228.960397 184.816953 \nL 229.307844 189.2306 \nL 229.655291 203.985384 \nL 230.002738 200.896288 \nL 230.350185 207.817388 \nL 231.045079 189.680736 \nL 231.392526 210.986069 \nL 231.739973 196.951456 \nL 232.08742 208.920709 \nL 232.434867 212.941132 \nL 232.782314 207.191272 \nL 233.129761 218.069162 \nL 233.477209 214.423226 \nL 233.824656 222.374529 \nL 234.172103 191.107509 \nL 234.51955 212.296277 \nL 234.866997 221.177849 \nL 235.214444 213.072509 \nL 235.561891 217.738691 \nL 235.909338 220.159272 \nL 236.256785 212.85359 \nL 236.604232 212.670556 \nL 236.951679 227.22849 \nL 237.299126 228.979572 \nL 237.646573 227.160057 \nL 237.99402 249.313626 \nL 238.341468 242.475957 \nL 238.688915 214.046052 \nL 239.036362 212.434765 \nL 239.383809 233.192614 \nL 239.731256 225.926011 \nL 240.078703 229.889481 \nL 240.42615 202.599738 \nL 240.773597 223.996956 \nL 241.468491 247.771733 \nL 241.815938 255.66097 \nL 242.163385 238.188934 \nL 242.858279 257.250334 \nL 243.205727 250.399028 \nL 243.553174 256.007673 \nL 243.900621 228.600515 \nL 244.595515 248.255371 \nL 244.942962 234.003316 \nL 245.637856 260.232841 \nL 245.985303 236.703032 \nL 246.33275 247.861275 \nL 246.680197 265.102471 \nL 247.027644 257.366346 \nL 247.722539 271.580296 \nL 248.069986 248.734008 \nL 248.417433 239.528489 \nL 248.76488 274.513231 \nL 249.112327 255.195186 \nL 249.459774 268.569246 \nL 249.807221 267.738528 \nL 250.154668 269.658611 \nL 250.502115 273.273723 \nL 250.849562 264.671672 \nL 251.197009 274.511269 \nL 251.544456 263.925026 \nL 251.891903 176.472193 \nL 252.23935 178.516411 \nL 252.586798 185.502851 \nL 252.934245 182.191955 \nL 253.281692 168.56352 \nL 253.629139 171.278654 \nL 253.976586 186.601549 \nL 254.324033 175.598074 \nL 254.67148 168.969552 \nL 255.018927 193.995277 \nL 255.366374 171.28552 \nL 255.713821 183.793445 \nL 256.061268 171.171655 \nL 256.756162 195.804013 \nL 257.10361 194.05518 \nL 257.451057 176.869825 \nL 257.798504 174.764711 \nL 258.145951 186.830065 \nL 258.493398 171.007571 \nL 258.840845 194.773631 \nL 259.188292 178.407114 \nL 259.535739 190.269962 \nL 260.230633 177.437768 \nL 260.57808 186.853648 \nL 260.925527 182.01389 \nL 261.272974 167.74674 \nL 261.620421 184.116101 \nL 261.967869 169.622361 \nL 262.315316 189.926559 \nL 262.662763 172.886396 \nL 263.01021 163.54806 \nL 263.357657 203.914325 \nL 264.052551 172.609627 \nL 264.399998 180.695604 \nL 264.747445 174.828534 \nL 265.094892 184.921015 \nL 265.442339 177.852521 \nL 265.789786 180.392749 \nL 266.137233 166.801991 \nL 266.48468 176.253284 \nL 266.832128 175.437216 \nL 267.179575 183.19787 \nL 267.527022 184.016525 \nL 267.874469 183.48678 \nL 268.221916 175.003296 \nL 268.569363 183.438811 \nL 268.91681 176.15065 \nL 269.264257 157.788322 \nL 269.959151 182.589575 \nL 270.306598 166.885802 \nL 271.001492 200.312532 \nL 271.34894 189.727571 \nL 271.696387 198.640196 \nL 272.043834 183.013079 \nL 272.391281 179.225244 \nL 272.738728 162.166778 \nL 273.086175 176.09021 \nL 273.433622 181.735325 \nL 273.781069 170.952883 \nL 274.128516 202.293932 \nL 274.475963 177.105091 \nL 274.82341 171.607125 \nL 275.170857 194.795591 \nL 275.518304 167.794931 \nL 276.213199 183.88124 \nL 276.560646 173.095525 \nL 276.908093 156.232457 \nL 277.25554 177.760516 \nL 277.602987 177.085441 \nL 277.950434 184.382375 \nL 278.297881 188.411727 \nL 278.645328 171.108646 \nL 278.992775 188.486668 \nL 279.340222 178.934219 \nL 279.687669 184.595331 \nL 280.035116 174.742516 \nL 280.382563 176.240945 \nL 280.730011 168.990288 \nL 281.077458 184.932577 \nL 281.424905 182.457545 \nL 281.772352 189.758385 \nL 282.119799 184.249578 \nL 282.467246 175.782066 \nL 282.814693 171.866159 \nL 283.16214 189.159197 \nL 283.509587 170.694703 \nL 283.857034 165.680864 \nL 284.204481 175.379608 \nL 284.551928 160.295966 \nL 284.899375 187.592543 \nL 285.59427 197.924461 \nL 285.941717 164.177065 \nL 286.636611 180.136434 \nL 286.984058 176.665936 \nL 287.331505 191.13023 \nL 287.678952 154.324911 \nL 288.026399 178.173442 \nL 288.373846 178.361101 \nL 288.721293 171.994073 \nL 289.416187 177.12767 \nL 289.763634 187.557991 \nL 290.458529 159.985661 \nL 290.805976 165.492225 \nL 291.153423 162.917698 \nL 291.50087 184.57405 \nL 291.848317 189.478905 \nL 292.195764 180.555977 \nL 292.543211 178.661375 \nL 292.890658 167.937966 \nL 293.238105 196.61952 \nL 293.585552 163.400396 \nL 293.932999 180.758022 \nL 294.280446 183.500037 \nL 294.975341 196.092821 \nL 295.322788 170.535023 \nL 295.670235 178.229555 \nL 296.017682 192.242877 \nL 296.365129 192.264125 \nL 296.712576 182.349621 \nL 297.060023 161.663893 \nL 297.40747 181.498833 \nL 297.754917 194.275847 \nL 298.102364 181.288025 \nL 298.449811 181.531054 \nL 298.797258 206.468789 \nL 299.144705 179.209344 \nL 299.492152 180.992471 \nL 300.187047 159.491101 \nL 300.534494 166.894824 \nL 300.881941 181.231765 \nL 301.229388 189.821673 \nL 301.576835 151.872502 \nL 301.924282 177.732358 \nL 302.619176 178.508826 \nL 302.966623 176.177848 \nL 303.661517 184.043081 \nL 304.008964 183.723652 \nL 304.356412 178.383646 \nL 304.703859 183.600191 \nL 305.051306 185.300766 \nL 305.398753 176.815519 \nL 305.7462 180.492947 \nL 306.093647 162.326228 \nL 306.441094 205.08312 \nL 306.788541 166.481815 \nL 307.135988 164.841814 \nL 307.483435 198.990321 \nL 307.830882 181.11252 \nL 308.178329 181.359812 \nL 308.525776 191.983832 \nL 308.873223 185.447049 \nL 309.220671 188.824827 \nL 309.568118 159.281728 \nL 309.915565 167.642989 \nL 310.263012 164.131338 \nL 310.610459 169.744173 \nL 310.957906 188.757574 \nL 311.305353 182.477044 \nL 311.6528 171.978929 \nL 312.000247 189.556867 \nL 312.347694 169.567611 \nL 312.695141 179.334149 \nL 313.042588 180.664894 \nL 313.390035 169.424219 \nL 313.737482 172.116087 \nL 314.08493 180.355416 \nL 314.432377 164.637294 \nL 314.779824 187.660606 \nL 315.127271 170.121367 \nL 315.474718 170.299107 \nL 315.822165 179.540124 \nL 316.169612 172.933253 \nL 316.517059 189.123108 \nL 316.864506 166.656307 \nL 317.211953 178.020932 \nL 317.5594 181.441406 \nL 317.906847 165.189716 \nL 318.254294 183.191992 \nL 318.601742 190.384922 \nL 318.949189 191.850229 \nL 319.296636 169.509753 \nL 319.644083 188.881678 \nL 319.99153 157.551345 \nL 320.338977 197.014011 \nL 320.686424 158.027112 \nL 321.033871 173.266683 \nL 321.381318 176.373427 \nL 321.728765 180.926995 \nL 322.076212 171.137463 \nL 322.423659 175.572588 \nL 322.771106 163.754192 \nL 323.118553 173.878399 \nL 323.466001 173.441436 \nL 323.813448 178.670024 \nL 324.508342 171.621057 \nL 324.855789 192.343201 \nL 325.203236 188.547277 \nL 325.550683 166.934461 \nL 325.89813 172.763748 \nL 326.245577 176.35159 \nL 326.593024 174.198308 \nL 326.940471 170.739189 \nL 327.287918 179.811798 \nL 327.635365 182.200363 \nL 327.982812 93.784597 \nL 328.33026 105.790333 \nL 328.677707 91.439223 \nL 329.025154 113.142626 \nL 329.372601 84.983236 \nL 329.720048 90.723549 \nL 330.067495 92.985683 \nL 330.762389 78.600909 \nL 331.109836 85.412272 \nL 331.457283 114.954342 \nL 332.152177 102.754808 \nL 332.847072 91.408859 \nL 333.194519 104.465557 \nL 333.541966 95.336107 \nL 333.889413 105.336471 \nL 334.23686 104.172326 \nL 334.584307 112.704903 \nL 334.931754 108.089039 \nL 335.279201 112.890149 \nL 335.626648 109.428758 \nL 335.974095 88.953956 \nL 336.321542 110.044214 \nL 336.668989 73.623198 \nL 337.016436 96.159302 \nL 337.363883 99.859152 \nL 337.711331 111.149681 \nL 338.058778 95.658025 \nL 338.406225 109.389413 \nL 338.753672 102.81048 \nL 339.101119 78.323063 \nL 339.448566 106.336014 \nL 339.796013 94.17736 \nL 340.14346 113.951762 \nL 340.490907 107.779214 \nL 340.838354 89.90722 \nL 341.185801 115.359005 \nL 341.533248 90.998539 \nL 342.228143 117.061257 \nL 342.57559 105.578353 \nL 342.923037 102.918985 \nL 343.270484 107.395061 \nL 343.617931 130.85746 \nL 343.965378 123.070131 \nL 344.312825 119.044339 \nL 344.660272 118.193242 \nL 345.007719 111.980951 \nL 345.355166 117.50912 \nL 345.702613 134.076885 \nL 346.05006 117.40003 \nL 346.397507 116.03819 \nL 346.744954 117.587982 \nL 347.092402 113.26197 \nL 347.439849 116.860442 \nL 347.787296 121.790269 \nL 348.134743 128.326368 \nL 348.48219 145.81052 \nL 348.829637 125.382811 \nL 349.177084 128.790596 \nL 349.524531 129.266057 \nL 349.871978 146.362032 \nL 350.219425 145.502905 \nL 350.566872 124.684626 \nL 350.914319 137.120025 \nL 351.261766 130.880699 \nL 351.609213 138.808383 \nL 351.956661 140.001826 \nL 352.304108 131.859748 \nL 352.651555 148.068047 \nL 352.999002 142.996106 \nL 353.346449 149.990986 \nL 353.693896 150.954762 \nL 354.041343 150.920794 \nL 354.38879 146.711232 \nL 354.736237 155.201748 \nL 355.083684 138.609356 \nL 355.431131 162.04357 \nL 355.778578 156.039367 \nL 356.126025 159.935711 \nL 356.473473 141.794964 \nL 356.82092 155.976154 \nL 357.168367 148.674232 \nL 357.515814 175.914096 \nL 357.863261 159.167792 \nL 358.210708 154.065022 \nL 358.558155 163.694843 \nL 358.905602 154.880235 \nL 359.253049 172.500599 \nL 359.600496 153.968351 \nL 359.947943 146.120037 \nL 360.29539 174.856014 \nL 360.642837 177.022197 \nL 360.990284 158.77371 \nL 361.337732 158.785618 \nL 361.685179 181.754297 \nL 362.032626 182.004038 \nL 362.380073 181.882875 \nL 362.72752 194.131611 \nL 363.074967 191.04284 \nL 363.422414 193.217162 \nL 363.769861 192.071546 \nL 364.117308 185.80341 \nL 364.464755 184.312182 \nL 364.812202 172.025279 \nL 365.159649 199.550091 \nL 365.507096 180.385499 \nL 365.854544 193.983818 \nL 366.201991 193.535141 \nL 366.549438 187.31221 \nL 366.896885 207.060436 \nL 367.244332 192.776687 \nL 367.591779 196.214301 \nL 367.939226 194.059474 \nL 368.286673 197.347484 \nL 368.63412 176.216203 \nL 368.981567 209.253313 \nL 369.329014 209.316086 \nL 369.676461 211.41652 \nL 370.023908 211.859057 \nL 370.371355 213.830501 \nL 370.718803 196.130573 \nL 371.06625 194.848436 \nL 371.413697 216.451424 \nL 371.761144 220.222613 \nL 372.108591 207.853835 \nL 372.456038 219.447437 \nL 372.803485 208.272081 \nL 373.150932 213.72102 \nL 373.498379 232.430334 \nL 373.845826 201.861928 \nL 374.193273 200.276022 \nL 374.54072 226.046285 \nL 374.888167 224.667789 \nL 375.235614 218.652446 \nL 375.583062 219.058463 \nL 375.930509 216.609072 \nL 376.277956 203.556246 \nL 376.625403 226.941618 \nL 377.320297 240.981006 \nL 378.015191 228.690744 \nL 378.362638 210.622541 \nL 378.710085 143.685451 \nL 379.057532 136.147882 \nL 379.404979 138.720988 \nL 380.099874 112.711959 \nL 380.447321 144.306371 \nL 380.794768 143.971563 \nL 381.142215 128.26313 \nL 381.489662 132.08396 \nL 381.837109 120.171514 \nL 382.532003 143.067066 \nL 382.87945 133.363519 \nL 383.226897 128.107776 \nL 383.921791 134.461235 \nL 384.269238 128.77501 \nL 384.616685 127.792961 \nL 384.964133 125.67376 \nL 385.659027 141.777099 \nL 386.006474 138.61344 \nL 386.353921 127.763612 \nL 386.701368 148.675265 \nL 387.048815 136.526544 \nL 387.396262 144.440424 \nL 387.743709 140.154636 \nL 388.091156 127.341495 \nL 388.438603 138.602901 \nL 388.78605 140.151582 \nL 389.133497 154.695986 \nL 389.480945 133.05506 \nL 389.828392 134.147631 \nL 390.175839 118.565061 \nL 390.523286 144.073014 \nL 390.870733 138.69534 \nL 391.21818 138.433255 \nL 391.565627 124.799892 \nL 391.913074 142.079082 \nL 392.607968 134.925054 \nL 392.955415 139.308304 \nL 393.302862 145.871816 \nL 393.650309 139.314131 \nL 393.997756 130.292955 \nL 394.345204 151.96673 \nL 394.692651 140.059312 \nL 395.040098 148.677832 \nL 395.387545 129.189282 \nL 395.734992 157.225874 \nL 396.777333 125.501734 \nL 397.12478 142.336275 \nL 397.472227 147.406893 \nL 397.819674 122.335408 \nL 398.167121 156.65809 \nL 398.514568 164.417805 \nL 398.862015 137.257406 \nL 399.209463 146.98552 \nL 399.55691 152.356079 \nL 399.904357 134.559287 \nL 400.251804 139.365094 \nL 400.946698 155.103189 \nL 401.294145 132.942952 \nL 401.641592 135.248659 \nL 401.989039 142.983678 \nL 402.336486 122.952459 \nL 402.683933 153.013076 \nL 403.03138 157.717146 \nL 403.726275 142.492746 \nL 404.073722 139.525711 \nL 404.421169 144.526198 \nL 404.768616 138.421816 \nL 405.116063 154.948383 \nL 405.46351 127.187713 \nL 405.810957 142.912258 \nL 406.158404 130.559814 \nL 406.505851 138.542401 \nL 407.200745 136.207119 \nL 407.548192 137.465084 \nL 407.895639 135.455783 \nL 408.243086 128.383184 \nL 408.590534 140.04933 \nL 408.937981 134.767036 \nL 409.285428 142.991852 \nL 409.632875 127.227193 \nL 409.980322 149.025774 \nL 410.327769 123.498773 \nL 411.022663 156.776745 \nL 411.37011 140.709091 \nL 411.717557 149.035659 \nL 412.065004 133.349527 \nL 412.412451 148.722136 \nL 412.759898 146.58396 \nL 413.107346 161.440899 \nL 413.454793 146.621855 \nL 413.80224 166.918879 \nL 414.149687 158.25119 \nL 414.497134 134.085375 \nL 414.844581 133.807984 \nL 415.192028 137.503568 \nL 415.539475 151.830827 \nL 415.886922 142.34276 \nL 416.234369 141.869667 \nL 416.581816 153.746396 \nL 416.929263 126.305413 \nL 417.27671 132.733955 \nL 417.624157 144.027259 \nL 418.319052 139.559379 \nL 418.666499 162.716203 \nL 419.013946 144.204995 \nL 419.70884 151.927048 \nL 420.056287 144.478425 \nL 420.403734 123.036593 \nL 421.098628 147.385042 \nL 421.793522 127.027458 \nL 422.140969 131.888251 \nL 422.835864 148.017059 \nL 423.183311 134.128031 \nL 423.530758 137.235315 \nL 423.878205 132.034186 \nL 424.225652 134.679653 \nL 424.573099 130.376999 \nL 424.920546 146.66879 \nL 425.267993 127.54848 \nL 425.61544 139.048888 \nL 425.962887 119.669594 \nL 426.310334 148.111778 \nL 426.657781 123.091262 \nL 427.005228 138.898296 \nL 427.352676 147.609705 \nL 428.04757 144.121108 \nL 428.395017 136.411517 \nL 428.742464 135.354753 \nL 429.089911 146.610222 \nL 429.437358 140.909669 \nL 429.784805 118.547742 \nL 430.132252 122.777852 \nL 430.479699 136.036903 \nL 430.827146 140.097003 \nL 431.174593 139.20817 \nL 431.52204 134.080093 \nL 431.869487 150.892251 \nL 432.216935 142.961857 \nL 432.564382 157.454102 \nL 432.911829 136.105811 \nL 433.259276 133.504599 \nL 433.606723 145.102448 \nL 433.95417 123.862321 \nL 434.301617 152.656752 \nL 434.649064 155.066382 \nL 434.996511 137.619532 \nL 435.691405 122.485162 \nL 436.038852 144.509718 \nL 436.386299 128.619383 \nL 436.733746 140.077333 \nL 437.081194 141.407475 \nL 437.428641 130.473887 \nL 437.776088 132.804711 \nL 438.123535 155.711683 \nL 438.470982 124.207837 \nL 438.818429 125.147839 \nL 439.165876 145.75566 \nL 439.513323 135.179363 \nL 439.86077 134.112655 \nL 440.208217 136.46145 \nL 440.555664 144.760397 \nL 440.903111 145.954219 \nL 441.250558 139.242193 \nL 441.598006 126.840997 \nL 441.945453 133.26503 \nL 442.2929 142.625578 \nL 442.640347 130.799846 \nL 442.987794 168.036563 \nL 443.335241 126.798543 \nL 443.682688 156.496076 \nL 444.030135 142.248378 \nL 444.377582 149.989504 \nL 444.725029 151.732396 \nL 445.072476 126.375883 \nL 445.419923 143.091891 \nL 445.76737 134.643037 \nL 446.114817 138.634583 \nL 446.462265 133.175074 \nL 446.809712 137.235937 \nL 447.157159 151.182009 \nL 447.504606 127.638794 \nL 448.1995 153.811163 \nL 448.546947 142.122184 \nL 448.894394 122.18725 \nL 449.241841 128.824559 \nL 449.589288 141.110391 \nL 449.936735 141.051605 \nL 450.284182 140.701707 \nL 450.631629 115.928433 \nL 450.979077 133.32208 \nL 451.326524 132.760247 \nL 451.673971 126.510129 \nL 452.021418 134.597082 \nL 452.368865 139.658485 \nL 452.716312 138.9452 \nL 453.063759 137.592333 \nL 453.411206 137.170145 \nL 453.758653 129.222496 \nL 454.1061 136.114652 \nL 454.453547 129.033411 \nL 454.800994 59.032157 \nL 455.148441 57.299742 \nL 455.495888 78.628472 \nL 455.843336 48.561639 \nL 456.190783 54.414407 \nL 456.53823 47.709339 \nL 456.885677 87.85833 \nL 457.233124 36.563125 \nL 457.580571 59.594773 \nL 457.928018 46.844312 \nL 458.275465 69.101417 \nL 458.622912 52.227875 \nL 458.970359 69.568601 \nL 459.317806 65.342373 \nL 459.665253 39.422208 \nL 460.0127 61.351947 \nL 460.360147 57.41867 \nL 460.707595 50.989904 \nL 461.402489 59.062676 \nL 461.749936 57.216816 \nL 462.097383 36.604387 \nL 462.44483 62.367576 \nL 462.792277 60.135148 \nL 463.139724 51.838571 \nL 463.487171 51.751023 \nL 463.834618 51.411303 \nL 464.182065 57.581711 \nL 464.529512 76.480533 \nL 464.876959 48.443684 \nL 465.224407 77.6672 \nL 465.571854 63.348192 \nL 465.919301 74.865375 \nL 466.266748 68.405644 \nL 466.614195 65.028568 \nL 466.961642 65.795714 \nL 467.309089 65.470711 \nL 467.656536 53.924 \nL 468.003983 66.626389 \nL 468.35143 74.583726 \nL 468.698877 62.925651 \nL 469.046324 61.411652 \nL 469.393771 87.138315 \nL 469.741218 69.182224 \nL 470.088666 68.975961 \nL 470.436113 80.145255 \nL 470.78356 63.80556 \nL 471.131007 80.438774 \nL 471.478454 78.531262 \nL 471.825901 82.556411 \nL 472.173348 81.577745 \nL 472.520795 94.012524 \nL 472.868242 98.550322 \nL 473.215689 68.300542 \nL 473.563136 94.478518 \nL 473.910583 97.007534 \nL 474.25803 110.033574 \nL 474.605478 95.50157 \nL 474.952925 103.632203 \nL 475.300372 74.182444 \nL 475.647819 81.804205 \nL 475.995266 100.417807 \nL 476.342713 71.25817 \nL 477.037607 100.110914 \nL 477.385054 123.680805 \nL 477.732501 75.510214 \nL 478.079948 114.614932 \nL 478.427395 118.449139 \nL 478.774842 92.172544 \nL 479.122289 78.790418 \nL 479.817184 100.575529 \nL 480.164631 101.500171 \nL 480.512078 100.085448 \nL 480.859525 102.315928 \nL 481.206972 108.501312 \nL 481.901866 114.589188 \nL 482.249313 122.993177 \nL 482.59676 119.404189 \nL 482.944207 135.244654 \nL 483.291654 120.112974 \nL 483.639101 130.583753 \nL 483.986548 133.071495 \nL 484.681443 109.977255 \nL 485.02889 116.167985 \nL 485.376337 143.119956 \nL 485.723784 138.450003 \nL 486.071231 119.878459 \nL 486.418678 141.007001 \nL 487.113572 127.850088 \nL 487.461019 144.806882 \nL 487.808466 135.05909 \nL 488.155913 151.199358 \nL 488.850808 136.779517 \nL 489.198255 157.789151 \nL 489.545702 138.142171 \nL 489.893149 144.179866 \nL 490.240596 139.579563 \nL 490.588043 144.253622 \nL 490.93549 133.791489 \nL 491.282937 147.833455 \nL 491.630384 154.820327 \nL 492.325278 147.327316 \nL 492.672725 153.674483 \nL 493.020172 155.005888 \nL 493.367619 163.894304 \nL 493.715067 164.721935 \nL 494.062514 173.564009 \nL 494.409961 142.303769 \nL 494.757408 174.345094 \nL 495.104855 169.863078 \nL 495.452302 171.309539 \nL 495.799749 155.86261 \nL 496.147196 169.282856 \nL 496.494643 154.541565 \nL 496.84209 175.473286 \nL 497.189537 166.788615 \nL 497.536984 179.383613 \nL 497.884431 149.781777 \nL 498.231879 185.204582 \nL 498.579326 170.854636 \nL 498.926773 168.579702 \nL 499.27422 171.894162 \nL 499.621667 179.099682 \nL 499.969114 179.547354 \nL 500.316561 178.762604 \nL 500.664008 181.716858 \nL 501.358902 159.359609 \nL 501.706349 188.81541 \nL 502.053796 198.107267 \nL 502.401243 185.811907 \nL 502.74869 159.216118 \nL 503.096138 194.942706 \nL 503.443585 170.631392 \nL 503.791032 171.069537 \nL 504.138479 194.87298 \nL 504.833373 173.972561 \nL 505.18082 195.413865 \nL 505.528267 102.84885 \nL 506.223161 111.404089 \nL 506.570608 107.735856 \nL 506.918055 113.751128 \nL 507.265502 100.884833 \nL 507.612949 109.260041 \nL 507.960397 99.009919 \nL 508.307844 117.535645 \nL 508.655291 98.18572 \nL 509.002738 93.093221 \nL 509.350185 122.303233 \nL 509.697632 97.993347 \nL 510.045079 89.280699 \nL 510.392526 114.468924 \nL 510.739973 84.855059 \nL 511.434867 109.500156 \nL 512.129761 96.631407 \nL 512.477209 101.670972 \nL 512.824656 96.973182 \nL 513.172103 112.075872 \nL 513.51955 100.913128 \nL 513.866997 117.286802 \nL 514.214444 103.932637 \nL 514.561891 112.161102 \nL 514.909338 110.487584 \nL 515.256785 90.236074 \nL 515.604232 91.785996 \nL 515.951679 96.810306 \nL 516.299126 114.394057 \nL 516.646573 106.455323 \nL 516.99402 90.751854 \nL 517.341468 101.84089 \nL 517.688915 79.45822 \nL 518.036362 99.348955 \nL 518.383809 101.470509 \nL 518.731256 106.680298 \nL 519.078703 102.165798 \nL 519.42615 105.156664 \nL 519.773597 96.334406 \nL 520.121044 93.815488 \nL 520.468491 111.80918 \nL 521.163385 122.706463 \nL 521.510832 98.594398 \nL 521.858279 115.230463 \nL 522.205727 126.077496 \nL 522.900621 78.283267 \nL 523.248068 104.059854 \nL 523.595515 95.389955 \nL 523.942962 103.21442 \nL 524.290409 105.104565 \nL 524.637856 94.645036 \nL 524.985303 107.129785 \nL 525.33275 101.410035 \nL 525.680197 100.874757 \nL 526.027644 111.094469 \nL 526.375091 94.014227 \nL 526.722539 106.057501 \nL 527.069986 112.060681 \nL 527.417433 98.76719 \nL 527.76488 113.568071 \nL 528.112327 104.038984 \nL 528.459774 104.439314 \nL 528.807221 93.173804 \nL 529.502115 104.645592 \nL 529.849562 95.975536 \nL 530.197009 90.089645 \nL 530.544456 98.675165 \nL 530.891903 104.324371 \nL 531.23935 117.953771 \nL 531.586798 115.419668 \nL 531.934245 107.598129 \nL 532.281692 110.738611 \nL 532.629139 106.390571 \nL 532.976586 103.884841 \nL 533.324033 99.014782 \nL 533.67148 105.200085 \nL 534.018927 99.463382 \nL 534.366374 103.811559 \nL 534.713821 124.82629 \nL 535.408715 105.956687 \nL 535.756162 116.931538 \nL 536.10361 98.285555 \nL 536.451057 88.691983 \nL 536.798504 91.900779 \nL 537.145951 106.648998 \nL 537.493398 89.086171 \nL 537.840845 102.904143 \nL 538.188292 107.899861 \nL 538.535739 110.737232 \nL 538.883186 107.524115 \nL 539.230633 108.396337 \nL 539.57808 102.63036 \nL 539.925527 102.719534 \nL 540.272974 104.329391 \nL 540.620421 99.845827 \nL 540.967869 92.066856 \nL 541.315316 94.535379 \nL 541.662763 119.59871 \nL 542.01021 130.594971 \nL 542.357657 94.64256 \nL 542.705104 118.331019 \nL 543.052551 106.546518 \nL 543.399998 116.265904 \nL 543.747445 122.755199 \nL 544.094892 98.5928 \nL 544.442339 96.328758 \nL 544.789786 110.066411 \nL 545.137233 130.796938 \nL 545.48468 109.708458 \nL 545.832128 100.020823 \nL 546.179575 119.26783 \nL 546.527022 102.118117 \nL 546.874469 104.138787 \nL 547.221916 97.986718 \nL 547.569363 102.695325 \nL 547.91681 113.923706 \nL 548.264257 91.544058 \nL 548.611704 105.474936 \nL 548.959151 104.093394 \nL 549.306598 113.396146 \nL 549.654045 108.317532 \nL 550.001492 112.834506 \nL 550.34894 105.469049 \nL 550.696387 86.021098 \nL 551.043834 117.754569 \nL 551.391281 120.210311 \nL 551.738728 88.397707 \nL 552.086175 105.67488 \nL 552.433622 110.375641 \nL 552.781069 92.734091 \nL 553.128516 109.649313 \nL 553.475963 84.562107 \nL 554.170857 108.323774 \nL 554.518304 81.03456 \nL 554.865751 109.459383 \nL 555.213199 95.534755 \nL 555.560646 100.056123 \nL 555.908093 89.688305 \nL 556.25554 86.970897 \nL 556.602987 102.685733 \nL 556.950434 110.734662 \nL 557.297881 98.19176 \nL 557.645328 109.868664 \nL 557.992775 82.101701 \nL 558.340222 101.392345 \nL 558.687669 106.517603 \nL 559.035116 100.806361 \nL 559.382563 116.540991 \nL 559.730011 112.626361 \nL 560.077458 90.203945 \nL 560.424905 109.290428 \nL 560.772352 113.260729 \nL 561.119799 96.899172 \nL 561.467246 90.164312 \nL 561.814693 94.923762 \nL 562.16214 92.019834 \nL 562.509587 110.027446 \nL 562.857034 116.828059 \nL 563.204481 86.663783 \nL 563.551928 105.403284 \nL 563.899375 104.611613 \nL 564.246822 88.292869 \nL 564.59427 96.193118 \nL 564.941717 97.174047 \nL 565.289164 79.513927 \nL 565.636611 108.393112 \nL 565.984058 92.151315 \nL 566.331505 101.067988 \nL 566.678952 98.833547 \nL 567.026399 85.799379 \nL 567.373846 96.258134 \nL 567.721293 95.888366 \nL 568.06874 90.328678 \nL 568.416187 105.080345 \nL 568.763634 109.90892 \nL 569.111081 111.969468 \nL 569.458529 121.405186 \nL 569.805976 79.907357 \nL 570.153423 112.396378 \nL 570.50087 103.251702 \nL 570.848317 103.764739 \nL 571.543211 92.389792 \nL 571.890658 91.868537 \nL 572.238105 106.678951 \nL 572.932999 97.638617 \nL 573.280446 103.049889 \nL 573.627893 83.867287 \nL 573.975341 95.305457 \nL 574.322788 92.730047 \nL 574.670235 93.408636 \nL 575.017682 88.085189 \nL 575.365129 98.245466 \nL 575.712576 113.405922 \nL 576.060023 95.864341 \nL 576.40747 106.62727 \nL 576.754917 105.297802 \nL 577.102364 93.746173 \nL 577.449811 86.964598 \nL 577.797258 99.436041 \nL 578.144705 96.407391 \nL 578.492152 81.986513 \nL 578.8396 96.975328 \nL 579.187047 72.586382 \nL 579.881941 117.392193 \nL 580.229388 91.424404 \nL 580.576835 95.190247 \nL 580.924282 85.761509 \nL 581.271729 92.044421 \nL 581.619176 22.025455 \nL 581.619176 22.025455 \n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"M 48.982812 333.36 \nL 48.982812 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"M 606.982813 333.36 \nL 606.982813 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"M 48.982812 333.36 \nL 606.982813 333.36 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"M 48.982812 7.2 \nL 606.982813 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"p4b19e9262b\">\n   <rect height=\"326.16\" width=\"558\" x=\"48.982812\" y=\"7.2\"/>\n  </clipPath>\n </defs>\n</svg>\n",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zmp1JXKxk9Vb"
