data_processing.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/data_processing.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": [
        "# Data exploration, preprocessing, labels, and dimensionality reduction"
      ],
      "metadata": {
        "id": "2KdChQsIj3dH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we will be exploring a dummy dataset and demonstrating ways we can preprocess our data, add labels, and perform dimensionality reduction."
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      "metadata": {
        "id": "ZlJhB8h_j-nd"
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      "cell_type": "markdown",
      "source": [
        "First, we will import our python libraries."
      ],
      "metadata": {
        "id": "D7pVut1wj8dM"
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      "cell_type": "code",
      "source": [
        "! pip install keras==3.0.1"
      ],
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: keras==3.0.1 in /usr/local/lib/python3.11/dist-packages (3.0.1)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.11/dist-packages (from keras==3.0.1) (1.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from keras==3.0.1) (1.26.4)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras==3.0.1) (13.9.4)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras==3.0.1) (0.0.8)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.11/dist-packages (from keras==3.0.1) (3.12.1)\n",
            "Requirement already satisfied: dm-tree in /usr/local/lib/python3.11/dist-packages (from keras==3.0.1) (0.1.9)\n",
            "Requirement already satisfied: attrs>=18.2.0 in /usr/local/lib/python3.11/dist-packages (from dm-tree->keras==3.0.1) (25.1.0)\n",
            "Requirement already satisfied: wrapt>=1.11.2 in /usr/local/lib/python3.11/dist-packages (from dm-tree->keras==3.0.1) (1.17.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras==3.0.1) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras==3.0.1) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras==3.0.1) (0.1.2)\n"
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      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import xarray as xr"
      ],
      "metadata": {
        "id": "Sqm5E0yIjxJe"
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      "execution_count": 6,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "print(xr.__version__)"
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        "id": "ftGvnjmfNYtT",
        "outputId": "dd1f3d25-089a-4862-e887-8afebba57777",
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2025.1.2\n"
          ]
        }
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      "cell_type": "markdown",
      "source": [
        "Next, we will create some toy weather data to explore.\n",
        "\n",
        "Adapted from https://docs.xarray.dev/en/latest/examples/weather-data.html"
      ],
      "metadata": {
        "id": "0RUR373IkTW1"
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      "source": [
        "# set a random seed for reproducibility\n",
        "np.random.seed(123)\n",
        "\n",
        "# create a fake time array and annual cycle\n",
        "times = pd.date_range(\"2000-01-01\", \"2005-12-31\", name=\"time\")\n",
        "annual_cycle = np.sin(2 * np.pi * (times.dayofyear.values / 365.25 - 0.28))\n",
        "base = 10 + 15 * annual_cycle.reshape(-1, 1)\n",
        "\n",
        "# create the maximum and minimum temperature arrays for the dummy data\n",
        "tmin_values = base + 3 * np.random.randn(annual_cycle.size, 3)\n",
        "tmax_values = base + 10 + 3 * np.random.randn(annual_cycle.size, 3)"
      ],
      "metadata": {
        "id": "xlCXVuIhjxGX"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# organize our data into an xarray dataset\n",
        "ds = xr.Dataset(\n",
        "    {\n",
        "        \"tmin\": ((\"time\", \"location\"), tmin_values),\n",
        "        \"tmax\": ((\"time\", \"location\"), tmax_values),\n",
        "    },\n",
        "    {\"time\": times, \"location\": [\"IA\", \"IN\", \"IL\"]},\n",
        ")"
      ],
      "metadata": {
        "id": "FTZ2OoeTjxDN"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# let's check out what the dataset looks like\n",
        "ds"
      ],
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          "height": 650
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              "Coordinates:\n",
              "  * time      (time) datetime64[ns] 18kB 2000-01-01 2000-01-02 ... 2005-12-31\n",
              "  * location  (location) &lt;U2 24B &#x27;IA&#x27; &#x27;IN&#x27; &#x27;IL&#x27;\n",
              "Data variables:\n",
              "    tmin      (time, location) float64 53kB -8.037 -1.788 ... -0.04978 -5.016\n",
              "    tmax      (time, location) float64 53kB 4.307 7.471 6.975 ... 0.5702 -2.562</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-88325623-e06c-4875-8caa-17f728a11b7b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88325623-e06c-4875-8caa-17f728a11b7b' 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'>time</span>: 2192</li><li><span class='xr-has-index'>location</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2081ba19-b7a8-47ab-a390-5ce04ca834c0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2081ba19-b7a8-47ab-a390-5ce04ca834c0' class='xr-section-summary' >Coordinates: <span>(2)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2005-12-31</div><input id='attrs-7436ec77-4561-45ad-a21d-283e6a0d4f82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7436ec77-4561-45ad-a21d-283e6a0d4f82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fec0fdb3-3f45-4c87-8593-ff816b63c62f' class='xr-var-data-in' type='checkbox'><label for='data-fec0fdb3-3f45-4c87-8593-ff816b63c62f' 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([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-02T00:00:00.000000000&#x27;,\n",
              "       &#x27;2000-01-03T00:00:00.000000000&#x27;, ..., &#x27;2005-12-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2005-12-30T00:00:00.000000000&#x27;, &#x27;2005-12-31T00:00:00.000000000&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>location</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;IA&#x27; &#x27;IN&#x27; &#x27;IL&#x27;</div><input id='attrs-9d927651-7737-4829-a1ad-3c74560372b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d927651-7737-4829-a1ad-3c74560372b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d09218d-94f7-453e-ba65-72d5f57914bc' class='xr-var-data-in' type='checkbox'><label for='data-5d09218d-94f7-453e-ba65-72d5f57914bc' 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([&#x27;IA&#x27;, &#x27;IN&#x27;, &#x27;IL&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ca89f98a-b62e-49b7-8f52-f5920ba2ecdb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca89f98a-b62e-49b7-8f52-f5920ba2ecdb' class='xr-section-summary' >Data variables: <span>(2)</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>tmin</span></div><div class='xr-var-dims'>(time, location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-8.037 -1.788 ... -0.04978 -5.016</div><input id='attrs-954d927d-f88e-43a9-a20f-81eabd25b5fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-954d927d-f88e-43a9-a20f-81eabd25b5fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4415392e-ef9f-42f8-b60c-ce89b9745a5a' class='xr-var-data-in' type='checkbox'><label for='data-4415392e-ef9f-42f8-b60c-ce89b9745a5a' 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([[ -8.03736932,  -1.78844117,  -3.93154201],\n",
              "       [ -9.34115662,  -6.55807323,   0.13203714],\n",
              "       [-12.13971902,  -6.14641918,  -1.06187252],\n",
              "       ...,\n",
              "       [ -1.69423063,  -7.21105647,  -4.97185463],\n",
              "       [ -3.31676476,  -5.71190415,  -4.3566398 ],\n",
              "       [ -7.16906477,  -0.04978273,  -5.01583554]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time, location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.307 7.471 6.975 ... 0.5702 -2.562</div><input id='attrs-074588f6-8b1f-486c-a5a6-52382bcdce7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-074588f6-8b1f-486c-a5a6-52382bcdce7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6b2ecb9-0c39-4c65-97e4-e1cf546246c3' class='xr-var-data-in' type='checkbox'><label for='data-e6b2ecb9-0c39-4c65-97e4-e1cf546246c3' 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([[ 4.30727833,  7.47109686,  6.97517223],\n",
              "       [ 0.26998796,  8.41457293,  4.49930431],\n",
              "       [-0.32266826,  4.35932559,  2.15393398],\n",
              "       ...,\n",
              "       [ 2.06023458,  1.27394633,  4.72942339],\n",
              "       [ 3.76377227,  6.30889438,  0.40184244],\n",
              "       [ 5.58355334,  0.57022093, -2.56200428]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5e024a7-a095-43b0-b9dd-e7754abac7b0' class='xr-section-summary-in' type='checkbox'  ><label for='section-c5e024a7-a095-43b0-b9dd-e7754abac7b0' class='xr-section-summary' >Indexes: <span>(2)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-593791fc-34c3-4dc3-aecf-01dfb69ad375' class='xr-index-data-in' type='checkbox'/><label for='index-593791fc-34c3-4dc3-aecf-01dfb69ad375' 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(DatetimeIndex([&#x27;2000-01-01&#x27;, &#x27;2000-01-02&#x27;, &#x27;2000-01-03&#x27;, &#x27;2000-01-04&#x27;,\n",
              "               &#x27;2000-01-05&#x27;, &#x27;2000-01-06&#x27;, &#x27;2000-01-07&#x27;, &#x27;2000-01-08&#x27;,\n",
              "               &#x27;2000-01-09&#x27;, &#x27;2000-01-10&#x27;,\n",
              "               ...\n",
              "               &#x27;2005-12-22&#x27;, &#x27;2005-12-23&#x27;, &#x27;2005-12-24&#x27;, &#x27;2005-12-25&#x27;,\n",
              "               &#x27;2005-12-26&#x27;, &#x27;2005-12-27&#x27;, &#x27;2005-12-28&#x27;, &#x27;2005-12-29&#x27;,\n",
              "               &#x27;2005-12-30&#x27;, &#x27;2005-12-31&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2192, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>location</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f0b05761-a663-4fab-b602-f3d67456f824' class='xr-index-data-in' type='checkbox'/><label for='index-f0b05761-a663-4fab-b602-f3d67456f824' 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([&#x27;IA&#x27;, &#x27;IN&#x27;, &#x27;IL&#x27;], dtype=&#x27;object&#x27;, name=&#x27;location&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15b0cede-4927-47ff-a8ad-e52b39c3f3a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15b0cede-4927-47ff-a8ad-e52b39c3f3a2' 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": 11
        }
      ]
    },
    {
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      "execution_count": 12,
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          "height": 300
        },
        "id": "Jgu94rIkjeuT",
        "outputId": "246096e3-7485-4ed3-dfca-cf7a0f7bbf98"
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        {
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              "<xarray.Dataset> Size: 53kB\n",
              "Dimensions:   (time: 2192)\n",
              "Coordinates:\n",
              "  * time      (time) datetime64[ns] 18kB 2000-01-01 2000-01-02 ... 2005-12-31\n",
              "    location  <U2 8B 'IN'\n",
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              "    tmin      (time) float64 18kB -1.788 -6.558 -6.146 ... -5.712 -0.04978\n",
              "    tmax      (time) float64 18kB 7.471 8.415 4.359 7.562 ... 1.274 6.309 0.5702"
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              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 53kB\n",
              "Dimensions:   (time: 2192)\n",
              "Coordinates:\n",
              "  * time      (time) datetime64[ns] 18kB 2000-01-01 2000-01-02 ... 2005-12-31\n",
              "    location  &lt;U2 8B &#x27;IN&#x27;\n",
              "Data variables:\n",
              "    tmin      (time) float64 18kB -1.788 -6.558 -6.146 ... -5.712 -0.04978\n",
              "    tmax      (time) float64 18kB 7.471 8.415 4.359 7.562 ... 1.274 6.309 0.5702</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-e5dc58dd-163c-4b75-803f-5185af46fe58' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5dc58dd-163c-4b75-803f-5185af46fe58' 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'>time</span>: 2192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-12197381-0583-44af-a2ff-993a5b11c326' class='xr-section-summary-in' type='checkbox'  checked><label for='section-12197381-0583-44af-a2ff-993a5b11c326' class='xr-section-summary' >Coordinates: <span>(2)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2005-12-31</div><input id='attrs-e2292173-876d-429e-af14-e58bf470c386' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2292173-876d-429e-af14-e58bf470c386' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe02e790-1dac-45bf-bc57-6039db6b3f09' class='xr-var-data-in' type='checkbox'><label for='data-fe02e790-1dac-45bf-bc57-6039db6b3f09' 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([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-02T00:00:00.000000000&#x27;,\n",
              "       &#x27;2000-01-03T00:00:00.000000000&#x27;, ..., &#x27;2005-12-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2005-12-30T00:00:00.000000000&#x27;, &#x27;2005-12-31T00:00:00.000000000&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>location</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;IN&#x27;</div><input id='attrs-62784863-47bd-45ee-8cbf-a9516f6235dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62784863-47bd-45ee-8cbf-a9516f6235dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2366f82-25e5-44cb-8b6a-a764e9345100' class='xr-var-data-in' type='checkbox'><label for='data-d2366f82-25e5-44cb-8b6a-a764e9345100' 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(&#x27;IN&#x27;, dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8dd65a56-751e-495d-a0f6-50da350ffa9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8dd65a56-751e-495d-a0f6-50da350ffa9c' class='xr-section-summary' >Data variables: <span>(2)</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>tmin</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.788 -6.558 ... -5.712 -0.04978</div><input id='attrs-31bbcb30-414f-4c8c-b66e-f5048952a1c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31bbcb30-414f-4c8c-b66e-f5048952a1c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4433f01d-f8f9-4443-be97-c540c5fe2171' class='xr-var-data-in' type='checkbox'><label for='data-4433f01d-f8f9-4443-be97-c540c5fe2171' 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([-1.78844117, -6.55807323, -6.14641918, ..., -7.21105647,\n",
              "       -5.71190415, -0.04978273])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.471 8.415 4.359 ... 6.309 0.5702</div><input id='attrs-f363c56f-766e-4b6b-9bef-85f40b829710' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f363c56f-766e-4b6b-9bef-85f40b829710' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a3360c1-92b9-41a7-9193-5dfaf2ec3fe0' class='xr-var-data-in' type='checkbox'><label for='data-3a3360c1-92b9-41a7-9193-5dfaf2ec3fe0' 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([7.47109686, 8.41457293, 4.35932559, ..., 1.27394633, 6.30889438,\n",
