autoencoder.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMXDck3wBJ0HYV1rV0jfxhn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mariajmolina/UMDAOSC650/blob/main/autoencoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import xarray as xr\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import keras\n",
        "from keras import layers"
      ],
      "metadata": {
        "id": "7IgRdMrZBxRF"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the seed using keras.utils.set_random_seed. This will set:\n",
        "# 1) `numpy` seed\n",
        "# 2) backend random seed\n",
        "# 3) `python` random seed\n",
        "keras.utils.set_random_seed(812)"
      ],
      "metadata": {
        "id": "Y-RbDEG114eE"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6Z5jbwtB58E",
        "outputId": "777a1816-e1a5-4b77-e659-f099894750f1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-09 16:34:42--  https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.79.172, 188.184.98.238, 188.184.103.159, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.79.172|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 109400556 (104M) [application/octet-stream]\n",
            "Saving to: ‘current_SP3hourly_20110427.nc’\n",
            "\n",
            "current_SP3hourly_2 100%[===================>] 104.33M  19.3MB/s    in 6.2s    \n",
            "\n",
            "2024-04-09 16:34:50 (16.8 MB/s) - ‘current_SP3hourly_20110427.nc’ saved [109400556/109400556]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import dataset (netcdf)\n",
        "ds = xr.open_dataset('current_SP3hourly_20110427.nc')"
      ],
      "metadata": {
        "id": "kvvqhZ8HB8Ot"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "dG2dD1ig-hhO",
        "outputId": "ff9fb5f3-6484-47d2-a6e0-88395ac34151"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.Dataset>\n",
              "Dimensions:      (starttime: 2224, y: 32, x: 32)\n",
              "Coordinates:\n",
              "  * starttime    (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime      (starttime) float64 ...\n",
              "    x_speed      (starttime) int64 ...\n",
              "    y_speed      (starttime) int64 ...\n",
              "Dimensions without coordinates: y, x\n",
              "Data variables:\n",
              "    uh25_grid    (starttime, y, x) float32 ...\n",
              "    uh03_grid    (starttime, y, x) float32 ...\n",
              "    ctt_grid     (starttime, y, x) float32 ...\n",
              "    dbz_grid     (starttime, y, x) float32 ...\n",
              "    mask         (starttime, y, x) int64 ...\n",
              "    row_indices  (starttime, y, x) int64 ...\n",
              "    col_indices  (starttime, y, x) int64 ...\n",
              "    lats         (starttime, y, x) float32 ...\n",
              "    lons         (starttime, y, x) float32 ..."
            ],
            "text/html": [
              "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
              "<defs>\n",
              "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
              "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
              "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
              "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
              "</symbol>\n",
              "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
              "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
              "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
              "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
              "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
              "</symbol>\n",
              "</defs>\n",
              "</svg>\n",
              "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
              " *\n",
              " */\n",
              "\n",
              ":root {\n",
              "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
              "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
              "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
              "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
              "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
              "  --xr-background-color: var(--jp-layout-color0, white);\n",
              "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
              "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
              "}\n",
              "\n",
              "html[theme=dark],\n",
              "body[data-theme=dark],\n",
              "body.vscode-dark {\n",
              "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
              "  --xr-border-color: #1F1F1F;\n",
              "  --xr-disabled-color: #515151;\n",
              "  --xr-background-color: #111111;\n",
              "  --xr-background-color-row-even: #111111;\n",
              "  --xr-background-color-row-odd: #313131;\n",
              "}\n",
              "\n",
              ".xr-wrap {\n",
              "  display: block !important;\n",
              "  min-width: 300px;\n",
              "  max-width: 700px;\n",
              "}\n",
              "\n",
              ".xr-text-repr-fallback {\n",
              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-header {\n",
              "  padding-top: 6px;\n",
              "  padding-bottom: 6px;\n",
              "  margin-bottom: 4px;\n",
              "  border-bottom: solid 1px var(--xr-border-color);\n",
              "}\n",
              "\n",
              ".xr-header > div,\n",
              ".xr-header > ul {\n",
              "  display: inline;\n",
              "  margin-top: 0;\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-obj-type,\n",
              ".xr-array-name {\n",
              "  margin-left: 2px;\n",
              "  margin-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-obj-type {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-sections {\n",
              "  padding-left: 0 !important;\n",
              "  display: grid;\n",
              "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
              "}\n",
              "\n",
              ".xr-section-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-section-item input {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-item input + label {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label {\n",
              "  cursor: pointer;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label:hover {\n",
              "  color: var(--xr-font-color0);\n",
              "}\n",
              "\n",
              ".xr-section-summary {\n",
              "  grid-column: 1;\n",
              "  color: var(--xr-font-color2);\n",
              "  font-weight: 500;\n",
              "}\n",
              "\n",
              ".xr-section-summary > span {\n",
              "  display: inline-block;\n",
              "  padding-left: 0.5em;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in + label:before {\n",
              "  display: inline-block;\n",
              "  content: '►';\n",
              "  font-size: 11px;\n",
              "  width: 15px;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label:before {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label:before {\n",
              "  content: '▼';\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label > span {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-summary,\n",
              ".xr-section-inline-details {\n",
              "  padding-top: 4px;\n",
              "  padding-bottom: 4px;\n",
              "}\n",
              "\n",
              ".xr-section-inline-details {\n",
              "  grid-column: 2 / -1;\n",
              "}\n",
              "\n",
              ".xr-section-details {\n",
              "  display: none;\n",
              "  grid-column: 1 / -1;\n",
              "  margin-bottom: 5px;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: '(';\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: ')';\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: ',';\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
              "Dimensions:      (starttime: 2224, y: 32, x: 32)\n",
              "Coordinates:\n",
              "  * starttime    (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime      (starttime) float64 ...\n",
              "    x_speed      (starttime) int64 ...\n",
              "    y_speed      (starttime) int64 ...\n",
              "Dimensions without coordinates: y, x\n",
              "Data variables:\n",
              "    uh25_grid    (starttime, y, x) float32 ...\n",
              "    uh03_grid    (starttime, y, x) float32 ...\n",
              "    ctt_grid     (starttime, y, x) float32 ...\n",
              "    dbz_grid     (starttime, y, x) float32 ...\n",
              "    mask         (starttime, y, x) int64 ...\n",
              "    row_indices  (starttime, y, x) int64 ...\n",
              "    col_indices  (starttime, y, x) int64 ...\n",
              "    lats         (starttime, y, x) float32 ...\n",
              "    lons         (starttime, y, x) float32 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cc06cf87-a0ec-4699-884c-9f377b740e55' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cc06cf87-a0ec-4699-884c-9f377b740e55' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>starttime</span>: 2224</li><li><span>y</span>: 32</li><li><span>x</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bec6ad7f-839e-4863-b25d-c653778ee75b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bec6ad7f-839e-4863-b25d-c653778ee75b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-6e87edee-f469-414e-a36e-b4f02fb2c491' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e87edee-f469-414e-a36e-b4f02fb2c491' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73d3752a-c9fd-417f-b151-3dffa5747489' class='xr-var-data-in' type='checkbox'><label for='data-73d3752a-c9fd-417f-b151-3dffa5747489' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6689ca73-5aa8-48b8-96fd-10413b6136d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6689ca73-5aa8-48b8-96fd-10413b6136d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2595c24e-6946-4115-8e86-6a8055a0ad53' class='xr-var-data-in' type='checkbox'><label for='data-2595c24e-6946-4115-8e86-6a8055a0ad53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68894b31-7588-493a-8ac3-f98ad6ec82ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68894b31-7588-493a-8ac3-f98ad6ec82ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a759a89-f477-4593-a8be-555d53431aa1' class='xr-var-data-in' type='checkbox'><label for='data-5a759a89-f477-4593-a8be-555d53431aa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37226455-dd1f-45d8-bac1-84dec87e78c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37226455-dd1f-45d8-bac1-84dec87e78c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae78e0cd-58c0-4d24-84a4-f972b776a234' class='xr-var-data-in' type='checkbox'><label for='data-ae78e0cd-58c0-4d24-84a4-f972b776a234' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-27b809fc-a2e6-4fd9-8d1e-ad66fd917b30' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27b809fc-a2e6-4fd9-8d1e-ad66fd917b30' class='xr-section-summary' >Data variables: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uh25_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-102f4be1-bb4c-43a9-af8b-2fb216c51ed1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-102f4be1-bb4c-43a9-af8b-2fb216c51ed1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14765d33-f176-4f8a-bb48-3833f4472137' class='xr-var-data-in' type='checkbox'><label for='data-14765d33-f176-4f8a-bb48-3833f4472137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uh03_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-86ae0f85-9599-4cf5-81b6-5825c83a93c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86ae0f85-9599-4cf5-81b6-5825c83a93c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7750978-5457-471a-a4f8-2ca8e484be59' class='xr-var-data-in' type='checkbox'><label for='data-e7750978-5457-471a-a4f8-2ca8e484be59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctt_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-234be6dd-ed4e-4887-96da-5ede1cbf87e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-234be6dd-ed4e-4887-96da-5ede1cbf87e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74417baf-3711-40bc-9b3a-88db1209329a' class='xr-var-data-in' type='checkbox'><label for='data-74417baf-3711-40bc-9b3a-88db1209329a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dbz_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d135c1ca-7877-41fc-bc0a-8137dfb2e664' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d135c1ca-7877-41fc-bc0a-8137dfb2e664' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a403079-3b35-4a0e-a032-1924f03b34b1' class='xr-var-data-in' type='checkbox'><label for='data-5a403079-3b35-4a0e-a032-1924f03b34b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3674c6d4-cd8e-41ad-9fc1-210697562887' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3674c6d4-cd8e-41ad-9fc1-210697562887' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d902c52c-0ea8-488f-a005-2e968d09d4e2' class='xr-var-data-in' type='checkbox'><label for='data-d902c52c-0ea8-488f-a005-2e968d09d4e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>row_indices</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-47b51df0-5b51-471d-b5c2-3920eae6e27a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b51df0-5b51-471d-b5c2-3920eae6e27a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2137b7d-069a-4137-a01b-38d864219804' class='xr-var-data-in' type='checkbox'><label for='data-b2137b7d-069a-4137-a01b-38d864219804' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>col_indices</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f019bc03-0de2-4840-aa6e-f2064630f5dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f019bc03-0de2-4840-aa6e-f2064630f5dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4defc940-788e-4186-80f4-618e41e5414c' class='xr-var-data-in' type='checkbox'><label for='data-4defc940-788e-4186-80f4-618e41e5414c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lats</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62dc2665-2d75-4e94-a075-628f9530d06f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62dc2665-2d75-4e94-a075-628f9530d06f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d8b814b-780a-4ae5-89b4-57b4e4d7c82f' class='xr-var-data-in' type='checkbox'><label for='data-7d8b814b-780a-4ae5-89b4-57b4e4d7c82f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lons</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2a849cf4-5fe8-4c37-b5a5-f65500add814' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a849cf4-5fe8-4c37-b5a5-f65500add814' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f526452-12a7-4ff7-9a43-c8972b4184a4' class='xr-var-data-in' type='checkbox'><label for='data-1f526452-12a7-4ff7-9a43-c8972b4184a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3554327f-42a4-4527-b58e-38b68e00ae21' class='xr-section-summary-in' type='checkbox'  ><label for='section-3554327f-42a4-4527-b58e-38b68e00ae21' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5f84b9c1-e1bb-471a-8e1c-dd420f8fd0e0' class='xr-index-data-in' type='checkbox'/><label for='index-5f84b9c1-e1bb-471a-8e1c-dd420f8fd0e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "       92640.0, 92640.0,\n",
              "       ...\n",
              "       92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "       92661.0, 92661.0],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-138c7991-9362-4137-abeb-42e9c1f1d15b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-138c7991-9362-4137-abeb-42e9c1f1d15b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ANN Autoencoder"
      ],
      "metadata": {
        "id": "wVklUrN4Ekw2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will again use the previous data, but this time we will focus on reconstructing one input variable (feature)."
