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      "source": [
        "--------\n",
        "\n",
        "# Clustering to find homogenous areas\n",
        "\n",
        "--------\n",
        "\n",
        "**Short description**\n",
        "\n",
        "This notebook performs clustering analysis on Sentinel-2 satellite data, utilizing the B02, B03 and B04 bands to identify and group areas with similar spectral characteristics for further analysis.\n",
        "\n",
        "In this notebook, you will search for, select, and obtain Sentinel-2 data for one day over a neighborhood in Barcelona, Spain. The selected data will be cloud-free to ensure accurate analysis of the study area. Specific bands, such as the  B02, B03 and B04 bands will be calculated and obtained over the region of interest. A clustering analysis will be performed on these bands to group areas with similar spectral characteristics, enabling a deeper understanding of the landscape patterns. This example demonstrates the application of clustering techniques on Sentinel-2 data to identify and visualize distinct land cover types.\n",
        "\n",
        "--------"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "050a2fc5",
      "metadata": {
        "id": "050a2fc5"
      },
      "source": [
        "### 1 - Import spacesense object(s) and other dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "ad7ca797",
      "metadata": {
        "colab": {
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        },
        "id": "ad7ca797",
        "outputId": "9a66ec72-3e06-4a89-e724-49ecc2c016e0"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your api key : ··········\n"
          ]
        }
      ],
      "source": [
        "from spacesense import Client, geoutils\n",
        "import datetime\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import json\n",
        "from skimage import exposure\n",
        "\n",
        "if \"SS_API_KEY\" not in os.environ:\n",
        "    from getpass import getpass\n",
        "    api_key = getpass('Enter your api key : ')\n",
        "    os.environ[\"SS_API_KEY\"] = api_key"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "836b0384",
      "metadata": {
        "id": "836b0384"
      },
      "source": [
        "### 2 - Define AOI and output options"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "4T3CqExepZMR",
      "metadata": {
        "id": "4T3CqExepZMR"
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      "outputs": [],
      "source": [
        "# A neighborhood of Barcelona\n",
        "aoi = {\n",
        "  \"type\": \"FeatureCollection\",\n",
        "  \"features\": [\n",
        "    {\n",
        "      \"type\": \"Feature\",\n",
        "      \"properties\": {},\n",
        "      \"geometry\": {\n",
        "        \"coordinates\": [\n",
        "          [\n",
        "            [\n",
        "              2.1719121924506055,\n",
        "              41.39760043017927\n",
        "            ],\n",
        "            [\n",
        "              2.1647389059867805,\n",
        "              41.39223018500084\n",
        "            ],\n",
        "            [\n",
        "              2.1682818096665244,\n",
        "              41.389200339725676\n",
        "            ],\n",
        "            [\n",
        "              2.175746693142031,\n",
        "              41.394800520638\n",
        "            ],\n",
        "            [\n",
        "              2.1719121924506055,\n",
        "              41.39760043017927\n",
        "            ]\n",
        "          ]\n",
        "        ],\n",
        "        \"type\": \"Polygon\"\n",
        "      }\n",
        "    }\n",
        "  ]\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "c05d9b03",
      "metadata": {
        "id": "c05d9b03"
      },
      "outputs": [],
      "source": [
        "# Define the TOI\n",
        "start_date = \"2021-06-16\"\n",
        "end_date = \"2021-06-16\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "05b56506",
      "metadata": {
        "id": "05b56506"
      },
      "outputs": [],
      "source": [
        "client = Client(id=\"cluster_zones\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "9b19c9d4",
      "metadata": {
        "id": "9b19c9d4"
      },
      "source": [
