firstcnn.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mariajmolina/UMDAOSC650/blob/main/firstcnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Our first CNN!"
      ],
      "metadata": {
        "id": "pwgWAFL8GZWw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we have some helper functions for normalizing and standardizing our data using numpy."
      ],
      "metadata": {
        "id": "WmqM9d_sEJHu"
      }
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "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_nosub(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.std(da)\n",
        "\n",
        "def z_score_median(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.median(da)) / np.std(da)\n",
        "\n",
        "def z_score_max(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.max(da)\n",
        "\n",
        "def min_max_scale(da):\n",
        "    \"\"\"\n",
        "    Scale training data by minimum and maximum values.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - np.min(da)) / (np.max(da) - np.min(da))"
      ],
      "metadata": {
        "id": "uA1hA1WUC-R-"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Supervised Learning Regression Example**"
      ],
      "metadata": {
        "id": "JBirR3cdICH8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we will proceed with training a neural network in a supervised learning regression application."
      ],
      "metadata": {
        "id": "rT22AWjwIIeX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use a dataset from Molina et al. (2021) consisting of thunderstorms generated using a 4-km horizontal resolution WRF simulation. We are using a small portion of the dataset, specifically thunderstorms simulated for April 27, 2011 over the continental United States."
      ],
      "metadata": {
        "id": "Z6MIe4xpeOQq"
      }
    },
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      "cell_type": "code",
      "source": [
        "!wget https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "YIDtIzo3IJFS",
        "outputId": "68f262be-e2f2-4a57-d8d3-323910eb4b45"
      },
      "execution_count": 64,
      "outputs": [
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          "text": [
            "--2025-03-06 17:39:40--  https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.48.194, 188.185.43.25, 188.185.45.92, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.48.194|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 109400556 (104M) [application/octet-stream]\n",
            "Saving to: ‘current_SP3hourly_20110427.nc.1’\n",
            "\n",
            "current_SP3hourly_2 100%[===================>] 104.33M  15.0MB/s    in 7.6s    \n",
            "\n",
            "2025-03-06 17:39:48 (13.7 MB/s) - ‘current_SP3hourly_20110427.nc.1’ saved [109400556/109400556]\n",
            "\n"
          ]
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's visualize the dataset. We can use python libraries for this."
      ],
      "metadata": {
        "id": "FTRe0rEpexSJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import xarray as xr"
      ],
      "metadata": {
        "id": "V-_wY-Lqewnh"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = xr.open_dataset('current_SP3hourly_20110427.nc')"
      ],
      "metadata": {
        "id": "pY1Bn-N8d7pB"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "YYoMRMW9ewrH",
        "outputId": "79306bc4-e596-4ba7-d06d-6f084783e352"
      },
      "execution_count": 71,
      "outputs": [
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              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 109MB\n",
              "Dimensions:      (starttime: 2224, y: 32, x: 32)\n",
              "Coordinates:\n",
              "  * starttime    (starttime) float64 18kB 9.264e+04 9.264e+04 ... 9.266e+04\n",
              "    endtime      (starttime) float64 18kB ...\n",
              "    x_speed      (starttime) int64 18kB ...\n",
              "    y_speed      (starttime) int64 18kB ...\n",
              "Dimensions without coordinates: y, x\n",
              "Data variables:\n",
              "    uh25_grid    (starttime, y, x) float32 9MB ...\n",
              "    uh03_grid    (starttime, y, x) float32 9MB ...\n",
              "    ctt_grid     (starttime, y, x) float32 9MB ...\n",
              "    dbz_grid     (starttime, y, x) float32 9MB ...\n",
              "    mask         (starttime, y, x) int64 18MB ...\n",
              "    row_indices  (starttime, y, x) int64 18MB ...\n",
              "    col_indices  (starttime, y, x) int64 18MB ...\n",
              "    lats         (starttime, y, x) float32 9MB ...\n",
              "    lons         (starttime, y, x) float32 9MB ...</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-f550a6ef-9741-40f3-9c43-41ba1d0fe6f3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f550a6ef-9741-40f3-9c43-41ba1d0fe6f3' 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-560b57eb-0ed1-41a3-9499-d001e8f9a68f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-560b57eb-0ed1-41a3-9499-d001e8f9a68f' 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-2df9bc72-ad74-4aba-b756-23796c3d574d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2df9bc72-ad74-4aba-b756-23796c3d574d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0981ceae-7ea2-4c7c-be4f-2b70aa9920b2' class='xr-var-data-in' type='checkbox'><label for='data-0981ceae-7ea2-4c7c-be4f-2b70aa9920b2' 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-33f97e68-1730-4670-890b-2769fef7be35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33f97e68-1730-4670-890b-2769fef7be35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4711a844-9aac-41cb-a577-7658012725f1' class='xr-var-data-in' type='checkbox'><label for='data-4711a844-9aac-41cb-a577-7658012725f1' 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-33123db5-0625-44e8-87a3-bf8a4308dae2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33123db5-0625-44e8-87a3-bf8a4308dae2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5f44638-8c74-4bde-984f-e4fb38e87377' class='xr-var-data-in' type='checkbox'><label for='data-d5f44638-8c74-4bde-984f-e4fb38e87377' 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-d9eda833-c6f7-42e8-aa32-9a7a815f8f65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9eda833-c6f7-42e8-aa32-9a7a815f8f65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5536452-0f1f-447c-9525-2deb9d6e2a0b' class='xr-var-data-in' type='checkbox'><label for='data-c5536452-0f1f-447c-9525-2deb9d6e2a0b' 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-02acdeff-d909-4698-b307-01f910ce0e82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02acdeff-d909-4698-b307-01f910ce0e82' 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-b9818cc8-845d-401e-83d5-26072a2c04f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9818cc8-845d-401e-83d5-26072a2c04f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f55157dc-8c5a-43dc-87f8-c16159ce832f' class='xr-var-data-in' type='checkbox'><label for='data-f55157dc-8c5a-43dc-87f8-c16159ce832f' 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-72d473e8-6eb2-4178-8c0d-68372ca7e854' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72d473e8-6eb2-4178-8c0d-68372ca7e854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c83e95f7-af9c-4c01-917e-bfbdd497ad98' class='xr-var-data-in' type='checkbox'><label for='data-c83e95f7-af9c-4c01-917e-bfbdd497ad98' 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-bf49cbec-252c-4a3e-a23f-cb07d725a1de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf49cbec-252c-4a3e-a23f-cb07d725a1de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c0cf816-d5a7-4a28-a04c-c4d846f38a86' class='xr-var-data-in' type='checkbox'><label for='data-4c0cf816-d5a7-4a28-a04c-c4d846f38a86' 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-12ec1e15-9048-4479-93a2-45b5979ab206' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12ec1e15-9048-4479-93a2-45b5979ab206' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1992ec53-15a1-4a4a-8b00-ffcfcf01db15' class='xr-var-data-in' type='checkbox'><label for='data-1992ec53-15a1-4a4a-8b00-ffcfcf01db15' 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-b15eba95-7b90-4fc0-afb6-c153f5fefb9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b15eba95-7b90-4fc0-afb6-c153f5fefb9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6857d69d-700e-4e7b-91b1-a73aac43d855' class='xr-var-data-in' type='checkbox'><label for='data-6857d69d-700e-4e7b-91b1-a73aac43d855' 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-f5101dee-1d4f-45e3-a4a7-d8d09fbce567' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5101dee-1d4f-45e3-a4a7-d8d09fbce567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdba446c-3f01-4965-9a1a-2630c8320259' class='xr-var-data-in' type='checkbox'><label for='data-bdba446c-3f01-4965-9a1a-2630c8320259' 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-046a10a2-fb14-4be4-b465-f22a343d116c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-046a10a2-fb14-4be4-b465-f22a343d116c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-584cc038-4b86-417a-aeb2-102210c64443' class='xr-var-data-in' type='checkbox'><label for='data-584cc038-4b86-417a-aeb2-102210c64443' 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-618328d0-290a-4401-8814-43dcb26f2a09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-618328d0-290a-4401-8814-43dcb26f2a09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e402e5c7-a621-4ffc-9109-81e20f386d42' class='xr-var-data-in' type='checkbox'><label for='data-e402e5c7-a621-4ffc-9109-81e20f386d42' 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-4cdcaf9f-927c-4f4d-8992-08b8316cae6c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cdcaf9f-927c-4f4d-8992-08b8316cae6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38fee3e9-feae-4245-9c39-72c6952d76d6' class='xr-var-data-in' type='checkbox'><label for='data-38fee3e9-feae-4245-9c39-72c6952d76d6' 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-590238b7-a7b4-4b6d-b4e6-1a18f012a4f8' class='xr-section-summary-in' type='checkbox'  ><label for='section-590238b7-a7b4-4b6d-b4e6-1a18f012a4f8' 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><input type='checkbox' disabled/><label></label><input id='index-4b864f13-793f-4e85-b73d-89b4eacfb643' class='xr-index-data-in' type='checkbox'/><label for='index-4b864f13-793f-4e85-b73d-89b4eacfb643' 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-fb0d6d86-fb6a-4e95-9e30-101cec75960f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb0d6d86-fb6a-4e95-9e30-101cec75960f' 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": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset contains 2,224 storm samples that were generated using the github reposity [deep-conus](https://github.com/mariajmolina/deep-conus). Each sample is 32x32 grid cells (i.e., pixels) with each grid cell spanning 4-km.\n",
        "\n",
        "Let's visualize some of the variables."
      ],
      "metadata": {
        "id": "yw6ewfZee-lr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# simulated radar reflectivity\n",
        "ds['dbz_grid'].isel(starttime=0).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "DsoCCgnSfnhJ",
        "outputId": "9d10d945-66e1-4d5c-f6a3-b41889b0c02f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x79c4de478110>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gridded updraft helicity from 2-5 km\n",
        "ds['uh25_grid'].isel(starttime=0).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "bFIWAsD0ewu-",
        "outputId": "bf74586a-a82c-49ec-ca56-7dd462b2aae4"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x79c47df80110>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gridded cloud top temperature\n",
        "ds['ctt_grid'].isel(starttime=0).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "dzBY7eYRfncK",
        "outputId": "82142bb2-3e94-4a90-8607-258b52dd8b4c"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x79c47de43710>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will preprocess our training and testing data. We need to split our data into a training and testing sets, and standardize."
      ],
      "metadata": {
        "id": "UlzHzBsPhpe1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def random_split(data, label, split=0.3, seed=0):\n",
        "    \"\"\"\n",
        "    Help spliting data randomly for training and testing.\n",
        "    \"\"\"\n",
        "    np.random.seed(0)\n",
        "    da_indx = np.random.permutation(data.shape[0])\n",
        "    data  = data[da_indx.astype(int)]\n",
        "    label = label[da_indx.astype(int)]\n",
        "    init_range = int(data.shape[0] * (1 - 0.3))\n",
        "    return data[:init_range], label[:init_range], data[init_range:], label[init_range:]"
      ],
      "metadata": {
        "id": "BYfUl7q0IJBl"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = xr.concat([\n",
        "    ds['dbz_grid'].expand_dims(dim='feature', axis=3),\n",
        "    ds['ctt_grid'].expand_dims(dim='feature', axis=3)],\n",
        "          dim='feature')"
      ],
      "metadata": {
        "id": "QmZSDOrvd50f"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(features.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rt4Sl3cii7SY",
        "outputId": "4e4315f2-272d-46cd-88d5-428823463fb6"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2224, 32, 32, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# grab the maximum of the dbz to use as our labels (regression)\n",
        "labels = ds['dbz_grid'].expand_dims(dim='feature', axis=3).max('x').max('y')"
      ],
      "metadata": {
        "id": "HgsBcfJ2ivWE"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZ57QrQXi3HV",
        "outputId": "6948d929-3803-4263-a1d4-e67f10c42d0b"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<xarray.DataArray 'dbz_grid' (starttime: 2224, feature: 1)> Size: 9kB\n",
            "array([[65.721054],\n",
            "       [58.54062 ],\n",
            "       [58.54062 ],\n",
            "       ...,\n",
            "       [45.41483 ],\n",
            "       [37.786957],\n",
            "       [42.48563 ]], dtype=float32)\n",
            "Coordinates:\n",
            "  * starttime  (starttime) float64 18kB 9.264e+04 9.264e+04 ... 9.266e+04\n",
            "    endtime    (starttime) float64 18kB ...\n",
            "    x_speed    (starttime) int64 18kB ...\n",
            "    y_speed    (starttime) int64 18kB ...\n",
            "Dimensions without coordinates: feature\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats, train_label, test_feats, test_label = random_split(features, labels)"
      ],
      "metadata": {
        "id": "1xt_5E0DjOPD"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_feats.shape[0], train_label.shape[0], test_feats.shape[0], test_label.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5kQ6CXcjOek",
        "outputId": "6a055293-de00-4808-ae66-01a14d900ad2"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1556 1556 668 668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalization of our training data (note each feature individually processed!)\n",
        "train_feats_standardized = xr.concat([\n",
        "    z_score_nosub(train_feats.isel(feature=0).expand_dims(dim='feature', axis=3)),\n",
        "    z_score_nosub(train_feats.isel(feature=1).expand_dims(dim='feature', axis=3))],\n",
        "                                     dim='feature')\n",
        "\n",
        "train_label_standardized = z_score(train_label)"
      ],
      "metadata": {
        "id": "-UM89woPj8Ep"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats.isel(feature=0,starttime=15).plot.hist(); plt.show()\n",
        "train_feats_standardized.isel(feature=0,starttime=15).plot.hist(); plt.show()"
      ],
      "metadata": {
        "id": "optlXJwYocz1",
        "outputId": "197d9717-0d8e-4c2d-c3da-1fbb066411d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        }
      },
      "execution_count": 101,
      "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": [
        "train_feats.isel(feature=0,starttime=15).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "1HV4mCLnlIMF",
        "outputId": "4f3c3900-af20-4a2f-ae33-c5531e7a98b3"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x79c4e1dfa390>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats_standardized.isel(feature=0,starttime=15).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "w2T48h8ykonf",
        "outputId": "ef929076-a333-4308-cc5e-89bc582badc2"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x79c4773d5790>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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evHnIy8tDXl6e7XcDBw7E7NmzcfLkSTRr1sy2/4EHHoBOp7P9fOONN2LhwoX4/fffkZCQgO+//x5msxlTpkyxi3vkkUfw/PPPO1znMWPG2A1O79mzJ9577z3cd999dnE9e/bEokWLUFpaCjc3N6xZswZWqxWjRo2yez4iIiLQunVrbNy4EU888USl5Xbu3Bnfffddjep45aDXijz00EP47LPPMHr0aDz33HPw9vbGkiVLsHPnTgAQvx+2bNmCuXPnYtSoUejfv79tf9nrLy8vD3v27EF0dDQAoH///mjVqhXmz59ve5/k5+ejsLAQDz74oO2uoBEjRsBsNmPZsmWYN28eWrduLXocCwsLUVhYWK6++fn5dscaDAbb50jZtZtMpnLHlbXyXLp0CSaTCWazGdOmTcODDz6IDh06iB6zqiQlJeGvf/0r5s2bh48++ggeHh5YtmyZ6BxlrUzr1q3D+PHjxS3IVI+0zpiutHr1auXh4aH0er267rrr1OzZs1VqaqpdTFWDX1esWKE6deqk3N3d7b41xcXF2WLKWkTmzZtX7vi6bGHx8PAoF3vTTTepdu3aldsfGxurbr31VtvPnp6eVX4LXLBgQYXXXx+ys7NtrR9l21/+8hc1YsQIBUBdvHixxueaP3++AqCeeeaZcr+75ZZbFAD19ttv2+3//fffFQA1d+5cpdQfz9GVz2mZ8ePHK3d3d1vLjeRxjI2NrdE38CtbwmrawlJSUqI6duyoxowZYxcDB1pYduzYUW1dd+/erZT6o4Vl+/btducoa3nYtGmTUkqplJQUBaDce04ppQIDAx1uYbmyZbGqes2ePduu9e6hhx6q8joTEhJq/LjVhVdffVV5e3vbym/VqpXttb1w4cIan+eXX35RQUFBqkuXLuVabMoe3379+pU7rl+/fnav/2uuuUYBUJs3b7aLKxvMW/aekjyOZc9BdduVn4mSFpYXXnhBBQYGqvPnz9ti6qKFRSml8vLyVEREhAKgVq1aJT6+sLBQXX/99QqACgkJUaNHj1arV69u0PGDVDGnamEZNWoUbrzxRqxduxbffvstXnzxRfzjH//AmjVrMHjw4CqP/c9//oNx48Zh2LBheOyxxxAWFgaDwYCUlBSkpqaWi6+LMQRVKWvtqel+pZTt/1arFTfffDMef/zxCmPbtGlTZdk5OTk1+qZnNBptY0Mq4+/vj3Xr1iE9PR3Hjx9HbGwsYmNj0bt3b4SGhta4z3vFihWYMWMGHnzwQTz11FPlfl/WohYeHm63PywsDMDlFpCq4spiS0pKUFBQAH9/f9HjuHLlSrvHrOz1d/U38/j4eNv/IyMjYbFYcObMGVs9gctzZ5w/f95W13feeQdHjhzBsmXLys1bkpeXh+PHjyMsLMzWclMTVqsVAPD3v/8dAwcOrDCmVatWdj/X5LVXn2r7nrBardDpdPjqq68qjK1uPJnZbMaFCxdqVMfQ0NBK61Nm8uTJGD9+PPbt2wej0YguXbrgzTffBFD9e7NMRkYGkpKS4O/vjy+//BK+vr52v6/udV42fqos9uDBg9W+dySP45gxY3DDDTfY/f7mm2/GY489hqSkJNu+Kz9HIyMjAaDCOWUyMzMRFBQEk8mEnJwcPPvss3j44YeRm5uL3NxcAJdbb5RSOH78OLy8vOzeUxJXTm+wf/9+3HXXXaLjPT09sWXLFmzcuBFffPEFvv76a6xevRr9+/fHt99+W+3rg+qPUyUswOUX/cMPP4yHH34YZ86cQbdu3fDcc8/ZEpYrm6qv9NFHHyE+Ph5r1qyxi5k9e3aNy67s3NX9rq61bNkS+fn5SExMrNXxU6dOLTfHSUVuuukmbNq0qUbnjImJQUxMDAAgOzsbu3btwsiRI2t07Lp163D//fdjxIgRWLx4cYUx3bt3x7///W+cPHnSbv+pU6cAwNZ9FxUVhYiIiHJxZbEeHh62D3/J43j99dfb/Vw2f0VVx5YN5N65cyeGDBli279z505YrVbb79PT01FSUlKuDOByMvPOO+9g7dq1GDZsWLX1LFOWOLm7u9f6dXK12NhYAMDRo0ftErOzZ8/a/uiVaej3g1IKcXFxNU4IrrR161a7QapVSUtLKzevTEW8vb3Rq1cv28/ff/89PD09K3yOr3b+/HkkJSWhuLgY69evt/2hv1KnTp3g7u5e6ev8yu7s7t2747vvvsPJkyfRtm1buzjgj/eO5HGMj4+3ew2U6dChQ6Wvt2bNmiE0NNTWPXalK298uHjxIvLz8zF//nzMnz+/XGxcXByGDh2KTz75pMo6VqSgoADjx49Hhw4d0Lt3b8yfPx/Dhw/HddddJzqPXq/HgAEDbPNPPf/883jyySexcePGOnu/kZzTJCwWiwX5+fl2d62EhYUhKirKdrcKcPmDoqIR/2VZr1LK9mG6Y8cObNu2zfaHtjplExdlZ2dX+Lua3mngqFGjRmHOnDn45ptvyn17zs7Oho+Pj934j6vV1RiWyiQnJ6O0tBTTpk2z23/48GF4eXnZPd5btmzBnXfeiT59+mDlypWV9gcPHToUU6dOxfLlyzFu3Dhb3BtvvAHg8re7MqNHj8Yrr7yC7777zrb/3LlzWLduHfr372871tHHsTr9+/dHUFAQli5dapewLF26FF5eXrjlllsAAHfeeWe5u9QAYPjw4RgyZAgmTpxoG4tTU2FhYejbty+WLVuGKVOmlPujd/bsWfEYrcTERLi7u+PVV19FUlKS7X1U0d1UVb1X6tqIESOQnJyMuXPn4j//+Y9dsqSUwoULFxAcHFzp8XU5hqUiW7duxZo1a/DQQw/ZfX5lZmYiJycHLVu2tI3dKSgowJAhQ3Dy5Els3Lix0juKfH19MWTIEHz++ec4fPgw2rVrB+DyLLJbt27FX//6V1vsqFGj8MILL+DNN9+0GwfzxhtvwM3NzbZMgqOPY02MHDkSb7/9NjIyMmzjbtavX49ff/3V9nkRFhaGtWvXljt20aJF2LZtG957770Kk7iamDFjBtLT07F9+3a0bdsW69evx9ixY7Fnz54Kx9ZU5MKFC+Vansvev1f+Laro8y49PR2FhYW25wu4/Nl07tw5xMTEiFpRqQKadUZd5eLFi8rb21uNHTtWLViwQL3++utq1KhRCoD65z//aYsr6yueNm2aWrVqlW0uj7feeksBULfffrtatmyZmjlzpgoICFDXXHONXT/rlfOwXM1sNquAgADVtm1b9cYbb6j33ntP/fbbb1WWW9U8LFe76aab1DXXXFNuf2xsrN3kYwUFBapbt27Kzc1N3X///Wrp0qXqpZdesp23IUevp6SkqHvuuUctWrRILVmyRCUlJdlG4V8NsL/z6Pjx48rf3195enqqxYsXq3fffdduu/JOB6WUbTK1m2++WS1evFg98MADSqfTqbvuussuLisrS0VGRipfX181e/ZstWDBAtWmTRvl6emp9u7da4tz5HGsyV1CSim1ePFiBUD9+c9/Vv/+97/VmDFjFIBq77goe7wqGsNSNu9PdQ4ePKgCAwNVcHCwmjlzpnr99dfVM888o4YMGWI3HqHsWn766Se748vGllw5DiU5OVkBUEOGDFH/+te/1IQJE1RUVJQKCQmxG8OSmZmpDAaD+tOf/qRWrFih3nvvPXX69Glb/Ssaw/Lhhx/alV9ZvcrGT1z5/JSNr+ndu7eaP3++Wrp0qXr88cdV69atK3wv15fjx4+rHj16qGeffVa98cYbatq0acrT01N17dq13DiUsrv1rrzzaOjQoQqAuu+++8q9H9auXWt3/MGDB5WPj4+KjIxUKSkpKiUlRUVGRqrQ0FB14sQJu9j77rtPAVCjRo1SixcvVnfccYcCUG7uEEcex6s/5yqSnp6ugoODVcuWLdWiRYvU888/rwIDA8vdzVaRyj43y14n1ZW9fv16pdPp1Jw5c2z7tmzZovR6vXrssceqPPZKU6dOVV27dlVPPfWU+ve//62ee+451axZM9W8eXPbHXVKlf+8U6ri927Z69nVJlmsD06TsBQXF6vHHntMde7cWfn6+ipvb2/VuXNntWTJEru4/Px8dffdd6uAgAC7QV9Wq1U9//zzKjY2VplMJtW1a1f1+eefq7Fjx9Y4YVFKqXXr1qkOHTooNzc3uzdJZeXWR8Ki1OWBY8nJyapVq1bKaDSqkJAQ1bt3b/XSSy/Z3Rpa3z7//HPVo0cP5evrq7y8vNSf/vQn9cEHH1QYe/UbuLrbSa8cwKrU5efw1VdfVW3atFHu7u4qOjpaPfXUUxVeb2pqqho+fLjy8/NTnp6eqn///urHH38sF1fbx7GmCYtSSr3++uuqbdu2tonpFi5caHdbcGUqS1i6d++uIiIialR2amqqGjNmjIqIiFDu7u6qWbNm6tZbb1UfffRRuWupScJisVjU3LlzVWRkZKUTx5X597//reLj45XBYLA7T30kLEop9fHHH6sbbrhBeXt7K29vb9WuXTs1adIkdeTIkRo9VnXhwoULaujQoSoiIkIZjUYVFxenZsyYUS5ZUarihKWqwd0VDerftWuXSkxMVN7e3srX11cNHTq0wsnpzGazmjNnjoqNjVXu7u6qVatWlQ4Aru3jWJOkQSmlDhw4oJKSkpSXl5cKCAhQ99xzj8rKyqr2uMo+N1999VUFQH399deVHpubm6tiY2NVt27d7KZLUOry7ft6vV5t27at2joodTnxGTp0qIqKilJGo1FFRUWpu+66q9zjzoSl4emUaqARd0RUrby8PAQFBeHll1/GpEmTtK4OkeZGjRqF48eP48cff9S6KqQxpxnDQkSXx/w0a9YMEydO1LoqRJpTSmHTpk22OWfItbGFhYiIXELZsiCV8fT0rHK5EtIWExYiInIJ1d2OP3bsWKxYsaJhKkNi7BIiIiKXUN3t7dIlYahhsYWFiIiInB5XdSIiIiKn1+S7hKxWK06dOgVfX98GnU6ciIgaH6UU8vLyEBUVVa8rNRcVFcFsNjt8HqPRaFsNu6lr8gnLqVOnbFNEExER1URGRgaaN29eL+cuKipCsKcPCmFx+FwRERFIS0tziaSlyScsZQvh7X/uIfh61GwtCa+2HcTl5B/8WRSffaz8ombVKS0oEh8j8cu3aeJjPAyybyDhXcqvPlsd7zDZ+hueIQGieN9Y+foxXm2vEcXrTPI1RHTGmr1ey+g9vUXxyk3+AWc+8D9R/G9rNojL8AzxE8VH9pWtw6S/YZQoHgDcsg6L4q25NVsd+kq6yJai+I/Oyh6n/JJSUTwAdArzrT7oCh/uPSUuQ6+XtXxfH1/1CvNXy8ipfuX6KxUV5OOZP99YbgXtumQ2m1EIC+5BMxgdGJlhhhUrs07CbDYzYWkKyrqBfD1M8POsYcLiXYs/LjVMhspYjO7iMkpKHM/Gq+Klly+b7iFsMvVxl7/kvIWPlZfJKIqv6evCrgzha0TnUZuERfYBJE5Y3D1F8QBg9pLVycdd/jr3MsmO8RPWSe8n+0MPAG75ssfWapF/udD5+ojiPS/J/qBazPKExVv4R9voJbsGADAIExYvH1mdPEpr92euIYYQeEIPo672CYvBxW6ZafIJCxERkTMy6HQwOJAYGaC7vBKVi2DCQkREpAG9DjA40JCjB1wqYeFtzUREROT02MJCRESkgTrpEnIhTFiIiIg0YHCwS0h+m0Tjxi4hIiIicnpsYSEiItIAu4RkmLAQERFpgF1CMuwSIiIiIqfnMi0svt17w9enZrONWvKyxee3CmeRvHQ+X1yGxSyb6Tb3RJ4oXjrNPgCEJYTK4jvHiMsweMhmrg1oLSvDLSpOFA/IZ66VzkILyGe6teScl8WfzxLFA8DZHftE8aWX5LOrunvJZuDVe8lmPq3NDL8wyZ4/fYR8Nt3CoHhRfP6J06L4nEslongA+GS//DUiJZ3p9kyBbMHAExdkU/MXF9bvEihXauguoaVLl2Lp0qU4fvw4AOCaa67BrFmzMHjw4FrXoSFp2sKydOlSJCQkwM/PD35+fujVqxe++uor2++LioowadIkBAcHw8fHByNHjsTp07I3KRERkTPS4fIf4dpu0lSnefPmeOGFF7Br1y7s3LkT/fv3x9ChQ3Hw4ME6uZ76pmnCUt2DN23aNHz22Wf48MMPsXnzZpw6dQojRozQsspERESN0m233YYhQ4agdevWaNOmDZ577jn4+Phg+/btWletRjTtErrtttvsfn7uueewdOlSbN++Hc2bN8ebb76JVatWoX///gCA5cuXo3379ti+fTv+9Kc/aVFlIiKiOlFXXUK5ubl2+00mE0ymqhd1tVgs+PDDD1FQUIBevXrVug4NyWkG3VosFrz//vu2B2/Xrl0oKSlBYmKiLaZdu3aIiYnBtm3bKj1PcXExcnNz7TYiIiJnU3aXkCMbAERHR8Pf39+2paSkVFrm/v374ePjA5PJhAcffBBr165Fhw4dGuiKHaP5oNv9+/ejV69eKCoqgo+Pj+3B27t3L4xGIwICAuziw8PDkZVV+UCwlJQUzJ07t55rTURE5JjLSYcjLSyXZWRkwM/vj4HeVbWutG3bFnv37kVOTg4++ugjjB07Fps3b24USYvmCUtlD15tJScnY/r06bafc3NzER0dXRdVJSIicjplN67UhNFoRKtWrQAA3bt3x08//YRXXnkFy5Ytq88q1gnNE5bKHrzRo0fDbDYjOzvbrpXl9OnTiIiIqPR8Nem7IyIi0pozTBxntVpRXFxcB2eqf5onLFcre/C6d+8Od3d3rF+/HiNHjgQAHDlyBOnp6Y1mgBAREVFlGnoeluTkZAwePBgxMTHIy8vDqlWrsGnTJnzzzTe1rkND0jRhqerB8/f3x4QJEzB9+nQEBQXBz88PU6ZMQa9evXiHEBERkdCZM2cwZswYZGZmwt/fHwkJCfjmm29w8803a121GtE0YanuwVu4cCH0ej1GjhyJ4uJiDBw4EEuWLNGyykRERHVC72CXkPQ23zfffLP2hTkBTROW6h48Dw8PLF68GIsXL3a8MDc3wM29RqHKLJ+auSDrgig++3iOuIyMYxfFx0g0jw+QH3NDW1F80LVdxWXovYXTnNfwebadXzgFPgBYC2XLHljFJcg/jC5uXi+KN+cVCksACs/KXoPNb7pGXIZPnHBphVay15SlSP7ek76mSgPlS1BcLJItveFvkn1839YmRBQPAP/+6YQovlC4fAgAmEtl746DJ2VTVVzXIlAUfym/4Wb74GrNMk4zDwsRERFRZZxu0C0REZErcIa7hBoTJixEREQaYMIiwy4hIiIicnpsYSEiItIAB93KMGEhIiLSgAEOdgmpOqtKo8CEhYiISAN6B1tY9A4c2xhxDAsRERE5PbawEBERacDhu4Rcq4GFCQsREZEWHB50yy4hIiIiIufiOi0spaVAaUmNQi3nM8WntxQVi+ILzsjXcfk13yyKzxeu0XHpqGw9JACIK5LVqfR8lriMwgMHRfHu3p6ieI9mUaJ4ACjNlj1Wxubx4jKsOedF8XkZp0Xx5lz5azCofawoPuCGfuIydFGtZfFFsnWdCt2Fa1MB8DHI1h/Slco+DwAg7aLs+2OEr0kU/95++XsvKkD2Xjov/IyqDS9j05kujV1CMq6TsBARETkRdgnJsEuIiIiInB5bWIiIiDSg1+kcmkvF1eZhYcJCRESkAZ1BB52+9kmHzsUSFnYJERERkdNjCwsREZEG9AYd9A60sLBLiIiIiOqfQQ+d3oGODp1rrX7IhIWIiEgDOr0OOgcmU9HBtVpYOIaFiIiInB5bWIiIiDSgN+igd6CFRe9iLSwuk7Doff2h9/GuUazBP1h8flOAryheWWTT5gPy5jDp1Pz5pfL+UL277CV0bl+quIzSgkuieJ1B9kh5nLkoigcAr7BAUbzRahGXYS2STZ3vGSqrk0eQvygeAHyiw0Xxel9ZnQDAaqzZ+9QWLzy/p07+XGR5NBPFXyiUl+HlLruSzHzZ9P85hTVbmuRKfh7uovgAL1k8AGQL61Us/Fw7nCVbuqG4MF8U7wid3rExLDrlWmNY2CVERERETs9lWliIiIicCbuEZJiwEBERaUBn4F1CEuwSIiIiIqfHFhYiIiINXG5hcWDQrXjIeePGhIWIiEgDHMMiwy4hIiIicnpsYSEiItKATqeDzoHFD3VW12phYcJCRESkAb1BD70DY1j0yrU6SZiwEBERacDh25qVa7WwuFZ6RkRERI2S67Sw6N0AQ83WuTCEytYNAQDPsGOi+Na3thWXce79/aL4CA/Z0xtkNIjiAcCcJ1vvpjaKLhaI4kM7x4vipWvwAPL1igyBYeIy3H0DRPFh3ZNE8VYP2fpXAKA79qPsgFqUYfGWreVlULJbOw05p0TxABDqHyWKP3ep/td4MQlfgz7CzwMAOH5O9t5rGSpbBwoAcn1KRfG/nZHVKa9Idn6zMN4RbGGRcZ2EhYiIyIlwDIuMa10tERERNUpsYSEiItKCg11CYJcQERER1Te9Tge9A/Ow6HWulbCwS4iIiIicHltYiIiINKAz6B1b/NDqWm0OTFiIiIg04PDihy42Nb9rpWdERETUKLGFhYiISAMOTxzHFhYiIiKqb2VjWBzZJFJSUnDdddfB19cXYWFhGDZsGI4cOVJPV1f3XKaFpSSsNUr8/GoUq/cJFZ/fO/O4KN4j2F9cRscT2aL4vMx8UXxgfIAoHgAKMi+I4ktrMe21R6CnLF742JpiW4niAcAQHCGK1/sGicuwnBdOIZ9zXhRem+9m+rBYUXxJWBtxGUp4q6bOUiIuQ0w4/X9RqSweAMK8a7Z0SBl34TdzQy1un5VOtR/oKbuG2hxzId8sis8ubIDXRy3pDXBwDIssfvPmzZg0aRKuu+46lJaW4oknnkBSUhIOHToEb2/5sgoNzWUSFiIiIlf29ddf2/28YsUKhIWFYdeuXejTp49Gtao5TbuEatI81bdvX+h0OrvtwQcf1KjGREREdUOn1zm8AUBubq7dVlxcXKPyc3JyAABBQfIWYC1omrCUNU9t374d3333HUpKSpCUlISCAvvVOCdOnIjMzEzbNn/+fI1qTEREVDf0er1tAcRabfrLf8Kjo6Ph7+9v21JSUqot22q14pFHHsH111+Pjh071vel1glNu4Rq2jzl5eWFiAjZmAEiIiJXkJGRAb8rxmiaTKZqj5k0aRIOHDiAH374oT6rVqec6i6hypqnVq5ciZCQEHTs2BHJyckoLCys9BzFxcXlmseIiIicTdltzY5sAODn52e3VZewTJ48GZ9//jk2btyI5s2bN8Sl1gmnGXRbWfPU3XffjdjYWERFRWHfvn2YMWMGjhw5gjVr1lR4npSUFMydO7ehqk1ERFQrDk/NLzxWKYUpU6Zg7dq12LRpE+Li4mpdthacJmGprHnqgQcesP2/U6dOiIyMxIABA5CamoqWLVuWO09ycjKmT59u+zk3NxfR0dH1V3EiIqJGYNKkSVi1ahXWrVsHX19fZGVlAQD8/f3h6SmbPkILTpGwlDVPbdmypdrmqZ49ewIAjh07VmHCYjKZatR/R0REpCWdXg+d3oEWFuGxS5cuBXD57tsrLV++HOPGjat1PRqKpglLbZqn9u7dCwCIjIys59oRERHVn7K7fRw5XkIpVeuynIGmCUt1zVOpqalYtWoVhgwZguDgYOzbtw/Tpk1Dnz59kJCQoGXViYiIqAFpmrBU1zxlNBrx/fff4+WXX0ZBQQGio6MxcuRIPPXUUxrUloiIqA45OOgWjhzbCGneJVSV6OhobN68uW4Ks1oubzVg8ZPP+aLv+xdRvNulHHEZzc9cFMVn/5ohij+xXRYPAEUXi0TxniFe4jJi48JE8aZo2ch3t4gYUTwA6Iweonirp3ztKF1z2THmANng8ku1WO/G20227klhqbwJ2miQxYvX/tILCwBwScmOifSRlxGpsmUHeMk+vi80k78Gs/JqNmNqGUstehwcWEqn0dPpHbxLyIHxL42RUwy6JSIicjUNPei2sXOtqyUiIqJGiS0sREREGrg8cZy8+/CP42s2zKGpYMJCRESkgYae6baxc62rJSIiokaJLSxEREQa0Ov10DswcNaRYxsjJixEREQaYJeQjGtdLRERETVKbGEhIiLSAFtYZJiwEBERaUCnc3DiOJ1rJSyudbVERETUKLlMC4v+6HbofWq2jo2uWVvx+Y8ZIkXxMZveF5cR0PsmUbze/X+i+G9W7RfFA0C+cD2aLrVowlQW2eRI7lEtZOf3E65FAwCWUlG47uJJeRnegaJwvZI9TkWySwAAWKyyxWICVIG4jN8veYriPd1kH2NBnvKPPZNwvZsAnXyBnEsIFsWXCp+LaD/5JGMebrL3a+qFS+IydqRdEB/TVLBLSMZlEhYiIiJnwoRFhgkLERGRBvQGPfQOJB2OHNsYudbVEhERUaPEFhYiIiIN6PQ6x+4S0svHSjVmTFiIiIg0wDEsMq51tURERNQosYWFiIhIA2xhkWHCQkREpAHOdCvjWldLREREjRJbWIiIiDSgMxigNxgcOt6VuEzCYsk+B0tJzab8NgZHic9f4B4uiteZPMRlQNh0eGbXEVG8oRbTiRuFt9WZ/IziMiJ6dxHFW/KyZQVcPCuLB2CIbic7wNNPXIbFV7ZkQG6J7PzSqd0B4Kuj50XxY9p6i8uIs2SJ4q1HdoniVYlZFA8Abi07i+L1gdHiMkrcZEsSGAyy956/Sd6gnlss+4NoVfLXVJdof1H8ocw8UbxZuHxIQ+IYFhnXuloiIiJqlFymhYWIiMiZsIVFhgkLERGRBnR6B+8ScuDYxogJCxERkQbYwiLjWldLREREjRJbWIiIiDSg0+sca2Hh4odERERU3ziGRca1rpaIiIgaJbawEBERaUCnN0Cnd2CmWweObYyYsBAREWlBb7i8OXK8C2GXEBERETk9l2lhcW8WB3efmq1rYvEJEZ//0Il8UXxCs5biMnTN2orilUW2rkfnzmGieAAIah0kio8Z2FtchntMG1F88bF9onhTqwRRPACUhsqev7PF8tH8e08XiOIHxMm+bc37/ldRPADMS2olileQr9tTvGm1KL7ofI4o3hToK4oHAPfmsufbLFwXCAA8s9NF8UUBMbLzu8u/n/p7yF5T0f7yNdKOnJO9zk1usutw5rWEoNeL14grd7wLcZmEhYiIyJnoDAaHVlx2tdWaXSs9IyIiokaJLSxERERa4KBbESYsREREWtDrHUxYXKuThAkLERGRBjjTrYxrXS0RERE1SmxhISIi0oLOwTEsOtcaw8IWFiIiIi2UDbp1ZBPYsmULbrvtNkRFRUGn0+GTTz6pn+uqJ0xYiIiIXEBBQQE6d+6MxYsXa12VWmGXEBERkQYaetDt4MGDMXjw4FqXpzWXSVgKY66Fm59fjWLPFpaKz39zS9kLR58dKy7Dqpc9XdGJ3UXxcUGhongAMARHiOJL0+XTwefs+K8o3iPYXxSv9w0QxQOAIfOgKD78fJa4jCGBsudDHSsSxS+IOC+KBwBDxgVRvCqRT82fnXFafIyEd3y8+BhLYLQofmtGnriM9qHNRPEeJbIp56VT2teGdJp9ADibV1wPNWkkOA+LiMskLERERE1Rbm6u3c8mkwkmk0mj2tQfTcewpKSk4LrrroOvry/CwsIwbNgwHDlyxC6mqKgIkyZNQnBwMHx8fDBy5EicPl2/38CIiIjqXdnEcbXeLv8Jj46Ohr+/v21LSUnR+MLqh6YJy+bNmzFp0iRs374d3333HUpKSpCUlISCgj+aFadNm4bPPvsMH374ITZv3oxTp05hxIgRGtaaiIjIcWWLHzqyAUBGRgZycnJsW3JyssZXVj807RL6+uuv7X5esWIFwsLCsGvXLvTp0wc5OTl48803sWrVKvTv3x8AsHz5crRv3x7bt2/Hn/70Jy2qTURE5DT8/PzgV8Mxmo2ZU41hycnJAQAEBQUBAHbt2oWSkhIkJibaYtq1a4eYmBhs27atwoSluLgYxcV/DOK6um+PiIjIKej1jq0HJDw2Pz8fx44ds/2clpaGvXv3IigoCDExMbWvRwNxmnlYrFYrHnnkEVx//fXo2LEjACArKwtGoxEBAQF2seHh4cjKqviui5SUFLu+vOho2eh+IiKiBtHAE8ft3LkTXbt2RdeuXQEA06dPR9euXTFr1qz6uLo65zQtLJMmTcKBAwfwww8/OHSe5ORkTJ8+3fZzbm4ukxYiInI6Or0BOgduTZYe27dvXyilal2e1pwiYZk8eTI+//xzbNmyBc2bN7ftj4iIgNlsRnZ2tl0ry+nTpxERUfH8H031di4iIiJXpmmXkFIKkydPxtq1a7FhwwbExcXZ/b579+5wd3fH+vXrbfuOHDmC9PR09OrVq6GrS0REVHd0+j/GsdRm0znNqI4GoWkLy6RJk7Bq1SqsW7cOvr6+tnEp/v7+8PT0hL+/PyZMmIDp06cjKCgIfn5+mDJlCnr16sU7hIiIqFFr6C6hxk7ThGXp0qUALverXWn58uUYN24cAGDhwoXQ6/UYOXIkiouLMXDgQCxZsqSBa0pERERa0jRhqcngHw8PDyxevNjh1SWzCkqQry+pUezxi7I1WQDg+mhfUfwJn5biMiJ+XiOKN3h6ieItOfK1ZcynM0XxZ3YeFpehN8peptK1hOApn7/A6i4bJ6Vzkz+2JcJ1lywXz4ridSYPUTwAWIWvEWWVrXcDAKYA2XvJp/v1ongVfY0oHgCsXoGi+P7HN4rLQLbssdJFtxfFZ3nI1ioCAOn4zNvahIjLkHph42/1XkaDKZvp1pHjXYhTDLolIiJyOQ08D0tj51pXS0RERI0SW1iIiIg0cOV6QLU93pUwYSEiItJCLWarLXe8C2HCQkREpAUmLCIcw0JEREROjy0sREREGtDp9dA5cKePI8c2RkxYiIiItKBzsEtIxy4hIiIiIqfCFhYiIiIt6HSOLWCo09VdXRoBl0lYLpVYYSip2dTXnx3IEp9fesw/B8aKy3j0YhdR/D9KPxXFmxLvFcUDQMYLT4rizx46Iy7Dr7lsqnZlkU8HL6VOHq33MqRLJeSlZYjipVPgA4DRX3aMe7x8GnxTh56iePOxn0Xxuovy16C6/k5RfPYP8qn53Txkyz34CM8fkhAjPAK4WCx7Lxlq8ffzQpFFflBToXNwxWUXW63Zta6WiIiIGiWXaWEhIiJyJkqnh3KglcSRYxsjJixERERaYJeQiGtdLRERETVKbGEhIiLSgk7n2J0+vEuIiIiI6p1ef3lz5HgXwoSFiIhIAxx0K8OEhYiIiOrEvn37ahybkJAgOjcTFiIiIi00wbuEunTpAp1OB6UUdNWMsbFYZJMGOt/VEhERuYKyhMWRzcmkpaXht99+Q1paGj7++GPExcVhyZIl2LNnD/bs2YMlS5agZcuW+Pjjj8XnZgsLERER1YnY2D+WnbnjjjuwaNEiDBkyxLYvISEB0dHRePrppzFs2DDRuV0mYXnnpxMwetVs9Q2DXn6rmMWqRPFmvVFcRqfm/qJ4d3MrUbwyeoriAcCcVyiKt5jrf92Q3LRMUbzHwEBxGTqjcD2aWozm17nLXyMStVlzSVllz59ObxCXYfWQrVfk1qmPrAAlv26LxSyKL8i6IC7DM9hPFG8tyBXF64tyRPEAEOgh+8xxu5guLqPIo5n4GAnp57m+Fp//tdYEu4SutH//fsTFxZXbHxcXh0OHDonP59xXS0RE1EQpnc52p1DtNueeh6V9+/ZISUmB2fxHwm82m5GSkoL27duLz+cyLSxERETUcF577TXcdtttaN68ue2OoH379kGn0+Gzzz4Tn48JCxERkRaaeJdQjx498Ntvv2HlypU4fPgwAGD06NG4++674e3tLT4fExYiIiItuMDU/N7e3njggQfq5FxMWIiIiKhOfPrppxg8eDDc3d3x6aefVhl7++23i87NhIWIiEgLTbBLaNiwYcjKykJYWFiVty3rdDrxxHFMWIiIiDTQFNcSslqtFf6/Ljjf1RIREbkCnf6PFZtrszlhwlKmpKQEAwYMwNGjR+vsnM57tURERNQoubu7ixZCrAkmLERERFpogmsJXekvf/kL3nzzzTo7n8uMYWkZ7g0P75pNzX80K7+eawMYLcXiY/afkE2trY8PEMWXmmRTogOA0ddLFB8orBMA+MdFiOJDEweI4pVwKngA0AfK6qSzlorLMMZfI4oPDJVNcV6bafMtF4VLEtTCpYAYUXy+WdZPHqSXv/cuWmQfld4RQeIyPIJl0+DrPGXzWLhdkE+bnxfRSRSf4RYlLiMrW/58SEiXTbEK4x3SBAfdXqm0tBRvvfUWvv/+e3Tv3r3c3CsLFiwQnc9lEhYiIiJqOAcOHEC3bt0AAL/++qvd73S1mEOGCQsREZEWmngLy8aNG+v0fExYiIiINFC2+KEjx7sSJixERERU54YPH15h149Op4OHhwdatWqFu+++G23btq3R+Zy7PYmIiKipauJ3Cfn7+2PDhg3YvXs3dDoddDod9uzZgw0bNqC0tBSrV69G586d8b///a9G52MLCxERkRaa+OKHERERuPvuu/Gvf/0Lev3l5MpqtWLq1Knw9fXF+++/jwcffBAzZszADz/8UO35nDs9IyIiokbpzTffxCOPPGJLVgBAr9djypQpeP3116HT6TB58mQcOHCgRudjwkJERKQFjbqEFi9ejBYtWsDDwwM9e/bEjz/+WMcXdllpaSkOHz5cbv/hw4dtCx96eHjU+BZn8dWOHTsWW7ZskR5GREREVyhb/NCRTWr16tWYPn06Zs+ejd27d6Nz584YOHAgzpyp+0kh7733XkyYMAELFy7EDz/8gB9++AELFy7EhAkTMGbMGADA5s2bcc01NZskUzyGJScnB4mJiYiNjcX48eMxduxYNGsmm2GTiIjI5WkwD8uCBQswceJEjB8/HgDw2muv4YsvvsBbb72FmTNn1r4uFVi4cCHCw8Mxf/58nD59GgAQHh6OadOmYcaMGQCApKQkDBo0qEbnE1/tJ598gpMnT+Khhx7C6tWr0aJFCwwePBgfffQRSkpKpKcjIiIiB+Tm5tptxcUVL3dgNpuxa9cuJCYm2vbp9XokJiZi27ZtdV4vg8GAJ598EpmZmcjOzkZ2djYyMzPxxBNPwGC4vDRITEwMfv/990rrfKVa3SUUGhqK6dOnY/r06di9ezeWL1+Oe++9Fz4+PvjLX/6Chx9+GK1bt67NqetNcz9PePl41ii2d3Sg+Py7M3NF8Y9+K1/X46HeLUTxpf4tRfE6JVuTBQAiencRxRsCQ8VlwM0oKyM4UhSvCs6L4gFAnT8pij/bqq+4DI/vnxPFn9j8syg+uGMLUTwABPXqLYq3xncXl5FbLHsdRpzeLYq3hMneFwAQlv2bKP6CQf7N99y+VFG895mLovjAG/uJ4gHA01+2NtB7PxeIy3jsxlhR/JmCEFH8xl/PieIb0uWJ42p/p0/ZsdHR0Xb7Z8+ejTlz5pSLP3fuHCwWC8LDw+32h4eHVzjWpC75+flV+rvBgwdj7969iI+Pr/IcDg26zczMxHfffYfvvvsOBoMBQ4YMwf79+9GhQwcsXLiw2uO3bNmC2267DVFRUdDpdPjkk0/sfj9u3DjbvdtlW02bjoiIiJyZUo5vAJCRkYGcnBzblpycrO2FCSlVswUnxS0sJSUl+PTTT7F8+XJ8++23SEhIwCOPPIK7777blkGtXbsW9913H6ZNm1bluQoKCtC5c2fcd999GDFiRIUxgwYNwvLly20/m0wmaZWJiIiaLD8/vypbMMqEhITAYDDYxpOUOX36NCIiZCvQa0GcsERGRsJqteKuu+7Cjz/+iC5dupSL6devHwICAqo91+DBgzF48OAqY0wmU6N4IImIiCSsSsFaw9aFyo6XMBqN6N69O9avX49hw4ZdPofVivXr12Py5Mm1rkdDEScsCxcuxB133AEPD49KYwICApCWluZQxcps2rQJYWFhCAwMRP/+/fHss88iODi4Ts5NRESkFfX/myPHS02fPh1jx47Ftddeix49euDll19GQUGB7a4hZyZOWO699976qEeFBg0ahBEjRiAuLg6pqal44oknMHjwYGzbts02wvhqxcXFdqONc3Nlg2GJiIiaqtGjR+Ps2bOYNWsWsrKy0KVLF3z99dflBuI2pJpOHOfUawndeeedtv936tQJCQkJaNmyJTZt2oQBAwZUeExKSgrmzp3bUFUkIiKqFau6vDlyfG1MnjzZqbqAajrotlFNzR8fH4+QkBAcO3as0pjk5GS70dIZGRkNWEMiIqKaUUo5vDmzefPmYcOGDeX2FxQUYN68ebaf8/Lyqr2lGWhkCcuJEydw/vx5REZWPs+GyWSyjZiu6chpIiIiqltz5szB4MGDsWDBArv9+fn5teoJ0bRLKD8/3661JC0tDXv37kVQUBCCgoIwd+5cjBw5EhEREUhNTcXjjz+OVq1aYeDAgRrWmoiIyHFadQk1pHfeeQeTJk3C/v37sWzZMhiNsolAr6RpC8vOnTvRtWtXdO3aFcDl0ctdu3bFrFmzYDAYsG/fPtx+++1o06YNJkyYgO7du+O///0v52IhIqImQTmwNQb9+vXDjh07sGPHDvTt29ehRRY1bWHp27dvlX1w33zzTZ2VtfnoORi9imoUO/vmVuLztxROOX9La/mt2akXa1b/Mi0DvEXxhuwTongAsLq7i+ILjx4Rl2EK8hfFG9pcK4rXWcyieAAo/u2gKN5r3w5xGSe3HRDFn9h+ShSfl5kvigeAoOtkj+3vJbLXIADEnfyvKN5SJJsOXp2ofAxcZbK2/yiKN+cVisvISZN9kJtzZdft3Uz2egIAj/AWovjkLkHiMqwW2Rp0N8bIPg+ceWr+pt7CUnb3T8uWLbF9+3aMGjUK3bt3x2uvvVar8zWqMSxERETUOFzZIOHn54cvv/wSw4cPt01aJ+XUtzUTERE1VY7e6ePsdwktX74c/v5/tIjp9XosWrQIXbt2xZYtW8TnY8JCRESkAev/b44c78zGjh1b4f7x48fXamZddgkRERFRvVi/fj1uvfVWtGzZEi1btsStt96K77//vlbnYsJCRESkAaUc35zZkiVLMGjQIPj6+mLq1KmYOnUq/Pz8MGTIECxevFh8PnYJERERaaCp3yX0/PPPY+HChXbLAPztb3/D9ddfj+effx6TJk0SnY8tLERERFTnsrOzMWjQoHL7k5KSkJOTIz4fExYiIiINNPW1hG6//XasXbu23P5169bh1ltvFZ+PXUJEREQaaIp3CS1atMj2/w4dOuC5557Dpk2b0KtXLwDA9u3b8b///Q+PPvqo+NxMWIiIiKhOLFy40O7nwMBAHDp0CIcOHbLtCwgIwFtvvYWnnnpKdG4mLERERBpQcOxOH2fsEEpLS6u3c7tMwnJtbCA8fXxrFGs06MTn97BcEsV7Gw3iMkyhXqL4GV/L1kyx1GLI+cIbhonivfZvEpdhaNdDFF8a0FwU7/7bdlE8AMBqEYWf3furuIiC07K1YkovlYriDbV4DVqLZGvktMzeJy9DLxtap4pla2yd+5/8+c49nimK17vLP1qN3rJVbL0jZeuRuce0EcUDgK5U9tgacrPEZUidLpWtJeTMrErB6kDG4six9WX69Ok1itPpdPjnP/8pOrfLJCxERETOxNFVl50vXQH27Nlj9/Pu3btRWlqKtm3bAgB+/fVXGAwGdO/eXXxuJixERERUJzZu3Gj7/4IFC+Dr64u3334bgYGBAICLFy9i/PjxuPHGG8Xn5m3NREREGiibOM6RzZn985//REpKii1ZAS4Pwn322WfF3UEAW1iIiIi04ej0+k6esOTm5uLs2bPl9p89exZ5eXni87GFhYiIiOrc8OHDMX78eKxZswYnTpzAiRMn8PHHH2PChAkYMWKE+HxsYSEiItKAFQpWB5pJHDm2Ibz22mv4+9//jrvvvhslJSUAADc3N0yYMAEvvvii+HxMWIiIiDTg6IrLTnhXsx0vLy8sWbIEL774IlJTUwEALVu2hLe3d63Ox4SFiIiI6o23tzcSEhIcPg8TFiIiIg04eqePs98lVNeYsBAREWmgqXcJ1TXeJUREREROz2VaWPJLSmEx12ytlUul8kW7Pa1m2QGypWgAAB4m2VojveKDRPEjI4TXAMDqEyKK1yf0E5eR7xUmivfQydaC0nnIB4C5JY0XxZeuf1hchleIbO2ozvfJ1lwKaBMtigcAU/vrRPGlWcfFZRj8ZWvkuIVEiOKLs+XzP+gMsu92bp6y9yoA+DQLFcVLnz9DVCtRPABYPWq2/ppDlOzzdtnW3+upIg2vqd8lVNdcJmEhIiJyJuwSkmHCQkREpIGmuFpzfeIYFiIiInJ6bGEhIiLSgMV6eXPkeFfChIWIiEgD7BKSYZcQEREROT22sBAREWnAqhQsbGGpMSYsREREGrg8Nb8jCUsdVqYRYJcQEREROT22sBAREWmAdwnJuEzC0q9FEHx8/WoUm5Ejn6I+MFg4vbulRFyGRdj+dzq/WBQ/7Yd8UTwAjOjsI4o/ck5cBIa3l123h0E2Nb/FTza1OwD8XiJ7vgNrMQ1+8Oj7xcdI6MyXxMeU+keJ4rNDrhGX4WsyiOLTc2Xv1+C4L0XxAOAZGig+RsoQHFmv578Q3E58TGGJ7C+iyU323gOAlA2/ieJ9Per3z1axteH+LPIuIRl2CREREZHTc5kWFiIiImdicfAuIUeObYyYsBAREWnACsfu9HGxISxMWIiIiLRgsSrx2MSrj3clHMNCRERETo8JCxERkQbU/98lVNtN1eMYlueeew69e/eGl5cXAgIC6q0cCSYsREREGrAox7f6Yjabcccdd+Chhx6qv0KEOIaFiIiI7MydOxcAsGLFCm0rcgUmLERERBqoq4njcnNz7fabTCaYTCaH6uaM2CVERESkgbK7hBzZACA6Ohr+/v62LSUlReMrqx9MWIiIiBqxjIwM5OTk2Lbk5OQK42bOnAmdTlfldvjw4Qaufc25TJfQ0q2/w+glW/dGYma/eFG8r8lTXEZ+sUUU7+EmW5OlNtb8nCmKT2ofJi5Dup5JcPFZUXyRT7goHgDijmwWxef8Vf6Np3DtfFG8e4jsOgzXDhbFA4Dy9BfF78qQr0/1xcHTovh5Sa1E8RfumiOKBwDf/WtF8fprbhSXYfUJEcUbcrNk8fJlflAinOdjS2q2uAyTm+x7c0ywlyg+7WyBKL4h5zapqy4hPz8/+PlVv1beo48+inHjxlUZEx8v+1vWkDRtYdmyZQtuu+02REVFQafT4ZNPPrH7vVIKs2bNQmRkJDw9PZGYmIijR49qU1kiIqI61NB3CYWGhqJdu3ZVbkajsX4utg5omrAUFBSgc+fOWLx4cYW/nz9/PhYtWoTXXnsNO3bsgLe3NwYOHIiioqIGrikREZHrSE9Px969e5Geng6LxYK9e/di7969yM+Xt5rWFU27hAYPHozBgytullZK4eWXX8ZTTz2FoUOHAgDeeecdhIeH45NPPsGdd97ZkFUlIiKqU3XVJVQfZs2ahbffftv2c9euXQEAGzduRN++feut3Ko47aDbtLQ0ZGVlITEx0bbP398fPXv2xLZt2yo9rri4GLm5uXYbERGRs7FalcNbfVmxYgXU/8+me+WmVbICOHHCkpV1eUBZeLj9QMLw8HDb7yqSkpJid3tXdHR0vdaTiIioNqwOjl9xsbUPnTdhqa3k5GS727syMjK0rhIRERE5yGlva46IiAAAnD59GpGRkbb9p0+fRpcuXSo9rqnO8EdERE2LM49hcUZO28ISFxeHiIgIrF+/3rYvNzcXO3bsQK9evTSsGRERkeMsSjm8uRJNW1jy8/Nx7Ngx289paWnYu3cvgoKCEBMTg0ceeQTPPvssWrdujbi4ODz99NOIiorCsGHDtKs0ERERNThNE5adO3eiX79+tp+nT58OABg7dixWrFiBxx9/HAUFBXjggQeQnZ2NG264AV9//TU8PDy0qjIREVGdcPROn/q8S8gZaZqw9O3bF6qKJi2dTod58+Zh3rx5DVir2nlh42+i+AFtQ8VluBtkPXgZFwrFZdS3k7nySf+83GVLDDQPkSW0JnOeKB4ArEWy6b49v3xFXIZeOtV+tyRRvDLJl6rQXcoRxQ8Ir81HjHypBNHZveV1covrKIq3KtlyEoD8sbX4RYjiZ319rPogB43vESM+prBEtuTI/hOyx0nKbJbVxxEWyGervfp4V+K0Y1iIiIiIyjjtXUJERERNGe8SkmHCQkREpAFH7/RxtbuE2CVERERETo8tLERERBqwWhUsvEuoxpiwEBERacDiYMLiyLGNERMWIiIiDTBhkeEYFiIiInJ6bGEhIiLSgMXqWCuJRT4/YaPGhIWIiEgD7BKSYZcQEREROT22sNSRnnFBonjpukAA8MsZ2Zo32YUl4jLqW9dIP/ExbYJkawPpDn8vii9J/1UUDwDGeNnaMr9+sElcRumlUlF87M2y6wi+bbQoHgByIxJE8T7ZaeIyBoQFiuLTi2Qrqry8RV4n6UflLdfI12kyuQk/E7Jl63J1iw2QnR9AoIe7KL69cB0vwPnWEmpIbGGRYcJCRESkAc7DIsMuISIiInJ6bGEhIiLSgEU52CXkYmsJMWEhIiLSAMewyLBLiIiIiJweW1iIiIg0wBYWGSYsREREGii1KhgcSDpKmbAQERFRfWMLiwzHsBAREZHTYwsLERGRBjhxnAwTljqSfrFQFG9yk0/d3cxPNu21uVS2lGemcKrv2ogPMImP8YZZFG8IihDFm4/tE8UDgC5QVsahLRniMtKFSyv0F354+bZvL4oHAI9mnUXxOotseQEA0F+STb0eHiibyr8htAvxEh/z6/lLovh9Wbmi+N/PyT6jAKBTc39R/IeHzorLyMyt/88dZ2VRyqG5VFxtHhZ2CREREZHTYwsLERGRBjjoVoYJCxERkQaYsMiwS4iIiIicHltYiIiINMAWFhkmLERERBqwKCssVtndnFcf70rYJUREREROjy0sREREGuDEcTJMWIiIiDRgsSroOYalxpiwEBERaaDUCugcWq25DivTCHAMCxERETk9trDUEek6PL1i5eufeLjJ8ksfk+zpzRGuXQMAhWaLKD6/RP6VwF+4/JDV5C2Kd4uMkxUAQGeRrW8U1ylUXIZl3xlRvGegbK2p0vNZongAgPDLoMEkXzNLZ5WtP1QgfE39tXesKB4A3PU6UXzqRfn6OJ8dqMXzIeBlNIiPMcguG0W1+MovXfOsKWGXkAwTFiIiIg0wYZFhlxARERE5PbawEBERaYAtLDJsYSEiItJA2Twstd3qax6W48ePY8KECYiLi4OnpydatmyJ2bNnw2yWjd2ra2xhISIiIpvDhw/DarVi2bJlaNWqFQ4cOICJEyeioKAAL730kmb1YsJCRESkAYtVOTQPS311CQ0aNAiDBg2y/RwfH48jR45g6dKlTFiIiIhcjVIKyoGkQ6nLx+bm5trtN5lMMJmE80FUIycnB0FBQXV6TimOYSEiImrEoqOj4e/vb9tSUlLq9PzHjh3Dq6++ir/+9a91el4ptrAQERFpwOrgwNmyYzMyMuDn52fbX1nrysyZM/GPf/yjynP+8ssvaNeune3nkydPYtCgQbjjjjswceLEWte1LjBhISIi0oBSytatU9vjAcDPz88uYanMo48+inHjxlUZEx8fb/v/qVOn0K9fP/Tu3Ruvv/56retZV5iw1BGDcOpud728N87HWL9PV1yobEp7ADh4Mrf6oCss2JwmLkPqlWtlHwC6hH7yQrJPicKvW/ikuIjuuRdE8da8i6J48+lMUTwAeJ/9VRS/WxcjLsPDXfbe8BZOzd/C3yiKB4ASi+w1VeKE82MM6xQhPiYzr1gU/3v2JXEZPWNky5Ss//WsKF66fEhDUlYHx7AIjw0NDUVoaM2WCTl58iT69euH7t27Y/ny5dDX4m9WXWPCQkRERDYnT55E3759ERsbi5deeglnz/6RJEZEyBPfuqJ9ylSFOXPmQKfT2W1X9q0RERE1VmVjWBzZ6sN3332HY8eOYf369WjevDkiIyNtm5acOmEBgGuuuQaZmZm27YcfftC6SkRERA5TVse3+jBu3Djb+JqrNy05fZeQm5ubpk1QREREpD2nb2E5evQooqKiEB8fj3vuuQfp6elVxhcXFyM3N9duIyIicjaVtWJINlfi1AlLz549sWLFCnz99ddYunQp0tLScOONNyIvL6/SY1JSUuwm0ImOjm7AGhMREdWMs45hcVZOnbAMHjwYd9xxBxISEjBw4EB8+eWXyM7OxgcffFDpMcnJycjJybFtGRkZDVhjIiIiqg9OP4blSgEBAWjTpg2OHTtWaUx9rKFARERU1xp6HpbGzqlbWK6Wn5+P1NRUzW+tIiIictj/Jyy13cCExXn8/e9/x+bNm3H8+HFs3boVw4cPh8FgwF133aV11YiIiKgBOXWX0IkTJ3DXXXfh/PnzCA0NxQ033IDt27fXeGphIiIiZ2VVCjoH7vSxuthdQk6dsLz//vtaV6HGLMKmucIS+foW/h6yp0u6XpHJzakb3Gps6k7Zuk4LbpLPvlQaJFsjp3TXBnEZlkuFonj3kHBRvHfiHaJ4AMgJaCmKjyqVf6CmbEgVxT+VKKuTu+zlAQCwCtcKM+jkhYzoLOvqXvOzbC2oUC/5GkqRPrJjBraUrQsEAO6lsvWHdqTLPgedei0h5eAYFiYsREREVN846FamaXylJiIioiaNLSxEREQasFoBnQOtJNZ6WkvIWTFhISIi0oCj0+u72hgWdgkRERGR02MLCxERkQaU9fLmyPGuhAkLERGRBqxW5eAYFnYJERERETkVtrAQERFpgPOwyDBhISIi0gATFhl2CREREZHTYwuLRnKKS8XH+JoMoviiUtkQ8rxa1MkZLRgQIYp3O3NUXIYy+YjiDdf0EJcBq2wNFJ2Xvyhe1WLWqawC2WvEX/iaBYBBHWRrIhWWyK5DGg8AZovsm2xWfrG4DGfkJ3z+jOY8cRlmo68oPr+oaXxOAVz8UIoJCxERkQbYJSTDhIWIiEgDXK1ZhmNYiIiIyOmxhYWIiEgDyqocmvyNXUJERERU77j4oQy7hIiIiMjpsYWFiIhIA7xLSIYJCxERkQasVgVw8cMaY5cQEREROT22sBAREWlAWS1Qwhmtrz7elTBh0ciWo+fEx9zQKlgUny+cav/3c4Wi+Np4ckBL8THPrU8VxRuyjsgK0MsbGou2fiaKv3AwTVyGwcMoig/s0lEUn33DOFE8ALTNOSY74JL8A7VvbHtR/Myv5EsrSHWLDRDFdwqTTTcPAH4esmnwIwM8RPG/5xSJ4gEgwMNbFH/JXX7dWfklovhrovxE8T8dvyiKb0hMWGTYJUREREROjy0sREREGlBWq4MtLPKFPBszJixEREQaUBYLlMWBhMWBYxsjJixEREQaUMrBMSzKtRIWjmEhIiIip8cWFiIiIg3wLiEZJixEREQaYMIiwy4hIiIicnpsYSEiItIAW1hkmLAQERFpgPOwyLBLiIiIiJweW1gakR+Fa2K46XWieHNp/WfrIeaz4mPmJrUSxVsOfCWKVwW5ongAyP41QxR/5ud0cRkmP9laQr4x4aL4YGuOKB4A1IVMUbzOU7YWDQB4XPhNfIzEY33jxcdE5xwWxZdmbBeXYS3IE8XPbHutKH7qTvlaQj7XNhfFl1iUuIyjFwpE8ScuXhKX4aysVgvgQAuLlV1CREREVN84hkWGXUJERERk5/bbb0dMTAw8PDwQGRmJe++9F6dOndK0TkxYiIiINFDWwuLIVl/69euHDz74AEeOHMHHH3+M1NRU/PnPf6638mqCXUJERERasFig9A4kHfW4+OG0adNs/4+NjcXMmTMxbNgwlJSUwN3dvd7KrQoTFiIiIg0o5dig24Za/PDChQtYuXIlevfurVmyArBLiIiIqFHLzc2124qLi+vkvDNmzIC3tzeCg4ORnp6OdevW1cl5a4sJCxERkQbKJo6r/XZ5Koro6Gj4+/vbtpSUlArLmzlzJnQ6XZXb4cN/3ML/2GOPYc+ePfj2229hMBgwZswYKCW/db2usEuIiIhIA8rBeVjKBt1mZGTAz8/Ptt9kMlUY/+ijj2LcuHFVnjM+/o95ikJCQhASEoI2bdqgffv2iI6Oxvbt29GrV69a19kRTFiIiIgaMT8/P7uEpTKhoaEIDQ2tVRnW/2/NqavuptpgwkJERKQBZbUCDqwHVF9rCe3YsQM//fQTbrjhBgQGBiI1NRVPP/00WrZsqVnrCsCEpUkzusmGKBWa5U2Tj/eTTXNe4il/yQWcOyorIy9bFO8WHi2KB4DCrPOieEuJ/LH1CJRNa+8eIbsOnaVUFA8A1vxsUbwhIExcBnSy1+3orhGi+Oa5v4riAcDiHSyK1xnlE2y5eVX/DflKpWkHRPHDEpJE8QBwrrBEFF9ikf8B9XI3iOLP5Gr3Db+u1VWXUF3z8vLCmjVrMHv2bBQUFCAyMhKDBg3CU089VWl3U0NgwkJEREQ2nTp1woYNG7SuRjmN4i6hxYsXo0WLFvDw8EDPnj3x448/al0lIiIihzjzTLfOyOkTltWrV2P69OmYPXs2du/ejc6dO2PgwIE4c+aM1lUjIiKqNavV4vDmSpw+YVmwYAEmTpyI8ePHo0OHDnjttdfg5eWFt956S+uqERERUQNx6jEsZrMZu3btQnJysm2fXq9HYmIitm3bVuExxcXFdrdd5ebm1ns9iYiIpJTFCugcGHRbi0HOjZlTt7CcO3cOFosF4eHhdvvDw8ORlZVV4TEpKSl2M/5FR8vvACEiIqpvSjk4hqWB1hJyFk6dsNRGcnIycnJybFtGRobWVSIiIiqHg25lnLpLKCQkBAaDAadPn7bbf/r0aUREVDz3gslk0vQ+cSIiIqp7Tt3CYjQa0b17d6xfv962z2q1Yv369ZrOtkdEROQotrDIOHULCwBMnz4dY8eOxbXXXosePXrg5ZdfRkFBAcaPH6911YiIiGpNWS2ODbplwuJcRo8ejbNnz2LWrFnIyspCly5d8PXXX5cbiFuZsqWwzZcK6rOaDUIJp9o3lMqmvDYLp+EGgDzhXVjGEvlLzpSXL4ovKbgkinfLl7828s2yxyq/VD4NvruwjFzhdRvy8kTxAKAKCoVlyJ47AFBG2YdwQbGnKD5XJ6+T1SJ83ebLHicA0LnJnm+L8LkoyJc/38WlsrtQajM1f7HwGHOh/PkTnf///1aU/e2oV5YSOFSKRf6Z3ZjpVIM8K9o5ceIE7xQiIiKRjIwMNG/evF7OXVRUhLi4uErvdpWIiIhAWloaPDw86qBmzq3JJyxWqxWnTp2Cr68vdDqdbX9ubi6io6ORkZFRo2W5mwpeN6/bFfC6ed21pZRCXl4eoqKioNfX3zDPoqIimM1mh89jNBpdIlkBGkGXkKP0en2VWbKfn59LvbHL8LpdC6/btfC6HePv718Htamah4eHyyQadcWp7xIiIiIiApiwEBERUSPgsgmLyWTC7NmzXW6SOV43r9sV8Lp53dT0NPlBt0RERNT4uWwLCxERETUeTFiIiIjI6TFhISIiIqfHhIWIiIicnssmLIsXL0aLFi3g4eGBnj174scff9S6SvVqzpw50Ol0dlu7du20rlad27JlC2677TZERUVBp9Phk08+sfu9UgqzZs1CZGQkPD09kZiYiKNHj2pT2TpU3XWPGzeu3PM/aNAgbSpbR1JSUnDdddfB19cXYWFhGDZsGI4cOWIXU1RUhEmTJiE4OBg+Pj4YOXIkTp8+rVGN60ZNrrtv377lnu8HH3xQoxrXjaVLlyIhIcE2OVyvXr3w1Vdf2X7fFJ9rsueSCcvq1asxffp0zJ49G7t370bnzp0xcOBAnDlzRuuq1atrrrkGmZmZtu2HH37Qukp1rqCgAJ07d8bixYsr/P38+fOxaNEivPbaa9ixYwe8vb0xcOBAFBUVNXBN61Z11w0AgwYNsnv+33vvvQasYd3bvHkzJk2ahO3bt+O7775DSUkJkpKSUFDwx2KW06ZNw2effYYPP/wQmzdvxqlTpzBixAgNa+24mlw3AEycONHu+Z4/f75GNa4bzZs3xwsvvIBdu3Zh586d6N+/P4YOHYqDBw8CaJrPNV1FuaAePXqoSZMm2X62WCwqKipKpaSkaFir+jV79mzVuXNnravRoACotWvX2n62Wq0qIiJCvfjii7Z92dnZymQyqffee0+DGtaPq69bKaXGjh2rhg4dqkl9GsqZM2cUALV582al1OXn1t3dXX344Ye2mF9++UUBUNu2bdOqmnXu6utWSqmbbrpJTZ06VbtKNZDAwED1xhtvuMxz7epcroXFbDZj165dSExMtO3T6/VITEzEtm3bNKxZ/Tt69CiioqIQHx+Pe+65B+np6VpXqUGlpaUhKyvL7rn39/dHz549m/xzDwCbNm1CWFgY2rZti4ceegjnz5/Xukp1KicnBwAQFBQEANi1axdKSkrsnu927dohJiamST3fV193mZUrVyIkJAQdO3ZEcnIyCgsLtahevbBYLHj//fdRUFCAXr16ucxz7eqa/OKHVzt37hwsFgvCw8Pt9oeHh+Pw4cMa1ar+9ezZEytWrEDbtm2RmZmJuXPn4sYbb8SBAwfg6+urdfUaRNlS7hU993WxzLszGzRoEEaMGIG4uDikpqbiiSeewODBg7Ft2zYYDAatq+cwq9WKRx55BNdffz06duwI4PLzbTQaERAQYBfblJ7viq4bAO6++27ExsYiKioK+/btw4wZM3DkyBGsWbNGw9o6bv/+/ejVqxeKiorg4+ODtWvXokOHDti7d2+Tf67JBRMWVzV48GDb/xMSEtCzZ0/Exsbigw8+wIQJEzSsGTWEO++80/b/Tp06ISEhAS1btsSmTZswYMAADWtWNyZNmoQDBw40yXFZVansuh944AHb/zt16oTIyEgMGDAAqampaNmyZUNXs860bdsWe/fuRU5ODj766COMHTsWmzdv1rpa1EBcrksoJCQEBoOh3Ojx06dPIyIiQqNaNbyAgAC0adMGx44d07oqDabs+XX15x4A4uPjERIS0iSe/8mTJ+Pzzz/Hxo0b0bx5c9v+iIgImM1mZGdn28U3lee7suuuSM+ePQGg0T/fRqMRrVq1Qvfu3ZGSkoLOnTvjlVdeafLPNV3mcgmL0WhE9+7dsX79ets+q9WK9evXo1evXhrWrGHl5+cjNTUVkZGRWlelwcTFxSEiIsLuuc/NzcWOHTtc6rkHgBMnTuD8+fON+vlXSmHy5MlYu3YtNmzYgLi4OLvfd+/eHe7u7nbP95EjR5Cent6on+/qrrsie/fuBYBG/XxXxGq1ori4uMk+13QVrUf9auH9999XJpNJrVixQh06dEg98MADKiAgQGVlZWldtXrz6KOPqk2bNqm0tDT1v//9TyUmJqqQkBB15swZratWp/Ly8tSePXvUnj17FAC1YMECtWfPHvX7778rpZR64YUXVEBAgFq3bp3at2+fGjp0qIqLi1OXLl3SuOaOqeq68/Ly1N///ne1bds2lZaWpr7//nvVrVs31bp1a1VUVKR11WvtoYceUv7+/mrTpk0qMzPTthUWFtpiHnzwQRUTE6M2bNigdu7cqXr16qV69eqlYa0dV911Hzt2TM2bN0/t3LlTpaWlqXXr1qn4+HjVp08fjWvumJkzZ6rNmzertLQ0tW/fPjVz5kyl0+nUt99+q5Rqms812XPJhEUppV599VUVExOjjEaj6tGjh9q+fbvWVapXo0ePVpGRkcpoNKpmzZqp0aNHq2PHjmldrTq3ceNGBaDcNnbsWKXU5Vubn376aRUeHq5MJpMaMGCAOnLkiLaVrgNVXXdhYaFKSkpSoaGhyt3dXcXGxqqJEyc2+gS9ousFoJYvX26LuXTpknr44YdVYGCg8vLyUsOHD1eZmZnaVboOVHfd6enpqk+fPiooKEiZTCbVqlUr9dhjj6mcnBxtK+6g++67T8XGxiqj0ahCQ0PVgAEDbMmKUk3zuSZ7OqWUarj2HCIiIiI5lxvDQkRERI0PExYiIiJyekxYiIiIyOkxYSEiIiKnx4SFiIiInB4TFiIiInJ6TFiIiIjI6TFhISIiIqfHhIWIiIicHhMWIiIicnpMWIhcyNmzZxEREYHnn3/etm/r1q0wGo12K90SETkbriVE5GK+/PJLDBs2DFu3bkXbtm3RpUsXDB06FAsWLNC6akRElWLCQuSCJk2ahO+//x7XXnst9u/fj59++gkmk0nrahERVYoJC5ELunTpEjp27IiMjAzs2rULnTp10rpKRERV4hgWIheUmpqKU6dOwWq14vjx41pXh4ioWmxhIXIxZrMZPXr0QJcuXdC2bVu8/PLL2L9/P8LCwrSuGhFRpZiwELmYxx57DB999BF+/vln+Pj44KabboK/vz8+//xzratGRFQpdgkRuZBNmzbh5Zdfxrvvvgs/Pz/o9Xq8++67+O9//4ulS5dqXT0iokqxhYWIiIicHltYiIiIyOkxYSEiIiKnx4SFiIiInB4TFiIiInJ6TFiIiIjI6TFhISIiIqfHhIWIiIicHhMWIiIicnpMWIiIiMjpMWEhIiIip8eEhYiIiJweExYiIiJyev8HE9NEsnSiddAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats.isel(feature=1,starttime=100).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "PJh6t50olRlG",
        "outputId": "16819f97-ed42-41ea-fcc3-9d3fb5de9706"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x79c477338110>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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Bb7/9NgoKCuDl5YWVK1fCbrdj+PDhTvcjPDwcjRs3xvr16/HMM8+UeNxWrVrhhx9+KFcdrx0cW5yHH34YX3zxBe6++2688sor8PPzw9y5c7F9+3YAEL8fNm3ahGnTpmH48OHo3r27Y33h6y8jIwO7du1CVFQUAKB79+5o1KgRZs6c6XifdOzYER07dnRse9ddd+Evf/kLYmNjkZCQgG+//RYARNcxOzsb2dnZReqbmZnptK3JZHJ8jhSeu9VqLbJd4SD+K1euwGq1Ii8vD5MmTcJDDz2EFi1aiK5ZaXr16oW//e1vmD59OpYvXw5vb2+8++67on0UtjqtWbMGY8aMEbcoUxXQO3K61tKlS5W3t7cyGo2qffv2aurUqerIkSNOZUobJLto0SLVsmVLZTabnb5FxcTEOMoUtpBMnz69yPYV2eLi7e1dpGyXLl1Us2bNiqyPjo5WAwYMcPzs4+NT6rfCN998s9jzrwypqamO1pDC5f7771dDhgxRANTly5fLva+ZM2cqAOqll14q8rv+/fsrAGrx4sVO648fP64AqGnTppW67+zsbGU0Gp1ahyTXMTo6ulzfyK9tGStvi0t+fr66+eab1ciRI53KwIUWl23btpVZ18Jv24UtLlu3bnXaR2FLxIYNG5RSSiUmJioARd5zSikVEhLicovLtS2NpdVr6tSpTq15Dz/8cKnnGRsbW+7rVhHeeecd5efn5zh+o0aNHK/tWbNmlXs/Bw8eVKGhoap169ZFWnAKr2+3bt2KbNetWzenz7SS3HPPPcpisTjGikmuY+E9KGu59jNR0uLy6quvqpCQEHXx4kVHmYoadJ+RkaHCw8MVALVkyRLx9tnZ2apTp04KgKpZs6a6++671dKlS6t0fCGVrlq1uAwfPhy33347Vq1ahe+//x6vv/46XnvtNaxcuRJ9+/YtdduPPvoIo0ePxqBBg/Dkk08iLCwMJpMJiYmJOHLkSJHyFTHGoDSFrT/lXa+UcvzfbrfjjjvuwFNPPVVs2SZNmpR67LS0tHJ987NYLEXGjlwvKCgIa9asQXJyMo4dO4bo6GhER0ejY8eOqFWrVrn7xBctWoQpU6bgoYcewnPPPVfk94UtbLVr13ZaHxYWBuBqi0hpfHx8UKNGDVy6dMmxTnIdP/74Y6drVvj6u/6beoMGDRz/j4iIgM1mw7lz5xz1BK7OvXHx4kXHOf373//GoUOH8O677xaZ9yQjIwPHjh1DWFiYoyWnPOx2OwDgiSeeQO/evYstc/2jsOV57VUmre8Ju90Og8GAb775ptiyZY03y8vLc3pdlKZWrVol1qfQhAkTMGbMGOzZswcWiwWtW7fGggULAJT93ix04sQJ9OrVC0FBQfj6668REBDg9PuS3g/A1fdE4fiq0kRFRSEvLw9ZWVkIDAwUXceRI0fitttuc/r9HXfcgSeffBK9evVyrLv2czQiIgIAip2T5syZMwgNDYXVakVaWhpefvllPPLII0hPT0d6ejqAq605SikcO3YMvr6+Tu8piV27duHcuXMAgL1792LEiBGi7X18fLBp0yasX78eX331Fb799lssXboU3bt3x/fff1/m64MqX7UKXICrL/5HHnkEjzzyCM6dO4e2bdvilVdecQQu1zZhX2v58uVo0KABVq5c6VRm6tSp5T52Sfsu63cVrWHDhsjMzHR0jUg9/vjjReZIKU6XLl2wYcOGcu2zXr16qFevHgAgNTUVO3bswNChQ8u17Zo1a/Dggw9iyJAhmDNnTrFl2rVrh/fffx+nTp1yWn/69GkAKLNbLyMjAxcuXHAqJ7mOnTp1cvq5cIKv0rYtHPC9fft29OvXz7F++/btsNvtjt8nJycjPz+/yDGAq0HNv//9b6xatQqDBg0qs56FCgMos9ms+XVyvejoaADAn3/+6RSgnT9/vkjgWNXvB6UUYmJiyh0YXGvz5s3FDmYtTlJSUpF5aYrj5+eHuLg4x89r166Fj49Psff4ehcvXkSvXr2Qm5uLdevWOf7gX6tly5Ywm81F3g/A1fdEebq5jx49Cm9vb0dAIrmODRo0cHoNFGrRokWJr7c6deqgVq1ajm6za137gMTly5eRmZmJmTNnYubMmUXKxsTEYODAgVi9enUZZ1hUVlYWxowZgxYtWqBjx46YOXMmBg8eXGR+m7IYjUb06NHDMX/VjBkz8Oyzz2L9+vUV9n4j7apN4GKz2ZCZmen0lEtYWBgiIyMdT7cAVz8wintCoDAKVko5PlS3bduGLVu2OP7glqVwAqTU1NRif1feJxNcNXz4cLz44ov47rvvinybTk1Nhb+/v9P4kOtV1BiXkiQkJKCgoACTJk1yWv/777/D19fX6Xpv2rQJ99xzDzp37oyPP/64xP7igQMH4vHHH8fChQsxevRoR7nCSebuuOMOAEBOTg7y8/OLfEN96aWXoJRCnz59HOtcvY5l6d69O0JDQzFv3jynwGXevHnw9fVF//79AVwdg3P9U20AMHjwYPTr1w/jxo1Dhw4dRMcOCwtD165d8e677+LRRx8t8sfv/Pnz4jFcPXv2hNlsxjvvvINevXo53kfFPX1V2nulog0ZMgQJCQmYNm0aPvroI6egSSmFS5cuoUaNGiVuX5FjXIqzefNmrFy5Eg8//LDT59eZM2eQlpaGhg0bOsb2ZGVloV+/fjh16hTWr19f4hNIAQEB6NevH7788kv8/vvvjlmhDx48iM2bN+Nvf/ubo2xx9/q3337D559/jr59+zreS65ex/IYOnQoFi9ejBMnTjjG5axbtw5//PGH4/MiLCwMq1atKrLt22+/jS1btuCTTz4pNpgrjylTpiA5ORlbt25F06ZNsW7dOowaNQq7du0qduxNcS5dulSkJbrw/Xvt36LiPu+Sk5ORnZ3tNIv3hQsXcOHCBdSrV0/UqkolqzaBS0ZGBurWrYu//OUvaNWqFfz9/bF27Vr8+uuveOONNxzl2rVrh6VLl2Ly5Mlo3749/P39ceedd2LAgAFYuXIlBg8ejP79+yMpKQnz589HixYtHAPdytKwYUMEBwdj/vz5CAgIgJ+fHzp06ICYmJgSj1sZnnzySXz++ecYMGAARo8ejXbt2iErKwt79+7F8uXLcezYMadHJ6/XokWLChvw9uqrr2Lfvn3o0KEDvLy8sHr1anz//fd4+eWXi3yLad68uVMrzvHjx3HXXXfBYDDgL3/5C5YtW+ZUPjY2FrGxsQCu/sF49tln8cILL6BPnz4YNGgQfvvtN7z//vsYMWKE41gpKSlo06YNRowY4fhw+O677/D111+jT58+GDhwoGP/rl7Hsvj4+OCll15CfHw8hg0bht69e+M///kPPvroI7zyyiuOD79mzZqVmI4gJiamSEtL165dsXHjxjK7cObMmYPbbrsNLVu2xLhx49CgQQOcPXsWW7ZswcmTJ/Hbb7+JzqdWrVp44oknkJiYiAEDBqBfv37YtWsXvvnmmyLXqXXr1jCZTHjttdeQlpYGq9WK7t27a27eL03Dhg3x8ssvIyEhAceOHcOgQYMQEBCApKQkrFq1CuPHj8cTTzxR4vYhISEV9i35+PHjGD58OO666y6Eh4dj//79mD9/PmJjY4sMYE5ISMDixYudWnHuu+8+/PLLL3jggQdw8OBBHDx40FHe39/f6bUwY8YMrFu3Dt27d8djjz0G4Oof99DQUKfByHfffTd8fHzQsWNHhIWF4cCBA3jvvffg6+uLV1991VHO1etYHs888wyWLVuGbt264fHHH0dmZiZef/11tGzZEmPGjAFwdYB7ca2Lq1evxi+//FLkd4sWLcKYMWMcX2pK8uOPP2Lu3LmYOnUq2rZtC+DqtBddu3bF888/X2zrTnGmT5+OTZs2oX///oiOjsa5c+cwd+5c1K1b16n77PrPO+BqF9v1791//vOfmDZtGtavX88cXhVFn6E1ReXm5qonn3xStWrVSgUEBCg/Pz/VqlUrNXfuXKdymZmZ6t5771XBwcFOg8PsdruaMWOGio6OVlarVbVp00Z9+eWXatSoUU4DyK6dgK44a9asUS1atFBeXl5Og25LOm5pE9Bdr0uXLuqmm24qsj46OrrI7KsZGRkqISFBNWrUSFksFlWzZk3VsWNH9Y9//MPpkdLK9uWXX6pbb71VBQQEKF9fX/V///d/6rPPPiu2LK57xLqsx1CvHeiq1NV7+M4776gmTZoos9msoqKi1HPPPed0vpcvX1b333+/atSokfL19VVWq1XddNNNasaMGcVeF63XsTyPQxd67733VNOmTR0T3M2aNcvpceKSoITBue3atVPh4eHlOvaRI0fUyJEjVXh4uDKbzapOnTpqwIABavny5UXO5ddff3XatvD+XDvA1mazqWnTpqmIiIgSJ6Ar9P7776sGDRook8nktJ+SBudeP7NrSfUqHBh6/eOoK1asULfddpvy8/NTfn5+qlmzZio+Pl4dOnSoXNeqIly6dEkNHDhQhYeHK4vFomJiYtSUKVOKDK5V6n/TElz7iHVpg8CLG/y/Y8cO1bNnT+Xn56cCAgLUwIEDizze/9Zbb6lbb71VhYaGKi8vLxUREaHuv/9+p5mpr6X1Ol7/OVeSffv2qV69eilfX18VHBys7rvvPpWSklLmdiV9br7zzjsKgPr2229L3DY9PV1FR0ertm3bFpmAb9KkScpoNKotW7aUWQellFq3bp0aOHCgioyMVBaLRUVGRqoRI0YUue7Xf94p9b9JUq9V+Hr2tFmmK5NBqSoamUdEZcrIyEBoaChmz56N+Ph4vatDpLvhw4fj2LFj+OWXX/SuClUT1aariIiujgmqU6cOxo0bp3dViHSnlMKGDRscc9YQAQBbXIiIyCMUphspiY+Pjy5Z5kmGgQsREXmEsh7jHzVqFBYtWlQ1lSHN2FVEREQeoazH4qWpZkgfbHEhIiIit8HsUUREROQ2bviuIrvdjtOnTyMgIKBKpyknIiL3o5RCRkYGIiMjKzUzdE5ODvLy8lzej8VicWTf9hQ3fOBy+vRpx9TTRERE5XHixAnUrVu3Uvadk5ODmGh/pJyzubyv8PBwJCUleVTwcsMHLoU5bRq+Pwkm3/LlqmhZu2h207LEBhRNhlaaSHPp2Y6LU9OULiqfaZe9kHdkx4jKA0CuXfYSsit5q5e/KbfsQte4zf+QqPyJAnl+lkybLLt4iFf50k5cKzlXVq/fM2X5XdLyyvd+uJb0/pmNdvExLmT7icpfOlJ6hvPr+ZyUf4tWwk9Ks/x2w5wtG27of0r2bd2adFFUHgAKjp8QlfcKk+XHAgB7beH7T3j7DDbZdS2w5WLjwbeK5EOrSHl5eUg5Z8PxHfURGKC9VSc9w47odseQl5fHwOVGUtg9ZPK1ljtwMftZxMfx9pddSl+LPDW6nzCdut0uK281mkXlrx6k8gMXq0n2x88vQHbePvnyt0GBTbaNj4Zkjt5m2f0wK9nr1stcFYGL/BulCbJ6GYUf2CZr5QcuJg09AKYC2R9YLy/ZeXgZ5fcbBtlr0Mso/+y0m4T1kgYu0Pb8SVUMLfAPMMA/QPtx7PDM4Q83fOBCRERUHdmUHcIGoSLbeyIGLkRERDqwQ8GusUWocHtPxMehiYiIyG2wxYWIiEgHdtjhSmePa1u7LwYuREREOrApBZsLk9e7sq07Y1cRERERuQ22uBAREemAg3O1YeBCRESkAzsUbAxcxNhVRERERG7DY1pclAJUOWf9TM2TTecOABfy/UXla5vTxMewGGSzkIZ7yY4RYz0vKg8AaTZfUflsu3xmzSDTFdkxlGwmTl+jfJpTm5LF/KnC6wQAJ3NCROVzhLP5Ftjl31tyhcewm+Qze2blyl4jdovsW2dekKg4AMCcJSuv5BNjQ/j2Rl6A7F5YAuWvQaOvcBsNg0WVt+w8bMLyXpmy97eqxMSK12NXkTa6trjMmzcPsbGxCAwMRGBgIOLi4vDNN984fp+Tk4P4+HjUqFED/v7+GDp0KM6ePatjjYmIiCpG4VNFriyeSNfApW7dunj11VexY8cObN++Hd27d8fAgQOxf/9+AMCkSZPwxRdfYNmyZdi4cSNOnz6NIUOG6FllIiIi0pGuXUV33nmn08+vvPIK5s2bh61bt6Ju3bpYsGABlixZgu7duwMAFi5ciObNm2Pr1q34v//7Pz2qTEREVCHs/11c2d4TVZsxLjabDcuWLUNWVhbi4uKwY8cO5Ofno2fPno4yzZo1Q7169bBly5YSA5fc3Fzk5uY6fk5PT6/0uhMREUnZXHyqyJVt3ZnuTxXt3bsX/v7+sFqteOihh7Bq1Sq0aNECKSkpsFgsCA4Odipfu3ZtpKSklLi/xMREBAUFOZaoqKhKPgMiIiI5m3J98US6By5NmzbF7t27sW3bNjz88MMYNWoUDhw4oHl/CQkJSEtLcywnTpyowNoSERGRnnTvKrJYLGjUqBEAoF27dvj111/x1ltv4e6770ZeXh5SU1OdWl3Onj2L8PDwEvdntVphtcoehyUiIqpqHOOije4tLtez2+3Izc1Fu3btYDabsW7dOsfvDh06hOTkZMTFxelYQyIiItfZYYDNhcUO+TxJNwJdW1wSEhLQt29f1KtXDxkZGViyZAk2bNiA7777DkFBQRg7diwmT56M0NBQBAYG4tFHH0VcXByfKCIiIvJQugYu586dw8iRI3HmzBkEBQUhNjYW3333He644w4AwKxZs2A0GjF06FDk5uaid+/emDt3rp5VJiIiqhB2dXVxZXtPpGvgsmDBglJ/7+3tjTlz5mDOnDkuH6sg3wR7fvnm4U7P9RbvP71Atk2GTX4Mm1nWLBhsyBeV9zXmll3IRaFemeJtIs2XReWz7LIxTpcK/ETlAeBAdqSofIFdPgd8rl04tblR1uMd5iO/Fycyg0Xl03Pkr/MCm+xaKbOwp9+gYT5+KQ0t+F5XKvevUEGQ/F6YA2SpTGCXj7pQRuHFEhYvCJClkCgoqLqRI4VdPq5s74mq3RgXIiIiopLo/lQRERGRJ2KLizYMXIiIiHRgVwbYlfbgw5Vt3Rm7ioiIiMhtsMWFiIhIB+wq0oaBCxERkQ5sMMLmQseHrQLr4k4YuBAREelAuTjGRXGMCxEREVH1xhYXIiIiHXCMizYMXIiIiHRgU0bYlAtjXDx0yn92FREREZHb8JgWl4JcLxiN5TvdzFxZbgsAOJsTKCpfy1L5OXtqmbJE5S0G+Rj15IIAUflG1rPiY9Qwys4jw+YjKr8nM0pUHgDO58pyuBTY5d8RjAbZ16k8YT6k08LXLCAfDJhbIP+IUXbZMQx5smsrTAEFALDJ0l/BK1t+DCVMoaSEKXVs3vIcTWY/X9kGOfJ8Z4Z82YkoX9kNNGUXiMobC6ruWR07DLC70H5gh2c2uXhM4EJERFSdcIyLNuwqIiIiIrfBFhciIiIduD44l11FREREVEWujnFxIckiu4qIiIiIqje2uBAREenA7mKuIk99qogtLkRERDooHOPiyiJRv359GAyGIkt8fHyx5RctWlSkrLe3d0WcukvY4kJERKQDO4xVOo/Lr7/+Cpvtf/PU7Nu3D3fccQeGDRtW4jaBgYE4dOiQ42eDQf9xNQxciIiIPECtWrWcfn711VfRsGFDdOnSpcRtDAYDwsPDK7tqIuwqIiIi0oFNGVxetMrLy8NHH32EBx54oNRWlMzMTERHRyMqKgoDBw7E/v37NR+zonhMi4sqMEIVlC9Ou6Jhyv/LObJp5v8w1hYfw9ckm05bOugr1Sac3htAkEk2t7mWgWjJBaGi8sfzaorK52qZA17ocq7s9QEA2Xny16GEl1E4ZzyAAGuOqLxJwzGyrwjPW5giQJnlAxrtNmEagiqYNV4ZZXWym+R/5GyhspQeplT5+9uQL7tY4in8c4XlbVU35b/NxcG5tv92FaWnpzutt1qtsFpLz1OxevVqpKamYvTo0SWWadq0KT744APExsYiLS0N//jHP9CxY0fs378fdevW1VxvV7HFhYiIyI1FRUUhKCjIsSQmJpa5zYIFC9C3b19ERkaWWCYuLg4jR45E69at0aVLF6xcuRK1atXCu+++W5HVF/OYFhciIqLqxK6MsLswc679vzPnnjhxAoGB/0uaWlZry/Hjx7F27VqsXLlSdDyz2Yw2bdrg8OHD8spWIAYuREREOqiorqLAwECnwKUsCxcuRFhYGPr37y87ns2GvXv3ol+/fqLtKhq7ioiIiDyE3W7HwoULMWrUKHh5ObddjBw5EgkJCY6fp0+fju+//x5Hjx7Fzp07cf/99+P48eN48MEHq7raTtjiQkREpAM74NKTQfKh78DatWuRnJyMBx54oMjvkpOTYTT+rz3j8uXLGDduHFJSUhASEoJ27dph8+bNaNGiheY6VwQGLkRERDpwfQI6+ba9evWCKiGr9IYNG5x+njVrFmbNmqWlapWKXUVERETkNtjiQkREpAMt+Yau394TMXAhIiLSgR0G2OHKGBf98wbpgYELERGRDtjioo1nnjURERG5JY9pcTHYDDCUM9+INC8JAGTklT5T4fXMJnk+jH0ZdUTl/8iS5UMyG+QP10mbKi/m+omP4e8ly9HkJyxvNcpymQBAjk321snMlb0+AMBokOXUMQnvnzTvEACE+2aIyv9xqVbZha6TnynLVWQwya6TzVueq8icZhKVN+aLD4F8P+HnjvA0lEl2Dld5i0pbNXwVNthkJ2I3yw6iTGZReVuBO+Uq8sy2B48JXIiIiKoTuzLA7so8Li5s6848M1wjIiIit8QWFyIiIh3YXewqcmXyOnfGwIWIiEgHrmeH9szAxTPPmoiIiNwSW1yIiIh0YIMBNhcmkXNlW3fGwIWIiEgH7CrSxjPPmoiIiNwSW1yIiIh0YINr3T1VN1Ve9cLAhYiISAfsKtLGcwIXm+HqUg4Fl2XTXAPAxSzZNOUXTYHiYxy1yqamV3ZZJK80pDpQBbI3jsEsTysQEpIlKt8w+KKovK9Xnqg8ABiF863XCUgTH8PbS3a/vU0a5pkXSs31EZU/nxIsPoYpTfax5H1e9rq1yU4BAGASZkewyz4OAAA2o+w8pBk6NGS2wJUw2b2wefuKj2FJk1XMJswroMyy61qQL/+M0opJFrXxzLMmIiIit6Rr4JKYmIj27dsjICAAYWFhGDRoEA4dOuRUpmvXrjAYDE7LQw89pFONiYiIKoaCAXYXFuWhj0PrGrhs3LgR8fHx2Lp1K3744Qfk5+ejV69eyMpy7hoYN24czpw541hmzpypU42JiIgqRmFXkSuLJ9J1jMu3337r9POiRYsQFhaGHTt2oHPnzo71vr6+CA8Pr+rqERERUTVTrcK1tLSrAxhDQ0Od1n/88ceoWbMmbr75ZiQkJCA7O7vEfeTm5iI9Pd1pISIiqm7syuDy4omqzVNFdrsdEydORKdOnXDzzTc71t97772Ijo5GZGQk9uzZgylTpuDQoUNYuXJlsftJTEzEtGnTqqraREREmthczA7tyrburNoELvHx8di3bx9++uknp/Xjx493/L9ly5aIiIhAjx49cOTIETRs2LDIfhISEjB58mTHz+np6YiKiqq8ihMREVGVqRaBy4QJE/Dll19i06ZNqFu3bqllO3ToAAA4fPhwsYGL1WqF1WqtlHoSERFVFFe7e9hVpAOlFB599FGsWrUKGzZsQExMTJnb7N69GwAQERFRybUjIiKqPHYYYXehu8eVbd2ZroFLfHw8lixZgjVr1iAgIAApKSkAgKCgIPj4+ODIkSNYsmQJ+vXrhxo1amDPnj2YNGkSOnfujNjYWD2rTkRERDrQNXCZN28egKuTzF1r4cKFGD16NCwWC9auXYvZs2cjKysLUVFRGDp0KJ577jkdaktERFRxbMoAmwvdPa5s68507yoqTVRUFDZu3Fghx/I/YoLJaipXWbtZvv8Cv/Lt23EMLVfeIEuAYrfI8uloaXWUvm3sQfKEKVk5svM+nSXPAyXla5blN0pJl9cpN1/2IjF7yXLFelvkuY0uX/aTbVCgIf+VWfa6tfkIc/xoyNljE6bg8RLmNgIAgzDVr3TusQJvDfdC9rGGAh/hBgCUSZhTTXgaRpvs9VSV40Y4xkWbajE4l4iIyNMoF7NDKw+dOdczz5qIiIjcEltciIiIdGCDATYXEiW6sq07Y+BCRESkA7tybZyKXTiM8UbBriIiIiJyG2xxISIi0oHdxcG5rmzrzhi4EBER6cAOA+wujFNxZVt35pnhGhEREbkltrgQERHpgDPnasPAhYiISAcc46KNZ541ERERuSWPaXEJPZQPL6/y5dHICpdflpwQWZNdgTDlCwAYZelxIM4kpKHVMT9AVj7PT36QnFRvUflTabLyMGqYDCFbnpNFynRF9r0iX5jjJ0uaywoAvGUJdbyCcsWHKMiWJQuzZclyWdmC7aLyAGAQ5lyypMlf59JcRdJ8SJpysFkr/xiGAtnr3HxF9rq1XBRe2AL560MrO1zMVeShg3M9JnAhIiKqTpSLTxUpBi5ERERUVZgdWhuOcSEiIiK3wRYXIiIiHfCpIm0YuBAREemAXUXaeGa4RkRERG6JLS5EREQ6YK4ibRi4EBER6YBdRdqwq4iIiIjcBltciIiIdMAWF208JnDxPpUOL1P5ph8v8A0R7z8jWjYFfH6AfLp1ryzZi9R6WbZ/S7q8TsY8WZ3yguWNfIbAfFF5lSu7F4Y8eZ2k0/ErL/m1LQgqEJU32GT3QpnkdfIJkE3h37DmBfEx9v0RJSpfECCb0t2vTqaoPABknfIXlTcUVH5KCOmTsDZZZoSrxxD+hbD5yI9hE6YVCDgh+zzwPpEuKl9gk6ep0IqBizbsKiIiIiK34TEtLkRERNUJW1y0YeBCRESkAwXXHmnWkN/9hsDAhYiISAdscdGGY1yIiIg8QP369WEwGIos8fHxJW6zbNkyNGvWDN7e3mjZsiW+/vrrKqxx8Ri4EBER6aCwxcWVReLXX3/FmTNnHMsPP/wAABg2bFix5Tdv3owRI0Zg7Nix2LVrFwYNGoRBgwZh3759Lp+7Kxi4EBER6aCqA5datWohPDzcsXz55Zdo2LAhunTpUmz5t956C3369MGTTz6J5s2b46WXXkLbtm3xz3/+syJOXzMGLkRERB4mLy8PH330ER544AEYDMUHQFu2bEHPnj2d1vXu3RtbtmypiiqWiINziYiIdFBRg3PT050n2bNarbBaS5/Zb/Xq1UhNTcXo0aNLLJOSkoLatWs7ratduzZSUlK0VbiCsMWFiIhIB0oZXF4AICoqCkFBQY4lMTGxzGMvWLAAffv2RWRkZGWfZoVjiwsREZEbO3HiBAIDAx0/l9Xacvz4caxduxYrV64stVx4eDjOnj3rtO7s2bMIDw/XXtkK4DGBi7J4QZnKd7rmDFnuEwCwpMtyk9i95M2DXldk5cuZmsnBkln50xlpOYRvYI6ovM0ma0jMy5G/DWzCxC/GAvn9NkjzGwnLG0122f4hnzfiVEaQ+BgmX1kumoBw2RsjxEf4RgIQ2Ej2GkxNqV12oetY0mTl84W5hwr8ZOUBea4iu4a/KAbhy9Bok73ODbl5svJ2WXlX2GFwaQK6wm0DAwOdApeyLFy4EGFhYejfv3+p5eLi4rBu3TpMnDjRse6HH35AXFycpvpWFI8JXIiIiKoTPSags9vtWLhwIUaNGgUvL+cQYOTIkahTp46jq+nxxx9Hly5d8MYbb6B///749NNPsX37drz33nua61wROMaFiIjIQ6xduxbJycl44IEHivwuOTkZZ86ccfzcsWNHLFmyBO+99x5atWqF5cuXY/Xq1bj55pursspFsMWFiIhIB9cOsNW6vVSvXr2gVPHdbRs2bCiybtiwYSVOUKcXBi5EREQ6YK4ibRi4EBER6UCPFpcbAce4EBERkdtgiwsREZEOlItdRZ7a4sLAhYiISAcKQAnjZMu9vSdiVxERERG5Dba4EBER6cAOAwwVMHOup/GYwMXubYbdq3xzZBsL5FOhBx8uEJXPCpdfertZVt6cJWtItKTJzgEAbFZhpTSoH3JJVN7LKLt/x9NCROUBIDVXdv/sGqb8h1E4tbmwvNEkb2i2FcgaabOyS8+ZUhz/ANn0+k1CL8j2b5btHwA2Hm4sKi8/a0CYRQL55Z/h/b/lNXQsSLNOZGlIbSH8uDXkCzfIzJKVr8Ip//lUkTbsKiIiIiK3oWvgkpiYiPbt2yMgIABhYWEYNGgQDh065FQmJycH8fHxqFGjBvz9/TF06NAi2SqJiIjcTeEEdK4snkjXwGXjxo2Ij4/H1q1b8cMPPyA/Px+9evVCVtb/mvYmTZqEL774AsuWLcPGjRtx+vRpDBkyRMdaExERuU4p1xdPpOsYl2+//dbp50WLFiEsLAw7duxA586dkZaWhgULFmDJkiXo3r07gKvpuJs3b46tW7fi//7v//SoNhEREemkWo1xSUtLAwCEhoYCAHbs2IH8/Hz07NnTUaZZs2aoV68etmzZUuw+cnNzkZ6e7rQQERFVN4WDc11ZPFG1CVzsdjsmTpyITp06OVJmp6SkwGKxIDg42Kls7dq1kZKSUux+EhMTERQU5FiioqIqu+pERERiDFy0qTaBS3x8PPbt24dPP/3Upf0kJCQgLS3NsZw4caKCakhERFRxODhXm2oxj8uECRPw5ZdfYtOmTahbt65jfXh4OPLy8pCamurU6nL27FmEh4cXuy+r1QqrVcssCkRERFTd6driopTChAkTsGrVKvz444+IiYlx+n27du1gNpuxbt06x7pDhw4hOTkZcXFxVV1dIiKiCsOnirTRtcUlPj4eS5YswZo1axAQEOAYtxIUFAQfHx8EBQVh7NixmDx5MkJDQxEYGIhHH30UcXFxfKKIiIjc2tXgw5WZcyuwMm5E18Bl3rx5AICuXbs6rV+4cCFGjx4NAJg1axaMRiOGDh2K3Nxc9O7dG3Pnzq3imhIREVF1oGvgosoRLnp7e2POnDmYM2eOS8cy5hXAaCtfLh5DnjwC9krPldUn31d8jOww2e0yVMNo3JAvv7bSRGIRPmmi8lpyFUGa58ckz3/l4y97TeXlyvJG2W0a8soIO5fz04UJeADUqCfLLWMxyXJsHcsIFZUHAHWh8sfNSXMP5dawyTbwFZYHYMiQfeaYcuSvKUum7L1kvnxFVL7g/EVZeZUvKu8K5irSploMziUiIvI0CuI8lkW290TV5nFoIiIiorKwxYWIiEgH7CrShoELERGRHthXpAkDFyIiIj24Om2/h7a4cIwLERERuQ22uBAREenA1dlvOQEdERERVRkOztWGXUVERETkNtjiQkREpAdlcG2ArYe2uHhM4GL3MsHuZSpXWeUlfzEY82TltRxDypQj6wDNipBNGQ8AOaGy8zDY5J2yyZdlU/Jn5cummdfST2zxk91wg4b8Cza7rEFUyxT+Uvbc8r2H/reBvE5ZubL7l57nLSp/JjVIVB6QT2Wf7yc+BOzesteIMUT2GvQyy6f8z8uR3W9TjvgQ8LoiT4chYbTIXk9GBUCWbUMzjnHRhl1FRERE5DY8psWFiIioWuEEdJowcCEiItIBnyrShl1FRERE5DbY4kJERKQXD+3ucQUDFyIiIh2wq0gbBi5ERER64OBcTRi4EBERUYXZs2dPucvGxsaK98/AhYiISBeG/y6ubF/9tG7dGgaDAUopGAyl19Fmk0+MyKeKiIiI9KAqYKmGkpKScPToUSQlJWHFihWIiYnB3LlzsWvXLuzatQtz585Fw4YNsWLFCk37Z4sLERERVZjo6GjH/4cNG4a3334b/fr1c6yLjY1FVFQUnn/+eQwaNEi8f48JXPKCLLCby5ezwuYjb4iyC3MPKWHKFwCwZAhzeghbEfP9NDTACY/hd0pD7hpjoKj8hQay/YcFZMo2AOBryReVzy2Qv9Uys62i8kYv2etDacgjZLDKmnXtRvlXQh/htT2bFSAqH+x3RVQeAFICfWUbyFu/AR/Z/QvwkyXUySuQf+gYr8g+Eyzp4kPAnCG7WIY04fvVIsvBZlCqynIVecLg3L179yImJqbI+piYGBw4cEDTPtlVREREpIfC7NCuLNVc8+bNkZiYiLy8/yUFzcvLQ2JiIpo3b65pnx7T4kJERERVa/78+bjzzjtRt25dxxNEe/bsgcFgwBdffKFpnwxciIiIdKDU1cWV7au7W2+9FUePHsXHH3+M33//HQBw9913495774Wfn5+mfTJwISIi0oMHjHEBAD8/P4wfP77C9sfAhYiIiCrM559/jr59+8JsNuPzzz8vtexdd90l3j8DFyIiIj24OsC2mg7OHTRoEFJSUhAWFlbq484Gg0HTBHQMXIiIiHRgUFcXV7avjux2e7H/ryh8HJqIiEgPN+jMuYXy8/PRo0cP/PnnnxW6XwYuREREVOHMZrMo4WJ5MXAhIiLSgwdMQHf//fdjwYIFFbpPjxnjkhtigs1cvimvC7zlLwabVbaN73l5v58lo0BUPjdIdnu19JdKp/j2PSc7BwAo8JGdh7mJbLBXq9BTovIAkGWTTcf/2/lI8THyc4X3T3gDvSwa5qUXvjUCgrPEh6jtlyEqn5EnuxepOT6i8gBg8JelIbBfkX+0GoQpG4xGWXmrWf4GL5BmGdHwGeKVLfxMyJKlbDB4y14fBrsBkL0EtfOAx6ELCgrwwQcfYO3atWjXrl2RuVvefPNN8T49JnAhIiKiqrVv3z60bdsWAPDHH384/c5g0NZixMCFiIhIDx7Q4rJ+/foK3yfHuBAREelBh6eKTp06hfvvvx81atSAj48PWrZsie3bt5dYfsOGDTAYDEWWlJQU+cErCFtciIiIPMDly5fRqVMndOvWDd988w1q1aqFP//8EyEhIWVue+jQIQQGBjp+DgsLK9cxBw8eXGyXkMFggLe3Nxo1aoR7770XTZs2Lfd5MHAhIiLSQxXPnPvaa68hKioKCxcudKyLiYkp17ZhYWEIDg4WHQ8AgoKCsHr1agQHB6Ndu3YAgJ07dyI1NRW9evXC0qVL8dprr2HdunXo1KlTufbJriIiIiIdFM6c68oi8fnnn+OWW27BsGHDEBYWhjZt2uD9998v17atW7dGREQE7rjjDvz888/lPmZ4eDjuvfdeHD16FCtWrMCKFStw5MgR3H///WjYsCEOHjyIUaNGYcqUKeXeJwMXIiIiN5aenu605ObmFlvu6NGjmDdvHho3bozvvvsODz/8MB577DEsXry4xH1HRERg/vz5jqAjKioKXbt2xc6dO8tVtwULFmDixIkwGv8XbhiNRjz66KN47733YDAYMGHCBOzbt6/c58uuIiIiIj1U0FNFUVFRTqunTp2KF198sUhxu92OW265BTNmzAAAtGnTBvv27cP8+fMxatSoYg/RtGlTp/EnHTt2xJEjRzBr1ix8+OGHZVaxoKAAv//+O5o0aeK0/vfff3ckWPT29hY9Gi0OXEaNGoWxY8eic+fO0k2JiIiogp04ccJp4KzVWvykexEREWjRooXTuubNm2PFihWi491666346aefylX2r3/9K8aOHYtnnnkG7du3BwD8+uuvmDFjBkaOHAkA2LhxI2666aZyH18cuKSlpaFnz56Ijo7GmDFjMGrUKNSpU0e6GyIiIo9mgIvZof/7b2BgoFPgUpJOnTrh0KFDTuv++OMPREdHi467e/duRERElKvsrFmzULt2bcycORNnz54FANSuXRuTJk1yjGvp1asX+vTpU+7ji8e4rF69GqdOncLDDz+MpUuXon79+ujbty+WL1+O/HzZtNhERERUNSZNmoStW7dixowZOHz4MJYsWYL33nsP8fHxjjIJCQmOlhAAmD17NtasWYPDhw9j3759mDhxIn788UenbUpjMpnw7LPP4syZM0hNTUVqairOnDmDZ555BibT1TQ89erVw/Hjx0scm3M9TWNcatWqhcmTJ2Py5MnYuXMnFi5ciL/+9a/w9/fH/fffj0ceeQSNGzfWsutKk+9vhN1Svjgt31fDAYQhoJZ8SCaf8uVaKiSN5L0vy/Mn+Z4u3wvNwSg/b4Nd9jL1Mctyn7TwPS0qDwAXCgJE5febwsXHUPmyF5USvga9fPNkGwDwscq+nHh7yXNT5dllr/OcArOovN2u4b1nluV1UtkaPloLZDcwN0923mYveW4qJcxvpDS8v41ZstehssnOw2CsxkM5q/hx6Pbt22PVqlVISEjA9OnTERMTg9mzZ+O+++5zlDlz5gySk5MdP+fl5eHvf/87Tp06BV9fX8TGxmLt2rXo1q2buLqltQr17dsXu3fvRoMGDcrcj0tPFZ05cwY//PADfvjhB5hMJvTr1w979+5FixYtMGvWrDK337RpE+68805ERkbCYDBg9erVTr8fPXp0kdn6JM1JRERE1ZYOM+cOGDAAe/fuRU5ODg4ePIhx48Y5/X7RokXYsGGD4+ennnoKhw8fxpUrV3Dx4kWsX79eU9BSFqXKfzLiwCU/Px8rVqzAgAEDEB0djWXLlmHixIk4ffo0Fi9ejLVr1+Kzzz7D9OnTy9xXVlYWWrVqhTlz5pRYpk+fPjhz5oxj+eSTT6RVJiIiohuEuA0tIiICdrsdI0aMwC+//ILWrVsXKdOtW7dyzbDXt29f9O3bt9QyVqsV4eHyZnYiIqJqzQOSLFYGceAya9YsDBs2DN7e3iWWCQ4ORlJSkksVK7RhwwaEhYUhJCQE3bt3x8svv4waNWpUyL6JiIj0omX22+u390TiwOWvf/1rZdSjWH369MGQIUMQExODI0eO4JlnnkHfvn2xZcsWx2jk6+Xm5jqNTE5PT6+q6hIREZEGlToBXVW65557HP9v2bIlYmNj0bBhQ2zYsAE9evQodpvExERMmzatqqpIRESkDbuKHCp1cK6eGjRogJo1a+Lw4cMllklISEBaWppjOXHiRBXWkIiIqJx0eKqoqk2fPh0//vhjkfVZWVlOD/FkZGSU61FowM0Cl5MnT+LixYulzthntVodswiWdzZBIiIiqngvvvgi+vbtizfffNNpfWZmpubeEV27ijIzM51aT5KSkrB7926EhoYiNDQU06ZNw9ChQxEeHo4jR47gqaeeQqNGjdC7d28da01EROQ6Txmc++9//xvx8fHYu3cv3n33XVgsFpf2p2uLy/bt29GmTRu0adMGADB58mS0adMGL7zwAkwmE/bs2YO77roLTZo0wdixY9GuXTv85z//KTGBFBERkdsonDnXlcUNdOvWDdu2bcO2bdvQtWtXnDt3zqX96dri0rVr11IH5Hz33XcVdqwCH0CVM97REsXaZLOUIydEywtOdhDvS7Lp1v2Sr4jKa3ElQp5PQTqV/eUsH1H5k3mhsgMACDDliMp7e8nzeBm8ZCkYjF6yF25YQKaoPADU9pVt422Sn7fZKJvS/ei5mqLydpv8+5qSZsMQ3gsAMAjTChRIUwRky1IEAID3edkx/FLkKR4M0in8fUqeiqNYJTyBWiJ55hPtPGBwbuHTQg0bNsTWrVsxfPhwtGvXDvPnz9e8T7ca40JERETu49rGicDAQHz99dcYPHgwBg0apHmf1fpxaCIiohuVJ4xxWbhwIYKCghw/G41GvP3222jTpg02bdqkaZ8MXIiIiPTgAV1Fo0aNKnb9mDFjMGbMGE37ZFcRERERVZp169ZhwIABaNiwIRo2bIgBAwZg7dq1mvfHwIWIiEgP6n/dRVoWd2hxmTt3Lvr06YOAgAA8/vjjePzxxxEYGIh+/fphzpw5mvbJriIiIiI9eEBX0YwZMzBr1ixMmDDBse6xxx5Dp06dMGPGDMTHx4v3yRYXIiIiqhSpqano06dPkfW9evVCWlqapn0ycCEiItKDB+Qquuuuu7Bq1aoi69esWYMBAwZo2ie7ioiIiHRwoz4O/fbbbzv+36JFC7zyyivYsGED4uLiAABbt27Fzz//jL///e+a9s/AhYiIiCrMrFmznH4OCQnBgQMHcODAAce64OBgfPDBB3juuefE+2fgQkRERBUmKSmpUvfvMYFLXgBgKmeuImuqfP9KmA4jL6jsMtezZMjKG2yydsT8YHnySruXbJhUXqDwQkF+bXPP+InKf2G9WXYAAF0iD5dd6Br+5jzxMYxmWdIUi0WWJybKP1VUHgDq+VwSlT+QESE+hq+X7FoV5MleICpfw9A+o+y9JM07BAAmizBnjzDdmV14nQD5Z6E5Q37eyiL7M2QI9JcdwC7sT7FVYeLCG/SposmTJ5ernMFgwBtvvCHev8cELkRERNXJjTrGZdeuXU4/79y5EwUFBWjatCkA4I8//oDJZEK7du007Z+BCxEREVWY9evXO/7/5ptvIiAgAIsXL0ZISAgA4PLlyxgzZgxuv/12Tfvn49BERER6uYEfhQaAN954A4mJiY6gBbg6WPfll1/W1E0EsMWFiIhIHzfoGJdrpaen4/z580XWnz9/HhkZwoGb/8UWFyIiIqoUgwcPxpgxY7By5UqcPHkSJ0+exIoVKzB27FgMGTJE0z7Z4kJERKSDG3Vw7rXmz5+PJ554Avfeey/y8/MBAF5eXhg7dixef/11Tftk4EJERKQHD+gq8vX1xdy5c/H666/jyJEjAICGDRvCz082bcW1GLgQERFRpfLz80NsbGyF7IuBCxERkQ48oauoMjBwISIi0oMHdBVVBj5VRERERG7DY1pc7N4AvMtX9r8Dn0WU8EraylmXa+WGyHJoGJRZVl6eZkTcVJkTLM8DIr22Pmdl8Xh6QUjZha5zqaZsYFnHGkfExzifLczJIlTf96J4mwBTjqj8gbO1xceoGZAlKm+2ynI05eXJc3IhS5gPyVf+ZvLykn3w5GTIzsOaIvs8AABrmuwNrrzk72+bn0VU3i7MbQSjrE4FBWZA/nbVhi0umnhM4EJERFSdcIyLNgxciIiI9MAWF004xoWIiIjcBltciIiI9MAWF00YuBAREemAY1y0YVcRERERuQ22uBAREemBXUWaMHAhIiLSAbuKtGFXEREREbkNtrgQERHpgV1FmnhM4GLKBkzlnIXbLpuB+uo20in/rfJXXFYdWfm8QNlU19bLsv0DgFe2rLxRNjs7AMCSKiyfLru2Xtnyacr/bFZLVL5lwCnxMVrXPCkqn1UgmwK+uc9pUXkAOHglUlTe2yLPn5FXIJtev3HYeVH5rBD5G/zYKdn9Vnnyxuz8fNl544qsvP8J2e4BwJQjey/ZNUz5b/ev3Cn8YZedQ0G+htwnWjFw0YRdRUREROQ2PKbFhYiIqDox/HdxZXtPxMCFiIhID+wq0oSBCxERkQ74OLQ2HONCREREboMtLkRERHpgV5EmDFyIiIj04qHBhyvYVURERERugy0uREREOuDgXG0YuBAREemBY1w0YVcRERERuQ2PaXHxPQeYypmiRAlThgBAvp+sfK403waAvCC7qLzdIgvHlVEex/rlyspb02XnAAD+Z2TbeGXLco2kWmQ5fgAgNctHVD6tQFYeAMKt6aLyYf6y8t6GPFF5ADif5y8qb/WS530psMteh75esnxIJ9ODROUBiL/ZGizy87ZYZIm88jKFryn5W0/cFVHgI/8MsZtln4XSfEhewnxLBdKcUS7Qo6vo1KlTmDJlCr755htkZ2ejUaNGWLhwIW655ZYSt9mwYQMmT56M/fv3IyoqCs899xxGjx6tveIu0rXFZdOmTbjzzjsRGRkJg8GA1atXO/1eKYUXXngBERER8PHxQc+ePfHnn3/qU1kiIqKKpCpgEbh8+TI6deoEs9mMb775BgcOHMAbb7yBkJCQErdJSkpC//790a1bN+zevRsTJ07Egw8+iO+++054shVH1xaXrKwstGrVCg888ACGDBlS5PczZ87E22+/jcWLFyMmJgbPP/88evfujQMHDsDb21uHGhMREbmn1157DVFRUVi4cKFjXUxMTKnbzJ8/HzExMXjjjTcAAM2bN8dPP/2EWbNmoXfv3pVa35Lo2uLSt29fvPzyyxg8eHCR3ymlMHv2bDz33HMYOHAgYmNj8e9//xunT58u0jJDRETkbgq7ilxZJD7//HPccsstGDZsGMLCwtCmTRu8//77pW6zZcsW9OzZ02ld7969sWXLFunpVphqOzg3KSkJKSkpThcsKCgIHTp0KPWC5ebmIj093WkhIiKqdiqoq+j6v3m5ucUPPjx69CjmzZuHxo0b47vvvsPDDz+Mxx57DIsXLy6xiikpKahdu7bTutq1ayM9PR1XrlzRfOquqLaBS0pKCgAUe8EKf1ecxMREBAUFOZaoqKhKrScREZEmFRS4REVFOf3dS0xMLPZwdrsdbdu2xYwZM9CmTRuMHz8e48aNw/z58yvxJCveDfdUUUJCAiZPnuz4OT09ncELERHdsE6cOIHAwEDHz1Zr8U9KRkREoEWLFk7rmjdvjhUrVpS47/DwcJw9e9Zp3dmzZxEYGAgfH/nTkhWh2gYu4eHhAK5eoIiICMf6s2fPonXr1iVuZ7VaS7xpRERE1UVFPQ4dGBjoFLiUpFOnTjh06JDTuj/++APR0dElbhMXF4evv/7aad0PP/yAuLg4eYUrSLXtKoqJiUF4eDjWrVvnWJeeno5t27bpesGIiIgqRBU/Dj1p0iRs3boVM2bMwOHDh7FkyRK89957iI+Pd5RJSEjAyJEjHT8/9NBDOHr0KJ566in8/vvvmDt3Lj777DNMmjRJ61m7TNcWl8zMTBw+fNjxc1JSEnbv3o3Q0FDUq1cPEydOxMsvv4zGjRs7HoeOjIzEoEGD9Ks0ERGRG2rfvj1WrVqFhIQETJ8+HTExMZg9ezbuu+8+R5kzZ84gOTnZ8XNMTAy++uorTJo0CW+99Rbq1q2Lf/3rX7o9Cg3oHLhs374d3bp1c/xcODZl1KhRWLRoEZ566ilkZWVh/PjxSE1NxW233YZvv/2Wc7gQEZHbMygFg9LeV6Rl2wEDBmDAgAEl/n7RokVF1nXt2hW7du0SH6uy6Bq4dO3aFaqUC28wGDB9+nRMnz7d5WNZL9vgZS7fNNxa+hyN+bKNrtSQTyudUU/Ws1cgTEOgNHQc2oWvIOtl+VToxgLZXOUFPrJrm69hfFm+cFrwPzJrl13oOi0DT4nKt/dJEpX/LUc+aL1AmA8jyFv+uKS3STb1vR2yKeADvIV5KgBk+8rGzXlbZWkIACC/QHZtpZ9TBg1T/ithZhK7Vf4hku8rO4g0JYv0Otk0pGPRjEkWNam2Y1yIiIiIrldtnyoiIiK6kemRZPFGwMCFiIhID+wq0oRdRUREROQ22OJCRESkA3YVacPAhYiISA/sKtKEgQsREZEO2OKiDce4EBERkdtgiwsREZEe2FWkCQMXIiIinXhqd48r2FVEREREbsNjWly8L+bBy6t8cZoyyeM50xVZfhXrBfEhYM6SJdXJipCdhzQHCAD4npclQDHlynMV5QWZReWvhMrO26YhV1FBhkVU/o9LNcXHaBaQIip/3uYvKp9q8xWVBwCLUfY6vzX0uPgYvqY8UfmP/mgvKn8lQ5Z3CAD8gnJE5euHXBIfY++xOqLy3tmynDrmK5X/1T7PT57nR8v7T7R/2a2DvSpbQJS6uriyvQfymMCFiIioOuFTRdqwq4iIiIjcBltciIiI9MCnijRh4EJERKQDg/3q4sr2nohdRUREROQ22OJCRESkB3YVacLAhYiISAd8qkgbBi5ERER64DwumnCMCxEREbkNtrgQERHpgF1F2nhM4GI+kAwvY/mmaTcEyKZOBwB7zSBZebN8fn1Lumy6dRhkt9ecIZ+O3+d0pqi8zU82VT4AXAmVTdGeFyibdlzLI4Ve6bL7l+kvn9c8X5iD4XxBoKj8hfwAUXkAaOorS0PQ1feQ+BjnhKkL3k+7XVTedEmWQgIAzDWyROUv58rvtyFNVi9zmmz/phz5C93mLUyf4S0+BOzCv0LGfPkxqi0OztWEXUVERETkNjymxYWIiKg6YVeRNgxciIiI9MCnijRhVxERERG5Dba4EBER6YBdRdowcCEiItIDnyrShF1FRERE5DbY4kJERKQDdhVpw8CFiIhID3Z1dXFlew/EwIWIiEgPHOOiCce4EBERkdvwnBYXJWiSy5MnwzBeluXsMVrk+VKMV2TbWM8L95+WLdsAEF+rgrAI8SFyg2W5h+zCdEhm2a3770FkdcqpI/9q5C1MynK+QJZ7KMhLfr8bW2W5ilpa5Dl73rocJdsgV5j3S0Nuqrx82TFOpweLj2HIl72mzFdk+1cm2f4BwCZ8LykNX4WNwhRs0lxFRmEKNiVP2aaZAS6OcamwmrgXzwlciIiIqhPOnKsJu4qIiIjIbbDFhYiISAd8HFobBi5ERER64FNFmrCriIiIiNwGW1yIiIh0YFAKBhcG2LqyrTtj4EJERKQHOzQ9nu+0vQdiVxERERG5Dba4EBER6YBdRdowcCEiItIDnyrSxGMCl4Lm9QAv73KVNdjkrwZjjmweakOOcJ5rAMYLqaLyKi1dVL4gSz4FvFdoiKi8lm8I5izhBsIp/L1y5HWyWWSTbRuN8mPsuFxPVD7YIpsD/pagY6LyABBukr2mgPK956719dmbROW9UmXT8du95ffiSqZVVF4JUwQAgDVD+JrKk52HkldJ/Do3aJgu3yScwl98DOntrspggDPnasIxLkREROQ2qnXg8uKLL8JgMDgtzZo107taRERELiucOdeVxRNV+66im266CWvXrnX87OVV7atMRERUNnYVaVLtowAvLy+Eh4frXQ0iIiKqBqp1VxEA/Pnnn4iMjESDBg1w3333ITk5udTyubm5SE9Pd1qIiIiqG4Pd9cUTVevApUOHDli0aBG+/fZbzJs3D0lJSbj99tuRkZFR4jaJiYkICgpyLFFRUVVYYyIionIq7CpyZfFA1Tpw6du3L4YNG4bY2Fj07t0bX3/9NVJTU/HZZ5+VuE1CQgLS0tIcy4kTJ6qwxkRERFSZqv0Yl2sFBwejSZMmOHz4cIllrFYrrFbZnAtERERVjhPQaVKtW1yul5mZiSNHjiAiIkLvqhAREbmkcMp/VxZPVK0DlyeeeAIbN27EsWPHsHnzZgwePBgmkwkjRozQu2pERERuRTo32qJFi4qU9/aWz4Zd0ap1V9HJkycxYsQIXLx4EbVq1cJtt92GrVu3olatWnpXjYiIyDU6zOMinRstMDAQhw4dcvxsMMjSQFSGah24fPrppxW2r6xIb3iZyxcpWjLlz5iZ02Q30ytfQ1IP4YtU5cvyIRm9NYwNCg0WFc/305DDJU12P6S5h3KD5A2PNuGlyj8v/5ayJ0P2RFzrJqVPFXC9UJMwqROAfGHCm6258pxcyZdk+a+MwreSklcJ9iyzqLxXmobXeaqsvFF4HgXWyv+DoyUfEnJlxQ122fu7Ws8uqwC48kizhnOTzo1mMBiq3Vxq1bqriIiI6EalxxgX6dxomZmZiI6ORlRUFAYOHIj9+/drPd0Kw8CFiIjIjV0/6WpubvHNWNK50Zo2bYoPPvgAa9aswUcffQS73Y6OHTvi5MmTlXk6ZarWXUVEREQ3LAUXx7hc/ef6iVanTp2KF198sUjxvn37Ov4fGxuLDh06IDo6Gp999hnGjh1bpHxcXBzi4uIcP3fs2BHNmzfHu+++i5deekl7vV3EwIWIiEgPFTQ498SJEwgMDHSsLu9cZuWZG+1aZrMZbdq0KXf5ysKuIiIiIjcWGBjotJQ3cJHOjWaz2bB3717d51Jj4EJERKQHewUsAmXNjTZy5EgkJCQ4yk+fPh3ff/89jh49ip07d+L+++/H8ePH8eCDD7py1i5jVxEREZEOXJ39VrptWXOjJScnw2j8X3vG5cuXMW7cOKSkpCAkJATt2rXD5s2b0aJFC811rggMXIiIiDxAWXOjbdiwwennWbNmYdasWZVYI20YuBAREelBh5lzbwQMXIiIiPTAwEUTDs4lIiIit+ExLS4FvgYoS/lydXhdke/fbpUl6VAW+aU3BPiJyotzD5nkiUbyIoNE5ZVJni/FnC0cOi/8EmIr5+viWtI8Mb4n5dc2W5aqSCzD7iPeJt0uy7n0c1YT8TFyMy2i8hbhy8MrS36/DTbZ/bOkiQ8Bg/A1Jc7RpOHLeb7wY8qgIQWbKV9WMbuX9P5V41YJtrho4jGBCxERUbViB+BK7ktXEjS6MQYuREREOqjqx6FvFBzjQkRERG6DLS5ERER64BgXTRi4EBER6cGuAIMLwYfdMwMXdhURERGR22CLCxERkR7YVaQJAxciIiJduBi4VOc5aioRu4qIiIjIbbDFhYiISA/sKtLEYwIXg63801HbrPKGKFOObApDm69ZfAy7VXa7lJfsPOwW+Xnn+8mmQpdP1w2YrsiubYGv7DyUhnZHc6asfJ4sMwIAQJllH0rpebIUD+fyA0XltWzz2eE24mMYL8qm/Jc+lGHKlZUHNKQB0fD3xC79NBb+0VIG+XvPK0d4DKMr08CWj8EmvLjSP+5VGQzYFVzq7uFTRURERETVm8e0uBAREVUryn51cWV7D8TAhYiISA8c46IJAxciIiI9cIyLJhzjQkRERG6DLS5ERER6YFeRJgxciIiI9KDgYuBSYTVxK+wqIiIiIrfBFhciIiI9sKtIEwYuREREerDbAbgwF4vdM+dxYVcRERERuQ2PaXGxZNjhZS5ndKqh9a3AV5azR0tOD2OBrGLSvEB2DTmaCnxkxzDmiw8hZhB+CTFnyW+4TZZOR9P9NuTLtklJk+URWn0lVlQeAFIv+IvKe52T5+QyyN5KKPCR3T9TjoacPcJcRVryX9nF510VeYFk5aW5jQCIuzuMBdL9y4pXxWeUA7uKNPGYwIWIiKhaYeCiCbuKiIiIyG2wxYWIiEgPnPJfEwYuREREOlDKDuVChmdXtnVnDFyIiIj0oJRrrSYc40JERERUvbHFhYiISA/KxTEuHtriwsCFiIhID3a7fOKpa3noGBd2FREREZHbYIsLERGRHthVpInHBC5e2TZ4eZVv/mot07NLp8tXwun4AXkqLmmd8n3ldbJZZdt4X5E3bRptwlQHwlH6WqYpl6ZTkE6dDgDeZ2VzwNsuyqb8z9FQJ2lWCLuGTxibt+w1YsqWVcqcJSoOAPDKlpVXwun7AaDAR1jeW/YaNOXJ9g8AXsL3q9cV+XtJnMrEXLmpDqSfN65QdjuUC11Fnvo4NLuKiIiIyG24ReAyZ84c1K9fH97e3ujQoQN++eUXvatERETkmsJcRa4sHqjaBy5Lly7F5MmTMXXqVOzcuROtWrVC7969ce7cOb2rRkREpJ1dub54oGofuLz55psYN24cxowZgxYtWmD+/Pnw9fXFBx98oHfViIiIqIpV68AlLy8PO3bsQM+ePR3rjEYjevbsiS1bthS7TW5uLtLT050WIiKiakepq3OxaF7Y4lLtXLhwATabDbVr13ZaX7t2baSkpBS7TWJiIoKCghxLVFRUVVSViIhIRNmVy4snqtaBixYJCQlIS0tzLCdOnNC7SkREREW51Npi99iZc6v1PC41a9aEyWTC2bNnndafPXsW4eHhxW5jtVphtVqronpERERUxap1i4vFYkG7du2wbt06xzq73Y5169YhLi5Ox5oRERG5hl1F2lTrFhcAmDx5MkaNGoVbbrkFt956K2bPno2srCyMGTNG76oRERFpp+yQz4l+/faep9oHLnfffTfOnz+PF154ASkpKWjdujW+/fbbIgN2S6L+O+q6oCCn3MdUBg3T8RtljVcGDdNKG6RTYwvrVJCv4byF16ogX8MbrUA2N31Bvuy87RoaHm15svO25WpIpyDcxF4gK680TPmvhJdKafiEsZtkrxFDjqxStlxR8avHEE6Xr2XKf5v02uYLD6Bhyn+D9P2aXwVT/qNyp/wvyL/6t0JVwRM7Bch3KVVRAaQvghuDQVXF3dHRyZMn+WQRERGJnDhxAnXr1q2Ufefk5CAmJqbEp2MlwsPDkZSUBG9v7wqomXu44QMXu92O06dPIyAgAIZrWgfS09MRFRWFEydOIDBQlpzOnfG8ed6egOfN89ZKKYWMjAxERkbCKGy1lsjJyUFenoZmsOtYLBaPCloAN+gqcpXRaCw1ag4MDPSoN3ghnrdn4Xl7Fp63a4KCgiqgNqXz9vb2uICjolTrp4qIiIiIrsXAhYiIiNyGxwYuVqsVU6dO9bjJ6njePG9PwPPmedON64YfnEtEREQ3Do9tcSEiIiL3w8CFiIiI3AYDFyIiInIbDFyIiIjIbXhs4DJnzhzUr18f3t7e6NChA3755Re9q1SpXnzxRRgMBqelWbNmelerwm3atAl33nknIiMjYTAYsHr1aqffK6XwwgsvICIiAj4+PujZsyf+/PNPfSpbgco679GjRxe5/3369NGnshUkMTER7du3R0BAAMLCwjBo0CAcOnTIqUxOTg7i4+NRo0YN+Pv7Y+jQoTh79qxONa4Y5Tnvrl27FrnfDz30kE41rhjz5s1DbGysY5K5uLg4fPPNN47f34j3mornkYHL0qVLMXnyZEydOhU7d+5Eq1at0Lt3b5w7d07vqlWqm266CWfOnHEsP/30k95VqnBZWVlo1aoV5syZU+zvZ86cibfffhvz58/Htm3b4Ofnh969eyMnp/xJOKujss4bAPr06eN0/z/55JMqrGHF27hxI+Lj47F161b88MMPyM/PR69evZCVleUoM2nSJHzxxRdYtmwZNm7ciNOnT2PIkCE61tp15TlvABg3bpzT/Z45c6ZONa4YdevWxauvvoodO3Zg+/bt6N69OwYOHIj9+/cDuDHvNZVAeaBbb71VxcfHO3622WwqMjJSJSYm6liryjV16lTVqlUrvatRpQCoVatWOX622+0qPDxcvf766451qampymq1qk8++USHGlaO689bKaVGjRqlBg4cqEt9qsq5c+cUALVx40al1NV7azab1bJlyxxlDh48qACoLVu26FXNCnf9eSulVJcuXdTjjz+uX6WqSEhIiPrXv/7lMfearvK4Fpe8vDzs2LEDPXv2dKwzGo3o2bMntmzZomPNKt+ff/6JyMhINGjQAPfddx+Sk5P1rlKVSkpKQkpKitO9DwoKQocOHW74ew8AGzZsQFhYGJo2bYqHH34YFy9e1LtKFSotLQ0AEBoaCgDYsWMH8vPzne53s2bNUK9evRvqfl9/3oU+/vhj1KxZEzfffDMSEhKQnZ2tR/Uqhc1mw6effoqsrCzExcV5zL2mq274JIvXu3DhAmw2G2rXru20vnbt2vj99991qlXl69ChAxYtWoSmTZvizJkzmDZtGm6//Xbs27cPAQEBelevShSmkC/u3ldEevnqrE+fPhgyZAhiYmJw5MgRPPPMM+jbty+2bNkCk8mkd/VcZrfbMXHiRHTq1Ak333wzgKv322KxIDg42KnsjXS/iztvALj33nsRHR2NyMhI7NmzB1OmTMGhQ4ewcuVKHWvrur179yIuLg45OTnw9/fHqlWr0KJFC+zevfuGv9f0Px4XuHiqvn37Ov4fGxuLDh06IDo6Gp999hnGjh2rY82oKtxzzz2O/7ds2RKxsbFo2LAhNmzYgB49euhYs4oRHx+Pffv23ZDjtkpT0nmPHz/e8f+WLVsiIiICPXr0wJEjR9CwYcOqrmaFadq0KXbv3o20tDQsX74co0aNwsaNG/WuFlUxj+sqqlmzJkwmU5HR5mfPnkV4eLhOtap6wcHBaNKkCQ4fPqx3VapM4f319HsPAA0aNEDNmjVviPs/YcIEfPnll1i/fj3q1q3rWB8eHo68vDykpqY6lb9R7ndJ512cDh06AIDb32+LxYJGjRqhXbt2SExMRKtWrfDWW2/d8PeanHlc4GKxWNCuXTusW7fOsc5ut2PdunWIi4vTsWZVKzMzE0eOHEFERITeVakyMTExCA8Pd7r36enp2LZtm0fdewA4efIkLl686Nb3XymFCRMmYNWqVfjxxx8RExPj9Pt27drBbDY73e9Dhw4hOTnZre93WeddnN27dwOAW9/v4tjtduTm5t6w95pKoPfoYD18+umnymq1qkWLFqkDBw6o8ePHq+DgYJWSkqJ31SrN3//+d7VhwwaVlJSkfv75Z9WzZ09Vs2ZNde7cOb2rVqEyMjLUrl271K5duxQA9eabb6pdu3ap48ePK6WUevXVV1VwcLBas2aN2rNnjxo4cKCKiYlRV65c0bnmrintvDMyMtQTTzyhtmzZopKSktTatWtV27ZtVePGjVVOTo7eVdfs4YcfVkFBQWrDhg3qzJkzjiU7O9tR5qGHHlL16tVTP/74o9q+fbuKi4tTcXFxOtbadWWd9+HDh9X06dPV9u3bVVJSklqzZo1q0KCB6ty5s841d83TTz+tNm7cqJKSktSePXvU008/rQwGg/r++++VUjfmvabieWTgopRS77zzjqpXr56yWCzq1ltvVVu3btW7SpXq7rvvVhEREcpisag6deqou+++Wx0+fFjvalW49evXKwBFllGjRimlrj4S/fzzz6vatWsrq9WqevTooQ4dOqRvpStAaeednZ2tevXqpWrVqqXMZrOKjo5W48aNc/tAvbjzBaAWLlzoKHPlyhX1yCOPqJCQEOXr66sGDx6szpw5o1+lK0BZ552cnKw6d+6sQkNDldVqVY0aNVJPPvmkSktL07fiLnrggQdUdHS0slgsqlatWqpHjx6OoEWpG/NeU/EMSilVde07RERERNp53BgXIiIicl8MXIiIiMhtMHAhIiIit8HAhYiIiNwGAxciIiJyGwxciIiIyG0wcCEiIiK3wcCFiIiI3AYDFyIiInIbDFyIiIjIbTBwIfIg58+fR3h4OGbMmOFYt3nzZlgsFqfMukRE1RVzFRF5mK+//hqDBg3C5s2b0bRpU7Ru3RoDBw7Em2++qXfViIjKxMCFyAPFx8dj7dq1uOWWW7B37178+uuvsFqteleLiKhMDFyIPNCVK1dw880348SJE9ixYwdatmypd5WIiMqFY1yIPNCRI0dw+vRp2O12HDt2TO/qEBGVG1tciDxMXl4ebr31VrRu3RpNmzbF7NmzsXfvXoSFheldNSKiMjFwIfIwTz75JJYvX47ffvsN/v7+6NKlC4KCgvDll1/qXTUiojKxq4jIg2zYsAGzZ8/Ghx9+iMDAQBiNRnz44Yf4z3/+g3nz5uldPSKiMrHFhYiIiNwGW1yIiIjIbTBwISIiIrfBwIWIiIjcBgMXIiIichsMXIiIiMhtMHAhIiIit8HAhYiIiNwGAxciIiJyGwxciIiIyG0wcCEiIiK3wcCFiIiI3AYDFyIiInIb/w/tT9ezctayNAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats_standardized.isel(feature=1,starttime=100).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "EO_ugNfykwE2",
        "outputId": "2c04ef62-8c29-4893-a92a-9563474bd23d"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x79c4772f5790>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's proceed to build a simple convolutional neural network that will take images as the input and predict the respective storm's maximum updraft helicity."
      ],
      "metadata": {
        "id": "yS-guBx7gOkQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras import layers"
      ],
      "metadata": {
        "id": "KDM-f2a1fnlB"
      },
      "execution_count": 117,
      "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(12)"
      ],
      "metadata": {
        "id": "AM2xE0yy6NPY"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lets use two input variables (dbz_grid and ctt_grid)\n",
        "number_of_features = 2"
      ],
      "metadata": {
        "id": "94Cj7eVlhByj"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "\n",
        "# input\n",
        "model.add(keras.Input(shape=(32, 32, number_of_features)))  # 32x32 images\n",
        "\n",
        "# data augmentation layers\n",
        "model.add(layers.RandomFlip(\"horizontal_and_vertical\"))\n",
        "#model.add(layers.RandomRotation(factor=(-0.5, 0.5)))\n",
        "\n",
        "# hidden layers\n",
        "model.add(layers.Conv2D(filters=16, kernel_size=(3,3), strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Conv2D(filters=32, kernel_size=(3,3), strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Conv2D(filters=64, kernel_size=(3,3), strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "# Now that we have 4x4 feature maps, now apply global max pooling.\n",
        "model.add(layers.GlobalMaxPooling2D())\n",
        "\n",
        "# Finally, we add last fully connected (dense) layers\n",
        "model.add(layers.Dense(32))\n",
        "model.add(layers.Dense(1, activation=\"linear\"))\n",
        "\n",
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "aGZqhCVfII4J",
        "outputId": "d8175c40-f28f-41a7-c8a0-d44499602b55"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_6\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ random_flip_2 (\u001b[38;5;33mRandomFlip\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m2\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │             \u001b[38;5;34m304\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m4,640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m32\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m32\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_17 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_max_pooling2d_5               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "│ (\u001b[38;5;33mGlobalMaxPooling2D\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ random_flip_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RandomFlip</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">304</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_max_pooling2d_5               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling2D</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m25,553\u001b[0m (99.82 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">25,553</span> (99.82 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m25,553\u001b[0m (99.82 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">25,553</span> (99.82 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Proceeding with compiling our model and selecting some metrics to use."
      ],
      "metadata": {
        "id": "AXkjSNM3mD2u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some common evaluation metrics used with regression problems are:\n",
        "\n",
        "Mean absolute error (MAE): $\\frac{1}{N} \\sum\\limits_{i = 1}^{N} \\lvert \\hat{y}_i - y_i \\rvert$\n",
        "\n",
        "Mean squared error (MSE): $\\frac{1}{N} \\sum\\limits_{i = 1}^{N} (\\hat{y}_i - y_i)^2$\n",
        "\n",
        "\n",
        "More evalution metrics can be found [here](https://scikit-learn.org/stable/modules/model_evaluation.html)."
      ],
      "metadata": {
        "id": "knrWFwJ5LU2A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "METRICS = [\n",
        "    keras.metrics.MeanSquaredError(name='mean_squared_error'),\n",
        "    keras.metrics.RootMeanSquaredError(name='root_mean_squared_error'),\n",
        "]"
      ],
      "metadata": {
        "id": "lPb4PhGRlZgN"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "    loss=\"mean_squared_error\",\n",
        "    metrics=METRICS\n",
        ")"
      ],
      "metadata": {
        "id": "o82dwXizjNTa"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10"
      ],
      "metadata": {
        "id": "QEdyoR97MTrv"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x=train_feats_standardized.values,\n",
        "    y=train_label_standardized.values,\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": "QfhYCYuVmXu7",
        "outputId": "4564ead3-0dc1-4674-d41d-ed695093bf94"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - loss: 1.5448 - mean_squared_error: 1.5448 - root_mean_squared_error: 1.2329 - val_loss: 0.8688 - val_mean_squared_error: 0.8688 - val_root_mean_squared_error: 0.9321\n",
            "Epoch 2/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - loss: 0.8129 - mean_squared_error: 0.8129 - root_mean_squared_error: 0.9015 - val_loss: 0.8857 - val_mean_squared_error: 0.8857 - val_root_mean_squared_error: 0.9411\n",
            "Epoch 3/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.7350 - mean_squared_error: 0.7350 - root_mean_squared_error: 0.8572 - val_loss: 0.8632 - val_mean_squared_error: 0.8632 - val_root_mean_squared_error: 0.9291\n",
            "Epoch 4/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 55ms/step - loss: 0.7058 - mean_squared_error: 0.7058 - root_mean_squared_error: 0.8401 - val_loss: 0.8178 - val_mean_squared_error: 0.8178 - val_root_mean_squared_error: 0.9043\n",
            "Epoch 5/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.6569 - mean_squared_error: 0.6569 - root_mean_squared_error: 0.8102 - val_loss: 0.7467 - val_mean_squared_error: 0.7467 - val_root_mean_squared_error: 0.8641\n",
            "Epoch 6/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.6394 - mean_squared_error: 0.6394 - root_mean_squared_error: 0.7994 - val_loss: 0.7054 - val_mean_squared_error: 0.7054 - val_root_mean_squared_error: 0.8399\n",
            "Epoch 7/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 37ms/step - loss: 0.5693 - mean_squared_error: 0.5693 - root_mean_squared_error: 0.7542 - val_loss: 0.7320 - val_mean_squared_error: 0.7320 - val_root_mean_squared_error: 0.8555\n",
            "Epoch 8/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 37ms/step - loss: 0.5209 - mean_squared_error: 0.5209 - root_mean_squared_error: 0.7210 - val_loss: 0.7087 - val_mean_squared_error: 0.7087 - val_root_mean_squared_error: 0.8419\n",
            "Epoch 9/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 63ms/step - loss: 0.4146 - mean_squared_error: 0.4146 - root_mean_squared_error: 0.6436 - val_loss: 0.6179 - val_mean_squared_error: 0.6179 - val_root_mean_squared_error: 0.7860\n",
            "Epoch 10/10\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - loss: 0.3684 - mean_squared_error: 0.3684 - root_mean_squared_error: 0.6067 - val_loss: 0.5071 - val_mean_squared_error: 0.5071 - val_root_mean_squared_error: 0.7121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"mean_squared_error\"], c='k', label='training')\n",
        "plt.plot(history.history[\"val_mean_squared_error\"], c='r', label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "KD2-89DcoW44",
        "outputId": "67eed4bb-6f22-4c04-b835-b22a1c93fb4a"
      },
      "execution_count": 145,
      "outputs": [
        {
          "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": [
        "Lets now evaluate the model using the test dataset. We need to standardize the data, but using the mean and standard deviation of the training set."