              "       0.57022093])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-715e366a-996f-4810-89a4-9c917b8c3177' class='xr-section-summary-in' type='checkbox'  ><label for='section-715e366a-996f-4810-89a4-9c917b8c3177' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2625c35b-25b4-46ae-b545-e25e59883361' class='xr-index-data-in' type='checkbox'/><label for='index-2625c35b-25b4-46ae-b545-e25e59883361' 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(DatetimeIndex([&#x27;2000-01-01&#x27;, &#x27;2000-01-02&#x27;, &#x27;2000-01-03&#x27;, &#x27;2000-01-04&#x27;,\n",
              "               &#x27;2000-01-05&#x27;, &#x27;2000-01-06&#x27;, &#x27;2000-01-07&#x27;, &#x27;2000-01-08&#x27;,\n",
              "               &#x27;2000-01-09&#x27;, &#x27;2000-01-10&#x27;,\n",
              "               ...\n",
              "               &#x27;2005-12-22&#x27;, &#x27;2005-12-23&#x27;, &#x27;2005-12-24&#x27;, &#x27;2005-12-25&#x27;,\n",
              "               &#x27;2005-12-26&#x27;, &#x27;2005-12-27&#x27;, &#x27;2005-12-28&#x27;, &#x27;2005-12-29&#x27;,\n",
              "               &#x27;2005-12-30&#x27;, &#x27;2005-12-31&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2192, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83705958-939d-47ab-864e-3e3e41d8fa96' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83705958-939d-47ab-864e-3e3e41d8fa96' 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": 12
        }
      ],
      "source": [
        "# we can select a location using the location string\n",
        "ds.sel(location=\"IN\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# if we prefer a pandas dataframe, we can easily convert\n",
        "df = ds.to_dataframe()\n",
        "df.head() # few first few rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "ixkAqRZik1C5",
        "outputId": "10fe7517-0015-4217-90ba-3c08f5fc3681"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         tmin      tmax\n",
              "time       location                    \n",
              "2000-01-01 IA       -8.037369  4.307278\n",
              "           IN       -1.788441  7.471097\n",
              "           IL       -3.931542  6.975172\n",
              "2000-01-02 IA       -9.341157  0.269988\n",
              "           IN       -6.558073  8.414573"
            ],
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6bbe80ab-8591-4f0c-9694-aedb67f5e696 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6576,\n  \"fields\": [\n    {\n      \"column\": \"tmin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.974887756635761,\n        \"min\": -13.506233981963057,\n        \"max\": 33.45605975961401,\n        \"num_unique_values\": 6576,\n        \"samples\": [\n          29.54754582742315,\n          -3.3195097382029166,\n          13.074127407421333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tmax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.066758261364901,\n        \"min\": -4.670115993218161,\n        \"max\": 46.91589687971752,\n        \"num_unique_values\": 6576,\n        \"samples\": [\n          34.8617751901723,\n          6.361539746696495,\n          23.75178983498861\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data exploration section"
      ],
      "metadata": {
        "id": "jKOfmXMfk-vq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# lets view some simple statistics of tmin for each location\n",
        "# we can group by location to more easily generate the stats\n",
        "df['tmin'].groupby('location').describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "By2I2raNk6Ar",
        "outputId": "20e66f7f-3edd-43ac-cf01-df448ffa4501"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           count       mean        std        min       25%        50%  \\\n",
              "location                                                                 \n",
              "IA        2192.0  10.090275  10.983128 -13.506234 -0.048902  10.005512   \n",
              "IL        2192.0  10.014051  10.976044 -13.306257 -0.015989   9.927050   \n",
              "IN        2192.0  10.048070  10.970363 -13.395763  0.047243  10.051561   \n",
              "\n",
              "                75%        max  \n",
              "location                        \n",
              "IA        20.129497  32.087925  \n",
              "IL        20.135186  33.302508  \n",
              "IN        20.022142  33.456060  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-87d4ecf7-357a-415a-84d3-0fe1d194ccea\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>location</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>IA</th>\n",
              "      <td>2192.0</td>\n",
              "      <td>10.090275</td>\n",
              "      <td>10.983128</td>\n",
              "      <td>-13.506234</td>\n",
              "      <td>-0.048902</td>\n",
              "      <td>10.005512</td>\n",
              "      <td>20.129497</td>\n",
              "      <td>32.087925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IL</th>\n",
              "      <td>2192.0</td>\n",
              "      <td>10.014051</td>\n",
              "      <td>10.976044</td>\n",
              "      <td>-13.306257</td>\n",
              "      <td>-0.015989</td>\n",
              "      <td>9.927050</td>\n",
              "      <td>20.135186</td>\n",
              "      <td>33.302508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IN</th>\n",
              "      <td>2192.0</td>\n",
              "      <td>10.048070</td>\n",
              "      <td>10.970363</td>\n",
              "      <td>-13.395763</td>\n",
              "      <td>0.047243</td>\n",
              "      <td>10.051561</td>\n",
              "      <td>20.022142</td>\n",
              "      <td>33.456060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87d4ecf7-357a-415a-84d3-0fe1d194ccea')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-87d4ecf7-357a-415a-84d3-0fe1d194ccea button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-87d4ecf7-357a-415a-84d3-0fe1d194ccea');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-189909ad-7b9e-486f-b2d1-50570047e167\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-189909ad-7b9e-486f-b2d1-50570047e167')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-189909ad-7b9e-486f-b2d1-50570047e167 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "summary": "{\n  \"name\": \"df['tmin']\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"IA\",\n          \"IL\",\n          \"IN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2192.0,\n        \"max\": 2192.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2192.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.038185131865465675,\n        \"min\": 10.014051135982465,\n        \"max\": 10.090275023545725,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10.090275023545725\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0063950307699156585,\n        \"min\": 10.970363358709097,\n        \"max\": 10.983127756566068,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10.983127756566068\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10017146250292784,\n        \"min\": -13.506233981963057,\n        \"max\": -13.306257018847221,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -13.506233981963057\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04886294738609381,\n        \"min\": -0.04890197512250034,\n        \"max\": 0.047243439708810064,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.04890197512250034\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06295497867997711,\n        \"min\": 9.927049725228528,\n        \"max\": 10.051561174658456,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10.005512307826283\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06368720182169264,\n        \"min\": 20.022142129292952,\n        \"max\": 20.13518588605553,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          20.129497174213256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7495089932180267,\n        \"min\": 32.08792522576775,\n        \"max\": 33.45605975961401,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          32.08792522576775\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now create simple stats for the tmax of each location\n",
        "df['tmax'].groupby('location').describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "RyALBRkYlLHc",
        "outputId": "34b02ac0-5127-4d48-d47a-92e177deba0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           count       mean        std       min        25%        50%  \\\n",
              "location                                                                 \n",
              "IA        2192.0  20.032035  11.070360 -2.518939   9.727204  20.159604   \n",
              "IL        2192.0  20.021919  11.104833 -3.547442  10.115834  19.868200   \n",
              "IN        2192.0  20.066633  11.029955 -4.670116   9.656586  20.418327   \n",
              "\n",
              "                75%        max  \n",
              "location                        \n",
              "IA        30.185495  44.799206  \n",
              "IL        30.332708  43.207552  \n",
              "IN        30.248937  46.915897  "
            ],
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              "      <th>IA</th>\n",
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              "      <td>30.248937</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df['tmax']\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"IA\",\n          \"IL\",\n          \"IN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2192.0,\n        \"max\": 2192.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2192.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.023447488079881423,\n        \"min\": 20.021919385382834,\n        \"max\": 20.066633362262323,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          20.03203507914089\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03747824525026957,\n        \"min\": 11.029955170025163,\n        \"max\": 11.104833377930849,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          11.07036026230279\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.075931947890277,\n        \"min\": -4.670115993218161,\n        \"max\": -2.5189390659043855,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -2.5189390659043855\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24729529654539487,\n        \"min\": 9.656585658571462,\n        \"max\": 10.11583415138651,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9.727203592318908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27522547478542964,\n        \"min\": 19.86819958160258,\n        \"max\": 20.41832706075339,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          20.159603560618528\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07384030797352577,\n        \"min\": 30.1854945123428,\n        \"max\": 30.332708028299674,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          30.1854945123428\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8603569031278024,\n        \"min\": 43.207551792750515,\n        \"max\": 46.91589687971752,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          44.79920584541566\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets take the mean of all locations and plot the time series\n",
        "ds.mean(dim=\"location\").to_dataframe().plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "DrPWwAIjlQVG",
        "outputId": "03093cca-4a5e-4e1d-af7d-4b62422a5f05"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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L5WgYBqjRFAiPVb7v38JqTV05ukL4WhCrDi3uR8FUvhwpncblAvr9D7h5gTwblfwsv4tlPyQElYc2eP6zNSpU80QmV6E7DFLV8wUVC/vwor2TWkryld9z8Qg3HgO/oZBwuwPELkDObIs1u1K67z8QZ7CAsL8KgLujggoqvpSVAL8+LC8tu/5ajwK6TwSS24rcwAD/vuFX8WyH1Dub1NLzn6NRUSCoFOUAeXQjAhmXR+MHAF0f8PwP4u2osrBQSHsn1agwDCsrAYLDqo6DZQgqM6sBLyYCJYXSaZ1ORHzVZ8UaFeu+hKbB76jkcvGotuWwKls+B7Z9ITMxqz8ICgaGzAJajxS/ZcVMj60BhUuZSkHllVTgjeZAofWD7BmOywX0nQpMWAcMecVzjj9RZte1xZbcqKBiJEw5T6OiINrlMRnr5E5n4EzWgUJB8dhqYN+vWpbG/qjVorxH2NroRJQ4GiNpC9iTEiHWvi3/GXampBDY9Im8tD0mA3ABncepm+Xr4YfJ7sQke5Z6k1tXLfny33+21nnZVOPKJgMqqKhFjcvhUR/xNCpKwnJb23OgIcSmVH1WOsiWFgDf3aldqAMnwN5RYbEZlDVQUCek9ihHUPEn4q3VObHWY/R5dgew6mXxGF7s+qvRFPhfBjD8HUsvR5jChSMezbxShr3le87N2xF4Jafq88a5yp+hI9SFvlrUdOyN+vCWIKjw4UWp4Kc2NkXBeSC+rrp7nUZMEuuACio+KHrHSRoVGd2rk2NQzb/W8//LkdLv3L08o+/QCM9/RZM5h7P/N+DbO4AGvZTfS6p/vuuCnHTu8ZmtQJ3Oyp+lA1SjohY5kv7tP3IN6oLDuTstlGDxoFGGo3bZIpCMRksJ4R2eSQeSWns+dxlvbHnsROkVYN278tOT2lXNltL3Sfm5cQJXLku7wg+JIJ8f+hoQlah9mexI5dLZiX/lpa+06avfk3y9XEIz84kWMYS0IYB6bY2Rs4ukUR9g1IdVxyHhyuxSODhcUNn8qcIbVNaHVIfpFC4e9Wyf5xMRD4xfDoz/m7tVni79cFHqH4k0kWjQAxgxR/y+cgdrVNhIThAE2l9iY2Dybs2LY0uULoO1GObxO3XnT+TrNhKSqaCiFjmNxhUMJNSrOg4KBsJjgJbXA00GKluCoBoVLlSjIs57nYAjf5OvhccC9brw2hQVVDgoHRSE3s+Od7DSEIRkGw0WfiG1w0xUUKZ9HwDpyUTza4GbWbvUXEEee58QAVspG7W9AOm1dUDOdtegIM8MdvJu4InDVedv+RK44wf120Mp6gW3QBFUlMLuBJsN9Q1ixicvk7y0FCikiPlIEYDkDt7JNipspDSZ1GhWGqk6qtYQaDWi6ljKmLv5UP/LZBC011aLkhcrIZVnuFhB9cZAeBwQnyojEzqr4KBaoxIA9ajKZwxLULl1IdD+NvHkbzQD5shwFe9Umg6Sn/bGz4HoJGDMt77XLh4GMgJgaUNyUiaiLQgJ17QotkVqzOEbb0sJKgn1gBrN/SuTQVBBRS1aeDoNCQOePAI8sk06rZMH2MN/iV/v9QRw03zuudBIYlJJnGyLcXQVkLlPXduMq131OSgIsgTj7HTpNI5FwfvYZjTwxCGgvkDIjA97AukbtCmWVZGaWIgNwkHBwJQDwOQ92pbJbkgJKnz7Rzm7zmyyA5JuT1aLVqpKOlsAfn9c/Hr/533PCe0SkMKpKubzh6pi0jynIo5MRDzw8DZ5vj8CASmBVuvt9AeXAqndleVpJ/wRVAAgriJ+0qM7PULdTw9oUy47IVVHEXHcYznvstoJn8FQjYpaDA9y52CNihoth1oNk1Nj/lw4WPVZbdtMbFxl/O3g5qYNtIIUIWlMKzOfag2A9rf6WxqbIlFJcXW4x3IElchq6otjIFRQUYvWM/Pr3hS/7uR+UawuK+NSGPEsO8MW9jQRxmQ2uOxTGjzLhrAF5bRJWmSoQR4WRsqYNjLBkGLYGqm+q8Uw7rGcoLf9CNpqC0IFFbVoPTPvMh5IauX5TNpx4WDTCsEX8PFDQPcJwvfdvQwY9BLQWIFjogO/e/4brhEzECO/29ttjHuWoUi8cGx7gMEvy8924Ivk8062QQPENSpXP+zR5lHEkRJUQnnL4cmtpfOMTVZfHgPRVVCZO3cu2rVrh7i4OMTFxSEtLQ1//FHlSKmoqAgTJ05EYmIiYmJiMHr0aGRmZupZJO2QWq544B/led691OPNlu9OGvCsYTsVoRdQaP9/JfXTPJ0ce/27+0Txe9a8CmTtB2bXA1a/qqycdkELrVHbmzz/6znYbsIfut4HJDYBeknYV/ERtElzuqAiMLt/ZLtnskGRRu57fe8Kj4a+2RB9y2MgugoqdevWxSuvvIKtW7diy5Yt6NevH0aMGIG9e/cCAB577DH89ttv+P7777FmzRqcPXsWN9xwg0SuFkFq1lqrvfI8I6sBTQeQ41ts+Ux5fnZB6AWUbbvC6uSHzJJO/udzniCFq2WktSNaCCrV6nvc7d+9FHhoo//5OY3IBODhrUD/aQpvdLhAIkRUdfL5ag3V5deB4HXZ6ZSXyUtX9yqPht5BWjpdd/0MHz6cc/zyyy9j7ty52LBhA+rWrYvPPvsMCxcuRL9+HtX9vHnz0LJlS2zYsAHdu1t8JqenrUOgOSUj1WWH24U7N385tVmffE2FJdQJCdEPrlWWZWWskKQW6opkZ5y8jd0MhOL1qB1Mo2uqL4sdycsAzu/XJ+/7Vloqrg8Jw0bE8vJyLFq0CAUFBUhLS8PWrVtRWlqKAQMGeNO0aNECqampWL9+vWA+xcXFyM3N5fyZgpiNSqSfA2ygxKPxQhgURn4g/3alET6Lc6TT2BmhtpniVHsSB+Cg2a8hBFp9bfxQOo1aLBIhWQzd/ajs3r0baWlpKCoqQkxMDH766Se0atUKO3bsQFhYGBISEjjpk5OTkZGRIZjf7NmzMWPGDJ1LLQMhjUrDPr7OyZQScBoVP2evPR/zGJI1Hew5jqwOXLnkf7nsBGfXj0N3NhmBuxwoLTThwQ4feI1sk07UhgUHtr8t3UfE5s2bY8eOHdi4cSMmTJiAu+66C/v27VOd39SpU5GTk+P9O3XKpO2RQur1+j38X7IIOEHFz04sNMIjrCRX7Jp6aD2QerX/5bITbtb6tcpdP8Vl5fhp+2lk5QZwDJ9P+gGz6wL5Bhv1u1xA4SWPZ2EnormgIibYOVBQCXDHoLqPiGFhYWjSpAk6d+6M2bNno3379njnnXeQkpKCkpISZGdnc9JnZmYiJSVFML/w8HDvLqLKP1O4cpl8Pki8So+dz8fH/xzFlRKRwSTQghVq3YnFpgBtbGKUrQWXTwI/jq86Vrl1fs7KI3js250Y9t5/GhXMhpzb4fl/aLlwmogE7Z/7z+vAaw2BuWnOFFaM1HI4UaOo1hM3j6LScmw6fgll5faqI8On7m63G8XFxejcuTNCQ0OxYkXVVtyDBw8iPT0daWkCMTGsxA93k89L2Jf0e2MNZi09gLdXHBJOFGjrr3p0LIHkCn7de9zjnYTgdzL4e7/H9X5WXoBE9BVDbMLx4L/6PvukQqNnO1Ap+HW8Q/9nOXHpRyONysPfbMfNH63HOysOy7uhOB84sgL4bixQcEGTMqhBVxuVqVOnYujQoUhNTUVeXh4WLlyI1atXY/ny5YiPj8f48eMxZcoUVK9eHXFxcXj44YeRlpZm/R0/G+YKX5OpDdlyQkAjE4jo0bEEkqqUv1S4Rp03Xwd2734gMFkY+4snGjpFGRcrBkYZu3UYhoFLarLWbAjwn4A3b6pREeSvfZ4lzXlrT+DxQTIiJ89mueUPiQBu+FiTcihFV0ElKysLY8eOxblz5xAfH4927dph+fLlGDhwIADgrbfeQlBQEEaPHo3i4mIMHjwYH3ygYLeHGVw+ASx7Rvi6zB07bidK/arRoS5IvmicSqAtFRoByU5s0MtAo74+py8VlCAmPARhIRopqANNo8oir6gUw9/7D/1aJGPa8FbCCVO7CV9zYt8aFq1pdgy/jppfK+1UNOeMpmVQgq6CymefiTspi4iIwJw5czBnzhw9i6EtRRLboXkd3KHMPGw4dhG3d6uP4KCqDsit9F0Kj1d4g43gz4DkxKiQIpCs5DUyvvbpvAIZkrBQ03cGeib7Cnq8shINa0Rj1RN9ZWQc4HXMNvQmxDf7fstpnLhYiM/XHhcXVMRwokZF4w0WPq1w5AfAqw3Ebzr5H3B2O1C7o6ZlkUOAbS8xAN7sdtBb/2DaL3uxcFM657zkoNDlPu6xlDt5O8PvWG7/3v88G/T0Pw+7QDUqxkAYAFfu96jSj18o0PBBDtaosOuQsDyrjabZicKgzt8pshrw1HHpdB/31bccAlBBRSlSalkByXfXqWzOcbmUSuVqXkRWJ84SKuF/t7AY//OMTACmngHG/e5/XlbHTweBF/OLsXT3OZSq3QngdnDbZEN6B/VYpnHy0g97Gz1BIJO0TWFz7f+Rzzuxr9T4OxHlQb08gWuA7g7fAoIRc4BfKoLhyZzdSi798AUeJ758lfC/m1b2JeExgbH7x0+Nyui563Dioh9OztylQJADltrYAtfRlb7XnfwOGgVbUCEIJYpEtK73AdkngfUfcLfkO/F30nhZlrGZ1olqVBRDeJXYW+4EghHym4Xk0k9ACSq8utDUvsTBs9NK/Fy/9ktIAXizZBsj5X+G8A4Kta6s3CIUl6nzZ+PoNiuhUVHMwBeBp09wzzFu1U4PLYtY/z9gBjBRWfwySbmn9ShF+ekNFVSUIqSanLgJuP0HwbgJ/IYh2VD4g4+j1et8QUVcC3KpoAT5xWW4UlKOolKJDikQvPwq+I5Ld5/Dy7/vg1upNbeYk7PyUmV5WRUpgYskqBC6g+MXCtB11goMeVulv5VAWfohaVSUfnWXC4jgOf0sOA+83gT49RHl5bMqYgNGz8lAzWbKshO6cMePQLOhwODZivLTG7r0oxShBlOzOXFXgPc2XtOQNhrjvbElecD2r4xxmGQ0/Ng8IobDeUWl6PTiXwA8fVRUaDD2zBgsvLbt4D7fiwIblYe+3gYA6JRaDUPb1pL/jMm7gFcE/Ic4RaOiUFApKi1Hcamv8PL3vioDW1k+QfgU5Xh2F/IHYCfA0XTo9HJu/tzTn2xbAFz/rj7PMJLCS8Dvj5OvtbtVXZ5Cw0+TAZ4/ixEA002N4auHWwyTeR/3sFxSUCFc/2UikL5R3vPsRPOh3GMRjcrhrHzvZ4YBCkrKUVouVpcBIKlIhG0gcSFfoffZiHjgzp/J15ysUWl3S9Vn1jvrdjPoOPMvzFzi6+6e7Yag04t/YXu6QueOf00DXqnnvOULgCtU+2ujIoi97C8kWf6scMT3kSLOR0WgNipOh995dJ+gKhtpOUUgQe5pVc+zFSKCCqlaRLVTAbH0o9yYVvEsHwBS2pHPbxH3l2QbSIIBu25ZGpUrpeW4wlt23HDsIhZtSsf3W6ve0cuFpZi0cLu68pQ5MDhkU/ZsXXrXz9Hz+SgscYjGTi3nDwpfUzFJAeznEy8AenGN4a9TyxwkFBvTChlPrXkdyE4nX7Mrfu76yRSL9uvk9f5KVOxseu7nPTiSlafM4FOoLv95XfHzLQlJo1LO0jxJvLO3frwBzyzejf3nuE4hyxxtX6aQViOrPku8m1tOXEL/N9ag1bTlyC9WIKw4TRPlxy7IvKJSrD960ccmzWZyChVUFMN/CQgzdpIQwjAMFqw74T2WXvoR4Px+4O229hOJxeDXqejA6/u9+7y+WiR9AAgql2U4aiIweu56bE/Pln+D04U+kqBSXLXUiOQqT6lKqiIzt1ja6JtYHocNuIBkxbEvL95e5bKd3XdKcn6/wkJZnCBlgkpBcRn+OXQepeVu3PrxBtz2yQZ8sf6EPmUzCCqoKMVHo8KtwoMZeeg6awW+2nCSexuAF37d6z2WnGTF1wVS04Ck1uTrB2zuyKy81OOOecWLwO7vuNdEAgoqls8CYelnk7pAYTlXFNqWOL0uiYJKnmfr550/A8lV76LSdjh39VHyha73C98ktV3a6ki1FwkblUKWFqVAiUbFaQQr2/Ny74ItGPv5Jry74jD2nvVo937cxo3TY7dwGQ7veXSA33nw1ggf/34HzucV47mf93DOrz96UdlzXC7g7j+Au34lX7f78s+SxzzumP8V8C6pFU7XAogx9HUgtraGGcqoy6wDQOkVDZ9pICQNRnGeZ+tn42u4SRV29Pt4y0FexLww23XJaOPHwNYFkG4v4tcLSqp+D/bKBcMwvnYrD65VVkY7oVCjsv6YZ6z5ZtMpwTT2ElOooKIciaUfoVlqVh53l4Wsjs7lEp6V2D068PYvVd1mtxfMVLqMB9qO1i4/KaHv0HLgg27ApwO1e6aRkDQqAloNpW5oBGsuKlH4JjtqVPIygT+eBH57RLr8pL6N1cbYwghbA3DXvM1oNW05zmazBOKUNkCfp1UX29Jo0Ne7XB7D5EpsplChgooisvYDX1zPPcczpi0slte5yJ6RCQkqWkQYtiFloluRCbDreQph7dqus1Y5uIKAFsM9n2OS/c9Pqs1u/8rzP3O3/88yA6KgQm4fSlXngjJe53Ei5bGhoFKSL52mkgjxiPC5V1iCCuv8P4fOAwB+2s5dzkB8XfnPthMa9fX931ij7IbG/TV5rhZQQUUOO78FPr4G+GyQ7zWeIFEgcytdZm4xvt54UjqhUzUqKpEM5ugDK31YNHDjPO7lbfP9LZJ1cbmA1G7AQxuAh7eaXRrrQxJUBISFDccuEc8rJjQKuO5N8jU7alTkCHDXvQlcNR5o1Nfn0su/V/ml2X2myncIyZOyj/BXraHcUtoLBX396cvscBh+qk2ia/p3v4YE5rRcKT+JGLzxBIkigqdKIf730x7c3q2+eCJBjUpgCSrb0i/DBaBUqQaE3XG6gn1f+k2fAFfd43f5LE1SSwDAx/8IGHTKRbLDtJk+mY8CjcqDXykT/AT7BZcLgvVmR42KnJhkXcYLXhKqJ9L8xMVfUIuv45vo0nGgus0FGAV9fc9XV3k/X8gv8X5WtdRjIfs+qlGxOkKRcZ2qUWk21OdUQXEZbvhgHUZ9sA5XSpR23mxBJcj3pc/aB+SeVV5Om1HuZjBr6QHZ6S+SPNeGRgLD3yHfwDD2W/jmQxIMNAoGuqZiuYLDqI89g4FQvdlRoyImrN72LefwcGYeBr21Bkt3n5PMlbRUTtSo8PuPdztI5m15FO76IVGmWBMNS+3ys05J7IqfcU4qVZor9mdi2i97UFIms2N0oqDS91lg9Cfew7yiUkz4aiu+21Jlva7I8RPAHWiCCBoVAFg1S2lJrQ/P3b3Qktmdn5FDMrBnZhyEbCrKS8jn7YQCY1rVVMZRia4JtL9FPK0d7afEhNXmQziHjy7agUOZ+d74Uz/zbU6U4nIBYxb5l4cVEdKoJHC18WJbuMsJbYltXEvGOhoVuvTjLyKCygQZ6uFyhkEQXBi/YAsAoFGNaIzrwVJVCjk/s5C0q4rgMN/Brc1oIDzWe/j+qiP4Y08G/tiT4T0nNOAKBn9jeBoVkqBSpjDujR2oWO6pRMh4WyhOEt89vCQv17KpBoAFUVDRWEuU2Bh4bB8QWY31DAGBxI71KfRdbvMVIPKKq3ZIMgyDyd/uEMyWqFFRXDibIjQpHfsz9/DzTYJZkDQq/d9Yg/0zhyAyTEBrT5d+HITIOjJ7gBWCP/Ce47uDDwomu+nXSCVtGqTy85a5zuf5ChBl5fLXsCseVPXRFUyendi9LknwBFmlfj8UY8dBlY+OSz8c4usAYVGsZzjIRkVo4kbY4cP+2uLepWUu/TgVIY1K9Uacw60nhYNfHjtfQDx/qVBEE2qhCqaCir+wBldFcVMqeOUPGXYDcQSnXXYeXA/8Tu7QeDMHH2M5ACUCGgDB3UDs0y4XWUPlhEGWj4+gYlI57ET6Bt9zhrxnDrJRERJUeIPtm38exOnLVX5Q0i8V8u/gZivHmFaIHd/IS2dVNLBREWKZ6GSaCirOoM1ooHZH7+ELv+wVSUxmvqwYFoQGY8fZViU7BdaRZfgLOCXQoQlrDNgaFRf5pffTzsiS8NqH8m3dnp1WRBr0UlMia5O+EVjziu95wnumuftxQWNaG05GhPol1ntXUubGuyuPKMr2hy2nkVNYig9WV90ne8L/84OKnmU5dGwHLy7ZJ3zRQuYF1imJ3ajXDbjxc87bsmizsMtiMW77uGomJ3uWYMdOrJLiPPJ5nqBC6oiEBDtBQYVfTyQ1qh2NFqUI4WqOVh/MUpzFDR+sw4EMguv3279XWyrrcvwf8nnCe6ZG6BPHSUs/AvGjWEs/auqvpNyN7rNX4LVlB1WWy8bv+Lr3zHluj0fMeS4BKqio5eYvNMuqMjaDICTZxc5bQcOiyef98MAo2PfVbM49Ji39XDqm+rmmI9QBs4014dlhoYYhb/+L3CLe4BMaqSovSyM0P+j7jM8pVVs9AXSY+SfGfKJgecmOkxGSdjKuDseeQq29FN/A+6iA3QWRr29U9Uyrk11Y4vt+akX1RkCN5tLpDIAKKmqJTTHuWeFxvufKinzP2QWZgsoPW0/LzlJwlhYRDzx5FJhakRdp6cfOYeEPEqJoN79W00dk8Q28nYiQmrvLvT6nSgUMuqXILizFuqMXvS7gvTjdmJbne0etoMfnm00KArMeXaHJM02nYW+gzzPAHYtRUuZGh5l/od30P3XQ8lUgEsneSKigYgdIMTF+neT5v/h+4Oub7aVhCY0in/fDN4zbzSArrwhD3v4HTZ5dinvmb65yux1do2rbs5AFvV1Vwytfrvp8z3LgriWeJUkNIQ4sdbtq+gzTERJUCOuP/g4Kf+3L5J0RyO/zQcDlE349y3BIwhXvvdZtUA0E3OXANVOBJv05mpSLBTq5WLDIzh8qqNiBlsPJ5/PPA7u+BQ4vt9fyhVDj92Ppp/+bazDj1304kJGHMjeDlQeysP0UwRhUyC/Nxg+BMhs6LWNrg4JDgYa9NF+a2XU6B1l5PK3KuN+BR3dq+hxzkd8hC/mekUs5f1IhtsTz++N+PctwSJrelHbcJHadFFgBls+nYFY/ejFfp77LIga11igFxcupy4RdLV0FYg39+b+qz3bRqGSfAkpZnRnblqLipTh9uVBwd48QlwpK8DvPFTdRQy+01W/5VGDDHEXPNJXsU8DPD3HPEToVhmFw74LNfj3qqR92oevLPNV5SBhQrYFf+VoKUod8zXPEpEt2+RdywWfXUM0WwomLFUQjtgKk8vIEZ901KrU7kc9n2XiJtxKWk0y2rc/lAiqoUAzk913neBEw4fHVktjUN3EOy+W0RVR0opxcB7zdBti5sOpcN9bWQZcLpeVu9Hx1FXq9JuDCXQHEKhEL8HVK2LOj5ZjbA9jxNfccoVO5kF+Cv/cr3/ETcPDrLrIa0OdJYlJFthEEfAbqZkM8EYV7TiGktskEpJISgqDCc1hZ5qdGSpJbviKf/6C7vs/VmoKLQBbPzxbLBohdi2M+JYfC8BsqqFCE2Jae7XtyBGG2b7ddAVvnc4+jEoHeTwIDZgAjPgAAFBZrZ0AYRBJUxJZFLPJSSrJ1AVCc43ueUH5qDyATvlRL8gZdQUKkwPKhTL7bwjMSd7k8EYVTbTaQkiAJKrwlXd3bJCmKsh15vRHwQTfuOdbStVZKdHG/QNaYANNYPxaEOMBG1/A9xxZU7KBR4Rva9X7Soy3qOVmXxxHfP6Fo1IA96hAAfhPyb0Aw/LTLkqDp8AUVYaG1X8skbDpxSfsiiLVNu0Ba+gni1qVWu34cS1kJcPk4+RproqWV40E3AwQLdX0W6RNtMoU0EZmNQctZQhCpcSQ2Boa+xjvJ8Q+v2fN1w8f5mr5y8o0frsfaIxfk32AXjYoC3HRQkAmvnkSEhhDiTEIDdH4fDKFE2rfJlRIbbrs2kq9HA3MEdtVFJXo/avVm/3fkAi7kF+PkRcJvZ5GJjvN6Zq2R6V79o3+OavZIwX6w2wPcY7toVAovAdu+AIp4yxUGzCDHzVNgd2J3QYWwFKh7MEKnsPxZ7rFIW9BNI0AMmGmz369EwOt0BYs2pWP4+/8ZVBibIuQlGQAGz/J+1Kpp3PX5Jlz10t/o8/pqYiBYK+AAEV5nSIJKrQ4+p+atPaHZI11yhQ7OwGRhQeWb24BTBI+chBmk1gMrcStpryeAzZ8CRdnc8w4UVKiNikpEbFSEInj7jRM0KhK7lJ5ZvNuggjiUavW9H/WYhBzKzEPNWLaTN2v0HzbvmQ2gnOSemPvjbU+/rKkkKlvkOLNVs2fqCklIAYgdsyE2Ff2fB6YQgnHZXlDxValTOUUlQcJtwV8/KoCAfYETBBUZSz+mUqLM7YGVMeTVjrOGYbLNe2YDkFj6KS13Y9QH6zR9JNFGpZKRHwpcsOGIZIBGRRDSjNn2gopv3Wke6TdQEGkLWmipiHmERhBS2uz3I+36sRKzagPlzoiWboj9mY9dJIDNnxlehzbvmQ2ANENgdf5q436IUalVeObHXXjhlz3ci7xgc6Qy2QaCjYphX4NkH2N7QYWw9GPHdmEFKpws7j2bgy83nITbzcDtZjD91734dou6KOlsiGMMK3Cfl8rfzy5xfwx2UKd8GY4BruiwY8viRIWptAeMTfY99/sUYOs8/wqkEJv3zAbwz+uEk/p2/uVuBudyrmDR5lNYsP4kithRQ4UMUO3mUwUwTKNCXJYjaVRy5AdBtCSELezURkUFzYYAXT2G69e9+x+e/3kPftl5BisOZGH+uhOaLPMS23loJDCU0N/8PQOYXQ+4qJ3Bvubs+MYz0zZYo1KiaqJoYXs+Bfy4TX5/tf6Z/to+/Mw2bfOTgAoqUhxb7XuOZZ2vx4T1cGY+DmcKvPBW3t2jFJKNig4Da5eX/8bn//H8EgQF+cb9OfGv5s82jOveIM7ItazPLUp8h5RZc/eAl4ILwIa5Hu+ffJLb+Nio7Dubi0saBn5bsO4E+UI3QriM/94ESguATzUebLSgvBTY+S3w84OembaQ/w+dKClTIajYtg/llvvtvw/LvlPE5EqaYW9rnKFyqKAiBSnMNctLrBLV+vieDdGzCcFxG4+3/j6EsZ8LbKsV2o1gRxU/4bvo9TVmLiEYzz6TDsTW1ueBRhKfCnS5l3PK7WawbE8G0hXGTBLjk38VBL7c9Z1mz9WF7+4Clj0DfDfWd729nBw3RUsZer6QoOID66FXCEE2zWbjh8BPArHIDECdRiXwCPbH909kgu85gw2/HWBmrjPBBEEluZX3o5IVl+S4cDw/rBWOns9HYXG5bH8CnMFbyI7Cjks/hO9iqN+P0EhPYL08/4LMmU7ZFZ9Tv+w8g8e+FY5uPKpjHfy0/Yzgdb+xulHlyf+q/r/O00QRdvoxjLbaKcf4tzn8l6mPV6VRsevSD0sTxDEHkIHoBg3JmwliwrHVwJVsshCjA1SjIkUdgUicFajpcBrXjEGLWrGy0zPsWZWgkzRndHyGm1REJ0qnsToEDcDaI4QljQq2PT8QQ9ukKH6MoqYu01GiJeA7IqzZjJhMzQ6qXk3JGlTZ7dzqLghMNkDffy4Pj3+3Ew2e+R0/bbe5jZkkHmHj283paPH8MkV3hgS58OKI1gBUaFdIgsrlE8BXo5Xl4wdUUJEivq7oZbUzo2AFEi6nUxNc+rGjRoUQm8ZoSSU02tjn6QFhR4hY66oe7V9QPQ73ryaft5OgwqfjWABAQTH3OyhpmuN7NsSxWdfii3vIrtAds23cZEFlW/plr1GpmAaRi03rvqK/fPpH5U7zgoNcuDOtAbY+NwAf3C48+R6/YLNviAOhZZ4zWxSXQy1UUBGjrARY9bJoErXbP5Vo4jidmuDSjx1fPt9KUNKBx0eG4rp2tXBvz4Z+FMGmamA2hKUKqa+lprWcuFiAeWuPc9XOMQKaGbv6qhj0EhDs6ZjZdiQMlE1KQoJcCApyCXqZtuXrSkJKUGl9g66PV7UDy7aVr76vqmyHiTHhGNSKsOW4gqJSN75Yf4J70gL1RQUVMS5JGw8q+Q1drIYm200+eIOKk5Z+CFWgZNaaGB2GOWM6Ia2xH8s3dvedAgBugqAi0Kn5E1DvUGY+Zvy2Dx+sOiKdeNVLnhhPdqPjHd6PuUVV9cowvhoWf3CMjYpUP3bj535l/1DfxqLXldpqeLBp3Ws0qXK5XFj9RF9c164W8Tq73QMA4szfcOCAXlpHCEaKfJR0OGrbGdeYViATOy798AbTP3afw+C3yQG5Ph93FZLjCIbN8PP9dYJGhfDbC32tJY/09Ptxm094dp8Ul5WL15+ENtKSsAVX3qv9f38eIt7SuX41vHNrB0WPuVxYijz+gGBHpAR9P9+vpFjyO19JUamKfs+WfSWgpRFwgxrRmD68tbzEKW2AW7/R7NlqoIKKGDJmhEbYVHCXfpy0PZn74k34WtiJUK+mNREaTG6uQtoDeWXg5fndXYDbrh1ZFULjQ/3q/tvkBAe58PP2M2j+3DIs3X1OOGHBBb+fZTis9iD1Rt3QqQ7uSquPd2/rKNg2AWB4e/KMVNAPRofbpUppHXTWSIaG+ObfslYcpg/37LwsLlOhUbFjXwloPqkSUq4S+9PmQzV9tlKooCKGDEHFnzYfEy5vd7ittyeLVpD8F09sIPBrosGvz30/A8dW+ZGhVSBXSmVf588SkMsFTP52BwDguV8PqM7HkrDaAzuWCkMQWxokRmPGiDaokxDpc20wa1fVGze1x88Te+DIy9zO/mz2FZzNvuJrl9XuZuHyfTkKOCXgY8kU/Bs8uzWsLno9lOBYrGZsOKLCPH3nxuNqlhdtJKiM/qzqs8ZCoaLdPyZrnqmgIoaMmBD+rDXL9QHAtVEREm4s+vLtXSx8TWHj51d15aGcXATfSdLLT4yYbSIMA6yaDRxaTr4+5BWfU0JVW3m+d7OauKo+OW7Uw/2aiBaH7ZPhEuKAznf7OJwTLYSlqSozu7mRXnOhV//vKX3QKbWqbsNCgtChXgJCeML2H3sycPUrK/HOCp5mRUhrCgBHVwKfDRS+bjQyf+PCkjKi9qNRTXENX0iwb/6lZW6EhnjOC/ahhHfCi1UndSQS2e+iuvfpy/HknWdK7CTNhgoqYsjQqPiz8iPXqyJnxhUu4H/FqurM/b+JXNTOOEx1GpKgYrDXRUkO/A6seQVYSJhpp7QFuk/wHl4pKUd2YQmK+FsMK6gUMkKDg/DDhKuJaa5tWwtPDm4uWBwfoW/42x4X/oNe4l2wckco3R6kXim2loWdtklSjKKS+CwBCRrMWxAZs/yi0nK0mrYcV730t8+1hCjxrfKkWX+Z2y2uYQU874RPe6zAqn0lCXa/pVKw6NW0JvG8X95qDcZiPbLFkGFMq8RGRa0Ey3lElICq1KovHyn6dCUpbTV5hJxaFfydiIKKxeT3HJFovfdUaVleXLIPn/FjGvGQU1dSHZhgO27gv6GuYQQFk329cGxUqtoMSXOq2ysnplGxGjIElZMXPWEc8op86/uhvo2x/1wuhrWrjbyiUnRMrYaRc9Z6r5M8qpaUyRBUAKBxfwDPES5YtK8k4hL47D9+yyl1OmtSDjlYrEe2GKTQ6jwVt5Tfj5s6CzuMCycYipHgrI+HRgmmsiRCHdnjh4SFLgGE6tovDWY0YbZhNY0Km13fc4/DqlTnUkIK4CtkkHzQSLnbFu7geBfElv3MRsghnYBGZeMxA7da20qjItAYanf0bk0Wai8/TkhDbEQo5t/dFTd2rou7ezREh3oJ3us3X1WXmH1JOYMwOYJKcivgpgW+50+ul77XKqjQqEy6pgnRboqPX271AfL4qBNUUBGD/UPE1QWePetRcbOTiMgHCVGheH54K8HrC+/rhpa14qTLIWd7slWj1ZIElXtXArHCToeE8FcUm7Rwm++W0JbX+ya0mqDCHjEXE2xBFMAfNP53XUuseqIv0hpV+aIJDnKhpUiIB+FlNMJ5K7ZLsQ6W1V7PZldpVA9m5vkk5diw+Nk6OT5a7OTbR6is968G2nhcrJOaRafUBHSuLz5RYRjyYFpa7pY9ySNqbX+6H1j2rLz7TUe5RqV9vQREhkkLu34LKgwVVKxBfmbVZ3cZZ/ZaidjSz5KHeyIuItR7zG8WnetXxx+P9sLKx/tgYKtkzLu7CzEfnyeEE4SbpU8KlsNU+B1ZSjugrjqVoY8xbcUJuduTl+w6h9eWHeSeTGoBTFgHhLEGZssJKtoZ//GFDJfLhYY1ovHK6KoOPdjlwjXNkwTzOEwYtCty8z1lRUGlVGRJl1U/f+7LFE4HYGBL5cK2EGXsfsQJggo7CWFA/L+b2svLnvW5cpv3g30ao1mK/FhpRDbM8e9+o2DXb/tbZd1S7najUIZzQqElXtnyi4FuHCzWI1uI7FNc1XUM2SCpsES4QSTFRsh6VKOaMfhk7FXIKSTvNvGxbCfNCLP2ynqW4fBbffvbVGclNGtVMjHYdSbH92Rya4+G52LlAGw1IzP9l/XYwl5QkLg91YkKmwPfTAj3WG0HFQCUFQlfq/gOX204KZlN27rxWpWIO+Gx09IP6V2JrSWVQpaNCQNuO3z7lg54fGAzNKihwBeQVW33hGD37bf/wH2nBrwgK4syN4MCAWN6Nn7bqGTt9exEbDbYz4yksZHobjCH/+Qe3ziPmOzGD4XXO8N46kmpATWYsBUPAHq9toqrGrbT9jq+YSDB3fu7Kw5jzCcbBLOYcb24B0Ul71vuFaGBk52LxTo3AzpbdttUvxuAJKjYTKNSwXM/71GUpb8/URl7dmp3Y1qec7DKoIFs5LQxhvFtl1JCyqbjlzDmkw1VWr/qjYBY813Ay4atgUztzr0WIm13AniEXp/AggQ02Z5M2omoA1RQEYL9I/afBtRoquj254cJ26YIIRZR+Z9D56sODFwb9BsZL8Obfx3CuqMXBa93r7CfEBoMkuLkaa4AEaGG4zrdaoKgwYKKlv4VxLQXZiFDUDEa22pUSILKwBc5h3NWHfVJItfhoFI7ips/Wo91Ry/i3i8qIvsGBQH3LFOUh6kcXFr1OSQSiKpRdSyzLsrKGcy6wbOUO3mA+Lg1ZWAzxUU0A10FldmzZ6NLly6IjY1FUlISRo4ciYMHuTYCRUVFmDhxIhITExETE4PRo0cjM1N8bdgQ2C+gCpuFxhKOjEiIzTJ2nMquOhAaSI+T4+SYCr8ja3Oj6qz4w3VkhXfKJkkxeG10O0EbH255BM6zB4e9P6spnn4YolFhL/2oFFSIxrQlKkukIzLcDihF7i90Tw/fXVYAcOPc9Zi7umJAD1Pmh8VU0gka5XDp8svRqNRPjFK9CJuZyxKQ7SD4lRUDC4YDP46vOhcc4jE5uOVr4I7FsgWVrg2r48bOdbHt+YGYPEBcEHmkv68gozjQY46vxkxrdBVU1qxZg4kTJ2LDhg3466+/UFpaikGDBqGgoMq3xmOPPYbffvsN33//PdasWYOzZ8/ihhv0DQ0uC/YAq0IVS1KrSTUzsVnGR/+wIjmTvIACnoZuNfh1F1+Hc+hW6TGvcc1oTiC4m7vUEzUA9RZHzpWNc4Er2arKpQ/6Cyrs30G9e327LP2Yp+WpFU/W/p3JvoJXl1WEI4gkewy2JJelt8STCBHxVbTo/u4Y37Mh7u/dCNVjxB3CCcHRxAgZ/JZrFw3bb/b+LDzRbDkMaNIfACSXdJ4e0gL1qntcWFSPVld3n/yr8Dfd86Oq5yhBV2PaZcu4Krf58+cjKSkJW7duRe/evZGTk4PPPvsMCxcuRL9+/QAA8+bNQ8uWLbFhwwZ0796dlK0x+KlRYXfZ17WthdUHszCiQx3B9IBnJju4dTKW75XQKA2cCTQZAHw3FigVMGy0ChKzgCIZQcUqjWjZioUVj/clpn1/TEdsqYjuO3/dCZ/rxy4IOKDjd2ZCfjbMwACNCvsRqjUqJIHKirt+dNCoyEWW/U+oPFsEWyNSDd0bJXqXezvWS8Cj/ZuiQQ0h/1EysheaaJYXe7QWVqAkX1ay77YIO3+c0LcxHuzTSKsSyYdnPK0Hhtqo5OR4dlxUr+7ZP79161aUlpZiwIAB3jQtWrRAamoq1q8nG6kWFxcjNzeX86cPrKauQnXIHp/fH9MRO14YhGoyJNyP7rwKd3RPFU8UEg40HWiPDk1ikC0oVqJmlB6wh7WrjenXt8b061vj2KxrsfSRXpxQ8QwjsKWcL1CtmKGgXDpjgM0M2/OqpjYqPz1gvZ0XOmhUpBw/VsI3sCficgEN+/hZImsTGSqvT3W5XHhsYDOM6ijsOFPovqoDgTq3khDNf8dJ/p0gvixzffvamsXv2ZZ+2fdkE4EYUxEJmjxTDMMEFbfbjcmTJ6NHjx5o06YNACAjIwNhYWFISEjgpE1OTkZGRgYxn9mzZyM+Pt77V69ePX0KLKOhH8kS8ifB3e7pcrnkuXyu4LLANmUf7NCZSQyypTLjHQHKx7ugIBda1Y5DLZ6XRuKWcv5vvO0LZQ8zgz7PaJYVR1BRq1Eh/UCXjgEn/lVZKp3QQKPCXnYE5Bt9yvKoCgA3zVdWIJshS2DzA86vITTRtJLWlO9yQmDMEWtnfjtwY/Hwwu2+QtHt3wPRhOV1AzZ3GCaoTJw4EXv27MGiRYv8ymfq1KnIycnx/p06JRIHRQ2Vfh8kln62p1/GgDeFjVf9aTPZhb4GiD2aJPomHPaW+ocYAcMAmeL+XZREn1Y7L+d7sSwkrfNa2smWwDfvdKcuT1Df4QmUc8Fw4LBvQDrTOLOVe5zUCoisrkjw5wfTG9gqGU2TYnDLVeITJ9kDtB0i2x63mADKhuPQVaAuraTp4w/2Av2RWLPQMsbgmewraPH8MizalM59OMkMwgCNryG986RJk7BkyRKsWrUKdetWqfBSUlJQUlKC7OxsTvrMzEykpKQQ8woPD0dcXBznTzO2LgBerAkc/gsoZmlLCI1mxf4s7Z7Lg2QwFRVGaCCRCbqVwW8YBvj+LuD8fslkJF4cUeU7JbnCcZ5c9TqfrFyuqr+A5LXRyoKK4NfWrmdiZGhU2vvj4Ozr0erv1Zp173GPI+KBJw4BY3+RnQW/hiJCg/HnY73x6o3tRO+TL6hYfKfKuV3AgmGSyZQEbdUSrjGtUF1aSFDha1QuHiEmE1va0UO2fWbxbt4ZQp0ZEPNH196ZYRhMmjQJP/30E1auXImGDblb8zp37ozQ0FCsWLHCe+7gwYNIT09HWlqankUj89sjABjg2zuApU+IJk2IChW9rniLF4sZ17fxyd9GEbk97P0J2Cfd8QtpVBrXjMGfj/XGLxN7yLLtEYPvSZWsUSFUcPpGjyRl9sxLaMbCKvOspeICIQDc0EnYmLt6dJUdj1BbkzSyNbue1JK+HggOVdTTk7ROcuwDZC8BW1lwBjy2RzL4YBV5wNUbzk8hVJdW8pfE16hk7CImE29hBgwSpDozYOlHV5PniRMnYuHChfjll18QGxvrtTuJj49HZGQk4uPjMX78eEyZMgXVq1dHXFwcHn74YaSlpZm740fGDx4fKS6oEAdDmbStG4/tzw9Ew6lVzn/kxrOxDDJ9ugiNbUFBLjRL5sbzUDsMhga7UFpedXe+XI3K54OA1DSPncW43xU7/dMOgW9eUeas3CJ8zN6+LoDYtuPq0WH45r7uiAwLJg64HVMT0LFeNWxPz5ZVYksTWR244l80ZLWz1xAB79O+D7C4oFIisHuOx5eEUATf3Kd/3y7LRsVKgrVMrYTYXEG9WwEFkEJi2F2jMnfuXOTk5KBv376oVauW9+/bb7/1pnnrrbcwbNgwjB49Gr1790ZKSgoWLzY5PLxPL8Rt0Mv2nMOTP5Al3krkuDAWLwK3DP5GZ7UqQhoV0iuntl9hB4YEZBrTVpK+3hOc0pJBHz21VCZTvS5lJJvWOBEd6iUQr/30UA+ESg6yNmmjtTsKXtp7Ngc9XlkpmYVaQYW97NilgYi/FKsLKjI9DpOaZgt/AwpW8OOEqwWvydr1YymNinhZdpzKxqC31uC/I8IevNWHvlBAOcGB49q3dX+s7ks/pL9x48Z500RERGDOnDm4dOkSCgoKsHjxYkH7FOPg/eBB3IHuwa+2SeYgFqxQDQo2x9gGhmEEnQtpOeTxX2Diluiu94tnYmbYAiEJraIzlttB+bsrQHJpw0ozVDFEdnu8/Pt+nMmW3hWkti7LWJq97x4QWd4OjQD6PQ8EhwunMRPZW7x924RWu1M616+G5slkoecye0OCkI3Kv/+nSTk0QUIrcednG3EoMx9/7xf2sWWaoJKxGzi5Dij0T0sphsXFdpMo5ak1w5XPAOorifApA0FD0v7TNH2OdkgPWl9tOIlv2Fbl7LsJt/ds6ol7kaIgtg/gqxIlCpGtRynK01AEZ1ue7yW341eqGv74zs6Ijwz1hiawnZ2UEPz6rNnC+1Guf49khW2wkiFtUtClQTU82r+ptODX+wngmmdVPUd3ZG7xJhnTijilVYyQppnTfwhpVLbO164g/iIxEcorkp74GrP0IxASY95Q4PfHdXusRdzyWRwZsSsq+XVSD+w9m4u+zWpqWgTBbbxd7gVWzNT0WZogY3Y97VfhrcukDmjWyLZoWycew9sri4aa1rgGJ4JrPkmjYmltgbiNilyUepwd1DoFA1slewdUaYHIhhqVsFjgriXew3gJI/kuDarh9m710VDlRCQiNBjfPyi8ZOGDVZeAhAYsHqRlSS39fch6LbWUjPRCAzsP9R6lNWLvYuCmebpkbYNf0AJUIwcS4xMS5EK7ugm4rWuqZh4CKxE2Q7DvNFeskyFdi48KxYN9GqNOgjKPvNOGcyNZXy4owbI953A+z0KeKcU4uop8vqKNybVfUuNxlhOsUFJOsYugwhoUWo/0BH6rICZcfO72xT3dMLKjeCgMJUSESnTBdvCnIkJJma82UFNBRbOcTMZHo6K8jgzRqJgEFVTkUK2+ZJKtzw3ArumDdCuCoEaFP+M6ugpwW9+ghdSBsdFyzIuPDMWd3at+w/dXHcGDX21Dl5f/xoV8GwgrZ7aIXpZbV8Fyd5wIIG20a5Nhg2OjUlXmnCulkoJKZJi2/k2eH9ZKIoW9Bx+S52ktZS+1vpUsB1+jEqzcJYO/GpU6CZF4aWQbn/NWqGMqqGhE9egwslM2jZAtqHw50qOCMx3xxr32yAXR60o81sphUr8mxPP/HRYvhxcLvKw+VGpUZBbtjm7SArcYQvZEXixYRUTYs9eKMv+y4wzaz/gTH6w+amhRbu3CjetVxh/YbapRYRgGlwtKiJpgw5d+7MD2r7jHKgQVLTQqpOp8bfnBqoMx33u29xsMFVQ0QuulHj75QsZUpDXsHV/rWhZZkHoQVvh6KYd4Wvc/yXERqEawPziUKRyvyfJU/PZyln7eH9PRG/5dLcF2WOuXA3v2muiJNvvcT3tMKQp/bOkw8y9eCnsKKk98vwsdX+R/Fw9arlCItXwraAJkU5TNPQ4Wt5Ui4a8AWLkrl8/c1Uex54wnoDCaDQKekvbZpDUO6Xmcz87TOeTlEpKgYqVgW2xYW4BLJZYR9OhkSC+y/Bm0SZ2eaD248NWGk3jkm+2S2USE+L9k8fYtHSRS2GRgqBRUancCuk8EYJ7igj/ByS8uw96zOeYURkPYxut8tNWoCLc5k7z3KyMvE8jcp0lW/mpUGAh3N/d9wVp+NuFloYKKjThPsqcgNRqrziRY5fJRcQsn1QzJ98uAcOWKEdldUVJWjud+3oPNJwgh2XlooQy5SsBBmdcOISbZ/4cYQeXSz6CXPP5KoL9GVAmbj7P8UVioXFqhqY2KyLXcKywvqu1vE8+opADIOqBJmRTxRjNgLsmfjvIO0F8/KgwjvOTOqUsToIKKjSA2Q5JGxRIeF0kNni2oiL+ISXHaO7qSHIxu/Ez4mlnCn4gH0IUb5KtgtQjBEBEajIX3dfM5/+6Kw54PCfWAoa8JZ5C1H1g2Fcg/73dZ/KJS48hyrW5decCyBeMSW8vzv8t9kkm1FArFXkvO0tOoD8Uz+iAN+KCbsRGh0zdKJikqLccKESdvbPytVzfDCNZnCX9ieYux5gVUULE7VhVUiHJK1Umfhs+iQWIUWtf2I1KvAJKvcZMBIhfNElSEdyVtzVDge0GjseHqxjV8zn27+VTVQbcHgE53kW/+oDuw4QNgyWRtCqOGJVM8sZsATsh6qeppWCMaC+7pql+5WHCWLFqPNOSZfjP+T+B/mcB1xnp79Tu0yIWKoInZFTGJ9v3sX35K+Fxkl2hFXzl18W6MXyC+608rGAhrVEr5E8uW0pGztYQKKjbixIUCuPkLr8SlHwsIKkSqyi62q+fWrqmC1/xBy7VxwxASVB7ehjLItzvR8rtP6NuYc+zzS0o9K4MfOt4gSgqBLSytGUvIl6qf7x9MQx+NnTgKMXPJvqql0dgU4AFCgM+iXEPKIpvoJO8ympH4reg88rfGGWqFpxw/bT+j+5NeHNEaIUEuvHNrB92fpRYqqNiIMZ9uxFM/igdDBGARQYXwwrM6ATP6A7/GarM6MNJv2f8FILGxZJEGtqqyGdFSRHtyUHPOsY+Rd4LENugQ4wc0AL72PhUalXVHLuBigTxPq3rQq6mvlmrHqeyqg+Q2QEpbboK5PfQtlBLSJpkipAAavJZW9fxrIHemNcCBF4fg6sY1lLmF6DxOtzLxob+Szfhhq7A1vRfLzAr4eMq17sgFvCDiPl8vbKhPERU6y0V+5/E9G+KTsVd5j7VUJvEdS/kIKt0fErdVCFHuI8IvsvYD34wBzvJ2RwUFI6+oFGM+lbYV0POVeudW32jOnMcFBQP387QqORI+bfSEb3TaoJc55YDC3YH3/Ol7zufFsEjfaXAxQoI9ooDYTil25G8AwJBXfRO53cDPDwHrP9CwdFRQ0QS5gcwMw8xIvzKQGhj0GhT8MjY7tUG7giiBJKh4Hb0JVxTf46mezgiv8H3ihEaI2yoYHRH4i5HAwd89zhB55ZAT7E1vSJs1/uU7IrSSD5sPeAbVMgRPyVABKlHUVaR2892ZZlmNivxv1iw5BqM71dXmqSKPffCrrdwTQYRx79gqjx+v5VM1KY/3UZrm5kRCtY2CbAhWWPohOZ0zWdPjd19fZC3/FkKzH7Yb7KlDW+C2rqnolJqg6bP5ruYV+f4weuknP4N8XqZTrbrVIlE9Wj8tEGlHlncnldUgvcMyvKjefFU9HQqjQZfy+xTg2BoNM9QIiXKEhVR1Zssn98YbN7fX5LFiSz8+wjNJyCvJ16QcfKigIsWD3O1qG45dNKkgCji309znC27xM7cT8HuLbrkJs28RoVOoUxncOsX7+YE+jTH7hraa+wlZ9URfznFGThFOXy6Up4pX4XVTF2QMsJv/NwCrn+jrt48KMYQm9VK+hkyBKKhIa8j0qj+/d/0AwBfX+5+HgYzv2RDt61btitTy3VYUcJPYcPX5namgIkVi1Q6HbemXcevHxi0BfPdAGiYPaKru5qz92hZGCRePkM/LnK1o0vkQEHqfc+Q6MzJD/U4UVDxfREijYkQU1Zqx3MFp/IIt6PnqKkyXY3tEUhmbQYj0AFstKtS7fq8XQjuOpINAmgCpPcpY+tFreVyqS/l151lcVBR41Cp1LlwOF4A3buqAbg2rY964Lpo+tU5CJPbMGCwvsYG7KKmgIkZf7jrbg19uFUioD10bVsfkAc3QPDlW+c0XjQ2uxkFwIJIpqOjUVwgNCI99u0NeBmaohUWeKaS98DeKqlz6t0jyObdg/UkZd1rErFmGZseILe2qfy5TlinULf1ES0SlVkuHegmi1x/5Zjtu/mi9/AzNXvqp9KA76CVfVxQVBAW5kJoYhW8fSMM1hHfQX2LCQ3BHd31cRKiFCiqV/P6E77lGfTmHpJDlRqBKbVp6RfuCyEagvCZ3AkJjzsoDWVUHda4iJwLMKf/+XwUvCS396LlMwaavQCeZfrFQ/Ear+LMJDpMsihFCn9CS5NHzvPX+ejwjVlMEZ0IfKENQiY3QR1B5ZXQ7PNCnkWiao+cLdHm2LgyZDUzZD1x1N/7cR/ZIa8Tb8+KINtKJSOj0blNBpZLNn/ie463BCWli9e53VQ08i+/VviBysaglvaxavO4N4WtGGykzDLDqZd/zkQkAPDsBSQQbJAgINcsP/5HS5llEUAkKNVt2BiDcf9z/BU+D22My99iM3X2kdyBUOip3x3rkOFH+Uj06DFOHttQwR5MbhCsYiKsNAMjKI4fPMCIulepn6LThwJojimXg/lgkVVx8ZCg+u0vbdUI+py9zZ6gLN6aj/xurceqSxMzVLCwqqDzSX469j0hHZbSgcvgv33MJ9YH2YwAIa1SMMqUR0gQYJSj5jUW2/AotL53J5mlF+e+VGbv7SM8Mk94Z2bZuPObd3QV/PtZbh0I5CE78KXK76NnE10GgUWTmCsceAwD8MlGX51rjTbUqvI6B5GBrx7SBSGucqGsx+H4qnv1pN46eL8DMJRLhwc0K/nboD/J5k6evg1qlSCcSK6PRA8MZgk3UjfO8xotCRQ0xaAAWkkcMWnlyDLLrqziPe+w2Q6NCaHQyBBUAuKZ5EpqpsbczErNVbJywDuQkPQmejI1iwBtrpBPpABVUAGEdOq+hkCL+GqGGixCwmPfxCMpn3Ts6lEaCS8eAfb+Qr8Umk8/zUORtUgFCP1VoMPuChQQVkmaKVVRBjYrJgoLXrmPiZmDgTN8EFtK4KHIZrhOy+5CibO6xke0xaz8w7zrgBMH1AM94/rstpzjHs0bx3P9ThHFV1aXZscn+feoan3N5fO+0BqGfu0o7IbTWyxsoSoUEGp0RtP6WascikXd1Q8iHS8c7gK4PyMqiQQ19nOwJvfiyNRCGCyqk8ladExpkjRCexfDWc81mnr+/pvFSWEdQsYCcIt8Gjf8+G2mj8s2twOUTwMn/JJM+9QM3HtmYbtbaQSKOyQ0iiC2omFgOAPWqS9seicIwmk1KqKACiKhQuZVsWlw6gfOSA5IZa9hXssnnR8wRvOXIy0MREhyEzScuYUd6Nq5rW0uXogm9+CFsjYrYb2x0fbrFZy9mu9kQan2SHaxRgpTb7auFqCTWY7BoBUFFNiW83SvbvwLS9LEJ8CH3LPl8437GPD9QYE2OSf37gJbytNKWYNMnQJP+HF9kaqFLP4WXgC9GkK9ZxChUqDO1pEZFYjCf8RvXIdij/Zt6HWp1aVAd9/VupJtGQEijEsZx6GWRpZ/ifGANIegXCyssW5CQ3tJrkKDy1Q3Aaw3J18Z7AtRZtQ4rWcXeOp/E292y/FnjCiLU9ns/xTnMLjQvCrUmmN0cWH0Uqb+a1K+JkaWRJlJkN9cfTwLvddLkMdYYic1k7dvCAecsIqgI2yJIdPgyjdw05fRm0cvz1p7gHA9vX1vHwnARqq6LBSX4dWfFjNEqxrTHVpPPu6SXfsxGsl0aoVEpK/YESBMiwRN7xpo1WMXd81nvU8vh5hVECF4faUlvugDu/GwjjvH90liNxv05hyR5Xy/7PdUYpNGzxkhsJiUiW3wrOtR/Dp3HpIXbfC6PTauvV6k4CNsiSNwow0W4prjdwM5vFN1ilHMyQHyp7JFvtuPkRQnHUJ8PAY6u1LhUAhwXsq6v+g5m91lC1Wn69uRNnwAvyfPYmVckM3yCFTCzXgW3mHGdvQnZ05nNv4cv4KGvtwHD3pJIaVD5r1z2PXfHj5xDksBvvcmJQT6bDHmKlRHTOlTMFsZ+vglLdp3zuTzj+tZ6lYqD8NKPRCMxevuikE3F3csEbzHbYIzNpYISIFjEbKsgC/hylDGF2fSxZJK9Z3MNKIgwQn5UJJd+9B5wlxK8TAvAN/ykCCCkTeRFwraqRgUAMnKLgBbXmV0MD79M4h4/uJbzXhzKzMP3W0+BD2nnqbkYUx4qqITHiFwU71CN2l0hJKh4H//oLrKxarnBs0WhXQip3QVvMXsLHpuI0GCgVgfphGYGfKzYFSA0c/1xQpqRpSEiLXxa5zc/kJEnncjKfDECKJVwwqUJQhoVrta2nNcua8QYrNUVQVarM0Jjsfdn4MAS7rkUrsv6QW/9g7VHLvrcyq/fQIEKKmEiDogsbqPiFZSq1Qfq9/BNUG6wYRtJo/L8RdEZtIXkFISFBHkK1MjXfwCHlS8ZUyA+TQYCyZ4O7YjAenvn+tWNK4+gwzcL2KiIMZ7g8ddknh/WSt2Nx1YD6es0LYsieBoV/kA6+wZjfKgsfuhqDGtXC1/f2w214yOIaczetu/l+7tU31q3mp9bhm2KNUZiM+Ev/SSy3KxbpGELCiqcA0JZt87TpTyCkNZdxZZSYKyNihTeapaKV5FPDhamO3f84P2dB731jzllkIH0b2q2g4iu5j6fQNcGfgiYMmLt6IbI0k9yXLhh7t47pVbD+2M6oUeTGlg3tT8xjbxWZ12NxQ0d6yA1kQoqFICrRbGMRoV83nJqwN8mK77FSks/Xov60EjxhEYvqdkMyZ0JFvrNrUJkmMy+JpEQr8oMV/qV8ASVyklV9egw/Pd0P0SGkb1qU5Sjd6gWK2ONkdhMfuUZNVlEOJFDzpXSKjf6VrAGP7pC8S1WGrO8ct+wt4HanYAuAhGog2jnCwjPUBOiwgSuSN0ZuDRJkhkD50GCZ9gT0t5idYNno1Jp7BkS5EJosLX6Ull9jRX6UQGsWzL9sVZLMhqSS3z2IGSlUZTAuqMX0fd1EV8RFoO0FdRojcr04cK2AKXlFe2hZjPg/lVAsyHkhC4qqADCa/6Wt1GxKEIemfPZ8VVCCfYXq2fpVCIZ8IT2Si2vlZZ0q7BimeRjpg+VVrXivJ+9/aSBBLigQlDh26wTPZtTYfFvg3K/9ddhn3NG+9wY16Mh1j1Ddvs97D3ezFRoSybVqIjiZhis2J+Jl3/fR16e3PMjsOs74wtmdQRehSe+24ky9uAgZ2eaEbBid1X+zpUR5q0pqFRw9x8idWhdvYWZyp4hbVK8u/kus70PGxRCIcAFFcIuFd5s2XKeAJViofIfyPD1+2GGjUrthEi8e1tH4jXO7FVo7V/P5UF3uXA0b5vAABi/YAs++fc4Fm87TU60+D7gtcbA2ncNLRva32bs8xQg9CYs25uBRxZtrzpx1T2GlEeSa18D4PE/1PXlvzF18S6UV7RdKwoq3q6m/tXAIJN27vmBmT15cJDLu6R7uYA1wa/TGXhAf8N+Kqjw4Q1CpL3stsJCgkrOFV8NllkmQUJq9tIylpAg5BdGL41KWTHw/lXAgmH65K8xQkMRW7jPzBXx8VF4AfjreSAvQ9uCCdFzCjDqQ9Ekj/QnGKsahJjQvnQ3u46s804DwMKNJ3GxoATfbDqFSsWPJQUV9oHQO8zuL8/t9Hg5tsjEwcyunGEYVIsKBVDhGJNNrfZAdE1dnx/gggphIOKMnC5kX7F+kK3L/IZTo1nVZ6Pi02ydL3p51YEsoidVs9ytBwe58GAf36ie5ezeQFCjopOgsvt74NIx4ORaVbfXt8jWRcVu1PlRgfWCtNTL47EB5gkqsl8FC00+AK6tUlnFoB5iQUElK69Yhn0Fq24/6u3xcrxbo2XKohzgiPINB5WY7T6/enSFRoUUeFJnu73AFlRI20x5kraQm/B3bu2gQ4HU0fHFv3AljOWvYMCMqs+CMWM05rdHRS9zgquxMPPlIz2b46JayD+FHmqgLfOAX/wL8LXs0d4aFUYeXQR8f3y1Md3QcsimTHrS4XK58PhAj6A/8Rr/w9MrQb58Z9A7c3QVcHqrolsqbVWs5HaAzYZjFRry0ivkBCQnmZl7tHn4FyM8Eb1VYrZ46l36IQoq+ooSgSuoMAyw9HHf87wKF3rfRnSoo0Oh1HO2EMATh4EnjwENe1Vd+OoGc12+SxAZap5hKsnQkzPjatIfaHOj7416LP2s8m/nRvXoMMN9VqQmRmHVE30xaxTX++iRLL7XXAMHrRMi2igJT82VSoBJ/ZrgnyevwRODmmtYMGku5BUb+jxRcs8BX44EPu0nHMm7AnYfWflOhQRbU1DxTjyFBJV9v2j3sNIrwM8PAfsr3OWf3S6eXgoTJ3UNa8QgqqJ/uVJC0DTrvMEgcAWVzD3A/t98z/MFFYOK4y8uAIhJAqITfaXbD4Rj7ehG2iRPoC0Rdr4wCCEm+lqQFFSCgoEbP/O9UQ81p4IZyd6zvp5zzWqnDWtEo3p0qOB1hgEQJO6dGLt/8NjnaAHpna5EYumnUgvgcrmQmhhluMv1TvUT5CU0YsDKPVv1+YsRoknZ2pO5q48CsEAEbQG8K1JlIrZT/PpVW9/r5wA7vga+vV3d/TzMEFN+eDAN/7u2Ja5tm4KIEE+/V1xGWD7T+fcOXEFFCBkalRWP9zGoMPLhdKqkQc9oaXzwyz6BtvjERwoPcEZAFlRk1JMeak4Fef7vJ19VtJnjgtiAvuJAFralvSeewepZwKqXNS4VAQmPwpJRn3Vm4jVNRP38eDHE7kx+f8GutY3HLwGoiJtlQby/MduOj49WnqfZwp4GKLb90oCrGlTHfb0bweVyobjMo0l5fflB34Q19NU+WrM16QF/oA4RcJPu0+n6dl6Na4pFXLYAREHFGpbrVqKcILzJc2akQ4ehQFAhrRGbGXBNzB5hx6lsjFshQwMlpgnRirjaopfNtv+MCgvBuB4N1d28maD58wcF/QXp5yft8DMDvlHvrR9vwJcbTgK12gG3/8i156uknKfdU/1uadtPmG2j8vMOEcFrxPu6PjswBJX9S4BXGwCH/2adFApbLh4N1KpwAxQSflY944EYtWtDY0g+ckrkCCp61KWCzvBSPkFQ0bIsCpEa4Bk5pdNM4yeQT8M+QC+CTRoLqxqA+kCqq9+naPwMBYIK4fcts0i/SdKSPf9zhUay6QCgJkETUFYMrH3H/4drPDm02GYvLrEpumYfGILKt7cDRdnA16OrzpF+9cQmwNDXvIdvrb+MiQu36V8+DeD0sUSNio6CCskfjQ0gCaElpPXXet24x3p8XxmD5IX8Yjz27Q7kFfs+38wxtl518W3RblliFOOxVfl0AJAj4CTOH0Z+AISLx9Oxou8PRWTs1s7vh4JRkdT2zB5Ua8R4YhANbaNiAC0rAv6aVnWs9ssoFFR+2XEGO09lC2enrhTGER4nnUYlgSGokPBpRC7g4a1A9YbAIzuASVvxzr/nzCiZKjizGqmln2XPAnO6aacJ4b/IfZ/VJl+dISlPZC396CH0SS39hMXihV/24qftZ8i3m6hTaZYcK7pdX5agwjDAj+OB05uBpU9pV7hKZBhA20ajIjRkfdhTQ78f/g2LZvv8WPF4H/wysQf6NFPhiEwrw27ZgooLW09ewqOLdmDEHOENCGZ7Se/XIkk8Qdf7dHt24AoqPi8i67h6Q6BGE0NL4y9cjQqhw2UvV2yYA5w/AOxcpM3D+S+kTSJQ14wN9zknS1DRZelHpM7C44GHt+DkJWHB0mxlwIgOdVArnhAwDzKXftjvX7GvY0C/kbF90uyBQDZS5dTCHYEijYrv72u2oBIfGYr29RLUacn4O4LUCrDsOhATfno8gqNZVe82UasL87VUTw2RMJi9+hHdnm2PEUUPJKTdolIdl0p0QLIRk76vVmuo/IfbZGZKcuhVUsYQBize9zFSUBn0EvDUMSA2RVRrYqYxbSVCg5M8QUVnpLZIwwaq9Uqk3lshR4VK2PaF+PWEVO9H0q9rEa/z6rRkxXncYy2Wfl5JFU7Xbxrn8ON/jhKTmS38RYd53qHwkCCkXyzEnjM8NwmRCbo9O4AFFfEf/dedZAvnTqkJOhTGfyQbsZ67fvh52yS6cGyE7/bootJyjJyzlmubxO/sdLH3EehQa7UHgqUHWSsgpIxyy+lmNLOlFchIQBDk2DDYRlKR4DTZC7Rs3OXALgltK6s+SbJAbIQ12qykoEIS6r67S5uHs/tFMb8twSGc1////jzkkyQ6LBg3dq6rTblUElrh86q4zI3er6/CsPf+w49bdbAnIxDAgor4wC2kBl5wT1c9SiOba5qT11wl+9gF1wNfjOR25FrNwkn2Pix8JG8L8cZN7TnHW09exs7TOfh9F9s+yQCNysXD5PMVu9AYhsFukXq0gEIFJWXkepE3/ussJRCE56LScvyxpyrYn9XllK0nL1d8kijpkb/8e5Cc9s2y+SE1PaHo5EYj6U+yQS/fc/kaBclUtHNKmNdubIcdLwxCYozvUrWRkLwNP/79TkNWHwJTUGEY4NwO0STBQeSqIc3CjWTuHZ2J5yU1Kpm7gWOrgCuXWSd1ElTyuEbIQnF+rMBo3iylWGCw5aD1rp+8TOFroR5/PysPZGn7TB0oJLnWhkyNimYIaVR8BZW3/+YKh1a3URk9d51nVwi7nJ00mv2zkTPAspbSSFuAmyWL77AyCsklUYF+XhPk1GMsOYo7m2pRYV5thpkIBZqkgopebPkc+PVh0SRWjP4JABGhwaiT4OusTlUnq9k0nPfsK9mcw/OEGCaTTYxSKwbRkE3vpZ8SfmwcFhWOCf/cKyLMwBqeQIV8Z8je9aMnBI3KX/u4M2driykePFoVVkm7P6T9Q+S0b9bSzy9ijsBMJsLEWGKy2nRSSwDiAlV/qd02BmFmuBPzezdDqWgM66W96FkpqNboTp5Z//ieHq+Vj/T33ZEku5/nqHU1+o78AF+89Vi+0HfgxSGYPEDEhbWJsB2+VQl//KUfje19xFTtFRoVKZnyPYuo2slY4F0iaFR89v3ZQFIJDw3iztRlGAkrRqE9W9WSlPVIjjNxuUROPVYs7Yq9IWaHdqhEaPJuhHO/wBJUKnv7aGkJdXNFzAorMPuGtvhxQhqmDm0BALj5qnq4vRvXilx2W2EvW2ilUVn2DPeYtxWPP6sxdZYjQUlZVUUK1qkWSz9uN7DrO+DiUfEZrAxBZf/MIWhdO97/MvmJmHOt8uhkibs16uyEpA0ZBt4PEyYAZvD5uKsEr1UGhvOix9KFHBuVzjosOelAqoQzQl2RI6gMmQ1A+P1W5bBOJ4QEFY7jzGFv6fLswBJU4AK2fw2c2iCZcsH6kwaURx5hIUHoXL+6V/Xmcrnw8qi2nDT3f7lFcP89B4kIsoopKwYO/8k9x4uVYQVDT7mwNSpeux89ln52LgQW3we810l8YPCGdBCuxMgwawh+r/MMk9lk3LMFGPa29g8tKwE2fgRcEDBGroToPrXq4z09GmJCH9/t6mbQvVGi4LWI0GCuMGaWRqXrA9o/VweiwkLQqpavx9RTlwqrDkZ9pM/D5dRjtQYAhPvIl0aKB3Y1EiGfNBzfU1fdo8uzA0tQcQUBv+iwpmsBTl4sxM87yF5LOXAig2ogQax51fccT6NidY+ft1xVz/uZvXNF0EBZi10/J9dXfeZ3aMPfATreCXS5DwjzzAi/2ZTu/zN1JiZceNBkgkK82iFyAlZd52d63MHLYf17wB9PAe9XaCHKfeMgCT6S9bl17ThL+KIBgDARW4DwkCDullohj7tnd6gvgCxjWk8ZrW6ADAApBEeEW06yNOapafo8WIO6IQVONQuXy4Vbu9TzOW9EPDxdBZV//vkHw4cPR+3ateFyufDzzz9zrjMMg2nTpqFWrVqIjIzEgAEDcPiwxMzIH/z0mNo0ydpRk3MKZWhLtF764WtTACClHeeQLYg/aJFZKxu2RmLDsaoOTNAURZPtyayXm6+hcQV7opFe938aPMca5BPiE3Fh1ceFQx538FJaEgA4vYV7vG2B7DKxNZB6bv5QipjRYlhIENDxDs+22oEvQnDJ7OM+6gsg1b6jang/lpZbZyAVQnJHpNxxQanQINPWJ7+4DL/vIm+JtlpQ3BEd6vick2wD73QAjq3x67m6vp4FBQVo37495syZQ7z+2muv4d1338WHH36IjRs3Ijo6GoMHD0ZRkYhzHH/wU1D5+t5u0olMRFbkX601KvyZb59ngH7/45xia1S6NKjm/zMNQnDpJycdeLstcPmERg+yZwgCEpV2VHxu+GCd8sxObZJOI2fpY/zfPqeOZOXhTHaVEbiZsZIUExYFjFsC9HhEH58+YgPs1NPAlH3eQ1khJ0yGNNhzfm85DipLCjzLtL8qcBMvU1CZvGg7/t5P3tWXHEsOS2EWpOUfSWHq8nHgi+v9eq6uPeLQoUPx0ksvYdSoUT7XGIbB22+/jeeeew4jRoxAu3bt8MUXX+Ds2bM+mhfN8EODcHePBkiKs1ajua4ddw++vDg1GmpULh3zPXfNVJ8otWyVutVmCAAwoS9Zy+MW2vUDANnpwMqXtSkAX6NiE8++JITsZYR8rHghzVZLC33P8Qlm+TUqEogRVK+Lz6n3Vh7hHIdbYHu3HHy0AzL8cChGzAYrPBYIqdpJI8suzmQ00ajs+8XT3ynQ2MkVVP7eT/aR9M6tHSyz46cSkrKvtNyNPWdyUCCpNVWPaW/n8ePHkZGRgQEDBnjPxcfHo1u3bli/fr3gfcXFxcjNzeX8yUasQfacInqrFe0s+BbtHKdgQvE+tHRWViTX42xVR2FFQSU5LgKPEbZLSxbVnzbB7jx9gjraV1DRlKVPSKcJYgkqr/iunwvB/22tYpBcyYsjWhPP+4y5IWFAssYGlwq2J9tVo8JBzvumxlbEz7AlIVZaj6yA5Aj1jz3nMOy9/6oiP5O8/fqJaTWRkeFZk0tO5m5bTE5O9l4jMXv2bMTHx3v/6tWT3zkJLnXU6Qz0n0a+Jn6nqfC3i+06zRIcHj9Ivolj6OrntyqXJ/SwOworGYexCQ/1fRXcboGln0pCozwBzDL2+PfwI7ylCQsKxXKRXXIfA0aZ7eLiUeCzQcDBZZ7jYHWeovmz7GgRQ2AzGNOtPvE8Q6qncN9dLX4hczlp9+kczP7jgLbP1gGSoMKpR700mEdX+J4Ll+9GwALOaH0IJvRNP1TE+zmSVeG48lrtbessWBXiTJ06FTk5Od6/U6dOyb9ZSKMy/i/O4EByo241FRwgvF0MABARB4QR3Fiz3dv7OyDK3OrMdghkRY0KAKKLatGlHwAIiwY+SAM+7AEc/0fZA4+tqvr8z+vcazZe+pFNLeGtzKIsvg84tRH45hbPsUJB5XBmHp75cRdOX+IuK0VZTKMSHORCX0JcL6KBd7qwBloVQpqA2p04h8Pf/w8/bZex09BkpDUqMvpBrSYPcbVlJ7XKLjQ2JCWPjzFtmPa+a0wTVFJSPI5sMjO5RkSZmZneayTCw8MRFxfH+ZON0O/OGxgu5PtucbRgm5F28z9uie85uds+pSjKIW9NJuBmdRRmhyoXIozgiVhSpgoOA3IqBOV9vyp7IC8eEgcbG9PKflH431Fuuyi4wD0OUiao3PDBOizafAo7T3OXLaPCrKVRAcjbvcnvj0DdKRWevdkJCCp3/aYuP5OR3Jik9ftWJrJFPiRMdjYk7YXZkCbHlwp431eH/su0HrFhw4ZISUnBihVV6rHc3Fxs3LgRaWka7msvZO2Xl9kXugkjlBVtVAa04i6b+TSi2h18b8pjL6v58Z3+eAY4tlpWUvZyT5LFrNgrIWlUGKFdP5XwNR/5WcD346QHCMlB2XptTS6yS87vzArkBl3k1Z3CTjFPwOAvMUb+AGImisT8BcNVPoQgqEzeA4Rb2z2DEINbS3hFltOGLh6V97AjfwMv1QTWf0C+XnBRXj6Q0JibhCzhSaw+VYYf0VVQyc/Px44dO7Bjxw4AHgPaHTt2ID09HS6XC5MnT8ZLL72EX3/9Fbt378bYsWNRu3ZtjBw50v+H7/4BmHcd8FrDqnMyDElLy93EWYsF2wxapMRxtCoppF1JSa08/xMq1rzZIcwrGx3DKDcWUzBbq2ybd3RPxdWNhb1umkndar7qSkl7GvaSjcsF/P44sPcn6QFCyshOpd2FFaghFoq+eiPtHyhVl9G+yyck4kyOik6CqDsxQiPJt1FpMQxIUGILaC3u6+Xb7v5v+SHWkYzO/V+ZdheL7/f8Xz6VfD33tLx8YENzAy8CabL2e8bjte8ofq6u+s4tW7bgmmuu8R5PmeLZWXPXXXdh/vz5eOqpp1BQUID7778f2dnZ6NmzJ5YtW4aICA1m3T+O9z0n4fp84cZ0PPvTbjw/rJXPNStqVACgQY1orxETsRO7fw1QlA2kbwC+u5OrBfnpAY+L9s2fejx63r1MvucrBTPZygH/4X5NLbnuCpBjglQp1mSWWY5flQuHgf/eFk8j5sHV4gxqJTJ7rdcVGDEHqN4YOLhU3QN8mrjIwN35bo+vEQn6NJMnzFgBQ0y8+MKfQjf98ZHWEvpI2tIz2VeQfrEQqYlR2i5VaOjXxi5LPz4I1efSJz1j0V/TgM7jgAj5hsW6Cip9+/YVnQG4XC7MnDkTM2fO1LMYVUhoVJ79yWO/8eKSfT7XrNdkPLDbDbGmQ8KAmCTPH4nvWcHF8s4C8XWlH1pa5HF6JgOGYbzGbFZUZVYSTLJRqRwV2oz2WPCHxwPFQluyXb4dfEkBsGwq0GqEx4hu0yfAls+kCyO0tdwGSM4CO97h+X94ubKM3eUVS228Vi6mYeg/DYiqLpn1qI6+3jatiuYKFdLyJn9Cp0DDFxrswtzbO0knNJiP7uyMB77cyjlXWFoxHoRoGGHZzy3JbCy4O1le+xMSVNh1881twN3yJysWrAod8UPataomIIjnTG3/uVycy7nimzBKzpKLzO8oU3XHMAze+LNKxWrFGUIlJMPkL9af8Hxofxsw7nfgARE30CX5vp3Uv294HER9dQPwUR95Qgpga42KbOTMYit3re1YCLxcCzhC2O4pplGRKfCFEIRUS0D4aoqN0b+90xP358IRIHMv91p5qWfX2qIxvIfwHRDKn8/umTEYVzepIZ3QYAa3TsGi+7tzznnNJYKCgSePAkNf970RANa/zz1mGODAUrLdSrECv14SWLG/LJOj0hMUVFj3nlyr6LmBJagoDCXPXm+36tIPm6y8Ygx951+kzV7pezFYQ2PBcztlJdt4/BLeX1XlAdSKa66VkLQ9n6894fkQFAQ06Cku7O34mmeoDI/32kp4EaUFia4J1GguL62dkSOoVK+wL/t5gqf+vrnNN43QwH3zF0CovCVkKzrWAsg+UxQLKvt/9cT9eb8zMPdqrpPG01uA8/u5y3AkezUFgkp4iLW2ebPh9+GcuoyuAXS7H7juTemMjq0GFt3mcalfScYe4MR/0vf2eFReYWFNDbRfuzbTVYTRqMCab6gJkPbas5c2LdhmlKFlOHiZQltWHndwltxObSKksvmckfJvcuWS+HU5PLZP0RZG26LKLkDB1lwF3jFDLapRIY0JM37bh2Pn89Vnyt7ezR5Yy8uAP58DXmsEZPJcGGjZd5gIv9sij7kyBuKz27jHF496fCnNv0763vZjpNNUYEUtPikStWoun/AY2MqACioVfL/F13EcW/UWZzEDsUoOZOTJS6ilEzGZHVc5bysayajNKpBmL2VuBqfYjsEUd9gqOppAEFIAmYIKw12GIM32hWZ4IdwOdcX+TMxeSu4UxaIVW41LBSUYOUeZ2pwDe/Bb9VLV570/Aeve8wjbv/Fm/U4RVHjHRO2AHI0BXzt9Zhs5HQkF/bAVNSpxEaGCsdG8yBWw3mkPfDZQVlL7vKE6QxrwXS4XXhzRGv1bJOGWLvbdngdAXmcjsSuqKi95L1tWLlejYtWZKyCs/n99OSsUgdIYPOet717cNORqVLZ9wTpg4DvjlSeojF+wBR/9QwiiCSDUggMCIDxm5hb5Ea9LqA3/+ZzwPSxj2ksFJVi25xzeX3lYfRlMgl+dqpcxgnnGt0oMaNntXuIdsKKNCgCM7iRjw4XGUEFFhOAgF+5Ma4DPxnVBRKh1115lIUe4kGNsXHpFtrpuwboTnGMrqjIrEZq9cDozJVqpsmIgY5eyQrQYpiy9RZG166N2R3mZZbF24PEHlvMHPQahJBTYndhJo+I3TDmwahZwkud2v5RggF8Jq93fOHcdHvxqG/7vz0PC6W0C0S5UTqDVYP6kT4HAwxFOXETnopVY1HQKTZJi8NuknvISh0Zr8kxn6PTYXLksO1geG9IYakXVm2pkRQgVmRmkbwD2/+aJs5Llu32bhNUCvYkhy35GiaC18BblhWgg8+W3OEPb1sL9vRvhYwENBgCgyQDha2w4wjNv6WdOV1Xl42PZXT96sHUBsPZt3/AXgtvuwQlTcOxCgT7lMgGi64wrl6VvZGunlTrLjEyo+uwKEnUqaeXxp21dmT5QGvVR7zOJhUVlNpUwDPBqA+D/mii+1UWwJ7BwO/HSW66zKjlLP8f/EdaqfD7Ys03v9GbZZWuabB+X25rvSGIHHZSLhj4YzKYHYYvqjlPZVQdyhD4G3OVIhrT04z9WC0hYCTFSsr9ckukKno1DbFT4EJUZja4hnOTBji9VXgJFbTKyWtVnl0s0YKJVl34kCWcFw2V/Xz9wlqAiw0W+EuywJfnuqxsQz/vMFuR0NksmAxs/9D1fInMWxdtpceJCoUBC+2DocpWGXi3NhrT+P3LOWq5xsqyMeBoVsWCOKokKteZArI+3fBXt2SGCCr8+iTYqTfpL20+x66OsSH4BUtrxTrhE7WSsrFEBRGwOg0OBKfuBx/ZqFg7EWYJKuUjUSglsu/QjUEQfQV2ufcW2L7nH53YBs2SEJo9OAsZWRRDeln4Z+85p5/zISNrLVWtqjQqNyh3dU3UoiP8IeaTu9ZoSTROjTsvU7lZFySMtq1ER5qUl+9TF/VGzLVzLHYMmwq8von2IywXU7yE/U3e5/AnynT/5PEtMo2LFiN5sfp3UU9iwNq62x8u5RtK2swSVMplOtQiQxnvZW39NREjr4/MCyO2g+Pn995a8+8KiOdZfP26VH3zLapgmoBJ2XV3ML8bmE77+Wfq3SMK6Z/rhxRFtjCiZYsT6J0UDrBotaZmIYSgBqy79iPHpf8fx7+EL0gn5qBFUZM6Knx7SQnneJiLoZVVKi8oWnvf9Avz6sLwHRvOWQ11BosGErSpAV9KyVhzeuLm