        "### 3 - Search S2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "2d70f6c8",
      "metadata": {
        "colab": {
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          "height": 179
        },
        "id": "2d70f6c8",
        "outputId": "3f0754af-a0bc-47f9-e636-ba83c2dafd6b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:spacesense.core:start_date and end_date are the same, adding 1 day to end_date\n"
          ]
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              "                         id       date   tile  valid_pixel_percentage  \\\n",
              "0  S2B_31TDF_20210616_0_L2A 2021-06-16  31TDF                   99.91   \n",
              "\n",
              "      platform relative_orbit_number  \\\n",
              "0  sentinel-2b                   008   \n",
              "\n",
              "                                          product_id              datetime  \\\n",
              "0  S2B_MSIL2A_20210616T103629_N0300_R008_T31TDF_2...  2021-06-16T10:49:42Z   \n",
              "\n",
              "   swath_coverage_percentage  no_data  cloud_shadows  vegetation  \\\n",
              "0                      100.0      0.0            0.0        1.83   \n",
              "\n",
              "   not_vegetated  water  cloud_medium_probability  cloud_high_probability  \\\n",
              "0          98.08    0.0                       0.0                    0.09   \n",
              "\n",
              "   thin_cirrus  snow  \n",
              "0          0.0   0.0  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "s2_search_result = client.s2_search(aoi=aoi, start_date=start_date, end_date=end_date, query_filters={\"valid_pixel_percentage\": {\">=\": 99}})\n",
        "s2_search_result.dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9841bf35",
      "metadata": {
        "id": "9841bf35"
      },
      "outputs": [],
      "source": [
        "#We remove duplicate dates\n",
        "s2_search_result.filter_duplicate_dates()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "67486634",
      "metadata": {
        "id": "67486634"
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      "source": [
        "### 4 - Specify bands\n",
        "\n",
        "Only selecting bands from S2 that we are interested in. In this urban example, we choose the RGB bands (2,3 and 4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "7165bb33",
      "metadata": {
        "id": "7165bb33"
      },
      "outputs": [],
      "source": [
        "s2_search_result.output_bands = [\"B02\",\"B03\",\"B04\"]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "d71f0f60",
      "metadata": {
        "id": "d71f0f60"
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      "source": [
        "### 5 - Obtain S2 data through Fuse function"
      ]
    },
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              "  content: '▼';\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label > span {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-summary,\n",
              ".xr-section-inline-details {\n",
              "  padding-top: 4px;\n",
              "  padding-bottom: 4px;\n",
              "}\n",
              "\n",
              ".xr-section-inline-details {\n",
              "  grid-column: 2 / -1;\n",
              "}\n",
              "\n",
              ".xr-section-details {\n",
              "  display: none;\n",
              "  grid-column: 1 / -1;\n",
              "  margin-bottom: 5px;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: '(';\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: ')';\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: ',';\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
              "Dimensions:  (time: 1, y: 94, x: 93)\n",
              "Coordinates:\n",
              "  * time     (time) datetime64[ns] 2021-06-16\n",
              "  * y        (y) float32 41.4 41.4 41.4 41.4 41.4 ... 41.39 41.39 41.39 41.39\n",
              "  * x        (x) float32 2.165 2.165 2.165 2.165 ... 2.175 2.175 2.176 2.176\n",
              "Data variables:\n",
              "    S2_B02   (time, y, x) float32 ...\n",
              "    S2_B03   (time, y, x) float32 ...\n",
              "    S2_B04   (time, y, x) float32 ...\n",
              "Attributes:\n",
