9. forecasting_with_cnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "l08c09_forecasting_with_cnn.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": "Tt5S6SiPi7ze"
      },
      "source": [
        "# Forecasting with a CNN"
      ]
    },
    {
      "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",
        "\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 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)\n",
        "  \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>2021-06-19T23:08:42.435168</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(#p84a70a8987)\" 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=\"m7029dc8f92\" 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=\"#m7029dc8f92\" 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(#p84a70a8987)\" 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=\"#m7029dc8f92\" 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(#p84a70a8987)\" 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=\"#m7029dc8f92\" 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(#p84a70a8987)\" 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=\"#m7029dc8f92\" 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(#p84a70a8987)\" 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=\"#m7029dc8f92\" 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(#p84a70a8987)\" 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=\"#m7029dc8f92\" 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(#p84a70a8987)\" 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=\"#m7029dc8f92\" 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(#p84a70a8987)\" 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=\"#m7029dc8f92\" 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(#p84a70a8987)\" 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=\"mf7f8e95397\" 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=\"#mf7f8e95397\" 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(#p84a70a8987)\" 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=\"#mf7f8e95397\" 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(#p84a70a8987)\" 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=\"#mf7f8e95397\" 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(#p84a70a8987)\" 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=\"#mf7f8e95397\" 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(#p84a70a8987)\" 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=\"#mf7f8e95397\" 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(#p84a70a8987)\" 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=\"#mf7f8e95397\" 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(#p84a70a8987)\" 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=\"#mf7f8e95397\" 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(#p84a70a8987)\" 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=\"#mf7f8e95397\" 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(#p84a70a8987)\" 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=\"p84a70a8987\">\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": "DI2GlupZ8OJW"
      },
      "source": [
        "## Preprocessing With 1D-Convolutional Layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6GFE82ci8OJW"
      },
      "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.Conv1D(filters=32, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\",\n",
        "                      input_shape=[None, 1]),\n",
        "  keras.layers.LSTM(32, return_sequences=True),\n",
        "  keras.layers.LSTM(32, 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-8 * 10**(epoch / 20))\n",
        "optimizer = keras.optimizers.SGD(lr=1e-8, 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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uungw0H58OJX"
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-8, 1e-4, 0, 30])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UG7cO0yr8OJY"
      },
      "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.Conv1D(filters=32, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\",\n",
        "                      input_shape=[None, 1]),\n",
        "  keras.layers.LSTM(32, return_sequences=True),\n",
        "  keras.layers.LSTM(32, return_sequences=True),\n",
        "  keras.layers.Dense(1),\n",
        "  keras.layers.Lambda(lambda x: x * 200)\n",
        "])\n",
        "optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "model_checkpoint = keras.callbacks.ModelCheckpoint(\n",
        "    \"my_checkpoint.h5\", save_best_only=True)\n",
        "early_stopping = keras.callbacks.EarlyStopping(patience=50)\n",
        "model.fit(train_set, epochs=500,\n",
        "          validation_data=valid_set,\n",
        "          callbacks=[early_stopping, model_checkpoint])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlqzLwfn8OJa"
      },
      "source": [
        "model = keras.models.load_model(\"my_checkpoint.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pj0rpT-48OJc"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vnDU8wm8OJd"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, rnn_forecast)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6tWOoE88OJe"
      },
      "source": [
        "keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfPTmghd8OJe"
      },
      "source": [
        "## Fully Convolutional Forecasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-cPF5CX8OJf"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 64\n",
        "train_set = seq2seq_window_dataset(x_train, window_size,\n",
        "                                   batch_size=128)\n",
        "\n",
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.InputLayer(input_shape=[None, 1]))\n",
        "for dilation_rate in (1, 2, 4, 8, 16, 32):\n",
        "    model.add(\n",
        "      keras.layers.Conv1D(filters=32,\n",
        "                          kernel_size=2,\n",
        "                          strides=1,\n",
        "                          dilation_rate=dilation_rate,\n",
        "                          padding=\"causal\",\n",
        "                          activation=\"relu\")\n",
        "    )\n",
        "model.add(keras.layers.Conv1D(filters=1, kernel_size=1))\n",
        "lr_schedule = keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-4 * 10**(epoch / 30))\n",
        "optimizer = keras.optimizers.Adam(lr=1e-4)\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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfWVZ8k-8OJf"
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-4, 1e-1, 0, 30])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVrxlzbk8OJg"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 64\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",
        "model.add(keras.layers.InputLayer(input_shape=[None, 1]))\n",
        "for dilation_rate in (1, 2, 4, 8, 16, 32):\n",
        "    model.add(\n",
        "      keras.layers.Conv1D(filters=32,\n",
        "                          kernel_size=2,\n",
        "                          strides=1,\n",
        "                          dilation_rate=dilation_rate,\n",
        "                          padding=\"causal\",\n",
        "                          activation=\"relu\")\n",
        "    )\n",
        "model.add(keras.layers.Conv1D(filters=1, kernel_size=1))\n",
        "optimizer = keras.optimizers.Adam(lr=3e-4)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "model_checkpoint = keras.callbacks.ModelCheckpoint(\n",
        "    \"my_checkpoint.h5\", save_best_only=True)\n",
        "early_stopping = keras.callbacks.EarlyStopping(patience=50)\n",
        "history = model.fit(train_set, epochs=500,\n",
        "                    validation_data=valid_set,\n",
        "                    callbacks=[early_stopping, model_checkpoint])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNwWZB0d8OJh"
      },
      "source": [
        "model = keras.models.load_model(\"my_checkpoint.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgYwn9VM8OJi"
      },
      "source": [
        "cnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
        "cnn_forecast = cnn_forecast[split_time - window_size:-1, -1, 0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCgshNPx8OJi"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, cnn_forecast)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epK1gFEN8OJj"
      },
      "source": [
        "keras.metrics.mean_absolute_error(x_valid, cnn_forecast).numpy()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}