9eCK5O0klsLbIphShHQBiRFnP3bMShMZUH5Wi7CUMdswPRv59aureorB3gVzML8afezPQv2Wy/gIMQXvQ5/XVyC/2HawZALUTrOtqX8zTdrmbkWfAyqjUqPA9BEsQadEdfVLy3OVCFRpkNUuZQfIEFUn/GhYjT2ibd6GEQS07AvKSyX6UQHzpx44CtA9Uo0JArpvySm77FnhoAwB1768VIBkBA374CKisiLISYG4PYM+P8u7r4HFvPmnhNoybt0kPM0DdGdw6GXUSIjkRgNcdvYj7v9yKrzac1L8AhOkVSUgBFGolTECs/cmKFwJ4ZmNq7HY63C476dNDWlg2tEOPJuLxudS944Fro8Jn4kIBR21SW5RXaBRE1+US3fVjZQeZbLo3Egn82Wa0Js+wR03IRemsvvkQIMaza8bKPj7EEOpj31t5BGXlanaRVGR4/B8ga694UgAYPAsY8x3Q+ymUlbuxZNc5rD54HicvVhng1kmIxLu3yfSbYSIf3tEZ/zx1DRJjfL3D/rlP2SxdFQ7a9SM2hsoeYBm3OtVxxztlJ7WyFuDO7vXx5s3t8eOENOJ1da+3ii6/hQzX8DZAtljXaayexajCFSTqR8UuPHttSwBAShzBvX6T/po8w1mish82KnZFSMCau/oo6laLxO3d6ivLMHM3kLkPsl/r8Dig2WAAAMPqOdceuej9/NYtHdC1oYjUbRFcLheCXWQjNkMUGAoGZat3b2EhwoK/mAEhB3c5UKzCTsyqnrIUEhIchBtEvIB6Bb5G11Rth282BDi0TDjTUxuUFSI4HIivI5ls6lB72aeIYlj78dWovHNrB+w4lY2mSbEC91iPSjtJXbbTVz5Dt5yN5ueHPFEtVWJPfYq4r5fDmSqDl80bCjU1IjQAWdl1PokYgqM6YwQV+VNki6/8oHdTYf8+YgaEHBg3UHBemwI5EO/y3y1fVZ0MjRK/iR3RWw73ESKxE+hcXxt/GXoi+50xaqmLsOtncOsUvDC8NcZ0s+ZuPhKVgkpmbjHO5SgzZJf9DF1yNYP9vwLLp5pdCsPRZcmqKFtJAbwfb/2YPFsLC7FXMxM1YrvrN/0erMAew+JyCkKCg9A8mTwrFFuX58CUy3NpzqbpYJ9TTlCvk/B+rXCWY0Ut+4OeU4AUebvK7BCGgDTjJ9p6KY3ppRaXCwfOcTWGdvDdxYetgHr6x93CCf15hi65OoQnBzc3uwiSiGlUfNq8zNmR52blZeF4HmURbjNBheT639vJNewNdB6n/UNdwUCXe2Unt7oxLSBsCCx76Sc7XbkG4BrfyYpswchmyK5HtShYdpMVgsKCzOfFIwOgzo5HBW4E4d4vtnDO2cJ3Fw/2ho70iwTnoBrUp71GED0htI9GNbQJqKQnYhoVnx1BcXpEvZR+sexivV4JSaPCGeu07shqtgT+dw5IkI7QXT3aY+g7oGWyRErzKSwhCyqqd6TJISbF55TuA7oB3NTZ993VXVhlCSpuN4PiMmGNnx0HWACY8ds+zF97nHvSoKWfnLimPufsWI3sMhNb5OTdQNNB/j3Dr7sdjh2WLBRpCiMUeFz95jZ56cKljb7sUI9soknGtJwjrXsTBggJl04GYPnk3vj4zs64o7tCI2kT6NeCLEzpKjjE1fI5patgZBADWvnWJbkaNWybLEHlpo/Wo+PMvwST2lWjAgDTf+MFWdXbmPaBf4COd+Jg2hs+l+y4+1SyzPF1gXrd/HqGvUYQtVx1T9VngdkwyR+JHTQBYmuaPpdCI+QvW0iFIxgw3bNHXsbWRTvUI5uocIk1aq07EwEjWtKMuWZsOAa1TrHFDHb69a0wjhCLymgNhxM0KsRAv6UEDUecjHAXcimuCoGx9eRlFJbYXKMitxnobaNSqz0w4n2URFlfKyoHdnco2Ar8jGNmrxFELS2GsQ7IVUkae+ygCRC1USGdHP4OUK2h/w/uPhG48XNZcUDsUI9swkiClZ5LPwKCyuJtZzjHdnNRHhsRirFpvpofooajvUwNnhgDphNPO0FQIY2yr/xxoOrgzp+B698DUtpq90gF/lNCbLAlXHYr0CO20cRN2udpEdiT5RMXBYKO+ulK3/qtSwvqdfUEzavdSTDJiQu+RkB2GGBN22+v4GUOtUEnxsblcnltQSrh7hgwRqPy3srDnGOSIzqrQ9L4EQUHzmRCJaFkmzInCCpCq1d/78v0fGh8jcdRmZbLXF3uk5002GYuCETRWqPyQjZQ03djhg1XeYjIUqapidnFfoZfd9uF8Fhgyj7g3hWCSf7Y4+t5lDizthiRYcHYN9N3SybgCVxGRIs3RIFWwW67fgAgrRHXfXnNWJYNSaO+2j5MYHBpYiOnT0KQBBWiRkULr7xCATodYKMiBH/XiGYb18f+CgTLNyoNtcPSj1y01qgItEuh8Cd2Q9b3oEs/MgkO9RhJEQZYIet5O2hUAJPCgcsUdoa3r23ZWCpK4Ay4zYcCt/8I9H5So9zJ7a9xTa6GoF41CWdeFoTUTIiu33UMH/BXpdbBxoiJH2ezWU62tBLKGvVRlNwONirJJBfvFew5w/LXw9710/lu3crjFI2KrO9BNSoSsA1pASDMVz0stK5mh5fP6kwb1srsIqiD99MfPc9aGnS5gKYDgH7PabNkITBI8wW8tMbiQeqsCMmQ2rsUE1Xxfao3Aup28f9hhB5ze/pl/O+nPf7nbWEe/25n1YFJ8aLs0Fc2SYpBixSylnLYe/9VHTTo5fkfnQT0eMSAktkbWRPRGr5bsRU9w6+77UC/57nHBEHlie93+pwD7OklUB7af68z2WTXyRGh9mxi/Brafy5X1I+EXwhMgp1gWxFCsF3470iFW/y7lwEd7gBu/8ETT+ZR8nsoyrC3WAe+zzqYoSJWkAURU5QcPc8OlWFOm7HLMsbNV0n7KkJsMvDkUY//D4Ocv9kZWTJqhzuAa/6nKLI55xmq7rILnccBUbxgeIRYGNwXvQobTBIswa7T2ejxCtnrrd22JldC8g1QVEqYrdbu4P/DBGbBThBUSL//rKUVu1VqNgNGzgESKyIYV2ug/AGprMjChN8s54rCiOoWRSzgG6edmGSPY5dl8puukun0MrqGx52DDoJKSZkbZeVum4h20iRGy/ABFRwC9HkKSGmn6hn2aF1qGf6O77mklj6nSsrIA0WNGHlOuAKdX3acFbxmV0dQpFITFWxXPwL0fwEY9ZH6hzlaUPGttBpa7l4KZufFfdZ7Kw5jNnsLr40Rkz9KOUY/xreZB/o0QqRYfCwLERsRih8nXC3/Bo0FlZIyN7rO+hv93lijab5mEhYShDdvbu89vv3TDYKTf7WGOc4WVEhc96bHUdldS7ynaidEcpI8NqAZljzckxjzxRHEJGmanVgnaoe1a7kQ5YmQcKDXFNUzhYqcfc6cvlxIjkNiM0galRs6aRjKgS2o8DrBN/46JHrryA4aOkczkTKdNSpSrvoH2iCcA5vO9ath5ojW8hJrLKicvFiA7MJSpF8qFPAsbE/6NKuKlr72yEVM+GorOWGH24E6nRXnH3iCSkxNj6Oyhr28p1rXjuMkuTOtPtrUUeBu3m6M/ABIVTCrkEBMLW1Hl9AAWfA/dbkQs5buJ9vj+NOhESSgmz9crz4/C0HSqGkazZgTekBeW+vWsDqWTe6F/7upvXRiiyBWY2XlbEFFe2NaqZ/LjuOtbPtDf97ryOo+Wn12XZa5zTF81gO+w7+MnCJywvAYZcFxK/NXUyinwX8RHaQEIFO9EXDPH8CnA4DTm/3OzoluKkhN4I7PNiK7sBRrDp7H8sd6827wR1DxrcCzQi+6zSAJqiXE/ckqCQ5lP0zWLS4X0CIlTjqhTeCOEdq/jFKxkpzy/pe7GV8NsD/v9VPHfNoke1JXWu6QioP+Dv8CT6PCorTcjdyiUp8X0S4W7FZB9yiuJkAaYLMLPYaZBzMJO0nUaI4Gvez5P3Ku9xTDMLhcIBFnyaZU2nx9sf4k3v5bfFlGNsHKNSp2fL/F3jHObJatUUmbpO5hPadwDqUFFfu9/6TXteXzy/DsT7t5Cf0YIknODlk/D9u2KCosGP8+dY36Z5kMX3MqqUkPU+bMMqAFlaHv/It20//E+dxiznnSlkq7IqiC0xD7dVN6IKPNtBrBPb56EvBclscnSwUv/b4fHV8UjlJrZ2JYwR7f/vuwNgKZiI1KoJBfXFa1nMYWGro/pC7D/tM4h1JyiFPe/5JyNxZuTOee1NhGhS30rTyQ5f18S5d6qFfdfg4dK+ELKjlXSlEmpjmNUKbRDGhB5UiWxzJ504lLnPNOMqIdOWetdpklkwOe2XBCZQ5DXgHa3sQ9F8LdWfaZUNgDB5AQxd3tc7lQC0GF/a46V1CResd2ns72PalWcOMvV0gJKjZ8/2Vr1TQWftmGzz9sPe39HBlqj11TQpA2Tcxbe0L4BoVRvp0jqMTYy/Jca94f05F4PiO3CEWkcPAAOB37dW+IPyAoBGjQk3hJSjVsR9ivnWaxioLD7Nmr+8kDvRuhU2oCRvB22Vwu1NjHicxBhRO3ySbwg2TyKa50scBpX9oMslLvd91qkaLXbY3GGpVSAS2D3QUV0lLPy0v3Y/bS/Th1ieD5XeHOH+cIKqExZpfAVIa1q42DLw0hXvtwzVHpDAQiz3phGOCaZ4Eek4EH/uVekllGW8F67zTzUBwcCofWlihTr22JxQ/1QDQvJlVhiX/xP3yIlt5236dZTTw3zNeXktXp1bQGqkWFCl6v2knFal8atVspQcXOSxaSaC2oCPjssosfGqV89M8xDH//P98LCl1kOEdQgXO2eqklPITc2I9fKCCe5yAVNIop96wrDpwB1OL6DHGikoCtGibtAvM1IJRRCcHqHJ21SInFyseVBYmzInz1MNGh3a0LZQkcHEZ/5hGgm/QXTRYa7MKCe7oiKVY4OJ1Vcblc2D5tkOD1KjmF1Q+GKPie7cdI503g5VFt5D/DQojJcJx3W2uHbwIalXCba1TEyCZpThuLv6t8nCOo8P0HVG8kmHT/uVyMnrtO5wLZDL+iWzpPUnFJaFT+VBORV+XSz7irG6BRTftrDPlG6g99vc03UYvrgCcU7ghqe6NHgA5QY1oAKK9sV21u9Pxv3A+ITACGvAoMfU06g8bCO04u5Bf7nDs++1ocn30tbu9WX0VprQ139UzbIfIQaccg7L/0oxiFoUecJajUZKl0ez8lmPTueZux9eRlAwplIxj1AfecqVFhfSaMf7K0VHyCgqFGqOMbodoVvlOowhKBNudyAd0mGFAi5+BmGGQXlngcWv4vE7hjsedC9weBbg+oznfv2Rz0J7h7d7lctnXmKIVbR42KN84VD7t59zUaBwkqDDD6E+Dqh4Fn0oEOtwkmPU+YIQQ8bu0Fld8mkY1v7QYpjLnvWrPMTluFVOcUY0VF4RSGvgI8ew6YvEe/AjmIxdvOoMPMv/DmX4cqgulpI0Qs2nRKk3yshljt/HfkAiuhgiGyRnPRy2L+ZuJF7I8ojhJU3EBKW2DQS0CEuPt7Ozoo0p1mg1XfSnKhf3u3VLSta98wBFJLPz5rzYmNgYa9udF8iShve/GRzujESAEKRQmLAhLq6VMYh/HbTk9g0HdXHNYsz1OXCrElADXP4+ZtRmZuhf8puYJK6tXAhHXAgBmCScqcFNzHYBwkqMjXCNDmUsE1z3r+dxoLVGugKotdp7Px3ZbTnHP/u7YlXhguM+iXRZEypvWJuO1yAXf9Btz8Jfd8M/JOLCWEhzrjNSVpVPSaNORc0Xjrs5MJJ08oer22CvvP5RpcGGtwtjKel1xBpVp9j0+fcGFbMidEQ9cUBUbzzugBAUXBuKhCpYLG1wBPHQeGvyudNpjse+L6930dyt3bqyHCtPI9YhItalW5eCatxRcLbDP06dhqdeAeq2h8Qru57AbfRgUAVh88L33jfasAF6EOomoI3tJ+xp9KihaYjPsdqN0JuOsXoFFfzzkZExa+Pxw7IrUy5hUqXC6g6wMeR43Nr/Ocq9sVaHcr94bK8afD7UDDPh7NPouL+cWCPlQCge+3nPLV9t3wsez7neOClUofgvyy4yxu6lwPPZsSOvao6uI3N+4PFJyXdgjHwglGdnd0r4/CknL0bFIDYz7Z4HNdUBPA/+5JLT0DQlydyjsVlyXCIRoVUmiKu+dvxolXrhO/sU4noNuDwIY53PMj3tewdAFIg57A/auqjp8+CYRJ+FMCMLRNio6FsgacZZprK3ZNFeUAu38AWo0Elk/l3lApqIRGAnf9yrn0/ZZTePKHXRjTLVW/AlucJ3/YBQDo1yIJbepUaPCkxh4WzugBAV3Cm9uRlY/3wXPX+Tq0uuOzjUi/SPAQKEW9bsCD/wL1uvpccrKtT2hwECZe0wTt6yWggLA7RVCL66MqZjwDQvWGFYfK6yws2BmvKT8eiCL42pgH/9NkWY3CIjKBG41aACdMRKQodzMoLuO99xHxQJfxQHSi73gTKyy8Tf91LwD4xhEKQC6x43sFydeTOKMHBIDqjTXJ5nabS72Nasbg3l5kHzKvLSdvjfOhfo+qzyK2P5O/3aGgZM5C0FsnX1BRIJjkF5N92ThlYFC064cPe+mn5XCP4bxD6sVuBDug3tk2aMlxvsvar/xxAM2fW4ZdpBhKAPe9bnuzqDsM507nlMPpNwNSULn+Pb+zeHpIC8wcYU9Pi3JYsuucvITVGlZ9FnEE98uOs36WyL4Idj78Tpw/8xKZsR4/r8I3i40I9UczxBYAR3zgf2EoqvFL4LQgD/dr6nNu95kcAMBsAb8nnB5g9CeKowGz8UvTaDO4Pmrk2945R1BJqOt3Fm3qxDnuJVQHqzHF+1+vTkRw2Ys/S+ALJgNnegTBIa/63Hom23dp7qWRzhGcSTYqsmELKn4MCgHLzV8CrW/QJCsHKFQ4jlQiRLzClrkFTAoUaEqlkn4x3ndZ3alw7ImDAlFQ0QDNgs/ZHcYN3PGjx3lex7Fml8aSlJYL9D6hkZ4Zf2wtoEEvoPm13OsJqcCjOzweQ1kwDIMHv/J1Kd+toXyDM6vDD0qoCI09hAYcra4HbpqnSVZOmMyx36tyIWEEIu+5hjaRVzcW3r3mND7+52hVAM2AXPrRgECQU2QZwIZEAE0GeLbYBTtnY5iW/LD1NA4LxO1Ax9uBxw8A45bIMk4EhJ1BOWFQqCTKnwixCmZfQrY+Drb9NhQnTOjqJ0Zj1RN9sXPaIFFHbIIaFWp54sMzQ1tIptl84jJ+21VhMkAFFXU4wUhMiqJSkZnAdW8CtdpXOYKjiDLwrX80y0vIGRTJ94hdiQ73Q+gNjZKdlLSdHKCeQQEAXe7z/G97k+osnCCoAEDDGtGIjwoVdcRWJqhRUbD0EyBCzYN95G1oOZqV7/mgYPIRcNNlMY0CKaaL0xDcrQJ4tt51GW9cYRxAZm4RkuMi/M5HUFDxx67DYoQLOAFkGEZ6Z9NV9wD7fgaaD5V8zq7TOSpKFyAMnuVZBqqr3i7CSVo+QGR5ByLCLXUwqhrvu041KsKITaqcMlMQg74z2jLkbW20KkIdopN2BAgJI//7WUbgwfAY4L6VQO8nRZNdpAFHxQkJ88SkClUvXDuoSQIQt1EpE/ImW8N3pxBFHt5xNiTc4zxPzj36FceaiGkUnDZTICGqUaEo5nKhNjFl3AFgowKQ7cAWbkzHqUuFWLQpHbOX7lftSPDPvRno/NLffpaQIoXTNM9iS4LlQm2x95NA94nAPct1KpVz8TafsGjZ3qUDbunn+AVhXxVCqmknoZWxeoGAwaITGd+zIT7777iuz0i/RPYa7CQbFTF6vVblyn1Aq2R0aaB8t9Mbfx7SskgUAZymeZbSWv5z6DwaJEYjNZFlJxUWDQyZJSt/GoyQixpBNzB6QRaDRAwg7R5ITw5aGXZ1m7VCk3zswCMEh1BacSQrH3fP24QRc3yDOwJAsINsVAB56/UX80ukExFw2PipGn8HxssFJXjjz4OC15226eC2rsLeyE9duoKxn2/CNW+sVp0/NeLmokbQtcTIPGfOHDRo0AARERHo1q0bNm3aZEo5nBJTRQw3I7LuqgChLaBOJD4qFKuf6Ivr2tYiXr9UUII1h86rqtf7vtiCVSIRhJ01JACPDWgGALimeU3BNMJbQv2jS4NquuRrBsPb10ZfgTr8dvMpv/J++sddeG/lEc65dnXjvZ+dpuSLjQjFgJbJomnUCn8lQlHWAxg1K4emN7lvv/0WU6ZMwQsvvIBt27ahffv2GDx4MLKysjR/ltTat5OWfq5vTw7F/uuOM2j+/DIs2yPTnT6BzNwi1ffalQY1opEYE0a81unFv3DX55vw0T/HFOd7SmDJpxIxr5l25JH+TfDHo73w6uh2gmkEt4Sq5NYu9TBtWCvMvaOzpvmaQY2KNjju6voY0pocCO+QkH8fmWw+ccnnHHt5xGlLP4A+2rjScje6zQosm6keTRIl09hSo/Lmm2/ivvvuw913341WrVrhww8/RFRUFD7//HPNn7XygLjwEx7inEEhrTG5wUz/bR/K3WQvqHIoK3cH1LIPGylD5J+2n1Gcp9A7Gxzkwp4Zgx1oTOtCy1pxiBBx/laqUDPldjN4b8VhHMggD9CxESG4p2dD1IjxDT5nN1Y/eQ3+eqw3OtcXt+FxuxmcvKgudhRpIAlhaZud1iYBfSYEZy5f0czY3i7IieelRig0VVApKSnB1q1bMWDAAO+5oKAgDBgwAOvXryfeU1xcjNzcXM6fXFYdFBdUnGSjosXyDonCUuFoygAQ449TL4sjVaVq+m+hLbszrm/t6LoUW2ZVuqa/Lf0y3vhL2JBWp1fBFGLCQ9A0ORaAuKuBp3/chT6vr8aXG04qfgapSYYGO1ujMlWGV1WlXJHoKwHxJVA7IkcbajuNyoULF1BeXo7kZO76YHJyMjIyMoj3zJ49G/Hx8d6/evXqyX5eRo74koWTBJU+zZIk0xTJeJH4SDWybx/orjhPuyC0hbgSVS+giEbFyYgJKkrtAaTSB9qWfJcL+H7raQDAuysOq7ifoFFhGaY4sWnWTojUNL+dp7Ix9J1/JdO9P6aTps81Gzn2Zba0UVHK1KlTkZOT4/07dUrccOzEhQKUlrux6fgl/L1fWKMSGuxy1OCQmhiF9VP7iaa574stmj/Xr8BzFkdqwJP0rsojr6iUGNIgLCQIIzvUUZSX3RDboqhU3pOq9/qJ8t3vO4F5a0/4db9U9Tupn9SS9UcvYtQHa7H3bA4e+26HZPqk2HD/wkpYEDmTDDXbk02tpRo1aiA4OBiZmZmc85mZmUhJIRuKhYeHIzxceK35yw0n8f2WU/h8XBdsOXEJD361Db2a1sAJkfXaXdMHIdRppuwAasWLzxL+PXxBcZ5SBslO7sQEnT9VoPSbP/jVVuL5Qy9Ju4l3Mi5WTW46fglTvtuBmSNao18L8s4MKQHy9m71NS2fVWhfN0GXfEmaQfYuPycu/WjBbRUxpsbN2+zoZVsxrmmRhM0nLoumUTqhA0zWqISFhaFz585YsaLKONPtdmPFihVIS0tTlefzP+/BrtM5eOfvw5i/7gQAz4BcLrJ2FhcRikh/IrsGEFICs5Ni0/CRXPoReZvcbgZ5RVzDurVHLmpRLMfBFjzu+HQjTl++gnvmC2v/xOSUN29u76glXTatasfhrVvaa5LXX/syMfuP/XC7GaJqnv2bOM0zrdZcyC9W7V3Z7tzXqxHeu60jujYUNva25dLPlClT8Mknn2DBggXYv38/JkyYgIKCAtx9991+5cs3ZDorYZ9CkSavqBQr9meKpnGyRuXOtAai18UcYY39fBPaTv9T9U6MQII9KJbIsIQVGxRu6FRXkzJZlW4NpbeDyuG+L7bgozXHsGT3OeKMl922nfqKX9eO7CdJKQwTuDHVQoODMLx9bSRGk105AOocBpouqNxyyy34v//7P0ybNg0dOnTAjh07sGzZMh8DWym+3ZzOOXbou2Qq932xBVO+2ymaxmleK9l0rl8Nb9/SQTiByHf/74hnme3HCiNHIZf87eslqC2eY1BqTBvIjj+1nhhk5hQRm3EgLPe8f1tHHHppKDY92x8vj2rjV15yFCpObrZae+O1xELapEmTMGnSJL/yeHHJflzdsmoHUCC8WEZy9Hw+NhzzdQTFJ8rBxrSAxwBOCDljRuXr++KSfcTr17Yh22YFEkoFFa3CQtgRqW7ufJ7yaNKnL19R/Bwn4HK5EBbiQlJcBOIjQxXdy/dAG8htEhDvC9XIMKZrVLSEvf3YgbaxpnLX59JhDT6+s7PjbX3E1ucrheP1Ry/izb8OEX3ZfPLvMVwrsm1xx6lsv8tod5RuJw5ojYoMCWLdUeVG83wCQVBhc4Eg4M34bS/uXbCZaKu2cCPXX02Amqh4mTq0JVLiIojX1LgLcNRwzm4/aiyLKcKQZll8Bgm49HYSYqr2yku3fbIB7644jEWEmCtFpW7sOyfspFDIu2ogodRBW6AaLgLyNMef/uu7zFhYUobt6Zd96k7OIOIKgIX1ghJfH1Pz1p7A3/uzsDXdd1dLRi5XsAngJgnAE3Jk/dR+GNnBN5QLFVTYlunOf5coJiDWrralZ+PeBZu9x0ey8hXnn1cUOMEevxzfFbXjfWddSjuyQB4U5Lh+JznXu+WjDRj1wTqvY7hKZv9xgJgH2+FbqIN39lVyhSCoVFJACMiqZl7s9HYrpCyQ2j1JwlGCCnt2EAhSv5Zk5BRh1+ls4rWcAItXIYbYDLbczXCcCqqJAnxfr4aqymVHejWtiZcIRotsGxV+dZe7GUxdvAvfbanSVgkJNpEOC+hIQs5Sayhhe/buMzkAgK83puO3nWdlPefpIS3w+MBmSHRAzCQpxnRLFbxWzLdHYRjsqahPKdY82defYjmC6b/tw3sKPSY7SlDhLv1QYUUJ3WevwPXvr8VhQuTVM9nSyz6BgpJdFqVlymcO17bVZoukXSAJfm/+dchrnMi/+seec/hm0yk89cMu7zmhmenW5weQLziM69vXRmxECDb9rz/xemiwCwzD4KkfduL/lh/kXNt5KhsPf7Nd1nMm9G2Mh/s39bu8dqAWQdNXCV9Q+XbzKR/nmULLkfUTo/0vnAMQi81FwlFbNNgzMbrrRx27Tud4g55VIseXRaCgpF0pjQIMBJ7RolB9tpm+HE1qxvgYyl4qKPFJK6RRcfoOtEreubUDSsrdgtHfF287g/CQIHy3xbPM88Tg5oqfEWDNUtTGsZjno+ujf475pJEK3kpRhqPe5J2sHRMuF7D+GPX8KcXEhduw/WSVcVhGrq9jPKXbRZ1MRKh8JWSlgJdbJH/pzMkO80gIfd+SMrLRMWl9O9Cbp8vlEhRSKvlmU9VSWSbhHaf44nKRtXV8jcrxC75OHLNlLZc7v+FqtanFUUs/n7KcaOVeCRyjRDF6NBH3XPn7rnMcr72vLz+I3KJSnMu5ghs+WItfdpyhggoLqQGBTVk5gyNZ+ejxykrZ9zjZYR4JpV+X3BRp+1TCrR9vMLsItkBI20dyO6CELg2qAQBu7FxPIiWlEkdpVNgUUdUbAODjO6/C9vRs3PHZRtn3/LL9DP47cgHb0rOxLX0Hvrmvu2j6oW1SMIKwDc2JhCvQqJS53fhi/QlFO3kCLY6KUsGMtMxD5WhlkDQAFF+EJmj+NjdPwNzL6NGkhp85BQ6OFVR+331OVrrXb2ync0nMJTo8BD2bKnshnv9lL+dYarvorFFtUU0ktoOTkLMdlI1SW6lA06goETLSLxbipd/3c+93M3jo620al4pCEUatYDykws9UbEQormmRpGGJrItYb6ZEM+WopR813HQVVb9JIRW3IVCEFACIYC39fH1vN4nULsU+JwJNo0LySSHELR+v5xw3eOZ3bDxODuvQu1lNv8pFoQih1sHgjZ2dHSCTiEh3xrf1ESPgBRWKNGIOeupVjzSwJObDFjzk2O6EEJxtiRFoxrR5xfINjc8RIqB/zXNdXskbN7VXXSY7E2jtxwxInn4pZNga6F8n9eBcUyKoOHbph6IdYhqVQFuqYFuxN0+JFUnpIVShoBJo40zjmjF+3b9kF3mJt6ZI8EgnE+QC9LDOC7DXXBTSzkgKmckDmmLbycu4tUs9HweMxWXlCJHZrqigQpFETHMQKGutbHZPH4SiUjdqxoajXd147Dot7JVSqSAXaP5/2tVN0DzPOgmBpeVjc03zJPy5L9PsYgQE2YUl+JjgQ0WIQLT5ToqNwLLJvQEAJy9yjbiLS92QazUQsEs/74/piBWP9zG7GLZAzJj28UHKnUfZndiIUO+M/YXhrQTTuVxAucL17EBU3besFWd2ERzDC9e31iyv9nXjvZ+dHpdGDf/7eQ8+WH1UdvpADp4J+PZt1EZFBsPa1fZb7RwoiC39BEI8FTHqVYsSva40AFegLaUBQCsqqGhGTLh2SvJAFJqVsPEY2ZCbQoYd2BJQ5ngwYAUViny+3kA2WAQCz6aCT1KccEyQNYfOo7hMmcVAoO36ATzr2Dd0rINh7QIrzpEeaNl82CJ2AMrPkpQofLcDnRDeDsixn2+SfS8VVAKUoW1SZKcV2gIKaOci2c6kVidrVUrK3PiE7hCQpF71KLx5SwcMbJWsSX6BrGLX0sYpgKtRFkqWLgAgPMC1zyF+SNFUUAkQHh/YjHNcWk57Ia2gKnJtCDRDYj3Qsg4TA8g/khrkCiod6iVgSOsU9AxwT7RKXTWwoYJKgPBw/6Z4cWQb7zENMaAd/sop9/ZsqE1BbE6Zm0bp9het5JS0RomYyeovXAEXP1mc9UflB7ydPKApPryzc8BPaEgalak/7pJ1LxVUAgi2SlzKLT5FPv4uf8VGhGpUEnszsFWKZBBNOQRyy9ZCo9K1YXV8c3/3gN7mLcVtn8gP7Eg1hR5IgspvAn6Q+AScoNK4ZrQM1+fOhO0PhUZE1g5/hb6OqQm4K60+pg0T3uocCMSEh+Dre8UDYMrh+QCuRy3GRNLOMyagxT//oIKKB380SgHn8G3F433NLoJpsIUTqlHRDn+rMjjIhRkj2kgnpEjy7m0dcW3bwN09pMWgSBpQwkIC2xDUH4ICTh1Axh/NM63CAII9oFKFinb4q52iEy4uCVHql8Kub19bw5LYDy3MINjtcfrwVmhUIxpTh7bwP+MAhdr3+A8VVAIItpdULZZ+9swY7HceTqCfn2EEqGqYS7/mgReWQSu0cBfAbo/jejTEyif6oja1V6GYSMAt/QQyozrWwSt/HMA1zWviUkGJ3/lp6QXTzjw9xDPbnL/uhKr7zRRUysvLUVoqP4KxEcSFMagTq26poajInIBxoaGhCA6myyMUih7QkSaASI6LwP6ZQxARGoTh7//nV17jrm6gTaEcQGRYMO66uoFqQcUMOYVhGGRkZCA7O9v4h0swqF4QeiSr06ocP26eg72EhASkpKSY7gTxtq6p+GZTuur7Az0sBkU/7uxeH1+KeDoXggoqAUZkmKcTKvfTZcV0DYOfBTqhfjhCUkulkJKUlISoqCjTB1c2kdlXkFOkTsvTMMX4uEEMw6CwsBBZWVkAgFq1zDXm9ddO5blhLbUpCAUAtUFj8+LINlRQociHxqnQFiXLYBuf7Y9/Dp3Hkz94nB0ZPYMtLy/3CimJif77LdGakHA3XGXKe/fa8ZGIiAjXoUTSREZ6bDiysrKQlJRk6jKQv/ZndSUCbVIoRhMwxrT9WiThg9s7mV0MyyAWEZminJqx4Xjz5vay0ibHRaBjajXvsdGCSqVNSlSUNQcktTY7NWLNEVIqqaxPs21+osJ8heZmyTRSPMW+BISgck3zmvh8XJeA9q/A59XR7VTfGxtBFXEkbuhUV7Zn1VBWJNGIMHNeQyst97Cx6y4oq9TnpH5NfM75875TKGYTEIIKdVHuS/dGieiYmiCZjrTe/edjvbUvkEOoFiUeyO2xAZ7gkGyfNhHUeJEDdZDlH9UJwQTp9mKKnQmILiE+kgoqJOTM/6J5thdDWqegVjzt9ISIChMWOno1rYFHBzT1OR9BvX5yqB4VpruBcd++fTF58mRdn2EmHeolcI4touwJSGjV+09A6PD5Ly1FPjHhIcgrKjO7GLZBzAsle2mgfmIUBrRMRlxkCMJCAmK+IJuQ4CC0SInF7jM5PtfG3zQMzVu3xVPTZyMhKgxl5W7kFytvn4sXL0ZoqHMnMHyDWrsup5nJ5+OuwtLdGfhh62mzixLwBISgMqpjHbOLYEn6Nk/CtvRs0TTUqZsyxJYt2EOFy+XCp3ddpXt57Ioce4/k2HCcyb6iKv/q1aurus8ujOhQmyPoSQnDjWtG47auqWiaHKt30WxDvxbJ6N4oUVRQGZtWH1+sV77dlqIMx0/l+rVIQpAWATAcyIN9GmP6cPFIs/ylH4oUYhoVA4uhEIZhUFhSZvgfoyCi4/OPPYQtG9bi688+RPt61RARFoIfFn2F9vWqYe3qFejYsSMiIyPRr18/ZGVl4Y8//kDLli0RFxeHMWPGoLCw0JsXf+mnQYMGmDVrFu655x7ExsYiNTUVH3/8sZZVbCh392iIL8d3xaRrmuDZa1sgTsJOb/GEHri3VyP0aVbToBLaA6k4PUEuF47PvhYvDG+FBfd0xT09GgIA2tQx3p+Pk3H8KDSoVbLZRbAsYSFBuPGqepj+2z7BNB1TE7DjVLb3+Lp2dOeUGGIysYXlFFwpLUeracsNf+6+mYOJ22lJPDVjNk4eP4ImzVth5swZqB4djr/XbQEAfPjWq/jg/fcRFRWFm2++GTfffDPCw8OxcOFC5OfnY9SoUXjvvffw9NNPC+b/xhtv4MUXX8Szzz6LH374ARMmTECfPn3QvHlzTb6rkQQHudCraU30aipP8KAGzGSkJhcMw8DlcuHuCgGlW8Pq6JiagJ5NaqDji38ZUMLAwPGCys1X1TO7CJZGavCcdE0TxISHoFNqNcRFhqKTjJ1CgYxYx0btBNQTFhKE2Lh4hIaGISIyEnVr10ZcZCgSYyIAAE8/Nw09evQAAIwfPx5Tp07F0aNH0ahRIwDAjTfeiFWrVokKKtdeey0eeughT35PP4233noLq1atsqWgIkWXBtWw+cRl73Ew1TprQkRoMIbzInhbZdu6nXGkoPLt/d3xyb/H8cLwVnTZx09CgoPw+CDnddR6IW5Ma2BBFBIZGox9M42Phi3H2V3N2HDERYTi6Pl877lKXz6RFdqYAT26eq8lJycjKirKK6RUntu0aZPoc9q1q/I14nK5kJKS4nWL7zR6NqnJEVSoEE2GXS0/PXQ1Rn2wjnO9uYyQDfUTrelY0U44UlDp1igR3RpZzzW4FZHaBiq23Zbii5hc3LNJDeMKohCXyyV7CcZoYiNCOQ7yAN9ZalhYGOcaf0ePy+WC2y0e4ErNPXaFAdc2iGpUyLAnHpGEvvDmq+oK3rvqib7IKypFclyELmULJKzZM1EMIywkCD9OuBqj53JnCkse7omkuHBTAubZGSE17zu3dsB11DOyIpolx6K4zI2Y8BBvbKqQ0DCUl9M4VVoTTDUqRNjVwteWtq+XgBCR/rFhjWi9ihVw0FGIgs71q2HWqLacc0lx4UiKpTMBpQj19yM61BHt1Ci+RIQGs5w1eiq2Tr1U7N6+FSdOnMCFCxccq/HQA7ZWir/Zii6Rk+G6FPBM4Cj+MWVgM8X30J6TAgAY0y0VKx7v4z2mMyx1SG1npPjH2AcmITg4GK1atULNmjWRnp5udpFsw729PDY7lWEcKNKwNaQuAG3qxHOOKcp5mBCLSgrHLf08d11Ls4vgCOiatTpotelDZbU2aNQEX/7yJ9rVTfBeGzduHCftuHHjfM5Nnz4d06dP9x6vXr2ac/3EiRM+z9yxY4fa4lqSKQObYVTHOmiaFIO3/z5sdnFsgdjrTOdy6nC5XIiNUObx3HGCCkU9bHUwVQWrg3ZeOkHr1W9Cg4PQrMLzrHw3e4GNywU0qhmN3CulaMCzOaFNUj0hCscXxwkqbgWeLinC0KUfdVCfCRS78Wh/30CZFA8ulwt/PdYHbobxbiy4rWsqvtmUjikDqdsGtSjV2DvORoXKKf5QVXl06UcdvSs8gVJ5hWJpWB3lYyqMGwOJ4CAXZ/fjrFFtsHPaIPRsal13A1ZHqd8ex2lUyqmkohrO0g8daVXRs2kNLLq/OxrViEbOlVIMfOsfjE2rb3axKBSKRrhcLsRHOTfythEonQg7TlChcop62FVHNSrq6V7hbDApLgIHXxqC8BDqNM9faGvUFtpNUsyELv1QSUU1bHfmVE7RBiqkUCgUCpeWtZRFl3acRsVN5RTV1KsehSkDmyEmPIQahVIoDqZz/WpmF4ESwMy+oS1qxITh+pbVcPXb0ukdKKhQScUfHqE7ACgUx9OnWU18MvYqNK/YrkyhGEmNmHDMvqEdcnNzZaV3oKBidgkoFIrmUAWfprhcLgxslWx2MSgUWTjORoVa01IozoPKKRRK4OI4QYVqVCgUJ+IRVcbfNAyvTZ9qclkoFIqROFBQoZIKheJklLrfplAo9saBgorZJaBQbAjDACUFxv8pmFg8/9hD2LJhLRZ8Mhculwsulwvz58+Hy+XC8uXL0bFjR0RGRqJfv37IysrCH3/8gZYtWyIuLg5jxoxBYWGhN69ly5ahZ8+eSEhIQGJiIoYNG4ajR496r3/xxReIiYnB4cNVwfseeughtGjRgpMPhULRH92MaV9++WX8/vvv2LFjB8LCwpCdne2TJj09HRMmTMCqVasQExODu+66C7Nnz0ZIiPpiUT8qFIoKSguBWbWNf+6zZ4GwaMlkLgBPzZiNk8ePoHOH9nj5pRcBAHv37gXgiY78/vvvIyoqCjfffDNuvvlmhIeHY+HChcjPz8eoUaPw3nvv4emnnwYAFBQUYMqUKWjXrh3y8/Mxbdo0jBo1Cjt27EBQUBDGjh2LJUuW4Pbbb8e6deuwfPlyfPrpp1i/fj2ioqJ0qw4KheKLboJKSUkJbrrpJqSlpeGzzz7zuV5eXo7rrrsOKSkpWLduHc6dO4exY8ciNDQUs2bNUv1cKqZQKM4kNi4eoaFhiIqKQkpKCgDgwIEDAICXXnoJPXr0AACMHz8eU6dOxdGjR9GoUSMAwI033ohVq1Z5BZXRo0dz8v78889Rs2ZN7Nu3D23atAEAfPTRR2jXrh0eeeQRLF68GNOnT0fnzp0N+a4UCqUK3QSVGTNmAADmz59PvP7nn39i3759+Pvvv5GcnIwOHTrgxRdfxNNPP43p06cjLCxM1XObJMWoLTKFEriERnm0G2Y8Vw4SZint2rXzfk5OTkZUVJRXSKk8t2nTJu/x4cOHMW3aNGzcuBEXLlyA2+0G4NHyVgoq1apVw2effYbBgwfj6quvxjPPPCPzS1EoFC0xzY/K+vXr0bZtWyQnV+3lHzx4MCZMmIC9e/eiY8eOxPuKi4tRXFzsPa50GLPo/m7Yd6EMozvV1bfgFIoTcblkLcFYldDQqiBxLpeLc1x5rlIYAYDhw4ejfv36+OSTT1C7dm243W60adMGJSUlnPv++ecfBAcH49y5cygoKEBsLHWQRqEYjWnGtBkZGRwhBYD3OCMjQ/C+2bNnIz4+3vtXr149AECbOgm4t1cjGkyPQnEwIaFhKC8v9yuPixcv4uDBg3juuefQv39/tGzZEpcvX/ZJt27dOrz66qv47bffEBMTg0mTJvn1XAqFog5FgsozzzzjtbYX+qtcM9aLqVOnIicnx/t36tQpXZ9HoVDMJ8jlQnxkKBo0qI8tmzfhxIkTnCUbJVSrVg2JiYn4+OOPceTIEaxcuRJTpkzhpMnLy8Odd96JRx55BEOHDsXXX3+Nb7/9Fj/88INWX4lCochE0dLP448/jnHjxommYa8Li5GSksJZMwaAzMxM7zUhwsPDER4eLusZFArFOdRPjMaM56birrvuQqtWrXDlyhXMmzdPcT5BQUFYtGgRHnnkEbRp0wbNmzfHu+++i759+3rTPProo4iOjvYa9rdt2xazZs3CAw88gLS0NNSpU0err0WhUCRwMTrv550/fz4mT57ssz35jz/+wLBhw3Du3DkkJSUBAD7++GM8+eSTyMrKki2M5ObmIj4+Hjk5OYiLUxY6mkIJRIqKinD8+HE0bNgQERERZhfHMdB6pVCUIXf81s2YNj09HZcuXUJ6ejrKy8uxY8cOAECTJk0QExODQYMGoVWrVrjzzjvx2muvISMjA8899xwmTpxINSYUCoVCoVAA6CioTJs2DQsWLPAeV+7iWbVqFfr27Yvg4GAsWbIEEyZMQFpaGqKjo3HXXXdh5syZehWJQqFQKBSKzdB96Udv6NIPhaIMukShD7ReKRRlyB2/HRfrh0KhUCgUinOgggqFEqDYXJlqOWh9Uij6QAUVCiXAqPTaSqMAa0tlffK94lIoFP8wzYU+hUIxh+DgYCQkJCArKwsAEBUVBZeLenRWC8MwKCwsRFZWFhISEhAcHGx2kSgUR0EFFQolAKl0qlgprFD8JyEhQdRZJYVCUQcVVCiUAMTlcqFWrVpISkpCaWmp2cWxPaGhoVSTQqHoBBVUKJQAJjg4mA6wFArF0lBjWgqFQqFQKJaFCioUCoVCoVAsCxVUKBQKhUKhWBbb26hUOlnKzc01uSQUCoVCoVDkUjluSzlLtL2gcvHiRQBAvXr1TC4JhUKhUCgUpVy8eBHx8fGC120vqFSvXh0AkJ6eLvpFldKlSxds3rzZsvnpkWdubi7q1auHU6dOaRbg0Q7fW+s89ahHgLZJ2ib9g9YlrUur5ZeTk4PU1FTvOC6E7QWVoCCPmU18fLymg0JwcLCl89MrTwCIi4vTLF+7fG898tSyHgHaJmmb1AZal9oRaHWpVz1WjuOC1zV/okOYOHGipfPTK0+tscv3DsS6pPUYeHlqjV2+N61La+YnFxdj85Cfubm5iI+PR05Oji6SXiBB61IbaD1qB61L7aB1qR20LrVBbj3aXqMSHh6OF154AeHh4WYXxfbQutQGWo/aQetSO2hdagetS22QW4+216hQKBQKhUJxLrbXqFAoFAqFQnEuVFChUCgUCoViWaigQqFQKBQKxbJQQYVCoVAoFIplMV1QmT17Nrp06YLY2FgkJSVh5MiROHjwICdNUVERJk6ciMTERMTExGD06NHIzMzkpElPT8d1112HqKgoJCUl4cknn0RZWRknzerVq9GpUyeEh4ejSZMmmD9/vt5fz1CMqstz585hzJgxaNasGYKCgjB58mQjvp6hGFWXixcvxsCBA1GzZk3ExcUhLS0Ny5cvN+Q7GoVRdfnff/+hR48eSExMRGRkJFq0aIG33nrLkO9oBEb2lZWsXbsWISEh6NChg15fyxSMqsvVq1fD5XL5/GVkZBjyPR0DYzKDBw9m5s2bx+zZs4fZsWMHc+211zKpqalMfn6+N82DDz7I1KtXj1mxYgWzZcsWpnv37szVV1/tvV5WVsa0adOGGTBgALN9+3Zm6dKlTI0aNZipU6d60xw7doyJiopipkyZwuzbt4957733mODgYGbZsmWGfl89Maoujx8/zjzyyCPMggULmA4dOjCPPvqokV/TEIyqy0cffZR59dVXmU2bNjGHDh1ipk6dyoSGhjLbtm0z9PvqiVF1uW3bNmbhwoXMnj17mOPHjzNffvklExUVxXz00UeGfl+9MKoeK7l8+TLTqFEjZtCgQUz79u2N+IqGYVRdrlq1igHAHDx4kDl37pz3r7y83NDva3dMF1T4ZGVlMQCYNWvWMAzDMNnZ2UxoaCjz/fffe9Ps37+fAcCsX7+eYRiGWbp0KRMUFMRkZGR408ydO5eJi4tjiouLGYZhmKeeeopp3bo151m33HILM3jwYL2/kmnoVZds+vTp40hBhY8RdVlJq1atmBkzZuj0TczHyLocNWoUc8cdd+j0TcxF73q85ZZbmOeee4554YUXHCeo8NGrLisFlcuXLxv3ZRyI6Us/fHJycgBUBRvcunUrSktLMWDAAG+aFi1aIDU1FevXrwcArF+/Hm3btkVycrI3zeDBg5Gbm4u9e/d607DzqExTmYcT0asuAxGj6tLtdiMvL08ySJedMaout2/fjnXr1qFPnz56fRVT0bMe582bh2PHjuGFF14w4quYjt5tskOHDqhVqxYGDhyItWvX6v11HIelghK63W5MnjwZPXr0QJs2bQAAGRkZCAsLQ0JCAidtcnKyd50vIyOD01gqr1deE0uTm5uLK1euIDIyUo+vZBp61mWgYWRd/t///R/y8/Nx8803a/wtrIERdVm3bl2cP38eZWVlmD59Ou69916dvo156FmPhw8fxjPPPIN///0XISGWGiJ0Qc+6rFWrFj788ENcddVVKC4uxqeffoq+ffti48aN6NSpk87fzDlYqhVOnDgRe/bswX///Wd2UWwPrUvtMKouFy5ciBkzZuCXX35BUlKSrs8yCyPq8t9//0V+fj42bNiAZ555Bk2aNMFtt92m2/PMQK96LC8vx5gxYzBjxgw0a9ZM07ytip5tsnnz5mjevLn3+Oqrr8bRo0fx1ltv4csvv9T8eU7FMks/kyZNwpIlS7Bq1SrUrVvXez4lJQUlJSXIzs7mpM/MzERKSoo3Dd8au/JYKk1cXJzjtCl612UgYVRdLlq0CPfeey++++47nyVKp2BUXTZs2BBt27bFfffdh8ceewzTp0/X/suYiJ71mJeXhy1btmDSpEkICQlBSEgIZs6ciZ07dyIkJAQrV67U98sZjBl9ZdeuXXHkyBGNvkGAYLaRjNvtZiZOnMjUrl2bOXTokM/1SqOmH374wXvuwIEDRKOmzMxMb5qPPvqIiYuLY4qKihiG8RjTtmnThpP3bbfd5ihjWqPqko1TjWmNrMuFCxcyERERzM8//6zjNzIPM9plJTNmzGDq16+v3ZcxESPqsby8nNm9ezfnb8KECUzz5s2Z3bt3c3bF2Bkz2+SAAQOYUaNGafhtnI/pgsqECROY+Ph4ZvXq1ZztW4WFhd40Dz74IJOamsqsXLmS2bJlC5OWlsakpaV5r1duExs0aBCzY8cOZtmyZUzNmjWJ25OffPJJZv/+/cycOXMctz3ZqLpkGIbZvn07s337dqZz587MmDFjmO3btzN79+417LvqjVF1+fXXXzMhISHMnDlzOM/Jzs429PvqiVF1+f777zO//vorc+jQIebQoUPMp59+ysTGxjL/+9//DP2+emHk+83Gibt+jKrLt956i/n555+Zw4cPM7t372YeffRRJigoiPn7778N/b52x3RBBQDxb968ed40V65cYR566CGmWrVqTFRUFDNq1Cjm3LlznHxOnDjBDB06lImMjGRq1KjBPP7440xpaSknzapVq5gOHTowYWFhTKNGjTjPcAJG1iXpOU6ZuTKMcXXZp08f4nPuuusug76p/hhVl++++y7TunVrJioqiomLi2M6duzIfPDBB47xWWHk+83GiYKKUXX56quvMo0bN2YiIiKY6tWrM3379mVWrlxp1Nd0DC6GYRhtF5MoFAqFQqFQtMEyxrQUCoVCoVAofKigQqFQKBQKxbJQQYVCoVAoFIploYIKhUKhUCgUy0IFFQqFQqFQKJaFCioUCoVCoVAsCxVUKBQKhUKhWBYqqFAoFAqFQrEsVFChUCimsHr1arhcLp/AbxQKhcKGeqalUCiG0LdvX3To0AFvv/02AKCkpASXLl1CcnIyXC6XuYWjUCiWJcTsAlAolMAkLCwMKSkpZheDQqFYHLr0Q6FQdGfcuHFYs2YN3nnnHbhcLrhcLsyfP5+z9DN//nwkJCRgyZIlaN68OaKionDjjTeisLAQCxYsQIMGDVCtWjU88sgjKC8v9+ZdXFyMJ554AnXq1EF0dDS6deuG1atXm/NFKRSK5lCNCoVC0Z133nkHhw4dQps2bTBz5kwAwN69e33SFRYW4t1338WiRYuQl5eHG264AaNGjUJCQgKWLl2KY8eOYfTo0ejRowduueUWAMCkSZOwb98+LFq0CLVr18ZPP/2EIUOGYPfu3WjatKmh35NCoWgPFVQoFIruxMfHIywsDFFRUd7lngMHDvikKy0txdy5c9G4cWMAwI033ogvv/wSmZmZiImJQatWrXDNNddg1apVuOWWW5Ceno558+YhPT0dtWvXBgA88cQTWLZsGebNm4dZs2YZ9yUpFIouUEGFQqFYhqioKK+QAgDJyclo0KABYmJiOOeysrIAALt370Z5eTmaNWvGyae4uBiJiYnGFJpCoegKFVQoFIplCA0N5Ry7XC7iObfbDQDIz89HcHAwtm7diuDgYE46tnBDoVDsCxVUKBSKIYSFhXGMYLWgY8eOKC8vR1ZWFnr16qVp3hQKxRrQXT8UCsUQGjRogI0bN+LEiRO4cOGCVyviD82aNcPtt9+OsWPHYvHixTh+/Dg2bdqE2bNn4/fff9eg1BQKxWyooEKhUAzhiSeeQHBwMFq1aoWaNWsiPT1dk3znzZuHsWPH4vHHH0fz5s0xcuRIbN68GampqZrkT6FQzIV6pqVQKBQKhWJZqEaFQqFQKBSKZaGCCoVCoVAoFMtCBRUKhUKhUCiWhQoqFAqFQqFQLAsVVCgUCoVCoVgWKqhQKBQKhUKxLFRQoVAoFAqFYlmooEKhUCgUCsWyUEGFQqFQKBSKZaGCCoVCoVAoFMtCBRUKhUKhUCiW5f8BLiYqaBKcaAAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we can also plot the time series for each location\n",
        "ds.sel(location='IA').to_dataframe().plot()\n",
        "plt.show()\n",
        "\n",
        "ds.sel(location='IL').to_dataframe().plot()\n",
        "plt.show()\n",
        "\n",
        "ds.sel(location='IN').to_dataframe().plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ukkr4ruolY-c",
        "outputId": "ea30e207-2b07-4d0d-c6a8-315af6c3e6e0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we may be interested in the number of times certain\n",
        "# temperatures occur, which we can view with a histogram\n",
        "# here we are viewing this just for Iowa and tmin\n",
        "ds.sel(location='IA')['tmin'].to_dataframe().plot.hist(\n",
        "    edgecolor='w', bins=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "MHWK4AUBlcxZ",
        "outputId": "73050ba0-aeb6-4a18-e65d-6fba3fe61e43"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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": [
        "# the number of bins can be modified too\n",
        "# Iowa's tmin\n",
        "ds.sel(location='IA')['tmin'].to_dataframe().plot.hist(\n",
        "    edgecolor='w', bins=10)\n",
        "plt.show()\n",
        "# Iowa's tmax\n",
        "ds.sel(location='IA')['tmax'].to_dataframe().plot.hist(\n",
        "    edgecolor='w', bins=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "4qxKGHaRlpP-",
        "outputId": "b3fbc6ab-d837-4717-a513-9be6a9c0d555"
      },
      "execution_count": 18,
      "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": [
        "# we can also view how tmin and tmax are related using a scatterplot\n",
        "plt.scatter(ds['tmin'], ds['tmax'], edgecolor='grey')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "9RS2RwCdl706",
        "outputId": "7284355c-4d1b-4c88-f78d-580c2c7032c4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# probability of a freeze by calendar month\n",
        "freeze = (ds[\"tmin\"] <= 0).groupby(\"time.month\").mean(\"time\")\n",
        "freeze"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "z6ps_fU9mG_R",
        "outputId": "dbbad428-cb14-410b-9098-526f64343a2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.DataArray 'tmin' (month: 12, location: 3)> Size: 288B\n",
              "array([[0.93548387, 0.93548387, 0.90860215],\n",
              "       [0.77647059, 0.77647059, 0.81764706],\n",
              "       [0.19892473, 0.16666667, 0.16129032],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.01612903, 0.01075269, 0.00537634],\n",
              "       [0.28333333, 0.3       , 0.29444444],\n",
              "       [0.84946237, 0.82258065, 0.84408602]])\n",
              "Coordinates:\n",
              "  * location  (location) <U2 24B 'IA' 'IN' 'IL'\n",
              "  * month     (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12"
            ],
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              "  display: inline-block;\n",
              "  content: \"►\";\n",
              "  font-size: 11px;\n",
              "  width: 15px;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label:before {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label:before {\n",
              "  content: \"▼\";\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label > span {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-summary,\n",
              ".xr-section-inline-details {\n",
              "  padding-top: 4px;\n",
              "  padding-bottom: 4px;\n",
              "}\n",
              "\n",
              ".xr-section-inline-details {\n",
              "  grid-column: 2 / -1;\n",
              "}\n",
              "\n",
              ".xr-section-details {\n",
              "  display: none;\n",
              "  grid-column: 1 / -1;\n",
              "  margin-bottom: 5px;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: \"(\";\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: \")\";\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: \",\";\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tmin&#x27; (month: 12, location: 3)&gt; Size: 288B\n",
              "array([[0.93548387, 0.93548387, 0.90860215],\n",
              "       [0.77647059, 0.77647059, 0.81764706],\n",
              "       [0.19892473, 0.16666667, 0.16129032],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.01612903, 0.01075269, 0.00537634],\n",
              "       [0.28333333, 0.3       , 0.29444444],\n",
              "       [0.84946237, 0.82258065, 0.84408602]])\n",
              "Coordinates:\n",
              "  * location  (location) &lt;U2 24B &#x27;IA&#x27; &#x27;IN&#x27; &#x27;IL&#x27;\n",
              "  * month     (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tmin'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>location</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0bd6e41f-dd62-4ec2-ae15-e2935d96d851' class='xr-array-in' type='checkbox' checked><label for='section-0bd6e41f-dd62-4ec2-ae15-e2935d96d851' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.9355 0.9355 0.9086 0.7765 0.7765 ... 0.3 0.2944 0.8495 0.8226 0.8441</span></div><div class='xr-array-data'><pre>array([[0.93548387, 0.93548387, 0.90860215],\n",
              "       [0.77647059, 0.77647059, 0.81764706],\n",
              "       [0.19892473, 0.16666667, 0.16129032],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        ],\n",
              "       [0.01612903, 0.01075269, 0.00537634],\n",
              "       [0.28333333, 0.3       , 0.29444444],\n",
              "       [0.84946237, 0.82258065, 0.84408602]])</pre></div></div></li><li class='xr-section-item'><input id='section-ccec3fa6-6334-4012-8246-4672d93f46e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccec3fa6-6334-4012-8246-4672d93f46e0' class='xr-section-summary' >Coordinates: <span>(2)</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'>location</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;IA&#x27; &#x27;IN&#x27; &#x27;IL&#x27;</div><input id='attrs-4a0d2b28-9488-471e-93fc-c3b4d5329d09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a0d2b28-9488-471e-93fc-c3b4d5329d09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1879e9e1-9107-4500-9513-8ec7b25f0d3e' class='xr-var-data-in' type='checkbox'><label for='data-1879e9e1-9107-4500-9513-8ec7b25f0d3e' 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([&#x27;IA&#x27;, &#x27;IN&#x27;, &#x27;IL&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-37309030-d05e-4ac0-9586-192654b4902c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37309030-d05e-4ac0-9586-192654b4902c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-294a7932-7937-4c5e-a5b9-180fff12d9c7' class='xr-var-data-in' type='checkbox'><label for='data-294a7932-7937-4c5e-a5b9-180fff12d9c7' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1313a02d-764c-4fe6-8c2f-1ff4bbda4286' class='xr-section-summary-in' type='checkbox'  ><label for='section-1313a02d-764c-4fe6-8c2f-1ff4bbda4286' class='xr-section-summary' >Indexes: <span>(2)</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>location</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-734129b1-9429-4134-965b-6d83c9c30e3e' class='xr-index-data-in' type='checkbox'/><label for='index-734129b1-9429-4134-965b-6d83c9c30e3e' 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([&#x27;IA&#x27;, &#x27;IN&#x27;, &#x27;IL&#x27;], dtype=&#x27;object&#x27;, name=&#x27;location&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b0caf63f-366a-4484-9691-48a756d1c873' class='xr-index-data-in' type='checkbox'/><label for='index-b0caf63f-366a-4484-9691-48a756d1c873' 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([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d7768b8-314a-41b5-b307-848970580d48' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3d7768b8-314a-41b5-b307-848970580d48' 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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# quick visualization\n",
        "freeze.to_pandas().plot(); plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "L9oyMW3Emmvf",
        "outputId": "0470a20d-4b96-4d0e-b5cb-8885b8d0fbd1"
      },
      "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": [
        "# monthly means\n",
        "# we can resample a time dimension to be monthly (1MS) average\n",
        "monthly_avg = ds.resample(time=\"1MS\").mean()\n",
