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      "cell_type": "markdown",
      "source": [
        "# Processing Time Series (GEE and XArray)\n",
        "\n",
        "## **Overview**\n",
        "\n",
        "This is a demonstration of how we can use the [XEE](https://github.com/google/Xee) package to extract time-series images from Earth Engine and then use XArray to apply smoothing and interpolation and save the processed images as GeoTIFF files locally.\n"
      ],
      "metadata": {
        "id": "VCi5DKHB7jag"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup and Data Download\n",
        "\n",
        "The following blocks of code will install the required packages and download the datasets to your Colab environment."
      ],
      "metadata": {
        "id": "YauILMlxtHa_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "if 'google.colab' in str(get_ipython()):\n",
        "    !pip install --upgrade xee\n",
        "    !pip install rioxarray\n"
      ],
      "metadata": {
        "id": "aQpTzbC0l73O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import ee\n",
        "import xarray\n",
        "import rioxarray as rxr\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import os\n",
        "import datetime\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "NaQVEndwjA-K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_folder = 'output'\n",
        "\n",
        "if not os.path.exists(output_folder):\n",
        "    os.mkdir(output_folder)"
      ],
      "metadata": {
        "id": "CZ5RXe5hzE-v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ee.Authenticate()"
      ],
      "metadata": {
        "id": "JMQYA2EHjVlB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ee.Initialize(opt_url='https://earthengine-highvolume.googleapis.com')"
      ],
      "metadata": {
        "id": "HE-KmIohjc9I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We start with the MODIS Vegetation Indices Version 6.1 data. We pre-process the data by applying cloud masking and pixel scaling."
      ],
      "metadata": {
        "id": "2MMuY8Ic-pX3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modis = ee.ImageCollection('MODIS/061/MOD13Q1')\n",
        "\n",
        "startDate = ee.Date.fromYMD(2020, 1, 1)\n",
        "endDate = startDate.advance(2, 'year')\n",
        "filtered = modis.filter(ee.Filter.date(startDate, endDate))\n",
        "\n",
        "# Cloud Masking\n",
        "def bitwiseExtract(input, fromBit, toBit):\n",
        "  maskSize = ee.Number(1).add(toBit).subtract(fromBit)\n",
        "  mask = ee.Number(1).leftShift(maskSize).subtract(1)\n",
        "  return input.rightShift(fromBit).bitwiseAnd(mask)\n",
        "\n",
        "def maskSnowAndClouds(image):\n",
        "  summaryQa = image.select('SummaryQA')\n",
        "  # Select pixels which are less than or equals to 1 (0 or 1)\n",
        "  qaMask = bitwiseExtract(summaryQa, 0, 1).lte(1)\n",
        "  maskedImage = image.updateMask(qaMask)\n",
        "  return maskedImage.copyProperties(image, ['system:index', 'system:time_start'])\n",
        "\n",
        "# MODIS VI values come as VI x 10000 that need to be scaled by 0.0001\n",
        "def scaleBands(image):\n",
        "  scaled = image.multiply(0.0001)\n",
        "  return scaled.copyProperties(image, ['system:index', 'system:time_start'])\n",
        "\n",
        "# Apply the function to all images in the collection\n",
        "maskedCol = filtered \\\n",
        "  .map(maskSnowAndClouds) \\\n",
        "  .map(scaleBands)\n"
      ],
      "metadata": {
        "id": "0xLu9-Oqj2pk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have an ImageCollection that we want to get it as a XArray Dataset. We define the region of interest and extract the ImageCollection using the 'ee' engine.\n",
        "\n",
        "Our region of interest is in South India with the UTM Zone 46N. So we request the pixels in EPSG:32643 at 250m resolution."
      ],
      "metadata": {
        "id": "meBFO01O-2SQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aoi = ee.Geometry.Rectangle(77, 12, 79, 15)\n",
        "\n",
        "ds = xarray.open_dataset(\n",
        "    maskedCol,\n",
        "    engine='ee',\n",
        "    crs='EPSG:32643',\n",
        "    scale=250,\n",
        "    geometry=aoi,\n",
        ")"
      ],
      "metadata": {
        "id": "heqlVMyJkfap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "THe result is a XArray Dataset containing the image time-series."
      ],
      "metadata": {
        "id": "XoBeHoaE_EWq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "id": "kX44ne6LlAss"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Select the 'NDVI' band."
      ],
      "metadata": {
        "id": "FkCYdbkQ2X__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "original_time_series = ds.NDVI.chunk(dict(X=100, Y=100, time=-1))\n",
        "original_time_series"
      ],
      "metadata": {
        "id": "CvJMUmj62OkC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "3129a789-f355-4a5c-edc6-81120ff619fd"
      },
      "execution_count": 34,
      "outputs": [
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              "<xarray.DataArray 'NDVI' (time: 46, X: 851, Y: 1341)>\n",
              "dask.array<xarray-<this-array>, shape=(46, 851, 1341), dtype=float64, chunksize=(46, 100, 100), chunktype=numpy.ndarray>\n",
              "Coordinates:\n",
              "  * time     (time) datetime64[ns] 2020-01-01 2020-01-17 ... 2021-12-19\n",
              "  * X        (X) float32 77.03 77.03 77.03 77.03 77.04 ... 78.99 79.0 79.0 79.0\n",
              "  * Y        (Y) float32 15.03 15.02 15.02 15.02 15.02 ... 12.01 12.0 12.0 12.0\n",
              "Attributes:\n",
              "    id:             NDVI\n",
              "    data_type:      {'type': 'PixelType', 'precision': 'double', 'min': -3.27...\n",
              "    dimensions:     [172800, 67200]\n",
              "    crs:            SR-ORG:6974\n",
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              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;NDVI&#x27; (time: 46, X: 851, Y: 1341)&gt;\n",
              "dask.array&lt;xarray-&lt;this-array&gt;, shape=(46, 851, 1341), dtype=float64, chunksize=(46, 100, 100), chunktype=numpy.ndarray&gt;\n",
              "Coordinates:\n",
              "  * time     (time) datetime64[ns] 2020-01-01 2020-01-17 ... 2021-12-19\n",
              "  * X        (X) float32 77.03 77.03 77.03 77.03 77.04 ... 78.99 79.0 79.0 79.0\n",
              "  * Y        (Y) float32 15.03 15.02 15.02 15.02 15.02 ... 12.01 12.0 12.0 12.0\n",
              "Attributes:\n",
              "    id:             NDVI\n",
              "    data_type:      {&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: -3.27...\n",
              "    dimensions:     [172800, 67200]\n",
              "    crs:            SR-ORG:6974\n",
              "    crs_transform:  [231.65635826395825, 0, -20015109.354, 0, -231.6563582639...</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'>'NDVI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 46</li><li><span class='xr-has-index'>X</span>: 851</li><li><span class='xr-has-index'>Y</span>: 1341</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-034b90b8-b3e5-4faf-aa50-6efafff77a56' class='xr-array-in' type='checkbox' checked><label for='section-034b90b8-b3e5-4faf-aa50-6efafff77a56' 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>dask.array&lt;chunksize=(46, 100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\n",
              "                <thead>\n",
              "                    <tr>\n",
              "                        <td> </td>\n",
              "                        <th> Array </th>\n",
              "                        <th> Chunk </th>\n",
              "                    </tr>\n",
              "                </thead>\n",
              "                <tbody>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Bytes </th>\n",
              "                        <td> 400.50 MiB </td>\n",
              "                        <td> 3.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (46, 851, 1341) </td>\n",
              "                        <td> (46, 100, 100) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 126 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
              "                    </tr>\n",
              "                </tbody>\n",
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              "  <text x=\"9.469974\" y=\"105.622099\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.469974,105.622099)\">46</text>\n",
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              "</table></div></div></li><li class='xr-section-item'><input id='section-f6ddebf7-df16-4276-8c6b-82bb50b69282' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f6ddebf7-df16-4276-8c6b-82bb50b69282' 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'>2020-01-01 ... 2021-12-19</div><input id='attrs-96bb288e-7493-4324-be19-e2e2be5ce892' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96bb288e-7493-4324-be19-e2e2be5ce892' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33fddfee-04d6-43bd-a8ce-489b0fc6da80' class='xr-var-data-in' type='checkbox'><label for='data-33fddfee-04d6-43bd-a8ce-489b0fc6da80' 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;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-17T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-02-02T00:00:00.000000000&#x27;, &#x27;2020-02-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-03-05T00:00:00.000000000&#x27;, &#x27;2020-03-21T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-04-06T00:00:00.000000000&#x27;, &#x27;2020-04-22T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-05-08T00:00:00.000000000&#x27;, &#x27;2020-05-24T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-06-09T00:00:00.000000000&#x27;, &#x27;2020-06-25T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-07-11T00:00:00.000000000&#x27;, &#x27;2020-07-27T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-08-12T00:00:00.000000000&#x27;, &#x27;2020-08-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-09-13T00:00:00.000000000&#x27;, &#x27;2020-09-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-10-15T00:00:00.000000000&#x27;, &#x27;2020-10-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-11-16T00:00:00.000000000&#x27;, &#x27;2020-12-02T00:00:00.000000000&#x27;,\n",
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              "       &#x27;2021-01-17T00:00:00.000000000&#x27;, &#x27;2021-02-02T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-02-18T00:00:00.000000000&#x27;, &#x27;2021-03-06T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-03-22T00:00:00.000000000&#x27;, &#x27;2021-04-07T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-04-23T00:00:00.000000000&#x27;, &#x27;2021-05-09T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-05-25T00:00:00.000000000&#x27;, &#x27;2021-06-10T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-06-26T00:00:00.000000000&#x27;, &#x27;2021-07-12T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-07-28T00:00:00.000000000&#x27;, &#x27;2021-08-13T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-08-29T00:00:00.000000000&#x27;, &#x27;2021-09-14T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-09-30T00:00:00.000000000&#x27;, &#x27;2021-10-16T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-11-17T00:00:00.000000000&#x27;,\n",
              "       &#x27;2021-12-03T00:00:00.000000000&#x27;, &#x27;2021-12-19T00:00:00.000000000&#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'>X</span></div><div class='xr-var-dims'>(X)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>77.03 77.03 77.03 ... 79.0 79.0</div><input id='attrs-806e76c2-1f71-403b-aa15-d322089be208' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-806e76c2-1f71-403b-aa15-d322089be208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26769625-796d-48fa-8796-714b3dc14232' class='xr-var-data-in' type='checkbox'><label for='data-26769625-796d-48fa-8796-714b3dc14232' 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([77.02657 , 77.0289  , 77.03122 , ..., 78.995834, 78.99815 , 79.00047 ],\n",
              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Y</span></div><div class='xr-var-dims'>(Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.03 15.02 15.02 ... 12.0 12.0</div><input id='attrs-70616b6b-ca87-4710-9693-d3f6e7f2efb9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70616b6b-ca87-4710-9693-d3f6e7f2efb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ead2f72e-7bac-4efe-82a1-f2ee4fd3ad03' class='xr-var-data-in' type='checkbox'><label for='data-ead2f72e-7bac-4efe-82a1-f2ee4fd3ad03' 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([15.027213, 15.024955, 15.022696, ..., 12.004432, 12.002172, 11.999912],\n",
              "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f8612ac-766f-4917-99b2-b772dd5d7139' class='xr-section-summary-in' type='checkbox'  ><label for='section-1f8612ac-766f-4917-99b2-b772dd5d7139' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d29e8ec8-05d1-4494-bb3a-6043318b0ed2' class='xr-index-data-in' type='checkbox'/><label for='index-d29e8ec8-05d1-4494-bb3a-6043318b0ed2' 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;2020-01-01&#x27;, &#x27;2020-01-17&#x27;, &#x27;2020-02-02&#x27;, &#x27;2020-02-18&#x27;,\n",
              "               &#x27;2020-03-05&#x27;, &#x27;2020-03-21&#x27;, &#x27;2020-04-06&#x27;, &#x27;2020-04-22&#x27;,\n",
              "               &#x27;2020-05-08&#x27;, &#x27;2020-05-24&#x27;, &#x27;2020-06-09&#x27;, &#x27;2020-06-25&#x27;,\n",
              "               &#x27;2020-07-11&#x27;, &#x27;2020-07-27&#x27;, &#x27;2020-08-12&#x27;, &#x27;2020-08-28&#x27;,\n",
              "               &#x27;2020-09-13&#x27;, &#x27;2020-09-29&#x27;, &#x27;2020-10-15&#x27;, &#x27;2020-10-31&#x27;,\n",
              "               &#x27;2020-11-16&#x27;, &#x27;2020-12-02&#x27;, &#x27;2020-12-18&#x27;, &#x27;2021-01-01&#x27;,\n",
              "               &#x27;2021-01-17&#x27;, &#x27;2021-02-02&#x27;, &#x27;2021-02-18&#x27;, &#x27;2021-03-06&#x27;,\n",
              "               &#x27;2021-03-22&#x27;, &#x27;2021-04-07&#x27;, &#x27;2021-04-23&#x27;, &#x27;2021-05-09&#x27;,\n",
              "               &#x27;2021-05-25&#x27;, &#x27;2021-06-10&#x27;, &#x27;2021-06-26&#x27;, &#x27;2021-07-12&#x27;,\n",
              "               &#x27;2021-07-28&#x27;, &#x27;2021-08-13&#x27;, &#x27;2021-08-29&#x27;, &#x27;2021-09-14&#x27;,\n",
              "               &#x27;2021-09-30&#x27;, &#x27;2021-10-16&#x27;, &#x27;2021-11-01&#x27;, &#x27;2021-11-17&#x27;,\n",
              "               &#x27;2021-12-03&#x27;, &#x27;2021-12-19&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>X</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fbd5a171-3505-412e-a7b9-7fab7b5e5b43' class='xr-index-data-in' type='checkbox'/><label for='index-fbd5a171-3505-412e-a7b9-7fab7b5e5b43' 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(Float64Index([77.02657318115234, 77.02890014648438, 77.03121948242188,\n",
              "               77.0335464477539, 77.03587341308594, 77.03819274902344,\n",
              "              77.04051971435547,  77.0428466796875,   77.045166015625,\n",
              "              77.04749298095703,\n",
              "              ...\n",
              "              78.97959899902344, 78.98191833496094, 78.98423767089844,\n",
              "              78.98655700683594, 78.98887634277344, 78.99119567871094,\n",
              "              78.99351501464844, 78.99583435058594, 78.99815368652344,\n",
              "              79.00047302246094],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;X&#x27;, length=851))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a400cec8-23b8-4711-a9d1-11a5552e1bc6' class='xr-index-data-in' type='checkbox'/><label for='index-a400cec8-23b8-4711-a9d1-11a5552e1bc6' 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(Float64Index([15.027213096618652, 15.024954795837402, 15.022695541381836,\n",
              "               15.02043628692627,  15.01817798614502, 15.015918731689453,\n",
              "              15.013660430908203, 15.011401176452637,  15.00914192199707,\n",
              "               15.00688362121582,\n",
              "              ...\n",
              "              12.020247459411621, 12.017988204956055, 12.015728950500488,\n",
              "              12.013469696044922, 12.011209487915039, 12.008950233459473,\n",
              "              12.006690979003906,  12.00443172454834, 12.002172470092773,\n",
              "               11.99991226196289],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;Y&#x27;, length=1341))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e4c0aa3-501d-4a7f-87cc-2006db4a13bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3e4c0aa3-501d-4a7f-87cc-2006db4a13bd' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>NDVI</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: -3.2768, &#x27;max&#x27;: 3.2767}</dd><dt><span>dimensions :</span></dt><dd>[172800, 67200]</dd><dt><span>crs :</span></dt><dd>SR-ORG:6974</dd><dt><span>crs_transform :</span></dt><dd>[231.65635826395825, 0, -20015109.354, 0, -231.65635826395834, 8895604.157336]</dd></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interpolate and Smooth the time-series\n",
        "\n",
        "We use XArray's excellent time-series processing functionality to fill the cloud-masked pixels with linearly interpolated values from temporal neighbors. We also apply a moving-window smoothing to remove noise."