      },
      "source": [
        "split_time = 1000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDs_w3kZ8OIw"
      },
      "source": [
        "## Simple RNN Forecasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YU4xRp9G8OIx"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = window_dataset(x_train, window_size, batch_size=128)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n",
        "                      input_shape=[None]),\n",
        "  keras.layers.SimpleRNN(100, return_sequences=True),\n",
        "  keras.layers.SimpleRNN(100),\n",
        "  keras.layers.Dense(1),\n",
        "  keras.layers.Lambda(lambda x: x * 200.0)\n",
        "])\n",
        "lr_schedule = keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-7 * 10**(epoch / 20))\n",
        "optimizer = keras.optimizers.SGD(lr=1e-7, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/home/ritesh/.local/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  warnings.warn(\n",
            "Epoch 1/100\n",
            "8/8 [==============================] - 2s 34ms/step - loss: 50.2173 - mae: 50.7170\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 23.0009 - mae: 23.4933\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 19.2075 - mae: 19.7004\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 16.9702 - mae: 17.4606\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 15.4094 - mae: 15.8988\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 14.7507 - mae: 15.2367\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 14.3058 - mae: 14.7931\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 14.0615 - mae: 14.5512\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 13.7812 - mae: 14.2687\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 13.5641 - mae: 14.0522\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 13.4094 - mae: 13.8972\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 13.0179 - mae: 13.5048\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 12.8762 - mae: 13.3629\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 12.3934 - mae: 12.8809\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 12.0738 - mae: 12.5614\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 11.8488 - mae: 12.3347\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 11.4459 - mae: 11.9320\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 0s 31ms/step - loss: 11.1944 - mae: 11.6819\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 10.9563 - mae: 11.4441\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 10.6491 - mae: 11.1317\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 10.3188 - mae: 10.8052\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 10.3916 - mae: 10.8774\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 9.8752 - mae: 10.3596\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 10.1089 - mae: 10.5951\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 9.8868 - mae: 10.3706\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 11.5285 - mae: 12.0163\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 10.3099 - mae: 10.7979\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 9.2476 - mae: 9.7351\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 10.3366 - mae: 10.8268\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 1s 29ms/step - loss: 11.8669 - mae: 12.3560\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 10.3245 - mae: 10.8129\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 11.5903 - mae: 12.0826\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 12.4465 - mae: 12.9401\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 0s 33ms/step - loss: 12.6757 - mae: 13.1683\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 13.7874 - mae: 14.2824\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 0s 33ms/step - loss: 13.5659 - mae: 14.0607\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 14.0512 - mae: 14.5457\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 13.7129 - mae: 14.2059\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 13.6162 - mae: 14.1056\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 13.7980 - mae: 14.2930\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 14.2962 - mae: 14.7912\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 15.6394 - mae: 16.1366\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 14.7359 - mae: 15.2309\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 16.2496 - mae: 16.7472\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 17.4995 - mae: 17.9950\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 17.9175 - mae: 18.4114\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 36.7272 - mae: 37.2246\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 0s 34ms/step - loss: 43.0683 - mae: 43.5683\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 42.2450 - mae: 42.7448\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 46.8209 - mae: 47.3209\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 48.2849 - mae: 48.7848\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 51.0403 - mae: 51.5399\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 55.9758 - mae: 56.4753\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 0s 31ms/step - loss: 59.9973 - mae: 60.4961\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 63.9028 - mae: 64.4022\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 65.2900 - mae: 65.7891\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 66.8681 - mae: 67.3670\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 68.8176 - mae: 69.3174\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 0s 31ms/step - loss: 86.0504 - mae: 86.5502\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 94.5714 - mae: 95.0712\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 0s 33ms/step - loss: 77.1481 - mae: 77.6464\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 0s 31ms/step - loss: 92.8479 - mae: 93.3470\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 152.4501 - mae: 152.9498\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 180.6510 - mae: 181.1510\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 150.5839 - mae: 151.0826\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 320.6530 - mae: 321.1530\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 1s 29ms/step - loss: 364.0673 - mae: 364.5673\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 552.0235 - mae: 552.5233\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 908.2772 - mae: 908.7772\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 1005.3674 - mae: 1005.8674\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 1277.0555 - mae: 1277.5555\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 474.7566 - mae: 475.2566\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 398.2926 - mae: 398.7919\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 2073.5896 - mae: 2074.0896\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 2959.4624 - mae: 2959.9600\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 2086.0427 - mae: 2086.5427\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 2384.9917 - mae: 2385.4917\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 1356.4625 - mae: 1356.9625\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 1847.7802 - mae: 1848.2802\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 2875.2422 - mae: 2875.7422\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 2569.8262 - mae: 2570.3262\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 4061.8091 - mae: 4062.3091\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 1225.3851 - mae: 1225.8851\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 1555.8503 - mae: 1556.3503\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 1732.8389 - mae: 1733.3389\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 1777.5637 - mae: 1778.0637\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 2103.5710 - mae: 2104.0710\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 7140.4414 - mae: 7140.9414\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 9922.1768 - mae: 9922.6768\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 18633.7090 - mae: 18634.2109\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 14234.3506 - mae: 14234.8506\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 13602.3779 - mae: 13602.8779\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 13449.9004 - mae: 13450.4004\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 10438.3066 - mae: 10438.8066\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 20509.0703 - mae: 20509.5684\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 21694.6445 - mae: 21695.1465\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 22940.7578 - mae: 22941.2578\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 18457.5645 - mae: 18458.0645\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 29397.7324 - mae: 29398.2324\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 32965.9766 - mae: 32966.4766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJTlFAXF8OIy"
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-7, 1e-4, 0, 30])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1e-07, 0.0001, 0.0, 30.0)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"252.317344pt\" version=\"1.1\" viewBox=\"0 0 380.675 252.317344\" width=\"380.675pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-06-17T12:40:28.076666</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M -0 252.317344 \nL 380.675 252.317344 \nL 380.675 0 \nL -0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 26.925 228.439219 \nL 361.725 228.439219 \nL 361.725 10.999219 \nL 26.925 10.999219 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_1\">\n      <defs>\n       <path d=\"M 0 0 \nL 0 3.5 \n\" id=\"me2e5a961fc\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#me2e5a961fc\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_1\">\n      <!-- $\\mathdefault{10^{-7}}$ -->\n      <g transform=\"translate(15.175 243.037656)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n        <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n        <path d=\"M 10.59375 35.5 \nL 73.1875 35.5 \nL 73.1875 27.203125 \nL 10.59375 27.203125 \nz\n\" id=\"DejaVuSans-8722\"/>\n        <path d=\"M 8.203125 72.90625 \nL 55.078125 72.90625 \nL 55.078125 68.703125 \nL 28.609375 0 \nL 18.3125 0 \nL 43.21875 64.59375 \nL 8.203125 64.59375 \nz\n\" id=\"DejaVuSans-55\"/>\n       </defs>\n       <use transform=\"translate(0 0.684375)\" xlink:href=\"#DejaVuSans-49\"/>\n       <use transform=\"translate(63.623047 0.684375)\" xlink:href=\"#DejaVuSans-48\"/>\n       <use transform=\"translate(128.203125 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-8722\"/>\n       <use transform=\"translate(186.855469 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_2\">\n     <g id=\"line2d_2\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"138.525\" xlink:href=\"#me2e5a961fc\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_2\">\n      <!-- $\\mathdefault{10^{-6}}$ -->\n      <g transform=\"translate(126.775 243.037656)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n       </defs>\n       <use transform=\"translate(0 0.765625)\" xlink:href=\"#DejaVuSans-49\"/>\n       <use transform=\"translate(63.623047 0.765625)\" xlink:href=\"#DejaVuSans-48\"/>\n       <use transform=\"translate(128.203125 39.046875)scale(0.7)\" xlink:href=\"#DejaVuSans-8722\"/>\n       <use transform=\"translate(186.855469 39.046875)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_3\">\n     <g id=\"line2d_3\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"250.125\" xlink:href=\"#me2e5a961fc\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_3\">\n      <!-- $\\mathdefault{10^{-5}}$ -->\n      <g transform=\"translate(238.375 243.037656)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 10.796875 72.90625 \nL 49.515625 72.90625 \nL 49.515625 64.59375 \nL 19.828125 64.59375 \nL 19.828125 46.734375 \nQ 21.96875 47.46875 24.109375 47.828125 \nQ 26.265625 48.1875 28.421875 48.1875 \nQ 40.625 48.1875 47.75 41.5 \nQ 54.890625 34.8125 54.890625 23.390625 \nQ 54.890625 11.625 47.5625 5.09375 \nQ 40.234375 -1.421875 26.90625 -1.421875 \nQ 22.3125 -1.421875 17.546875 -0.640625 \nQ 12.796875 0.140625 7.71875 1.703125 \nL 7.71875 11.625 \nQ 12.109375 9.234375 16.796875 8.0625 \nQ 21.484375 6.890625 26.703125 6.890625 \nQ 35.15625 6.890625 40.078125 11.328125 \nQ 45.015625 15.765625 45.015625 23.390625 \nQ 45.015625 31 40.078125 35.4375 \nQ 35.15625 39.890625 26.703125 39.890625 \nQ 22.75 39.890625 18.8125 39.015625 \nQ 14.890625 38.140625 10.796875 36.28125 \nz\n\" id=\"DejaVuSans-53\"/>\n       </defs>\n       <use transform=\"translate(0 0.684375)\" xlink:href=\"#DejaVuSans-49\"/>\n       <use transform=\"translate(63.623047 0.684375)\" xlink:href=\"#DejaVuSans-48\"/>\n       <use transform=\"translate(128.203125 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-8722\"/>\n       <use transform=\"translate(186.855469 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_4\">\n     <g id=\"line2d_4\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"361.725\" xlink:href=\"#me2e5a961fc\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_4\">\n      <!-- $\\mathdefault{10^{-4}}$ -->\n      <g transform=\"translate(349.975 243.037656)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n       </defs>\n       <use transform=\"translate(0 0.684375)\" xlink:href=\"#DejaVuSans-49\"/>\n       <use transform=\"translate(63.623047 0.684375)\" xlink:href=\"#DejaVuSans-48\"/>\n       <use transform=\"translate(128.203125 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-8722\"/>\n       <use transform=\"translate(186.855469 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_5\">\n     <g id=\"line2d_5\">\n      <defs>\n       <path d=\"M 0 0 \nL 0 2 \n\" id=\"m72723e5dc8\" style=\"stroke:#000000;stroke-width:0.6;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"60.519948\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_6\">\n     <g id=\"line2d_6\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"80.171732\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_7\">\n     <g id=\"line2d_7\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"94.114895\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_8\">\n     <g id=\"line2d_8\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"104.930052\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_9\">\n     <g id=\"line2d_9\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"113.76668\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_10\">\n     <g id=\"line2d_10\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"121.237941\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_11\">\n     <g id=\"line2d_11\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"127.709843\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_12\">\n     <g id=\"line2d_12\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"133.418464\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_13\">\n     <g id=\"line2d_13\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"172.119948\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_14\">\n     <g id=\"line2d_14\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"191.771732\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_15\">\n     <g id=\"line2d_15\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"205.714895\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_16\">\n     <g id=\"line2d_16\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"216.530052\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_17\">\n     <g id=\"line2d_17\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"225.36668\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_18\">\n     <g id=\"line2d_18\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"232.837941\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_19\">\n     <g id=\"line2d_19\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"239.309843\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_20\">\n     <g id=\"line2d_20\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"245.018464\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_21\">\n     <g id=\"line2d_21\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"283.719948\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_22\">\n     <g id=\"line2d_22\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"303.371732\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_23\">\n     <g id=\"line2d_23\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"317.314895\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_24\">\n     <g id=\"line2d_24\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"328.130052\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_25\">\n     <g id=\"line2d_25\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"336.96668\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_26\">\n     <g id=\"line2d_26\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"344.437941\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_27\">\n     <g id=\"line2d_27\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"350.909843\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_28\">\n     <g id=\"line2d_28\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"356.618464\" xlink:href=\"#m72723e5dc8\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"matplotlib.axis_2\">\n    <g id=\"ytick_1\">\n     <g id=\"line2d_29\">\n      <defs>\n       <path d=\"M 0 0 \nL -3.5 0 \n\" id=\"m78934af565\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m78934af565\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_5\">\n      <!-- 0 -->\n      <g transform=\"translate(13.5625 232.238437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_2\">\n     <g id=\"line2d_30\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m78934af565\" y=\"192.199219\"/>\n      </g>\n     </g>\n     <g id=\"text_6\">\n      <!-- 5 -->\n      <g transform=\"translate(13.5625 195.998437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_3\">\n     <g id=\"line2d_31\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m78934af565\" y=\"155.959219\"/>\n      </g>\n     </g>\n     <g id=\"text_7\">\n      <!-- 10 -->\n      <g transform=\"translate(7.2 159.758437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_4\">\n     <g id=\"line2d_32\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m78934af565\" y=\"119.719219\"/>\n      </g>\n     </g>\n     <g id=\"text_8\">\n      <!-- 15 -->\n      <g transform=\"translate(7.2 123.518437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_5\">\n     <g id=\"line2d_33\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m78934af565\" y=\"83.479219\"/>\n      </g>\n     </g>\n     <g id=\"text_9\">\n      <!-- 20 -->\n      <g transform=\"translate(7.2 87.278437)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_6\">\n     <g id=\"line2d_34\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m78934af565\" y=\"47.239219\"/>\n      </g>\n     </g>\n     <g id=\"text_10\">\n      <!-- 25 -->\n      <g transform=\"translate(7.2 51.038437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_7\">\n     <g id=\"line2d_35\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m78934af565\" y=\"10.999219\"/>\n      </g>\n     </g>\n     <g id=\"text_11\">\n      <!-- 30 -->\n      <g transform=\"translate(7.2 14.798437)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 40.578125 39.3125 \nQ 47.65625 37.796875 51.625 33 \nQ 55.609375 28.21875 55.609375 21.1875 \nQ 55.609375 10.40625 48.1875 4.484375 \nQ 40.765625 -1.421875 27.09375 -1.421875 \nQ 22.515625 -1.421875 17.65625 -0.515625 \nQ 12.796875 0.390625 7.625 2.203125 \nL 7.625 11.71875 \nQ 11.71875 9.328125 16.59375 8.109375 \nQ 21.484375 6.890625 26.8125 6.890625 \nQ 36.078125 6.890625 40.9375 10.546875 \nQ 45.796875 14.203125 45.796875 21.1875 \nQ 45.796875 27.640625 41.28125 31.265625 \nQ 36.765625 34.90625 28.71875 34.90625 \nL 20.21875 34.90625 \nL 20.21875 43.015625 \nL 29.109375 43.015625 \nQ 36.375 43.015625 40.234375 45.921875 \nQ 44.09375 48.828125 44.09375 54.296875 \nQ 44.09375 59.90625 40.109375 62.90625 \nQ 36.140625 65.921875 28.71875 65.921875 \nQ 24.65625 65.921875 20.015625 65.03125 \nQ 15.375 64.15625 9.8125 62.3125 \nL 9.8125 71.09375 \nQ 15.4375 72.65625 20.34375 73.4375 \nQ 25.25 74.21875 29.59375 74.21875 \nQ 40.828125 74.21875 47.359375 69.109375 \nQ 53.90625 64.015625 53.90625 55.328125 \nQ 53.90625 49.265625 50.4375 45.09375 \nQ 46.96875 40.921875 40.578125 39.3125 \nz\n\" id=\"DejaVuSans-51\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-51\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"line2d_36\">\n    <path clip-path=\"url(#pab5f7fb08a)\" d=\"M 30.730606 -1 \nL 32.505 61.728362 \nL 38.085 89.223477 \nL 43.664998 105.439019 \nL 49.244998 116.752088 \nL 54.825 121.526131 \nL 60.404999 124.750777 \nL 65.985002 126.521573 \nL 71.565002 128.552822 \nL 77.144998 130.126966 \nL 82.724998 131.247577 \nL 88.304999 134.085684 \nL 93.885001 135.112869 \nL 99.465001 138.611896 \nL 105.045 140.928357 \nL 110.624999 142.559195 \nL 116.205 145.479509 \nL 121.785 147.302492 \nL 127.365 149.027972 \nL 132.944999 151.254789 \nL 138.525 153.648785 \nL 144.105001 153.120994 \nL 149.685 156.863878 \nL 155.265001 155.169807 \nL 160.845 156.779445 \nL 166.425 144.880985 \nL 172.004997 153.713386 \nL 177.585002 161.412472 \nL 183.164998 153.519726 \nL 188.744999 142.428222 \nL 194.324999 153.607443 \nL 199.905 144.43299 \nL 205.484999 138.226864 \nL 211.065 136.565903 \nL 216.645001 128.508321 \nL 222.225002 130.113611 \nL 227.805001 126.59617 \nL 233.384999 129.048035 \nL 238.964998 129.748701 \nL 244.545001 128.431188 \nL 250.124999 124.8203 \nL 255.704999 115.084989 \nL 261.284999 121.633112 \nL 266.865001 110.662274 \nL 272.444997 101.602606 \nL 278.024998 98.57303 \nL 282.100448 -1 \n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"M 26.925 228.439219 \nL 26.925 10.999219 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"M 361.725 228.439219 \nL 361.725 10.999219 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"M 26.925 228.439219 \nL 361.725 228.439219 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"M 26.925 10.999219 \nL 361.725 10.999219 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"pab5f7fb08a\">\n   <rect height=\"217.44\" width=\"334.8\" x=\"26.925\" y=\"10.999219\"/>\n  </clipPath>\n </defs>\n</svg>\n",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3yNjxWE8OIz",