        "# plot the variables for Iowa\n",
        "monthly_avg.sel(location=\"IA\").to_dataframe().plot(style=\"s-\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "hgHrxMFknvUL",
        "outputId": "57c1ab95-d7d2-4c12-b4b2-65781c0fcfd4"
      },
      "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": [
        "# calculate monthly anomalies\n",
        "climatology = ds.groupby(\"time.month\").mean(\"time\")\n",
        "anomalies = ds.groupby(\"time.month\") - climatology\n",
        "anomalies.mean(\"location\").to_dataframe()[[\"tmin\", \"tmax\"]].plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "I_YW-_s-n7lu",
        "outputId": "a7fc3beb-86d2-43f0-d92e-ece22c1a4589"
      },
      "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": [
        "# we can calculate standardized anomalies\n",
        "climatology_mean = ds.groupby(\"time.month\").mean(\"time\")\n",
        "climatology_std = ds.groupby(\"time.month\").std(\"time\")\n",
        "stand_anomalies = xr.apply_ufunc(\n",
        "    lambda x, m, s: (x - m) / s,\n",
        "    ds.groupby(\"time.month\"),\n",
        "    climatology_mean,\n",
        "    climatology_std,\n",
        ")\n",
        "\n",
        "stand_anomalies.mean(\"location\").to_dataframe()[[\"tmin\", \"tmax\"]].plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "5tWJoGIMn_0-",
        "outputId": "a04c6e9d-90f9-4d00-a858-527aa243759c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stand_anomalies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "gFqVg3eqNuIy",
        "outputId": "048a290d-c93d-46bf-8857-dea12afad708"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.Dataset> Size: 140kB\n",
              "Dimensions:   (time: 2192, location: 3)\n",
              "Coordinates:\n",
              "  * time      (time) datetime64[ns] 18kB 2000-01-01 2000-01-02 ... 2005-12-31\n",
              "  * location  (location) <U2 24B 'IA' 'IN' 'IL'\n",
              "    month     (time) int64 18kB 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12\n",
              "Data variables:\n",
              "    tmin      (time, location) float64 53kB -1.025 0.8885 ... 0.9589 -0.5782\n",
              "    tmax      (time, location) float64 53kB -0.2732 0.6729 ... -1.772 -2.917"
            ],
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              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: \"(\";\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: \")\";\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: \",\";\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 140kB\n",
              "Dimensions:   (time: 2192, location: 3)\n",
              "Coordinates:\n",
              "  * time      (time) datetime64[ns] 18kB 2000-01-01 2000-01-02 ... 2005-12-31\n",
              "  * location  (location) &lt;U2 24B &#x27;IA&#x27; &#x27;IN&#x27; &#x27;IL&#x27;\n",
              "    month     (time) int64 18kB 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12\n",
              "Data variables:\n",
              "    tmin      (time, location) float64 53kB -1.025 0.8885 ... 0.9589 -0.5782\n",
              "    tmax      (time, location) float64 53kB -0.2732 0.6729 ... -1.772 -2.917</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-fd822ee0-3f27-4e00-a291-48b20d3cff1e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fd822ee0-3f27-4e00-a291-48b20d3cff1e' 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'>time</span>: 2192</li><li><span class='xr-has-index'>location</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-80b2c9dd-3248-48f9-9dd6-60feb9bf5111' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80b2c9dd-3248-48f9-9dd6-60feb9bf5111' class='xr-section-summary' >Coordinates: <span>(3)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2005-12-31</div><input id='attrs-ad65bd13-1950-4e32-9fe6-4fb43870f211' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad65bd13-1950-4e32-9fe6-4fb43870f211' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02df040a-58c7-4c78-b012-ebe2adb9f233' class='xr-var-data-in' type='checkbox'><label for='data-02df040a-58c7-4c78-b012-ebe2adb9f233' 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([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-02T00:00:00.000000000&#x27;,\n",
              "       &#x27;2000-01-03T00:00:00.000000000&#x27;, ..., &#x27;2005-12-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2005-12-30T00:00:00.000000000&#x27;, &#x27;2005-12-31T00:00:00.000000000&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>location</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;IA&#x27; &#x27;IN&#x27; &#x27;IL&#x27;</div><input id='attrs-c28836ea-d7cf-416f-864f-bdce0af6f700' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c28836ea-d7cf-416f-864f-bdce0af6f700' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92999d9b-416d-4060-9aef-1330f79fb005' class='xr-var-data-in' type='checkbox'><label for='data-92999d9b-416d-4060-9aef-1330f79fb005' 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([&#x27;IA&#x27;, &#x27;IN&#x27;, &#x27;IL&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 ... 12 12 12 12 12 12</div><input id='attrs-88a1b696-0866-446d-97aa-f252d6304137' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88a1b696-0866-446d-97aa-f252d6304137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97f98b93-1b35-42fe-a0a7-41e55f85cc5a' class='xr-var-data-in' type='checkbox'><label for='data-97f98b93-1b35-42fe-a0a7-41e55f85cc5a' 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([ 1,  1,  1, ..., 12, 12, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93965423-af9f-46d4-bb9d-b94e4cf52cf4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93965423-af9f-46d4-bb9d-b94e4cf52cf4' class='xr-section-summary' >Data variables: <span>(2)</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>tmin</span></div><div class='xr-var-dims'>(time, location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.025 0.8885 ... 0.9589 -0.5782</div><input id='attrs-7ddfae1f-6550-41fa-9162-351aa64f4013' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ddfae1f-6550-41fa-9162-351aa64f4013' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd8fd70e-654f-4ee4-ad59-b724230be141' class='xr-var-data-in' type='checkbox'><label for='data-dd8fd70e-654f-4ee4-ad59-b724230be141' 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([[-1.02469979,  0.88849084,  0.12664117],\n",
              "       [-1.43464802, -0.63174323,  1.43825709],\n",
              "       [-2.31459652, -0.50053594,  1.05289461],\n",
              "       ...,\n",
              "       [ 0.48540682, -1.16373159, -0.56434758],\n",
              "       [-0.07405304, -0.71936657, -0.3711948 ],\n",
              "       [-1.40235005,  0.95894765, -0.57815582]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time, location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2732 0.6729 ... -1.772 -2.917</div><input id='attrs-fa2060b2-698a-4cd6-99f0-e027830ff88f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa2060b2-698a-4cd6-99f0-e027830ff88f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9c21292-2111-40a9-b77c-1a4217b55d95' class='xr-var-data-in' type='checkbox'><label for='data-c9c21292-2111-40a9-b77c-1a4217b55d95' 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([[-0.27320988,  0.67290463,  0.49429527],\n",
              "       [-1.72149668,  0.99139847, -0.24613607],\n",
              "       [-1.93409872, -0.37755124, -0.94754088],\n",
              "       ...,\n",
              "       [-1.46307888, -1.55891349, -0.62270956],\n",
              "       [-0.92608093, -0.03316049, -1.98422696],\n",
              "       [-0.35244015, -1.77216518, -2.91669453]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7a39891-eb43-48fd-a43c-ccdd88713173' class='xr-section-summary-in' type='checkbox'  ><label for='section-d7a39891-eb43-48fd-a43c-ccdd88713173' class='xr-section-summary' >Indexes: <span>(2)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e405e987-55e7-4fbc-887b-053623b509f5' class='xr-index-data-in' type='checkbox'/><label for='index-e405e987-55e7-4fbc-887b-053623b509f5' 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(DatetimeIndex([&#x27;2000-01-01&#x27;, &#x27;2000-01-02&#x27;, &#x27;2000-01-03&#x27;, &#x27;2000-01-04&#x27;,\n",
              "               &#x27;2000-01-05&#x27;, &#x27;2000-01-06&#x27;, &#x27;2000-01-07&#x27;, &#x27;2000-01-08&#x27;,\n",
              "               &#x27;2000-01-09&#x27;, &#x27;2000-01-10&#x27;,\n",
              "               ...\n",
              "               &#x27;2005-12-22&#x27;, &#x27;2005-12-23&#x27;, &#x27;2005-12-24&#x27;, &#x27;2005-12-25&#x27;,\n",
              "               &#x27;2005-12-26&#x27;, &#x27;2005-12-27&#x27;, &#x27;2005-12-28&#x27;, &#x27;2005-12-29&#x27;,\n",
              "               &#x27;2005-12-30&#x27;, &#x27;2005-12-31&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2192, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>location</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b76475a2-23d2-428c-9f9b-67a3ad41692d' class='xr-index-data-in' type='checkbox'/><label for='index-b76475a2-23d2-428c-9f9b-67a3ad41692d' 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([&#x27;IA&#x27;, &#x27;IN&#x27;, &#x27;IL&#x27;], dtype=&#x27;object&#x27;, name=&#x27;location&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-579f089e-b729-4da0-844b-6b73128e9049' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-579f089e-b729-4da0-844b-6b73128e9049' 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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we can visualize correlations between locations\n",
        "correlation_matrix = np.corrcoef(stand_anomalies['tmax'].transpose('location','time'))\n",
        "correlation_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdQd7GHhM0XT",
        "outputId": "659244ce-68e2-4a20-90cc-b89bd9370446"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.18154861, 0.20928759],\n",
              "       [0.18154861, 1.        , 0.23578394],\n",
              "       [0.20928759, 0.23578394, 1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import pearsonr\n",
        "pearsonr(stand_anomalies['tmax'].isel(location=0), stand_anomalies['tmax'].isel(location=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UI84sQcoN9WA",
        "outputId": "eb9f85b3-3fa1-4d6a-cfaf-6c2a6d3eb9c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PearsonRResult(statistic=0.18154861474897227, pvalue=1.0684505560033999e-17)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "# Calculate the correlation matrix\n",
        "corr = stand_anomalies.drop_vars('month').to_dataframe().corr()\n",
        "\n",
        "# Create the heatmap\n",
        "sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "kWxeJqpONqbj",
        "outputId": "4f448607-f783-42a1-967d-72b7bbe9c165"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "For more details on feature selection, see: https://scikit-learn.org/stable/modules/feature_selection.html"
      ],
      "metadata": {
        "id": "YLB76BdfPXiX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dimensionality reduction with principal component analysis"
      ],
      "metadata": {
        "id": "QG8BA0zWoNns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "LtfpNnTGoLA7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What is PCA?**\n",
        "\n",
        "\"Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in the data set. It accomplishes this reduction by identifying directions, called principal components, along which the variation in the data is maximal. By using a few components, each sample can be represented by relatively few numbers instead of by values for thousands of variables. Samples can then be plotted, making it possible to visually assess similarities and differences between samples and determine whether samples can be grouped.\" Sourced from https://www.nature.com/articles/nbt0308-303"
      ],
      "metadata": {
        "id": "QnGGkukxoU7Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will perform dimensionality reduction in this case by representing our three cities using just two values (rather than three)."
      ],
      "metadata": {
        "id": "Ktjr93aNo0Im"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize object\n",
        "pca = PCA(n_components=2)"
      ],
      "metadata": {
        "id": "I650r41WoSlp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the pca object to the data (tmax in this case)\n",
        "pca.fit(stand_anomalies['tmax'].values)\n",
        "# and now transform the arrays of interest\n",
        "X = pca.transform(stand_anomalies['tmax'].values)"
      ],
      "metadata": {
        "id": "J5q37qMSpAVW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# we have modified the arrays from\n",
        "print(stand_anomalies['tmax'].values.shape)\n",
        "# to have a shape now of\n",
        "print(X.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cnGGrR4vpCKx",
        "outputId": "3aad1df4-f993-4281-f6da-1fe80fe83de8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2192, 3)\n",
            "(2192, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets visualize the two pcs/transformed arrays\n",
        "# first lets view the original data (first two dimensions/states)\n",
        "plt.scatter(stand_anomalies['tmax'][:,0],stand_anomalies['tmax'][:,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "F9dwLYTBpSE_",
        "outputId": "832b35d8-6aed-43c9-fa40-daa634578ae5"
      },
      "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": [
        "# and now the transformed data\n",
        "plt.scatter(X[:,0], X[:,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "u-QlVpYDpbTc",
        "outputId": "a65bc466-ca49-4eea-ea50-2418d366f5c0"
      },
      "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": [
        "# what is the varianced explained by these two pcs?\n",
        "print(pca.explained_variance_ratio_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HyWAn_4oplXT",
        "outputId": "a8162892-c448-4e40-9609-45ba60f91c13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.4728429  0.27401653]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Other dimensionality reduction can also be feature selection and feature extraction. See https://scikit-learn.org/stable/modules/feature_selection.html for examples\n",
        "\n",
        "# Inputs and Labels section\n",
        "\n",
        "When using various input variables (i.e., features or channels), normalizing or standardizing the variables is needed to make sure they have a similar dynamic range. Otherwise, some input variables may have much larger magnitudes than other input variables, limiting their contributions to the learning process. By normalizing/standardizing our features, we facilitate meaningful comparisons between features. This practice can also improve machine learning model convergence.\n",
        "\n",
        "Standardization involves centering the respective data to have a mean of zero and a standard deviation of one, which can be achieved by subtracting the mean and dividing by the standard deviation (i.e., z-score). Normalization involves the scaling of data to a set range, such as from 0 to +1 or -1 to +1, by using the minimum and maximum values of the respective variable.\n",
        "\n",
        "Here we have some helper functions for normalizing and standardizing our data using numpy."