              "    transform:        [ 1.18459751e-04  0.00000000e+00  2.16473355e+00  0.000...\n",
              "    crs:              +init=epsg:4326\n",
              "    res:              [1.18459751e-04 9.09349018e-05]\n",
              "    descriptions:     [&#x27;B02&#x27;, &#x27;B03&#x27;, &#x27;B04&#x27;]\n",
              "    AREA_OR_POINT:    Area\n",
              "    _FillValue:       nan\n",
              "    s2_data_lineage:  {&quot;Data origin&quot;: &quot;S3 bucket (ARN=arn:aws:s3:::sentinel-c...\n",
              "    ulx, uly:         [ 2.16473355 41.39765899]</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-c9f10c49-5443-4347-a36d-97a17c252261' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c9f10c49-5443-4347-a36d-97a17c252261' 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>: 1</li><li><span class='xr-has-index'>y</span>: 94</li><li><span class='xr-has-index'>x</span>: 93</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9edee35b-96c4-404d-a786-68d9c5cf2bf2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9edee35b-96c4-404d-a786-68d9c5cf2bf2' 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'>2021-06-16</div><input id='attrs-3f8d6d48-cd53-4342-a0d7-371fe62ff855' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f8d6d48-cd53-4342-a0d7-371fe62ff855' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5830148e-aead-4a35-bdb9-2e784cb589f2' class='xr-var-data-in' type='checkbox'><label for='data-5830148e-aead-4a35-bdb9-2e784cb589f2' 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;2021-06-16T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</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'>41.4 41.4 41.4 ... 41.39 41.39</div><input id='attrs-2a451eff-1318-4fd2-8d1b-a79d5906d84e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a451eff-1318-4fd2-8d1b-a79d5906d84e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27a37cff-1b0c-4f15-9a0b-aa33761d4dc9' class='xr-var-data-in' type='checkbox'><label for='data-27a37cff-1b0c-4f15-9a0b-aa33761d4dc9' 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'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([41.397614, 41.397522, 41.39743 , 41.397343, 41.39725 , 41.39716 ,\n",
              "       41.397068, 41.396976, 41.396885, 41.396797, 41.396706, 41.396614,\n",
              "       41.396523, 41.39643 , 41.39634 , 41.39625 , 41.39616 , 41.39607 ,\n",
              "       41.395977, 41.395885, 41.395794, 41.395706, 41.395615, 41.395523,\n",
              "       41.39543 , 41.39534 , 41.39525 , 41.395157, 41.39507 , 41.394978,\n",
              "       41.394886, 41.394794, 41.394703, 41.39461 , 41.394524, 41.394432,\n",
              "       41.39434 , 41.39425 , 41.394157, 41.394066, 41.39398 , 41.393887,\n",
              "       41.393795, 41.393703, 41.393612, 41.39352 , 41.393433, 41.39334 ,\n",
              "       41.39325 , 41.393158, 41.393066, 41.392975, 41.392887, 41.392796,\n",
              "       41.392704, 41.392612, 41.39252 , 41.39243 , 41.392338, 41.39225 ,\n",
              "       41.39216 , 41.392067, 41.391975, 41.391884, 41.391792, 41.391705,\n",
              "       41.391613, 41.39152 , 41.39143 , 41.39134 , 41.391247, 41.39116 ,\n",
              "       41.391068, 41.390976, 41.390884, 41.390793, 41.3907  , 41.390614,\n",
              "       41.390522, 41.39043 , 41.39034 , 41.390247, 41.390156, 41.390068,\n",
              "       41.389977, 41.389885, 41.389793, 41.3897  , 41.38961 , 41.389523,\n",
              "       41.38943 , 41.38934 , 41.389248, 41.389156], dtype=float32)</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'>2.165 2.165 2.165 ... 2.176 2.176</div><input id='attrs-b59e1773-5e5b-4311-9ea7-4f6b20ca2424' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b59e1773-5e5b-4311-9ea7-4f6b20ca2424' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d92e16dc-1b09-4fae-8c74-aaecfeec3787' class='xr-var-data-in' type='checkbox'><label for='data-d92e16dc-1b09-4fae-8c74-aaecfeec3787' 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'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([2.164793, 2.164911, 2.16503 , 2.165148, 2.165267, 2.165385, 2.165504,\n",
              "       2.165622, 2.16574 , 2.165859, 2.165977, 2.166096, 2.166214, 2.166333,\n",
              "       2.166451, 2.16657 , 2.166688, 2.166806, 2.166925, 2.167043, 2.167162,\n",