        "tags": [
          "outputPrepend"
        ]
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = window_dataset(x_train, window_size, batch_size=128)\n",
        "valid_set = window_dataset(x_valid, window_size, batch_size=128)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n",
        "                      input_shape=[None]),\n",
        "  keras.layers.SimpleRNN(100, return_sequences=True),\n",
        "  keras.layers.SimpleRNN(100),\n",
        "  keras.layers.Dense(1),\n",
        "  keras.layers.Lambda(lambda x: x * 200.0)\n",
        "])\n",
        "optimizer = keras.optimizers.SGD(lr=1.5e-6, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "early_stopping = keras.callbacks.EarlyStopping(patience=50)\n",
        "model_checkpoint = keras.callbacks.ModelCheckpoint(\n",
        "    \"my_checkpoint\", save_best_only=True)\n",
        "model.fit(train_set, epochs=500,\n",
        "          validation_data=valid_set,\n",
        "          callbacks=[early_stopping, model_checkpoint])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 9.2316 - val_loss: 6.1083 - val_mae: 6.5866\n",
            "Epoch 13/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 8.3674 - mae: 8.8467 - val_loss: 6.2306 - val_mae: 6.7114\n",
            "Epoch 14/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 8.3383 - mae: 8.8220 - val_loss: 5.7182 - val_mae: 6.2003\n",
            "Epoch 15/500\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 8.4830 - mae: 8.9645 - val_loss: 6.1668 - val_mae: 6.6477\n",
            "Epoch 16/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 7.8410 - mae: 8.3221 - val_loss: 7.1294 - val_mae: 7.6134\n",
            "Epoch 17/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.9632 - mae: 8.4469 - val_loss: 7.2040 - val_mae: 7.6893\n",
            "Epoch 18/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.4941 - mae: 7.9735 - val_loss: 6.2208 - val_mae: 6.7036\n",
            "Epoch 19/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 7.4794 - mae: 7.9629 - val_loss: 5.8516 - val_mae: 6.3349\n",
            "Epoch 20/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.2516 - mae: 7.7336 - val_loss: 5.8175 - val_mae: 6.2993\n",
            "Epoch 21/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 7.2804 - mae: 7.7615 - val_loss: 5.6090 - val_mae: 6.0905\n",
            "Epoch 22/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.1217 - mae: 7.5992 - val_loss: 5.2088 - val_mae: 5.6918\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 23/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 7.3137 - mae: 7.7910 - val_loss: 5.3233 - val_mae: 5.8034\n",
            "Epoch 24/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 7.2680 - mae: 7.7480 - val_loss: 5.9505 - val_mae: 6.4389\n",
            "Epoch 25/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 7.6200 - mae: 8.1031 - val_loss: 6.6005 - val_mae: 7.0809\n",
            "Epoch 26/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.8836 - mae: 7.3656 - val_loss: 6.4162 - val_mae: 6.8926\n",
            "Epoch 27/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.5925 - mae: 7.0718 - val_loss: 5.8378 - val_mae: 6.3248\n",
            "Epoch 28/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.0312 - mae: 7.5140 - val_loss: 5.6763 - val_mae: 6.1614\n",
            "Epoch 29/500\n",
            "8/8 [==============================] - 1s 42ms/step - loss: 6.6603 - mae: 7.1396 - val_loss: 5.6038 - val_mae: 6.0875\n",
            "Epoch 30/500\n",
            "8/8 [==============================] - 1s 50ms/step - loss: 7.0819 - mae: 7.5631 - val_loss: 8.3007 - val_mae: 8.7922\n",
            "Epoch 31/500\n",
            "8/8 [==============================] - 1s 43ms/step - loss: 6.4866 - mae: 6.9621 - val_loss: 6.3720 - val_mae: 6.8495\n",
            "Epoch 32/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.1873 - mae: 6.6609 - val_loss: 5.2598 - val_mae: 5.7392\n",
            "Epoch 33/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.5050 - mae: 6.9888 - val_loss: 5.2490 - val_mae: 5.7277\n",
            "Epoch 34/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 6.4857 - mae: 6.9655 - val_loss: 5.4383 - val_mae: 5.9173\n",
            "Epoch 35/500\n",
            "8/8 [==============================] - 1s 44ms/step - loss: 6.0975 - mae: 6.5742 - val_loss: 5.3990 - val_mae: 5.8759\n",
            "Epoch 36/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 6.0247 - mae: 6.5033 - val_loss: 5.3508 - val_mae: 5.8273\n",
            "Epoch 37/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 5.9693 - mae: 6.4466 - val_loss: 5.0254 - val_mae: 5.5085\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 38/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 6.2703 - mae: 6.7460 - val_loss: 4.9862 - val_mae: 5.4679\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 39/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.3089 - mae: 6.7863 - val_loss: 5.0002 - val_mae: 5.4845\n",
            "Epoch 40/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 6.6673 - mae: 7.1477 - val_loss: 5.5825 - val_mae: 6.0636\n",
            "Epoch 41/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 6.3527 - mae: 6.8321 - val_loss: 6.4424 - val_mae: 6.9252\n",
            "Epoch 42/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 5.9507 - mae: 6.4282 - val_loss: 5.0356 - val_mae: 5.5097\n",
            "Epoch 43/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.1178 - mae: 6.5932 - val_loss: 5.0035 - val_mae: 5.4783\n",
            "Epoch 44/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 6.0757 - mae: 6.5564 - val_loss: 4.9765 - val_mae: 5.4604\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 45/500\n",
            "8/8 [==============================] - 1s 56ms/step - loss: 6.1126 - mae: 6.5907 - val_loss: 4.9839 - val_mae: 5.4654\n",
            "Epoch 46/500\n",
            "8/8 [==============================] - 0s 48ms/step - loss: 5.9623 - mae: 6.4384 - val_loss: 5.1971 - val_mae: 5.6715\n",
            "Epoch 47/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 7.1637 - mae: 7.6499 - val_loss: 6.7116 - val_mae: 7.1970\n",
            "Epoch 48/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.4838 - mae: 7.9710 - val_loss: 10.5735 - val_mae: 11.0683\n",
            "Epoch 49/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 6.8848 - mae: 7.3676 - val_loss: 6.1742 - val_mae: 6.6535\n",
            "Epoch 50/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 6.6975 - mae: 7.1795 - val_loss: 5.0918 - val_mae: 5.5723\n",
            "Epoch 51/500\n",
            "8/8 [==============================] - 0s 55ms/step - loss: 7.0777 - mae: 7.5635 - val_loss: 5.2914 - val_mae: 5.7696\n",
            "Epoch 52/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 6.4533 - mae: 6.9356 - val_loss: 5.8080 - val_mae: 6.2870\n",
            "Epoch 53/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 6.2474 - mae: 6.7314 - val_loss: 9.4445 - val_mae: 9.9404\n",
            "Epoch 54/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.6254 - mae: 7.1117 - val_loss: 5.1423 - val_mae: 5.6190\n",
            "Epoch 55/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.7724 - mae: 7.2524 - val_loss: 5.7710 - val_mae: 6.2558\n",
            "Epoch 56/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 6.9317 - mae: 7.4196 - val_loss: 5.7375 - val_mae: 6.2175\n",
            "Epoch 57/500\n",
            "8/8 [==============================] - 0s 52ms/step - loss: 5.8178 - mae: 6.2985 - val_loss: 5.1172 - val_mae: 5.5917\n",
            "Epoch 58/500\n",
            "8/8 [==============================] - 1s 58ms/step - loss: 6.0435 - mae: 6.5191 - val_loss: 7.4312 - val_mae: 7.9162\n",
            "Epoch 59/500\n",
            "8/8 [==============================] - 0s 52ms/step - loss: 5.7947 - mae: 6.2725 - val_loss: 6.6451 - val_mae: 7.1294\n",
            "Epoch 60/500\n",
            "8/8 [==============================] - 0s 53ms/step - loss: 5.6307 - mae: 6.1075 - val_loss: 5.5807 - val_mae: 6.0649\n",
            "Epoch 61/500\n",
            "8/8 [==============================] - 0s 49ms/step - loss: 5.4900 - mae: 5.9733 - val_loss: 5.1648 - val_mae: 5.6429\n",
            "Epoch 62/500\n",
            "8/8 [==============================] - 0s 55ms/step - loss: 5.7959 - mae: 6.2788 - val_loss: 6.7675 - val_mae: 7.2550\n",
            "Epoch 63/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 5.8492 - mae: 6.3293 - val_loss: 5.5543 - val_mae: 6.0376\n",
            "Epoch 64/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.0058 - mae: 6.4879 - val_loss: 5.3563 - val_mae: 5.8395\n",
            "Epoch 65/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.2938 - mae: 6.7772 - val_loss: 5.0566 - val_mae: 5.5311\n",
            "Epoch 66/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 5.7496 - mae: 6.2315 - val_loss: 6.1685 - val_mae: 6.6490\n",
            "Epoch 67/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 5.4430 - mae: 5.9245 - val_loss: 4.9655 - val_mae: 5.4472\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 68/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 5.5585 - mae: 6.0357 - val_loss: 5.0738 - val_mae: 5.5519\n",
            "Epoch 69/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 5.8952 - mae: 6.3778 - val_loss: 5.3735 - val_mae: 5.8562\n",
            "Epoch 70/500\n",
            "8/8 [==============================] - 0s 52ms/step - loss: 6.3723 - mae: 6.8538 - val_loss: 8.4082 - val_mae: 8.8989\n",
            "Epoch 71/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 5.9511 - mae: 6.4321 - val_loss: 5.0425 - val_mae: 5.5215\n",
            "Epoch 72/500\n",
            "8/8 [==============================] - 0s 49ms/step - loss: 5.4763 - mae: 5.9503 - val_loss: 5.0605 - val_mae: 5.5426\n",
            "Epoch 73/500\n",
            "8/8 [==============================] - 1s 54ms/step - loss: 5.8305 - mae: 6.3057 - val_loss: 5.1049 - val_mae: 5.5825\n",
            "Epoch 74/500\n",
            "8/8 [==============================] - 1s 55ms/step - loss: 5.6705 - mae: 6.1504 - val_loss: 7.8165 - val_mae: 8.3056\n",
            "Epoch 75/500\n",
            "8/8 [==============================] - 1s 55ms/step - loss: 5.9062 - mae: 6.3867 - val_loss: 6.9280 - val_mae: 7.4136\n",
            "Epoch 76/500\n",
            "8/8 [==============================] - 1s 56ms/step - loss: 5.5544 - mae: 6.0323 - val_loss: 5.3406 - val_mae: 5.8188\n",
            "Epoch 77/500\n",
            "8/8 [==============================] - 1s 57ms/step - loss: 5.4786 - mae: 5.9570 - val_loss: 7.4991 - val_mae: 7.9860\n",
            "Epoch 78/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 5.9442 - mae: 6.4282 - val_loss: 5.0672 - val_mae: 5.5435\n",
            "Epoch 79/500\n",
            "8/8 [==============================] - 0s 52ms/step - loss: 6.6318 - mae: 7.1173 - val_loss: 5.0748 - val_mae: 5.5574\n",
            "Epoch 80/500\n",
            "8/8 [==============================] - 1s 53ms/step - loss: 5.7917 - mae: 6.2715 - val_loss: 7.9717 - val_mae: 8.4605\n",
            "Epoch 81/500\n",
            "8/8 [==============================] - 0s 49ms/step - loss: 5.4488 - mae: 5.9271 - val_loss: 6.9996 - val_mae: 7.4858\n",
            "Epoch 82/500\n",
            "8/8 [==============================] - 0s 49ms/step - loss: 5.3072 - mae: 5.7875 - val_loss: 6.3978 - val_mae: 6.8798\n",
            "Epoch 83/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 5.2079 - mae: 5.6898 - val_loss: 5.0611 - val_mae: 5.5427\n",
            "Epoch 84/500\n",
            "8/8 [==============================] - 0s 49ms/step - loss: 5.1165 - mae: 5.5955 - val_loss: 5.0673 - val_mae: 5.5487\n",
            "Epoch 85/500\n",
            "8/8 [==============================] - 0s 49ms/step - loss: 5.1233 - mae: 5.5969 - val_loss: 5.1983 - val_mae: 5.6755\n",
            "Epoch 86/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 5.1349 - mae: 5.6123 - val_loss: 4.9879 - val_mae: 5.4746\n",
            "Epoch 87/500\n",
            "8/8 [==============================] - 1s 45ms/step - loss: 5.0676 - mae: 5.5474 - val_loss: 4.9456 - val_mae: 5.4310\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 88/500\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 5.6988 - mae: 6.1808 - val_loss: 4.9810 - val_mae: 5.4567\n",
            "Epoch 89/500\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 5.6044 - mae: 6.0865 - val_loss: 5.5546 - val_mae: 6.0348\n",
            "Epoch 90/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 5.0368 - mae: 5.5087 - val_loss: 5.2880 - val_mae: 5.7700\n",
            "Epoch 91/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.9507 - mae: 5.4261 - val_loss: 6.5972 - val_mae: 7.0798\n",
            "Epoch 92/500\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 5.0542 - mae: 5.5284 - val_loss: 5.5571 - val_mae: 6.0364\n",
            "Epoch 93/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 4.9388 - mae: 5.4120 - val_loss: 5.1257 - val_mae: 5.6040\n",
            "Epoch 94/500\n",
            "8/8 [==============================] - 1s 46ms/step - loss: 5.1850 - mae: 5.6616 - val_loss: 8.8054 - val_mae: 9.2980\n",
            "Epoch 95/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 5.6227 - mae: 6.0971 - val_loss: 6.0098 - val_mae: 6.4888\n",
            "Epoch 96/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.9709 - mae: 5.4468 - val_loss: 5.8050 - val_mae: 6.2818\n",
            "Epoch 97/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 5.0279 - mae: 5.5052 - val_loss: 4.9141 - val_mae: 5.3892\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 98/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.9666 - mae: 5.4429 - val_loss: 4.9100 - val_mae: 5.3930\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 99/500\n",
            "8/8 [==============================] - 0s 51ms/step - loss: 4.9774 - mae: 5.4514 - val_loss: 4.9773 - val_mae: 5.4530\n",
            "Epoch 100/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 5.0688 - mae: 5.5446 - val_loss: 4.9497 - val_mae: 5.4250\n",
            "Epoch 101/500\n",
            "8/8 [==============================] - 0s 51ms/step - loss: 4.9040 - mae: 5.3803 - val_loss: 5.6018 - val_mae: 6.0794\n",
            "Epoch 102/500\n",
            "8/8 [==============================] - 0s 51ms/step - loss: 5.0183 - mae: 5.4926 - val_loss: 6.4838 - val_mae: 6.9686\n",
            "Epoch 103/500\n",
            "8/8 [==============================] - 0s 51ms/step - loss: 5.1880 - mae: 5.6656 - val_loss: 5.9399 - val_mae: 6.4176\n",
            "Epoch 104/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 4.8851 - mae: 5.3596 - val_loss: 5.7728 - val_mae: 6.2629\n",
            "Epoch 105/500\n",
            "8/8 [==============================] - 0s 51ms/step - loss: 5.3819 - mae: 5.8553 - val_loss: 4.9261 - val_mae: 5.4131\n",
            "Epoch 106/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.8987 - mae: 5.3722 - val_loss: 4.9417 - val_mae: 5.4202\n",
            "Epoch 107/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 4.7787 - mae: 5.2532 - val_loss: 6.7641 - val_mae: 7.2507\n",
            "Epoch 108/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 4.9333 - mae: 5.4066 - val_loss: 5.0794 - val_mae: 5.5598\n",
            "Epoch 109/500\n",
            "8/8 [==============================] - 1s 44ms/step - loss: 4.8609 - mae: 5.3372 - val_loss: 5.9341 - val_mae: 6.4115\n",
            "Epoch 110/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 4.8677 - mae: 5.3451 - val_loss: 4.9018 - val_mae: 5.3773\n",
            "INFO:tensorflow:Assets written to: my_checkpoint/assets\n",
            "Epoch 111/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 4.9848 - mae: 5.4589 - val_loss: 5.3269 - val_mae: 5.8103\n",
            "Epoch 112/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 5.0177 - mae: 5.4949 - val_loss: 5.1845 - val_mae: 5.6697\n",
            "Epoch 113/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.7052 - mae: 5.1779 - val_loss: 5.0071 - val_mae: 5.4804\n",
            "Epoch 114/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.9924 - mae: 5.4697 - val_loss: 5.4819 - val_mae: 5.9591\n",
            "Epoch 115/500\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 5.2149 - mae: 5.6952 - val_loss: 8.4176 - val_mae: 8.9079\n",
            "Epoch 116/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 5.5694 - mae: 6.0533 - val_loss: 4.9970 - val_mae: 5.4694\n",
            "Epoch 117/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 5.2112 - mae: 5.6912 - val_loss: 5.8994 - val_mae: 6.3906\n",
            "Epoch 118/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 5.2597 - mae: 5.7376 - val_loss: 5.9524 - val_mae: 6.4276\n",
            "Epoch 119/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 5.3134 - mae: 5.7963 - val_loss: 6.3334 - val_mae: 6.8156\n",
            "Epoch 120/500\n",
            "8/8 [==============================] - 1s 47ms/step - loss: 5.1218 - mae: 5.6007 - val_loss: 4.9825 - val_mae: 5.4659\n",
            "Epoch 121/500\n",
            "8/8 [==============================] - 0s 54ms/step - loss: 4.9230 - mae: 5.4008 - val_loss: 5.5421 - val_mae: 6.0270\n",
            "Epoch 122/500\n",
            "8/8 [==============================] - 1s 54ms/step - loss: 5.5261 - mae: 6.0098 - val_loss: 5.3486 - val_mae: 5.8317\n",
            "Epoch 123/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 5.0603 - mae: 5.5404 - val_loss: 5.4108 - val_mae: 5.8932\n",
            "Epoch 124/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 4.8919 - mae: 5.3721 - val_loss: 4.9513 - val_mae: 5.4383\n",
            "Epoch 125/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 4.8796 - mae: 5.3609 - val_loss: 5.1420 - val_mae: 5.6219\n",
            "Epoch 126/500\n",
            "8/8 [==============================] - 1s 59ms/step - loss: 4.6771 - mae: 5.1532 - val_loss: 5.1613 - val_mae: 5.6438\n",
            "Epoch 127/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.7393 - mae: 5.2200 - val_loss: 4.9283 - val_mae: 5.4133\n",
            "Epoch 128/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 4.7002 - mae: 5.1783 - val_loss: 5.3663 - val_mae: 5.8474\n",
            "Epoch 129/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.8500 - mae: 5.3240 - val_loss: 6.5757 - val_mae: 7.0580\n",
            "Epoch 130/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 4.6848 - mae: 5.1605 - val_loss: 4.9521 - val_mae: 5.4268\n",
            "Epoch 131/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 5.2800 - mae: 5.7530 - val_loss: 5.7696 - val_mae: 6.2572\n",
            "Epoch 132/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 5.6002 - mae: 6.0814 - val_loss: 5.9902 - val_mae: 6.4686\n",
            "Epoch 133/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.7737 - mae: 5.2488 - val_loss: 6.4223 - val_mae: 6.9041\n",
            "Epoch 134/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.8784 - mae: 5.3525 - val_loss: 5.4584 - val_mae: 5.9371\n",
            "Epoch 135/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 4.6794 - mae: 5.1575 - val_loss: 5.6242 - val_mae: 6.1015\n",
            "Epoch 136/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.8044 - mae: 5.2784 - val_loss: 4.9364 - val_mae: 5.4245\n",
            "Epoch 137/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.8204 - mae: 5.2968 - val_loss: 5.0082 - val_mae: 5.4846\n",
            "Epoch 138/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 4.7712 - mae: 5.2487 - val_loss: 5.0347 - val_mae: 5.5087\n",
            "Epoch 139/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.5266 - mae: 4.9986 - val_loss: 4.9584 - val_mae: 5.4356\n",
            "Epoch 140/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 5.1097 - mae: 5.5900 - val_loss: 4.9829 - val_mae: 5.4644\n",
            "Epoch 141/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.7073 - mae: 5.1828 - val_loss: 5.3065 - val_mae: 5.7885\n",
            "Epoch 142/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.4624 - mae: 4.9319 - val_loss: 6.4504 - val_mae: 6.9324\n",
            "Epoch 143/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 4.6605 - mae: 5.1331 - val_loss: 6.7925 - val_mae: 7.2776\n",
            "Epoch 144/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 5.0673 - mae: 5.5472 - val_loss: 4.9931 - val_mae: 5.4676\n",
            "Epoch 145/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 4.5280 - mae: 5.0033 - val_loss: 5.0017 - val_mae: 5.4770\n",