      ],
      "metadata": {
        "id": "h1BNPkpox4GW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "time_series_interpolated = original_time_series.interpolate_na('time', use_coordinate=False)\n",
        "time_series_smooth = time_series_interpolated.rolling(time=3, center=True).mean()\n",
        "time_series_smooth"
      ],
      "metadata": {
        "id": "BvsoysnGxpK6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot and Extract the Time-Series at a Single Location"
      ],
      "metadata": {
        "id": "VXziNzdSxx14"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "original_ts = original_time_series.interp(Y=13.16, X=77.35)\n",
        "smooth_ts = time_series_smooth.interp(Y=13.16, X=77.35)"
      ],
      "metadata": {
        "id": "-5J0g36ZljdQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(15, 7)\n",
        "original_ts.plot.line(\n",
        "    ax=ax, x='time',\n",
        "    marker='o', color='#66c2a4', linestyle='--', linewidth=1, markersize=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "FeR6FMTx4ZT0",
        "outputId": "9bdf3f1e-5416-4d73-a16e-7cc923f95d32"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
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n5/A12g65ecI5g/6FIo6ur12R0amjAAAAA+UUl0d+25a/lxJsINi2rVf2bpFlWbIO+xnfMgMAgIEyzZ2rmybMVnZ8spzWx7XNjPQCSrYQYXTqKAAAwECZlTFaszPHDMq927o69fT2d1XsrdDUtBxtqN1zxPTR+blFg/LaAAAg/Exz53YvSfHCrmIdbG+WbduyrMO/7kOwoWgDAADDgmVZsm1b7YEuxTgjB+y+VS0NWrzlLdV1tHRP2Sj2Vmh5RYn2NdcryhmhBWPP4ltmAAAwKK4omCqHLEq2EEHRBgAAho3fb1+rmrYm/b+iOQN2z33NDZJladHUi7unhx76lnmnr0YRlkN5iWkD9noAAACfdmj66IbaPWpob9W5WWMNJ8KxULQBAIBhIys+SetqytUZ8CvS4Tzh+/jtgIq9FTrNnafpI3I1NW2knI4jl7Yd7WJXcwAAMDR2+mr02r5SnZKUrqz4ZNNxcBQUbQAAYNgY40pXZ8CviqaDJ1yCNXa06X9K31ZpfbUyTnUpJyG115LtkLerdqqxs03zciadaGwAAIJSsbdCy3aXqLrVJ0+sS/PzCrvXDcPQ+1zeFG2s3aentq3R96bO7R7phuDCPxUAADBs5CSkKNoZoe0NB07o+vLGWv1ow/9pX3Odvl14vnISUo97TVWrT6/u26rAIO12CgCACcXeCi3ZukqVLfXqsgOqbKnXkq2rVOytMB0tbEU6nLpu7ExVNNXpH3u3mI6Do6BoAwAAw4bTcmhUolt7mur6fe0un1c//fAfSoqK1X+d+lmNS87o03VTUrPV2Nmussbafr8mAADBatnukiN22LYkLa8oMRcKKnC5ddHICXq/pkJ+vuQLSkwdBQAAw8oN42cpLiKqz+fbti3LspSXmKorCqbq3Myx/VrfbZTLrfiIaG08uI812wAAw0Z1q6+7ZDvEllTV4jMRB59yaV6RLs0rYupokOKfCgAAGFbiI6NlWVafzj3Y1qyfbvyHdvpq5LQcmpM9od+bKDgshwpTs7Sxdu+JxAUAICh5Yl3q7U/T+MjoIc+CniIdTkU6nNrf0qA11btMx8FhKNoAAMCwErBt/WTDK3pz/7Zjnre1br/u/+D/VN/eelI7lErSeVnj9PmCU2Xbh3/3DwBAaJqfV9g9XVSf+rWho1XvUu4EhbUHyvT77Wu1t7n/S2Zg8FC0AQCAYcXxyWi20vrqXn9u27ZW7NmsRze9rryEVN1x6sXK7cOmB8eSn5imwtTsPo+kAwAg2E1z5+qmCbOVHZ+sCMuh7Phk3TzhHF0/7mx2Hg0Sl+QWyhObqKdK18gfYL22YMEabQAAYNg5JSld7x4o615/7dNaujr0emWp5uVM1OfyiuQYoPVNNtdVapfPq0vzigbkfgAAmDbNnXvUUm1fc51eqyzVVaNPP+mR4TgxkQ6nvjJ2pn6y4RWt2LtZl+QWmo4EMaINAAAMQ6ckpauho1XetqbuY5XNDWroaFV8ZLR+MH2+Ls+fOmAlmyRVt/i0Ys9mtXV1Dtg9AQAIVgfbW/RudZke2/yG2vz82WdKfmKa5uZM1Gv7SvkMEiQo2gAAwLDT3NkuSbp7/d913/rlemHXev14wwq9tPtDSVJsP3Yl7auitJHqsgPaUr9/wO8NAECwKUzN1jcnn6+yRq8eKVmppk/+7MXQuyS3UHdO+6xiIiJNR4Eo2gAAwDBT7K3Qk9vWSPp4Y4R9LfX6576PlB2frH8bNX3QXtcdk6CsuCRtrN03aK8BAEAwGZfs0cLCOappbdKvt7zBpkCGRDqcSomOU0tXhz5kF3TjKNoAAMCwsmx3iXrbkqAj0KUY5+B+01uUmq2Sg/sUsFmQGAAQHvIS0/SfUy7SFwqmsSmQYW/t367FW9/SnqaDpqOENYo2AAAwrFS3+tTb9+lVLb5Bf+0Z6aP0+YJTFeAbfQBAGMmIc2m0a4T8dkC/2/auKih6jJiTPV5ZcUl6atu76gr4TccJWxRtAABgWPHEuo4Y0Wbp478EDLas+CSdnTFaEey+BgAIQ+3+Lu1trtPPNr6qbQ3VpuOEnYhPdiGtbKnXy3s2m44TtijaAADAsDI/r1C21F22WZJsSfNzi4bk9SuaDupv5R8OyWsBABBM4iKitLBwjvISUvWLTa9rI+uFDbnchFRdnDNZr+zZrPr2FtNxwhJFGwAAGFamuXN104TZyo5PVoTlUHZ8sm6ecI5OdecMyevXtDbp5T2b5G1rGpLXAwAgmMREROq2yedpUkqWfrPlLaaRGvDZnEn6f0UXKjk6znSUsBRhOgAAAMBAm+bO1TR3rpHXnpiSKafl0MbavTo/e7yRDAAAmBTpcOrGCbP0Xs1u5cSnmI4TdiIcTo1yuRWwbZU1ejXaNcJ0pLDCiDYAAIABFBsRqbFJ6dp4cJ/pKAAAGOO0HDozvUCWZekD7x79ffdG2WwWNKTeqd6p//7wn9rdWGs6SlihaAMAABhgRakjta3hgFq7Ok1HAQDAuJq2Ri2rKNFzO99nZ+4hNDN9lLLjk/XUtjXqZBfSIUPRBgAAMMCmukfqc3lFssVfJgAAuGjkRF0zZobe3L9dS0vflj8QMB0pLDgdDi0YN1PVrY1aXlFiOk7YoGgDAAAYYKnR8ZqXM0lxEVGmowAAEBRmZ47RDeNn6QPvHv1p13rTccJGdnyKLsmdrFf2bFFlc73pOGGBzRAAAAAGgbetSWuqd+mS3MlyWHy3CQDA9BG5io+MUnpMoiSp2FuhZbtLVN3qkyfWpfl5hcY2MxrO5o2cpBExCcqISzIdJSzwqQ8AAGAQ+DratKyiRLt8XtNRAAAIGuOTM5QaE693qndpydZV2tdSry47oMqWei3ZukrF3grTEYcdp8OhM9IL5LAseduaTMcZ9ijaAAAABkF+YpoSI2PYfRQAgF6s2LOpx3NbkiWxltgg2nSwUne995LK+BJwUFG0AQAADAKHZakwNYuiDQCAXtS2NR9xzJZU1eIb+jBhYkJKhnISUtiFdJBRtAEAAAySotSR2t/SoJrWRtNRAAAIKp5Yl6zDjlmSMuJcJuKEBafl0HVjZ+pAa6O+v/YvunX1H3Xf+uVM1x1gFG0AAACDZEJKhi7MniAnmyEAANDD/LzC7umi+uRXW9L83CJzocJAVatPAdlq6mpnbbxBwqc+AACAQRLjjNQXR01Taky86SgAAASVae5c3TRhtrLjkxVhOZQdn6ybJ5yjU905pqMNa8t2l/QYScjaeAMvwnQAAACA4ay5s13vHijTWZ5Rio2IMh0HAICgMc2dq2nuXNMxwkp1q0/2YcdYG29gMaINAABgELX5O/WnXeu1uW6/6SgAACDMsTbe4KNoAwAAGERpMQkaGZ+sjQf3mo4CAADC3OFr40msjTfQKNoAAAAGWWFqtjYdrJTfDpiOAgAAwtjha+M5LEvjkzysjTeAWKMNAABgkE1JHan/27NZO301GpvkMR0HAACEsU+vjffKns16afdG+Tpa5YqKNZxseGBEGwAAwCDLS0zTORmnKNYZaToKAABAt1kZY+S0HNpQyxIXA8WybfvwDSfCns/nU1JSkhoaGuRysSAgAAAAAAAYng62Nys1Ot50jKDX166IEW0AAABDoDPg17oD5TrY3mw6CgAAQLfU6HjZtq3GjjbTUYYFijYAAIAhELBtPb1tjYq9FaajAAAA9PDnsg/03xv/qQCTHk8aRRsAAMAQiHZGaHxyhjbW7jMdBQAAoIepaSNV1erTlrpK01FCHkUbAADAEClKzdZ23wG1dHWYjgIAANBttGuE8hPS9Oq+j0xHCXkUbQAAAEOkMC1bAdvW5oN8WwwAAIKHZVmakz1eW+urtLe5znSckEbRBgAAMERSo+N1buYpio+MNh0FAACgh2nuXOUlpKq2jY2bToZl26x0d7i+btkKAAAAAAAwXNi2LcuyTMcISn3tihjRBgAAMIQCtq2tdVWqbvGZjgIAANCDZVmqb2/Rlrr9pqOELIo2AACAIba09G2tqtphOgYAAMAR/rF3q/7no9Xq8HeZjhKSKNoAAACGkMOyVJSWrY0H95qOAgAAcITzs8eppatTaw7sMh0lJFG0AQAADLGi1JGqbm1k+igAAAg67pgEnZo2Uiv3faQAy/r3G0UbAADAEJuQnKFIh1MbD+4zHQUAAOAIc0ZOUHVrozbxWaXfjBdtjz32mPLz8xUTE6MZM2Zo3bp1xzy/vr5et956qzIzMxUdHa2xY8fq5ZdfPql7AgAADKUoZ4TOyTxFCZHRpqMAAAAcYVSiWxfnTFJaTLzpKCHHaNH2/PPPa+HChbrnnntUXFysKVOmaO7cuTpw4ECv53d0dOjCCy9UeXm5XnjhBZWWlurxxx9Xdnb2Cd8TAADAhH8fNV0zPaNMxwAAADiCZVm6PH+qsuNTTEcJOZZtm5twO2PGDJ1++un61a9+JUkKBALKycnRbbfdpu9///tHnL948WL99Kc/1UcffaTIyMgBuWdvfD6fkpKS1NDQIJfLdYLvDgAA4Nj2NddJspQdn2w6CgAAwBE+rN2rPU11mp9XaDqKcX3tioyNaOvo6ND69es1Z86cf4VxODRnzhytWbOm12teeuklzZw5U7feeqs8Ho8mT56sBx54QH6//4TvKUnt7e3y+Xw9HgAAAIPtydI1+r89m0zHAAAA6FVNW6OW7ylRXXuL6Sghw1jR5vV65ff75fF4ehz3eDyqqqrq9Zpdu3bphRdekN/v18svv6y77rpLP/vZz3T//fef8D0l6cEHH1RSUlL3Iycn5yTfHQAAwPEVpY3UpoOV8gcCpqMAAAAc4WzPGEU5IvRGZanpKCHD+GYI/REIBJSenq7f/va3mj59uq688kr913/9lxYvXnxS9120aJEaGhq6H3v27BmgxAAAAEdXlJqtVn+ndvhYSxYAAASf2IhIzc4Yo7eqdqjN32k6TkgwVrS53W45nU5VV1f3OF5dXa2MjIxer8nMzNTYsWPldDq7j02YMEFVVVXq6Og4oXtKUnR0tFwuV48HAADAYMtNSFVSVKw2HtxnOgoAAECvzssaq7auTq09UGY6SkgwVrRFRUVp+vTpWrlyZfexQCCglStXaubMmb1ec/bZZ2vHjh0KfGp6xbZt25SZmamoqKgTuicAAIApDsvS2Z7RiouIMh0FAACgV2kxCbq98Hyd5RltOkpIMDp1dOHChXr88cf19NNPa+vWrbrlllvU3NysBQsWSJKuvfZaLVq0qPv8W265RQcPHtTtt9+ubdu2afny5XrggQd066239vmeAAAAweSy/Cm6JJedvAAAQPAan5yhSIdTAds2HSXoRZh88SuvvFI1NTW6++67VVVVpalTp2rFihXdmxlUVFTI4fhXF5iTk6NXXnlF3/72t1VUVKTs7Gzdfvvt+t73vtfnewIAAASbho5WNXW2KTs+xXQUAACAXq3Ys1kf1VfpW4UXmI4S1Czbpo48nM/nU1JSkhoaGlivDQAADLrHNr+h1q5OfWfKhaajAAAA9KrYW6ElW1dp0dR5yk9MMx1nyPW1KwqpXUcBAACGo8LUbO301ai5s910FAAAgF5NTRspd0yCVu77yHSUoEbRBgAAYFhRarYCsrWprtJ0FAAAgF45LIcuyBqn9727dbC92XScoEXRBgAAYFhydJxyE1K1sXaf6SgAAABHdZZntGKdUdrWcMB0lKBldDMEAAAAfOz0EXna39JgOgYAAMBRxURE6oEzLlOMM9J0lKBF0QYAABAELho50XQEAACA44pxRsofCMjb1iRPHBtIHo6powAAAEGi3d+lyuZ60zEAAACO6fld7+sXm19XwA6YjhJ0KNoAAACCxJ/LivXrLW/Ktm3TUQAAAI7qLM9oeduatKF2r+koQYeiDQAAIEgUpmarpq1JVa0+01EAAACOKj8xTWNcI/Tqvo9MRwk6FG0AAABBYlySR5EOpzby7TAAAAhyF46coJ2+GpX5vKajBBWKNgAAgCAR5YzQxORMbTy4z3QUAACAYypKzdaU1JHqCHSZjhJUKNoAAACCyJS0kXJYlvwsLgwAAIKYw3Lo65PO1bjkDNNRgkqE6QAAAAD4l9iISDV3duibbz8vT6xL8/MKNc2dazoWAABAr/Y01amqpUGnp+ebjhIUGNEGAAAQJIq9FVqydZUqW+rVZQdU2VKvJVtXqdhbYToaAABAr96vKdcfdqxTa1en6ShBgaINAAAgSCzbXSJLkv3Jc1uSJWl5RYm5UAAAAMfwmaxx6gh06e3qHaajBAWKNgAAgCBR3errLtkOsSVVtfhMxAEAADiulOg4nT4iT6/tK2WNWVG0AQAABA1PrEvWYccsSRlxLhNxAAAA+mRO9gTVtjdrg3eP6SjGUbQBAAAEifl5hd3TRQ+xJc3PLTKUCAAA4PhyE1J11ejTVJDoNh3FOIo2AACAIDHNnaubJsxWdnyyHLIU7YjQzRPO0anuHNPRAAAAjum8rHFKjYk3HcO4CNMBAAAA8C/T3Lma5s5VwA7IYfGdKAAACB1vV+3UnuY6XTX6NNNRjKFoAwAACEKHSjYKNwAAECq6An69XlmqrXX75W1rkifWpfl5hZrmzjUdbcjwqQ0AACBI/XjDK3pp90bTMQAAAPokNiJKklTV6lOXHVBlS72WbF2lYm+F4WRDh6INAAAgSCVFxWiXz2s6BgAAQJ+s2LO5x/NDmzwtrygxkscEijYAAIAgVZDoVnlTrQJ2wHQUAACA46pu9R1xzJZU1XLk8eGKog0AACBIFSS61e7v0v4w+nAKAABClyfWJeuwY5akjDiXiThGULQBAAAEqbzEVFmyVNbI9FEAABD85ucVdk8X1Se/2pLm5xaZCzXELNu2bdMhgo3P51NSUpIaGhrkcoVP6woAAIJPdatPI2IS2HkUAACEhGJvhZZXlKiqxaeMOJfm5xbpVHeO6Vgnra9dUcQQZgIAAEA/eWL50g8AAISOae5cTXPnmo5hDF+NAgAABLGyRq9+suEVtXZ1mo4CAACA46BoAwAACGLRjkjtavSqvLHWdBQAAAAcB0UbAABAEMuIcynGGcmGCAAAACGANdoAAGGv2FuhZbtLVN3qkyfWpfl5hWG9rgSCi8OyVJCYRtEGAAAQAhjRBgAIa8XeCi3ZukqVLfXqsgOqbKnXkq2rVOytMB0N6DYq0a2yRq/YLB4AACC4MaINABDWlu0ukSXpUH1hS7IkLa8oYVQbgsaM9AKNSUrv/vcTAAAAwYmiDQAQ1qpbfTp8jJAtqarFZyIO0CtPnEueOJfpGAAAADgOpo4CAMKaJ9Z1xAghSx8vQA8Ek7erdurtqp2mYwAAAOAYKNoAAGFtfl5hj+l4h6aRzs8tMhcK6EVpQ5VWVW03HQMAAADHQNEGAAhr09y5umnCbGXHJyvCcig7Plk3TzhHp7pzTEcDeihIdGtPU506A37TUQAAAHAUrNEGAAh709y5bHyAoFeQ6FaXHdDepjoVuNym4wAAAKAXjGgDAAAIASM/GXW5q9FrOgoAAACOgqINAAAgBEQ4nPpc/hTlJqSajgIAAICjYOooAABAiJg7cqLpCAAAADgGRrQBAACEiNauDr17oEzNne2mowAAAKAXFG0AAAAhorWrU0+WvqMdvhrTUQAAANALijYAAIAQkRIdJ1dkjMrYEAEAACAoUbQBAACECMuyNMrl1i4fRRsAAEAwomgDAAAIIQWJbpU31SpgB0xHAQAAwGEo2gAAAELI2CSPJiRnqKWr03QUAAAAHCbCdAAAAAD03SiXW7dMPNd0DAAAAPSCEW0AAAAhprWrU1UtDaZjAAAA4DAUbQAAACHmz2XFWrJ1lekYAAAAOAxFGwAAQIgpSHRrf0uDWlmnDQAAIKhQtAEAAISYUS63bEnljbWmowAAAOBTKNoAAABCjCfWpVhnpMoavaajAAAA4FOComh77LHHlJ+fr5iYGM2YMUPr1q076rlPPfWULMvq8YiJielxznXXXXfEOfPmzRvstwEAADAkHJalMUnpaunqMB0FAAAAnxJhOsDzzz+vhQsXavHixZoxY4YeeeQRzZ07V6WlpUpPT+/1GpfLpdLS0u7nlmUdcc68efP05JNPdj+Pjo4e+PAAAACG3Drx3F4/AwEAAMAc4yPaHn74Yd1www1asGCBJk6cqMWLFysuLk5Lly496jWWZSkjI6P74fF4jjgnOjq6xzkpKSmD+TYAAACGlGVZCti2/IGA6SgAAAD4hNGiraOjQ+vXr9ecOXO6jzkcDs2ZM0dr1qw56nVNTU3Ky8tTTk6OLrvsMm3evPmIc9544w2lp6dr3LhxuuWWW1Rbe/TFgtvb2+Xz+Xo8AAAAglmbv1ML1/yv1nsrTEcBAADAJ4wWbV6vV36//4gRaR6PR1VVVb1eM27cOC1dulR/+9vf9Ic//EGBQEBnnXWW9u7d233OvHnz9Lvf/U4rV67UT37yE7355pu6+OKL5ff7e73ngw8+qKSkpO5HTk7OwL1JAACAQRDjjFRiZAwbIgAAAAQR42u09dfMmTM1c+bM7udnnXWWJkyYoCVLluiHP/yhJOmqq67q/nlhYaGKioo0evRovfHGG7rggguOuOeiRYu0cOHC7uc+n4+yDQAABL38xDSKNgAAgCBidESb2+2W0+lUdXV1j+PV1dXKyMjo0z0iIyN16qmnaseOHUc9Z9SoUXK73Uc9Jzo6Wi6Xq8cDAAAg2I1yubWnqU6dgd5H7QMAAGBoGS3aoqKiNH36dK1cubL7WCAQ0MqVK3uMWjsWv9+vkpISZWZmHvWcvXv3qra29pjnAAAAhJqCRLe67ID2NdebjgIAAAAFwa6jCxcu1OOPP66nn35aW7du1S233KLm5mYtWLBAknTttddq0aJF3effd999+sc//qFdu3apuLhY11xzjXbv3q2vfe1rkj7eKOE///M/9e6776q8vFwrV67UZZddpjFjxmju3LlG3iMAAMBgyElI0UMzPq/8xDTTUQAAAKAgWKPtyiuvVE1Nje6++25VVVVp6tSpWrFiRfcGCRUVFXI4/tUH1tXV6YYbblBVVZVSUlI0ffp0vfPOO5o4caIkyel0auPGjXr66adVX1+vrKwsXXTRRfrhD3+o6OhoI+8RAABgMDgth5KiYk3HAAAAwCcs27Zt0yGCjc/nU1JSkhoaGlivDQAABLV1B8r1dvVOfbvwyA2fAAAAMDD62hUZnzoKAACAE+ewLH1UXyVfR6vpKAAAAGGPog0AACCEjUp0S5LKGmsNJwEAAABFGwAAQAhLiY6TKzJGuxq9pqMAAACEPYo2AACAEGZZlka53CrzUbQBAACYZnzXUQAAAJycS3IL5bAs0zEAAADCHkUbAABAiMtNSDUdAQAAAGLqKAAAQMizbVt/Ld+gkoP7TEcBAAAIaxRtAAAAIc6yLJUcrNQH3j2mowAAAIQ1ijYAAIBhoCAxTWXsPAoAAGAURRsAAMAwUJDo1v6WBrV2dZqOAgAAELYo2gAAAIaBUS63bEnljbWmowAAAIQtijYAAIBhwBPr0vzcQqVGx5mOAgAAELYiTAcAAADAyXNYli7NKzIdAwAAIKwxog0AAGCYqG1r0uuVpbJt23QUAACAsETRBgAAMExUtjTouZ3vy9vWZDoKAABAWKJoAwAAGCYKEtMkSWVsiAAAAGAERRsAAMAwkRAZo/SYRJU1ek1HAQAACEsUbQAAAMNIfmIaRRsAAIAhFG0AAADDSFFatnITUk3HAAAACEsRpgMAAABg4Jw+Il+nj8g3HQMAACAsMaINAABgmDnY1qzqVp/pGAAAAGGnXyPafL6+fWBzuVwnFAYAAAAn739KVys1Ol5fGz/LdBQAAICw0q+iLTk5WZZlHfXntm3Lsiz5/f6TDgYAAIATU5Do1gfePaZjAAAAhJ1+FW2vvfbaMYs2AAAAmFeQ6Nar+z6Sr6NVrqhY03EAAADCRr+KtqKiIqWmsosVAABAMBuV6JYklTXWakraSMNpAAAAwke/NkPIysrSVVddpX/+85+DlQcAAAAnKSU6TtlxyWruajcdBQAAIKz0q2h7/PHHVVNTo3nz5ik/P18/+MEPVF5ePkjRAAAAcCIsy9Ld0y/RWZ7RpqMAAACElX4VbV/+8pe1cuVK7dixQ1/5ylf09NNPa8yYMbrwwgv1/PPPq6OjY7ByAgAAoJ86/F0K2LbpGAAAAGGjX0XbIQUFBbr33ntVVlamFStWKD09XV/96leVmZmpb37zmwOdEQAAAP1U3lir29/5k/a31JuOAgAAEDZOqGj7tDlz5uiZZ57R7373O0nSY489dtKhAAAAcHIy4lyyJe3y1ZqOAgAAEDZOqmjbvXu3fvCDH6igoEBXXnmlpk2bpmeeeWagsgEAAOAExTgjlR2frLJGr+koAAAAYSOivxe0t7frz3/+s5YuXao33nhD2dnZuu6667RgwQLl5+cPQkQAAACciFGJadrhqzEdAwAAIGz0q2j7+te/rueee04tLS267LLL9PLLL+vCCy+UZVmDlQ8AAAAnKD/Rrberd6nd36VoZ7+/XwUAAEA/9esT1+rVq3XPPffommuuUVpa2mBlAgAAwAA4fUSeTh+RpyhKNgAAgCHRr09dGzdu7P7fXq9X5eXlsixL+fn5FG8AAABBhoINAABgaPV7M4TNmzfrnHPOkcfj0YwZM3TGGWcoPT1d559/vkpLSwcjIwAAAE7QS+Uf6qnSNaZjAAAAhIV+fc1ZVVWlc889VyNGjNDDDz+s8ePHy7ZtbdmyRY8//rhmz56tTZs2KT09fbDyAgAAoJ821e2TbdusqwsAADDI+lW0/fznP1deXp7efvttxcTEdB+fN2+ebrnlFs2aNUs///nP9eCDDw54UAAAAPRfgcut5Xs2ydvWpBGxiabjAAAADGv9mjr6z3/+U9/73vd6lGyHxMbG6j//8z/1yiuvDFg4AAAAnJyCxI/X0S1r9BpOAgAAMPz1q2jbtWuXpk2bdtSfn3baadq1a9dJhwIAAMDASIiMUXpMosoaa01HAQAAGPb6NXW0sbFRLpfrqD9PTExUU1PTSYcCAADAwFkw7iylRMeZjgEAADDs9XvP98bGxl6njkqSz+eTbdsnHQoAAAADZ5TLbToCAABAWOhX0WbbtsaOHXvMn7ObFQAAQHBp7mzXi+Uf6DOZ45STkGI6DgAAwLDVr6Lt9ddfH6wcAAAAGCTRzgi9W12mrLhkijYAAIBB1K+i7dxzzx2sHAAAABgkEQ6n8hJT2XkUAABgkPWraHM4HMedGmpZlrq6uk4qFAAAAAZWfqJbG7x7TMcAAAAY1vpVtP3lL3856s/WrFmjX/ziFwoEAicdCgAAAANrVKJbK/d9JF9Hq1xRsabjAAAADEv9Ktouu+yyI46Vlpbq+9//vv7+97/rS1/6ku67774BCwcAAICBcUpSuv5t1HQ5LYfpKAAAAMPWCX/Sqqys1A033KDCwkJ1dXVpw4YNevrpp5WXlzeQ+QAAADAAkqJiNSd7vOIjo01HAQAAGLb6XbQ1NDToe9/7nsaMGaPNmzdr5cqV+vvf/67JkycPRj4AAAAMkPLGWr1dtdN0DAAAgGGrX0XbQw89pFGjRmnZsmX64x//qHfeeUezZ88erGwAAAAYQB/VV+n5Xe8rYLOmLgAAwGCwbNu2+3qyw+FQbGys5syZI6fTedTzXnzxxQEJZ4rP51NSUpIaGhrkcrlMxwEAABgQ2+qr9bOSV3X3tM8qOz7FdBwAAICQ0deuqF8j2q699lr9+7//u1JTU5WUlHTUR3899thjys/PV0xMjGbMmKF169Yd9dynnnpKlmX1eMTExPQ4x7Zt3X333crMzOwuBrdv397vXAAAAMNJbmKqLFkqa6w1HQUAAGBY6teuo0899dSAB3j++ee1cOFCLV68WDNmzNAjjzyiuXPnqrS0VOnp6b1e43K5VFpa2v3csqweP3/ooYf0i1/8Qk8//bQKCgp01113ae7cudqyZcsRpRwAAEC4iHFGKjs+Wbt8Xs3KGGM6DgAAwLBjfH/3hx9+WDfccIMWLFigiRMnavHixYqLi9PSpUuPeo1lWcrIyOh+eDye7p/Ztq1HHnlEd955py677DIVFRXpd7/7nSorK/XXv/51CN4RAABA8JrpKVB2fLLpGAAAAMOS0aKto6ND69ev15w5c7qPORwOzZkzR2vWrDnqdU1NTcrLy1NOTo4uu+wybd68uftnZWVlqqqq6nHPpKQkzZgx46j3bG9vl8/n6/EAAAAYjlKj4/V21U7duvqPum/9chV7K0xHAgAAGDaMFm1er1d+v7/HiDRJ8ng8qqqq6vWacePGaenSpfrb3/6mP/zhDwoEAjrrrLO0d+9eSeq+rj/3fPDBB3usMZeTk3Oybw0AACDoFHsrtGTrKu1rqVeXHVBlS72WbF1F2QYAADBAjE8d7a+ZM2fq2muv1dSpU3XuuefqxRdf1IgRI7RkyZITvueiRYvU0NDQ/dizZ88AJgYAAAgOy3aX6NMr29qSLEnLK0oMJQIAABhejBZtbrdbTqdT1dXVPY5XV1crIyOjT/eIjIzUqaeeqh07dkhS93X9uWd0dLRcLlePBwAAwHBT3eqTfdgxW1JVC8tmAAAADASjRVtUVJSmT5+ulStXdh8LBAJauXKlZs6c2ad7+P1+lZSUKDMzU5JUUFCgjIyMHvf0+Xxau3Ztn+8JAEdT7K3QfeuXs7YRgJDkiXXJOuyYJSkjji8ZAQAABoLxqaMLFy7U448/rqefflpbt27VLbfcoubmZi1YsECSdO2112rRokXd59933336xz/+oV27dqm4uFjXXHONdu/era997WuSPt6R9Fvf+pbuv/9+vfTSSyopKdG1116rrKwsXX755SbeIoBh4tDaRpWsbQQgRM3PK+yeLqpPfrUlzc8tMhcKAABgGIkwHeDKK69UTU2N7r77blVVVWnq1KlasWJF92YGFRUVcjj+1QfW1dXphhtuUFVVlVJSUjR9+nS98847mjhxYvc53/3ud9Xc3Kwbb7xR9fX1mjVrllasWKGYmJghf38Aho9Daxsdmnb16bWNprlzzQUDgD6a5s7VTRNma3lFiapafMqIc2l+bpFOdbMRFAAAwECwbNs+fKmOsOfz+ZSUlKSGhgbWawPQ7dbVf1SXHTjieITl0GOzrjaQCAAAAAAwFPraFRmfOgoAocITe+R/TFnbCAAAAABwCEUbAPTRRSMn9njO2kYAAAAAgE8zvkYbAISKlq6Oj0ewxbpU09bE2kYAAAAAgB4o2gCgDwK2rTf2l2qaO1c3TphtOg4AAAAAIAgxdRQA+mBL3X5Vtzbq/KxxpqMAAAAAAIIURRsA9MEol1vXjZ2p0a4RpqMAAAAAAIIUU0cBoA/iIqI00zPKdAwAAAAAQBBjRBsAHMdL5R/qpd0bTccAAAAAAAQ5RrQBwDG0+Tu1srJUn8k8xXQUAACAk1LsrdCy3SWqbvXJE+vS/LxCTXPnmo4FAMMKI9oA4BjWVpep3d+lczPHmo4CAABwwoq9FVqydZUqW+rVZQdU2VKvJVtXqdhbYToaAAwrFG0AcBS2bev1ylJNTRup1Jh403EAAABO2LLdJbIk2Z88tyVZkpZXlJgLBQDDEEUbABzF7qaD2t/q03lZ40xHAQAAOCnVrb7uku0QW9K+5gatqd6l+vYWE7EAYNhhjTYAOIr8xDTdO/1SeWITTUcBAAA4KZ5Ylypb6o8o2yIcDj21bY0kKTMuSae58zQ/r3DoAwLAMEHRBgC96PB3KcLhVEacy3QUAACAkzY/r1BLtq7qfn5oGun1487WGNcIfVRfpa31VWrzd0qSmjvb9estb2p8coYmJGeoINEtp4MJUQBwPBRtANCLl3Zv1E5fjb475SJZlmU6DgAAwEmZ5s5VYWq2ttRVypKljDiX5ucW6VR3jiTp9PR8nZ6e331+q79TrsgYvVZZqmUVJYp2RmhySpZuGD/rqJ+N2NUUACjaAOAI7f4uvV29U7MyRlOyAQCAYaMwJUtjXCM0L2fScc91xyToponnKGAHVNFUp631+9XS1SnLstQV8Ove4uUalejuHvG2q9GrJVtXdY+UO7Sr6U0TZlO2AQgrFG0AcJi1B8rU2tWpczPHmo4CAAAwYM7N6v9nG4flUH5imvIT07qPdQT8mpo2UlvrqvTugTJJUoTlOOquphRtAMIJRRsAfIpt23q9slRFadlyxySYjgMAADAg9jbXybalnISUk75XXESUvlAwTSqQGjva9FFDlZZ+9E6vu5pWtfhO+vUAIJSwmiUAfEp9R6va/J06L3Oc6SgAAAAD5uWKTXpmx9oBv29iVIxOH5GvzLgkHb7ghiWxsRSAsEPRBgCfkhIdpx+dfpnGJ3tMRwEAABgQATugrfVVmpiSNWivMT+vsHu6qPSvXU3n5xYN2msCQDCiaAOAT/g6WlXT2iiH5WATBAAAMGzsbjyolq4OTUzOGLTXmObO1U0TZis7PlkRlkPZ8cm6ecI53buaAkC4YI02APjEq/s+0qqqHXpoxucV6XCajgMAADAgNtftV6wzUgUu96C+zjR3LhsfAAh7jGgDAEkd/i6trtqhmZ5RlGwAAGBYiXZG6EzPKDkt/voHAIONEW0AIOm9mt1q6erQeZn93/YeAAAgmF04coLpCAAQNvhKA0DYs21br1WWanJqlkbEJpqOAwAAMGC8bU2qa28xHQMAwgZFG4Cw1xnwKy8hVRdk8W0vAAAYXlbs2ayfl7xqOgYAhA2mjgIIe1HOCF079kzTMQAAAAaUbdvaUrdfRWkjTUcBgLDBiDYAYa2uvUVv7d+uDn+X6SgAAAADqrq1UbXtzZqUkmk6CgCEDYo2AGHtzf3b9OeyYvlt23QUAACAAbWlbr+clkOnJKWbjgIAYYOiDUDY6gz4tbpqh2Z6Ris2ItJ0HAAAgAHVHuhUYWqWYpx8zgGAocIabQDC1vs1u9XY2a7zMseajgIAADDgLs6ZbDoCAIQdRrQBCEu2beu1ylJNTMmUJ85lOg4AAMCAaupsV5u/03QMAAg7jGgDELbmjZyo5Og40zEAAAAG3D/2btG6mnI9ePrlsizLdBwACBsUbQDCkmVZmj4iz3QMAACAQbGlbr/GJqVTsgHAEGPqKICwU9/eoiVb3tLBtmbTUQAAAAacr6NVe5rrNDEly3QUAAg7FG0Aws5bVTu0uW4/O40CAIBhaUt9lSRpYnKG4SQAEH4o2gCEla6AX6v2b9eZngLFRkSZjgMAADDgvK1NyktIlSsq1nQUAAg7rNEGIKys91bI19mm87LGmY4CAAAwKObnFeri3EmmYwBAWGJEG4Cw8tb+7ZqQnKHMuCTTUQAAAAZch79LATsgp8Vf9QDABEa0AQgrN004Ry1d7aZjAAAADIrXKkv15v5t+tHpl8vBjqMAMOQo2gCEjYAdkCsqRq6oGNNRAAAABsWWuv3Kjk+mZAMAQxhPDCAs+DpatWjdX7W94YDpKAAAAIOi3d+lnb4aTUzONB0FAMIWRRuAsLCqaodaujqUxdpsAABgmNrWUK0uO6CJKVmmowBA2KJoAzDs+QMBvbV/u2akFyg+Mtp0HAAAgEGxu/Gg0qLj5YlNNB0FAMIWa7QBGPaKaytU39Gq87LGmo4CAAAwaObnFer87HGyWJ8NAIxhRBuAYW97wwGNTfIoOz7FdBQAAIBBEbBtSVJcRJThJAAQ3hjRBmDY+48xZ6itq9N0DAAAgEGzumqHXqss1V2nflZOB+MpAMAU/gsMYFg70Noo27YVExFpOgoAAMCg2Vq3X3ERkZRsAGAY/xUGMGw1drTp3vXLtLpqp+koAAAAg8ZvB7S1vkoTk9ltFABMo2gDMGytrt4hy7J0qjvHdBQAAIBBU95Yq1Z/pyalZJqOAgBhj6INwLDktwN6s3K7zhiRr4TIaNNxAAAABs1H9VWKi4hSXmKq6SgAEPbYDAHAsFLsrdCy3SWqam2Q37aVGecyHQkAAGBQXZwzSWeMyJfDYhwFAJhG0QZg2Cj2VmjJ1lWyJNmfHHuh7AOlxSRomjvXZDQAAIBB47AcGhGbaDoGAEBBMnX0scceU35+vmJiYjRjxgytW7euT9c999xzsixLl19+eY/j1113nSzL6vGYN2/eICQHEEyW7S7pUbJJkiVpeUWJoUQAAACD6wPvHj304T/U4e8yHQUAoCAo2p5//nktXLhQ99xzj4qLizVlyhTNnTtXBw4cOOZ15eXl+s53vqPZs2f3+vN58+Zp//793Y8//vGPgxEfQBCpbvX1KNmkj0u3qhafiTgAAACDruTgPrV0tivKyWQlAAgGxou2hx9+WDfccIMWLFigiRMnavHixYqLi9PSpUuPeo3f79eXvvQl3XvvvRo1alSv50RHRysjI6P7kZKSMlhvAUCQ8MQeuR6bJSmDddoAAMAwZNu2ttTv18SULNNRAACfMFq0dXR0aP369ZozZ073MYfDoTlz5mjNmjVHve6+++5Tenq6rr/++qOe88Ybbyg9PV3jxo3TLbfcotra2qOe297eLp/P1+MBIPTMzyvs8fzQNNL5uUVG8gAAAAymqlaf6tpbNDElw3QUAMAnjI4v9nq98vv98ng8PY57PB599NFHvV6zevVqPfHEE9qwYcNR7ztv3jx9/vOfV0FBgXbu3Kk77rhDF198sdasWSOn03nE+Q8++KDuvffek3ovAMyb5s6VOyZBTZ1t6goElBHn0vzcIp3qzjEdDQAAYMBtrtuvCMuhsUme458MABgSITWRv7GxUV/+8pf1+OOPy+12H/W8q666qvt/FxYWqqioSKNHj9Ybb7yhCy644IjzFy1apIULF3Y/9/l8ysnhL+ZAqClvrJW3rUm3TjxXRWkjTccBAAAYVLMzxmhUopv12QAgiBj9L7Lb7ZbT6VR1dXWP49XV1crIOHL4886dO1VeXq5LL720+1ggEJAkRUREqLS0VKNHjz7iulGjRsntdmvHjh29Fm3R0dGKjo4+2bcDwLBoZ4Q+kzlWk1NZpwQAAAx/0c4IjXIdfQACAGDoGV2jLSoqStOnT9fKlSu7jwUCAa1cuVIzZ8484vzx48erpKREGzZs6H587nOf03nnnacNGzYcdRTa3r17VVtbq8zMzEF7LwDMy4xL0tVjTpfDMr7PCwAAwKDa1lCt32x5U61dHaajAAA+xfgY44ULF+orX/mKTjvtNJ1xxhl65JFH1NzcrAULFkiSrr32WmVnZ+vBBx9UTEyMJk+e3OP65ORkSeo+3tTUpHvvvVdf+MIXlJGRoZ07d+q73/2uxowZo7lz5w7pewMwdDbU7lG7v0sz0gtMRwEAABh0G2v3qayxVjHOSNNRAACfYrxou/LKK1VTU6O7775bVVVVmjp1qlasWNG9QUJFRYUcjr6PTnE6ndq4caOefvpp1dfXKysrSxdddJF++MMfMj0UGKZs29bfd5coLSaeog0AAISFrfX7NTE5U5ZlmY4CAPgUy7Zt23SIYOPz+ZSUlKSGhga5XC7TcU5asbdCy3aXqLrVJ0+sS/PzCjXNnWs6FjBgynxe/fjDV3TbpM9ocmq26TgAAACDqqGjVd9d+6KuH3e2zkjPNx0HAMJCX7siFjIa5oq9FVqydZUqW+rVZQdU2VKvJVtXqdhbYToaMGDeqtqutOh4TUxhHUYAADD8banbL0vShOQjN5ADAJhF0TbMLdtdIkvSoWGLtiRL0vKKEnOhgAHU0tWh92p2a1bGGDZBAAAAYWFSSpZunnCOEqNiTEcBABzG+BptGFzVrT4dPjfYllTV4jMRBxhwkQ6nvjTmDEazAQCAsOGKitFUd47pGACAXjD8Y5jzxLrU2/KoGXGhv/YcIH1ctM30jFJSVKzpKAAAAINuX3Od/rB9rZo7201HAQD0gqJtmJufV9g9XfTT5mSPNxEHGFC7fF498dHbau3qMB0FAABgSHxYu0/rasoV7WRyEgAEI4q2YW6aO1c3TZit7PhkRVgOZcS6FOOMPGI6KRCK3ty/TbsavYp2RpqOAgAAMCS21u/X+KQMRTicpqMAAHrB1yBhYJo7V9Pcud3PmzvbFR8ZbTARcPKaO9v1fs1uXZpXJIfV2wRpAACA4aWtq1M7fDW6ctRppqMAAI6CEW1h6FDJ9uq+j/TW/u2G0wAnZs2BMtmSzvKMNh0FAABgSJQ2VCtg22wCBQBBjBFtYczb1qQ392+TJ9alccke03GAfnm7aoempo2Ui23tAQBAmBgZn6yrRp+m9NhE01EAAEdh2bbNcl2H8fl8SkpKUkNDg1yu4bs7p98O6BebXteepjotmjpXI/gDGyHkYHuzugIBPmgCAAAAAAZdX7sipo6GMafl0I3jZykuIlK/3vKm2ro6TUcC+sS2baVGx1OyAQCAsFHb1qS/lm9QU2e76SgAgGOgaAtz8ZHR+vrEzyg91iW/HTAdBziups423fn+S9rRcMB0FAAAgCGz6WClXtm7RU6Lv8IBQDBjjTYoKz5Jt0w8R9LHOxnFREQaTgQc3ZrqMtW3t8gTO3yndQMAABxuS/1+jUp0K5bP6gAQ1Pg6BN3q2lt01/sv6b2actNRgF7Ztq1VVdt1qjtHiWyCAAAAwoQ/ENBH9VWalJJlOgoA4Dgo2tAtOSpW45Mz9PS2d1XRdNB0HOAI2xqqVd3aqNkZp5iOAgAAMGR2NXrV5u/SxJRM01EAAMdB0YZulmXpy6fMUFZckn69+U35OlpNRwJ6KDlYKU+sS2OT0k1HAQAAGDKuqBhdnDNJuQkppqMAAI7Dsm3bNh0i2PR1y9bhqq69RQ9uWKHMuCR9a/L5sizLdCRA0sdTRxs72+Vi2igAADCk2FuhZbtLVN3qkyfWpfl5hZrmzjUdCwAwyPraFTGiDUdIiY7T1yeeqyvyp1KyIWg0d7bLsixKNgAAYEyxt0JLtq5SZUu9uuyAKlvqtWTrKhV7KwbtNZs62/XavlI1d7YP2msAAAYORRt6lZ+YpvzENHUF/Npat990HIS5gG3rxxte0Uu7N5qOAgAAwtiy3SWyJB2aEmRLsiQtrygZtNfcWrdfz+96Xx0B/6C9BgBg4FC04ZjertqpRze9ri2UbTCotL5KB9oaNTE5w3QUAAAQxqpbfTp83R1bUlWLb9Bec0v9fmXFJSklOm7QXgMAMHAo2nBMszPHaFJKph7/aLWqB/EDBHAsb1XtUGZckka7RpiOAgAAwpgntvc1eWzZeu9AuQZ6+WvbtrWlbj+7jQJACKFowzE5LIe+Nv5suSJj9NiWN9Xa1WE6EsJMQ0erNtTu0TkZY1gzEAAAGDU/r7DH80OfTHLiU/XUtjWq72gd0NerbGlQfUerJqVkDeh9AQCDh6INxxUbEaWvTzpXjZ1tWldTbjoOwkxlc4OSImM1I73AdBQAABDmprlzNd2dK4csRVgOZccn6+YJ52jRqfN072mXKiU6Th3+Lv3vrvVqGIDSzWFZOsszSmMY1Q8AIcOyB3p88zDQ1y1bw01dewtrQ8CIgB2Qw+J7AQAAYN4vN70uSbpt8nm9/ry8sVa/2PSauuyAPpszWRdkj1ekwzmUEQEAg6CvXRF/c0WfHSrZ3q3epbUHygynQTioaW1UY0cbJRsAAAgKtm2rvLFW+YlpRz0nPzFNPzztczrbM1p/K/9Q965fpq0nsLFYh79L7x0oV1tX58lEBgAMMf72in7b1nBAv9v2rsp8XtNRMMy9UPaBfrH5ddMxAAAAJEkB29bFuZM1JW3kMc+Lj4zWlaNP013TLtGI2ET57cAn1wf6/FrbfQf0P6Vv62B780llBgAMLYo29NvVY05XXmKqfrP1LdW1t5iOg2Gqvr1FG2v36izPKNNRAAAAJElOh0NzsscrNyG1T+dnxSfpm5PO0+TUbNm2rV9tfkPP7linps724167uW6/kqNilRmXdLKxAQBDiKIN/RbpcOrmCefIIUu/2fKmOvxdpiNhGHq7eqciHA6dySYIAAAgSGyuq9S2+up+XXNo13Rb0oTkTK09UK673n9Jr+37SP7A0Ue4ba3br0kpWey6DgAhJsJ0AIQmV1Ssvj7pXC396G098MH/qaatSZ5Yl+bnFWqaO9d0PIS4gB3Q6qodOn1EvmIjokzHAQAAkCS9XLFJyVFxGpvs6fe1DsvShSMnaEZ6gV7a/aH+tGu91taU63tT5spxWJlW196iypYGfTZ38kBFBwAMEYo2nDBvW5P2t/pk6eNv6Cpb6rVk6yrdNGE2ZRtOSktXp3IT0jQ7c4zpKAAAAJIkvx3Q7qaDmpqXc1L3cUXF6JpTZujczFO0v8Unh2WptatTvo5WeeJcKvZW6K9lG2RJWl6xSU7LwWdrAAghFG04Yct2l3SXbPrk148/EJTwYQAnJSEyWrdMPMd0DAAAgG6VzQ3qDPhVcIwdR/sjJyFVOZ+s9fZa5UdaXrFJk1IytfHgvu7P2FUtDXyRDQAhhqINJ6y61dddsh3y8QcCn4k4GCbq21u00+fV1LSRcjpYRhIAwl2xt0LLdpeoutXHMhUwqryxVg5Z3eXYQLowe4IsWfrb7g8l8UU2AIQy/haLE+aJdenwpVktSRlxLhNxMEysqtqhp7etUUfAbzoKAMCwYm+FlmxdpcqWenXZge5lKoq9FaajIQzFRURppmeUop0DP1Yhyhmhz+ZOVoR15F/P+CIbAEILRRtO2Py8wu5v2Q6xJc3PLTKUCKHObwf0dtVOnZ6er9iISNNxAACGHWuZCmCoTR+Rq2vHnjmor8EX2QAQ+ijacMKmuXN104TZyo5PVoTlUKTDqbFJ6TrVfXILxCJ8bTpYqbqOFp2TwSYIAACWqUDw6PB3qaLpoPx2YFBf5/Avsg8VzXyRDQChgzXacFKmuXO714uoa29RUlSM4UQIZauqtis3IVV5A7TIMAAgtHliXapsqe9RtjG6ByaUN9XqZxtf1V3TPquR8SmD9jqHvsheXlGiqhafMuJcmp9bxBfZABBCKNowYFKi4yRJnQG/Ih1Ow2kQiopSR8pFWQsA+MT8vEIt2brqiOmj52eNN5gK4ai8sVZRDqcy45IG/bU+/UU2ACD0MHUUA+ofe7foh8Uvy7YPn+gBHN85madoahrf2AIAPnb4MhUZsS7FOiP15v5tgz6FD/i08sZa5SakytnLZgUAAHwaI9owoPIT0vTn1g+0veGAxiZ7TMdBiPDbAf257AOdlzlWI2ITTccBAASRw0f37GmqU2VLPYUHhlR5Yy2jzAAAfcInFAyoU5LSlR6TqLerd5qOghBSUrtPK/d9pDZ/p+koAIAgl5OQohnpBbJtW8XeCgUY2YZB1u7vktPhUAFryAIA+oCiDQPKsiydlTFK670Vau3qMB0HIeKtqu3KT0xTTkKq6SgAgBCxv6VBj29drWd3vMeSFRhU0c4I/fC0zzGiDQDQJxRtGHAz00fJaVkqbzxoOgpCgLetSVvq9uucjFNMRwEAhJCs+GR9eewMraraob/t/tB0HAxjnQG/pI+/UAYA4HhYow0DLjk6Tj+d8QVFOfnXC8e3umqHop2ROm1EnukoAIAQc5ZntJo62/Xnsg+UGBmjC7LZjRQD79db3lRSVKyuGzvTdBQAQAigCcGgiHJGqN3fpc5AlxIiY0zHQRA7yzNa+QlpiqaYBQCcgItGTlRjZ7tKDu7TeVlj5WCTBAwg27ZV3lirC7IocQEAfcPfbDEobNvWjz54WZNSsnTl6NNMx0EQS49NVDo7jQIATsLn86fKbwfksBzqCvgV4XCajoRhoqatSS1dHcpnIwQAQB/xlR8GhWVZmpKWo7UHyrrXtQAO9/vt72rdgTLTMQAAIc6yLEU4nKpqadDd7/9dO301piNhmChv9EoSRRsAoM8o2jBozvaMVnNXhzbU7jEdBUGm2Fuhe97/u1ZX7dSLZRtU7K0wHQkAMAykRscrNTpev9r8hvY115mOg2GgoqlOI2ISlBAZbToKACBEULRh0GTEuTTGNUJvV+00HQVBpNhboSVbV6mq1SdJqu9o0ZKtqyjbAAAnLcoZoVsnnau06Hg9uul1eduaTEdCiPt8wVR9p+hC0zEAACGEog2DalbGGPltW11MH8Unlu0ukfWp57YkS9LyihJDiQAAw0lsRJS+Ofk8RTmcWrzlLQVs23QkhDCH5VBydJzpGACAEMJmCBhUZ6YXaKZnlOkYCCLVrT4d/lceW1JVi89EHADAMOSKitW3Ci9QY2ebHJZ1/AuAXuxpOqhndrynG8afrbSYBNNxAAAhghFtGFSWZSlg29pWX62AHTAdB0HAE+vS4X/lsfTxVGMAAAaKOyZBBYludQX8erliE5szod92NXq1u6lWiZExpqMAAEIIRRsGXUXTQf2s5FVtra8yHQVBYH5uYfd0UX3yqy1pfm6RuVAAgGFrf0uDXt6zSf/z0dvy86Uf+qG8sVbZccmKcjIJCADQd0FRtD322GPKz89XTEyMZsyYoXXr1vXpuueee06WZenyyy/vcdy2bd19993KzMxUbGys5syZo+3btw9CcvRFXkKqsuKS2BQBkqTGrjZlxyUrKz5ZEZZD2fHJunnCOTrVnWM6GgBgGMpJSNWN42dpY+1ePbtjnWzWbEMflTfWqiAxzXQMAECIMV60Pf/881q4cKHuueceFRcXa8qUKZo7d64OHDhwzOvKy8v1ne98R7Nnzz7iZw899JB+8YtfaPHixVq7dq3i4+M1d+5ctbW1DdbbwDFYlqWzM0ZrQ+1eNXXyzyDcranepdSYON097RI9Nutq3TXtEko2AMCgKkobqWvHnqnVVTv1190fmo6DENDW1an9LQ3KT3SbjgIACDHGi7aHH35YN9xwgxYsWKCJEydq8eLFiouL09KlS496jd/v15e+9CXde++9GjWq50L7tm3rkUce0Z133qnLLrtMRUVF+t3vfqfKykr99a9/HeR3g6M5M71AkvTugXKzQWBUVYtPZY21mpnOBhkAgKE10zNKXyyYpg5/F6PacFwRDodun3y+JqdmmY4CAAgxRhcc6Ojo0Pr167Vo0aLuYw6HQ3PmzNGaNWuOet19992n9PR0XX/99Vq1alWPn5WVlamqqkpz5szpPpaUlKQZM2ZozZo1uuqqq464X3t7u9rb27uf+3zsfjjQEiJj9JmssYp1RpqOAoPePbBLcRFRKkobaToKACAMXThygmzblmVZ8nW0yhUVO6ivV+yt0LLdJapu9ckT69L8vEJNc+cO6mtiYEQ4nJqQkmk6BgAgBBkd0eb1euX3++XxeHoc93g8qqrqfeH81atX64knntDjjz/e688PXdefez744INKSkrqfuTkMI1tMPz7qOk6O2O06RgwaEPtXp3mzlWkw2k6CgAgTFmWpV0+r+5472/aWLt30F6n2FuhJVtXqbKlXl12QJUt9VqydZWKvRWD9poYOP/Yu0XvMRMDAHACQmoLncbGRn35y1/W448/Lrd74NZLWLRokRYuXNj93OfzUbYNkprWRu1trmdNrjD1/alz1eHvMh0DABDm8hJTNSklS7/9aLU+mzNJ79dUnPSos8rmetW0Namho1X1Ha16vbJU0sc7ax/61ZK0vKKEUW0h4LXKUp0+Il+nK990FABAiDFatLndbjmdTlVXV/c4Xl1drYyMjCPO37lzp8rLy3XppZd2HwsEPt6mPSIiQqWlpd3XVVdXKzPzX8O9q6urNXXq1F5zREdHKzo6+mTfDvpgTfUuraz8SBNTvqBotkoPK/5AQDHOSMUwfRgAYJjTcuhr48/Wjz74P/1t98bu44dGnd00YbYKU7PV0NEqvx2QJ9alDn+XXt6zSQ0drZ882tTQ0aofnnapYiOi9EJZsTbX7ZclS66oGLV0dRzxurY+Xq8Uwa2ho1V17S3sOAoAOCFGm46oqChNnz5dK1eu1OWXXy7p4+Js5cqV+sY3vnHE+ePHj1dJSUmPY3feeacaGxv16KOPKicnR5GRkcrIyNDKlSu7izWfz6e1a9fqlltuGey3hOM4K2O0lu/ZpPXe3TrLwzTScNHm79Sd772ka8acoamMZgQABIFIh1PWYccOjT777dbVsj95NsY1Qv855SI5LYfWHSiXKypGSVGxGu1yK+lTa7xdM2aGnA6HEiOj5bAcum/9clW21OvT2y5YkjLiXIP5tjAAyhtrJUn5CRRtAID+Mz6kaOHChfrKV76i0047TWeccYYeeeQRNTc3a8GCBZKka6+9VtnZ2XrwwQcVExOjyZMn97g+OTlZknoc/9a3vqX7779fp5xyigoKCnTXXXcpKyuru8yDOe6YBI1PztDbVTsp2sLIB949auxsU05CiukoAAB0O9Da2OtxS9JXxs5UUlSsUqPjJUlOh0MPnHH5Ue+VGhPf4/n8vEIt2bpKlnpOH52fW3TSuTG4yhq9ckXGKCU6znQUAEAIMl60XXnllaqpqdHdd9+tqqoqTZ06VStWrOjezKCiokIOR//2bPjud7+r5uZm3Xjjjaqvr9esWbO0YsUKxcTEDMZbQD/N8ozW/5S+raoWH9/qhol3D5RpbJJHaTEJpqMAANDNE+vqddRZVnySZnpGndS9p7lzddOE2VpeUaKqFp/iI6PU0NGm9Fj+LAx245I8SomOk2UdPuYRAIDjs2zbto9/Wnjx+XxKSkpSQ0ODXC6KoIHWGfDrhV3FmpM9XiNiE03HwSA72N6sO9b9VdeOPZNRjACAoHJoZ9BDo84O/XrzhHMGfOOmroBfj21+UxfnTNLYZM+A3hsAAAy+vnZF/RsqBgyASIdTV485nZItTGxrOKAoZ4SmpbHDGgAguBwadZYdn6wIy6Hs+ORBKdkkKcLh1O2F51OyBbn69ha9W71Lbf5O01EAACHK+NRRhKeAbWt11Q5lxLk0NokPnMPZmekFKkrNVkwEu40CAILPNHeuprmH7sug2rYmrdizWf8++jRFOpxD9rrom48aqvXktjV6ODVb4h8PAOAEMKINRjgsS6uqduife7eajoJB1NrVqYAdUFxElOkoAAAEhc6AX6urd+rVfXwGCkbljbXyxCYqPjLadBQAQIiiaIMxszyjtelgpRo6Wk1HwSD5S/kHeujDf5qOAQBA0MiIS9L5WeP0fxWbVd/eYjoODlPe6FV+YprpGACAEEbRBmNOT8+X0+HQmupdpqNgEHQG/HqvZrfGJqWbjgIAQFC5JLdQUU6n/lK+wXQUfEpXwK89TXXKT3CbjgIACGEUbTAmLiJK09w5ertqp9j8dvgpObhPLV0dmukZZToKAABBJS4iSpflTdV6bwWj2oJIS1enJqdma0zSCNNRAAAhjM0QYNSc7Ak6kNooW7YsWabjYAC9W12m/IQ0ZcYlmY4CAEDQOTtjlCamZCg5Os50FHzCFRWjWyaeYzoGACDEMaINRuUmpOq0EXlyWPyrOJz4AwF525p0pqfAdBQAAIKSw3IoLSZBHf4uVTQdNB0Hkva3NKi1q8N0DABAiKPdgHEHWhv1Px+t5oPNMOJ0OHTXtM/qnMxTTEcBACCovVBWrF9uel1tXZ2mo4S9JVveYt08AMBJo2iDcVEOp96vqdC6mnLTUTBAalobZVmWnIxUBADgmOaNnKRWf6f+b88m01HCWmtXh6pafSpIZCMEAMDJ4W/BMC45Ok6TU7P0dtVO01EwAPY11+nO91/S1roq01EAAAh6qTHxmjtyol7d95FqWhtNxwlbu5sOypaUn5hmOgoAIMRRtCEozPKM1u6mg9rbXGc6Ck7SmuoyxUdE6xR27AIAoE/mjpyoxMgYvVBWbDpK2CpvrFWMM0KeWJfpKACAEEfRhqBQmJotV2QMo9pCnN8OaO2BMp0+Ik8RDqfpOAAAhIQoZ4S+dMoZmpUxxnSUsNXc1aExrnQ5LMt0FABAiIswHQCQPl48/4bxs5QRx7eIoWxrXZV8nW2a6RllOgoAACGlMDVbkmTbtmyJwmeIfaHgVNm2bToGAGAYYEQbgsbYZI9cUbGmY+Ak+DpbNcY1QnkJqaajAAAQcvx2QL/Y/Lre3L/NdJSw4rcDCti2LMpNAMAAoGhDUHlz/zY98dHbpmPgBJ3lGa3vFF3IB1UAAE6A03IoNTpeL+3eqKbOdtNxwsbG2n369pr/5fccADAgKNoQVCIdTr1XU67atibTUdBPlc0Naupsp2QDAOAkXJY3RQHb1ku7PzQdJWyUN3oV44xQQmS06SgAgGGAog1BZbo7T9HOCL1Tvct0FPTTMzvWamkpoxEBADgZrqgYzc8t1Fv7d7Ab+xApa6xVfmKa6RgAgGGCog1BJdoZodNG5Oud6p0K2AHTcdBHNa2N2uGr0Yz0AtNRAAAIeedljVVOQor2NzeYjjLsBWxbu5so2gAAA4eiDUFnlme0Dra3aKfPazoK+ujdA2WKdkZoalqO6SgAAIS8CIdTi6bO0+np+aajDHsHWhvV5u9SfoLbdBQAwDBB0Yagk5+YprunXaJTktJNR0Ef2Latdw+Uabo7V9HOCNNxAAAYFhyWpdauTi2vKFFnwG86zrCVEefSQzM+rzFJI0xHAQAMExRtCDqWZSk7Plm2bcvP9NGg19LVoez4ZM30jDIdBQCAYaWho1XLKkr0z71bTUcZ1pKiYhXpcJqOAQAYJijaEJQCdkAPblih1yu3mY6C44iPjNbXJ56rsUke01EAABhWMuJcOj9rnFbs2ay69hbTcYal325dpbf2bzcdAwAwjFC0ISg5LIfSYhL0dtUO2bZtOg6OosPfpQ21e9TFlBYAAAbF/NxCRTmd+kv5B6ajDDudAb821O5