      ],
      "metadata": {
        "id": "qqoNw_88CTV3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# selecting a variable\n",
        "ds['dbz_grid']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "x1tCi9N9_Fhu",
        "outputId": "18d29a54-1b69-422b-c8ca-c0658f9f839b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.DataArray 'dbz_grid' (starttime: 2224, y: 32, x: 32)>\n",
              "[2277376 values with dtype=float32]\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "Dimensions without coordinates: y, x"
            ],
            "text/html": [
              "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
              "<defs>\n",
              "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
              "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
              "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
              "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
              "</symbol>\n",
              "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
              "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
              "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
              "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
              "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
              "</symbol>\n",
              "</defs>\n",
              "</svg>\n",
              "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
              " *\n",
              " */\n",
              "\n",
              ":root {\n",
              "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
              "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
              "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
              "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
              "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
              "  --xr-background-color: var(--jp-layout-color0, white);\n",
              "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
              "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
              "}\n",
              "\n",
              "html[theme=dark],\n",
              "body[data-theme=dark],\n",
              "body.vscode-dark {\n",
              "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
              "  --xr-border-color: #1F1F1F;\n",
              "  --xr-disabled-color: #515151;\n",
              "  --xr-background-color: #111111;\n",
              "  --xr-background-color-row-even: #111111;\n",
              "  --xr-background-color-row-odd: #313131;\n",
              "}\n",
              "\n",
              ".xr-wrap {\n",
              "  display: block !important;\n",
              "  min-width: 300px;\n",
              "  max-width: 700px;\n",
              "}\n",
              "\n",
              ".xr-text-repr-fallback {\n",
              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-header {\n",
              "  padding-top: 6px;\n",
              "  padding-bottom: 6px;\n",
              "  margin-bottom: 4px;\n",
              "  border-bottom: solid 1px var(--xr-border-color);\n",
              "}\n",
              "\n",
              ".xr-header > div,\n",
              ".xr-header > ul {\n",
              "  display: inline;\n",
              "  margin-top: 0;\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-obj-type,\n",
              ".xr-array-name {\n",
              "  margin-left: 2px;\n",
              "  margin-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-obj-type {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-sections {\n",
              "  padding-left: 0 !important;\n",
              "  display: grid;\n",
              "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
              "}\n",
              "\n",
              ".xr-section-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-section-item input {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-item input + label {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label {\n",
              "  cursor: pointer;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label:hover {\n",
              "  color: var(--xr-font-color0);\n",
              "}\n",
              "\n",
              ".xr-section-summary {\n",
              "  grid-column: 1;\n",
              "  color: var(--xr-font-color2);\n",
              "  font-weight: 500;\n",
              "}\n",
              "\n",
              ".xr-section-summary > span {\n",
              "  display: inline-block;\n",
              "  padding-left: 0.5em;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in + label:before {\n",
              "  display: inline-block;\n",
              "  content: '►';\n",
              "  font-size: 11px;\n",
              "  width: 15px;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label:before {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label:before {\n",
              "  content: '▼';\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label > span {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-summary,\n",
              ".xr-section-inline-details {\n",
              "  padding-top: 4px;\n",
              "  padding-bottom: 4px;\n",
              "}\n",
              "\n",
              ".xr-section-inline-details {\n",
              "  grid-column: 2 / -1;\n",
              "}\n",
              "\n",
              ".xr-section-details {\n",
              "  display: none;\n",
              "  grid-column: 1 / -1;\n",
              "  margin-bottom: 5px;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: '(';\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: ')';\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: ',';\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;dbz_grid&#x27; (starttime: 2224, y: 32, x: 32)&gt;\n",
              "[2277376 values with dtype=float32]\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "Dimensions without coordinates: y, x</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'dbz_grid'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>starttime</span>: 2224</li><li><span>y</span>: 32</li><li><span>x</span>: 32</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9d11a62d-02d7-407d-aace-27d2ace10f52' class='xr-array-in' type='checkbox' checked><label for='section-9d11a62d-02d7-407d-aace-27d2ace10f52' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[2277376 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-110334bb-c69a-4b20-8cbe-bcdfc7ef5162' class='xr-section-summary-in' type='checkbox'  checked><label for='section-110334bb-c69a-4b20-8cbe-bcdfc7ef5162' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-d954f8aa-270b-48c8-bcd2-ee04d64f5a34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d954f8aa-270b-48c8-bcd2-ee04d64f5a34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17f64645-267f-450d-b809-cbc13d2ab13c' class='xr-var-data-in' type='checkbox'><label for='data-17f64645-267f-450d-b809-cbc13d2ab13c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-33114745-a3f7-40e1-ac20-003db8711e7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33114745-a3f7-40e1-ac20-003db8711e7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45e940cf-bd02-4a91-8b2a-fd7101495be6' class='xr-var-data-in' type='checkbox'><label for='data-45e940cf-bd02-4a91-8b2a-fd7101495be6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3183a2a9-ac4d-483e-8e44-4b4c975ed27b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3183a2a9-ac4d-483e-8e44-4b4c975ed27b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46c77c77-5c3e-47d7-a8ab-a844ddff6b16' class='xr-var-data-in' type='checkbox'><label for='data-46c77c77-5c3e-47d7-a8ab-a844ddff6b16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36f9e2b7-eed7-4faa-be94-6bf16b745edc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36f9e2b7-eed7-4faa-be94-6bf16b745edc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44448f17-8ef5-4d05-9311-20e57307af61' class='xr-var-data-in' type='checkbox'><label for='data-44448f17-8ef5-4d05-9311-20e57307af61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a89de42-a49e-43f2-a0b1-e72785638ee4' class='xr-section-summary-in' type='checkbox'  ><label for='section-6a89de42-a49e-43f2-a0b1-e72785638ee4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c57608fa-e66d-4821-9772-7d7e6f18caf5' class='xr-index-data-in' type='checkbox'/><label for='index-c57608fa-e66d-4821-9772-7d7e6f18caf5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "       92640.0, 92640.0,\n",
              "       ...\n",
              "       92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "       92661.0, 92661.0],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2800aa17-32a4-4967-8b02-13f4ab2ae71a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2800aa17-32a4-4967-8b02-13f4ab2ae71a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape data to be samples, features\n",
        "features_data = ds['dbz_grid'].stack(new_dim=([\"y\", \"x\"])).values\n",
        "print(features_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dg1nX786FXrR",
        "outputId": "d3a24309-fef2-4053-e95d-05f0775de6b1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2224, 1024)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "32*32"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQdZUIgwuCyE",
        "outputId": "dd4eff49-8925-4975-9be4-21fde67bd046"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1024"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vcr9t3cIsXN",
        "outputId": "ace22dc4-6965-4a33-d1fd-02179be584cb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2224, 1024)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split dataset into training and testing set\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    features_data,\n",
        "    features_data,\n",
        "    test_size=0.3,\n",
        "    shuffle=False,\n",
        "    random_state=42)"
      ],
      "metadata": {
        "id": "b7dfQyq4JIbF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now rescale your data\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train = scaler.transform(X_train) # input\n",
        "X_test = scaler.transform(X_test) # input\n",
        "y_train = scaler.transform(y_train) # label\n",
        "y_test = scaler.transform(y_test) # label"
      ],
      "metadata": {
        "id": "ffojuEJYJISv"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shapes\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3WNN1oQYKvbq",
        "outputId": "bf9fb692-c030-458b-b1a3-3794bb89f18d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1556, 1024) (668, 1024) (1556, 1024) (668, 1024)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create fully connected autoencoder\n",
        "model = keras.Sequential()\n",
        "\n",
        "# input\n",
        "model.add(keras.Input(shape=(1024))) # features\n",
        "\n",
        "# encoder\n",
        "# hidden layers\n",
        "model.add(layers.Dense(32, activation=\"relu\"))\n",
        "model.add(layers.Dense(8, activation=\"relu\"))\n",
        "\n",
        "# desired dimensions\n",
        "model.add(layers.Dense(2, activation=\"relu\"))\n",
        "\n",
        "# decoder\n",
        "model.add(layers.Dense(8, activation=\"relu\"))\n",
        "model.add(layers.Dense(32, activation=\"relu\"))\n",
        "\n",
        "# Finally, we add last layer\n",
        "model.add(layers.Dense(1024, activation='linear')) # back to the original shape\n",
        "\n",
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4JnAwI1J9Bk",
        "outputId": "3f846c11-ffb4-40f6-95b9-b7eca9d2976d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 32)                32800     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 8)                 264       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 18        \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 8)                 24        \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 32)                288       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1024)              33792     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67186 (262.45 KB)\n",