            "Epoch 146/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.5581 - mae: 5.0336 - val_loss: 5.0172 - val_mae: 5.4885\n",
            "Epoch 147/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.4518 - mae: 4.9248 - val_loss: 6.6480 - val_mae: 7.1297\n",
            "Epoch 148/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 4.7889 - mae: 5.2636 - val_loss: 5.6387 - val_mae: 6.1176\n",
            "Epoch 149/500\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 4.5493 - mae: 5.0241 - val_loss: 4.9822 - val_mae: 5.4559\n",
            "Epoch 150/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.7236 - mae: 5.2000 - val_loss: 6.0009 - val_mae: 6.4782\n",
            "Epoch 151/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 5.1126 - mae: 5.5922 - val_loss: 7.1998 - val_mae: 7.6906\n",
            "Epoch 152/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 5.5811 - mae: 6.0627 - val_loss: 7.2075 - val_mae: 7.6981\n",
            "Epoch 153/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 5.1775 - mae: 5.6604 - val_loss: 5.4814 - val_mae: 5.9606\n",
            "Epoch 154/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.6057 - mae: 5.0829 - val_loss: 5.0336 - val_mae: 5.5064\n",
            "Epoch 155/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.7348 - mae: 5.2129 - val_loss: 5.1809 - val_mae: 5.6616\n",
            "Epoch 156/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 4.7502 - mae: 5.2277 - val_loss: 6.2518 - val_mae: 6.7316\n",
            "Epoch 157/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 4.7090 - mae: 5.1840 - val_loss: 5.7431 - val_mae: 6.2192\n",
            "Epoch 158/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 4.4960 - mae: 4.9674 - val_loss: 5.5493 - val_mae: 6.0291\n",
            "Epoch 159/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 4.5836 - mae: 5.0614 - val_loss: 5.9668 - val_mae: 6.4452\n",
            "Epoch 160/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 4.6556 - mae: 5.1280 - val_loss: 5.8042 - val_mae: 6.2809\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb56cd96b80>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KuPtKFe8OI0"
      },
      "source": [
        "model = keras.models.load_model(\"my_checkpoint\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxq09qOg8OI1"
      },
      "source": [
        "rnn_forecast = model_forecast(\n",
        "    model,\n",
        "    series[split_time - window_size:-1],\n",
        "    window_size)[:, 0]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkC_JssS8OI2"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, rnn_forecast)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"370.91625pt\" version=\"1.1\" viewBox=\"0 0 612.165625 370.91625\" width=\"612.165625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-06-17T12:45:48.256517</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 370.91625 \nL 612.165625 370.91625 \nL 612.165625 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 46.965625 333.36 \nL 604.965625 333.36 \nL 604.965625 7.2 \nL 46.965625 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_1\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 72.329261 333.36 \nL 72.329261 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_2\">\n      <defs>\n       <path d=\"M 0 0 \nL 0 3.5 \n\" id=\"m183c31542c\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"72.329261\" xlink:href=\"#m183c31542c\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_1\">\n      <!-- 1000 -->\n      <g transform=\"translate(59.604261 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n        <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_2\">\n     <g id=\"line2d_3\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 182.605941 333.36 \nL 182.605941 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_4\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"182.605941\" xlink:href=\"#m183c31542c\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_2\">\n      <!-- 1100 -->\n      <g transform=\"translate(169.880941 347.958438)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_3\">\n     <g id=\"line2d_5\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 292.882621 333.36 \nL 292.882621 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_6\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"292.882621\" xlink:href=\"#m183c31542c\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_3\">\n      <!-- 1200 -->\n      <g transform=\"translate(280.157621 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_4\">\n     <g id=\"line2d_7\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 403.159301 333.36 \nL 403.159301 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_8\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"403.159301\" xlink:href=\"#m183c31542c\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_4\">\n      <!-- 1300 -->\n      <g transform=\"translate(390.434301 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 40.578125 39.3125 \nQ 47.65625 37.796875 51.625 33 \nQ 55.609375 28.21875 55.609375 21.1875 \nQ 55.609375 10.40625 48.1875 4.484375 \nQ 40.765625 -1.421875 27.09375 -1.421875 \nQ 22.515625 -1.421875 17.65625 -0.515625 \nQ 12.796875 0.390625 7.625 2.203125 \nL 7.625 11.71875 \nQ 11.71875 9.328125 16.59375 8.109375 \nQ 21.484375 6.890625 26.8125 6.890625 \nQ 36.078125 6.890625 40.9375 10.546875 \nQ 45.796875 14.203125 45.796875 21.1875 \nQ 45.796875 27.640625 41.28125 31.265625 \nQ 36.765625 34.90625 28.71875 34.90625 \nL 20.21875 34.90625 \nL 20.21875 43.015625 \nL 29.109375 43.015625 \nQ 36.375 43.015625 40.234375 45.921875 \nQ 44.09375 48.828125 44.09375 54.296875 \nQ 44.09375 59.90625 40.109375 62.90625 \nQ 36.140625 65.921875 28.71875 65.921875 \nQ 24.65625 65.921875 20.015625 65.03125 \nQ 15.375 64.15625 9.8125 62.3125 \nL 9.8125 71.09375 \nQ 15.4375 72.65625 20.34375 73.4375 \nQ 25.25 74.21875 29.59375 74.21875 \nQ 40.828125 74.21875 47.359375 69.109375 \nQ 53.90625 64.015625 53.90625 55.328125 \nQ 53.90625 49.265625 50.4375 45.09375 \nQ 46.96875 40.921875 40.578125 39.3125 \nz\n\" id=\"DejaVuSans-51\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-51\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_5\">\n     <g id=\"line2d_9\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 513.435981 333.36 \nL 513.435981 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_10\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"513.435981\" xlink:href=\"#m183c31542c\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_5\">\n      <!-- 1400 -->\n      <g transform=\"translate(500.710981 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"text_6\">\n     <!-- Time -->\n     <g transform=\"translate(313.732031 361.636562)scale(0.1 -0.1)\">\n      <defs>\n       <path d=\"M -0.296875 72.90625 \nL 61.375 72.90625 \nL 61.375 64.59375 \nL 35.5 64.59375 \nL 35.5 0 \nL 25.59375 0 \nL 25.59375 64.59375 \nL -0.296875 64.59375 \nz\n\" id=\"DejaVuSans-84\"/>\n       <path d=\"M 9.421875 54.6875 \nL 18.40625 54.6875 \nL 18.40625 0 \nL 9.421875 0 \nz\nM 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 64.59375 \nL 9.421875 64.59375 \nz\n\" id=\"DejaVuSans-105\"/>\n       <path d=\"M 52 44.1875 \nQ 55.375 50.25 60.0625 53.125 \nQ 64.75 56 71.09375 56 \nQ 79.640625 56 84.28125 50.015625 \nQ 88.921875 44.046875 88.921875 33.015625 \nL 88.921875 0 \nL 79.890625 0 \nL 79.890625 32.71875 \nQ 79.890625 40.578125 77.09375 44.375 \nQ 74.3125 48.1875 68.609375 48.1875 \nQ 61.625 48.1875 57.5625 43.546875 \nQ 53.515625 38.921875 53.515625 30.90625 \nL 53.515625 0 \nL 44.484375 0 \nL 44.484375 32.71875 \nQ 44.484375 40.625 41.703125 44.40625 \nQ 38.921875 48.1875 33.109375 48.1875 \nQ 26.21875 48.1875 22.15625 43.53125 \nQ 18.109375 38.875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 21.1875 51.21875 25.484375 53.609375 \nQ 29.78125 56 35.6875 56 \nQ 41.65625 56 45.828125 52.96875 \nQ 50 49.953125 52 44.1875 \nz\n\" id=\"DejaVuSans-109\"/>\n       <path d=\"M 56.203125 29.59375 \nL 56.203125 25.203125 \nL 14.890625 25.203125 \nQ 15.484375 15.921875 20.484375 11.0625 \nQ 25.484375 6.203125 34.421875 6.203125 \nQ 39.59375 6.203125 44.453125 7.46875 \nQ 49.3125 8.734375 54.109375 11.28125 \nL 54.109375 2.78125 \nQ 49.265625 0.734375 44.1875 -0.34375 \nQ 39.109375 -1.421875 33.890625 -1.421875 \nQ 20.796875 -1.421875 13.15625 6.1875 \nQ 5.515625 13.8125 5.515625 26.8125 \nQ 5.515625 40.234375 12.765625 48.109375 \nQ 20.015625 56 32.328125 56 \nQ 43.359375 56 49.78125 48.890625 \nQ 56.203125 41.796875 56.203125 29.59375 \nz\nM 47.21875 32.234375 \nQ 47.125 39.59375 43.09375 43.984375 \nQ 39.0625 48.390625 32.421875 48.390625 \nQ 24.90625 48.390625 20.390625 44.140625 \nQ 15.875 39.890625 15.1875 32.171875 \nz\n\" id=\"DejaVuSans-101\"/>\n      </defs>\n      <use xlink:href=\"#DejaVuSans-84\"/>\n      <use x=\"57.958984\" xlink:href=\"#DejaVuSans-105\"/>\n      <use x=\"85.742188\" xlink:href=\"#DejaVuSans-109\"/>\n      <use x=\"183.154297\" xlink:href=\"#DejaVuSans-101\"/>\n     </g>\n    </g>\n   </g>\n   <g id=\"matplotlib.axis_2\">\n    <g id=\"ytick_1\">\n     <g id=\"line2d_11\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 46.965625 307.591542 \nL 604.965625 307.591542 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_12\">\n      <defs>\n       <path d=\"M 0 0 \nL -3.5 0 \n\" id=\"mf6ebbe7abc\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#mf6ebbe7abc\" y=\"307.591542\"/>\n      </g>\n     </g>\n     <g id=\"text_7\">\n      <!-- 40 -->\n      <g transform=\"translate(27.240625 311.39076)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_2\">\n     <g id=\"line2d_13\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 46.965625 238.201675 \nL 604.965625 238.201675 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_14\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#mf6ebbe7abc\" y=\"238.201675\"/>\n      </g>\n     </g>\n     <g id=\"text_8\">\n      <!-- 60 -->\n      <g transform=\"translate(27.240625 242.000894)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-54\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_3\">\n     <g id=\"line2d_15\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 46.965625 168.811808 \nL 604.965625 168.811808 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_16\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#mf6ebbe7abc\" y=\"168.811808\"/>\n      </g>\n     </g>\n     <g id=\"text_9\">\n      <!-- 80 -->\n      <g transform=\"translate(27.240625 172.611027)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 31.78125 34.625 \nQ 24.75 34.625 20.71875 30.859375 \nQ 16.703125 27.09375 16.703125 20.515625 \nQ 16.703125 13.921875 20.71875 10.15625 \nQ 24.75 6.390625 31.78125 6.390625 \nQ 38.8125 6.390625 42.859375 10.171875 \nQ 46.921875 13.96875 46.921875 20.515625 \nQ 46.921875 27.09375 42.890625 30.859375 \nQ 38.875 34.625 31.78125 34.625 \nz\nM 21.921875 38.8125 \nQ 15.578125 40.375 12.03125 44.71875 \nQ 8.5 49.078125 8.5 55.328125 \nQ 8.5 64.0625 14.71875 69.140625 \nQ 20.953125 74.21875 31.78125 74.21875 \nQ 42.671875 74.21875 48.875 69.140625 \nQ 55.078125 64.0625 55.078125 55.328125 \nQ 55.078125 49.078125 51.53125 44.71875 \nQ 48 40.375 41.703125 38.8125 \nQ 48.828125 37.15625 52.796875 32.3125 \nQ 56.78125 27.484375 56.78125 20.515625 \nQ 56.78125 9.90625 50.3125 4.234375 \nQ 43.84375 -1.421875 31.78125 -1.421875 \nQ 19.734375 -1.421875 13.25 4.234375 \nQ 6.78125 9.90625 6.78125 20.515625 \nQ 6.78125 27.484375 10.78125 32.3125 \nQ 14.796875 37.15625 21.921875 38.8125 \nz\nM 18.3125 54.390625 \nQ 18.3125 48.734375 21.84375 45.5625 \nQ 25.390625 42.390625 31.78125 42.390625 \nQ 38.140625 42.390625 41.71875 45.5625 \nQ 45.3125 48.734375 45.3125 54.390625 \nQ 45.3125 60.0625 41.71875 63.234375 \nQ 38.140625 66.40625 31.78125 66.40625 \nQ 25.390625 66.40625 21.84375 63.234375 \nQ 18.3125 60.0625 18.3125 54.390625 \nz\n\" id=\"DejaVuSans-56\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-56\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_4\">\n     <g id=\"line2d_17\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 46.965625 99.421941 \nL 604.965625 99.421941 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_18\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#mf6ebbe7abc\" y=\"99.421941\"/>\n      </g>\n     </g>\n     <g id=\"text_10\">\n      <!-- 100 -->\n      <g transform=\"translate(20.878125 103.22116)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_5\">\n     <g id=\"line2d_19\">\n      <path clip-path=\"url(#p66244f0a83)\" d=\"M 46.965625 30.032074 \nL 604.965625 30.032074 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_20\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#mf6ebbe7abc\" y=\"30.032074\"/>\n      </g>\n     </g>\n     <g id=\"text_11\">\n      <!-- 120 -->\n      <g transform=\"translate(20.878125 33.831293)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"text_12\">\n     <!-- Value -->\n     <g transform=\"translate(14.798438 184.01125)rotate(-90)scale(0.1 -0.1)\">\n      <defs>\n       <path d=\"M 28.609375 0 \nL 0.78125 72.90625 \nL 11.078125 72.90625 \nL 34.1875 11.53125 \nL 57.328125 72.90625 \nL 67.578125 72.90625 \nL 39.796875 0 \nz\n\" id=\"DejaVuSans-86\"/>\n       <path d=\"M 34.28125 27.484375 \nQ 23.390625 27.484375 19.1875 25 \nQ 14.984375 22.515625 14.984375 16.5 \nQ 14.984375 11.71875 18.140625 8.90625 \nQ 21.296875 6.109375 26.703125 6.109375 \nQ 34.1875 6.109375 38.703125 11.40625 \nQ 43.21875 16.703125 43.21875 25.484375 \nL 43.21875 27.484375 \nz\nM 52.203125 31.203125 \nL 52.203125 0 \nL 43.21875 0 \nL 43.21875 8.296875 \nQ 40.140625 3.328125 35.546875 0.953125 \nQ 30.953125 -1.421875 24.3125 -1.421875 \nQ 15.921875 -1.421875 10.953125 3.296875 \nQ 6 8.015625 6 15.921875 \nQ 6 25.140625 12.171875 29.828125 \nQ 18.359375 34.515625 30.609375 34.515625 \nL 43.21875 34.515625 \nL 43.21875 35.40625 \nQ 43.21875 41.609375 39.140625 45 \nQ 35.0625 48.390625 27.6875 48.390625 \nQ 23 48.390625 18.546875 47.265625 \nQ 14.109375 46.140625 10.015625 43.890625 \nL 10.015625 52.203125 \nQ 14.9375 54.109375 19.578125 55.046875 \nQ 24.21875 56 28.609375 56 \nQ 40.484375 56 46.34375 49.84375 \nQ 52.203125 43.703125 52.203125 31.203125 \nz\n\" id=\"DejaVuSans-97\"/>\n       <path d=\"M 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 0 \nL 9.421875 0 \nz\n\" id=\"DejaVuSans-108\"/>\n       <path d=\"M 8.5 21.578125 \nL 8.5 54.6875 \nL 17.484375 54.6875 \nL 17.484375 21.921875 \nQ 17.484375 14.15625 20.5 10.265625 \nQ 23.53125 6.390625 29.59375 6.390625 \nQ 36.859375 6.390625 41.078125 11.03125 \nQ 45.3125 15.671875 45.3125 23.6875 \nL 45.3125 54.6875 \nL 54.296875 54.6875 \nL 54.296875 0 \nL 45.3125 0 \nL 45.3125 8.40625 \nQ 42.046875 3.421875 37.71875 1 \nQ 33.40625 -1.421875 27.6875 -1.421875 \nQ 18.265625 -1.421875 13.375 4.4375 \nQ 8.5 10.296875 8.5 21.578125 \nz\nM 31.109375 56 \nz\n\" id=\"DejaVuSans-117\"/>\n      </defs>\n      <use xlink:href=\"#DejaVuSans-86\"/>\n      <use x=\"60.658203\" xlink:href=\"#DejaVuSans-97\"/>\n      <use x=\"121.9375\" xlink:href=\"#DejaVuSans-108\"/>\n      <use x=\"149.720703\" xlink:href=\"#DejaVuSans-117\"/>\n      <use x=\"213.099609\" xlink:href=\"#DejaVuSans-101\"/>\n     </g>\n    </g>\n   </g>\n   <g id=\"line2d_21\">\n    <path clip-path=\"url(#p66244f0a83)\" d=\"M 72.329261 198.841266 \nL 73.432028 207.02649 \nL 74.534795 221.981101 \nL 75.637562 234.186242 \nL 76.740329 210.798119 \nL 77.843095 216.030561 \nL 78.945862 207.272234 \nL 80.048629 211.72701 \nL 81.151396 204.481652 \nL 82.254163 231.915854 \nL 83.356929 199.71863 \nL 84.459696 219.084488 \nL 85.562463 186.451153 \nL 86.66523 234.345741 \nL 87.767997 192.212996 \nL 88.870763 218.830902 \nL 89.97353 233.500287 \nL 92.179064 227.625741 \nL 93.281831 214.643345 \nL 94.384597 212.863831 \nL 95.487364 231.817231 \nL 96.590131 222.217909 \nL 97.692898 184.562034 \nL 98.795665 191.685236 \nL 99.898431 214.012522 \nL 101.001198 220.849438 \nL 102.103965 219.352708 \nL 103.206732 210.717395 \nL 104.309499 239.027858 \nL 105.412265 225.673646 \nL 106.515032 250.077543 \nL 107.617799 214.128558 \nL 108.720566 209.748305 \nL 109.823333 229.278247 \nL 110.926099 193.511403 \nL 112.028866 241.999151 \nL 113.131633 246.056794 \nL 114.2344 216.677555 \nL 115.337167 202.323603 \nL 116.439933 191.192367 \nL 117.5427 228.280133 \nL 118.645467 201.521953 \nL 119.748234 220.816315 \nL 120.851 223.056179 \nL 121.953767 204.644804 \nL 123.056534 208.569744 \nL 124.159301 247.143434 \nL 125.262068 194.093227 \nL 126.364834 195.676123 \nL 127.467601 230.37821 \nL 128.570368 212.568487 \nL 129.673135 210.772227 \nL 130.775902 214.727429 \nL 131.878668 228.702259 \nL 132.981435 230.712568 \nL 134.084202 219.41 \nL 135.186969 198.527279 \nL 136.289736 209.344887 \nL 137.392502 225.107377 \nL 138.495269 205.193695 \nL 139.598036 267.897645 \nL 140.700803 198.455789 \nL 141.80357 248.464295 \nL 142.906336 224.472198 \nL 144.009103 237.507697 \nL 145.11187 240.442603 \nL 146.214637 197.74406 \nL 147.317404 225.892613 \nL 148.42017 211.665353 \nL 149.522937 218.386828 \nL 150.625704 209.193409 \nL 151.728471 216.031609 \nL 152.831238 239.51579 \nL 153.934004 199.870712 \nL 155.036771 225.612581 \nL 156.139538 243.943095 \nL 157.242305 224.259698 \nL 158.345072 190.690704 \nL 159.447838 201.867455 \nL 160.550605 222.555911 \nL 161.653372 222.456919 \nL 162.756139 221.867716 \nL 163.858906 180.151306 \nL 164.961672 209.440955 \nL 166.064439 208.494869 \nL 167.167206 197.970121 \nL 168.269973 211.587967 \nL 169.37274 220.111005 \nL 170.475506 218.909885 \nL 171.578273 216.631754 \nL 172.68104 215.920821 \nL 173.783807 202.537552 \nL 174.886574 214.143447 \nL 175.98934 202.219147 \nL 177.092107 84.342076 \nL 178.194874 81.424813 \nL 179.297641 117.340859 \nL 180.400408 66.710479 \nL 181.503174 76.566117 \nL 182.605941 65.275265 \nL 183.708708 132.883276 \nL 184.811475 46.505845 \nL 185.914242 85.28948 \nL 187.017008 63.818622 \nL 188.119775 101.297984 \nL 189.222542 72.884155 \nL 190.325309 102.084689 \nL 191.428076 94.968026 \nL 192.530842 51.320334 \nL 193.633609 88.248436 \nL 194.736376 81.62508 \nL 195.839143 70.799501 \nL 198.044676 84.393467 \nL 199.147443 81.285172 \nL 200.25021 46.575326 \nL 201.352977 89.958681 \nL 202.455744 86.199434 \nL 203.55851 72.228595 \nL 204.661277 72.081171 \nL 205.764044 71.509108 \nL 206.866811 81.899629 \nL 207.969578 113.723885 \nL 209.072344 66.51185 \nL 210.175111 115.722146 \nL 211.277878 91.609967 \nL 212.380645 111.004076 \nL 213.483412 100.126353 \nL 214.586178 94.4396 \nL 215.688945 95.731419 \nL 216.791712 95.184137 \nL 217.894479 75.740308 \nL 218.997246 97.130216 \nL 220.100012 110.529798 \nL 221.202779 90.898439 \nL 222.305546 88.348974 \nL 223.408313 131.670823 \nL 224.51108 101.434058 \nL 225.613846 101.086726 \nL 226.716613 119.895012 \nL 227.81938 92.380143 \nL 228.922147 120.389277 \nL 230.024914 117.177164 \nL 231.12768 123.955225 \nL 232.230447 122.307222 \nL 233.333214 143.246494 \nL 234.435981 150.887821 \nL 235.538748 99.949369 \nL 236.641514 144.031195 \nL 237.744281 148.289875 \nL 238.847048 170.22479 \nL 239.949815 145.753941 \nL 241.052582 159.445342 \nL 242.155348 109.854069 \nL 243.258115 122.688565 \nL 244.360882 154.03253 \nL 245.463649 104.929801 \nL 247.669182 153.515744 \nL 248.771949 193.205744 \nL 249.874716 112.089936 \nL 250.977483 177.939466 \nL 252.08025 184.395994 \nL 253.183016 140.1481 \nL 254.285783 117.613565 \nL 255.38855 137.988153 \nL 256.491317 154.298123 \nL 257.594083 155.855153 \nL 258.69685 153.472861 \nL 259.799617 157.228828 \nL 260.902384 167.64457 \nL 263.107917 177.896115 \nL 264.210684 192.047827 \nL 265.313451 186.00423 \nL 266.416218 212.678432 \nL 267.518985 187.197773 \nL 268.621751 204.829811 \nL 269.724518 209.018989 \nL 271.930052 170.129952 \nL 273.032819 180.554696 \nL 274.135585 225.939873 \nL 275.238352 218.076009 \nL 276.341119 186.802866 \nL 277.443886 222.38181 \nL 279.649419 200.226515 \nL 280.752186 228.780536 \nL 281.854953 212.365955 \nL 282.95772 239.545005 \nL 284.060487 229.040976 \nL 285.163253 215.26303 \nL 286.26602 250.641741 \nL 287.368787 217.557642 \nL 288.471554 227.724686 \nL 289.574321 219.978106 \nL 290.677087 227.848885 \nL 291.779854 210.231407 \nL 292.882621 233.877066 \nL 293.985388 245.642455 \nL 295.088155 238.205438 \nL 296.190921 233.024765 \nL 297.293688 243.712937 \nL 298.396455 245.954928 \nL 299.499222 260.922379 \nL 300.601989 262.316051 \nL 301.704755 277.205467 \nL 302.807522 224.565473 \nL 303.910289 278.520757 \nL 305.013056 270.973365 \nL 306.115823 273.4091 \nL 307.218589 247.397585 \nL 308.321356 269.996313 \nL 309.424123 245.173041 \nL 310.52689 280.420551 \nL 311.629657 265.796191 \nL 312.732423 287.00526 \nL 313.83519 237.157901 \nL 314.937957 296.807352 \nL 316.040724 272.643076 \nL 317.143491 268.812252 \nL 318.246257 274.393564 \nL 319.349024 286.527141 \nL 320.451791 287.280988 \nL 321.554558 285.959525 \nL 322.657325 290.934277 \nL 323.760091 273.301386 \nL 324.862858 253.28628 \nL 325.965625 302.887727 \nL 327.068392 318.534545 \nL 328.171159 297.830044 \nL 329.273925 253.044651 \nL 330.376692 313.205653 \nL 331.479459 272.26715 \nL 332.582226 273.004955 \nL 333.684993 313.088239 \nL 335.890526 277.893439 \nL 336.993293 313.999049 \nL 338.09606 158.126231 \nL 339.198827 164.853994 \nL 340.301593 172.532638 \nL 341.40436 166.355597 \nL 342.507127 176.484882 \nL 343.609894 154.818968 \nL 344.712661 168.922215 \nL 345.815427 151.661748 \nL 346.918194 182.857736 \nL 348.020961 150.273856 \nL 349.123728 141.698454 \nL 350.226495 190.886012 \nL 351.329261 149.949914 \nL 352.432028 135.278441 \nL 353.534795 177.693599 \nL 354.637562 127.825984 \nL 355.740329 149.700949 \nL 356.843095 169.326551 \nL 359.048629 147.656505 \nL 360.151396 156.142769 \nL 361.254163 148.23203 \nL 362.356929 173.663872 \nL 363.459696 154.866615 \nL 364.562463 182.438703 \nL 365.66523 159.951251 \nL 366.767997 173.807393 \nL 367.870763 170.989309 \nL 368.97353 136.887225 \nL 370.076297 139.497182 \nL 371.179064 147.957758 \nL 372.281831 177.567528 \nL 373.384597 164.199272 \nL 374.487364 137.755762 \nL 375.590131 156.428899 \nL 376.692898 118.738095 \nL 377.795665 152.23266 \nL 378.898431 155.805204 \nL 380.001198 164.578114 \nL 381.103965 156.976021 \nL 382.206732 162.012425 \nL 383.309499 147.156377 \nL 384.412265 142.9147 \nL 385.515032 173.214781 \nL 387.720566 191.565021 \nL 388.823333 150.96204 \nL 389.926099 178.975976 \nL 391.028866 197.241598 \nL 392.131633 153.210682 \nL 393.2344 116.759558 \nL 394.337167 160.165475 \nL 395.439933 145.565989 \nL 396.5427 158.741825 \nL 397.645467 161.924693 \nL 398.748234 144.311599 \nL 399.851 165.335018 \nL 400.953767 