              "       2.16728 , 2.167399, 2.167517, 2.167636, 2.167754, 2.167873, 2.167991,\n",
              "       2.16811 , 2.168228, 2.168346, 2.168465, 2.168583, 2.168702, 2.16882 ,\n",
              "       2.168939, 2.169057, 2.169176, 2.169294, 2.169413, 2.169531, 2.16965 ,\n",
              "       2.169768, 2.169887, 2.170005, 2.170123, 2.170242, 2.17036 , 2.170479,\n",
              "       2.170597, 2.170716, 2.170834, 2.170953, 2.171071, 2.17119 , 2.171308,\n",
              "       2.171427, 2.171545, 2.171663, 2.171782, 2.1719  , 2.172019, 2.172137,\n",
              "       2.172256, 2.172374, 2.172493, 2.172611, 2.172729, 2.172848, 2.172966,\n",
              "       2.173085, 2.173203, 2.173322, 2.17344 , 2.173559, 2.173677, 2.173796,\n",
              "       2.173914, 2.174033, 2.174151, 2.174269, 2.174388, 2.174506, 2.174625,\n",
              "       2.174743, 2.174862, 2.17498 , 2.175099, 2.175217, 2.175336, 2.175454,\n",
              "       2.175573, 2.175691], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93be17d9-77b5-497e-a4d2-580f02616046' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93be17d9-77b5-497e-a4d2-580f02616046' class='xr-section-summary' >Data variables: <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>S2_B02</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-38842d4f-a2b8-4725-945b-ef2db064f0ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38842d4f-a2b8-4725-945b-ef2db064f0ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bea37dbc-0778-4106-aead-00dba80355bc' class='xr-var-data-in' type='checkbox'><label for='data-bea37dbc-0778-4106-aead-00dba80355bc' 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'><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>transform :</span></dt><dd>[ 1.18459751e-04  0.00000000e+00  2.16473355e+00  0.00000000e+00\n",
              " -9.09349018e-05  4.13976590e+01]</dd><dt><span>crs :</span></dt><dd>+init=epsg:4326</dd><dt><span>res :</span></dt><dd>[1.18459751e-04 9.09349018e-05]</dd><dt><span>descriptions :</span></dt><dd></dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>color_description :</span></dt><dd>BLUE</dd><dt><span>band_wavelength :</span></dt><dd>490 nm</dd><dt><span>scene_id :</span></dt><dd>S2B_31TDF_20210616_0_L2A</dd><dt><span>scene_classification :</span></dt><dd>{&quot;not_vegetated&quot;: 98.08, &quot;saturated_or_defective&quot;: 0.0, &quot;snow&quot;: 0.0, &quot;no_data&quot;: 0.0, &quot;thin_cirrus&quot;: 0.0, &quot;cloud_medium_probability&quot;: 0.0, &quot;water&quot;: 0.0, &quot;dark_area_pixels&quot;: 0.0, &quot;cloud_high_probability&quot;: 0.09, &quot;vegetation&quot;: 1.83, &quot;cloud_shadows&quot;: 0.0, &quot;unclassified&quot;: 0.0}</dd></dl></div><div class='xr-var-data'><pre>[8742 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>S2_B03</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-74114ae5-5a4d-4934-bb0c-a6bfb1465ce4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74114ae5-5a4d-4934-bb0c-a6bfb1465ce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e024de8-5f3e-480d-8dd3-dc5b0cacba7d' class='xr-var-data-in' type='checkbox'><label for='data-3e024de8-5f3e-480d-8dd3-dc5b0cacba7d' 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'><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>transform :</span></dt><dd>[ 1.18459751e-04  0.00000000e+00  2.16473355e+00  0.00000000e+00\n",
              " -9.09349018e-05  4.13976590e+01]</dd><dt><span>crs :</span></dt><dd>+init=epsg:4326</dd><dt><span>res :</span></dt><dd>[1.18459751e-04 9.09349018e-05]</dd><dt><span>descriptions :</span></dt><dd></dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>color_description :</span></dt><dd>GREEN</dd><dt><span>band_wavelength :</span></dt><dd>560 nm</dd><dt><span>scene_id :</span></dt><dd>S2B_31TDF_20210616_0_L2A</dd><dt><span>scene_classification :</span></dt><dd>{&quot;not_vegetated&quot;: 98.08, &quot;saturated_or_defective&quot;: 0.0, &quot;snow&quot;: 0.0, &quot;no_data&quot;: 0.0, &quot;thin_cirrus&quot;: 0.0, &quot;cloud_medium_probability&quot;: 0.0, &quot;water&quot;: 0.0, &quot;dark_area_pixels&quot;: 0.0, &quot;cloud_high_probability&quot;: 0.09, &quot;vegetation&quot;: 1.83, &quot;cloud_shadows&quot;: 0.0, &quot;unclassified&quot;: 0.0}</dd></dl></div><div class='xr-var-data'><pre>[8742 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>S2_B04</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0478520f-4443-47ed-b5b7-0ad0611f43be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0478520f-4443-47ed-b5b7-0ad0611f43be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ceb513f-ad05-423b-ac31-40ef045ecaeb' class='xr-var-data-in' type='checkbox'><label for='data-7ceb513f-ad05-423b-ac31-40ef045ecaeb' 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'><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>transform :</span></dt><dd>[ 1.18459751e-04  0.00000000e+00  2.16473355e+00  0.00000000e+00\n",
              " -9.09349018e-05  4.13976590e+01]</dd><dt><span>crs :</span></dt><dd>+init=epsg:4326</dd><dt><span>res :</span></dt><dd>[1.18459751e-04 9.09349018e-05]</dd><dt><span>descriptions :</span></dt><dd></dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>color_description :</span></dt><dd>RED</dd><dt><span>band_wavelength :</span></dt><dd>665 nm</dd><dt><span>scene_id :</span></dt><dd>S2B_31TDF_20210616_0_L2A</dd><dt><span>scene_classification :</span></dt><dd>{&quot;not_vegetated&quot;: 98.08, &quot;saturated_or_defective&quot;: 0.0, &quot;snow&quot;: 0.0, &quot;no_data&quot;: 0.0, &quot;thin_cirrus&quot;: 0.0, &quot;cloud_medium_probability&quot;: 0.0, &quot;water&quot;: 0.0, &quot;dark_area_pixels&quot;: 0.0, &quot;cloud_high_probability&quot;: 0.09, &quot;vegetation&quot;: 1.83, &quot;cloud_shadows&quot;: 0.0, &quot;unclassified&quot;: 0.0}</dd></dl></div><div class='xr-var-data'><pre>[8742 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f47b9498-bc59-452a-824c-82633dc38b7a' class='xr-section-summary-in' type='checkbox'  ><label for='section-f47b9498-bc59-452a-824c-82633dc38b7a' 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-432b8cb4-e98b-4fb9-8f96-eb84403dccc1' class='xr-index-data-in' type='checkbox'/><label for='index-432b8cb4-e98b-4fb9-8f96-eb84403dccc1' 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;2021-06-16&#x27;], 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>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-86447845-1ad9-46aa-98da-929072de0b72' class='xr-index-data-in' type='checkbox'/><label for='index-86447845-1ad9-46aa-98da-929072de0b72' 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([41.397613525390625,  41.39752197265625, 41.397430419921875,\n",
              "              41.397342681884766,  41.39725112915039, 41.397159576416016,\n",
              "               41.39706802368164, 41.396976470947266,  41.39688491821289,\n",
              "               41.39679718017578, 41.396705627441406,  41.39661407470703,\n",
              "              41.396522521972656,  41.39643096923828, 41.396339416503906,\n",
              "                41.3962516784668,  41.39616012573242,  41.39606857299805,\n",
              "               41.39597702026367,   41.3958854675293,  41.39579391479492,\n",
              "               41.39570617675781,  41.39561462402344,  41.39552307128906,\n",
              "               41.39543151855469,  41.39533996582031,  41.39524841308594,\n",
              "               41.39515686035156,  41.39506912231445,  41.39497756958008,\n",
              "                41.3948860168457,  41.39479446411133,  41.39470291137695,\n",
              "               41.39461135864258,  41.39452362060547, 41.394432067871094,\n",
              "               41.39434051513672, 41.394248962402344,  41.39415740966797,\n",
              "              41.394065856933594, 41.393978118896484,  41.39388656616211,\n",
              "              41.393795013427734,  41.39370346069336, 41.393611907958984,\n",
              "               41.39352035522461,   41.3934326171875, 41.393341064453125,\n",
              "               41.39324951171875, 41.393157958984375,     41.39306640625,\n",
              "              41.392974853515625, 41.392887115478516,  41.39279556274414,\n",
              "              41.392704010009766,  41.39261245727539, 41.392520904541016,\n",
              "               41.39242935180664, 41.392337799072266, 41.392250061035156,\n",
              "               41.39215850830078, 41.392066955566406,  41.39197540283203,\n",
              "              41.391883850097656,  41.39179229736328,  41.39170455932617,\n",
              "                41.3916130065918,  41.39152145385742,  41.39142990112305,\n",