      ],
      "metadata": {
        "id": "rwxlG2G0xcQ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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)\n",
        "\n",
        "def z_score_nosub_testing(da, testdata):\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 testdata / np.std(da)"
      ],
      "metadata": {
        "id": "7zaFmAIUorvC"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# normalization of our training data (note each feature individually processed!)\n",
        "test_feats_standardized = xr.concat([\n",
        "    z_score_nosub_testing(\n",
        "        train_feats.isel(feature=0).expand_dims(dim='feature', axis=3),\n",
        "        test_feats.isel(feature=0).expand_dims(dim='feature', axis=3)\n",
        "        ),\n",
        "    z_score_nosub_testing(\n",
        "        train_feats.isel(feature=1).expand_dims(dim='feature', axis=3),\n",
        "        test_feats.isel(feature=1).expand_dims(dim='feature', axis=3)\n",
        "        )],\n",
        "                                     dim='feature')\n",
        "\n",
        "test_label_standardized = z_score_testing(train_label, test_label)"
      ],
      "metadata": {
        "id": "3jR6rF-lx55_"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data using `evaluate`\n",
        "print(\"Evaluate on test data\")\n",
        "results = model.evaluate(test_feats_standardized.values, test_label_standardized.values, batch_size=128)\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXfumUFnor4-",
        "outputId": "c037602d-2594-4d12-df14-8d0abf729210"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate on test data\n",
            "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.6155 - mean_squared_error: 0.6155 - root_mean_squared_error: 0.7844\n",
            "[0.6060198545455933, 0.6060198545455933, 0.7784727811813354]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate predictions using the trained model\n",
        "predictions = model.predict(test_feats_standardized.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSHfeMExzVqb",
        "outputId": "da5f05a3-c44a-4efe-d566-bb3601e5c122"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize predictions vs ground truth\n",
        "plt.scatter(predictions, test_label_standardized.values)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "xzOMgkXozb6V",
        "outputId": "d8cf4d03-a2c6-4fb0-8446-bd592ea373b0"
      },
      "execution_count": 150,
      "outputs": [
        {
          "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": [
        "# Tutorial Practice: How does batch size impact model training?\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "6soWOXwjyo8t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# recreate the model object"
      ],
      "metadata": {
        "id": "PZlR8mqqy3py"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# recompile the model"
      ],
      "metadata": {
        "id": "HOB_0mQEzJwz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# retrain the model"
      ],
      "metadata": {
        "id": "5oNHXJ4_zJ3I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the new model's skill"
      ],
      "metadata": {
        "id": "rMHPapkbzJ9m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Other sample questions for practice:\n",
        "\n",
        "*   How does the learning rate impact model training?\n",
        "*   Does data augmentation help with model skill?\n",
        "*   Does reducing regularization impact overfitting (hint: reduce dropout percentage)?"
      ],
      "metadata": {
        "id": "hsqlwFRY1aXd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# more cells for practicing"
      ],
      "metadata": {
        "id": "8rhORSiS4Dz3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Supervised Learning Classification Example\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "Hd9l4RhAC6gu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using our work from the previous section, we will now use the data and reframe our problem to be a classification task. As before, we will use two input features."
      ],
      "metadata": {
        "id": "CFMnVVa5zti6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "However, we will use updraft helicity values (which represent storm updraft rotation) and convert this continuous value into two classes, \"not potentially severe\" and \"potentially severe\". More strongly rotating storms have a greater likelihood of producing severe hazards like tornadoes and large hail. We will use 75 m2/s2 as the delineation mark between the two classes. The two classes will be creating using integer encoding first, with 0 representing non-severe and 1 representing potentially severe, and then converting using one-hot encoding for subsequent training."
      ],
      "metadata": {
        "id": "y5R40_674lUy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = xr.concat([\n",
        "    ds['dbz_grid'].expand_dims(dim='feature', axis=3),\n",
        "    ds['ctt_grid'].expand_dims(dim='feature', axis=3)],\n",
        "          dim='feature')\n",
        "\n",
        "labels = ds['uh25_grid'].expand_dims(dim='feature', axis=3).max('x').max('y')\n",
        "\n",
        "train_feats, train_label, test_feats, test_label = random_split(features, labels)\n",
        "\n",
        "# normalization of our training data (note each feature individually processed!)\n",
        "train_feats_standardized = xr.concat([\n",
        "    z_score(train_feats.isel(feature=0).expand_dims(dim='feature', axis=3)),\n",
        "    z_score(train_feats.isel(feature=1).expand_dims(dim='feature', axis=3))],\n",
        "                                     dim='feature')\n",
        "\n",
        "train_label_ints = xr.where(train_label >= 75, 1, 0)"
      ],
      "metadata": {
        "id": "hj6bAjZ7zt5v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_label.values[-2:])\n",
        "print(train_label_ints.values[-2:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnlyxpuOzuCl",
        "outputId": "6419f7da-9130-47c1-895a-fa5798673af1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[162.55905  ]\n",
            " [  1.8748366]]\n",
            "[[1]\n",
            " [0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert labels into classes using one-hot encoding\n",
        "onehot_layer = keras.layers.CategoryEncoding(num_tokens=2, output_mode=\"one_hot\")\n",
        "train_y = onehot_layer(train_label_ints.astype(int))"
      ],
      "metadata": {
        "id": "LzGNl7wUzuKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_y[-2:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7axDV9KzuY3",
        "outputId": "c1344e4f-24c3-4697-b37a-8ad12fffba96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
              "array([[0., 1.],\n",
              "       [1., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# out of more than 1000 samples, only 189 are the \"severe\" class\n",
        "print(train_y[:,1].numpy().sum())\n",
        "print(train_y[:,1].numpy().shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsRwqJjG7_Ec",
        "outputId": "2f2df4a6-2cb9-49c5-fa35-d16040fd681c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "189.0\n",
            "(1556,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class imbalance is a major issue with a minority class that is a rare event, like severe hazards. To address this limitation, we create class weights that help the machine learning model learn that the minority class is important to get correct."
      ],
      "metadata": {
        "id": "12FrqE9C7_mS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generate class weights due to class imbalance issues\n",
        "# use the second class as the positive one (using 1 index)\n",
        "counts = np.bincount(train_y[:,1].numpy().astype(int))\n",
        "\n",
        "print(\"Number of positive samples in training data: {} ({:.2f}% of total)\".format(\n",
        "    counts[1], 100 * float(counts[1]) / len(train_y)))\n",
        "\n",
        "weight_for_0 = 1.0 / counts[0]\n",
        "weight_for_1 = 1.0 / counts[1]\n",
        "\n",
        "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
        "print(class_weight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asYu-Bio70M6",
        "outputId": "c2857cec-a728-4f21-e3b2-38e17de83d59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of positive samples in training data: 189 (12.15% of total)\n",
            "{0: 0.000731528895391368, 1: 0.005291005291005291}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# metrics that are useful for classification, particularly for two classes\n",
        "METRICS = [\n",
        "    keras.metrics.BinaryCrossentropy(name='cross entropy'),  # same as model's loss\n",
        "    keras.metrics.TruePositives(name='tp'),\n",
        "    keras.metrics.FalsePositives(name='fp'),\n",
        "    keras.metrics.TrueNegatives(name='tn'),\n",
        "    keras.metrics.FalseNegatives(name='fn'),\n",
        "    keras.metrics.BinaryAccuracy(name='accuracy'),\n",
        "    keras.metrics.Precision(name='precision'),\n",
        "    keras.metrics.Recall(name='recall'),\n",
        "    keras.metrics.AUC(name='auc'),\n",
        "    keras.metrics.AUC(name='prc', curve='PR'), # precision-recall curve\n",
        "]"
      ],
      "metadata": {
        "id": "xHmN3l2z70QS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "\n",
        "# input\n",
        "model.add(keras.Input(shape=(32, 32, number_of_features)))  # 32x32 images\n",
        "\n",
        "# data augmentation layers\n",
        "#model.add(layers.RandomFlip(\"horizontal_and_vertical\"))\n",
        "#model.add(layers.RandomRotation(factor=(-0.5,0.5)))\n",
        "\n",
        "# hidden layers\n",
        "model.add(layers.Conv2D(32, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Conv2D(64, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Conv2D(128, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "# Now that we have 4x4 feature maps, now apply global max pooling.\n",
        "model.add(layers.GlobalMaxPooling2D())\n",
        "\n",
        "# Finally, we add last fully connected (dense) layers\n",
        "model.add(layers.Dense(64))\n",
        "model.add(layers.Dense(2, activation=\"softmax\"))\n",
        "\n",
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHBpYKnh70WR",
        "outputId": "53175725-91e9-4f03-acaa-f468edbcacee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 32, 32, 32)        608       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 16, 16, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 16, 16, 32)        0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 8, 8, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 8, 8, 64)          0         \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPoolin  (None, 4, 4, 128)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 4, 4, 128)         0         \n",
            "                                                                 \n",
            " global_max_pooling2d_1 (Gl  (None, 128)               0         \n",
            " obalMaxPooling2D)                                               \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 101346 (395.88 KB)\n",
            "Trainable params: 101346 (395.88 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "    loss=keras.losses.BinaryCrossentropy(), # categorical loss function\n",
        "    metrics=METRICS\n",
        ")"
      ],
      "metadata": {
        "id": "xS8iZ6Ro70bD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10"
      ],
      "metadata": {
        "id": "m0jmE_7k823F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Fit model on training data\")\n",
        "history = model.fit(\n",
        "    x=train_feats_standardized.values,\n",
        "    y=train_y,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        "    class_weight=class_weight,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3wCne8Q826e",
        "outputId": "1fc23ce2-824e-4764-e222-fc4cd4422722"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fit model on training data\n",
            "Epoch 1/10\n",
            "44/44 [==============================] - 8s 127ms/step - loss: 7.3008e-04 - cross entropy: 0.6251 - tp: 778.0000 - fp: 622.0000 - tn: 778.0000 - fn: 622.0000 - accuracy: 0.5557 - precision: 0.5557 - recall: 0.5557 - auc: 0.6789 - prc: 0.7216 - val_loss: 0.5818 - val_cross entropy: 0.5818 - val_tp: 83.0000 - val_fp: 73.0000 - val_tn: 83.0000 - val_fn: 73.0000 - val_accuracy: 0.5321 - val_precision: 0.5321 - val_recall: 0.5321 - val_auc: 0.7129 - val_prc: 0.7584\n",
            "Epoch 2/10\n",
            "44/44 [==============================] - 4s 99ms/step - loss: 5.8429e-04 - cross entropy: 0.5035 - tp: 947.0000 - fp: 453.0000 - tn: 947.0000 - fn: 453.0000 - accuracy: 0.6764 - precision: 0.6764 - recall: 0.6764 - auc: 0.8062 - prc: 0.8262 - val_loss: 0.4368 - val_cross entropy: 0.4368 - val_tp: 121.0000 - val_fp: 35.0000 - val_tn: 121.0000 - val_fn: 35.0000 - val_accuracy: 0.7756 - val_precision: 0.7756 - val_recall: 0.7756 - val_auc: 0.8727 - val_prc: 0.8823\n",
            "Epoch 3/10\n",
            "44/44 [==============================] - 4s 82ms/step - loss: 5.1437e-04 - cross entropy: 0.4644 - tp: 1042.0000 - fp: 358.0000 - tn: 1042.0000 - fn: 358.0000 - accuracy: 0.7443 - precision: 0.7443 - recall: 0.7443 - auc: 0.8480 - prc: 0.8557 - val_loss: 0.4306 - val_cross entropy: 0.4306 - val_tp: 129.0000 - val_fp: 27.0000 - val_tn: 129.0000 - val_fn: 27.0000 - val_accuracy: 0.8269 - val_precision: 0.8269 - val_recall: 0.8269 - val_auc: 0.8882 - val_prc: 0.8919\n",
            "Epoch 4/10\n",
            "44/44 [==============================] - 5s 108ms/step - loss: 4.2505e-04 - cross entropy: 0.3559 - tp: 1180.0000 - fp: 220.0000 - tn: 1180.0000 - fn: 220.0000 - accuracy: 0.8429 - precision: 0.8429 - recall: 0.8429 - auc: 0.9219 - prc: 0.9216 - val_loss: 0.2431 - val_cross entropy: 0.2431 - val_tp: 148.0000 - val_fp: 8.0000 - val_tn: 148.0000 - val_fn: 8.0000 - val_accuracy: 0.9487 - val_precision: 0.9487 - val_recall: 0.9487 - val_auc: 0.9866 - val_prc: 0.9870\n",
            "Epoch 5/10\n",
            "44/44 [==============================] - 4s 80ms/step - loss: 4.0987e-04 - cross entropy: 0.3486 - tp: 1207.0000 - fp: 193.0000 - tn: 1207.0000 - fn: 193.0000 - accuracy: 0.8621 - precision: 0.8621 - recall: 0.8621 - auc: 0.9237 - prc: 0.9198 - val_loss: 0.2236 - val_cross entropy: 0.2236 - val_tp: 148.0000 - val_fp: 8.0000 - val_tn: 148.0000 - val_fn: 8.0000 - val_accuracy: 0.9487 - val_precision: 0.9487 - val_recall: 0.9487 - val_auc: 0.9931 - val_prc: 0.9933\n",
            "Epoch 6/10\n",
            "44/44 [==============================] - 4s 80ms/step - loss: 3.7793e-04 - cross entropy: 0.3000 - tp: 1260.0000 - fp: 140.0000 - tn: 1260.0000 - fn: 140.0000 - accuracy: 0.9000 - precision: 0.9000 - recall: 0.9000 - auc: 0.9486 - prc: 0.9445 - val_loss: 0.2968 - val_cross entropy: 0.2968 - val_tp: 141.0000 - val_fp: 15.0000 - val_tn: 141.0000 - val_fn: 15.0000 - val_accuracy: 0.9038 - val_precision: 0.9038 - val_recall: 0.9038 - val_auc: 0.9685 - val_prc: 0.9686\n",
            "Epoch 7/10\n",
            "44/44 [==============================] - 5s 112ms/step - loss: 2.9649e-04 - cross entropy: 0.2460 - tp: 1269.0000 - fp: 131.0000 - tn: 1269.0000 - fn: 131.0000 - accuracy: 0.9064 - precision: 0.9064 - recall: 0.9064 - auc: 0.9624 - prc: 0.9579 - val_loss: 0.4005 - val_cross entropy: 0.4005 - val_tp: 131.0000 - val_fp: 25.0000 - val_tn: 131.0000 - val_fn: 25.0000 - val_accuracy: 0.8397 - val_precision: 0.8397 - val_recall: 0.8397 - val_auc: 0.8950 - val_prc: 0.8836\n",
            "Epoch 8/10\n",
            "44/44 [==============================] - 4s 81ms/step - loss: 2.7169e-04 - cross entropy: 0.2344 - tp: 1271.0000 - fp: 129.0000 - tn: 1271.0000 - fn: 129.0000 - accuracy: 0.9079 - precision: 0.9079 - recall: 0.9079 - auc: 0.9675 - prc: 0.9639 - val_loss: 0.3004 - val_cross entropy: 0.3004 - val_tp: 138.0000 - val_fp: 18.0000 - val_tn: 138.0000 - val_fn: 18.0000 - val_accuracy: 0.8846 - val_precision: 0.8846 - val_recall: 0.8846 - val_auc: 0.9419 - val_prc: 0.9370\n",
            "Epoch 9/10\n",
            "44/44 [==============================] - 4s 80ms/step - loss: 2.3649e-04 - cross entropy: 0.1940 - tp: 1298.0000 - fp: 102.0000 - tn: 1298.0000 - fn: 102.0000 - accuracy: 0.9271 - precision: 0.9271 - recall: 0.9271 - auc: 0.9770 - prc: 0.9746 - val_loss: 0.2613 - val_cross entropy: 0.2613 - val_tp: 137.0000 - val_fp: 19.0000 - val_tn: 137.0000 - val_fn: 19.0000 - val_accuracy: 0.8782 - val_precision: 0.8782 - val_recall: 0.8782 - val_auc: 0.9625 - val_prc: 0.9626\n",
            "Epoch 10/10\n",
            "44/44 [==============================] - 4s 93ms/step - loss: 2.0359e-04 - cross entropy: 0.1631 - tp: 1315.0000 - fp: 85.0000 - tn: 1315.0000 - fn: 85.0000 - accuracy: 0.9393 - precision: 0.9393 - recall: 0.9393 - auc: 0.9819 - prc: 0.9792 - val_loss: 0.2113 - val_cross entropy: 0.2113 - val_tp: 143.0000 - val_fp: 13.0000 - val_tn: 143.0000 - val_fn: 13.0000 - val_accuracy: 0.9167 - val_precision: 0.9167 - val_recall: 0.9167 - val_auc: 0.9783 - val_prc: 0.9788\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"accuracy\"])\n",
        "plt.plot(history.history[\"val_accuracy\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "H1FKdrnY829W",
        "outputId": "0afcbf6e-6bca-4a81-fcea-bc432aba349d"
      },
      "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": [
        "# normalization of our training data (note each feature individually processed!)\n",
        "test_feats_standardized = xr.concat([\n",
        "    z_score_testing(\n",
        "        train_feats.isel(feature=0).expand_dims(dim='feature', axis=3),\n",
        "        test_feats.isel(feature=0).expand_dims(dim='feature', axis=3)\n",
        "        ),\n",
        "    z_score_testing(\n",
        "        train_feats.isel(feature=1).expand_dims(dim='feature', axis=3),\n",
        "        test_feats.isel(feature=1).expand_dims(dim='feature', axis=3)\n",
        "        )],\n",
        "                                     dim='feature')\n",
        "\n",
        "test_label_ints = xr.where(test_label >= 75, 1, 0)\n",
        "test_y = onehot_layer(test_label_ints.astype(int))"
      ],
      "metadata": {
        "id": "zMbEUR_O83Aw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data using `evaluate`\n",
        "print(\"Evaluate on test data\")\n",
        "results = model.evaluate(test_feats_standardized.values, test_y, batch_size=128)\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZ4Zljo_-a7c",
        "outputId": "6e47a10c-f0c0-4ffd-9c13-9871dd3ef44f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate on test data\n",
            "6/6 [==============================] - 0s 70ms/step - loss: 0.2836 - cross entropy: 0.2836 - tp: 605.0000 - fp: 63.0000 - tn: 605.0000 - fn: 63.0000 - accuracy: 0.9057 - precision: 0.9057 - recall: 0.9057 - auc: 0.9548 - prc: 0.9461\n",
            "[0.28363198041915894, 0.28363198041915894, 605.0, 63.0, 605.0, 63.0, 0.9056886434555054, 0.9056886434555054, 0.9056886434555054, 0.954757034778595, 0.946064293384552]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate predictions using the trained model\n",
        "predictions = model.predict(test_feats_standardized.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fcwNVSZ-a_X",
        "outputId": "e5191ee7-11f5-45f4-e0d2-ed31e4145504"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21/21 [==============================] - 1s 21ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_ints = xr.where(predictions[:,1]>0.5, 1, 0)"
      ],
      "metadata": {
        "id": "tHLyxWLVAqed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.cluster import contingency_matrix\n",
        "contingency_matrix(test_label_ints.astype(int).values.squeeze(), pred_ints)\n",
        "# tend to predict more false positives"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrZYzjM9_eLj",
        "outputId": "1495ca11-b9ec-46d2-fe28-650495dd2151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[515,  61],\n",
              "       [  2,  90]])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_label_ints.astype(int).sum().values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UwAmxuqcBJ1K",
        "outputId": "0e0f8aea-f86a-4613-fa09-bbd83f770529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(92)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_ints.sum() - 92"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bb-w77AwBfUV",
        "outputId": "e49fa2a5-d2e9-42ad-81b0-b6326639c809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tutorial Practice: What happens if we do not use class weights during training?\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "KmokO1cq9_RS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# recreate the model object"
      ],
      "metadata": {
        "id": "0a43SpER-Mxd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# recompile the model"
      ],
      "metadata": {
        "id": "ITEZzW3y-M02"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# retrain the model without class weights"
      ],
      "metadata": {
        "id": "Xak_WCCB-M3-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the new model's skill"
      ],
      "metadata": {
        "id": "qwdW6YEP-NCM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# References\n",
        "\n",
        "Campbell, T., G. M. Lackmann, M. J. Molina, and M. D. Parker (2024). Severe Convective Storms in Limited Instability Organized by Pattern and Distribution. Weather and Forecasting. https://journals.ametsoc.org/view/journals/wefo/39/1/WAF-D-23-0130.1.xml\n",
        "\n",
        "DuVivier, A. K., M. J. Molina, A. L. Deppenmeier, M. M. Holland, L. Landrum, K. Krumhardt, and S. Jenouvrier (2023). Projections of Winter Polynyas and Their Biophysical Impacts in the Ross Sea Antarctica. Climate Dynamics. https://link.springer.com/article/10.1007/s00382-023-06951-z\n",
        "\n",
        "Molina, M. J., D. J. Gagne, and A. F. Prein (2021). A benchmark to test generalization capabilities of deep learning methods to classify severe convective storms in a changing climate. Earth and Space Science. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EA001490\n",
        "\n",
        "Molina, M. J., T. A. O'Brien, G. Anderson, M. Ashfaq, K. E. Bennett, W. D. Collins, K. Dagon, J. M. Restrepo, and P. A. Ullrich (2023). A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena. Artificial Intelligence for the Earth Systems. https://journals.ametsoc.org/view/journals/aies/2/4/AIES-D-22-0086.1.xml\n",
        "\n",
        "Molina, M. J., J. H. Richter, A. A. Glanville, K. Dagon, J. Berner, A. Hu, and G. A. Meehl (2023). Subseasonal Representation and Predictability of North American Weather Regimes using Cluster Analysis. Artificial Intelligence for the Earth Systems. https://journals.ametsoc.org/view/journals/aies/2/2/AIES-D-22-0051.1.xml\n",
        "\n",
        "Passarella, L.S. and Mahajan, S., 2023. Assessing Tropical Pacific–Induced Predictability of Southern California Precipitation Using a Novel Multi-Input Multioutput Autoencoder. Artificial Intelligence for the Earth Systems, 2(4), p.e230003. https://journals.ametsoc.org/view/journals/aies/2/4/AIES-D-23-0003.1.xml"
      ],
      "metadata": {
        "id": "Y8DYzxtePqCH"
      }
    }
  ]
}