155.703371 \nL 402.056534 154.802001 \nL 403.159301 172.01126 \nL 404.262068 143.249362 \nL 405.364834 163.529369 \nL 406.467601 173.638291 \nL 407.570368 151.25301 \nL 408.673135 176.176627 \nL 409.775902 160.130331 \nL 410.878668 160.804457 \nL 411.981435 141.834151 \nL 413.084202 152.46067 \nL 414.186969 161.151816 \nL 415.289736 146.552065 \nL 416.392502 136.640649 \nL 417.495269 151.098046 \nL 418.598036 160.610902 \nL 419.700803 183.561831 \nL 420.80357 179.294584 \nL 421.906336 166.123675 \nL 423.009103 171.412021 \nL 424.11187 164.090235 \nL 425.214637 159.870765 \nL 426.317404 151.669937 \nL 427.42017 162.085542 \nL 428.522937 152.425348 \nL 429.625704 159.747364 \nL 430.728471 195.134657 \nL 431.831238 177.158476 \nL 432.934004 163.359606 \nL 434.036771 181.840463 \nL 435.139538 150.441972 \nL 436.242305 134.287088 \nL 437.345072 139.690469 \nL 438.447838 164.525407 \nL 439.550605 134.950871 \nL 440.653372 158.219342 \nL 441.756139 166.631771 \nL 442.858906 171.409699 \nL 443.961672 165.99904 \nL 445.064439 167.4678 \nL 446.167206 157.758311 \nL 447.269973 157.908472 \nL 448.37274 160.619355 \nL 449.475506 153.069356 \nL 450.578273 139.970129 \nL 451.68104 144.126946 \nL 452.783807 186.33179 \nL 453.886574 204.8487 \nL 454.98934 144.307429 \nL 456.092107 184.197089 \nL 457.194874 164.352838 \nL 458.297641 180.719583 \nL 459.400408 191.647089 \nL 460.503174 150.959349 \nL 461.605941 147.146866 \nL 462.708708 170.280084 \nL 463.811475 205.188798 \nL 464.914242 169.677318 \nL 466.017008 153.364038 \nL 467.119775 185.774611 \nL 468.222542 156.895729 \nL 469.325309 160.298393 \nL 470.428076 149.938751 \nL 471.530842 157.867706 \nL 472.633609 176.775491 \nL 473.736376 139.089776 \nL 474.839143 162.548371 \nL 475.94191 160.221954 \nL 477.044676 175.887118 \nL 478.147443 167.335098 \nL 479.25021 174.941357 \nL 480.352977 162.538458 \nL 481.455744 129.789509 \nL 482.55851 183.226388 \nL 483.661277 187.361682 \nL 484.764044 133.791547 \nL 485.866811 162.885061 \nL 486.969578 170.800805 \nL 488.072344 141.093706 \nL 489.175111 169.577721 \nL 490.277878 127.332674 \nL 492.483412 167.345609 \nL 493.586178 121.392538 \nL 494.688945 169.257892 \nL 495.791712 145.809822 \nL 496.894479 153.42348 \nL 497.997246 135.964822 \nL 499.100012 131.388902 \nL 500.202779 157.851553 \nL 501.305546 171.405371 \nL 502.408313 150.284027 \nL 503.51108 169.947093 \nL 504.613846 123.189526 \nL 505.716613 155.673582 \nL 506.81938 164.304148 \nL 507.922147 154.686826 \nL 509.024914 181.182811 \nL 510.12768 174.590856 \nL 511.230447 136.833121 \nL 512.333214 168.973384 \nL 513.435981 175.659086 \nL 514.538748 148.107403 \nL 515.641514 136.766383 \nL 516.744281 144.780953 \nL 517.847048 139.890948 \nL 518.949815 170.21447 \nL 520.052582 181.666213 \nL 521.155348 130.871744 \nL 522.258115 162.427715 \nL 523.360882 161.094597 \nL 524.463649 133.615008 \nL 525.566416 146.918459 \nL 526.669182 148.570272 \nL 527.771949 118.831902 \nL 528.874716 167.46237 \nL 529.977483 140.112352 \nL 531.08025 155.127389 \nL 532.183016 151.36475 \nL 533.285783 129.41615 \nL 534.38855 147.027941 \nL 535.491317 146.405277 \nL 536.594083 137.043163 \nL 537.69685 161.883908 \nL 538.799617 170.014881 \nL 539.902384 173.484696 \nL 541.005151 189.373765 \nL 542.107917 119.494409 \nL 543.210684 174.203581 \nL 544.313451 158.804605 \nL 545.416218 159.668522 \nL 546.518985 149.43328 \nL 547.621751 140.513931 \nL 548.724518 139.636176 \nL 549.827285 164.575845 \nL 552.032819 149.352575 \nL 553.135585 158.464766 \nL 554.238352 126.162646 \nL 555.341119 145.4237 \nL 556.443886 141.086896 \nL 557.546653 142.229591 \nL 558.649419 133.26529 \nL 559.752186 150.374465 \nL 560.854953 175.90358 \nL 561.95772 146.364821 \nL 563.060487 164.488818 \nL 564.163253 162.25009 \nL 565.26602 142.797979 \nL 566.368787 131.378295 \nL 567.471554 152.379306 \nL 568.574321 147.279278 \nL 569.677087 122.995559 \nL 570.779854 148.235643 \nL 571.882621 107.166416 \nL 572.985388 142.375299 \nL 574.088155 182.616174 \nL 575.190921 138.888287 \nL 576.293688 145.229696 \nL 577.396455 129.35238 \nL 578.499222 139.932351 \nL 579.601989 22.025455 \nL 579.601989 22.025455 \n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\n   </g>\n   <g id=\"line2d_22\">\n    <path clip-path=\"url(#p66244f0a83)\" d=\"M 72.329261 209.216296 \nL 73.432028 204.5467 \nL 74.534795 196.873035 \nL 75.637562 202.087227 \nL 76.740329 213.736469 \nL 77.843095 211.715659 \nL 78.945862 207.232095 \nL 80.048629 201.723501 \nL 81.151396 202.744084 \nL 82.254163 201.08168 \nL 83.356929 208.73859 \nL 84.459696 202.216666 \nL 85.562463 204.828289 \nL 86.66523 194.753518 \nL 87.767997 204.468137 \nL 88.870763 199.452893 \nL 89.97353 202.592251 \nL 91.076297 212.270553 \nL 92.179064 214.226457 \nL 93.281831 220.737686 \nL 94.384597 211.835489 \nL 95.487364 208.743857 \nL 96.590131 212.328707 \nL 97.692898 216.302007 \nL 98.795665 200.009904 \nL 99.898431 190.458474 \nL 101.001198 194.65579 \nL 102.103965 202.785113 \nL 103.206732 208.480797 \nL 104.309499 204.510012 \nL 105.412265 212.572736 \nL 106.515032 215.959589 \nL 107.617799 228.901737 \nL 109.823333 210.505312 \nL 110.926099 213.046022 \nL 112.028866 202.398913 \nL 113.131633 215.978992 \nL 114.2344 224.796881 \nL 115.337167 219.566383 \nL 116.439933 208.342067 \nL 117.5427 194.858287 \nL 118.645467 203.44226 \nL 119.748234 201.705263 \nL 120.851 207.566889 \nL 121.953767 206.950982 \nL 123.056534 202.100039 \nL 124.159301 202.622771 \nL 125.262068 215.432595 \nL 126.364834 205.681713 \nL 127.467601 198.265364 \nL 128.570368 205.771579 \nL 129.673135 203.468042 \nL 130.775902 206.864425 \nL 131.878668 205.247073 \nL 132.981435 208.446598 \nL 134.084202 214.787174 \nL 135.186969 216.480839 \nL 136.289736 203.990165 \nL 137.392502 199.321628 \nL 138.495269 207.14387 \nL 139.598036 204.216194 \nL 140.700803 227.091961 \nL 141.80357 213.272024 \nL 142.906336 224.359767 \nL 144.009103 221.253325 \nL 145.11187 223.698344 \nL 146.214637 229.048051 \nL 147.317404 212.617629 \nL 148.42017 213.079983 \nL 149.522937 206.344525 \nL 150.625704 210.694018 \nL 151.728471 204.887106 \nL 152.831238 205.123537 \nL 153.934004 214.496347 \nL 155.036771 206.446117 \nL 156.139538 211.491086 \nL 157.242305 218.476634 \nL 158.345072 219.38636 \nL 159.447838 206.811379 \nL 160.550605 197.074922 \nL 161.653372 201.969462 \nL 162.756139 208.317503 \nL 163.858906 213.973138 \nL 164.961672 194.356996 \nL 166.064439 193.462702 \nL 167.167206 197.205023 \nL 168.269973 195.334643 \nL 170.475506 201.224645 \nL 171.578273 205.976458 \nL 172.68104 207.217536 \nL 173.783807 207.450421 \nL 174.886574 200.193554 \nL 175.98934 202.438168 \nL 177.092107 199.288699 \nL 178.194874 162.054512 \nL 179.297641 134.392931 \nL 180.400408 135.046744 \nL 181.503174 121.080228 \nL 182.605941 115.114945 \nL 183.708708 112.326819 \nL 184.811475 120.914895 \nL 185.914242 112.608964 \nL 187.017008 106.82164 \nL 188.119775 114.275471 \nL 189.222542 108.570919 \nL 190.325309 115.657874 \nL 191.428076 111.229023 \nL 192.530842 122.432852 \nL 193.633609 104.973946 \nL 194.736376 112.662513 \nL 195.839143 112.258685 \nL 196.94191 108.74766 \nL 198.044676 109.273489 \nL 199.147443 111.939244 \nL 200.25021 112.027892 \nL 201.352977 104.166395 \nL 203.55851 114.000499 \nL 204.661277 107.639223 \nL 205.764044 109.417593 \nL 206.866811 108.222546 \nL 207.969578 110.654992 \nL 209.072344 117.060632 \nL 210.175111 113.428162 \nL 211.277878 117.435608 \nL 212.380645 122.026403 \nL 213.483412 118.66099 \nL 214.586178 123.625913 \nL 215.688945 117.290843 \nL 216.791712 121.20919 \nL 217.894479 116.548276 \nL 218.997246 115.613404 \nL 220.100012 114.42156 \nL 221.202779 122.612293 \nL 222.305546 117.475631 \nL 223.408313 116.193735 \nL 224.51108 126.896484 \nL 225.613846 125.277544 \nL 226.716613 120.15507 \nL 227.81938 127.95026 \nL 228.922147 121.871473 \nL 230.024914 125.143129 \nL 231.12768 128.906599 \nL 232.230447 129.523459 \nL 233.333214 131.537519 \nL 234.435981 137.018689 \nL 235.538748 144.904327 \nL 236.641514 131.7942 \nL 239.949815 151.57811 \nL 241.052582 151.951789 \nL 242.155348 152.454377 \nL 244.360882 133.40946 \nL 245.463649 147.127951 \nL 246.566416 132.989591 \nL 247.669182 134.870823 \nL 248.771949 144.154084 \nL 249.874716 160.86513 \nL 250.977483 146.123939 \nL 252.08025 155.898671 \nL 253.183016 170.738357 \nL 254.285783 152.762992 \nL 256.491317 141.480733 \nL 257.594083 149.039545 \nL 258.69685 148.740803 \nL 259.799617 153.4865 \nL 260.902384 151.7868 \nL 263.107917 161.754288 \nL 264.210684 166.2045 \nL 265.313451 173.832477 \nL 266.416218 176.242107 \nL 267.518985 187.787161 \nL 268.621751 185.65973 \nL 269.724518 189.687426 \nL 270.827285 194.503905 \nL 271.930052 188.727063 \nL 273.032819 180.13311 \nL 274.135585 174.932894 \nL 275.238352 192.693083 \nL 276.341119 200.282282 \nL 277.443886 195.188237 \nL 278.546653 198.752652 \nL 279.649419 199.75145 \nL 280.752186 198.869015 \nL 281.854953 207.9623 \nL 282.95772 204.935784 \nL 284.060487 215.827397 \nL 285.163253 219.521781 \nL 286.26602 214.800859 \nL 287.368787 223.820928 \nL 288.471554 219.358381 \nL 289.574321 220.746157 \nL 290.677087 214.974397 \nL 291.779854 215.80998 \nL 292.882621 210.516086 \nL 293.985388 216.385017 \nL 295.088155 224.828712 \nL 296.190921 227.064128 \nL 297.293688 228.708532 \nL 298.396455 228.308581 \nL 299.499222 232.937705 \nL 300.601989 242.812309 \nL 302.807522 257.817778 \nL 303.910289 242.894326 \nL 305.013056 251.81312 \nL 307.218589 264.809056 \nL 308.321356 256.489692 \nL 309.424123 254.198106 \nL 310.52689 247.571189 \nL 311.629657 259.781385 \nL 312.732423 262.30432 \nL 313.83519 267.224694 \nL 314.937957 254.293517 \nL 316.040724 264.876634 \nL 317.143491 268.269655 \nL 318.246257 267.983275 \nL 319.349024 267.997423 \nL 320.451791 269.727974 \nL 321.554558 276.775409 \nL 322.657325 280.589464 \nL 323.760091 281.875185 \nL 324.862858 273.8755 \nL 325.965625 264.232047 \nL 327.068392 273.98735 \nL 328.171159 289.941985 \nL 329.273925 296.009508 \nL 330.376692 277.330951 \nL 331.479459 279.537581 \nL 332.582226 277.101548 \nL 333.684993 279.229376 \nL 334.787759 287.472189 \nL 335.890526 284.80977 \nL 336.993293 283.76641 \nL 338.09606 292.459718 \nL 339.198827 237.423347 \nL 340.301593 198.981328 \nL 341.40436 188.414371 \nL 342.507127 172.277516 \nL 343.609894 175.730174 \nL 344.712661 165.066257 \nL 345.815427 158.17272 \nL 346.918194 153.694106 \nL 348.020961 165.425642 \nL 350.226495 150.148802 \nL 351.329261 165.397637 \nL 352.432028 157.929301 \nL 353.534795 151.454335 \nL 354.637562 158.806203 \nL 355.740329 148.97276 \nL 356.843095 147.882456 \nL 357.945862 158.109959 \nL 359.048629 154.767073 \nL 360.151396 152.352705 \nL 361.254163 153.1678 \nL 362.356929 151.804906 \nL 363.459696 156.576068 \nL 364.562463 158.1242 \nL 365.66523 163.481491 \nL 366.767997 162.503896 \nL 367.870763 163.226848 \nL 368.97353 166.397679 \nL 370.076297 153.256687 \nL 371.179064 149.688883 \nL 372.281831 148.039953 \nL 373.384597 158.84215 \nL 374.487364 159.446251 \nL 375.590131 151.929633 \nL 376.692898 152.100418 \nL 377.795665 142.019427 \nL 378.898431 145.320411 \nL 380.001198 151.900542 \nL 381.103965 153.327839 \nL 382.206732 155.595852 \nL 383.309499 155.467869 \nL 384.412265 154.002086 \nL 385.515032 147.349771 \nL 386.617799 158.209884 \nL 387.720566 165.028485 \nL 388.823333 173.617302 \nL 389.926099 164.413028 \nL 391.028866 166.510627 \nL 392.131633 176.899784 \nL 393.2344 165.883761 \nL 394.337167 151.41741 \nL 395.439933 150.775112 \nL 397.645467 151.555504 \nL 398.748234 157.715845 \nL 399.851 149.456873 \nL 400.953767 154.966552 \nL 402.056534 154.864271 \nL 403.159301 154.166201 \nL 404.262068 159.122204 \nL 405.364834 153.593493 \nL 406.467601 155.639316 \nL 407.570368 161.871762 \nL 408.673135 156.486414 \nL 409.775902 162.302537 \nL 410.878668 160.854118 \nL 411.981435 158.728222 \nL 413.084202 152.684191 \nL 414.186969 151.400813 \nL 415.289736 155.241735 \nL 416.392502 150.616953 \nL 417.495269 147.431246 \nL 418.598036 147.220808 \nL 419.700803 152.826415 \nL 420.80357 162.147687 \nL 421.906336 167.967437 \nL 423.009103 165.116789 \nL 424.11187 165.420851 \nL 426.317404 160.653077 \nL 427.42017 156.634117 \nL 428.522937 157.131994 \nL 429.625704 154.752143 \nL 430.728471 155.150334 \nL 431.831238 169.310479 \nL 432.934004 169.687599 \nL 434.036771 166.370414 \nL 435.139538 169.139773 \nL 436.242305 160.836251 \nL 437.345072 151.096512 \nL 438.447838 147.999639 \nL 439.550605 152.842667 \nL 440.653372 147.208341 \nL 441.756139 150.367154 \nL 442.858906 157.285415 \nL 443.961672 158.383581 \nL 445.064439 162.071003 \nL 446.167206 161.316948 \nL 447.269973 159.676224 \nL 448.37274 156.358536 \nL 449.475506 158.506694 \nL 450.578273 154.710956 \nL 451.68104 149.812367 \nL 452.783807 147.384817 \nL 454.98934 176.289568 \nL 456.092107 164.242322 \nL 457.194874 168.974996 \nL 458.297641 166.341641 \nL 459.400408 167.493542 \nL 460.503174 177.600766 \nL 461.605941 164.68702 \nL 462.708708 157.412154 \nL 463.811475 158.96423 \nL 464.914242 176.638418 \nL 466.017008 171.283165 \nL 467.119775 164.563193 \nL 468.222542 168.231583 \nL 469.325309 161.830628 \nL 470.428076 161.519101 \nL 471.530842 156.920683 \nL 472.633609 153.960369 \nL 473.736376 161.821151 \nL 474.839143 153.794136 \nL 475.94191 155.651016 \nL 477.044676 156.294849 \nL 478.147443 161.361868 \nL 479.25021 163.463014 \nL 480.352977 164.791974 \nL 481.455744 163.53676 \nL 482.55851 149.624879 \nL 483.661277 161.872504 \nL 484.764044 170.453221 \nL 485.866811 156.666993 \nL 486.969578 157.339917 \nL 488.072344 161.606373 \nL 489.175111 152.485479 \nL 490.277878 158.40481 \nL 491.380645 149.498272 \nL 492.483412 145.529975 \nL 493.586178 155.551647 \nL 494.688945 143.961091 \nL 495.791712 152.315382 \nL 496.894479 151.742278 \nL 497.997246 150.297352 \nL 499.100012 146.971777 \nL 500.202779 141.282287 \nL 501.305546 149.185051 \nL 502.408313 155.024574 \nL 503.51108 154.954878 \nL 504.613846 157.225354 \nL 505.716613 148.334354 \nL 506.81938 147.868109 \nL 507.922147 156.925209 \nL 509.024914 152.870885 \nL 510.12768 163.749104 \nL 511.230447 165.15885 \nL 512.333214 155.869263 \nL 513.435981 157.013725 \nL 514.538748 165.061334 \nL 515.641514 156.212157 \nL 516.744281 150.516869 \nL 518.949815 145.226601 \nL 520.052582 153.614669 \nL 521.155348 164.662694 \nL 522.258115 151.162873 \nL 523.360882 154.099549 \nL 524.463649 157.478885 \nL 525.566416 147.414808 \nL 526.669182 148.523139 \nL 527.771949 149.010083 \nL 528.874716 139.734181 \nL 529.977483 148.735667 \nL 531.08025 149.673345 \nL 532.183016 147.877003 \nL 533.285783 151.722716 \nL 534.38855 142.890585 \nL 535.491317 146.074651 \nL 536.594083 146.081428 \nL 537.69685 144.544969 \nL 538.799617 149.517092 \nL 539.902384 157.954156 \nL 541.005151 161.292861 \nL 542.107917 170.388369 \nL 543.210684 153.76621 \nL 544.313451 157.835676 \nL 545.416218 161.003171 \nL 546.518985 156.105323 \nL 547.621751 156.117235 \nL 548.724518 147.828589 \nL 549.827285 147.272557 \nL 550.930052 150.548845 \nL 552.032819 155.756182 \nL 553.135585 150.191207 \nL 554.238352 154.21112 \nL 555.341119 144.507672 \nL 556.443886 144.744712 \nL 557.546653 145.933089 \nL 558.649419 143.710604 \nL 559.752186 142.425849 \nL 560.854953 144.744659 \nL 561.95772 157.184537 \nL 563.060487 151.508705 \nL 564.163253 156.457376 \nL 565.26602 157.730298 \nL 566.368787 151.165282 \nL 567.471554 145.735542 \nL 568.574321 147.753546 \nL 569.677087 148.825533 \nL 570.779854 139.982047 \nL 571.882621 144.768456 \nL 572.985388 135.320339 \nL 574.088155 137.218698 \nL 575.190921 157.548051 \nL 576.293688 148.634128 \nL 577.396455 148.811478 \nL 578.499222 143.221939 \nL 579.601989 142.127822 \nL 579.601989 142.127822 \n\" style=\"fill:none;stroke:#ff7f0e;stroke-linecap:square;stroke-width:1.5;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"M 46.965625 333.36 \nL 46.965625 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"M 604.965625 333.36 \nL 604.965625 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"M 46.965625 333.36 \nL 604.965625 333.36 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"M 46.965625 7.2 \nL 604.965625 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"p66244f0a83\">\n   <rect height=\"326.16\" width=\"558\" x=\"46.965625\" y=\"7.2\"/>\n  </clipPath>\n </defs>\n</svg>\n",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1mwfgEK08OI3"
      },
      "source": [
        "keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.3282213"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNG7s8jt8OI4"
      },
      "source": [
        "## Sequence-to-Sequence Forecasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsKGxfiE8OI4"
      },
      "source": [
        "def seq2seq_window_dataset(series, window_size, batch_size=32,\n",
        "                           shuffle_buffer=1000):\n",
        "    series = tf.expand_dims(series, axis=-1)\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
        "    ds = ds.shuffle(shuffle_buffer)\n",
        "    ds = ds.map(lambda w: (w[:-1], w[1:]))\n",
        "    return ds.batch(batch_size).prefetch(1)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Nk2C7WP8OI5"
      },
      "source": [
        "for X_batch, Y_batch in seq2seq_window_dataset(tf.range(10), 3,\n",
        "                                               batch_size=1):\n",
        "    print(\"X:\", X_batch.numpy())\n",
        "    print(\"Y:\", Y_batch.numpy())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X: [[[1]\n  [2]\n  [3]]]\nY: [[[2]\n  [3]\n  [4]]]\nX: [[[2]\n  [3]\n  [4]]]\nY: [[[3]\n  [4]\n  [5]]]\nX: [[[4]\n  [5]\n  [6]]]\nY: [[[5]\n  [6]\n  [7]]]\nX: [[[3]\n  [4]\n  [5]]]\nY: [[[4]\n  [5]\n  [6]]]\nX: [[[5]\n  [6]\n  [7]]]\nY: [[[6]\n  [7]\n  [8]]]\nX: [[[6]\n  [7]\n  [8]]]\nY: [[[7]\n  [8]\n  [9]]]\nX: [[[0]\n  [1]\n  [2]]]\nY: [[[1]\n  [2]\n  [3]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JSc-Btk8OI7"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = seq2seq_window_dataset(x_train, window_size,\n",
        "                                   batch_size=128)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.SimpleRNN(100, return_sequences=True,\n",
        "                         input_shape=[None, 1]),\n",
        "  keras.layers.SimpleRNN(100, return_sequences=True),\n",
        "  keras.layers.Dense(1),\n",
        "  keras.layers.Lambda(lambda x: x * 200)\n",
        "])\n",
        "lr_schedule = keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-7 * 10**(epoch / 30))\n",
        "optimizer = keras.optimizers.SGD(lr=1e-7, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/home/ritesh/.local/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  warnings.warn(\n",
            "Epoch 1/100\n",
            "8/8 [==============================] - 2s 31ms/step - loss: 52.3618 - mae: 52.8614\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 0s 31ms/step - loss: 25.4714 - mae: 25.9671\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 0s 33ms/step - loss: 23.5029 - mae: 23.9972\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 21.1367 - mae: 21.6294\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 19.4762 - mae: 19.9674\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 18.6742 - mae: 19.1647\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 18.1698 - mae: 18.6587\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 17.7679 - mae: 18.2568\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 17.4389 - mae: 17.9280\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 17.0989 - mae: 17.5875\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 0s 37ms/step - loss: 16.7679 - mae: 17.2569\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 1s 51ms/step - loss: 16.4296 - mae: 16.9185\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 1s 43ms/step - loss: 16.0786 - mae: 16.5664\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 0s 40ms/step - loss: 15.7223 - mae: 16.2095\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 0s 34ms/step - loss: 15.3619 - mae: 15.8493\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 14.9962 - mae: 15.4842\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 14.6109 - mae: 15.0986\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 0s 36ms/step - loss: 14.2178 - mae: 14.7052\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 1s 45ms/step - loss: 13.8218 - mae: 14.3087\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 0s 40ms/step - loss: 13.3872 - mae: 13.8739\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 0s 35ms/step - loss: 12.9509 - mae: 13.4372\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 0s 33ms/step - loss: 12.5123 - mae: 12.9977\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 0s 35ms/step - loss: 12.0817 - mae: 12.5669\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 11.6471 - mae: 12.1312\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 11.2324 - mae: 11.7161\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 0s 38ms/step - loss: 10.8651 - mae: 11.3486\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 0s 37ms/step - loss: 10.5209 - mae: 11.0050\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 10.1947 - mae: 10.6790\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 0s 33ms/step - loss: 9.8873 - mae: 10.3706\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 9.5928 - mae: 10.0763\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 1s 30ms/step - loss: 9.3295 - mae: 9.8131\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 9.0906 - mae: 9.5738\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 8.8725 - mae: 9.3552\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 8.6903 - mae: 9.1725\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 8.5312 - mae: 9.0130\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 8.3607 - mae: 8.8433\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 8.2321 - mae: 8.7129\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 8.0739 - mae: 8.5555\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 7.9503 - mae: 8.4314\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 8.4994 - mae: 8.9838\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 0s 31ms/step - loss: 8.9800 - mae: 9.4655\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 8.7825 - mae: 9.2684\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 9.2915 - mae: 9.7797\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 9.8246 - mae: 10.3147\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 0s 34ms/step - loss: 10.0401 - mae: 10.5308\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 9.1706 - mae: 9.6581\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 10.7949 - mae: 