            "Trainable params: 67186 (262.45 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define list of metrics\n",
        "METRICS = [\n",
        "    keras.metrics.MeanSquaredError(name='mean_squared_error'),\n",
        "    keras.metrics.RootMeanSquaredError(name='root_mean_squared_error'),\n",
        "    keras.metrics.MeanAbsoluteError(name='mean_absolute_error'),\n",
        "    keras.metrics.MeanAbsolutePercentageError(name=\"mean_absolute_percentage_error\"),\n",
        "    keras.metrics.R2Score()\n",
        "]\n",
        "\n",
        "# compile your nn\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "    loss=\"mean_squared_error\",\n",
        "    metrics=METRICS\n",
        ")\n",
        "\n",
        "# set hyperparameters\n",
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10"
      ],
      "metadata": {
        "id": "NTMiTP9ELYO9"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train the NN now\n",
        "history = model.fit(\n",
        "    x=X_train,\n",
        "    y=y_train,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5dS4BDILYLA",
        "outputId": "bc6e3f81-97bb-4741-a9a1-5f084bfd7815"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "44/44 [==============================] - 2s 14ms/step - loss: 0.8096 - mean_squared_error: 0.8096 - root_mean_squared_error: 0.8998 - mean_absolute_error: 0.7182 - mean_absolute_percentage_error: 1093.3037 - r2_score: 0.1849 - val_loss: 0.7432 - val_mean_squared_error: 0.7432 - val_root_mean_squared_error: 0.8621 - val_mean_absolute_error: 0.7084 - val_mean_absolute_percentage_error: 222.2127 - val_r2_score: 0.2861\n",
            "Epoch 2/10\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.6787 - mean_squared_error: 0.6787 - root_mean_squared_error: 0.8239 - mean_absolute_error: 0.6675 - mean_absolute_percentage_error: 1927.2611 - r2_score: 0.3165 - val_loss: 0.7075 - val_mean_squared_error: 0.7075 - val_root_mean_squared_error: 0.8411 - val_mean_absolute_error: 0.6863 - val_mean_absolute_percentage_error: 269.2519 - val_r2_score: 0.3187\n",
            "Epoch 3/10\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.6383 - mean_squared_error: 0.6383 - root_mean_squared_error: 0.7989 - mean_absolute_error: 0.6366 - mean_absolute_percentage_error: 1577.7681 - r2_score: 0.3572 - val_loss: 0.6642 - val_mean_squared_error: 0.6642 - val_root_mean_squared_error: 0.8150 - val_mean_absolute_error: 0.6557 - val_mean_absolute_percentage_error: 325.8496 - val_r2_score: 0.3619\n",
            "Epoch 4/10\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.5946 - mean_squared_error: 0.5946 - root_mean_squared_error: 0.7711 - mean_absolute_error: 0.5960 - mean_absolute_percentage_error: 1805.5078 - r2_score: 0.4012 - val_loss: 0.6290 - val_mean_squared_error: 0.6290 - val_root_mean_squared_error: 0.7931 - val_mean_absolute_error: 0.6271 - val_mean_absolute_percentage_error: 400.1971 - val_r2_score: 0.3969\n",
            "Epoch 5/10\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.5559 - mean_squared_error: 0.5559 - root_mean_squared_error: 0.7456 - mean_absolute_error: 0.5638 - mean_absolute_percentage_error: 1820.6925 - r2_score: 0.4402 - val_loss: 0.5922 - val_mean_squared_error: 0.5922 - val_root_mean_squared_error: 0.7695 - val_mean_absolute_error: 0.6021 - val_mean_absolute_percentage_error: 357.4614 - val_r2_score: 0.4317\n",
            "Epoch 6/10\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.5072 - mean_squared_error: 0.5072 - root_mean_squared_error: 0.7122 - mean_absolute_error: 0.5331 - mean_absolute_percentage_error: 1543.3330 - r2_score: 0.4890 - val_loss: 0.5642 - val_mean_squared_error: 0.5642 - val_root_mean_squared_error: 0.7511 - val_mean_absolute_error: 0.5882 - val_mean_absolute_percentage_error: 313.4367 - val_r2_score: 0.4596\n",
            "Epoch 7/10\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.4746 - mean_squared_error: 0.4746 - root_mean_squared_error: 0.6889 - mean_absolute_error: 0.5103 - mean_absolute_percentage_error: 1505.6624 - r2_score: 0.5216 - val_loss: 0.5515 - val_mean_squared_error: 0.5515 - val_root_mean_squared_error: 0.7427 - val_mean_absolute_error: 0.5720 - val_mean_absolute_percentage_error: 369.2470 - val_r2_score: 0.4740\n",
            "Epoch 8/10\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.4643 - mean_squared_error: 0.4643 - root_mean_squared_error: 0.6814 - mean_absolute_error: 0.5024 - mean_absolute_percentage_error: 1566.7017 - r2_score: 0.5320 - val_loss: 0.5328 - val_mean_squared_error: 0.5328 - val_root_mean_squared_error: 0.7300 - val_mean_absolute_error: 0.5591 - val_mean_absolute_percentage_error: 372.7177 - val_r2_score: 0.4909\n",
            "Epoch 9/10\n",
            "44/44 [==============================] - 0s 7ms/step - loss: 0.4573 - mean_squared_error: 0.4573 - root_mean_squared_error: 0.6762 - mean_absolute_error: 0.4975 - mean_absolute_percentage_error: 1737.6608 - r2_score: 0.5391 - val_loss: 0.5297 - val_mean_squared_error: 0.5297 - val_root_mean_squared_error: 0.7278 - val_mean_absolute_error: 0.5533 - val_mean_absolute_percentage_error: 380.4408 - val_r2_score: 0.4925\n",
            "Epoch 10/10\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.4538 - mean_squared_error: 0.4538 - root_mean_squared_error: 0.6736 - mean_absolute_error: 0.4948 - mean_absolute_percentage_error: 1552.0354 - r2_score: 0.5426 - val_loss: 0.5179 - val_mean_squared_error: 0.5179 - val_root_mean_squared_error: 0.7196 - val_mean_absolute_error: 0.5494 - val_mean_absolute_percentage_error: 379.2081 - val_r2_score: 0.5050\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize training and validation curves\n",
        "plt.plot(history.history[\"mean_squared_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_squared_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "KIo2XzMzX7xU",
        "outputId": "cfb3086c-dc94-46a6-97fe-766783ad26c0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize training and validation curves\n",
        "plt.plot(history.history[\"mean_absolute_percentage_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_absolute_percentage_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "W3Z_lSlmw-ag",
        "outputId": "dc5ddfee-5841-472e-aac2-5b921112721a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGgCAYAAAC0f12xAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABLd0lEQVR4nO3deXxM9/4/8NdkmyySiYRkhCB2iVhqiSB2gnCrtL2Wlm60GlR9b6tUa7mtWL4t3ehyW+3vlqtXW9raKtZYgqCxi10smUSRmSCyzHx+f8x3joxEJTGTM2fm9Xw8zoOZ85k57xHJeeVzPp/zUQkhBIiIiIgUxE3uAoiIiIgqigGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUp0IBJikpCe3bt4e/vz9CQkIwePBgZGRkWLW5e/cuEhMTERwcjGrVqmHo0KHIzs62apOZmYmEhAT4+voiJCQEb7zxBoqLi63abNu2DY899hjUajUaNWqEb7/9tnKfkIiIiJyOR0Uab9++HYmJiWjfvj2Ki4sxbdo09O3bF8ePH4efnx8A4PXXX8fatWuxcuVKaDQajB8/HkOGDMGuXbsAAEajEQkJCdBqtdi9ezeysrIwatQoeHp6Ys6cOQCA8+fPIyEhAa+88gqWLVuGzZs346WXXkKtWrUQHx9frlpNJhOuXr0Kf39/qFSqinxMIiIikokQAnl5eQgLC4Ob21/0s4hHkJOTIwCI7du3CyGEyM3NFZ6enmLlypVSmxMnTggAIjU1VQghxLp164Sbm5vQ6XRSmyVLloiAgABRUFAghBDizTffFFFRUVbH+vvf/y7i4+PLXdulS5cEAG7cuHHjxo2bArdLly795Xm+Qj0w99Pr9QCAoKAgAMCBAwdQVFSE3r17S22aNWuGunXrIjU1FR07dkRqaiqio6MRGhoqtYmPj8e4ceNw7NgxtGnTBqmpqVbvYWkzadKkB9ZSUFCAgoIC6bH4v0W2L126hICAgEf5mERERFRFDAYDwsPD4e/v/5ftKh1gTCYTJk2ahM6dO6NFixYAAJ1OBy8vLwQGBlq1DQ0NhU6nk9qUDC+W/ZZ9f9XGYDAgPz8fPj4+pepJSkrCrFmzSj0fEBDAAENERKQwDxv+UelZSImJiTh69ChWrFhR2bewqalTp0Kv10vbpUuX5C6JiIiI7KRSPTDjx4/HmjVrkJKSgjp16kjPa7VaFBYWIjc316oXJjs7G1qtVmqzb98+q/ezzFIq2eb+mUvZ2dkICAgos/cFANRqNdRqdWU+DhERESlMhXpghBAYP348Vq1ahS1btiAiIsJqf9u2beHp6YnNmzdLz2VkZCAzMxOxsbEAgNjYWBw5cgQ5OTlSm+TkZAQEBCAyMlJqU/I9LG0s70FERESuTSUso13L4dVXX8Xy5cvxyy+/oGnTptLzGo1G6hkZN24c1q1bh2+//RYBAQGYMGECAGD37t0AzNOoW7dujbCwMMyfPx86nQ7PPvssXnrpJatp1C1atEBiYiJeeOEFbNmyBRMnTsTatWvLPY3aYDBAo9FAr9dzDAwREZFClPv8Xe55yeagU+a2dOlSqU1+fr549dVXRfXq1YWvr6944oknRFZWltX7XLhwQfTv31/4+PiIGjVqiP/5n/8RRUVFVm22bt0qWrduLby8vESDBg2sjlEeer1eABB6vb5CryMiIiL5lPf8XaEeGCVhDwwREZHylPf8zbWQiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgXFROTg7mzJlTaskGIiIiJWCAcVHvvPMO3n77bTz++OMoKiqSuxwiIqIKYYBxQSaTCb/88gsAYO/evZgxY4bMFREREVUMA4wLSktLQ3Z2Njw9PQEAc+fOLbV4JhERkSNjgHFBv/76KwBgyJAhGDNmDIQQeOaZZ3Dt2jWZKyMiIiofBhgXZAkwf/vb37Bo0SI0b94cOp0Ozz33HJx0aSwiInIyDDAu5ty5czh69Cjc3d3Rv39/+Pr6YsWKFVCr1Vi3bh0+/vhjuUskIiJ6KAYYF/Pbb78BALp27Yrq1asDAFq2bIkPPvgAAPDmm2/ijz/+kK0+IiKi8mCAcTElLx+V9Oqrr+Jvf/sbCgsLMWzYMNy6dUuO8oiIiMqFAcaF3Lx5E9u3bwcADBo0yGqfSqXCN998g9q1a+PUqVOYOHGiHCUSERGVCwOMC9mwYQOMRiOioqLQsGHDUvuDg4OxbNkyqFQqLF26FP/5z39kqJKIiOjhGGBcyIMuH5XUrVs3TJ8+HQDwyiuv4Ny5c1VSGxERUUUwwLiIwsJCrF+/HsBfBxgAePfdd9GpUycYDAaMGDGCSw0QEZHDYYBxETt27IBer0dISAg6dOjwl209PDywfPlyBAYGYu/evXj33XerqEoiIqLyYYBxEZbLR4MGDYKb28O/7PXq1cNXX30FAJg3bx42bdpk1/qIiIgqggHGBQghyjX+5X5PPvkkxo4dCyEEnn32WS41QEREDoMBxgUcPXoUFy5cgLe3N3r37l2h1y5cuJBLDRARkcNhgHEBlt6XPn36wNfXt0Kv9fX1xQ8//CAtNfDRRx/Zo0QiIqIKYYBxAb/88guAil0+Kik6OhoffvghAPNSAwcPHrRZbURERJXBAOPkrl69irS0NADAwIEDK/0+48aNw+OPP46ioiIMHz6cSw0QEZGsGGCc3Jo1awAAMTEx0Gq1lX4flUqFr7/+mksNEBGRQ/CQuwCyr8rMPnoQy1IDPXv2xNKlS9GnTx8MHz78kd+XlKO4uBh6vR56vR65ubnS30s+vn37Np544gl07NhR7nKJyImphJNOKzEYDNBoNNDr9QgICJC7HFncvn0bwcHBKCgowJEjR9CiRQubvO+MGTMwe/Zs+Pv7Iz09HQ0aNLDJ+5J9mUwm5OXllQofDwoiZe27fft2uY7l5eWFdevWoVevXnb+VETkbMp7/maAcWKrV6/GE088gYiICJw9exYqlcom71tcXIzu3btj165d6NChA3bu3AlPT0+bvDeVTQiBO3fuPFL4MBgMNpsG7+vrC41Gg8DAQGg0Gqu/nzp1Ctu2bUO1atWwdetWtGvXzibHJCLXUN7zNy8hObGSl49sFV4A81IDy5YtQ+vWrbFv3z68++67SEpKstn7uyKDwYBFixbh3LlzDwwiRqPRJsfy8vKyCh4PCiIP2hcQEPCXgbWgoAADBgzAli1b0L9/f+zcuRNNmza1Se1ERBbsgXFSRqMRtWrVwrVr17B582b07NnT5sf46aef8OSTT0KlUmHjxo0VvkkemWVlZWHAgAFIT09/aFs3N7dyh48HtfP29rb7ZzIYDOjRowcOHjyIunXrYvfu3ahdu7bdj0tEysdLSC4eYHbv3o3OnTsjMDAQOTk5drvE88orr+CLL76AVqvFoUOHEBISYpfjOKtTp04hPj4eFy5cQEhICCZNmoTq1as/MHz4+fnZtDfNnnJychAXF4dTp04hMjISO3bsQFBQkNxlEZGD4yUkF2e5fDRgwAC7jk/58MMPsWPHDhw/fhzPPfcc1qxZU67FIgnYu3cvEhIScP36dTRq1AgbNmxAw4YN5S7LZkJCQvD777+jc+fOOH78OAYOHIjk5GT4+fnJXRoROQGeaZyULadP/xVfX1+sWLECarUa69evx8cff2zX4zmLNWvWoEePHrh+/Trat2+PXbt2OVV4sahfvz5+//13VK9eHampqXjqqadQVFQkd1lE5AQYYJzQ6dOnceLECXh4eKBfv352Px6XGqiYr7/+GoMHD0Z+fj769euHLVu2OPWltxYtWmDNmjXw8fHB+vXr8dxzz8FkMsldFhEpHAOME/rtt98AAN27d4dGo6mSY44bNw6DBw9GUVERhg0bxqUGyiCEwHvvvYeXXnoJRqMRo0ePxq+//opq1arJXZrdderUCT/99BM8PDywfPlyvP7661zZnIgeCQOME6qqy0clWZYaqFOnDk6fPo0JEyZU2bGVwGg04tVXX8U777wDAJg2bRqWLl3qUvfP6d+/P7799lsAwMcff4w5c+bIWxARKVqFA0xKSgoGDRqEsLAwqFQqrF692mq/SqUqc1uwYIHUpn79+qX2z5071+p9Dh8+jLi4OHh7eyM8PBzz58+v3Cd0MdevX8fOnTsBAIMGDarSYwcFBeH777+Hm5sbvv32WyxfvrxKj++o8vPz8eSTT+Lzzz+HSqXCp59+ivfff18xs4lsaeTIkVi0aBEAYPr06fjiiy/kLYiIFKvCAeb27dto1aoVPvvsszL3Z2VlWW3ffPMNVCoVhg4datVu9uzZVu1K/sZuMBjQt29f1KtXDwcOHMCCBQswc+ZMfPnllxUt1+WsX78eRqMRLVu2RP369av8+N26dcP06dMBmKdYnzt3rsprcCQ3btxAnz59sHr1aqjVaqxcuRKJiYlylyWr1157DW+//TYA86XHH3/8UeaKiEiRxCMAIFatWvWXbR5//HHRs2dPq+fq1asnFi5c+MDXLF68WFSvXl0UFBRIz02ZMkU0bdq03LXp9XoBQOj1+nK/xhk89dRTAoCYPn26bDUUFRWJLl26CACiQ4cOorCwULZa5HTx4kXRvHlzAUBoNBqxfft2uUtyGCaTSYwdO1YAEF5eXmLz5s1yl0REDqK852+7joHJzs7G2rVr8eKLL5baN3fuXAQHB6NNmzZYsGABiouLpX2pqano2rUrvLy8pOfi4+ORkZGBmzdvlnmsgoICGAwGq83VFBQUYMOGDQCqdvzL/SxLDQQGBmLfvn3SuA9XcuTIEXTq1AknTpxA7dq1sXPnTnTt2lXushyGSqXC4sWLMXToUBQWFuLxxx/H/v375S6LiBTErgHmu+++g7+/P4YMGWL1/MSJE7FixQps3boVL7/8MubMmYM333xT2q/T6RAaGmr1GstjnU5X5rGSkpKs7lwaHh5u40/j+LZv3468vDzUqlULbdu2lbWWunXr4l//+hcAYN68eUhOTpa1nqq0fft2xMXF4cqVK4iMjERqaqrNVgJ3Ju7u7li2bBl69uyJW7duoX///jh16pTcZRGRQtg1wHzzzTcYOXJkqbVXJk+ejO7du6Nly5Z45ZVX8MEHH+CTTz5BQUFBpY81depUq5V3L1269KjlK45l9tGgQYMc4m64Q4cOxcsvvwwAGDVqFHJycmSuyP5WrlyJvn37Qq/XIy4uDjt37nTJMF1earUaq1atwmOPPYY///wTffr0wZUrV+Qui8hu7t69i6ysLLnLcAp2O8vt2LEDGRkZeOmllx7aNiYmBsXFxbhw4QIAQKvVIjs726qN5bFWqy3zPdRqNQICAqw2VyKEkGX69MMsXLgQUVFR0Ol0Tn8Ds08++QR///vfUVhYiCeeeEK6Ay39tYCAAKxfvx5NmjRBZmYm4uPjcePGDbnLIrK533//HY0bN0adOnUwY8YMq6ETVHF2CzBff/012rZti1atWj20bXp6Otzc3KS7kcbGxiIlJcXqluPJyclo2rQpTwgPcOjQIVy6dAm+vr52WXm6snx8fLBixQp4e3tj/fr1+Oijj+QuyeaEEJg6dSomTpwIIQReffVVrFy5Ej4+PnKXphiWdZPCwsJw7NgxDBw4ELdv35a7LCKbMBgMGDt2LPr164fLly/DZDJh9uzZ6Nmzp0teLbCZio4OzsvLE3/88Yf4448/BADx4Ycfij/++ENcvHjRagSxr6+vWLJkSanX7969WyxcuFCkp6eLs2fPiu+//17UrFlTjBo1SmqTm5srQkNDxbPPPiuOHj0qVqxYIXx9fcUXX3xR7jpdbRbSrFmzBAAxePBguUsp0+LFiwUA4enpKQ4cOCB3OTZTWFgoRo0aJQAIAOK9994TJpNJ7rIU68iRI6J69eoCgOjfv7/LzmAj57Fp0yZRt25d6WfExIkTxdKlS4W/v78AIKpXr/7Q2byuprzn7woHmK1bt0pfiJLb6NGjpTZffPGF8PHxEbm5uaVef+