      ],
      "metadata": {
        "id": "GM_MnFE1puo-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "freq = plt.hist(ds['tmax'].isel(location=0), edgecolor='grey')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "sNHswgkuprVd",
        "outputId": "0eb891a2-550a-4b16-850d-ef00fdaca344"
      },
      "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": [
        "# matplotlib bins things for us nicely!\n",
        "# we can see these values... what is the frequency per bin?\n",
        "print(freq[0])\n",
        "# what is the range of the bins?\n",
        "print(freq[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vj1H31A8p0_A",
        "outputId": "34033716-b2df-4800-eac5-bbb8e6aeec49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 49. 283. 338. 251. 222. 236. 293. 363. 148.   9.]\n",
            "[-2.51893907  2.21287543  6.94468992 11.67650441 16.4083189  21.14013339\n",
            " 25.87194788 30.60376237 35.33557686 40.06739135 44.79920585]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create some helper functions to rescale our data."
      ],
      "metadata": {
        "id": "WhUf-GyLqE7j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import QuantileTransformer\n",
        "from scipy.stats import gaussian_kde, linregress, pearsonr\n",
        "\n",
        "# z-score\n",
        "# the function applied to the training data needs to return the\n",
        "# mean and std values\n",
        "def z_score(da):\n",
        "    \"\"\"\n",
        "    Compute z-score for training data.\n",
        "    Normalizing data for ML model training.\n",
        "    Args: da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - np.mean(da)) / np.std(da), np.mean(da), np.std(da)\n",
        "\n",
        "# when applied to test data, input the mean and std from training set\n",
        "def z_score_test(da, mn, std):\n",
        "    \"\"\"\n",
        "    Compute z-score for training data.\n",
        "    Normalizing data for ML model training.\n",
        "    Args: da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - mn) / std\n",
        "\n",
        "# the function applied to the training data needs to return the\n",
        "# min and max values\n",
        "def min_max_scale(da):\n",
        "    \"\"\"\n",
        "    Scale training data by minimum and maximum values.\n",
        "    Args: da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - np.min(da)) / (\n",
        "        np.max(da) - np.min(da)), np.min(da), np.max(da)\n",
        "\n",
        "# when applied to test data, input the min and max from training set\n",
        "def min_max_scale_test(da, min, max):\n",
        "    \"\"\"\n",
        "    Scale training data by minimum and maximum values.\n",
        "    Args: da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - min) / (max - min)\n",
        "\n",
        "def boxcox_transform(y, lam):\n",
        "    \"\"\"\n",
        "    Apply the boxcox transformation to a 1D array.\n",
        "\n",
        "    Parameters:\n",
        "    - y: Input array (1D)\n",
        "    - lam: Lambda parameter\n",
        "    \"\"\"\n",
        "    if lam == 0:\n",
        "        return np.log(y)\n",
        "    else:\n",
        "        return (y**lam - 1) / lam\n",
        "\n",
        "def yeo_johnson_transform(y, lam):\n",
        "    \"\"\"\n",
        "    Apply the Yeo-Johnson transformation to a 1D array.\n",
        "\n",
        "    Parameters:\n",
        "    - y: Input array (1D)\n",
        "    - lam: Lambda parameter\n",
        "    \"\"\"\n",
        "    y_transformed = np.zeros_like(y, dtype=np.float64)\n",
        "\n",
        "    # Case 1: y >= 0, lambda != 0\n",
        "    pos = (y >= 0) & (lam != 0)\n",
        "    y_transformed[pos] = ((y[pos] + 1)**lam - 1) / lam\n",
        "\n",
        "    # Case 2: y >= 0, lambda == 0\n",
        "    pos_zero = (y >= 0) & (lam == 0)\n",
        "    y_transformed[pos_zero] = np.log(y[pos_zero] + 1)\n",
        "\n",
        "    # Case 3: y < 0, lambda != 2\n",
        "    neg = (y < 0) & (lam != 2)\n",
        "    y_transformed[neg] = -((-y[neg] + 1)**(2 - lam) - 1) / (2 - lam)\n",
        "\n",
        "    # Case 4: y < 0, lambda == 2\n",
        "    neg_two = (y < 0) & (lam == 2)\n",
        "    y_transformed[neg_two] = -np.log(-y[neg_two] + 1)\n",
        "\n",
        "    return y_transformed\n",
        "\n",
        "def compute_kde(data):\n",
        "    \"\"\"\n",
        "    Compute kernel density estimation\n",
        "    \"\"\"\n",
        "    if type(data)==xr.core.dataarray.DataArray:\n",
        "        data = data.values\n",
        "    kde = gaussian_kde(data)\n",
        "    x = np.linspace(min(data), max(data), 1000)  # Generate x values for the curve\n",
        "    return x, kde(x)\n",
        "\n",
        "def detrend(arr, time_array, deg=1):\n",
        "    \"\"\"\n",
        "    Detrend a 1D array or grouped data by removing a polynomial trend.\n",
        "    \"\"\"\n",
        "    x = time_array\n",
        "    coeffs = np.polyfit(x, arr, deg=deg) # Fit the polynomial\n",
        "    trend = np.polyval(coeffs, x) # Generalized trend calculation\n",
        "    return arr - trend\n",
        "\n",
        "def compute_rolling_mean(group, window=30):\n",
        "    # Apply rolling mean to the grouped data\n",
        "    # assumes time dimension is named 'datetime' (edit as needed)\n",
        "    rolling_mean = group.rolling(datetime=window, min_periods=1, center=True).mean(skipna=True)\n",
        "    return rolling_mean\n",
        "\n",
        "def rolling_month(group):\n",
        "    return group.rolling(datetime=30, min_periods=1, center=True).mean(skipna=True)\n",
        "\n",
        "def month_year_create(yr_array):\n",
        "    # helper function for creating tiled data\n",
        "    return yr_array + np.tile(np.arange(0.,1.,1/12), len(np.unique(yr_array)))[:yr_array.shape[0]]\n",
        "\n",
        "def percent_diff(mean1, mean2):\n",
        "    # computing percent difference between two average values\n",
        "    return ((mean1 - mean2) / mean2) * 100"
      ],
      "metadata": {
        "id": "nUTHMUF-qCTW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# apply zscore to first location's tmax\n",
        "zscore, mn, std = z_score(ds['tmax'].isel(location=0))\n",
        "print(mn.values, std.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YshMrlWqLC3",
        "outputId": "134bf123-b6ff-4c90-c7ab-753a5fe98524"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20.211218503369558 10.986009532508573\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# notice that the rescaling just modifies the x-axis values\n",
        "plt.hist(z_score(ds['tmax'].isel(location=0))[0], edgecolor='grey')\n",
        "plt.title('z-score')\n",
        "plt.show()\n",
        "\n",
        "plt.hist(min_max_scale(ds['tmax'].isel(location=0))[0], edgecolor='grey')\n",
        "plt.title('min-max')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "UdDHDK3prHZf",
        "outputId": "54adadb3-bf9a-47a9-9921-b299bebf5534"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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g3YEyatQoWZZ13OMOh0MLFizQggULjrsmMTFRa9asae9DAwCAH4mw+BQPAAD4cSFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMZp92+SBX7sDh8+bPcI7dK1a1e5XC67xwCAdiFQgDaKVZP8lrR27Vq7R2mXiMgozZyRQ6QACCsECtBG0Y5mRTikkka3vP6Y77+DAVwR9bo8eq98Ph+BAiCsEChAO3n9MfrKirN7jLbx2z0AAJwcLpIFAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGCfkgdLS0qI5c+bI7XYrNjZW/fv31wMPPCDLsgJrLMvS3Llz1adPH8XGxsrj8WjPnj2hHgUAAISpkAfKI488ouXLl2vZsmX6+OOP9cgjj2jhwoVaunRpYM3ChQu1ZMkSrVixQmVlZYqLi1NGRobq6+tDPQ4AAAhDUaE+4TvvvKMJEybommuukST169dPL7zwgrZv3y7pf++eFBYW6v7779eECRMkSatXr1ZSUpLWr1+vSZMmhXokAAAQZkIeKCNGjNBTTz2lTz/9VGeffbb+/e9/65///KcWL14sSdq7d68qKyvl8XgC93G5XBo2bJhKS0tbDZSGhgY1NDQEbtfU1IR6bAD4Qbxer3w+n91jtNnhw4ftHgE4oZAHyuzZs1VTU6MBAwYoMjJSLS0teuihh5SVlSVJqqyslCQlJSUF3S8pKSlw7LsKCgo0f/78UI8KACHh9Xq1ZOky+Vua7R4F6DRCHih/+ctf9Pzzz2vNmjU699xztWvXLuXm5iolJUWTJ08+qXPm5+crLy8vcLumpkapqamhGhkAfhCfzyd/S7NKGt3y+mPsHqdNfhLp1eAuB+0eAziukAfKPffco9mzZwd+VHP++efriy++UEFBgSZPnqzk5GRJUlVVlfr06RO4X1VVlS644IJWz+l0OuV0OkM9KgCElNcfo6+sOLvHaBOX/xu7RwBOKOSf4vH5fIqICD5tZGSk/H6/JMntdis5OVnFxcWB4zU1NSorK1N6enqoxwEAAGEo5O+gjB8/Xg899JDS0tJ07rnn6l//+pcWL16sW2+9VZLkcDiUm5urBx98UGeddZbcbrfmzJmjlJQUTZw4MdTjAAA6mXC7wLdr165yuVx2jxF2Qh4oS5cu1Zw5c3TnnXfq0KFDSklJ0e233665c+cG1syaNUt1dXWaPn26qqurdckll2jjxo2KiQmPn90CAE69WDXJb0lr1661e5R2iYiM0swZOURKO4U8ULp3767CwkIVFhYed43D4dCCBQu0YMGCUD88AKCTinY0K8KhsLoY2RVRr8uj98rn8xEo7RTyQAEAoCOF08XI8ts9QPjiywIBAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHH7VPfAjwLe/Agg3BArQifHtrwDCFYECdGJ8+yuAcEWgAD8CfPsrgHDDRbIAAMA4vIMCwEjhdGFvOM0KhAsCBYBRwvXCXgChRaAAMEo4Xtj7k0ivBnc5aPcYQKdCoAAwUjhd2Ovyf2P3CECnw0WyAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA43RIoBw4cEA33XSTevToodjYWJ1//vnasWNH4LhlWZo7d6769Omj2NhYeTwe7dmzpyNGAQAAYSjkgfL1119r5MiR6tKli/7xj3/oo48+0mOPPabTTjstsGbhwoVasmSJVqxYobKyMsXFxSkjI0P19fWhHgcAAIShqFCf8JFHHlFqaqpWrlwZ2Od2uwP/bVmWCgsLdf/992vChAmSpNWrVyspKUnr16/XpEmTQj0SAAAIMyF/B+W1117TkCFDdP3116t379668MIL9fTTTweO7927V5WVlfJ4PIF9LpdLw4YNU2lpaavnbGhoUE1NTdAGAAA6r5AHyueff67ly5frrLPO0qZNm3THHXdo5syZevbZZyVJlZWVkqSkpKSg+yUlJQWOfVdBQYFcLldgS01NDfXYAADAICEPFL/fr4suukgPP/ywLrzwQk2fPl3Tpk3TihUrTvqc+fn58nq9ga2ioiKEEwMAANOEPFD69Omjc845J2jfwIEDtX//fklScnKyJKmqqipoTVVVVeDYdzmdTsXHxwdtAACg8wp5oIwcOVK7d+8O2vfpp5+qb9++kv53wWxycrKKi4sDx2tqalRWVqb09PRQjwMAAMJQyD/Fc9ddd2nEiBF6+OGHdcMNN2j79u166qmn9NRTT0mSHA6HcnNz9eCDD+qss86S2+3WnDlzlJKSookTJ4Z6HAAAEIZCHigXX3yx1q1bp/z8fC1YsEBut1uFhYXKysoKrJk1a5bq6uo0ffp0VVdX65JLLtHGjRsVExMT6nEAAEAYCnmgSNK4ceM0bty44x53OBxasGCBFixY0BEPDwAAwhzfxQMAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjBNl9wAIDa/XK5/PZ/cYbXb48GG7RwAAGIxA6QS8Xq+WLF0mf0uz3aMAABASBEon4PP55G9pVkmjW15/jN3jtMlPIr0a3OWg3WMAAAxFoHQiXn+MvrLi7B6jTVz+b+weAQBgMC6SBQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGKfDA+UPf/iDHA6HcnNzA/vq6+uVnZ2tHj16qFu3bsrMzFRVVVVHjwIAAMJEhwbKu+++qyeffFI/+9nPgvbfddddev311/Xyyy+rpKREBw8e1LXXXtuRowAAgDDSYYFSW1urrKwsPf300zrttNMC+71er5555hktXrxYV1xxhQYPHqyVK1fqnXfe0bZt21o9V0NDg2pqaoI2AADQeXVYoGRnZ+uaa66Rx+MJ2l9eXq6mpqag/QMGDFBaWppKS0tbPVdBQYFcLldgS01N7aixAQCAATokUF588UXt3LlTBQUFxxyrrKxUdHS0EhISgvYnJSWpsrKy1fPl5+fL6/UGtoqKio4YGwAAGCIq1CesqKjQb3/7W23evFkxMTEhOafT6ZTT6QzJuQAAgPlC/g5KeXm5Dh06pIsuukhRUVGKiopSSUmJlixZoqioKCUlJamxsVHV1dVB96uqqlJycnKoxwEAAGEo5O+gXHnllXr//feD9k2ZMkUDBgzQvffeq9TUVHXp0kXFxcXKzMyUJO3evVv79+9Xenp6qMcBAABhKOSB0r17d5133nlB++Li4tSjR4/A/qlTpyovL0+JiYmKj4/XjBkzlJ6eruHDh4d6HAAAEIZCHiht8fjjjysiIkKZmZlqaGhQRkaGnnjiCTtGAQAABjolgfLWW28F3Y6JiVFRUZGKiopOxcMDAIAww3fxAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADBOlN0DAADQ2R0+fNjuEdqta9eucrlctj0+gQIAQAeJVZP8lrR27Vq7R2m3iMgozZyRY1ukECgAAHSQaEezIhxSSaNbXn+M3eO0mSuiXpdH75XP5yNQAADorLz+GH1lxdk9Rtv57R6Ai2QBAICBCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcUIeKAUFBbr44ovVvXt39e7dWxMnTtTu3buD1tTX1ys7O1s9evRQt27dlJmZqaqqqlCPAgAAwlTIA6WkpETZ2dnatm2bNm/erKamJl111VWqq6sLrLnrrrv0+uuv6+WXX1ZJSYkOHjyoa6+9NtSjAACAMBUV6hNu3Lgx6PaqVavUu3dvlZeX67LLLpPX69UzzzyjNWvW6IorrpAkrVy5UgMHDtS2bds0fPjwUI8EAADCTIdfg+L1eiVJiYmJkqTy8nI1NTXJ4/EE1gwYMEBpaWkqLS1t9RwNDQ2qqakJ2gAAQOfVoYHi9/uVm5urkSNH6rzzzpMkVVZWKjo6WgkJCUFrk5KSVFlZ2ep5CgoK5HK5AltqampHjg0AAGzWoYGSnZ2tDz74QC+++OIPOk9+fr68Xm9gq6ioCNGEAADARCG/BuVbOTk52rBhg7Zu3arTTz89sD85OVmNjY2qrq4OehelqqpKycnJrZ7L6XTK6XR21KgAAMAwIX8HxbIs5eTkaN26ddqyZYvcbnfQ8cGDB6tLly4qLi4O7Nu9e7f279+v9PT0UI8DAADCUMjfQcnOztaaNWv06quvqnv37oHrSlwul2JjY+VyuTR16lTl5eUpMTFR8fHxmjFjhtLT0/kEDwAAkNQBgbJ8+XJJ0qhRo4L2r1y5Urfccosk6fHHH1dERIQyMzPV0NCgjIwMPfHEE6EeBQAAhKmQB4plWd+7JiYmRkVFRSoqKgr1wwMAgE6A7+IBAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcaLsHsBEXq9XPp/P7jHa7PDhw3aPAABASBEo3+H1erVk6TL5W5rtHgUAgB8tAuU7fD6f/C3NKml0y+uPsXucNvlJpFeDuxy0ewwAAEKGQDkOrz9GX1lxdo/RJi7/N3aPAABASHGRLAAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMY2ugFBUVqV+/foqJidGwYcO0fft2O8cBAACGsC1QXnrpJeXl5WnevHnauXOnBg0apIyMDB06dMiukQAAgCFsC5TFixdr2rRpmjJlis455xytWLFCXbt21Z///Ge7RgIAAIaIsuNBGxsbVV5ervz8/MC+iIgIeTwelZaWHrO+oaFBDQ0Ngdter1eSVFNTE/LZjh49qvr6esU1fa0Wvy/k5+8Izsga1VvM3NGY+dRg5lODmU+NcJxZkuIiGlSveh09elRxcXEhO++3/25blvX9iy0bHDhwwJJkvfPOO0H777nnHmvo0KHHrJ83b54liY2NjY2Nja0TbBUVFd/bCra8g9Je+fn5ysvLC9z2+/06cuSIevToIYfDYeNkraupqVFqaqoqKioUHx9v9zhoA16z8MLrFX54zcJLR71elmXp6NGjSklJ+d61tgRKz549FRkZqaqqqqD9VVVVSk5OPma90+mU0+kM2peQkNCRI4ZEfHw8fxHDDK9ZeOH1Cj+8ZuGlI14vl8vVpnW2XCQbHR2twYMHq7i4OLDP7/eruLhY6enpdowEAAAMYtuPePLy8jR58mQNGTJEQ4cOVWFhoerq6jRlyhS7RgIAAIawLVB+9atf6csvv9TcuXNVWVmpCy64QBs3blRSUpJdI4WM0+nUvHnzjvmxFMzFaxZeeL3CD69ZeDHh9XJYVls+6wMAAHDq8F08AADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BEoH2rdvn6ZOnSq3263Y2Fj1799f8+bNU2Njo92j4QQeeughjRgxQl27dg2L31j8Y1RUVKR+/fopJiZGw4YN0/bt2+0eCcexdetWjR8/XikpKXI4HFq/fr3dI+EECgoKdPHFF6t79+7q3bu3Jk6cqN27d9syC4HSgT755BP5/X49+eST+vDDD/X4449rxYoVuu++++weDSfQ2Nio66+/XnfccYfdo6AVL730kvLy8jRv3jzt3LlTgwYNUkZGhg4dOmT3aGhFXV2dBg0apKKiIrtHQRuUlJQoOztb27Zt0+bNm9XU1KSrrrpKdXV1p3wWfg/KKfboo49q+fLl+vzzz+0eBd9j1apVys3NVXV1td2j4P8YNmyYLr74Yi1btkzS/74mIzU1VTNmzNDs2bNtng4n4nA4tG7dOk2cONHuUdBGX375pXr37q2SkhJddtllp/SxeQflFPN6vUpMTLR7DCAsNTY2qry8XB6PJ7AvIiJCHo9HpaWlNk4GdE5er1eSbPl3i0A5hT777DMtXbpUt99+u92jAGHp8OHDamlpOeYrMZKSklRZWWnTVEDn5Pf7lZubq5EjR+q888475Y9PoJyE2bNny+FwnHD75JNPgu5z4MABjRkzRtdff72mTZtm0+Q/XifzmgHAj1l2drY++OADvfjii7Y8vm1fFhjO7r77bt1yyy0nXHPGGWcE/vvgwYMaPXq0RowYoaeeeqqDp0Nr2vuawUw9e/ZUZGSkqqqqgvZXVVUpOTnZpqmAzicnJ0cbNmzQ1q1bdfrpp9syA4FyEnr16qVevXq1ae2BAwc0evRoDR48WCtXrlREBG9a2aE9rxnMFR0drcGDB6u4uDhwoaXf71dxcbFycnLsHQ7oBCzL0owZM7Ru3Tq99dZbcrvdts1CoHSgAwcOaNSoUerbt68WLVqkL7/8MnCM/9sz1/79+3XkyBHt379fLS0t2rVrlyTpzDPPVLdu3ewdDsrLy9PkyZM1ZMgQDR06VIWFhaqrq9OUKVPsHg2tqK2t1WeffRa4vXfvXu3atUuJiYlKS0uzcTK0Jjs7W2vWrNGrr76q7t27B67tcrlcio2NPbXDWOgwK1eutCS1usFckydPbvU1e/PNN+0eDf/f0qVLrbS0NCs6OtoaOnSotW3bNrtHwnG8+eabrf59mjx5st2joRXH+zdr5cqVp3wWfg8KAAAwDhdEAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMM7/A45QOfNR4PKPAAAAAElFTkSuQmCC\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": "markdown",
      "source": [
        "As we can see on the x-axis in the above images, z-score results in our data having a mean of zero while min-max results in our data spanning values from 0 to +1, with both methods not changing the actual distribution of our data."
      ],
      "metadata": {
        "id": "MV0G5PJ1reJg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that sklearn has handy functions for rescaling data, which saves the min/max and mean/std values as object attributes.\n",
        "\n",
        "*   https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html\n",
        "*   https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FE9phS-Etmaz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train and test data splitting"
      ],
      "metadata": {
        "id": "3FmOzbuewq5J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To split our data into train and test sets, we can use random splits or split by years (or some other existing data dimension that reduces the chances of there being autocorrelation in our train/test sets).\n",
        "\n",
        "**Note that we would need to split our train/test datasets before rescaling it.**"
      ],
      "metadata": {
        "id": "QLhpa14kuCvG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# helper function for random split\n",
        "def random_split(data, label, split=0.3, seed=0):\n",
        "    \"\"\"\n",
        "    Help splitting data randomly for training and testing.\n",
        "    \"\"\"\n",
        "    np.random.seed(seed)\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 (\n",
        "        data[:init_range], label[:init_range],\n",
        "        data[init_range:], label[init_range:]\n",
        "        )"
      ],
      "metadata": {
        "id": "jaiWHBllrPMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X, train_Y, test_X, test_Y = random_split(\n",
        "    ds['tmin'].isel(location=0),\n",
        "    ds['tmax'].isel(location=0), split=0.3, seed=0)"
      ],
      "metadata": {
        "id": "LSA_AsGnutAZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_X.shape, train_Y.shape)\n",
        "print(test_X.shape, test_Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKL2HIorvH5q",
        "outputId": "918bd583-e4b5-41d7-d8d5-27cf0703bf0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(511,) (511,)\n",
            "(220,) (220,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that sklearn has helper functions for this preprocessing too!\n",
        "\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split"
      ],
      "metadata": {
        "id": "TpLiUYHpvfyY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Label preprocessing\n",
        "\n",
        "Lastly, what if we want to convert our continuous temperature data into discrete classes? We can use integer encoding or one-hot encoding for this.\n",
        "\n",
        "The pandas `cut` method can be leveraged to convert continuous data to discrete classes. Make sure to supply the `labels` attribute so `pd.cut` returns encoded classes.\n",
        "\n",
        "Of course, sklearn has functions to help us with encoding data into classes too.\n",
        "\n",
        "*   https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder\n",
        "*   https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html#sklearn.preprocessing.LabelBinarizer\n",
        "\n"
      ],
      "metadata": {
        "id": "FL-pNJ_Bw0hB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# lets convert our tmax into 10 classes\n",
        "sample_array = ds['tmax'].isel(location=0).values\n",
        "sample_cut, sample_bins = pd.cut(\n",
        "    sample_array, bins=10, labels=range(0,10), retbins=True)"
      ],
      "metadata": {
        "id": "mU3duRwKvcJA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pd cut outputs our continuous array encoded with integer classes\n",
        "sample_cut"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwEgeGR-yGJG",
        "outputId": "cf0e7f32-da93-468e-9f55-07e23a0ecb2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 0, 1, 1, 0, ..., 2, 1, 2, 2, 2]\n",
              "Length: 731\n",
              "Categories (10, int64): [0 < 1 < 2 < 3 ... 6 < 7 < 8 < 9]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# here are the left side of the bins\n",
        "sample_bins"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UIfrP3_738dJ",
        "outputId": "9256d9e0-4062-4b8a-e939-1836ce11515e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.55182814,  1.05318194,  5.61259786, 10.17201378, 14.7314297 ,\n",
              "       19.29084562, 23.85026154, 28.40967746, 32.96909338, 37.5285093 ,\n",
              "       42.08792523])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "metadata": {
        "id": "lAPZSg-SyNYn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create the object\n",
        "onehotencode = OneHotEncoder()"
      ],
      "metadata": {
        "id": "92oh4MLs2q6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape the input array to be (samples, features)\n",
        "sample_onehot = onehotencode.fit_transform(X=sample_cut.reshape(-1,1))"
      ],
      "metadata": {
        "id": "THf425-CyUwP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_onehot.toarray().shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHKyIuLe0kqn",
        "outputId": "d941f2c6-a92a-4020-f048-799b858367fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(731, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_onehot.toarray()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHcZupv_5g5-",
        "outputId": "8e878453-21e7-4dce-a138-ca231d451658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_cut"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fG7OPMeF6HWz",
        "outputId": "e716cd31-0495-426a-a620-4d29147bc790"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 0, 1, 1, 0, ..., 2, 1, 2, 2, 2]\n",
              "Length: 731\n",
              "Categories (10, int64): [0 < 1 < 2 < 3 ... 6 < 7 < 8 < 9]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g6xbcG-O6L_5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}