11.2851\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 21.6432 - mae: 22.1388\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 42.4324 - mae: 42.9310\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 53.0824 - mae: 53.5810\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 30.3502 - mae: 30.8472\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 21.6446 - mae: 22.1403\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 13.3187 - mae: 13.8101\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 9.7645 - mae: 10.2545\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 7.7063 - mae: 8.1915\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 9.7062 - mae: 10.1969\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 9.9227 - mae: 10.4134\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 11.1099 - mae: 11.6035\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 11.4349 - mae: 11.9293\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 11.4739 - mae: 11.9669\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 11.4033 - mae: 11.8944\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 14.3109 - mae: 14.8025\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 27.3466 - mae: 27.8417\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 1s 28ms/step - loss: 26.4622 - mae: 26.9582\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 30.2780 - mae: 30.7758\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 59.7148 - mae: 60.2147\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 51.4859 - mae: 51.9853\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 56.8440 - mae: 57.3439\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 57.0448 - mae: 57.5437\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 54.7092 - mae: 55.2079\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 61.6619 - mae: 62.1616\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 28.3010 - mae: 28.7969\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 34.7211 - mae: 35.2198\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 42.9168 - mae: 43.4151\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 32.8757 - mae: 33.3727\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 16.8995 - mae: 17.3927\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 41.9154 - mae: 42.4126\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 82.7875 - mae: 83.2858\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 41.1973 - mae: 41.6939\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 45.2238 - mae: 45.7230\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 45.7564 - mae: 46.2556\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 49.0538 - mae: 49.5535\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 49.0057 - mae: 49.5052\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 49.4648 - mae: 49.9646\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 50.1054 - mae: 50.6051\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 49.4669 - mae: 49.9662\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 1s 32ms/step - loss: 50.0162 - mae: 50.5158\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 50.7769 - mae: 51.2765\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 58.3083 - mae: 58.8083\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 62.8067 - mae: 63.3065\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 65.4932 - mae: 65.9930\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 62.8643 - mae: 63.3642\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 0s 34ms/step - loss: 81.3788 - mae: 81.8786\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 79.6877 - mae: 80.1875\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 92.4389 - mae: 92.9387\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 98.2926 - mae: 98.7924\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 92.1470 - mae: 92.6467\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 121.6875 - mae: 122.1871\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 133.2452 - mae: 133.7450\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 183.0946 - mae: 183.5946\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGNsWceq8OI8"
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-7, 1e-4, 0, 30])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1e-07, 0.0001, 0.0, 30.0)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"252.317344pt\" version=\"1.1\" viewBox=\"0 0 380.675 252.317344\" width=\"380.675pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-06-17T12:49:08.846743</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M -0 252.317344 \nL 380.675 252.317344 \nL 380.675 0 \nL -0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 26.925 228.439219 \nL 361.725 228.439219 \nL 361.725 10.999219 \nL 26.925 10.999219 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_1\">\n      <defs>\n       <path d=\"M 0 0 \nL 0 3.5 \n\" id=\"mc8b03b1172\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#mc8b03b1172\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_1\">\n      <!-- $\\mathdefault{10^{-7}}$ -->\n      <g transform=\"translate(15.175 243.037656)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n        <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n        <path d=\"M 10.59375 35.5 \nL 73.1875 35.5 \nL 73.1875 27.203125 \nL 10.59375 27.203125 \nz\n\" id=\"DejaVuSans-8722\"/>\n        <path d=\"M 8.203125 72.90625 \nL 55.078125 72.90625 \nL 55.078125 68.703125 \nL 28.609375 0 \nL 18.3125 0 \nL 43.21875 64.59375 \nL 8.203125 64.59375 \nz\n\" id=\"DejaVuSans-55\"/>\n       </defs>\n       <use transform=\"translate(0 0.684375)\" xlink:href=\"#DejaVuSans-49\"/>\n       <use transform=\"translate(63.623047 0.684375)\" xlink:href=\"#DejaVuSans-48\"/>\n       <use transform=\"translate(128.203125 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-8722\"/>\n       <use transform=\"translate(186.855469 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_2\">\n     <g id=\"line2d_2\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"138.525\" xlink:href=\"#mc8b03b1172\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_2\">\n      <!-- $\\mathdefault{10^{-6}}$ -->\n      <g transform=\"translate(126.775 243.037656)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n       </defs>\n       <use transform=\"translate(0 0.765625)\" xlink:href=\"#DejaVuSans-49\"/>\n       <use transform=\"translate(63.623047 0.765625)\" xlink:href=\"#DejaVuSans-48\"/>\n       <use transform=\"translate(128.203125 39.046875)scale(0.7)\" xlink:href=\"#DejaVuSans-8722\"/>\n       <use transform=\"translate(186.855469 39.046875)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_3\">\n     <g id=\"line2d_3\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"250.125\" xlink:href=\"#mc8b03b1172\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_3\">\n      <!-- $\\mathdefault{10^{-5}}$ -->\n      <g transform=\"translate(238.375 243.037656)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 10.796875 72.90625 \nL 49.515625 72.90625 \nL 49.515625 64.59375 \nL 19.828125 64.59375 \nL 19.828125 46.734375 \nQ 21.96875 47.46875 24.109375 47.828125 \nQ 26.265625 48.1875 28.421875 48.1875 \nQ 40.625 48.1875 47.75 41.5 \nQ 54.890625 34.8125 54.890625 23.390625 \nQ 54.890625 11.625 47.5625 5.09375 \nQ 40.234375 -1.421875 26.90625 -1.421875 \nQ 22.3125 -1.421875 17.546875 -0.640625 \nQ 12.796875 0.140625 7.71875 1.703125 \nL 7.71875 11.625 \nQ 12.109375 9.234375 16.796875 8.0625 \nQ 21.484375 6.890625 26.703125 6.890625 \nQ 35.15625 6.890625 40.078125 11.328125 \nQ 45.015625 15.765625 45.015625 23.390625 \nQ 45.015625 31 40.078125 35.4375 \nQ 35.15625 39.890625 26.703125 39.890625 \nQ 22.75 39.890625 18.8125 39.015625 \nQ 14.890625 38.140625 10.796875 36.28125 \nz\n\" id=\"DejaVuSans-53\"/>\n       </defs>\n       <use transform=\"translate(0 0.684375)\" xlink:href=\"#DejaVuSans-49\"/>\n       <use transform=\"translate(63.623047 0.684375)\" xlink:href=\"#DejaVuSans-48\"/>\n       <use transform=\"translate(128.203125 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-8722\"/>\n       <use transform=\"translate(186.855469 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_4\">\n     <g id=\"line2d_4\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"361.725\" xlink:href=\"#mc8b03b1172\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_4\">\n      <!-- $\\mathdefault{10^{-4}}$ -->\n      <g transform=\"translate(349.975 243.037656)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n       </defs>\n       <use transform=\"translate(0 0.684375)\" xlink:href=\"#DejaVuSans-49\"/>\n       <use transform=\"translate(63.623047 0.684375)\" xlink:href=\"#DejaVuSans-48\"/>\n       <use transform=\"translate(128.203125 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-8722\"/>\n       <use transform=\"translate(186.855469 38.965625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_5\">\n     <g id=\"line2d_5\">\n      <defs>\n       <path d=\"M 0 0 \nL 0 2 \n\" id=\"ma18f09ae4f\" style=\"stroke:#000000;stroke-width:0.6;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"60.519948\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_6\">\n     <g id=\"line2d_6\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"80.171732\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_7\">\n     <g id=\"line2d_7\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"94.114895\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_8\">\n     <g id=\"line2d_8\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"104.930052\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_9\">\n     <g id=\"line2d_9\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"113.76668\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_10\">\n     <g id=\"line2d_10\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"121.237941\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_11\">\n     <g id=\"line2d_11\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"127.709843\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_12\">\n     <g id=\"line2d_12\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"133.418464\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_13\">\n     <g id=\"line2d_13\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"172.119948\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_14\">\n     <g id=\"line2d_14\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"191.771732\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_15\">\n     <g id=\"line2d_15\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"205.714895\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_16\">\n     <g id=\"line2d_16\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"216.530052\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_17\">\n     <g id=\"line2d_17\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"225.36668\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_18\">\n     <g id=\"line2d_18\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"232.837941\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_19\">\n     <g id=\"line2d_19\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"239.309843\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_20\">\n     <g id=\"line2d_20\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"245.018464\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_21\">\n     <g id=\"line2d_21\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"283.719948\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_22\">\n     <g id=\"line2d_22\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"303.371732\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_23\">\n     <g id=\"line2d_23\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"317.314895\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_24\">\n     <g id=\"line2d_24\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"328.130052\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_25\">\n     <g id=\"line2d_25\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"336.96668\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_26\">\n     <g id=\"line2d_26\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"344.437941\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_27\">\n     <g id=\"line2d_27\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"350.909843\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_28\">\n     <g id=\"line2d_28\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"356.618464\" xlink:href=\"#ma18f09ae4f\" y=\"228.439219\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"matplotlib.axis_2\">\n    <g id=\"ytick_1\">\n     <g id=\"line2d_29\">\n      <defs>\n       <path d=\"M 0 0 \nL -3.5 0 \n\" id=\"m59c14bea54\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m59c14bea54\" y=\"228.439219\"/>\n      </g>\n     </g>\n     <g id=\"text_5\">\n      <!-- 0 -->\n      <g transform=\"translate(13.5625 232.238437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_2\">\n     <g id=\"line2d_30\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m59c14bea54\" y=\"192.199219\"/>\n      </g>\n     </g>\n     <g id=\"text_6\">\n      <!-- 5 -->\n      <g transform=\"translate(13.5625 195.998437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_3\">\n     <g id=\"line2d_31\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m59c14bea54\" y=\"155.959219\"/>\n      </g>\n     </g>\n     <g id=\"text_7\">\n      <!-- 10 -->\n      <g transform=\"translate(7.2 159.758437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_4\">\n     <g id=\"line2d_32\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m59c14bea54\" y=\"119.719219\"/>\n      </g>\n     </g>\n     <g id=\"text_8\">\n      <!-- 15 -->\n      <g transform=\"translate(7.2 123.518437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_5\">\n     <g id=\"line2d_33\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m59c14bea54\" y=\"83.479219\"/>\n      </g>\n     </g>\n     <g id=\"text_9\">\n      <!-- 20 -->\n      <g transform=\"translate(7.2 87.278437)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_6\">\n     <g id=\"line2d_34\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m59c14bea54\" y=\"47.239219\"/>\n      </g>\n     </g>\n     <g id=\"text_10\">\n      <!-- 25 -->\n      <g transform=\"translate(7.2 51.038437)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_7\">\n     <g id=\"line2d_35\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m59c14bea54\" y=\"10.999219\"/>\n      </g>\n     </g>\n     <g id=\"text_11\">\n      <!-- 30 -->\n      <g transform=\"translate(7.2 14.798437)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 40.578125 39.3125 \nQ 47.65625 37.796875 51.625 33 \nQ 55.609375 28.21875 55.609375 21.1875 \nQ 55.609375 10.40625 48.1875 4.484375 \nQ 40.765625 -1.421875 27.09375 -1.421875 \nQ 22.515625 -1.421875 17.65625 -0.515625 \nQ 12.796875 0.390625 7.625 2.203125 \nL 7.625 11.71875 \nQ 11.71875 9.328125 16.59375 8.109375 \nQ 21.484375 6.890625 26.8125 6.890625 \nQ 36.078125 6.890625 40.9375 10.546875 \nQ 45.796875 14.203125 45.796875 21.1875 \nQ 45.796875 27.640625 41.28125 31.265625 \nQ 36.765625 34.90625 28.71875 34.90625 \nL 20.21875 34.90625 \nL 20.21875 43.015625 \nL 29.109375 43.015625 \nQ 36.375 43.015625 40.234375 45.921875 \nQ 44.09375 48.828125 44.09375 54.296875 \nQ 44.09375 59.90625 40.109375 62.90625 \nQ 36.140625 65.921875 28.71875 65.921875 \nQ 24.65625 65.921875 20.015625 65.03125 \nQ 15.375 64.15625 9.8125 62.3125 \nL 9.8125 71.09375 \nQ 15.4375 72.65625 20.34375 73.4375 \nQ 25.25 74.21875 29.59375 74.21875 \nQ 40.828125 74.21875 47.359375 69.109375 \nQ 53.90625 64.015625 53.90625 55.328125 \nQ 53.90625 49.265625 50.4375 45.09375 \nQ 46.96875 40.921875 40.578125 39.3125 \nz\n\" id=\"DejaVuSans-51\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-51\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"line2d_36\">\n    <path clip-path=\"url(#p66ade2911d)\" d=\"M 29.789486 -1 \nL 30.644999 43.822848 \nL 34.364999 58.089888 \nL 38.085 75.240088 \nL 41.804999 87.275803 \nL 45.524999 93.088271 \nL 49.244998 96.744261 \nL 52.964999 99.657642 \nL 56.685 102.042099 \nL 60.404999 104.506393 \nL 64.124999 106.905614 \nL 67.845001 109.357742 \nL 71.565002 111.90172 \nL 75.285001 114.483991 \nL 79.005 117.096227 \nL 82.724998 119.746805 \nL 86.445001 122.539769 \nL 90.165001 125.388396 \nL 93.885001 128.258949 \nL 97.605001 131.409122 \nL 101.325 134.571054 \nL 105.045 137.749802 \nL 108.765 140.871179 \nL 112.485001 144.021339 \nL 116.205 147.027095 \nL 119.925001 149.688892 \nL 123.644998 152.183385 \nL 127.365 154.547928 \nL 131.085001 156.775885 \nL 134.805 158.910631 \nL 138.525 160.818835 \nL 142.245001 162.550308 \nL 145.965002 164.131204 \nL 149.685 165.452111 \nL 153.404999 166.605064 \nL 157.125 167.840771 \nL 160.845 168.773155 \nL 164.565 169.919258 \nL 168.285001 170.815512 \nL 172.004997 166.83558 \nL 175.725 163.352134 \nL 179.445001 164.783595 \nL 183.164998 161.094198 \nL 186.885 157.23062 \nL 190.605 155.668898 \nL 194.324999 161.971042 \nL 198.045001 150.197798 \nL 201.765 71.569292 \nL 203.556594 -1 \nM 212.711389 -1 \nL 212.925001 8.46113 \nL 216.645001 71.559159 \nL 220.365001 131.904971 \nL 224.085002 157.66589 \nL 227.805001 172.584113 \nL 231.525 158.088808 \nL 235.244999 156.519151 \nL 238.964998 147.914881 \nL 242.684999 145.5592 \nL 246.405 145.276337 \nL 250.124999 145.788382 \nL 253.844998 124.713776 \nL 257.564998 30.231341 \nL 261.284999 36.641413 \nL 265.005001 8.98422 \nL 265.179081 -1 \nM 290.670938 -1 \nL 291.044999 23.313926 \nL 292.988719 -1 \nM 302.489122 -1 \nL 305.925002 105.951519 \nL 308.119298 -1 \n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"M 26.925 228.439219 \nL 26.925 10.999219 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"M 361.725 228.439219 \nL 361.725 10.999219 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"M 26.925 228.439219 \nL 361.725 228.439219 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"M 26.925 10.999219 \nL 361.725 10.999219 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"p66ade2911d\">\n   <rect height=\"217.44\" width=\"334.8\" x=\"26.925\" y=\"10.999219\"/>\n  </clipPath>\n </defs>\n</svg>\n",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9lDnb0X8OI9"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = seq2seq_window_dataset(x_train, window_size,\n",
        "                                   batch_size=128)\n",
        "valid_set = seq2seq_window_dataset(x_valid, window_size,\n",
        "                                   batch_size=128)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.SimpleRNN(100, return_sequences=True,\n",
        "                         input_shape=[None, 1]),\n",
        "  keras.layers.SimpleRNN(100, return_sequences=True),\n",
        "  keras.layers.Dense(1),\n",
        "  keras.layers.Lambda(lambda x: x * 200.0)\n",
        "])\n",
        "optimizer = keras.optimizers.SGD(lr=1e-6, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "early_stopping = keras.callbacks.EarlyStopping(patience=10)\n",
        "model.fit(train_set, epochs=500,\n",
        "          validation_data=valid_set,\n",
        "          callbacks=[early_stopping])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "8/8 [==============================] - 2s 110ms/step - loss: 54.1429 - mae: 54.6414 - val_loss: 17.9208 - val_mae: 18.4157\n",
            "Epoch 2/500\n",
            "8/8 [==============================] - 1s 57ms/step - loss: 34.1722 - mae: 34.6694 - val_loss: 20.5482 - val_mae: 21.0441\n",
            "Epoch 3/500\n",
            "8/8 [==============================] - 0s 53ms/step - loss: 27.5075 - mae: 28.0027 - val_loss: 37.8713 - val_mae: 38.3707\n",
            "Epoch 4/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 21.0646 - mae: 21.5578 - val_loss: 25.2685 - val_mae: 25.7675\n",
            "Epoch 5/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 17.1768 - mae: 17.6667 - val_loss: 15.3556 - val_mae: 15.8505\n",
            "Epoch 6/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 15.0383 - mae: 15.5247 - val_loss: 9.4217 - val_mae: 9.9070\n",
            "Epoch 7/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 13.9508 - mae: 14.4372 - val_loss: 9.1617 - val_mae: 9.6475\n",
            "Epoch 8/500\n",
            "8/8 [==============================] - 1s 58ms/step - loss: 13.0593 - mae: 13.5433 - val_loss: 9.8610 - val_mae: 10.3507\n",
            "Epoch 9/500\n",
            "8/8 [==============================] - 0s 51ms/step - loss: 12.4298 - mae: 12.9131 - val_loss: 8.1729 - val_mae: 8.6573\n",
            "Epoch 10/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 11.8905 - mae: 12.3737 - val_loss: 8.1780 - val_mae: 8.6607\n",
            "Epoch 11/500\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 11.4248 - mae: 11.9069 - val_loss: 8.2508 - val_mae: 8.7357\n",
            "Epoch 12/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 11.0519 - mae: 11.5337 - val_loss: 7.7045 - val_mae: 8.1899\n",
            "Epoch 13/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 10.6913 - mae: 11.1735 - val_loss: 7.8013 - val_mae: 8.2838\n",
            "Epoch 14/500\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 10.3653 - mae: 10.8473 - val_loss: 7.5620 - val_mae: 8.0455\n",
            "Epoch 15/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 10.0908 - mae: 10.5734 - val_loss: 7.4425 - val_mae: 7.9259\n",
            "Epoch 16/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 9.8478 - mae: 10.3300 - val_loss: 7.3019 - val_mae: 7.7855\n",
            "Epoch 17/500\n",
            "8/8 [==============================] - 1s 44ms/step - loss: 9.6206 - mae: 10.1028 - val_loss: 7.2305 - val_mae: 7.7128\n",
            "Epoch 18/500\n",
            "8/8 [==============================] - 1s 44ms/step - loss: 9.4222 - mae: 9.9045 - val_loss: 7.2973 - val_mae: 7.7811\n",
            "Epoch 19/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 9.2415 - mae: 9.7235 - val_loss: 7.0410 - val_mae: 7.5227\n",
            "Epoch 20/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 9.0733 - mae: 9.5550 - val_loss: 6.9231 - val_mae: 7.4054\n",
            "Epoch 21/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 8.9267 - mae: 9.4083 - val_loss: 6.8684 - val_mae: 7.3498\n",
            "Epoch 22/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 8.7896 - mae: 9.2713 - val_loss: 6.7681 - val_mae: 7.2486\n",
            "Epoch 23/500\n",
            "8/8 [==============================] - 0s 51ms/step - loss: 8.6424 - mae: 9.1235 - val_loss: 6.9130 - val_mae: 7.3982\n",