DAARETEyM0Go3w9vYWzZs3F3PmzBF37961anfo0CHRpUsXoVarRe3atcXcuXMrVKerBZi2bdsKAOKbb76Ru5QymUwm8cQTTwgAonHjxiIvL0/ukh5ZXl6eiI+PFwCEu7u7w/7bK82uXbuEj4+PACBGjhwpjEaj3CURVVheXp4YN26cdI6MiIgQ27Ztk/afOXNGtGvXTtr/6quvijt37shYseOwW4BRClcKMJcuXRIAhEqlEtnZ2XKX80DXr18XderUEQDEc889J3c5j0Sn00mh0dfXV6xdu1bukpzKunXrhIeHhwAgXnvtNfZqkaJs27ZNREREWIWTsn5pKygoEG+88YbULjo6Whw7dkyGih0LA4wLBRjL5ZlOnTrJXcpDbd++Xbi5uQkAYtmyZXKXUymnT58WDRs2FABEjRo1xN69e+UuySl9//33VpfmiBzdrVu3xMSJE6X/t3Xr1hWbNm166Os2bNggQkJCBADh4+MjvvrqK5cO7QwwLhRg+vXrJwBU+DKbXGbMmCEACH9/f3HmzBm5y6mQtLQ0UbNmTalLOCMjQ+6SnNqiRYukk0FFxsARVbWdO3eKRo0aSf9fx44dW6HzT1ZWlujTp4/0+qefflrcvHnTfgU7MAYYFwkwBoNBeHl5CQDi+PHjcpdTLkVFRSIuLk4AEO3bt7daMsKRbdiwQfj5+QkAok2bNiIrK0vuklzC22+/LQAINzc38eOPP8pdDpGVO3fuiP/5n/8RKpVKABB16tQRGzZsqNR7GY1GMW/ePOnyaf369UVqaqqNK3Z8DDAuEmB+/PFHAUA0atRIUV2OFy9elGabTJkyRe5yHuq7776Tfqj06dNHGAwGuUtyGVw3iRxVamqqaNq0qdRr8vzzz9uk12TPnj3SGBp3d3eRlJTkUoPZGWBcJMBYpvBOnjxZ7lIq7KeffpK+8Tdu3Ch3OWUymUwiKSlJqnPkyJGK6TFyJsXFxWLIkCECgKhWrZrYv3+/3CWRC8vPzxdTpkyRxvPVqlVLrFmzxqbHyM3NFcOGDZN+9vTq1UtcvXrVpsdwVAwwLhBgioqKRHBwsABgNT1PSV555RUBQGi1WoebQVVcXCzGjx8v/QD5xz/+4VK/BTma/Px80aNHDwFA1KxZk+OPSBZpaWkiMjJS+rnw7LPPihs3btjlWCaTSXz99dfC19dX+n+/bt06uxzLkTDAuECASUlJkW6EVFRUJHc5lXLnzh0RFRUl3bjMUQJCfn6+ePLJJ6UfUgsXLpS7JBLm7+vHHntMABD16tUTly9flrskchF3794Vb7/9tnB3dxcARGhoqFi9enWVHPvEiROiZcuW0s+jyZMnO3VPMAOMCwSYf/zjHwKAeOaZZ+Qu5ZEcOXJEeHt7S3d2ltvNmzdFt27dpDEXK1askLskKiE7O1s0btxYABBRUVHi+vXrcpdETu7gwYMiOjpaChDDhg0Tf/75Z5XWkJ+fLyZMmCDV0LZtW3H69OkqraGqMMC4QIBp0qSJACBWrlwpdymPbMmSJQ6x1MDly5dFixYtBAAREBAgtmzZIlst9GDnz58XYWFhAoCIjY0Vt27dkrskckKFhYVi5syZ0gD+GjVqyP7zdvXq1SIoKEgaD/bvf/9b1nrsgQHGyQPMyZMnpR4CZ5gR4whLDRw7dkyEh4dLg/LS09OrvAYqP66bRPZ06NAh0aZNG6nHY+jQoQ4zTu/SpUuia9euUm2jRo1yivOABQOMkweY+fPnCwAiPj5e7lJs5vr161KAKLm2VlXYuXOndDJs2rSpOH/+fJUenyqH6yaRrRUVFYn33ntPeHp6CgAiKChIrFixwuFuU1FcXCxmzZolzYRq3Lix0yyUywDj5AGmS5cuAoD47LPP5C7FplJSUqRvyO+//75Kjrlq1SppDE7Hjh2r/No2PZq1a9dy3SSyiWPHjlktsPj44487/A0rU1JSpDXmPD09xcKFCxX/PcAA48QBJicnRzrJZ2Zmyl2Ozc2cObPKlhpYsmSJ9G85aNAgcfv2bbsej+yj5LpJ77//vtzlkMIUFxeLefPmSXc1DwwMFP/+978VEwSuX78uBg8eLH0PDBgwQOTk5MhdVqUxwDhxgPn222+l29k7o6pYasBkMonp06dL3/BjxoxR7FR0MuO6SVQZJ0+eFB07dpT+7yQkJIgrV67IXVaFmUwm8dlnnwm1Wi2N41PqXasZYJw4wFjuSDpjxgy5S7GbzMxMuy01UFRUJF544QXpB9bMmTMV85sW/bVp06Zx3SQql+LiYvHBBx9Il48DAgLE0qVLFf+z4NChQ6J58+YCgFCpVGLatGmKG+DOAOOkASY/P19aUNBZBmw9yM8//2zzpQZu3bolEhISpJMcf1N3Llw3icrj9OnT0jhCy2QIZ7ocf+vWLTFmzBjp88XGxipqYgIDjJMGmHXr1gkAonbt2or/TaE8xo0bJ9318lGnMF67dk3ExMQIAMLb21v88ssvNqqSHAnXTaIHMRqN4uOPP5Zmrvn7+4uvvvrKaX+W/vDDD0Kj0QgAQqPRyH4Pm/JigHHSAGNZO2jcuHFyl1IlSi410K9fv0pPkz137px047+goCCxa9cuG1dKjoTrJtH9zp49K91h27I44oULF+Quy+7Onz9vNcZn7NixDj9ZgQHGCQOMyWSS7j66fv16ucupMkePHn2kpQYOHjwotFqtACDq1q0rTpw4YYcqydFw3SQSwtzrsnjxYunSu5+fn1i8eLHT9rqUpbCwUEydOlWoVCoBQERGRoojR47IXdYDMcA4YYDZv3+/1C1+9+5ducupUp9//rl0n4OKXBJITk4W/v7+AoBo2bKlImcXUOVx3STXduHCBdGrVy+p96Fbt27i7Nmzcpclm02bNkm/zHl7e4slS5Y4ZJBjgHHCAPPuu+9Kt7R2NSaTSRrX0KhRo3LdNnvZsmXS3TS7d+8ucnNzq6BScjT3r5vk6N3n9OhMJpP46quvpF9efHx8xMcff8w7NQtzqO/fv78U6oYMGSJu3Lghd1lWGGCcMMC0bt1aABDfffed3KXIoiJLDfzv//6v9A369NNPu1yPFVnjukmu49KlSyI+Pl76/u/cubPTrtpcWUajUXzwwQfSL3jh4eFix44dcpclYYBxsgBz8eJFaervtWvX5C5HNg9basBoNIrJkydLP7wmTZrE37pICMF1k5ydyWQSS5culWbdeHt7iw8++EAUFxfLXZrD2r9/v2jUqJF0bpk9e7ZD/HsxwDhZgPn0008FABEXFyd3KbKbNWtWmUsN3L17VwwfPlwKLwsWLHDI67skH66b5JyuXLki3d8JgIiJieFg/XIyGAzimWeekf7tunfvLvuAdwYYJwswffr0kU7Krq64uFhaSt6y1IBerxc9e/YUAISHh0eVLQRJysN1k5yHyWQS//73v0VgYKB088J58+Y5RC+C0vy///f/pJlawcHB4tdff5WtFgYYJwowubm50rVK3s/CrORSA2PHjhWtWrWSZmj9/vvvcpdHDq7kuklffvml3OVQJeh0OqsFDNu1ayeOHTsmd1mKdurUKenWAwDExIkTZRk/yADjRAHmhx9+EABE06ZN5S7FoaxatUr6RgMgQkJCnH55BbKdkusm/fTTT3KXQ+VkMpnEihUrRHBwsHRrhffff5+LsdrI3bt3xeuvvy79XG3durU4efJkldZQ3vO3G8jh/frrrwCAv/3tbzJX4lgGDx6MV199FQDQqFEjpKam4rHHHpO5KlKK9957D2PHjoXJZMLw4cOxZcsWuUuih7h27RqefvppDBs2DNevX0ebNm2wf/9+TJs2DR4eHnKX5xTUajU+/PBDrF27FjVq1EB6ejratm2Lb7/9FkIIucuzVjV5quo5Sw9MYWGhdH3Xkaa5OYri4mKxYcMG3uOFKoXrJjk+k8kkjh8/Lj755BNRs2ZNaZzbrFmzOB3ezq5cuSKNLQQgRowYUSXn1PKev1VCOFqksg2DwQCNRgO9Xo+AgAC5y6m0bdu2oUePHggODkZ2djbc3d3lLonIqdy9excDBgzA1q1bUbNmTezcuRNNmjSRuyyXVVxcjEOHDmHHjh1ISUnBzp07ce3aNWl/dHQ0vvvuO7Rp00bGKl2H0WjEvHnz8O6778JoNKJBgwZYsWIF2rdvb7djlvf8zT43B2e5fDRw4ECGFyI78Pb2xurVq9GjRw8cPHgQffv2xa5du1C7dm25S3MJd+/eRVpamhRYdu/ejby8PKs23t7e6NixIxISEjBx4kR4eXnJVK3rcXd3x7Rp09C9e3eMGDEC586dQ6dOnZCUlITJkyfDzU2+kSjsgXFgQgg0btwYZ8+exU8//YQhQ4bIXRKR08rJyUGXLl1w+vRpREVFISUlBUFBQXKX5XTy8vKwe/dupKSkYMeOHdi3bx8KCgqs2mg0GnTp0gVxcXHo2rUr2rZty9DiAHJzczFmzBj8+OOPAID4+Hh89913CA0Ntelxynv+ZoBxYMePH0dUVBTUajX+/PNPVKtWTe6SiJzahQsX0LlzZ1y9ehWdOnVCcnIyfH195S5L0a5du4adO3dKgeWPP/6AyWSyahMaGoquXbtKgaVFixbscXZQQgj861//wmuvvYb8/HzMnDkTM2bMsOkxeAnJCVguH/Xq1YvhhagK1K9fH7///jvi4uKwe/duREVFoU2bNmjSpAmaNGmCxo0bo0mTJggJCYFKpZK7XIeUmZkpXQ7asWMHTpw4UapNgwYNpLASFxeHRo0a8d9TIVQqFcaMGYPOnTtjwYIFmDZtmmy1MMA4ME6fJqp6LVq0wNq1axEfH48LFy7gwoULpdoEBARIoabk1rhxY8X2+FaGEAIZGRlSWElJSUFmZmapdi1atLAKLBxfpHyRkZFYunSprDXwEpKDys7ORq1atSCEwOXLl/kNT1TFrl+/jgMHDuDUqVNW24ULF/7yfhharbbMcNOgQQOo1eoq/AS2ZzQacejQISmw7Nixw2qGEGAe9Nm2bVspsHTu3BnBwcEyVUxKxDEwCg8w33zzDV588UW0a9cOaWlpcpdDRP/n7t27OHfuXKlgc+rUKWRnZz/wdW5ubqhXr16Z4SY8PNwhx3wUFBQgLS1NCiy7du164AwhS+9Kx44decmbHgnHwCgcLx8ROSZvb29ERkYiMjKy1D6DwYDTp0+XCjYZGRnIy8vD+fPncf78efz+++9Wr1Or1WjUqFGZ4aZmzZpVNj7EMkPI0ruyd+/eMmcIde7cWQosbdu2VXzPEikTe2AcUH5+PoKDg5Gfn4/09HS0atVK7pKI6BEIIZCTk1Nmr82ZM2dQWFj4wNfac7yNZYaQZfzKg2YIlRy/Eh0d7ZC9ReQ87HYJKSUlBQsWLMCBAweQlZWFVatWYfDgwdL+5557Dt99953Va+Lj47Fhwwbp8Y0bNzBhwgT89ttvcHNzw9ChQ/HRRx9ZdTsePnwYiYmJSEtLQ82aNTFhwgS8+eab5a5TyQFmzZo1GDRoEOrWrYsLFy5wdD6REzMajcjMzCwVbE6fPm3z8TaXLl2yGnBb1gyhiIgIqynNnCFEVc1ul5Bu376NVq1a4YUXXnjgjdX69etnNTr5/m+kkSNHIisrC8nJySgqKsLzzz+PsWPHYvny5VLxffv2Re/evfH555/jyJEjeOGFFxAYGIixY8dWtGTFKXn5iD84iJybu7s7IiIiEBERgfj4eKt9Dxtvo9PpoNPpkJKSYvW6+8fb5ObmIiUlBRcvXix1/KioKCmwxMXFoU6dOnb9vES28kiXkFQqVZk9MLm5uVi9enWZrzlx4gQiIyORlpaGdu3aAQA2bNiAAQMG4PLlywgLC8OSJUvw9ttvQ6fTSXdffOutt7B69WqcPHmyXLUptQfGZDKhdu3a0Ol02LhxI/r06SN3SUTkgPR6fZnjbU6dOlVqoK1FyRlCcXFx6NKlC2cIkcORdRDvtm3bEBISgurVq6Nnz5547733pG+S1NRUBAYGSuEFAHr37g03Nzfs3bsXTzzxBFJTU9G1a1erW0fHx8dj3rx5uHnzJqpXr17qmAUFBVaDzQwGgz0+mt3t378fOp0O/v7+6Natm9zlEJGD0mg0aNeundXPUsA83iY7O9sq3Hh7e3OGEDkdmweYfv36YciQIYiIiMDZs2cxbdo09O/fH6mpqXB3d4dOp0NISIh1ER4eCAoKgk6nAwDodDpERERYtbGstaDT6coMMElJSZg1a5atP06Vs1w+6t+/P9f+IKIKU6lU0Gq10Gq1iIuLk7scIruxeYAZNmyY9Pfo6Gi0bNkSDRs2xLZt29CrVy9bH04ydepUTJ48WXpsMBgQHh5ut+PZC6dPExERPZzd18Fu0KABatSogTNnzgAwj5rPycmxalNcXIwbN25Aq9VKbe6/IZTlsaXN/dRqNQICAqw2pTl//jyOHDkCd3d39O/fX+5yiIiIHJbdA8zly5dx/fp11KpVCwAQGxuL3NxcHDhwQGqzZcsWmEwmxMTESG1SUlJQVFQktUlOTkbTpk3LvHzkLH777TcAQFxcHIKCgmSuhoiIyHFVOMDcunUL6enpSE9PB2DuNUhPT0dmZiZu3bqFN954A3v27MGFCxewefNmPP7442jUqJE0PbB58+bo168fxowZg3379mHXrl0YP348hg0bhrCwMADAiBEj4OXlhRdffBHHjh3DDz/8gI8++sjqEpEz4uUjIiKichIVtHXrVgGg1DZ69Ghx584d0bdvX1GzZk3h6ekp6tWrJ8aMGSN0Op3Ve1y/fl0MHz5cVKtWTQQEBIjnn39e5OXlWbU5dOiQ6NKli1Cr1aJ27dpi7ty5FapTr9cLAEKv11f0I8ri5s2bwsPDQwAQZ86ckbscIiIiWZT3/M2lBBzEf/7zH4wYMQKRkZE4duyY3OUQERHJorznb7uPgaHy4eUjIiKi8mOAcQCFhYVYv349AAYYIiKi8mCAcQA7duyAXq9HSEgIOnToIHc5REREDo8BxgFYLh8NHDiQy9QTERGVAwOMzIQQHP9CRERUQQwwMjt69CguXLgAb29v9O7dW+5yiIiIFIEBRmaW3pc+ffrAz89P5mqIiIiUgQFGZrx8REREVHEMMDLKysrCvn37AJgH8BIREVH5MMDIaM2aNQCAmJiYB66yTURERKUxwMiIl4+IiIgqhwFGJrdv38amTZsAMMAQERFVFAOMTDZt2oS7d+8iIiICUVFRcpdDRESkKAwwMil5+UilUslcDRERkbIwwMjAaDTit99+A8DLR0RERJXBACODffv24dq1a9BoNIiLi5O7HCIiIsVhgJGB5fLRgAED4OnpKXM1REREysMAIwNOnyYiIno0DDBV7MyZMzh+/Dg8PDzQr18/ucshIiJSJAaYKmYZvNutWzcEBgbKWwwREZFCMcBUMV4+IiIienQMMFXoxo0b2LFjBwBg0KBBMldDRESkXAwwVWjdunUwGo2Ijo5GRESE3OUQEREpFgNMFeLlIyIiIttggKkiBQUF2LBhAwAGGCIiokfFAFNFtm/fjry8PGi1WrRr107ucoiIiBSNAaaKWC4fDRo0CG5u/GcnIiJ6FDyTVgEhBMe/EBER2RADTBU4dOgQLl26BB8fH/Tq1UvucoiIiBSPAaYKWHpf+vbtCx8fH5mrISIiUj4GmCpgCTCPP/64zJUQERE5BwYYO7t8+TIOHDgAlUqFhIQEucshIiJyCgwwdrZmzRoAQGxsLEJCQmSuhoiIyDkwwNgZZx8RERHZHgOMHd26dQubN28GwABDRERkSwwwdrRx40YUFhaiUaNGaNasmdzlEBEROQ0GGDsqeflIpVLJXA0REZHzYICxE6PRKA3g5eUjIiIi26pwgElJScGgQYMQFhYGlUqF1atXS/uKioowZcoUREdHw8/PD2FhYRg1ahSuXr1q9R7169eHSqWy2ubOnWvV5vDhw4iLi4O3tzfCw8Mxf/78yn1CmaSmpuL69euoXr06OnfuLHc5RERETqXCAeb27dto1aoVPvvss1L77ty5g4MHD+Kdd97BwYMH8fPPPyMjI6PMHojZs2cjKytL2iZMmCDtMxgM6Nu3L+rVq4cDBw5gwYIFmDlzJr788suKlisby+WjhIQEeHh4yFwNERGRc6nwmbV///7o379/mfs0Gg2Sk5Otnvv000/RoUMHZGZmom7dutLz/v7+0Gq1Zb7PsmXLUFhYiG+++QZeXl6IiopCeno6PvzwQ4wdO7aiJcuC06eJiIjsx+5jYPR6PVQqFQIDA62enzt3LoKDg9GmTRssWLAAxcXF0r7U1FR07doVXl5e0nPx8fHIyMjAzZs3yzxOQUEBDAaD1SaXjIwMZGRkwNPTE/Hx8bLVQURE5Kzsem3j7t27mDJlCoYPH46AgADp+YkTJ+Kxxx5DUFAQdu/ejalTpyIrKwsffvghAECn0yEiIsLqvUJDQ6V91atXL3WspKQkzJo1y46fpvx+++03AECPHj2sPjcRERHZht0CTFFREZ5++mkIIbBkyRKrfZMnT5b+3rJlS3h5eeHll19GUlIS1Gp1pY43depUq/c1GAwIDw+vXPGPiJePiIiI7MsuAcYSXi5evIgtW7Y8tBciJiYGxcXFuHDhApo2bQqtVovs7GyrNpbHDxo3o1arKx1+bOnPP//Erl27AACDBg2SuRoiIiLnZPMxMJbwcvr0aWzatAnBwcEPfU16ejrc3NykxQ5jY2ORkpKCoqIiqU1ycjKaNm1a5uUjR7Ju3TqYTCa0bt3aatAyERER2U6Fe2Bu3bqFM2fOSI/Pnz+P9PR0BAUFoVatWnjyySdx8OBBrFmzBkajETqdDgAQFBQELy8vpKamYu/evejRowf8/f2RmpqK119/Hc8884wUTkaMGIFZs2bhxRdfxJQpU3D06FF89NFHWLhwoY0+tv3w8hEREVEVEBW0detWAaDUNnr0aHH+/Pky9wEQW7duFUIIceDAARETEyM0Go3w9vYWzZs3F3PmzBF37961Os6hQ4dEly5dhFqtFrVr1xZz586tUJ16vV4AEHq9vqIfsdLy8/OFn5+fACD2799fZcclIiJyFuU9f6uEEEKO4GRvBoMBGo0Ger2+ymYCrV+/HgMGDEBYWBguX77M9Y+IiIgqqLznb66FZENcvJGIiKhqMMDYiBCC41+IiIiqCAOMjRw8eBBXr16Fn58fevToIXc5RERETo0BxkYsvS/x8fHw9vaWuRoiIiLnxgBjI7x8REREVHUYYGwgMzNTuhlfQkKC3OUQERE5PQYYG7As3ti5c2fUqFFD5mqIiIicHwOMDfDyERERUdVigHlEBoMBW7duBcAAQ0REVFUYYB7R77//jqKiIjRt2hRNmjSRuxwiIiKXwADziHj5iIiIqOoxwDyC4uJirF27FgADDBERUVVigHkEu3btws2bNxEcHIzY2Fi5yyEiInIZDDCPwHL5aODAgXB3d5e5GiIiItfBAFNJQgj88ssvAHj5iIiIqKoxwFTSyZMncfbsWXh5eaFv375yl0NERORSGGAqyXL5qFevXqhWrZrM1RAREbkWBphK4vRpIiIi+TDAVEJOTg5SU1MBmAfwEhERUdVigKmEtWvXQgiBtm3bok6dOnKXQ0RE5HIYYCqBl4+IiIjkxQBTQfn5+di4cSMABhgiIiK5MMBU0ObNm3Hnzh2Eh4ejVatWcpdDRETkkhhgKqjk5SOVSiVzNURERK6JAaaC/P39ERQUxMtHREREMlIJIYTcRdiDwWCARqOBXq9HQECATd+7uLgYKpWK6x8RERHZWHnP3x5VWJPT8PDgPxsREZGceAmJiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSnwgEmJSUFgwYNQlhYGFQqFVavXm21XwiBd999F7Vq1YKPjw969+6N06dPW7W5ceMGRo4ciYCAAAQGBuLFF1/ErVu3rNocPnwYcXFx8Pb2Rnh4OObPn1/xT0dEREROqcIB5vbt22jVqhU+++yzMvfPnz8fH3/8MT7//HPs3bsXfn5+iI+Px927d6U2I0eOxLFjx5CcnIw1a9YgJSUFY8eOlfYbDAb07dsX9erVw4EDB7BgwQLMnDkTX375ZSU+IhERETkd8QgAiFWrVkmPTSaT0Gq1YsGCBdJzubm5Qq1Wi//85z9CCCGOHz8uAIi0tDSpzfr164VKpRJXrlwRQgixePFiUb16dVFQUCC1mTJlimjatGm5a9Pr9QKA0Ov1lf14REREVMXKe/626RiY8+fPQ6fToXfv3tJzGo0GMTExSE1NBQCkpqYiMDAQ7dq1k9r07t0bbm5u2Lt3r9Sma9eu8PLyktrEx8cjIyMDN2/eLPPYBQUFMBgMVhsRERE5J5sGGJ1OBwAIDQ21ej40NFTap9PpEBISYrXfw8MDQUFBVm3Keo+Sx7hfUlISNBqNtIWHhz/6ByIiIiKH5DSzkKZOnQq9Xi9tly5dkrskIiIishObBhitVgsAyM7Otno+Oztb2qfVapGTk2O1v7i4GDdu3LBqU9Z7lDzG/dRqNQICAqw2IiIick42DTARERHQarXYvHmz9JzBYMDevXsRGxsLAIiNjUVubi4OHDggtdmyZQtMJhNiYmKkNikpKSgqKpLaJCcno2nTpqhevbotSyYiIiIFqnCAuXXrFtLT05Geng7APHA3PT0dmZmZUKlUmDRpEt577z38+uuvOHLkCEaNGoWwsDAMHjwYANC8eXP069cPY8aMwb59+7Br1y6MHz8ew4YNQ1hYGABgxIgR8PLywosvvohjx47hhx9+wEcffYTJkyfb7IMTERGRglV0etPWrVsFgFLb6NGjhRDmqdTvvPOOCA0NFWq1WvTq1UtkZGRYvcf169fF8OHDRbVq1URAQIB4/vnnRV5enlWbQ4cOiS5dugi1Wi1q164t5s6dW6E6OY2aiIhIecp7/lYJIYSM+cluDAYDNBoN9Ho9x8MQEREpRHnP304zC4mIiIhcBwMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKY7NA0z9+vWhUqlKbYmJiQCA7t27l9r3yiuvWL1HZmYmEhIS4Ovri5CQELzxxhsoLi62dalERESkUB62fsO0tDQYjUbp8dGjR9GnTx889dRT0nNjxozB7Nmzpce+vr7S341GIxISEqDVarF7925kZWVh1KhR8PT0xJw5c2xdLhERESmQzQNMzZo1rR7PnTsXDRs2RLdu3aTnfH19odVqy3z9xo0bcfz4cWzatAmhoaFo3bo1/vnPf2LKlCmYOXMmvLy8bF0yERERKYxdx8AUFhbi+++/xwsvvACVSiU9v2zZMtSoUQMtWrTA1KlTcefOHWlfamoqoqOjERoaKj0XHx8Pg8GAY8eOPfBYBQUFMBgMVhsRERE5J5v3wJS0evVq5Obm4rnnnpOeGzFiBOrVq4ewsDAcPnwYU6ZMQUZGBn7++WcAgE6nswovAKTHOp3ugcdKSkrCrFmzbP8hiIiIyOHYNcB8/fXX6N+/P8LCwqTnxo4dK/09OjoatWrVQq9evXD27Fk0bNiw0seaOnUqJk+eLD02GAwIDw+v9PsRERGR47JbgLl48SI2bdok9aw8SExMDADgzJkzaNiwIbRaLfbt22fVJjs7GwAeOG4GANRqNdRq9SNWTUREREpgtzEwS5cuRUhICBISEv6yXXp6OgCgVq1aAIDY2FgcOXIEOTk5Upvk5GQEBAQgMjLSXuUSERGRgtilB8ZkMmHp0qUYPXo0PDzuHeLs2bNYvnw5BgwYgODgYBw+fBivv/46unbtipYtWwIA+vbti8jISDz77LOYP38+dDodpk+fjsTERPawEBEREQA7BZhNmzYhMzMTL7zwgtXzXl5e2LRpExYtWoTbt28jPDwcQ4cOxfTp06U27u7uWLNmDcaNG4fY2Fj4+flh9OjRVveNISIiItemEkIIuYuwB4PBAI1GA71ej4CAALnLISIionIo7/mbayERERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHi2DzAzJw5EyqVympr1qyZtP/u3btITExEcHAwqlWrhqFDhyI7O9vqPTIzM5GQkABfX1+EhITgjTfeQHFxsa1LJSIiIoXysMebRkVFYdOmTfcO4nHvMK+//jrWrl2LlStXQqPRYPz48RgyZAh27doFADAajUhISIBWq8Xu3buRlZWFUaNGwdPTE3PmzLFHuURERKQwdgkwHh4e0Gq1pZ7X6/X4+uuvsXz5cvTs2RMAsHTpUjRv3hx79uxBx44dsXHjRhw/fhybNm1CaGgoWrdujX/+85+YMmUKZs6cCS8vL3uUTERERApilzEwp0+fRlhYGBo0aICRI0ciMzMTAHDgwAEUFRWhd+/eUttmzZqhbt26SE1NBQCkpqYiOjoaoaGhUpv4+HgYDAYcO3bsgccsKCiAwWCw2oiIiMg52TzAxMTE4Ntvv8WGDRuwZMkSnD9/HnFxccjLy4NOp4OXlxcCAwOtXhMaGgqdTgcA0Ol0VuHFst+y70GSkpKg0WikLTw83LYfjIiIiByGzS8h9e/fX/p7y5YtERMTg3r16uG///0vfHx8bH04ydSpUzF58mTpscFgYIghIiJyUnafRh0YGIgmTZrgzJkz0Gq1KCwsRG5urlWb7OxsacyMVqstNSvJ8riscTUWarUaAQEBVhsRERE5J7sHmFu3buHs2bOoVasW2rZtC09PT2zevFnan5GRgczMTMTGxgIAYmNjceTIEeTk5EhtkpOTERAQgMjISHuXS0RERApg80tI//jHPzBo0CDUq1cPV69exYwZM+Du7o7hw4dDo9HgxRdfxOTJkxEUFISAgABMmDABsbGx6NixIwCgb9++iIyMxLPPPov58+dDp9Nh+vTpSExMhFqttnW5REREpEA2DzCXL1/G8OHDcf36ddSsWRNdunTBnj17ULNmTQDAwoUL4ebmhqFDh6KgoADx8fFYvHix9Hp3d3esWbMG48aNQ2xsLPz8/DB69GjMnj3b1qUSERGRQqmEEELuIuzBYDBAo9FAr9dzPAwREZFClPf8zbWQiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxbL6YIxFVUG4usHEjsG0bEBgIREWZt2bNAG9vmYsjInJMDDBEVU0I4PhxYO1a87ZrF2A0lm7n5gY0anQv0Fi2Jk0Atbrq6yYiciAMMERVIT8f2Lr1Xmi5eNF6f7NmQHy8ud2xY+YtNxc4dcq8rVp1r627O9C4celg07gx4OVVpR+LiEguDDBE9nLx4r3AsmULcPfuvX1qNdCjBzBgAJCQADRoYP1aIYCsrHthpuRmMAAnT5q3n3669xoPD3PvzP3BplEjwNOzaj4zEVEVUQkhhNxF2IPBYIBGo4Fer0dAQIDc5ZArKC4Gdu++F1qOHbPeX6eOOawkJAA9ewJ+fhU/hhDAlStlB5tbt8p+jacn0LRp6WDTsKE59BDRPUIAhYXmXzgsW0GB9eOynqtIm6IiQKW6t7m5Kffxk08C0dE2/RKU9/zNn15Ej+LaNWD9emDdOuD3382XfSzc3IBOne71skRHm7/hH4VKZQ5CdeqYLzlZCAFculQ61Bw/Dty+DRw9at5KUqvLDjYNGpgvUxHZkhCAyWQO+kZj5f4sKip/WHiUkEHl17y5zQNMeTHAEFWEEMAff9zrZdm3z/ycRXAw0K+fObDExwNBQVVTl0oF1K1r3vr3v/e8yQRkZpYdbPLzgcOHzVtJ3t7mMTn3B5uICHMoI2W4cwc4cADYswfQ6coXEiobLMr73krk7W0O+97e97aKPr7/OUvPp8lk/vlh2R72uDxt7PEef9WmcWPZvjS8hET0MHl5wKZN5sCybp15bEpJrVubA8uAAUBMjDJ6L0wm4MIFc5g5evResDl50nqsTkk+PubftqKigBYt7gWbunUZbOQmBHD2rDms7NkDpKYChw4pJzS4uZm/bzw8/vpPT89HCw4Vfezl9ei9plRh5T1/M8AQleXUqXu9LCkp5q5rCz8/oHfve6Gldm356rQ1oxE4d650j83Jk+ZxAWXx8wMiI617a1q0MF/m4g9/+zAYgLS0e2Flzx7g+vXS7WrVAmJj7413elhAkOtP/j+hEhhgGGCoIgoKzEHFElrOnLHe36jRvQG4Xbu63n1YiovNv+HfH2wyMqzDXUkBAeYgEx1t/WdwcNXWrnQmkzlAluxdOXbM+tIlYO4taNsW6Njx3hYeznBAisMAwwBDD3PlivmS0Lp1QHKyebCrhaenOahYQkuTJvLV6ciKisxh7/5gc+qUOfSURau9F2YswSYysnKzspzRjRvA3r33AsvevYBeX7pd/frWYaV1a9cL1uSUGGAYYOh+RqN50K2llyU93Xp/rVr3Zgz17g34+8tSplMoKDCHmKNHgSNH7s2COn++7PYqlXmQ8P3BpkkT576HTXGx+d+lZO/KqVOl2/n6Au3bWwcWrbbq6yWqAgwwDDAEmH+b3bjRHFg2bAD+/PPePpUK6NDhXi9L69YcjGpveXnmGVD3B5vs7LLbW+5hc3+wqVdPmV+r7GzrsJKWZp4tdL8mTe4FldhY8+fmPXvIRTDAMMC4JiHMJ0RLL8vu3eYxBBaBgebpzQkJ5unONWvKViqVcO3avTBTMtjk5ZXd3s/PPFj4/mATEuI4Yz4KC829fCUH2l64ULpdQIB59polrHTowHFC5NIYYBhgXMedO+Zb9VumOWdmWu9v0eLepaFOnfibrFIIYf5a3h9sTpx48IyoGjVKDxyOijKHBHvXevmydVg5eLD0TdFUKnM9lrDSsaP5njtK7E0ishMGGAYY53Xlyr0TRWqq+WZdJU8U3t5Ar173pjnXqydfrWR7xcXA6dOlg82ZM6Vn5ljUrVu6t6ZZs8oPes3PN/+/s4SVPXuAq1dLtwsOtg4r7dvbP0wRKRwDDAOMcygsNN/51hJWUlPNt8y/X71698ay9OhhvukauZY7d8y9M/cHmytXym5vWdX7/mBz/1IKQpjvjVOyd+XQodKzrNzdgVat7oWVjh3N919xlEtaRArBAMMAo0xXr1qHlft7VwBzd3vLluYThWXjiYIe5ObNe6HGEmyOHLFet6okb2/ztO4WLcyDwPfssR78bWG5SZwlrLRta54tRESPhAGGAcbxWQY5lgws949fAczd8CXDSvv2QLVqVV4uOREhzEtClBwwfOTIvTWi7uflBTz2mHVg4U3iiOyCq1GT48nKKt27cv+6O25u5m78koGlUSOeKMi2VCogLMy8lVzV22g036vmyBHzDfmqVTP/H+RN4ogcDgMM2UdRUenelYsXS7cLCirdu8IbyJFc3N3NgblRI+CJJ+Suhoj+AgMM2YZOZx1W9u8vu3elRQvrwNK4MXtXiIiowhhgqOKKisyzMEoGlrJu0BUUdG8KqeUGXexdISIiG2CAoYfLzi7du3L/QEeVqnTvSpMm7F0hIiK7YIAha0VFwOHD1oGlrAX4qlcv3bvC2V5ERFRFGGBcXU6OdVhJSyu7dyUqqnTvCm9/TkREMmGAcRVCmO9ge+iQeUtPN9/h9ty50m0DA0v3rmg0VV0xERHRA9k8wCQlJeHnn3/GyZMn4ePjg06dOmHevHlo2rSp1KZ79+7Yvn271etefvllfP7559LjzMxMjBs3Dlu3bkW1atUwevRoJCUlwYML8T1cQYH5hlyWoGIJLTdvlm6rUpnvOlqyd6VpU/auEBGRQ7N5Gti+fTsSExPRvn17FBcXY9q0aejbty+OHz8OPz8/qd2YMWMwe/Zs6bFviVtwG41GJCQkQKvVYvfu3cjKysKoUaPg6emJOXPm2LpkZbt2zbpX5dAh83ow96/TAphXYW7WzHxTrlatzFv79uYeFyIiIgWx+1IC165dQ0hICLZv346uXbsCMPfAtG7dGosWLSrzNevXr8fAgQNx9epVhIaGAgA+//xzTJkyBdeuXYOXl9dDj+t0SwkYjeYVeO8PK2WtgAuYQ0nJoNK6tbmnhXcTJSIiB+YwSwno9XoAQFBQkNXzy5Ytw/fffw+tVotBgwbhnXfekXphUlNTER0dLYUXAIiPj8e4ceNw7NgxtGnTptRxCgoKUFBi0T+DwWCPj1M18vLMM4FKBpUjR8peowUw3zW0ZFBp1YrrtBARkVOza4AxmUyYNGkSOnfujBYtWkjPjxgxAvXq1UNYWBgOHz6MKVOmICMjAz///DMAQKfTWYUXANJjnU5X5rGSkpIwa9YsO30SOxHCvHjh/b0qZ8+W3d7Hx7wKc8mgEh3Nm8MREZHLsWuASUxMxNGjR7Fz506r58eOHSv9PTo6GrVq1UKvXr1w9uxZNGzYsFLHmjp1KiZPniw9NhgMCA8Pr1zh9lBQYF4c7v6wkptbdvvatUv3qjRqZF6rhYiIyMXZLcCMHz8ea9asQUpKCurUqfOXbWNiYgAAZ86cQcOGDaHVarFv3z6rNtnZ2QAArVZb5nuo1WqoHWV8R05O6aBy8uSDB9ZGRloHlVatgBo1qrpqIiIixbB5gBFCYMKECVi1ahW2bduGiIiIh74mPT0dAFCrVi0AQGxsLN5//33k5OQgJCQEAJCcnIyAgABERkbauuTKMxqBU6dKh5WsrLLbBwWVDirNm3NgLRERUQXZPMAkJiZi+fLl+OWXX+Dv7y+NWdFoNPDx8cHZs2exfPlyDBgwAMHBwTh8+DBef/11dO3aFS1btgQA9O3bF5GRkXj22Wcxf/586HQ6TJ8+HYmJifL3sqxYAWzZYg4rR4+WPbBWpbo3sNYSVlq3Nl8W4sBaIiKiR2bzadSqB5ygly5diueeew6XLl3CM888g6NHj+L27dsIDw/HE088genTp1tNl7p48SLGjRuHbdu2wc/PD6NHj8bcuXPLfSM7u02jfvppYOXKe4/9/MwDaUv2qkRHA9Wq2e6YRERELqK852+73wdGLnYLMCtXmi8TWXpVGjbkXWuJiIhsxGHuA+N0nnrKvBEREZFs2HVAREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREiuO0q1ELIQCYl+UmIiIiZbCcty3n8Qdx2gCTl5cHAAgPD5e5EiIiIqqovLw8aDSaB+5XiYdFHIUymUy4evUq/P39oVKpbPa+BoMB4eHhuHTpEgICAmz2vlR5/Jo4Fn49HAu/Ho6FX4+HE0IgLy8PYWFhcHN78EgXp+2BcXNzQ506dez2/gEBAfzP52D4NXEs/Ho4Fn49HAu/Hn/tr3peLDiIl4iIiBSHAYaIiIgUhwGmgtRqNWbMmAG1Wi13KfR/+DVxLPx6OBZ+PRwLvx6247SDeImIiMh5sQeGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBpoI+++wz1K9fH97e3oiJicG+ffvkLsklJSUloX379vD390dISAgGDx6MjIwMucui/zN37lyoVCpMmjRJ7lJc2pUrV/DMM88gODgYPj4+iI6Oxv79++UuyyUZjUa88847iIiIgI+PDxo2bIh//vOfD13vhx6MAaYCfvjhB0yePBkzZszAwYMH0apVK8THxyMnJ0fu0lzO9u3bkZiYiD179iA5ORlFRUXo27cvbt++LXdpLi8tLQ1ffPEFWrZsKXcpLu3mzZvo3LkzPD09sX79ehw/fhwffPABqlevLndpLmnevHlYsmQJPv30U5w4cQLz5s3D/Pnz8cknn8hdmmJxGnUFxMTEoH379vj0008BmNdbCg8Px4QJE/DWW2/JXJ1ru3btGkJCQrB9+3Z07dpV7nJc1q1bt/DYY49h8eLFeO+999C6dWssWrRI7rJc0ltvvYVdu3Zhx44dcpdCAAYOHIjQ0FB8/fXX0nNDhw6Fj48Pvv/+exkrUy72wJRTYWEhDhw4gN69e0vPubm5oXfv3khNTZWxMgIAvV4PAAgKCpK5EteWmJiIhIQEq+8Tksevv/6Kdu3a4amnnkJISAjatGmDr776Su6yXFanTp2wefNmnDp1CgBw6NAh7Ny5E/3795e5MuVy2sUcbe3PP/+E0WhEaGio1fOhoaE4efKkTFURYO4JmzRpEjp37owWLVrIXY7LWrFiBQ4ePIi0tDS5SyEA586dw5IlSzB58mRMmzYNaWlpmDhxIry8vDB69Gi5y3M5b731FgwGA5o1awZ3d3cYjUa8//77GDlypNylKRYDDCleYmIijh49ip07d8pdisu6dOkSXnvtNSQnJ8Pb21vucgjmYN+uXTvMmTMHANCmTRscPXoUn3/+OQOMDP773/9i2bJlWL58OaKiopCeno5JkyYhLCyMX49KYoAppxo1asDd3R3Z2dlWz2dnZ0Or1cpUFY0fPx5r1qxBSkoK6tSpI3c5LuvAgQPIycnBY489Jj1nNBqRkpKCTz/9FAUFBXB3d5exQtdTq1YtREZGWj3XvHlz/PTTTzJV5NreeOMNvPXWWxg2bBgAIDo6GhcvXkRSUhIDTCVxDEw5eXl5oW3btti8ebP0nMlkwubNmxEbGytjZa5JCIHx48dj1apV2LJlCyIiIuQuyaX16tULR44cQXp6urS1a9cOI0eORHp6OsOLDDp37lzq1gKnTp1CvXr1ZKrItd25cwdubtanXHd3d5hMJpkqUj72wFTA5MmTMXr0aLRr1w4dOnTAokWLcPv2bTz//PNyl+ZyEhMTsXz5cvzyyy/w9/eHTqcDAGg0Gvj4+Mhcnevx9/cvNf7Iz88PwcHBHJckk9dffx2dOnXCnDlz8PTTT2Pfvn348ssv8eWXX8pdmksaNGgQ3n//fdStWxdRUVH4448/8OGHH+KFF16QuzTlElQhn3zyiahbt67w8vISHTp0EHv27JG7JJcEoMxt6dKlcpdG/6dbt27itddek7sMl/bbb7+JFi1aCLVaLZo1aya+/PJLuUtyWQaDQbz22muibt26wtvbWzRo0EC8/fbboqCgQO7SFIv3gSEiIiLF4RgYIiIiUhwGGCIiIlIcBhgiIiJSHAYYIiIiUhwGGCIiIlIcBhgiIiJSHAYYIiIiUhwGGCIiIlIcBhgiIiJSHAYYIiIiUhwGGCIiIlIcBhgiIiJSnP8PbCA8KWuMaF0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluation mode\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuwjUjxQFpH0",
        "outputId": "a85ad239-a8fa-442c-d4fe-45635085bb63"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21/21 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check prediction shape matches test data shape\n",
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bP_iZHp-FpE8",
        "outputId": "a726680e-3817-4b99-a320-56886c3607b2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(668, 1024)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G05BTQjzFpB3",
        "outputId": "e5eae6f8-97d4-4ef2-d8bc-a7cac1d361e1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(668, 1024)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize obs (x-axis) to nn predictions (y-axis)\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "8Gqwmol2Fo9Z",
        "outputId": "97abc147-f338-4341-d8b6-61c219bfaf2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations (MSE)\n",
        "mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "id": "YF-NMyDDGLw0",
        "outputId": "7c1dd9c9-4b02-4c20-dbb0-8ae4498023b1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.75690186"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to extract model layers, we can first print out model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "tOlStrPvwcRG",
        "outputId": "4312b9d2-fb71-473f-c26b-68cfc288adff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 32)                32800     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 8)                 264       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 18        \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 8)                 24        \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 32)                288       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1024)              33792     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67186 (262.45 KB)\n",
            "Trainable params: 67186 (262.45 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model layers are contained as a list\n",
        "model.layers"
      ],
      "metadata": {
        "id": "wYrVfb4Nw2W7",
        "outputId": "88927bd1-a786-496a-da95-ffb38773d4c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.src.layers.core.dense.Dense at 0x7ab6e1cb0550>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7ab6e1cb26e0>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7ab6e1cb2b00>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7ab6e1cc80a0>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7ab6e1cc8b20>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7ab6e1cc8df0>]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets select the first layer\n",
        "model.layers[0].input"
      ],
      "metadata": {
        "id": "t9WTz86DwZ3_",
        "outputId": "cd600682-0415-4351-e98e-d8a79d99d67a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 1024) dtype=float32 (created by layer 'input_1')>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# and the bottleneck layer\n",
        "model.layers[1].output"
      ],
      "metadata": {
        "id": "7evftiBgwoko",
        "outputId": "70e49be7-2681-42fb-95a4-31919ef89e63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 8) dtype=float32 (created by layer 'dense_1')>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we define the input layer to the ANN and the output of the bottleneck layer\n",
        "bottleneck = keras.backend.function([model.layers[0].input], [model.layers[2].output])"
      ],
      "metadata": {
        "id": "xKcPKNXfIK1d"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# input test data into the bottleneck function\n",
        "bottleneck_output = bottleneck(X_test)"
      ],
      "metadata": {
        "id": "z5wKXdZZI6T4"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bottleneck_output[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUlfSJhEy6Av",
        "outputId": "ecc1910c-27e4-422d-ab38-7b4416ccdd95"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(668, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we can visualize what these two nodes in the bottleneck output\n",
        "plt.scatter(bottleneck_output[0][:,0],bottleneck_output[0][:,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "2UdtAmxdw9Ch",
        "outputId": "e6e274eb-4e7a-4425-806d-dde963431d0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# saving model\n",
        "model.save('autoencodersample.keras')"
      ],
      "metadata": {
        "id": "FP6Vu34_LYD_"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading model\n",
        "model2 = keras.models.load_model('autoencodersample.keras')"
      ],
      "metadata": {
        "id": "uunVHVJcKEMt"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# inference with loaded model\n",
        "ypred2 = model2.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jupJauZbKNZg",
        "outputId": "23b3d6e5-81cc-48fc-bad3-1a17bf538458"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21/21 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use autoencoders for the task of anomaly detection. For anomaly detection, we would note instances where reconstruction error for an image is higher, indicating that the sample was likely not similar to those in the training set distribution."