lBgUAYEBRtCFone0ZrcqWBpU31pqOgqP4sHavfrPlLdW2N5uOAgDAsBQbEaXL86dq7YFyVbf4TMcZVvY21clvB1SQyEYIAICBw8rlCFoTUzIUHxGlX2x+XR3+LnliXZqfV6hp7lzT0fCJNQd2aVSiW55Yl+koAAAMW2d5RikrLlmeOP68HUhljbWKsBzKjk82HQUAMIwwog1Ba0PtXjV3dailq0NddkCVLfVasnWVir0VpqNBHy/QvKWuik0QAAAYZA7LoVEut2zblretyXScYaO8yauRCSlshAAAGFAUbQhay3aX9HhuS7IkLa8o6fV8DK21B8rktCxNZ4QhAABDYllFiX6y4RW1dnWajjIszBs5Sf9WMM10DADAMEPRhqBV3XrkOiS2pCrWJwkKKdFxunDkBMVHRpuOAgBAWDg7Y7Ra/Z36vz2bTEcZFrLikzUmKd10DADAMEPRhqDliXXJOuyYpY+3uod5p4/I1+X5U03HAAAgbKRGx2veyElaue8jHWhtNB0npJU31urPZR+ow99lOgoAYJihaEPQmp9X2D1d9BBb0vzcIkOJcEixt0J7mg6ajgEAQNi5aOQEuaJi9MKuYtNRQtqWuv1atX+7IlifDQAwwCjaELSmuXN104TZyo5PlkOWLEnXjztbp7pzTEcLa/5AQM/uWKc1B8pMRwEAIOxEOSN05ajTNC7ZI9u2TccJWeWNtcpPTJPDOnz+BAAAJyfCdADgWKa5czXNnStvW5PufO9v6ggwvN+0TXWVauxs18z0AtNRAAAIS1M/+dKx2FuhZbtLVN3qkyfWpfl5hZrGJkXHZdu2yhq9OjtjtOkoAIBhiBFtCAnumARNSctRXXuL6Shh790DZRoZn6ychFTTUQAACFvFNRVasnWV9rXUq8sOqLKlXku2rlKxt8J0tKBX19EiX2eb8hPTTEcBAAxDjGhDyLh5wmxZDO83qrmzXRtr97IJAgAAhi2rKOnx/NC6tssrShjVdhxOy6FLciZrVOII01EAAMMQI9oQMizLUoe/Szt9NaajhK2Abev87HE6Iz3fdBQAAMJadavviGO2pKqWI4+jp6SoWH0uf4pcUTGmowAAhiGKNoSUV/d9pEdLXlNrV4fpKGEpMSpGXyiYpqSoWNNRAAAIa55Ylw4f529JyohzmYgTUtbX7Nb+lgbTMQAAwxRFG0LKTM8odQb8Wnug3HSUsFPT2qgVezarzd9pOgoAAGFvfl5h93TRQ2xJ83OLDCUKDQE7oKe3v6sPa/eajgIAGKYo2hBSUqLjVJQ2Um/t386W9kPs7eqdemXvFjkt/rMBAIBp09y5umnCbGXHJyvCcmhkfLJunnCOkqJi1Rnwm44XtKpafGr3d6mAjRAAAIOEzRAQcs7NPEWPbnpNuxq9Gu1iEduhELBtrT1QptPcuYp0OE3HAQAA+rhs+/TGBy1dHbpj3V81JildN02YzZ/ZvShrrJUlKTeBog0AMDgo2hByxidnaJo7V4EgH9FW7K3Qst0lqm71yRPr0vy8wpDdBWx7Q7UOtrfoTM8o01EAAMBRxEVE6YYJs/TrzW/q8a2rdeOEWYqgbOuhvLFWGXFJio2INB0FADBMMQcMIcdhWbppwmydkpRuOspRFXsrtGTrKlW21KvLDqiypV5Ltq5SsbfCdLQTsqa6TOkxiRqV6DYdBQAAHMOklCzdMvEcba6r1OMfrZY/EDAdKaikRMdpeoh+8QkACA0UbQhZO3012nSw0nSMXi3bXSJLHy9KrE9+tSQtrygxF+oknOkp0BcKTpVlHb6/GQAACDaTU7N188RzVNnSIF9nm+k4QeWzuZN1aR4bRgAABg9TRxGyXq8sVUVTnSalZAZdAVTd6tPhE1ttfbwAbygan5xhOgIAAOiHwtRsTUzOlNPhUGtXp6IcTjkd4f0de1Nnm9r9XUqNjg+6z44AgOEjvP+0RUg7J+MUVbf6tK3hgOkoR/DEutTbx7eMONeQZzlZfynfoK11+03HAAAA/eR0OBSwbf1y82t6ovRt+e3wnka69kC57n7/72H/+wAAGFwUbQhZpySlKyPWpbf2bzcd5QhneUZ1Txf9tItzJpuIc8IOtjfrlT2bdbC9xXQUAABwAhyWpQuzJ+qD2j16svSdsC6ZyhtrlZOQygYRAIBBxdRRhCzLsjQ78xS9WPaBfB1tckXFmI4kSbJtWx/U7pE7JkExzghVtfiUEefSJTmFmjYiV76OVrmiYk3HPKZDO6bub2mQLclp0ckDABCqTnXn6Ibxs/T41tWyZGnBuJlyhOGf7eWNtZqcmmU6BgBgmKNoQ0ibmV6glq4OOYJonY2Sg/u0w1ejb04+T5NSen6Yq29v0b3Fy/XZnMm6cOQEQwmP7dCOqZ/ezOHJbe8oyunUNHbpAgAgJE1z5+pr48/W/5S+rZmeUZqYkmk60pBq7mzXgbZGFSSmmY4CABjmKNoQ0uIjo/W5INo5KmAH9JfyDRqX5NHE5CM/wCZHx+mcjDF6oaxYCZHRmukZZSDlsR2+Y6r0rx1TKdoAAAhd00fkKSchVemxiZI+HoUfLpsCeNualBARrfxEt+koAIBhLvzGjGPY6Qr49dLujdpWX206it49UKbKlgZ9vuDUo35wvTx/qmZljNHvtr2rDbV7hjjh8Q23HVMBAMC/HCrZXt23Vb/fvlYB+/A/9YenvMQ0/feZX9CImATTUQAAwxxFG0Ke03Low9o9Wln5kekompCcqf8Yc7ryjzEtwbIsfWnM6ZrqztEz29epw981hAmPL6mX9eMsheaOqQAAoHcJkTF6p3qnntkRHmWb3w7IsqywGcEHADCHqaMIeZZl6ZyMU/TczvdV196ilOg4Izls21ZKdJzOzRx73HMdlkNfHXeWatuaFOUMjv8bBmxbyytKVNveLEnd00cP/To/N3im6AIAgJNzZnqBbNvW09vWyCFLV485I6jWvB1Itm3rjnV/1dyRk3R+9jjTcQAAwxwj2jAsnJFeoEiHU29X7TTy+i1dHbq3eHm/pq9GOpzKiEtSZ8Cvp7etMTo1s7WrQ7/Z8qaWV5Toc3lFunH8bGXHJyvCcig7Plk3TzhHp7pzjOUDAAADb6ZnlL58yplaVbVDK/eZnxkwWA62N6u+o1VpMfGmowAAwkBQFG2PPfaY8vPzFRMToxkzZmjdunVHPffFF1/UaaedpuTkZMXHx2vq1Kn6/e9/3+Oc6667rnto+KHHvHnzBvttwKDYiEidnp6v1VU75LcDQ/76r+zZrNq2JnlOYHplh79LZT6vHt20Ugc/GU021J7fuV7bGw7o1kmf0SW5hZo+Ild3TbtEj826WndNu4SSDQCAYersjNG6eeI5mpUxxnSUQVPeWCtJ7DgKABgSxou2559/XgsXLtQ999yj4uJiTZkyRXPnztWBAwd6PT81NVX/9V//pTVr1mjjxo1asGCBFixYoFdeeaXHefPmzdP+/fu7H3/84x+H4u3AoItGTtDXxp8th4Z22kNde4tWVpZqTvaEXtc3O574yGjdXniBJOnRktfU1Nk20BGPqrWrU5J0RcFU3XHqxSpMzR6y1wYAAMFhalqOYiMidaC1UX/fvVH2MFuzrayxVmnR8XKdwOc0AAD6y3jR9vDDD+uGG27QggULNHHiRC1evFhxcXFaunRpr+d/5jOf0RVXXKEJEyZo9OjRuv3221VUVKTVq1f3OC86OloZGRndj5SUlKNmaG9vl8/n6/FA6PHEujQmKX3IF7ldVlGiKEeELho58YTvkRIdp29NvkDNXe365aY3Bn2DhIAd0F/KN+je4mVq7mxXUlRs9y5kAAAgPO3wHdCyihL9b1nxsCrbdjfVHnOjKgAABpLRVdg7Ojq0fv16LVq0qPuYw+HQnDlztGbNmuNeb9u2XnvtNZWWluonP/lJj5+98cYbSk9PV0pKis4//3zdf//9Skvr/Q/YBx98UPfee+/JvRkEhQOtjXqy9B1dP/5suYdg+3Z/IKA9TQf12dzJio2IPKl7eeJc+ubk87Wxdp8iHc4BSnik5s52/U/p29paV6UrCqYqLiJq0F4LAACEjrM8o9Xu79JzO99XTWujatuaVd3qkyfWpfl5hZrmzjUd8YTcNuk8tfo7TccAAIQJyzb4dVVlZaWys7P1zjvvaObMmd3Hv/vd7+rNN9/U2rVre72uoaFB2dnZam9vl9Pp1K9//Wt99atf7f75c889p7i4OBUUFGjnzp264447lJCQoDVr1sjpPLLAaG9vV3t7e/dzn8+nnJwcNTQ0yOXq/5pbMKfd36XvrX1Rn8kaq8vzpw7Ja/7/9u48Pqr63v/4eyYbCSTsBAKEsCZBwhqWqmyXHawLgrSgIg8BcSuy3PZy+6teCsq1ogLVQrEWKVBBKUorFllkiSJhxyBBdkJYQiBAyL7M9/cHNyOBmYAyyUkmr+c/mpkzJ5/zyJszZz7zPd+vwxgZGfnYPDtA9Fh6qpoG15bdg/tNzrys+Qe3KrsgT2Oj7lfrmg08tm8AAOAd/nZ4u75O+WGBqaIVyJ+J7l5hm20AANyt9PR0Va9e/ba9IktHtP1UwcHB2rdvnzIyMrRx40ZNnjxZzZo1U69evSRJv/jFL5zbxsTEqG3btmrevLk2b96sPn363LK/gIAABQQElFX5KEUBPr7qFtpMX58/pgfCY+RbiiPDTmdcVoEpVNPgOpKH54W7nJult77doK71muqJll09djtsTkG+qvkFaFJMnzIZ8QcAACqeosUDihhdv9JZk5RQ4RptO1NPKv7CST3XuqfsZTy9CACgcrJ0jrY6derIx8dHKSkpxR5PSUlR/fr13b7ObrerRYsWat++vaZMmaJhw4Zp1qxZbrdv1qyZ6tSpo6NHj3qsdpRfPeq3UHp+jvZfSi6132GM0fJjO7X0SHypzGFSMyBIT7Tsqq9TjmnVyX13ta9C49CWs4dVaBxqUb2e/qvdAJpsAADArZTsW+crNpLOZ1W8eYy/v5KiSzkZNNkAAGXG0kabv7+/OnXqpI0bNzofczgc2rhxY7FbSW/H4XAUu/XzZsnJybp06ZIaNOA2ucogrGoNtQipq/jUk6X2Ow5cPquj6al6JKJ9qS2+0C20mR5r1knrkg/qi9Pf/aR9XMvL0ZyEL7X82C4dT78oSWW+WAQAAKhYQgNDXI7Vrx9U8aZUOXnt0v/dfQAAQNmw/NbRyZMna/To0YqNjVWXLl00Z84cZWZmasyYMZKkJ598Ug0bNnSOWJs1a5ZiY2PVvHlz5ebm6vPPP9eSJUs0f/58SVJGRoamT5+uRx99VPXr19exY8f061//Wi1atNCAAQMsO06UrTGR9yrEr0qp7NthHFp1Yq9aVQ/VPTXDSuV3FOnTMEoZ+bn6OuW4eodFyt/nzv/Jnrp2SfMTt6rA4dCkmD5qWb1eKVYKAAC8xQNNYvTnxDjn3GxF+jaMtqqknySvsEBnMq+oR4OWVpcCAKhELG+0jRgxQqmpqXr55Zd1/vx5tW/fXmvXrlVoaKgkKSkpSXb7DwPvMjMz9dxzzyk5OVmBgYGKiorS0qVLNWLECEmSj4+Pvv32Wy1evFhXrlxRWFiY+vfvrxkzZjAPWyVSdGtkvqPQ4yt4xl84qbNZV/Vf7bqVyeiwB5u0Vf9GreXv4yuHMXd068O5rKv6w/51alStpiZE91DNgKBSrxMAAHiHjnXC9Ux0d61JStD5rHTVqVJNl3OzFH/hhLrWi/DoQk2lKSkjTQ4ZNQ2ubXUpAIBKxNJVR8urO11JAuVb/IUTWnVir2Z2fsijzbaM/Bztv3RG99Vv7rF93on0vBz98btNGhrRXtFuVgstasQZY7Qt5bi61IvweKMRAABUPomXz2nugU16KvJn6lavqdXl3JHM/FwlXjmvDrUby8deMZqDAIDyy6tXHQXuRES12rqSl63dF5M8dkHoMA5V86tS5k02SQry9VOwX4DmH9yqSW373DLfyNW8bC1MjFOfhlHqWCfckhoBAIB3iq7ZQL9u179CjA7bczFJn51KUEp2ukIDQ2S32SrcaqkAgIqLRhu8VmhQiKJq1FfcuSMeabRlFeRp1t61+mWLzmrtZkRZafK1+2hCdA/NObBRfzywWUPC2+jr88eUkp2umgFByizIk7/dR9X9A8u8NgAA4P2ahVz/ku9A2hnVrRKs0HK4OMKei0nF5pc7k3VFf06M0zPR3Wm2AQDKBGOo4dV61G+ho+mpOpN55a739UXyQV3Oy1JYUPW7L+wn8vfx1fOteynA7qOPju/W2awrKjAOpeZkKKsgTz9v0lbNQ+paVh8AAPBuhcahlcf3aEHiVuUU5ltdzi0+O5VwyyIONklrkhIsqggAUNnQaINXa1e7kWr4B+pYeupd7edKbpY2njmkvg2jVMPihQWq+gUo4P9WH735InLT2e8tqQkAAFQOPja7xkf30KWcTP3t8HaVt+meU7LTdXNFRtL5rHQrygEAVEI02uDVfO0+mhH74F0v6/5ZUoL87T4a0Ki1hyq7O6k5Gbc8xkUkAAAoC2FVq2t0q27afTFJG84csrqcYuoFBt/ymE1S/XJ4mysAwDvRaIPX8/fxVaHDoUsumlN3otA4lJJ9TYPD2yjQ19/D1f00oYEhst30GBeRAACgrHSq20T9Gkbr65RjKnAUWl2OU/+bvhQtuo30gfC2ltQDAKh8bKa8jfcuB+50yVZUHO8f+loXsq9pWoeBP+n1xhgZGdlt5aM3ffNEv0X/nRDdQx3qNLa2OAAAUCkUGofyCgsV6OtndSnKyM9RvsOhmgFB2p2apM9PJ+h8VrrqB4XogfC2XB8BAO7anfaKWHUUlUJs3Sb608EtOnXtkpr8iGXpT2dcVlZBriJr1JftljFk1ulYJ1zPRHfXmiQuIgEAgDV8bHYF+tqVmn1Na5O/0y+bd5av3afM60jLzdTchC8V4l9FU9r2U6e64epUlxVGAQDWoNGGSqFNrTDV9A/S1vNH9cQdNtqMMfro+C5l5ufp/3UcLLut/DTapOvNNpapBwAAVssoyNX2lBPysdk1skWXMv3dKVnpmnNgo2yy6fGWXcv0dwMA4Er5uA8OKGU+Nrvur99COy+cVHbBnS1F/93lszp89YIeadq+3DXZAAAAyoumwXU0onmstpw7om9SjpfZ703KSNMb365TgI+f/rNdf4UGMuULAMB6NNpQadxXv7lCg0KUlpt5220dxqFVJ/apZUg9takZVgbVAQAAVFzd67fQfaHNtezoDiVlpJXJ77yYk6G6VYI1tW0/1QwIKpPfCQDA7XDrKCqNmgFB+m2HQXe07Y7UUzqTdUW/aTdANkazAQAAlMhms+mXLTorNeeaLudmKbxarVL7XaczLqtR1RrqWCdc7Ws35s4DAEC5wog2VDpJGWk6n5Ve4jbtajXS05H3qllInTKqCgAAoGLzs/tockxftavdSMYYOYzx+O/YnnJcr+39t3amnpQkmmwAgHKHRhsqFYcx+nNinP59+oDbbQodDgX6+qlLvaZlWBkAAEDFZ7PZZIzRkiPx+tepbz26741nDmnR4W/0s9Bm6lS3iUf3DQCAp9BoQ6Vit9nUvX4L7b6YpMz83Fuezy7I0+92/VP7LyVbUB0AAEDFZ7PZVDewmj4/fcAj11TGGP3z5H59dHy3+jeK1hMtu8rHxscYAED5xDsUKp17Q5vLYYy+uXDilue+SD6o9PycUp1XBAAAwNsNbHSP2tdupL9+v00p2SVP2XE7DhmdzrysRyLa69GmHZk/FwBQrtFoQ6UT4l9FHes01tZzR2RumDvkal62Npw5pD4No1i5CgAA4C7YbDY91epeVfevogUHtyq3sOBH76PQ4dDZzKvysdn1bOueGtj4nlKoFAAAz6LRhkqpd1ik2tZqqHxHofOxz059K3+7jwY0am1hZQAAAN4h0NdPE6J7qk2thvL9kbd65hUWaH7iFr2VsEG5hQUsegAAqDB8rS4AsELzkLpqHlLX+bPDGF3Oy9agxm0U5OtvYWUAAADeI6xqdT3atIMk6VpejoL9q9z2NVkFeXr3u81KykjTs617KsCHjywAgIqDdy1UWtkFefr70Z06nXFZqTnXFBoYolr1qlpdFgAAgNc5lp6qOQkb9WKb3mpVPdTtdul52Zp7YJPScjP1UkyfYl+MAgBQEXDrKCqtfZeStSP1pM5lX1WBcehs1hUtPBSnPReTrC4NAADAq0QE11ZEcB29l/iVruRmud3uWn6OCo1DU9v2o8kGAKiQaLSh0lqfnFjsZyPJJml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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(15, 7)\n",
        "original_ts.plot.line(\n",
        "    ax=ax, x='time',\n",
        "    marker='^', color='#66c2a4', linestyle='--', linewidth=1, markersize=2)\n",
        "smooth_ts.plot.line(\n",
        "    ax=ax, x='time',\n",
        "    marker='o', color='#238b45', linestyle='-', linewidth=1, markersize=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "aim3uBoq66C0",
        "outputId": "e7bf42d5-d93d-4afd-c0ce-7715271e67bd"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = smooth_ts.to_pandas()\n",
        "df"
      ],
      "metadata": {
        "id": "eFLOQqpS4115"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_filename = 'smoothed_time_series.csv'\n",
        "output_filepath = os.path.join(output_folder, output_filename)\n",
        "df.reset_index().to_csv(output_filepath, index=False)"
      ],
      "metadata": {
        "id": "21tHUORk5HPW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download Smoothed Time-Series Images"
      ],
      "metadata": {
        "id": "tytabV-N5f-O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save the original time-series images using `rioxarray` as GeoTIFF files."
      ],
      "metadata": {
        "id": "ehxlKsUY780n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for time in original_time_series.time.values:\n",
        "  image = original_time_series.sel(time=time)\n",
        "  # transform the image to suit rioxarray format\n",
        "  image = image \\\n",
        "    .rename({'Y': 'y', 'X': 'x'}) \\\n",
        "    .transpose('y', 'x') \\\n",
        "    .rio.write_crs('EPSG:4326')\n",
        "\n",
        "  date = np.datetime_as_string(time, unit='D')\n",
        "  output_file = f'original_{date}.tif'\n",
        "  output_path = os.path.join(output_folder, output_file)\n",
        "  image.rio.to_raster(output_path, driver='COG')"
      ],
      "metadata": {
        "id": "Fik-Cya_5pWO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save the smoothed time-series images."
      ],
      "metadata": {
        "id": "R8NrAucw8Akr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for time in time_series_smooth.time.values:\n",
        "  image = time_series_smooth.sel(time=time)\n",
        "  # transform the image to suit rioxarray format\n",
        "  image = image \\\n",
        "    .rename({'Y': 'y', 'X': 'x'}) \\\n",
        "    .transpose('y', 'x') \\\n",
        "    .rio.write_crs('EPSG:4326')\n",
        "\n",
        "  date = np.datetime_as_string(time, unit='D')\n",
        "  output_file = f'smoothed_{date}.tif'\n",
        "  output_path = os.path.join(output_folder, output_file)\n",
        "  image.rio.to_raster(output_path, driver='COG')\n"
      ],
      "metadata": {
        "id": "32072E2AyCfU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}