            "Epoch 24/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 8.5131 - mae: 8.9936 - val_loss: 6.7497 - val_mae: 7.2318\n",
            "Epoch 25/500\n",
            "8/8 [==============================] - 1s 56ms/step - loss: 8.3955 - mae: 8.8765 - val_loss: 6.6384 - val_mae: 7.1199\n",
            "Epoch 26/500\n",
            "8/8 [==============================] - 0s 48ms/step - loss: 8.2890 - mae: 8.7705 - val_loss: 6.4467 - val_mae: 6.9236\n",
            "Epoch 27/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 8.1784 - mae: 8.6595 - val_loss: 6.4515 - val_mae: 6.9306\n",
            "Epoch 28/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 8.0964 - mae: 8.5775 - val_loss: 6.5570 - val_mae: 7.0407\n",
            "Epoch 29/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 7.9966 - mae: 8.4773 - val_loss: 6.2728 - val_mae: 6.7472\n",
            "Epoch 30/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 7.9174 - mae: 8.3981 - val_loss: 6.6967 - val_mae: 7.1815\n",
            "Epoch 31/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 7.8317 - mae: 8.3122 - val_loss: 6.1268 - val_mae: 6.6057\n",
            "Epoch 32/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 7.7520 - mae: 8.2328 - val_loss: 6.6857 - val_mae: 7.1703\n",
            "Epoch 33/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.6775 - mae: 8.1583 - val_loss: 6.0753 - val_mae: 6.5512\n",
            "Epoch 34/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 7.5826 - mae: 8.0634 - val_loss: 6.3348 - val_mae: 6.8185\n",
            "Epoch 35/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 7.4951 - mae: 7.9749 - val_loss: 6.0378 - val_mae: 6.5139\n",
            "Epoch 36/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 7.4325 - mae: 7.9133 - val_loss: 6.1865 - val_mae: 6.6674\n",
            "Epoch 37/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.3559 - mae: 7.8373 - val_loss: 6.0786 - val_mae: 6.5581\n",
            "Epoch 38/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.2913 - mae: 7.7732 - val_loss: 6.0684 - val_mae: 6.5479\n",
            "Epoch 39/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 7.2333 - mae: 7.7151 - val_loss: 6.1053 - val_mae: 6.5854\n",
            "Epoch 40/500\n",
            "8/8 [==============================] - 0s 48ms/step - loss: 7.1725 - mae: 7.6538 - val_loss: 5.9208 - val_mae: 6.3990\n",
            "Epoch 41/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 7.1129 - mae: 7.5941 - val_loss: 5.9483 - val_mae: 6.4270\n",
            "Epoch 42/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 7.0588 - mae: 7.5404 - val_loss: 5.9384 - val_mae: 6.4174\n",
            "Epoch 43/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 7.0068 - mae: 7.4883 - val_loss: 5.8196 - val_mae: 6.2968\n",
            "Epoch 44/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 6.9536 - mae: 7.4349 - val_loss: 5.9777 - val_mae: 6.4585\n",
            "Epoch 45/500\n",
            "8/8 [==============================] - 1s 53ms/step - loss: 6.9029 - mae: 7.3843 - val_loss: 5.8453 - val_mae: 6.3236\n",
            "Epoch 46/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 6.8559 - mae: 7.3373 - val_loss: 5.9745 - val_mae: 6.4567\n",
            "Epoch 47/500\n",
            "8/8 [==============================] - 0s 48ms/step - loss: 6.8096 - mae: 7.2905 - val_loss: 5.6930 - val_mae: 6.1683\n",
            "Epoch 48/500\n",
            "8/8 [==============================] - 0s 53ms/step - loss: 6.7795 - mae: 7.2602 - val_loss: 5.8916 - val_mae: 6.3713\n",
            "Epoch 49/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 6.7324 - mae: 7.2131 - val_loss: 5.7787 - val_mae: 6.2573\n",
            "Epoch 50/500\n",
            "8/8 [==============================] - 0s 41ms/step - loss: 6.6880 - mae: 7.1684 - val_loss: 5.7283 - val_mae: 6.2063\n",
            "Epoch 51/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.6452 - mae: 7.1259 - val_loss: 5.7470 - val_mae: 6.2248\n",
            "Epoch 52/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.6150 - mae: 7.0959 - val_loss: 5.7311 - val_mae: 6.2093\n",
            "Epoch 53/500\n",
            "8/8 [==============================] - 0s 48ms/step - loss: 6.5714 - mae: 7.0514 - val_loss: 5.6912 - val_mae: 6.1687\n",
            "Epoch 54/500\n",
            "8/8 [==============================] - 1s 53ms/step - loss: 6.5348 - mae: 7.0153 - val_loss: 5.6069 - val_mae: 6.0830\n",
            "Epoch 55/500\n",
            "8/8 [==============================] - 0s 46ms/step - loss: 6.5013 - mae: 6.9819 - val_loss: 5.7314 - val_mae: 6.2103\n",
            "Epoch 56/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.4726 - mae: 6.9530 - val_loss: 5.6089 - val_mae: 6.0850\n",
            "Epoch 57/500\n",
            "8/8 [==============================] - 0s 53ms/step - loss: 6.4448 - mae: 6.9249 - val_loss: 5.7441 - val_mae: 6.2233\n",
            "Epoch 58/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.4214 - mae: 6.9012 - val_loss: 5.5397 - val_mae: 6.0159\n",
            "Epoch 59/500\n",
            "8/8 [==============================] - 1s 48ms/step - loss: 6.3940 - mae: 6.8735 - val_loss: 5.5486 - val_mae: 6.0250\n",
            "Epoch 60/500\n",
            "8/8 [==============================] - 0s 48ms/step - loss: 6.3687 - mae: 6.8481 - val_loss: 5.6414 - val_mae: 6.1190\n",
            "Epoch 61/500\n",
            "8/8 [==============================] - 1s 55ms/step - loss: 6.3471 - mae: 6.8263 - val_loss: 5.4949 - val_mae: 5.9711\n",
            "Epoch 62/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.3239 - mae: 6.8034 - val_loss: 5.6962 - val_mae: 6.1758\n",
            "Epoch 63/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 6.3027 - mae: 6.7813 - val_loss: 5.4634 - val_mae: 5.9396\n",
            "Epoch 64/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.2800 - mae: 6.7595 - val_loss: 5.6596 - val_mae: 6.1384\n",
            "Epoch 65/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 6.2597 - mae: 6.7382 - val_loss: 5.4920 - val_mae: 5.9685\n",
            "Epoch 66/500\n",
            "8/8 [==============================] - 0s 51ms/step - loss: 6.2370 - mae: 6.7161 - val_loss: 5.5227 - val_mae: 5.9986\n",
            "Epoch 67/500\n",
            "8/8 [==============================] - 1s 61ms/step - loss: 6.2181 - mae: 6.6968 - val_loss: 5.5079 - val_mae: 5.9838\n",
            "Epoch 68/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 6.1976 - mae: 6.6767 - val_loss: 5.5154 - val_mae: 5.9910\n",
            "Epoch 69/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 6.1796 - mae: 6.6588 - val_loss: 5.5412 - val_mae: 6.0176\n",
            "Epoch 70/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.1641 - mae: 6.6427 - val_loss: 5.3948 - val_mae: 5.8713\n",
            "Epoch 71/500\n",
            "8/8 [==============================] - 0s 47ms/step - loss: 6.1562 - mae: 6.6361 - val_loss: 5.5790 - val_mae: 6.0581\n",
            "Epoch 72/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.1331 - mae: 6.6120 - val_loss: 5.4278 - val_mae: 5.9045\n",
            "Epoch 73/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 6.1158 - mae: 6.5956 - val_loss: 5.5849 - val_mae: 6.0648\n",
            "Epoch 74/500\n",
            "8/8 [==============================] - 0s 42ms/step - loss: 6.0946 - mae: 6.5735 - val_loss: 5.4474 - val_mae: 5.9229\n",
            "Epoch 75/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.0770 - mae: 6.5565 - val_loss: 5.5412 - val_mae: 6.0205\n",
            "Epoch 76/500\n",
            "8/8 [==============================] - 0s 45ms/step - loss: 6.0624 - mae: 6.5409 - val_loss: 5.4268 - val_mae: 5.9025\n",
            "Epoch 77/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.0439 - mae: 6.5231 - val_loss: 5.4290 - val_mae: 5.9047\n",
            "Epoch 78/500\n",
            "8/8 [==============================] - 0s 43ms/step - loss: 6.0280 - mae: 6.5074 - val_loss: 5.4693 - val_mae: 5.9471\n",
            "Epoch 79/500\n",
            "8/8 [==============================] - 0s 44ms/step - loss: 6.0133 - mae: 6.4930 - val_loss: 5.4171 - val_mae: 5.8927\n",
            "Epoch 80/500\n",
            "8/8 [==============================] - 1s 45ms/step - loss: 5.9990 - mae: 6.4783 - val_loss: 5.5149 - val_mae: 5.9946\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb55602a400>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mglBRex8OI_"
      },
      "source": [
        "rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
        "rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zl_FkcdI8OJA"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, rnn_forecast)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"370.91625pt\" version=\"1.1\" viewBox=\"0 0 612.165625 370.91625\" width=\"612.165625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-06-17T12:50:08.117216</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 370.91625 \nL 612.165625 370.91625 \nL 612.165625 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 46.965625 333.36 \nL 604.965625 333.36 \nL 604.965625 7.2 \nL 46.965625 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_1\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 72.329261 333.36 \nL 72.329261 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_2\">\n      <defs>\n       <path d=\"M 0 0 \nL 0 3.5 \n\" id=\"m23d88873ca\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"72.329261\" xlink:href=\"#m23d88873ca\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_1\">\n      <!-- 1000 -->\n      <g transform=\"translate(59.604261 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n        <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_2\">\n     <g id=\"line2d_3\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 182.605941 333.36 \nL 182.605941 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_4\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"182.605941\" xlink:href=\"#m23d88873ca\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_2\">\n      <!-- 1100 -->\n      <g transform=\"translate(169.880941 347.958438)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_3\">\n     <g id=\"line2d_5\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 292.882621 333.36 \nL 292.882621 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_6\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"292.882621\" xlink:href=\"#m23d88873ca\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_3\">\n      <!-- 1200 -->\n      <g transform=\"translate(280.157621 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_4\">\n     <g id=\"line2d_7\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 403.159301 333.36 \nL 403.159301 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_8\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"403.159301\" xlink:href=\"#m23d88873ca\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_4\">\n      <!-- 1300 -->\n      <g transform=\"translate(390.434301 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 40.578125 39.3125 \nQ 47.65625 37.796875 51.625 33 \nQ 55.609375 28.21875 55.609375 21.1875 \nQ 55.609375 10.40625 48.1875 4.484375 \nQ 40.765625 -1.421875 27.09375 -1.421875 \nQ 22.515625 -1.421875 17.65625 -0.515625 \nQ 12.796875 0.390625 7.625 2.203125 \nL 7.625 11.71875 \nQ 11.71875 9.328125 16.59375 8.109375 \nQ 21.484375 6.890625 26.8125 6.890625 \nQ 36.078125 6.890625 40.9375 10.546875 \nQ 45.796875 14.203125 45.796875 21.1875 \nQ 45.796875 27.640625 41.28125 31.265625 \nQ 36.765625 34.90625 28.71875 34.90625 \nL 20.21875 34.90625 \nL 20.21875 43.015625 \nL 29.109375 43.015625 \nQ 36.375 43.015625 40.234375 45.921875 \nQ 44.09375 48.828125 44.09375 54.296875 \nQ 44.09375 59.90625 40.109375 62.90625 \nQ 36.140625 65.921875 28.71875 65.921875 \nQ 24.65625 65.921875 20.015625 65.03125 \nQ 15.375 64.15625 9.8125 62.3125 \nL 9.8125 71.09375 \nQ 15.4375 72.65625 20.34375 73.4375 \nQ 25.25 74.21875 29.59375 74.21875 \nQ 40.828125 74.21875 47.359375 69.109375 \nQ 53.90625 64.015625 53.90625 55.328125 \nQ 53.90625 49.265625 50.4375 45.09375 \nQ 46.96875 40.921875 40.578125 39.3125 \nz\n\" id=\"DejaVuSans-51\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-51\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_5\">\n     <g id=\"line2d_9\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 513.435981 333.36 \nL 513.435981 7.2 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_10\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"513.435981\" xlink:href=\"#m23d88873ca\" y=\"333.36\"/>\n      </g>\n     </g>\n     <g id=\"text_5\">\n      <!-- 1400 -->\n      <g transform=\"translate(500.710981 347.958438)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"text_6\">\n     <!-- Time -->\n     <g transform=\"translate(313.732031 361.636562)scale(0.1 -0.1)\">\n      <defs>\n       <path d=\"M -0.296875 72.90625 \nL 61.375 72.90625 \nL 61.375 64.59375 \nL 35.5 64.59375 \nL 35.5 0 \nL 25.59375 0 \nL 25.59375 64.59375 \nL -0.296875 64.59375 \nz\n\" id=\"DejaVuSans-84\"/>\n       <path d=\"M 9.421875 54.6875 \nL 18.40625 54.6875 \nL 18.40625 0 \nL 9.421875 0 \nz\nM 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 64.59375 \nL 9.421875 64.59375 \nz\n\" id=\"DejaVuSans-105\"/>\n       <path d=\"M 52 44.1875 \nQ 55.375 50.25 60.0625 53.125 \nQ 64.75 56 71.09375 56 \nQ 79.640625 56 84.28125 50.015625 \nQ 88.921875 44.046875 88.921875 33.015625 \nL 88.921875 0 \nL 79.890625 0 \nL 79.890625 32.71875 \nQ 79.890625 40.578125 77.09375 44.375 \nQ 74.3125 48.1875 68.609375 48.1875 \nQ 61.625 48.1875 57.5625 43.546875 \nQ 53.515625 38.921875 53.515625 30.90625 \nL 53.515625 0 \nL 44.484375 0 \nL 44.484375 32.71875 \nQ 44.484375 40.625 41.703125 44.40625 \nQ 38.921875 48.1875 33.109375 48.1875 \nQ 26.21875 48.1875 22.15625 43.53125 \nQ 18.109375 38.875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 21.1875 51.21875 25.484375 53.609375 \nQ 29.78125 56 35.6875 56 \nQ 41.65625 56 45.828125 52.96875 \nQ 50 49.953125 52 44.1875 \nz\n\" id=\"DejaVuSans-109\"/>\n       <path d=\"M 56.203125 29.59375 \nL 56.203125 25.203125 \nL 14.890625 25.203125 \nQ 15.484375 15.921875 20.484375 11.0625 \nQ 25.484375 6.203125 34.421875 6.203125 \nQ 39.59375 6.203125 44.453125 7.46875 \nQ 49.3125 8.734375 54.109375 11.28125 \nL 54.109375 2.78125 \nQ 49.265625 0.734375 44.1875 -0.34375 \nQ 39.109375 -1.421875 33.890625 -1.421875 \nQ 20.796875 -1.421875 13.15625 6.1875 \nQ 5.515625 13.8125 5.515625 26.8125 \nQ 5.515625 40.234375 12.765625 48.109375 \nQ 20.015625 56 32.328125 56 \nQ 43.359375 56 49.78125 48.890625 \nQ 56.203125 41.796875 56.203125 29.59375 \nz\nM 47.21875 32.234375 \nQ 47.125 39.59375 43.09375 43.984375 \nQ 39.0625 48.390625 32.421875 48.390625 \nQ 24.90625 48.390625 20.390625 44.140625 \nQ 15.875 39.890625 15.1875 32.171875 \nz\n\" id=\"DejaVuSans-101\"/>\n      </defs>\n      <use xlink:href=\"#DejaVuSans-84\"/>\n      <use x=\"57.958984\" xlink:href=\"#DejaVuSans-105\"/>\n      <use x=\"85.742188\" xlink:href=\"#DejaVuSans-109\"/>\n      <use x=\"183.154297\" xlink:href=\"#DejaVuSans-101\"/>\n     </g>\n    </g>\n   </g>\n   <g id=\"matplotlib.axis_2\">\n    <g id=\"ytick_1\">\n     <g id=\"line2d_11\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 46.965625 307.591542 \nL 604.965625 307.591542 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_12\">\n      <defs>\n       <path d=\"M 0 0 \nL -3.5 0 \n\" id=\"medd5a165dc\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#medd5a165dc\" y=\"307.591542\"/>\n      </g>\n     </g>\n     <g id=\"text_7\">\n      <!-- 40 -->\n      <g transform=\"translate(27.240625 311.39076)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_2\">\n     <g id=\"line2d_13\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 46.965625 238.201675 \nL 604.965625 238.201675 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_14\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#medd5a165dc\" y=\"238.201675\"/>\n      </g>\n     </g>\n     <g id=\"text_8\">\n      <!-- 60 -->\n      <g transform=\"translate(27.240625 242.000894)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-54\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_3\">\n     <g id=\"line2d_15\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 46.965625 168.811808 \nL 604.965625 168.811808 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_16\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#medd5a165dc\" y=\"168.811808\"/>\n      </g>\n     </g>\n     <g id=\"text_9\">\n      <!-- 80 -->\n      <g transform=\"translate(27.240625 172.611027)scale(0.1 -0.1)\">\n       <defs>\n        <path d=\"M 31.78125 34.625 \nQ 24.75 34.625 20.71875 30.859375 \nQ 16.703125 27.09375 16.703125 20.515625 \nQ 16.703125 13.921875 20.71875 10.15625 \nQ 24.75 6.390625 31.78125 6.390625 \nQ 38.8125 6.390625 42.859375 10.171875 \nQ 46.921875 13.96875 46.921875 20.515625 \nQ 46.921875 27.09375 42.890625 30.859375 \nQ 38.875 34.625 31.78125 34.625 \nz\nM 21.921875 38.8125 \nQ 15.578125 40.375 12.03125 44.71875 \nQ 8.5 49.078125 8.5 55.328125 \nQ 8.5 64.0625 14.71875 69.140625 \nQ 20.953125 74.21875 31.78125 74.21875 \nQ 42.671875 74.21875 48.875 69.140625 \nQ 55.078125 64.0625 55.078125 55.328125 \nQ 55.078125 49.078125 51.53125 44.71875 \nQ 48 40.375 41.703125 38.8125 \nQ 48.828125 37.15625 52.796875 32.3125 \nQ 56.78125 27.484375 56.78125 20.515625 \nQ 56.78125 9.90625 50.3125 4.234375 \nQ 43.84375 -1.421875 31.78125 -1.421875 \nQ 19.734375 -1.421875 13.25 4.234375 \nQ 6.78125 9.90625 6.78125 20.515625 \nQ 6.78125 27.484375 10.78125 32.3125 \nQ 14.796875 37.15625 21.921875 38.8125 \nz\nM 18.3125 54.390625 \nQ 18.3125 48.734375 21.84375 45.5625 \nQ 25.390625 42.390625 31.78125 42.390625 \nQ 38.140625 42.390625 41.71875 45.5625 \nQ 45.3125 48.734375 45.3125 54.390625 \nQ 45.3125 60.0625 41.71875 63.234375 \nQ 38.140625 66.40625 31.78125 66.40625 \nQ 25.390625 66.40625 21.84375 63.234375 \nQ 18.3125 60.0625 18.3125 54.390625 \nz\n\" id=\"DejaVuSans-56\"/>\n       </defs>\n       <use xlink:href=\"#DejaVuSans-56\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_4\">\n     <g id=\"line2d_17\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 46.965625 99.421941 \nL 604.965625 99.421941 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_18\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#medd5a165dc\" y=\"99.421941\"/>\n      </g>\n     </g>\n     <g id=\"text_10\">\n      <!-- 100 -->\n      <g transform=\"translate(20.878125 103.22116)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_5\">\n     <g id=\"line2d_19\">\n      <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 46.965625 30.032074 \nL 604.965625 30.032074 \n\" style=\"fill:none;stroke:#b0b0b0;stroke-linecap:square;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"line2d_20\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.965625\" xlink:href=\"#medd5a165dc\" y=\"30.032074\"/>\n      </g>\n     </g>\n     <g id=\"text_11\">\n      <!-- 120 -->\n      <g transform=\"translate(20.878125 33.831293)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"text_12\">\n     <!-- Value -->\n     <g transform=\"translate(14.798438 184.01125)rotate(-90)scale(0.1 -0.1)\">\n      <defs>\n       <path d=\"M 28.609375 0 \nL 0.78125 72.90625 \nL 11.078125 72.90625 \nL 34.1875 11.53125 \nL 57.328125 72.90625 \nL 67.578125 72.90625 \nL 39.796875 0 \nz\n\" id=\"DejaVuSans-86\"/>\n       <path d=\"M 34.28125 27.484375 \nQ 23.390625 27.484375 19.1875 25 \nQ 14.984375 22.515625 14.984375 16.5 \nQ 14.984375 11.71875 18.140625 8.90625 \nQ 21.296875 6.109375 26.703125 6.109375 \nQ 34.1875 6.109375 38.703125 11.40625 \nQ 43.21875 16.703125 43.21875 25.484375 \nL 43.21875 27.484375 \nz\nM 52.203125 31.203125 \nL 52.203125 0 \nL 43.21875 0 \nL 43.21875 8.296875 \nQ 40.140625 3.328125 35.546875 0.953125 \nQ 30.953125 -1.421875 24.3125 -1.421875 \nQ 15.921875 -1.421875 10.953125 3.296875 \nQ 6 8.015625 6 15.921875 \nQ 6 25.140625 12.171875 29.828125 \nQ 18.359375 34.515625 30.609375 34.515625 \nL 43.21875 34.515625 \nL 43.21875 35.40625 \nQ 43.21875 41.609375 39.140625 45 \nQ 35.0625 48.390625 27.6875 48.390625 \nQ 23 48.390625 18.546875 47.265625 \nQ 14.109375 46.140625 10.015625 43.890625 \nL 10.015625 52.203125 \nQ 14.9375 54.109375 19.578125 55.046875 \nQ 24.21875 56 28.609375 56 \nQ 40.484375 56 46.34375 49.84375 \nQ 52.203125 43.703125 52.203125 31.203125 \nz\n\" id=\"DejaVuSans-97\"/>\n       <path d=\"M 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 0 \nL 9.421875 0 \nz\n\" id=\"DejaVuSans-108\"/>\n       <path d=\"M 8.5 21.578125 \nL 8.5 54.6875 \nL 17.484375 54.6875 \nL 17.484375 21.921875 \nQ 17.484375 14.15625 20.5 10.265625 \nQ 23.53125 6.390625 29.59375 6.390625 \nQ 36.859375 6.390625 41.078125 11.03125 \nQ 45.3125 15.671875 45.3125 23.6875 \nL 45.3125 54.6875 \nL 54.296875 54.6875 \nL 54.296875 0 \nL 45.3125 0 \nL 45.3125 8.40625 \nQ 42.046875 3.421875 37.71875 1 \nQ 33.40625 -1.421875 27.6875 -1.421875 \nQ 18.265625 -1.421875 13.375 4.4375 \nQ 8.5 10.296875 8.5 21.578125 \nz\nM 31.109375 56 \nz\n\" id=\"DejaVuSans-117\"/>\n      </defs>\n      <use xlink:href=\"#DejaVuSans-86\"/>\n      <use x=\"60.658203\" xlink:href=\"#DejaVuSans-97\"/>\n      <use x=\"121.9375\" xlink:href=\"#DejaVuSans-108\"/>\n      <use x=\"149.720703\" xlink:href=\"#DejaVuSans-117\"/>\n      <use x=\"213.099609\" xlink:href=\"#DejaVuSans-101\"/>\n     </g>\n    </g>\n   </g>\n   <g id=\"line2d_21\">\n    <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 72.329261 198.841266 \nL 73.432028 207.02649 \nL 74.534795 221.981101 \nL 75.637562 234.186242 \nL 76.740329 210.798119 \nL 77.843095 216.030561 \nL 78.945862 207.272234 \nL 80.048629 211.72701 \nL 81.151396 204.481652 \nL 82.254163 231.915854 \nL 83.356929 199.71863 \nL 84.459696 219.084488 \nL 85.562463 186.451153 \nL 86.66523 234.345741 \nL 87.767997 192.212996 \nL 88.870763 218.830902 \nL 89.97353 233.500287 \nL 92.179064 227.625741 \nL 93.281831 214.643345 \nL 94.384597 212.863831 \nL 95.487364 231.817231 \nL 96.590131 222.217909 \nL 97.692898 184.562034 \nL 98.795665 191.685236 \nL 99.898431 214.012522 \nL 101.001198 220.849438 \nL 102.103965 219.352708 \nL 103.206732 210.717395 \nL 104.309499 239.027858 \nL 105.412265 225.673646 \nL 106.515032 250.077543 \nL 107.617799 214.128558 \nL 108.720566 209.748305 \nL 109.823333 229.278247 \nL 110.926099 193.511403 \nL 112.028866 241.999151 \nL 113.131633 246.056794 \nL 114.2344 216.677555 \nL 115.337167 202.323603 \nL 116.439933 191.192367 \nL 117.5427 228.280133 \nL 118.645467 201.521953 \nL 119.748234 220.816315 \nL 120.851 223.056179 \nL 121.953767 204.644804 \nL 123.056534 208.569744 \nL 124.159301 247.143434 \nL 125.262068 