      ],
      "metadata": {
        "id": "E0t93MSi1QW1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# for get the mean absolute error between actual and reconstruction/prediction\n",
        "prediction_loss = keras.losses.mae(ypred2, y_test)\n",
        "\n",
        "# for check the prediction loss threshold for 2% of outliers\n",
        "loss_threshold = np.percentile(prediction_loss, 98)\n",
        "\n",
        "print(f'The prediction loss threshold for 2% of outliers is {loss_threshold:.2f}')\n",
        "# for visualize the threshold\n",
        "plt.hist(prediction_loss, bins=30, alpha=0.8)\n",
        "plt.axvline(x=loss_threshold, color='orange')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "WGap0EHv2EV-",
        "outputId": "5dc91b88-df2f-4bab-c145-b4fb360aa2ef"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The prediction loss threshold for 2% of outliers is 1.11\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# same as above but for inverse transformed data\n",
        "# for get the mean absolute error between actual and reconstruction/prediction\n",
        "rescaled_pred = scaler.inverse_transform(ypred2)\n",
        "rescaled_test = scaler.inverse_transform(X_test)\n",
        "\n",
        "prediction_loss = keras.losses.mae(rescaled_pred, rescaled_test)\n",
        "\n",
        "# for check the prediction loss threshold for 2% of outliers\n",
        "loss_threshold = np.percentile(prediction_loss, 98)\n",
        "\n",
        "print(f'The prediction loss threshold for 2% of outliers is {loss_threshold:.2f}')\n",
        "# for visualize the threshold\n",
        "plt.hist(prediction_loss, bins=30, alpha=0.8)\n",
        "plt.axvline(x=loss_threshold, color='orange')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "bQMnms3FHR2i",
        "outputId": "31b1ca1b-2ede-4322-d638-c32b380952bf"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The prediction loss threshold for 2% of outliers is 21.70\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CNN Autoencoder"
      ],
      "metadata": {
        "id": "CnJOrJ4WErKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def z_score(da):\n",
        "    \"\"\"\n",
        "    Compute z-score for training data.\n",
        "    Normalizing data for ML model training.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - np.mean(da)) / np.std(da)\n",
        "\n",
        "def z_score_testing(da, testdata):\n",
        "    \"\"\"\n",
        "    Compute z-score for testing data.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (testdata - np.mean(da)) / np.std(da)"
      ],
      "metadata": {
        "id": "8tBktm3K4JNl"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape data to be samples, features\n",
        "features_data = ds['dbz_grid'].values\n",
        "print(features_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVRljSszBQIe",
        "outputId": "4173ace3-4ab3-412b-d60a-ae502c674946"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2224, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = features_data[:1800], features_data[1800:], features_data[:1800], features_data[1800:]"
      ],
      "metadata": {
        "id": "Whxd2_cZ3k8W"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = z_score_testing(X_train, X_test) # input\n",
        "y_test = z_score_testing(y_train, y_test) # label\n",
        "\n",
        "X_train = z_score(X_train) # input\n",
        "y_train = z_score(y_train) # label"
      ],
      "metadata": {
        "id": "6xmsBq_k3k2r"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shapes\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXCWZMpj3kw6",
        "outputId": "049d1e6b-f8d6-48e3-9a43-ea37a2596a4b"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1800, 32, 32) (424, 32, 32) (1800, 32, 32) (424, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "\n",
        "# input\n",
        "model.add(keras.Input(shape=(32, 32, 1)))  # 32x32 images\n",
        "\n",
        "# encoder\n",
        "# hidden layers\n",
        "model.add(layers.Conv2D(16, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "\n",
        "model.add(layers.Conv2D(8, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "\n",
        "model.add(layers.Conv2D(4, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "\n",
        "# decoder\n",
        "model.add(layers.Conv2D(4, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.UpSampling2D(2))\n",
        "\n",
        "model.add(layers.Conv2D(8, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.UpSampling2D(2))\n",
        "\n",
        "model.add(layers.Conv2D(16, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.UpSampling2D(2))\n",
        "\n",
        "# Finally, we add last layer\n",
        "model.add(layers.Conv2D(1, (3, 3), activation='linear', padding='same'))\n",
        "\n",
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzQMwzPt3kpH",
        "outputId": "0f9d4e93-237a-4a1d-a52a-f33ccd68e717"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_8 (Conv2D)           (None, 32, 32, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 16, 16, 16)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 16, 16, 8)         1160      \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 8, 8, 8)           0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 8, 8, 4)           292       \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPoolin  (None, 4, 4, 4)           0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 4, 4, 4)           148       \n",
            "                                                                 \n",
            " up_sampling2d_3 (UpSamplin  (None, 8, 8, 4)           0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 8, 8, 8)           296       \n",
            "                                                                 \n",
            " up_sampling2d_4 (UpSamplin  (None, 16, 16, 8)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 16, 16, 16)        1168      \n",
            "                                                                 \n",
            " up_sampling2d_5 (UpSamplin  (None, 32, 32, 16)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_14 (Conv2D)          (None, 32, 32, 1)         145       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3369 (13.16 KB)\n",
            "Trainable params: 3369 (13.16 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "METRICS = [\n",
        "    keras.metrics.MeanSquaredError(name='mean_squared_error'),\n",
        "    keras.metrics.RootMeanSquaredError(name='root_mean_squared_error'),\n",
        "]\n",
        "\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "    loss=\"mean_squared_error\",\n",
        "    metrics=METRICS\n",
        ")\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10"
      ],
      "metadata": {
        "id": "teBOl4-45KCx"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x=X_train,\n",
        "    y=y_train,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkufMFbc5J9C",
        "outputId": "a7e73f94-df31-4971-e395-244024364a9a"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "51/51 [==============================] - 4s 52ms/step - loss: 0.5640 - mean_squared_error: 0.5640 - root_mean_squared_error: 0.7510 - val_loss: 0.4640 - val_mean_squared_error: 0.4640 - val_root_mean_squared_error: 0.6811\n",
            "Epoch 2/10\n",
            "51/51 [==============================] - 3s 49ms/step - loss: 0.2904 - mean_squared_error: 0.2904 - root_mean_squared_error: 0.5389 - val_loss: 0.3856 - val_mean_squared_error: 0.3856 - val_root_mean_squared_error: 0.6210\n",
            "Epoch 3/10\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 0.2478 - mean_squared_error: 0.2478 - root_mean_squared_error: 0.4978 - val_loss: 0.3562 - val_mean_squared_error: 0.3562 - val_root_mean_squared_error: 0.5968\n",
            "Epoch 4/10\n",
            "51/51 [==============================] - 3s 64ms/step - loss: 0.2260 - mean_squared_error: 0.2260 - root_mean_squared_error: 0.4754 - val_loss: 0.3326 - val_mean_squared_error: 0.3326 - val_root_mean_squared_error: 0.5767\n",
            "Epoch 5/10\n",
            "51/51 [==============================] - 2s 46ms/step - loss: 0.2122 - mean_squared_error: 0.2122 - root_mean_squared_error: 0.4606 - val_loss: 0.3182 - val_mean_squared_error: 0.3182 - val_root_mean_squared_error: 0.5641\n",
            "Epoch 6/10\n",
            "51/51 [==============================] - 2s 49ms/step - loss: 0.2013 - mean_squared_error: 0.2013 - root_mean_squared_error: 0.4486 - val_loss: 0.3044 - val_mean_squared_error: 0.3044 - val_root_mean_squared_error: 0.5517\n",
            "Epoch 7/10\n",
            "51/51 [==============================] - 4s 72ms/step - loss: 0.1920 - mean_squared_error: 0.1920 - root_mean_squared_error: 0.4381 - val_loss: 0.2939 - val_mean_squared_error: 0.2939 - val_root_mean_squared_error: 0.5422\n",
            "Epoch 8/10\n",
            "51/51 [==============================] - 3s 49ms/step - loss: 0.1848 - mean_squared_error: 0.1848 - root_mean_squared_error: 0.4299 - val_loss: 0.2842 - val_mean_squared_error: 0.2842 - val_root_mean_squared_error: 0.5331\n",
            "Epoch 9/10\n",
            "51/51 [==============================] - 2s 48ms/step - loss: 0.1773 - mean_squared_error: 0.1773 - root_mean_squared_error: 0.4211 - val_loss: 0.2732 - val_mean_squared_error: 0.2732 - val_root_mean_squared_error: 0.5227\n",
            "Epoch 10/10\n",
            "51/51 [==============================] - 2s 48ms/step - loss: 0.1712 - mean_squared_error: 0.1712 - root_mean_squared_error: 0.4137 - val_loss: 0.2667 - val_mean_squared_error: 0.2667 - val_root_mean_squared_error: 0.5164\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"mean_squared_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_squared_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "sr2Dr77c5J05",
        "outputId": "678bb6da-45d4-451c-ace3-5c64eb460380"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# saving model\n",
        "model.save('autoencodercnn.keras')\n",
        "\n",
        "# loading model\n",
        "model2 = keras.models.load_model('autoencodercnn.keras')"
      ],
      "metadata": {
        "id": "KMa7dgXE5WpG"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# inference with loaded model\n",
        "ypred2 = model2.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmdp8MjZoHze",
        "outputId": "35121fb1-bc6d-40ae-8af6-7cabf55950a6"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 15ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test.shape, ypred2[:,:,:,0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmxkhle1obQ0",
        "outputId": "7b6d1a3c-0172-4cb1-cce7-ac99c88cee3e"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(424, 32, 32) (424, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations\n",
        "plt.scatter(np.ndarray.flatten(y_test), np.ndarray.flatten(ypred2[:,:,:,0]))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "W9flZbSJoHs-",
        "outputId": "3a2c249b-0ea2-4df8-9542-80791d230295"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations\n",
        "sample_indx = 15\n",
        "\n",