194.093227 \nL 126.364834 195.676123 \nL 127.467601 230.37821 \nL 128.570368 212.568487 \nL 129.673135 210.772227 \nL 130.775902 214.727429 \nL 131.878668 228.702259 \nL 132.981435 230.712568 \nL 134.084202 219.41 \nL 135.186969 198.527279 \nL 136.289736 209.344887 \nL 137.392502 225.107377 \nL 138.495269 205.193695 \nL 139.598036 267.897645 \nL 140.700803 198.455789 \nL 141.80357 248.464295 \nL 142.906336 224.472198 \nL 144.009103 237.507697 \nL 145.11187 240.442603 \nL 146.214637 197.74406 \nL 147.317404 225.892613 \nL 148.42017 211.665353 \nL 149.522937 218.386828 \nL 150.625704 209.193409 \nL 151.728471 216.031609 \nL 152.831238 239.51579 \nL 153.934004 199.870712 \nL 155.036771 225.612581 \nL 156.139538 243.943095 \nL 157.242305 224.259698 \nL 158.345072 190.690704 \nL 159.447838 201.867455 \nL 160.550605 222.555911 \nL 161.653372 222.456919 \nL 162.756139 221.867716 \nL 163.858906 180.151306 \nL 164.961672 209.440955 \nL 166.064439 208.494869 \nL 167.167206 197.970121 \nL 168.269973 211.587967 \nL 169.37274 220.111005 \nL 170.475506 218.909885 \nL 171.578273 216.631754 \nL 172.68104 215.920821 \nL 173.783807 202.537552 \nL 174.886574 214.143447 \nL 175.98934 202.219147 \nL 177.092107 84.342076 \nL 178.194874 81.424813 \nL 179.297641 117.340859 \nL 180.400408 66.710479 \nL 181.503174 76.566117 \nL 182.605941 65.275265 \nL 183.708708 132.883276 \nL 184.811475 46.505845 \nL 185.914242 85.28948 \nL 187.017008 63.818622 \nL 188.119775 101.297984 \nL 189.222542 72.884155 \nL 190.325309 102.084689 \nL 191.428076 94.968026 \nL 192.530842 51.320334 \nL 193.633609 88.248436 \nL 194.736376 81.62508 \nL 195.839143 70.799501 \nL 198.044676 84.393467 \nL 199.147443 81.285172 \nL 200.25021 46.575326 \nL 201.352977 89.958681 \nL 202.455744 86.199434 \nL 203.55851 72.228595 \nL 204.661277 72.081171 \nL 205.764044 71.509108 \nL 206.866811 81.899629 \nL 207.969578 113.723885 \nL 209.072344 66.51185 \nL 210.175111 115.722146 \nL 211.277878 91.609967 \nL 212.380645 111.004076 \nL 213.483412 100.126353 \nL 214.586178 94.4396 \nL 215.688945 95.731419 \nL 216.791712 95.184137 \nL 217.894479 75.740308 \nL 218.997246 97.130216 \nL 220.100012 110.529798 \nL 221.202779 90.898439 \nL 222.305546 88.348974 \nL 223.408313 131.670823 \nL 224.51108 101.434058 \nL 225.613846 101.086726 \nL 226.716613 119.895012 \nL 227.81938 92.380143 \nL 228.922147 120.389277 \nL 230.024914 117.177164 \nL 231.12768 123.955225 \nL 232.230447 122.307222 \nL 233.333214 143.246494 \nL 234.435981 150.887821 \nL 235.538748 99.949369 \nL 236.641514 144.031195 \nL 237.744281 148.289875 \nL 238.847048 170.22479 \nL 239.949815 145.753941 \nL 241.052582 159.445342 \nL 242.155348 109.854069 \nL 243.258115 122.688565 \nL 244.360882 154.03253 \nL 245.463649 104.929801 \nL 247.669182 153.515744 \nL 248.771949 193.205744 \nL 249.874716 112.089936 \nL 250.977483 177.939466 \nL 252.08025 184.395994 \nL 253.183016 140.1481 \nL 254.285783 117.613565 \nL 255.38855 137.988153 \nL 256.491317 154.298123 \nL 257.594083 155.855153 \nL 258.69685 153.472861 \nL 259.799617 157.228828 \nL 260.902384 167.64457 \nL 263.107917 177.896115 \nL 264.210684 192.047827 \nL 265.313451 186.00423 \nL 266.416218 212.678432 \nL 267.518985 187.197773 \nL 268.621751 204.829811 \nL 269.724518 209.018989 \nL 271.930052 170.129952 \nL 273.032819 180.554696 \nL 274.135585 225.939873 \nL 275.238352 218.076009 \nL 276.341119 186.802866 \nL 277.443886 222.38181 \nL 279.649419 200.226515 \nL 280.752186 228.780536 \nL 281.854953 212.365955 \nL 282.95772 239.545005 \nL 284.060487 229.040976 \nL 285.163253 215.26303 \nL 286.26602 250.641741 \nL 287.368787 217.557642 \nL 288.471554 227.724686 \nL 289.574321 219.978106 \nL 290.677087 227.848885 \nL 291.779854 210.231407 \nL 292.882621 233.877066 \nL 293.985388 245.642455 \nL 295.088155 238.205438 \nL 296.190921 233.024765 \nL 297.293688 243.712937 \nL 298.396455 245.954928 \nL 299.499222 260.922379 \nL 300.601989 262.316051 \nL 301.704755 277.205467 \nL 302.807522 224.565473 \nL 303.910289 278.520757 \nL 305.013056 270.973365 \nL 306.115823 273.4091 \nL 307.218589 247.397585 \nL 308.321356 269.996313 \nL 309.424123 245.173041 \nL 310.52689 280.420551 \nL 311.629657 265.796191 \nL 312.732423 287.00526 \nL 313.83519 237.157901 \nL 314.937957 296.807352 \nL 316.040724 272.643076 \nL 317.143491 268.812252 \nL 318.246257 274.393564 \nL 319.349024 286.527141 \nL 320.451791 287.280988 \nL 321.554558 285.959525 \nL 322.657325 290.934277 \nL 323.760091 273.301386 \nL 324.862858 253.28628 \nL 325.965625 302.887727 \nL 327.068392 318.534545 \nL 328.171159 297.830044 \nL 329.273925 253.044651 \nL 330.376692 313.205653 \nL 331.479459 272.26715 \nL 332.582226 273.004955 \nL 333.684993 313.088239 \nL 335.890526 277.893439 \nL 336.993293 313.999049 \nL 338.09606 158.126231 \nL 339.198827 164.853994 \nL 340.301593 172.532638 \nL 341.40436 166.355597 \nL 342.507127 176.484882 \nL 343.609894 154.818968 \nL 344.712661 168.922215 \nL 345.815427 151.661748 \nL 346.918194 182.857736 \nL 348.020961 150.273856 \nL 349.123728 141.698454 \nL 350.226495 190.886012 \nL 351.329261 149.949914 \nL 352.432028 135.278441 \nL 353.534795 177.693599 \nL 354.637562 127.825984 \nL 355.740329 149.700949 \nL 356.843095 169.326551 \nL 359.048629 147.656505 \nL 360.151396 156.142769 \nL 361.254163 148.23203 \nL 362.356929 173.663872 \nL 363.459696 154.866615 \nL 364.562463 182.438703 \nL 365.66523 159.951251 \nL 366.767997 173.807393 \nL 367.870763 170.989309 \nL 368.97353 136.887225 \nL 370.076297 139.497182 \nL 371.179064 147.957758 \nL 372.281831 177.567528 \nL 373.384597 164.199272 \nL 374.487364 137.755762 \nL 375.590131 156.428899 \nL 376.692898 118.738095 \nL 377.795665 152.23266 \nL 378.898431 155.805204 \nL 380.001198 164.578114 \nL 381.103965 156.976021 \nL 382.206732 162.012425 \nL 383.309499 147.156377 \nL 384.412265 142.9147 \nL 385.515032 173.214781 \nL 387.720566 191.565021 \nL 388.823333 150.96204 \nL 389.926099 178.975976 \nL 391.028866 197.241598 \nL 392.131633 153.210682 \nL 393.2344 116.759558 \nL 394.337167 160.165475 \nL 395.439933 145.565989 \nL 396.5427 158.741825 \nL 397.645467 161.924693 \nL 398.748234 144.311599 \nL 399.851 165.335018 \nL 400.953767 155.703371 \nL 402.056534 154.802001 \nL 403.159301 172.01126 \nL 404.262068 143.249362 \nL 405.364834 163.529369 \nL 406.467601 173.638291 \nL 407.570368 151.25301 \nL 408.673135 176.176627 \nL 409.775902 160.130331 \nL 410.878668 160.804457 \nL 411.981435 141.834151 \nL 413.084202 152.46067 \nL 414.186969 161.151816 \nL 415.289736 146.552065 \nL 416.392502 136.640649 \nL 417.495269 151.098046 \nL 418.598036 160.610902 \nL 419.700803 183.561831 \nL 420.80357 179.294584 \nL 421.906336 166.123675 \nL 423.009103 171.412021 \nL 424.11187 164.090235 \nL 425.214637 159.870765 \nL 426.317404 151.669937 \nL 427.42017 162.085542 \nL 428.522937 152.425348 \nL 429.625704 159.747364 \nL 430.728471 195.134657 \nL 431.831238 177.158476 \nL 432.934004 163.359606 \nL 434.036771 181.840463 \nL 435.139538 150.441972 \nL 436.242305 134.287088 \nL 437.345072 139.690469 \nL 438.447838 164.525407 \nL 439.550605 134.950871 \nL 440.653372 158.219342 \nL 441.756139 166.631771 \nL 442.858906 171.409699 \nL 443.961672 165.99904 \nL 445.064439 167.4678 \nL 446.167206 157.758311 \nL 447.269973 157.908472 \nL 448.37274 160.619355 \nL 449.475506 153.069356 \nL 450.578273 139.970129 \nL 451.68104 144.126946 \nL 452.783807 186.33179 \nL 453.886574 204.8487 \nL 454.98934 144.307429 \nL 456.092107 184.197089 \nL 457.194874 164.352838 \nL 458.297641 180.719583 \nL 459.400408 191.647089 \nL 460.503174 150.959349 \nL 461.605941 147.146866 \nL 462.708708 170.280084 \nL 463.811475 205.188798 \nL 464.914242 169.677318 \nL 466.017008 153.364038 \nL 467.119775 185.774611 \nL 468.222542 156.895729 \nL 469.325309 160.298393 \nL 470.428076 149.938751 \nL 471.530842 157.867706 \nL 472.633609 176.775491 \nL 473.736376 139.089776 \nL 474.839143 162.548371 \nL 475.94191 160.221954 \nL 477.044676 175.887118 \nL 478.147443 167.335098 \nL 479.25021 174.941357 \nL 480.352977 162.538458 \nL 481.455744 129.789509 \nL 482.55851 183.226388 \nL 483.661277 187.361682 \nL 484.764044 133.791547 \nL 485.866811 162.885061 \nL 486.969578 170.800805 \nL 488.072344 141.093706 \nL 489.175111 169.577721 \nL 490.277878 127.332674 \nL 492.483412 167.345609 \nL 493.586178 121.392538 \nL 494.688945 169.257892 \nL 495.791712 145.809822 \nL 496.894479 153.42348 \nL 497.997246 135.964822 \nL 499.100012 131.388902 \nL 500.202779 157.851553 \nL 501.305546 171.405371 \nL 502.408313 150.284027 \nL 503.51108 169.947093 \nL 504.613846 123.189526 \nL 505.716613 155.673582 \nL 506.81938 164.304148 \nL 507.922147 154.686826 \nL 509.024914 181.182811 \nL 510.12768 174.590856 \nL 511.230447 136.833121 \nL 512.333214 168.973384 \nL 513.435981 175.659086 \nL 514.538748 148.107403 \nL 515.641514 136.766383 \nL 516.744281 144.780953 \nL 517.847048 139.890948 \nL 518.949815 170.21447 \nL 520.052582 181.666213 \nL 521.155348 130.871744 \nL 522.258115 162.427715 \nL 523.360882 161.094597 \nL 524.463649 133.615008 \nL 525.566416 146.918459 \nL 526.669182 148.570272 \nL 527.771949 118.831902 \nL 528.874716 167.46237 \nL 529.977483 140.112352 \nL 531.08025 155.127389 \nL 532.183016 151.36475 \nL 533.285783 129.41615 \nL 534.38855 147.027941 \nL 535.491317 146.405277 \nL 536.594083 137.043163 \nL 537.69685 161.883908 \nL 538.799617 170.014881 \nL 539.902384 173.484696 \nL 541.005151 189.373765 \nL 542.107917 119.494409 \nL 543.210684 174.203581 \nL 544.313451 158.804605 \nL 545.416218 159.668522 \nL 546.518985 149.43328 \nL 547.621751 140.513931 \nL 548.724518 139.636176 \nL 549.827285 164.575845 \nL 552.032819 149.352575 \nL 553.135585 158.464766 \nL 554.238352 126.162646 \nL 555.341119 145.4237 \nL 556.443886 141.086896 \nL 557.546653 142.229591 \nL 558.649419 133.26529 \nL 559.752186 150.374465 \nL 560.854953 175.90358 \nL 561.95772 146.364821 \nL 563.060487 164.488818 \nL 564.163253 162.25009 \nL 565.26602 142.797979 \nL 566.368787 131.378295 \nL 567.471554 152.379306 \nL 568.574321 147.279278 \nL 569.677087 122.995559 \nL 570.779854 148.235643 \nL 571.882621 107.166416 \nL 572.985388 142.375299 \nL 574.088155 182.616174 \nL 575.190921 138.888287 \nL 576.293688 145.229696 \nL 577.396455 129.35238 \nL 578.499222 139.932351 \nL 579.601989 22.025455 \nL 579.601989 22.025455 \n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\n   </g>\n   <g id=\"line2d_22\">\n    <path clip-path=\"url(#pd92a9f5c59)\" d=\"M 72.329261 222.753414 \nL 73.432028 210.754661 \nL 74.534795 204.199094 \nL 75.637562 213.953815 \nL 76.740329 228.71597 \nL 77.843095 223.353545 \nL 78.945862 215.536782 \nL 80.048629 210.560026 \nL 81.151396 211.780617 \nL 82.254163 210.337889 \nL 83.356929 220.692714 \nL 84.459696 213.910933 \nL 85.562463 213.682469 \nL 86.66523 202.903116 \nL 87.767997 214.787333 \nL 88.870763 212.055324 \nL 89.97353 212.09045 \nL 91.076297 225.192173 \nL 92.179064 230.606268 \nL 93.281831 230.752887 \nL 94.384597 222.099681 \nL 95.487364 215.185152 \nL 96.590131 223.355477 \nL 97.692898 227.998709 \nL 98.795665 206.405459 \nL 99.898431 192.916676 \nL 101.001198 204.99783 \nL 102.103965 218.49601 \nL 103.206732 221.592248 \nL 104.309499 214.85409 \nL 105.412265 224.619015 \nL 106.515032 230.21099 \nL 107.617799 241.725167 \nL 108.720566 230.441373 \nL 109.823333 216.825798 \nL 110.926099 221.000667 \nL 112.028866 211.766058 \nL 113.131633 224.548406 \nL 114.2344 242.165776 \nL 115.337167 231.936512 \nL 116.439933 213.173211 \nL 117.5427 199.051103 \nL 118.645467 212.381012 \nL 119.748234 215.936772 \nL 120.851 216.871009 \nL 121.953767 219.457141 \nL 123.056534 213.260006 \nL 124.159301 209.635424 \nL 125.262068 229.408667 \nL 126.364834 217.932541 \nL 127.467601 202.716343 \nL 128.570368 215.492762 \nL 129.673135 218.924059 \nL 130.775902 215.876632 \nL 131.878668 214.795565 \nL 132.981435 220.641018 \nL 134.084202 228.599912 \nL 135.186969 227.434485 \nL 136.289736 210.574743 \nL 137.392502 205.870048 \nL 138.495269 218.576082 \nL 139.598036 216.240226 \nL 140.700803 240.097969 \nL 141.80357 227.520883 \nL 142.906336 234.940925 \nL 144.009103 233.517242 \nL 145.11187 234.895674 \nL 146.214637 239.300265 \nL 147.317404 221.308144 \nL 148.42017 217.398876 \nL 149.522937 217.652407 \nL 150.625704 219.159061 \nL 151.728471 214.51252 \nL 152.831238 214.805094 \nL 153.934004 227.105051 \nL 155.036771 218.562609 \nL 156.139538 218.750759 \nL 157.242305 232.91997 \nL 158.345072 232.85255 \nL 159.447838 212.013448 \nL 160.550605 200.947132 \nL 162.756139 223.340177 \nL 163.858906 225.259474 \nL 164.961672 201.150582 \nL 166.064439 198.369365 \nL 167.167206 209.928131 \nL 168.269973 206.509143 \nL 169.37274 207.051648 \nL 170.475506 215.082528 \nL 171.578273 219.002305 \nL 172.68104 218.802984 \nL 173.783807 217.282805 \nL 174.886574 209.232575 \nL 175.98934 211.055097 \nL 177.092107 209.314897 \nL 179.297641 112.897674 \nL 180.400408 129.155842 \nL 181.503174 129.541062 \nL 182.605941 103.940816 \nL 183.708708 103.589822 \nL 184.811475 124.701924 \nL 185.914242 124.709574 \nL 187.017008 94.851925 \nL 188.119775 106.945256 \nL 189.222542 114.220969 \nL 190.325309 117.107087 \nL 191.428076 111.551456 \nL 192.530842 122.138477 \nL 193.633609 108.413025 \nL 194.736376 100.430771 \nL 195.839143 115.860212 \nL 196.94191 110.192347 \nL 198.044676 103.302912 \nL 199.147443 110.815957 \nL 200.25021 113.331387 \nL 201.352977 101.594054 \nL 202.455744 99.768276 \nL 203.55851 118.250385 \nL 204.661277 111.360924 \nL 205.764044 101.567557 \nL 206.866811 104.872565 \nL 207.969578 109.478103 \nL 209.072344 121.838332 \nL 210.175111 119.300191 \nL 211.277878 112.66929 \nL 212.380645 127.618827 \nL 213.483412 124.194174 \nL 214.586178 125.59643 \nL 215.688945 120.503576 \nL 216.791712 119.114344 \nL 217.894479 120.419586 \nL 218.997246 114.859455 \nL 220.100012 112.233777 \nL 221.202779 126.899872 \nL 222.305546 124.904156 \nL 223.408313 113.349784 \nL 224.51108 129.08784 \nL 225.613846 138.211883 \nL 226.716613 121.376402 \nL 227.81938 127.450292 \nL 228.922147 129.91032 \nL 230.024914 126.153017 \nL 231.12768 136.162777 \nL 232.230447 137.128329 \nL 233.333214 137.373733 \nL 234.435981 145.509382 \nL 235.538748 157.197057 \nL 236.641514 140.816095 \nL 237.744281 135.455945 \nL 238.847048 158.526387 \nL 239.949815 168.853101 \nL 241.052582 162.728362 \nL 242.155348 158.67335 \nL 243.258115 148.018909 \nL 244.360882 133.351649 \nL 245.463649 153.018906 \nL 246.566416 147.109078 \nL 247.669182 131.475579 \nL 248.771949 152.636915 \nL 249.874716 182.454065 \nL 250.977483 161.027418 \nL 252.08025 153.611228 \nL 253.183016 186.964125 \nL 254.285783 170.578345 \nL 255.38855 140.568731 \nL 256.491317 141.556014 \nL 257.594083 160.393008 \nL 258.69685 164.781624 \nL 259.799617 160.941681 \nL 260.902384 160.432052 \nL 262.005151 168.814508 \nL 263.107917 175.81554 \nL 264.210684 178.955745 \nL 265.313451 187.116699 \nL 266.416218 190.35442 \nL 267.518985 201.523069 \nL 268.621751 199.658249 \nL 269.724518 199.299922 \nL 270.827285 207.197419 \nL 271.930052 200.339537 \nL 273.032819 183.93123 \nL 274.135585 180.564732 \nL 275.238352 207.472126 \nL 276.341119 221.318521 \nL 277.443886 204.549029 \nL 278.546653 206.538948 \nL 279.649419 214.750831 \nL 280.752186 209.082251 \nL 281.854953 217.878277 \nL 282.95772 218.671798 \nL 284.060487 228.076928 \nL 285.163253 233.316029 \nL 286.26602 224.489391 \nL 287.368787 234.473105 \nL 289.574321 229.041394 \nL 290.677087 224.2323 \nL 291.779854 225.209299 \nL 292.882621 219.278176 \nL 293.985388 226.507037 \nL 295.088155 238.280132 \nL 296.190921 241.19329 \nL 297.293688 238.487049 \nL 298.396455 239.036132 \nL 299.499222 243.82409 \nL 300.601989 255.30203 \nL 301.704755 262.741712 \nL 302.807522 270.975988 \nL 303.910289 251.163887 \nL 305.013056 261.03456 \nL 307.218589 278.058721 \nL 308.321356 265.822941 \nL 309.424123 263.218505 \nL 310.52689 254.072333 \nL 311.629657 270.352048 \nL 312.732423 273.092262 \nL 313.83519 280.139247 \nL 314.937957 261.907413 \nL 316.040724 274.915856 \nL 317.143491 278.679062 \nL 318.246257 281.135464 \nL 319.349024 276.787056 \nL 320.451791 278.9469 \nL 321.554558 287.013911 \nL 322.657325 293.214725 \nL 323.760091 291.899636 \nL 324.862858 282.808985 \nL 325.965625 271.071082 \nL 327.068392 281.345848 \nL 328.171159 301.859633 \nL 329.273925 312.643698 \nL 330.376692 288.731889 \nL 331.479459 285.280766 \nL 332.582226 282.196096 \nL 333.684993 291.886017 \nL 334.787759 296.869642 \nL 335.890526 294.526797 \nL 336.993293 296.50851 \nL 338.09606 302.793314 \nL 339.198827 232.424356 \nL 340.301593 192.236817 \nL 342.507127 179.133572 \nL 343.609894 182.90898 \nL 345.815427 164.128421 \nL 346.918194 163.853422 \nL 348.020961 174.810628 \nL 349.123728 170.662864 \nL 350.226495 154.06977 \nL 351.329261 172.914361 \nL 352.432028 176.738316 \nL 353.534795 152.946589 \nL 354.637562 163.706857 \nL 355.740329 163.569239 \nL 356.843095 149.992893 \nL 357.945862 167.61504 \nL 359.048629 170.744154 \nL 360.151396 159.136577 \nL 361.254163 159.363188 \nL 362.356929 161.023474 \nL 363.459696 168.375527 \nL 364.562463 170.443242 \nL 365.66523 173.686389 \nL 366.767997 174.90158 \nL 367.870763 172.585761 \nL 368.97353 176.620974 \nL 370.076297 161.935052 \nL 371.179064 149.05675 \nL 372.281831 155.246367 \nL 373.384597 172.332547 \nL 374.487364 175.515766 \nL 375.590131 157.659702 \nL 376.692898 154.639963 \nL 377.795665 151.171184 \nL 378.898431 148.928952 \nL 380.001198 163.610771 \nL 381.103965 167.515645 \nL 382.206732 165.215814 \nL 383.309499 165.1584 \nL 384.412265 162.161372 \nL 385.515032 154.449907 \nL 386.617799 166.596893 \nL 387.720566 183.065869 \nL 388.823333 189.504067 \nL 389.926099 173.606423 \nL 391.028866 170.716466 \nL 392.131633 192.279169 \nL 393.2344 180.147748 \nL 394.337167 147.212946 \nL 395.439933 150.450323 \nL 396.5427 165.381093 \nL 397.645467 162.232338 \nL 398.748234 165.833865 \nL 399.851 159.689432 \nL 400.953767 162.012266 \nL 402.056534 167.868571 \nL 403.159301 162.816428 \nL 404.262068 168.825228 \nL 406.467601 161.179251 \nL 407.570368 174.493278 \nL 408.673135 169.01981 \nL 409.775902 169.120608 \nL 410.878668 173.228323 \nL 411.981435 167.191597 \nL 413.084202 159.079878 \nL 414.186969 156.890639 \nL 415.289736 165.269178 \nL 416.392502 162.385177 \nL 417.495269 151.199084 \nL 418.598036 153.328897 \nL 420.80357 177.851623 \nL 421.906336 183.413422 \nL 423.009103 175.218957 \nL 424.11187 172.81568 \nL 425.214637 173.315066 \nL 426.317404 168.905592 \nL 427.42017 163.400757 \nL 428.522937 164.669788 \nL 429.625704 164.903387 \nL 430.728471 163.756356 \nL 431.831238 182.620297 \nL 432.934004 188.079815 \nL 434.036771 174.069253 \nL 435.139538 176.255606 \nL 436.242305 171.955321 \nL 437.345072 153.376623 \nL 438.447838 149.401206 \nL 439.550605 163.102253 \nL 440.653372 159.920384 \nL 441.756139 155.143796 \nL 442.858906 168.556265 \nL 443.961672 174.41175 \nL 446.167206 170.952633 \nL 447.269973 168.265782 \nL 448.37274 164.915087 \nL 449.475506 166.784513 \nL 450.578273 164.319191 \nL 451.68104 155.73805 \nL 452.783807 152.337511 \nL 453.886574 173.714341 \nL 454.98934 198.982572 \nL 456.092107 177.22084 \nL 457.194874 169.567133 \nL 458.297641 178.941901 \nL 459.400408 179.415187 \nL 460.503174 189.448242 \nL 461.605941 175.558939 \nL 462.708708 156.980929 \nL 463.811475 167.027615 \nL 464.914242 194.551313 \nL 466.017008 188.82982 \nL 467.119775 166.741658 \nL 468.222542 174.236173 \nL 469.325309 177.13783 \nL 470.428076 167.07227 \nL 471.530842 162.422553 \nL 472.633609 161.990507 \nL 473.736376 173.780967 \nL 475.94191 158.408278 \nL 477.044676 167.74612 \nL 478.147443 174.839031 \nL 479.25021 175.422035 \nL 480.352977 175.21263 \nL 481.455744 172.859197 \nL 482.55851 155.586641 \nL 483.661277 166.216888 \nL 484.764044 190.603478 \nL 485.866811 167.937446 \nL 486.969578 155.10327 \nL 488.072344 173.813763 \nL 489.175111 165.299777 \nL 490.277878 162.794511 \nL 491.380645 158.192917 \nL 492.483412 148.560779 \nL 493.586178 166.298575 \nL 494.688945 156.587874 \nL 495.791712 154.088431 \nL 496.894479 165.573002 \nL 500.202779 146.143421 \nL 501.305546 155.61957 \nL 502.408313 172.603099 \nL 503.51108 166.958131 \nL 504.613846 164.39434 \nL 505.716613 156.403297 \nL 506.81938 150.900607 \nL 507.922147 169.112932 \nL 509.024914 167.348777 \nL 510.12768 172.146595 \nL 511.230447 180.710106 \nL 512.333214 163.197625 \nL 513.435981 160.466781 \nL 514.538748 179.066682 \nL 515.641514 168.886904 \nL 516.744281 151.303773 \nL 517.847048 151.445918 \nL 518.949815 154.626649 \nL 520.052582 164.719208 \nL 521.155348 180.635275 \nL 522.258115 162.410271 \nL 523.360882 154.307181 \nL 524.463649 169.76468 \nL 525.566416 158.274154 \nL 526.669182 149.881454 \nL 527.771949 157.712325 \nL 528.874716 147.606399 \nL 529.977483 153.844244 \nL 531.08025 163.701934 \nL 532.183016 156.36346 \nL 533.285783 159.836712 \nL 534.38855 151.391548 \nL 535.491317 149.19953 \nL 536.594083 157.434018 \nL 537.69685 152.957574 \nL 538.799617 157.491802 \nL 539.902384 172.47546 \nL 541.005151 176.119735 \nL 542.107917 183.624918 \nL 543.210684 162.277047 \nL 544.313451 157.121909 \nL 545.416218 175.078268 \nL 548.724518 154.297757 \nL 549.827285 151.634041 \nL 550.930052 161.955223 \nL 552.032819 168.695101 \nL 553.135585 159.551417 \nL 554.238352 160.543597 \nL 555.341119 153.19795 \nL 556.443886 147.826974 \nL 557.546653 154.822078 \nL 558.649419 152.980153 \nL 559.752186 148.447275 \nL 560.854953 152.197643 \nL 561.95772 170.955147 \nL 563.060487 167.388482 \nL 564.163253 161.397126 \nL 565.26602 168.641949 \nL 566.368787 161.282855 \nL 567.471554 148.15748 \nL 568.574321 153.138604 \nL 569.677087 160.535471 \nL 570.779854 147.446625 \nL 571.882621 146.565381 \nL 572.985388 143.853887 \nL 574.088155 139.855679 \nL 575.190921 172.04577 \nL 576.293688 168.529372 \nL 577.396455 149.012545 \nL 578.499222 147.620825 \nL 579.601989 148.713962 \nL 579.601989 148.713962 \n\" style=\"fill:none;stroke:#ff7f0e;stroke-linecap:square;stroke-width:1.5;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"M 46.965625 333.36 \nL 46.965625 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"M 604.965625 333.36 \nL 604.965625 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"M 46.965625 333.36 \nL 604.965625 333.36 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"M 46.965625 7.2 \nL 604.965625 7.2 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"pd92a9f5c59\">\n   <rect height=\"326.16\" width=\"558\" x=\"46.965625\" y=\"7.2\"/>\n  </clipPath>\n </defs>\n</svg>\n",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cznEtSVK8OJB"
      },
      "source": [
        "keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.458883"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}