        "plt.imshow(y_test[sample_indx,:,:], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(ypred2[sample_indx,:,:,0], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(ypred2[sample_indx,:,:,0] - y_test[sample_indx,:,:], cmap='seismic', vmin=-1, vmax=1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EqkYWhIWo1zD",
        "outputId": "372bd6cd-6fa2-43f9-b81f-7c3d89123199"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAr00lEQVR4nO3df2xV933/8dc5x/de22CbGIKNi6GQtNCUwDSWUCsto4HxY1JEGjQlbaWRLkqUzERLWNfWU5s02SZnqdSmjSj5YxmsUgltppIo0UqWkGLUDehgQTTtZgW+bJCBTUOLDf5xfX3P5/sHjTcnkHzexpeP7Twf0pXAfvvjz7nnXL98/ePlyDnnBADAFRaH3gAA4IOJAAIABEEAAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQRFnoDbxTmqY6efKkqqqqFEVR6O0AAIycczp37pwaGhoUx5d+njPmAujkyZNqbGwMvQ0AwGU6ceKEZs6cecnXlyyANm3apG984xvq6OjQokWL9OSTT+rGG29837erqqqSJM194CHFuXKv9+UMT5QGq1P/YUnO8EXKyLa0ihX+b5Cb2mda+0NXdXnPNlT6z0rS/MkdpvnUcIKmlp03rZ0Y7vSKaMC0djYqes9mokHT2olsDVi5qOA9W27cS0Xsv3ZGtos87xLv2VPFatPaRwfqvGc7BmpMa//sV7NN82+dm+Q9mz9TYVo7GvD/IJT0275qlPT5z8eGy6qY79f/e/LRoY/nl1KSAPrBD36gjRs36qmnntKSJUv0xBNPaNWqVWpvb9f06dPf823f/rJbnCtX4htAhpBIy8dOADlDACWVtg9YZZP6vWezk7Kmtcsn2y6b1HAnVpTZ1rYEUGXsHyiSlDU8ljOR7fxYA6jccJyWWUmqfI8vkbxTxrSylDGc+8qif1hJUnne/1rJDdh2XtabM80nRb+PVZIU9/rPSlKU+N+HsYwBlBoCyHZ6JOl9v41Skh9C+OY3v6m7775bX/jCF3TdddfpqaeeUmVlpf7+7/++FO8OADAOjXoADQwM6ODBg1qxYsX/vpM41ooVK7R37953zefzeXV3dw+7AQAmvlEPoLfeekvFYlF1dcO/PltXV6eOjnd/76C1tVU1NTVDN34AAQA+GIL/HlBLS4u6urqGbidOnAi9JQDAFTDqP4Qwbdo0JUmizs7OYS/v7OxUfX39u+ZzuZxyOds3/AAA49+oPwPKZrNavHixdu3aNfSyNE21a9cuNTU1jfa7AwCMUyX5MeyNGzdq/fr1+r3f+z3deOONeuKJJ9TT06MvfOELpXh3AIBxqCQBdPvtt+tXv/qVHnroIXV0dOh3fud3tHPnznf9YAIA4IOrZE0IGzZs0IYNG0q1/DBpxvZLfRYu6792avxlxMrpPd6zjVedNa09o9L/x9mn586Z1q6K/X/JVZJqDe0GH868ZVp7kuG3/mPjL38mhvNZtNRxGNe2sv6Sq+Xr8BlrPaPhd2InGZsqLNfhbwxtD5KUKzM2WySGAy23/UK0DL8smhq/ne7yhl9E7TWs63kqg/8UHADgg4kAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEUbIqnssVDUqR598gj9/n747/X856xIZWE2sNxuCg/x9ZPz9gW/w3SaX3bFlkqBGRVFvmXyEkSZnIUD2SMS2tqth/betnW/5nRyqUsFpHkoolXN5Sr2O7UmyVQ/3OdvIt8+cHbY+f1FitZBHFtpPpEv/5eLB0zyliQzuR85zlGRAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAhizHbBZc5LScFvNrVUSBl64yQprfbvYXIZW8dTZfmA9+ykjP+sJNVVdHvPzi7/tWntmVnb/KQ47z8bGQqnJNXE/o1tqbOdn6KhCLBgXNtYeafYcNmWG6/xct/SRUkFZ+wNjP0/x010xrR2Uf7HebpQbVrbKlvmf932JVnT2s7Qp2f9GOQsT0Es15XnKM+AAABBEEAAgCAIIABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCDGbBVPlEpR0W/WkqKxrdFGccG/fiLN2/I8X/C/+wdS/7oUSeor+pe9pIZKE0nqSXOmedPaznZJ5p3/Ce031uVY9DvbfVgw1PxI0oChMyUf2epy+g31R3njXVgV+7+B5Rgl6Vyxwnu2a9B/diQiQ11ObLhPJCkt5dOEwE9BeAYEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAiCAAIABEEAAQCCGMNdcE5R6teZ5MpsPVwmhlotQx3UhaVT/30XS1gIlYsLJVtbkibFee/Zqw2zkjQ1rvSe7TP0xlkVnK1/LY5Kdz5TY6eaRWJ5QEiqibPes/3Odu4rDdfK5MS29lW5XtN8LvHv03PG3sAuw8eJ1NBdKUmu379j0rJt31meAQEAghj1APr617+uKIqG3ebPnz/a7wYAMM6V5EtwH//4x/XKK6/87zspG7Nf6QMABFKSZCgrK1N9fX0plgYATBAl+R7QG2+8oYaGBs2dO1ef//zndfz48UvO5vN5dXd3D7sBACa+UQ+gJUuWaOvWrdq5c6c2b96sY8eO6VOf+pTOnTt30fnW1lbV1NQM3RobG0d7SwCAMWjUA2jNmjX6oz/6Iy1cuFCrVq3SP/3TP+ns2bP64Q9/eNH5lpYWdXV1Dd1OnDgx2lsCAIxBJf/pgClTpuijH/2ojhw5ctHX53I55XK5Um8DADDGlPz3gM6fP6+jR49qxowZpX5XAIBxZNQD6Itf/KLa2tr0X//1X/rXf/1XfeYzn1GSJPrsZz872u8KADCOjfqX4N5880199rOf1ZkzZ3T11Vfrk5/8pPbt26err77atE48KMWe8WhpHolsTSKm+ahoW3tw0L8Go3/Qdqr6ixnv2UJqW/u8yk3zU5Pz/nuRrUpkUP53urUupyj/biXjZaWMsbYpjvzvl0wJa376jfdhmfyvcf8r9oLquN97tjK21TBVlpWutqkssd2HiWE+zdnWTsssHzwNj03P0VEPoO3bt4/2kgCACYguOABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEAQQACCIkv85hpGydMEp8i/WKhZsXWNJ3n/eGe/NQq//G/RV2Jqyugf8+9rOF21/DiM19rX1pFnv2X7n3x0mSQXn3wVn6VOTpKIzFrYZWPdSHvlfK3EJP6/sNRYepoY+vcR4n2SiQdO8RUVSMM0PGPoUs4ntPszm/I9zcMD2+LH0aJZilmdAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBBjtorHJPUfNTaJmObNzSCD/vlfKBgralL/+V5DVY4kxYbqI0lKDZ/nWKt4+t2A92zBWK1juVQMl6AkKTa+RcZQaaPIupvSiQ21TdbPhhNTzY/tPkmM13hs2EtZbNtLZNmLrc3INl+CWZ4BAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAICZGF5xBbOyCiw39bkneVsSUnPfP/3xSblr7Tee/lzJjT1ZNrs80n0/9LzNrR5p0ynvS0h1mna+0XCiydYdd2Iv/+hnj55VFw156Utu+KyP/rr5+Y1dfj/PvMLT2HfYVM6b5QVe6z+Wd4bFsvKxsLGt7zvIMCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAiCAAIABDF2u+Cc/LuHLBVs1q4kQzVZZO6Z8994mrd9rjBYSLxnzxdsPVlWvy6b5D37VrbatPbZsi7v2dT4+VYm8u9fG1C/be04b5ovRv4Xrq3FzMba1Fdw/m9RMD42C87/w1d/artXCqn/40eSUkNfm2VWkkwVeca1S9od54FnQACAIMwBtGfPHt1yyy1qaGhQFEV67rnnhr3eOaeHHnpIM2bMUEVFhVasWKE33nhjtPYLAJggzAHU09OjRYsWadOmTRd9/eOPP67vfOc7euqpp7R//35NmjRJq1atUn+/7UsUAICJzfw9oDVr1mjNmjUXfZ1zTk888YS++tWvau3atZKk733ve6qrq9Nzzz2nO+644/J2CwCYMEb1e0DHjh1TR0eHVqxYMfSympoaLVmyRHv37r3o2+TzeXV3dw+7AQAmvlENoI6ODklSXV3dsJfX1dUNve6dWltbVVNTM3RrbGwczS0BAMao4D8F19LSoq6urqHbiRMnQm8JAHAFjGoA1dfXS5I6OzuHvbyzs3Pode+Uy+VUXV097AYAmPhGNYDmzJmj+vp67dq1a+hl3d3d2r9/v5qamkbzXQEAxjnzT8GdP39eR44cGfr/sWPHdOjQIdXW1mrWrFl64IEH9Nd//df6yEc+ojlz5uhrX/uaGhoadOutt47mvgEA45w5gA4cOKBPf/rTQ//fuHGjJGn9+vXaunWrvvSlL6mnp0f33HOPzp49q09+8pPauXOnysvLTe/HxRdufsP+60aprXsiGfCvtjC0pUiS0n7/tYvltoqNdMC/SqRvwFZTkhgP9Nxgznu2N7XVAvU6/7Ut1TqSJEPVi1XG2JhSHtmqYUplwPtBeUGPoYqn37h2v/O/bvNpaVvHYsNjwjIrSZHxWjGtHbiKx3xWli1bJvce5URRFOnRRx/Vo48+elkbAwBMbMF/Cg4A8MFEAAEAgiCAAABBEEAAgCAIIABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgihtQdJliJyhp8i/bkpR0bgPQw+TtbPJUk0WFYyLF/3nB1NjB9eg7bKxrF9wts6z/tS/DyyT2LrgyqOCYdZ2YZUbL5a4hJ8rFuW/91S2ffc6//leY/ee5dwPprbrKhPbzmcc+X8QSmLDByxJUQkL2wynR5aHpu8sz4AAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIMZsFY/cb28+DDHqjJFbivqJIYYaDGMziNJ+/wPt682Z1u6P/StQJKm8zL8C50zFZNPaZzOV/vuI/at1JCmO+71nJ1l6lSRlIuvFUsK1DU0v5dGAaelSfoY7YHjAFYwP/IrEdq1Y1GT7TPM9uaz3bF/Wf1aS0qx/BFjuQt9ZngEBAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgxmwXXJReuPmw9ruNFZGh3y3pMxTHSXKx/50y2Ge7DOKsrZiur+DfHdc9aOul60395y3dYZKUlvDCysi2F0u/W2wpGZSUyvOBJvtnrP4r22UND6CM7weT36pIjJ13kX+hXt7QvyZJ53Llhlnj4yfn/9gs5vyvwaLn3TFOP3QDAMY7AggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEAQQACAIAggAEMSYreKxiA3NMM7WIqPI+VdsxAXr2v6VKYP+27iwF8uZLdg+D7HWq+QH/Ss8BlNbRY1FeWQ7QZlo0Hu2KrbV31TGWdN8KRVS/zNaMNb8WBRLuHZsrOLJxf7nXpLKDQ/+NGs7zl/lJ3vPliW244xy/h8Q05z/BxXn+XGTZ0AAgCAIIABAEOYA2rNnj2655RY1NDQoiiI999xzw15/5513KoqiYbfVq1eP1n4BABOEOYB6enq0aNEibdq06ZIzq1ev1qlTp4ZuzzzzzGVtEgAw8Zh/CGHNmjVas2bNe87kcjnV19ePeFMAgImvJN8D2r17t6ZPn6558+bpvvvu05kzZy45m8/n1d3dPewGAJj4Rj2AVq9ere9973vatWuX/vZv/1ZtbW1as2aNisWL/7hfa2urampqhm6NjY2jvSUAwBg06r8HdMcddwz9+/rrr9fChQt1zTXXaPfu3Vq+fPm75ltaWrRx48ah/3d3dxNCAPABUPIfw547d66mTZumI0eOXPT1uVxO1dXVw24AgImv5AH05ptv6syZM5oxY0ap3xUAYBwxfwnu/Pnzw57NHDt2TIcOHVJtba1qa2v1yCOPaN26daqvr9fRo0f1pS99Sddee61WrVo1qhsHAIxv5gA6cOCAPv3pTw/9/+3v36xfv16bN2/W4cOH9Q//8A86e/asGhoatHLlSv3VX/2Vcrmc6f1EqVOUehagGaqVokFbD1NkGTf2tSkyvEFq7cnyn0+7bf1rxQrbXnor/M99Z1+Vae1TFTXes5OTftPalu64ftdrWruUis7WBxYbLvLE2ARo+RJLYnwAWfrdMpGtBNI6b7m2is72hafqjP/aV1X2mdYuGHoa8+X+/YWp57k0B9CyZcves2jupZdesi4JAPgAogsOABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACGLU/x7QaInkX5VmaZAy1EddWNsQ0UnB1mXlktIVzbky/7Wt90lUtHXBOUOPXW8hY1q7e7Dce/Y3g5NMa09Jerxne9Iu09pWln63vBs0rZ2+R7XW5TJeWiaWrr6KxH9WkqpK2BuYj217sXTBlZfZ1q7IDXjP9k32v67SxG+WZ0AAgCAIIABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEGO2ikepoSLG0AxjrZ2xiIq2+djWmGJiaRKJ/ds4JEmprS1HqaG6p5jaPic6V/Cv4jmV1JjWnmy4EzuTc6a1G9Ne03zBUMXTY5i1KloebEb9zvbhqGCYLzfW32SMD2bL+gVne8BVGB6gtTnjdZUm3rNdFf5VVpHzu/94BgQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIYu11w7rc3n1H/OiPT7NA+PEWGWUlKBvzfIDH2tVk675K+0vV7SVK+xv/znMKg7QR1G7rgJpXZ7sS8tfTOoGi5sCTlIv+HalG23jNLc1zBGS9yg8R4n5QbOtIsvX6SVJP0mOYnWQsVDSoS//OZS2wFk1WZvPdsnBj68TxneQYEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAiCAAIABDF2q3gMYkNDhLWKJ0r960GiorGmxJWuAicy3CeW+0+SjOMmqfE+6SlkvWd/k1SY1u7MVnvPHi+rNa09u6zbNF9uOKG9xsvQ8lloQaW7ZjOWi1ZSeeRfUWOZlaTy2DafGLqvrMdZZagRqiqzVQ7li/4RUFbmf4xFz1meAQEAgjAFUGtrq2644QZVVVVp+vTpuvXWW9Xe3j5spr+/X83NzZo6daomT56sdevWqbOzc1Q3DQAY/0wB1NbWpubmZu3bt08vv/yyCoWCVq5cqZ6e/22OffDBB/XCCy/o2WefVVtbm06ePKnbbrtt1DcOABjfTN8D2rlz57D/b926VdOnT9fBgwe1dOlSdXV16emnn9a2bdt08803S5K2bNmij33sY9q3b58+8YlPjN7OAQDj2mV9D6irq0uSVFt74RuwBw8eVKFQ0IoVK4Zm5s+fr1mzZmnv3r0XXSOfz6u7u3vYDQAw8Y04gNI01QMPPKCbbrpJCxYskCR1dHQom81qypQpw2br6urU0dFx0XVaW1tVU1MzdGtsbBzplgAA48iIA6i5uVmvv/66tm/fflkbaGlpUVdX19DtxIkTl7UeAGB8GNHvAW3YsEEvvvii9uzZo5kzZw69vL6+XgMDAzp79uywZ0GdnZ2qr6+/6Fq5XE65XG4k2wAAjGOmZ0DOOW3YsEE7duzQq6++qjlz5gx7/eLFi5XJZLRr166hl7W3t+v48eNqamoanR0DACYE0zOg5uZmbdu2Tc8//7yqqqqGvq9TU1OjiooK1dTU6K677tLGjRtVW1ur6upq3X///WpqauIn4AAAw5gCaPPmzZKkZcuWDXv5li1bdOedd0qSvvWtbymOY61bt075fF6rVq3Sd7/73VHZLABg4jAFkHPvXzJVXl6uTZs2adOmTSPelJUzfCHRMitJaeLffRUP2tZOBgylXdaaOUPnXTxg6/eyzqvgf6f39pbu+4FlsX+XlSSdzld5z9Zl/HvjJKnXWEpYkP/ez6UZ09qVkf+Fm0S2CzFjuHAtfXeSNCXu859Nek1rZ417yRjuw6IrN61dNHzQio3nZ1LZgPdsRc5/tlj069KjCw4AEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEAQQACAIAggAEAQBBAAIYkR/jmGsiQwNK5ZZSSWNaEstkHXfllog69rmveT9D7RoqO2RpMKgf6VNb8FWUdM14F+Z8lZhsmntXxUnmeZjw53em9rqjK5OznnPlstWUZMYK21MaxtqfqoS/9oeSSqP/KpkRmJS7F9pI0kZw4O50rh2UuZ/XZVn/PcxWOY3yzMgAEAQBBAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQxNjtgot+e/PgPOdGxL9uysxSkxUP2jYSpf7zSd52BxbLbfNxv//8YL9/t5skFTL+l3B3n3+3myQlkf99eCrbb1r7fwavMs1Xxf5dZgVne1jXJudN8xYZw6XSb3ysZQz9eJMiW0eapXtPkgrO/7q1zI4llke97yzPgAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgxm4VTyw5z3i0VPH4rjmita2VQJZ5ayWQYT4atC0d21pNlBjmi8YqnsEy/wPNG6p1JOl84t+VdCY/ybR2Z6HGNN+fZLxnrVUvGcMFMCXpNa1dcHnTvEVsPJ+lVB4VvGerEv9aJUmqSf3nM5Z+L0nni/71VBUZ/2Mc9JzlGRAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAhi7HbBFaXIMx7LBvw7oVL/Sq0L28j6zyaGfUhSVPSfj5yx9yr1L5qLbfVR9i64Pv+9JIb7W5KKiX/vmX+T1QXnDbO/ztpW/5/8FNP8uTL/zi5rF1x5bL1n/CVJ6j07ydCnJkkZ+a9t6WqTpDjyX1uSsoa9FCLbA2hS7N+nZz33OcO5ryjzny2U+fUL8gwIABCEKYBaW1t1ww03qKqqStOnT9ett96q9vb2YTPLli1TFEXDbvfee++obhoAMP6ZAqitrU3Nzc3at2+fXn75ZRUKBa1cuVI9PT3D5u6++26dOnVq6Pb444+P6qYBAOOf6XtAO3fuHPb/rVu3avr06Tp48KCWLl069PLKykrV19ePzg4BABPSZX0PqKurS5JUW1s77OXf//73NW3aNC1YsEAtLS3q7b30H7HK5/Pq7u4edgMATHwj/im4NE31wAMP6KabbtKCBQuGXv65z31Os2fPVkNDgw4fPqwvf/nLam9v149+9KOLrtPa2qpHHnlkpNsAAIxTIw6g5uZmvf766/rpT3867OX33HPP0L+vv/56zZgxQ8uXL9fRo0d1zTXXvGudlpYWbdy4cej/3d3damxsHOm2AADjxIgCaMOGDXrxxRe1Z88ezZw58z1nlyxZIkk6cuTIRQMol8spl8uNZBsAgHHMFEDOOd1///3asWOHdu/erTlz5rzv2xw6dEiSNGPGjBFtEAAwMZkCqLm5Wdu2bdPzzz+vqqoqdXR0SJJqampUUVGho0ePatu2bfrDP/xDTZ06VYcPH9aDDz6opUuXauHChSU5AADA+GQKoM2bN0u68Mum/9eWLVt05513KpvN6pVXXtETTzyhnp4eNTY2at26dfrqV786ahsGAEwM5i/BvZfGxka1tbVd1obeFrkLNx/xoLEnzbYT70nrPuKCoQvOVk0lyX9t674TY3VYXPC/D8v6/WclyWX859PI1pM1mPjfL70DtpLBs4VK03yxhK1Z54oV3rPWTrXEcOFaO9IsexmQ7dwnznYdpobzM2Dsa7P0u2UiW7FjpaHYsbLMf7bgOUsXHAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABDEiP8eUKlFqVOUelahGJpkvNd8e97QbGGp1pGkeMC/piQuGvdtOM60zFY7EqW2z1uKhrocQ/ORJPveLYpl/sdZKNrqVboK5ab5vqJ/1U9ZbKtjmZbxr+LJxbYqHlM1jPHT4dhQ85OVtaLGdpypobqnGOdNa09JLv0Xpd/JWvOTNZyfSYl/Fc+AZ8UPz4AAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQY7YLLk0ipYlfv1ISla73rJjzn7d2pFl6z5ytykpxwb8ny8XGLjjjXpIB//PjjOcnyfvPp/51ahcUDWuntn0PpLaHXmy4xsuMvWcWls4zSep3/nd64vyvWUkqGHrPUuPn2hnjAy4xFFJaZiUpEw2WZNbK0jGYes7yDAgAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEAQQACAIAggAEAQBBAAIYsxW8SiSd1VNlBpqMPK2bZT1+9eDlPXY6jssdTlWlsaUsj5rhZBt3lKXUyw3bsXQPGI99y7xP87etypNax+LbXUsFdmC9+z0SedNa58r97/TM8YeJkuFkLVGxlLzkzHWE/W4rGk+a1i/aOngMrLW/MTy/xiUiQz1Xp7nnWdAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgiDHbBZeWRSpmPDuTDJVqcWrrX4sHDT1zvf59XZKtw87Fxv6oTFKSfUhSkrfdh0nWf+9Jn2lplfleI5JSw6wkxYZqsmJqW7tQ8D8/kpQt899MaikClDSY+n8eWnC2fZ83lPsllgeypP7UvwsuMXTSSVJStO3F2pFn0ZP699KlxucURec/f7ZQ4T1bGPS7TngGBAAIwhRAmzdv1sKFC1VdXa3q6mo1NTXpxz/+8dDr+/v71dzcrKlTp2ry5Mlat26dOjs7R33TAIDxzxRAM2fO1GOPPaaDBw/qwIEDuvnmm7V27Vr94he/kCQ9+OCDeuGFF/Tss8+qra1NJ0+e1G233VaSjQMAxjfT94BuueWWYf//m7/5G23evFn79u3TzJkz9fTTT2vbtm26+eabJUlbtmzRxz72Me3bt0+f+MQnRm/XAIBxb8TfAyoWi9q+fbt6enrU1NSkgwcPqlAoaMWKFUMz8+fP16xZs7R3795LrpPP59Xd3T3sBgCY+MwB9POf/1yTJ09WLpfTvffeqx07dui6665TR0eHstmspkyZMmy+rq5OHR0dl1yvtbVVNTU1Q7fGxkbzQQAAxh9zAM2bN0+HDh3S/v37dd9992n9+vX65S9/OeINtLS0qKura+h24sSJEa8FABg/zL8HlM1mde2110qSFi9erH/7t3/Tt7/9bd1+++0aGBjQ2bNnhz0L6uzsVH19/SXXy+VyyuVy9p0DAMa1y/49oDRNlc/ntXjxYmUyGe3atWvode3t7Tp+/Liampou990AACYY0zOglpYWrVmzRrNmzdK5c+e0bds27d69Wy+99JJqamp01113aePGjaqtrVV1dbXuv/9+NTU18RNwAIB3MQXQ6dOn9cd//Mc6deqUampqtHDhQr300kv6gz/4A0nSt771LcVxrHXr1imfz2vVqlX67ne/O6KNRc4pcp71GZbmkchYaeO7B0kuMT6hjA1rl3DfccFWO1LM2r5ymwz476XM1vQiZ9iKuc7IcJ8Xs7ZzP9DrX68iSWnRf/1sma0W5lgy1Xv219lJprWryvq9Z6/K+Fe9SNLkxH/tRLYqHmu1Thz5P4aslUNFwxeqrPs+Xaj2nh001Pb4zpo+kjz99NPv+fry8nJt2rRJmzZtsiwLAPgAogsOABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABCEuQ271NxvK2SKBf+ajcGCf/2EoTFDkhQP+r+BGxw0re1dNaQRVPEYxtPE1n8zWLBdNqml0iaxHWdxwLB23ri24TDTnO3Ccn0F03xa9L+2ipm8ae2CBrxnBwb8ZyVpoMz/OPMZ231SlvjfJ9YqnqKx0iYaI1U8g8Z99xf87/NCj/+5f3vWvc/HuMi938QV9uabb/JH6QBgAjhx4oRmzpx5ydePuQBK01QnT55UVVWVov/zmXN3d7caGxt14sQJVVf7F+iNNxznxPFBOEaJ45xoRuM4nXM6d+6cGhoaFMeXfgY35r4EF8fxeyZmdXX1hD75b+M4J44PwjFKHOdEc7nHWVNT874z/BACACAIAggAEMS4CaBcLqeHH35YuVwu9FZKiuOcOD4IxyhxnBPNlTzOMfdDCACAD4Zx8wwIADCxEEAAgCAIIABAEAQQACCIcRNAmzZt0oc//GGVl5dryZIl+tnPfhZ6S6Pq61//uqIoGnabP39+6G1dlj179uiWW25RQ0ODoijSc889N+z1zjk99NBDmjFjhioqKrRixQq98cYbYTZ7Gd7vOO+88853ndvVq1eH2ewItba26oYbblBVVZWmT5+uW2+9Ve3t7cNm+vv71dzcrKlTp2ry5Mlat26dOjs7A+14ZHyOc9myZe86n/fee2+gHY/M5s2btXDhwqFfNm1qatKPf/zjoddfqXM5LgLoBz/4gTZu3KiHH35Y//7v/65FixZp1apVOn36dOitjaqPf/zjOnXq1NDtpz/9aegtXZaenh4tWrRImzZtuujrH3/8cX3nO9/RU089pf3792vSpElatWqV+vv9i2jHgvc7TklavXr1sHP7zDPPXMEdXr62tjY1Nzdr3759evnll1UoFLRy5Ur19PQMzTz44IN64YUX9Oyzz6qtrU0nT57UbbfdFnDXdj7HKUl33333sPP5+OOPB9rxyMycOVOPPfaYDh48qAMHDujmm2/W2rVr9Ytf/ELSFTyXbhy48cYbXXNz89D/i8Wia2hocK2trQF3Nboefvhht2jRotDbKBlJbseOHUP/T9PU1dfXu2984xtDLzt79qzL5XLumWeeCbDD0fHO43TOufXr17u1a9cG2U+pnD592klybW1tzrkL5y6Tybhnn312aOY//uM/nCS3d+/eUNu8bO88Tuec+/3f/333Z3/2Z+E2VSJXXXWV+7u/+7srei7H/DOggYEBHTx4UCtWrBh6WRzHWrFihfbu3RtwZ6PvjTfeUENDg+bOnavPf/7zOn78eOgtlcyxY8fU0dEx7LzW1NRoyZIlE+68StLu3bs1ffp0zZs3T/fdd5/OnDkTekuXpaurS5JUW1srSTp48KAKhcKw8zl//nzNmjVrXJ/Pdx7n277//e9r2rRpWrBggVpaWtTb2xtie6OiWCxq+/bt6unpUVNT0xU9l2OujPSd3nrrLRWLRdXV1Q17eV1dnf7zP/8z0K5G35IlS7R161bNmzdPp06d0iOPPKJPfepTev3111VVVRV6e6Ouo6NDki56Xt9+3USxevVq3XbbbZozZ46OHj2qv/zLv9SaNWu0d+9eJca/xTQWpGmqBx54QDfddJMWLFgg6cL5zGazmjJlyrDZ8Xw+L3ackvS5z31Os2fPVkNDgw4fPqwvf/nLam9v149+9KOAu7X7+c9/rqamJvX392vy5MnasWOHrrvuOh06dOiKncsxH0AfFGvWrBn698KFC7VkyRLNnj1bP/zhD3XXXXcF3Bku1x133DH07+uvv14LFy7UNddco927d2v58uUBdzYyzc3Nev3118f99yjfz6WO85577hn69/XXX68ZM2Zo+fLlOnr0qK655porvc0Rmzdvng4dOqSuri794z/+o9avX6+2trYruocx/yW4adOmKUmSd/0ERmdnp+rr6wPtqvSmTJmij370ozpy5EjorZTE2+fug3ZeJWnu3LmaNm3auDy3GzZs0Isvvqif/OQnw/5sSn19vQYGBnT27Nlh8+P1fF7qOC9myZIlkjTuzmc2m9W1116rxYsXq7W1VYsWLdK3v/3tK3oux3wAZbNZLV68WLt27Rp6WZqm2rVrl5qamgLurLTOnz+vo0ePasaMGaG3UhJz5sxRfX39sPPa3d2t/fv3T+jzKl34q79nzpwZV+fWOacNGzZox44devXVVzVnzpxhr1+8eLEymcyw89ne3q7jx4+Pq/P5fsd5MYcOHZKkcXU+LyZNU+Xz+St7Lkf1RxpKZPv27S6Xy7mtW7e6X/7yl+6ee+5xU6ZMcR0dHaG3Nmr+/M//3O3evdsdO3bM/cu//ItbsWKFmzZtmjt9+nTorY3YuXPn3GuvveZee+01J8l985vfdK+99pr77//+b+ecc4899pibMmWKe/75593hw4fd2rVr3Zw5c1xfX1/gndu813GeO3fOffGLX3R79+51x44dc6+88or73d/9XfeRj3zE9ff3h966t/vuu8/V1NS43bt3u1OnTg3dent7h2buvfdeN2vWLPfqq6+6AwcOuKamJtfU1BRw13bvd5xHjhxxjz76qDtw4IA7duyYe/75593cuXPd0qVLA+/c5itf+Ypra2tzx44dc4cPH3Zf+cpXXBRF7p//+Z+dc1fuXI6LAHLOuSeffNLNmjXLZbNZd+ONN7p9+/aF3tKouv32292MGTNcNpt1H/rQh9ztt9/ujhw5Enpbl+UnP/mJk/Su2/r1651zF34U+2tf+5qrq6tzuVzOLV++3LW3t4fd9Ai813H29va6lStXuquvvtplMhk3e/Zsd/fdd4+7T54udnyS3JYtW4Zm+vr63J/+6Z+6q666ylVWVrrPfOYz7tSpU+E2PQLvd5zHjx93S5cudbW1tS6Xy7lrr73W/cVf/IXr6uoKu3GjP/mTP3GzZ8922WzWXX311W758uVD4ePclTuX/DkGAEAQY/57QACAiYkAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQfx/N33T8th3sdkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations (MSE)\n",
        "mean_squared_error(np.ndarray.flatten(y_test), np.ndarray.flatten(ypred2[:,:,:,0]))"
      ],
      "metadata": {
        "id": "rtT5yvYC5mdS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22ceeabc-cb77-4bf0-91d0-af947a8a9527"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3524576"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CNN Autoencoder Noise Removal"
      ],
      "metadata": {
        "id": "It_XWynZEtsc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "4d682kwu-tLe"
      },
      "outputs": [],
      "source": [
        "def noise(array):\n",
        "    \"\"\"Adds random noise to each image in the supplied array.\"\"\"\n",
        "    noise_factor = 0.4\n",
        "    noisy_array = array + noise_factor * np.random.normal(\n",
        "        loc=0.0, scale=1.0, size=array.shape\n",
        "    )\n",
        "    return noisy_array"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape data to be samples, features\n",
        "features_data = ds['dbz_grid'].values\n",
        "print(features_data.shape)\n",
        "\n",
        "X_train, X_test, y_train, y_test = features_data[:1800], features_data[1800:], features_data[:1800], features_data[1800:]\n",
        "\n",
        "X_test = z_score_testing(X_train, X_test) # input\n",
        "y_test = z_score_testing(y_train, y_test) # label\n",
        "\n",
        "X_train = z_score(X_train) # input\n",
        "y_train = z_score(y_train) # label\n",
        "\n",
        "# check shapes\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "id": "SfUSdJXiBP9T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b8a7db1-6e25-40b1-9681-daf08fe79349"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2224, 32, 32)\n",
            "(1800, 32, 32) (424, 32, 32) (1800, 32, 32) (424, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy of the data with added noise\n",
        "noisy_train_data = noise(X_train)\n",
        "noisy_test_data = noise(X_test)"
      ],
      "metadata": {
        "id": "CEXwYXwZBP2h"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(noisy_train_data[0,:,:], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(X_train[0,:,:], vmin=-1, vmax=1)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ApqtvKmTBZ1j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "outputId": "a190157c-20dc-498e-dd31-c0542cb9624d"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loading model\n",
        "model_transfer = keras.models.load_model('autoencodercnn.keras')"
      ],
      "metadata": {
        "id": "UoM47qIh6SMb"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_transfer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "224Hh5Ok6X_R",
        "outputId": "1c9f1e37-8e85-4165-cd41-d24e8718e680"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.engine.sequential.Sequential at 0x7ab6dae8e860>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model_transfer.fit(\n",
        "    x=noisy_train_data,\n",
        "    y=y_train,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        ")"
      ],
      "metadata": {
        "id": "Lyv_iWs2BZny",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1467629-0aba-4f27-f021-69404e33954f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "51/51 [==============================] - 4s 53ms/step - loss: 0.1712 - mean_squared_error: 0.1712 - root_mean_squared_error: 0.4137 - val_loss: 0.2647 - val_mean_squared_error: 0.2647 - val_root_mean_squared_error: 0.5145\n",
            "Epoch 2/10\n",
            "51/51 [==============================] - 2s 48ms/step - loss: 0.1665 - mean_squared_error: 0.1665 - root_mean_squared_error: 0.4080 - val_loss: 0.2593 - val_mean_squared_error: 0.2593 - val_root_mean_squared_error: 0.5092\n",
            "Epoch 3/10\n",
            "51/51 [==============================] - 4s 75ms/step - loss: 0.1632 - mean_squared_error: 0.1632 - root_mean_squared_error: 0.4040 - val_loss: 0.2548 - val_mean_squared_error: 0.2548 - val_root_mean_squared_error: 0.5047\n",
            "Epoch 4/10\n",
            "51/51 [==============================] - 3s 63ms/step - loss: 0.1593 - mean_squared_error: 0.1593 - root_mean_squared_error: 0.3992 - val_loss: 0.2504 - val_mean_squared_error: 0.2504 - val_root_mean_squared_error: 0.5004\n",
            "Epoch 5/10\n",
            "51/51 [==============================] - 2s 49ms/step - loss: 0.1578 - mean_squared_error: 0.1578 - root_mean_squared_error: 0.3973 - val_loss: 0.2473 - val_mean_squared_error: 0.2473 - val_root_mean_squared_error: 0.4973\n",
            "Epoch 6/10\n",
            "51/51 [==============================] - 3s 50ms/step - loss: 0.1553 - mean_squared_error: 0.1553 - root_mean_squared_error: 0.3941 - val_loss: 0.2452 - val_mean_squared_error: 0.2452 - val_root_mean_squared_error: 0.4951\n",
            "Epoch 7/10\n",
            "51/51 [==============================] - 3s 67ms/step - loss: 0.1525 - mean_squared_error: 0.1525 - root_mean_squared_error: 0.3905 - val_loss: 0.2411 - val_mean_squared_error: 0.2411 - val_root_mean_squared_error: 0.4910\n",
            "Epoch 8/10\n",
            "51/51 [==============================] - 3s 54ms/step - loss: 0.1515 - mean_squared_error: 0.1515 - root_mean_squared_error: 0.3893 - val_loss: 0.2390 - val_mean_squared_error: 0.2390 - val_root_mean_squared_error: 0.4889\n",
            "Epoch 9/10\n",
            "51/51 [==============================] - 3s 49ms/step - loss: 0.1498 - mean_squared_error: 0.1498 - root_mean_squared_error: 0.3870 - val_loss: 0.2371 - val_mean_squared_error: 0.2371 - val_root_mean_squared_error: 0.4869\n",
            "Epoch 10/10\n",
            "51/51 [==============================] - 2s 49ms/step - loss: 0.1476 - mean_squared_error: 0.1476 - root_mean_squared_error: 0.3841 - val_loss: 0.2353 - val_mean_squared_error: 0.2353 - val_root_mean_squared_error: 0.4851\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"mean_squared_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_squared_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "QZGBAhGzrD2g",
        "outputId": "e0bd22a2-1e4f-4462-9d8b-31e0c4241659"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference with model\n",
        "ypred = model_transfer.predict(noisy_test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cm0_RjscrDuL",
        "outputId": "b575ec9d-d382-43b8-9030-4079a30c42ba"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 15ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations\n",
        "sample_indx = 42\n",
        "\n",
        "plt.imshow(noisy_test_data[sample_indx, :, :], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(y_test[sample_indx,:,:], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(ypred[sample_indx,:,:,0], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(ypred[sample_indx,:,:,0] - y_test[sample_indx,:,:], cmap='seismic', vmin=-1, vmax=1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6fTN2v2YrizE",
        "outputId": "e3b161a9-76fc-4563-b58b-1e580f128ad3"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LCcZfhT0ruF2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}