              "               41.39133834838867,   41.3912467956543,  41.39115905761719,\n",
              "               41.39106750488281,  41.39097595214844,  41.39088439941406,\n",
              "               41.39079284667969,  41.39070129394531,   41.3906135559082,\n",
              "               41.39052200317383,  41.39043045043945,  41.39033889770508,\n",
              "                41.3902473449707,  41.39015579223633,  41.39006805419922,\n",
              "              41.389976501464844,  41.38988494873047, 41.389793395996094,\n",
              "               41.38970184326172, 41.389610290527344, 41.389522552490234,\n",
              "               41.38943099975586, 41.389339447021484,  41.38924789428711,\n",
              "              41.389156341552734],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</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-dc990ca7-4df1-4c58-a973-0c0ef68a63e3' class='xr-index-data-in' type='checkbox'/><label for='index-dc990ca7-4df1-4c58-a973-0c0ef68a63e3' 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([ 2.164792776107788, 2.1649112701416016,  2.165029525756836,\n",
              "              2.1651480197906494,  2.165266513824463, 2.1653850078582764,\n",
              "                2.16550350189209, 2.1656219959259033,  2.165740489959717,\n",
              "               2.165858745574951, 2.1659772396087646,  2.166095733642578,\n",
              "              2.1662142276763916,  2.166332721710205, 2.1664512157440186,\n",
              "               2.166569709777832, 2.1666879653930664,   2.16680645942688,\n",
              "              2.1669249534606934,  2.167043447494507, 2.1671619415283203,\n",
              "               2.167280435562134, 2.1673989295959473, 2.1675171852111816,\n",
              "               2.167635679244995, 2.1677541732788086,  2.167872667312622,\n",
              "              2.1679911613464355,  2.168109655380249, 2.1682281494140625,\n",
              "               2.168346405029297, 2.1684648990631104,  2.168583393096924,\n",
              "              2.1687018871307373,  2.168820381164551, 2.1689388751983643,\n",
              "              2.1690573692321777,  2.169175624847412, 2.1692941188812256,\n",
              "               2.169412612915039, 2.1695311069488525,  2.169649600982666,\n",
              "              2.1697680950164795,  2.169886589050293, 2.1700048446655273,\n",
              "               2.170123338699341, 2.1702418327331543, 2.1703603267669678,\n",
              "              2.1704788208007812, 2.1705973148345947,  2.170715808868408,\n",
              "              2.1708340644836426,  2.170952558517456, 2.1710710525512695,\n",
              "               2.171189546585083, 2.1713080406188965,   2.17142653465271,\n",
              "              2.1715450286865234,  2.171663284301758, 2.1717817783355713,\n",
              "              2.1719002723693848, 2.1720187664031982, 2.1721372604370117,\n",
              "               2.172255754470825, 2.1723742485046387,  2.172492504119873,\n",
              "              2.1726109981536865,    2.1727294921875, 2.1728479862213135,\n",
              "               2.172966480255127, 2.1730849742889404,  2.173203468322754,\n",
              "              2.1733217239379883, 2.1734402179718018, 2.1735587120056152,\n",
              "              2.1736772060394287,  2.173795700073242, 2.1739141941070557,\n",
              "               2.174032688140869, 2.1741509437561035,  2.174269437789917,\n",
              "              2.1743879318237305,  2.174506425857544, 2.1746249198913574,\n",
              "               2.174743413925171, 2.1748619079589844, 2.1749801635742188,\n",
              "              2.1750986576080322, 2.1752171516418457,  2.175335645675659,\n",
              "              2.1754541397094727,  2.175572633743286, 2.1756911277770996],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e0108f9-d74d-4ba1-b6ee-62aa72277493' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e0108f9-d74d-4ba1-b6ee-62aa72277493' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>[ 1.18459751e-04  0.00000000e+00  2.16473355e+00  0.00000000e+00\n",
              " -9.09349018e-05  4.13976590e+01]</dd><dt><span>crs :</span></dt><dd>+init=epsg:4326</dd><dt><span>res :</span></dt><dd>[1.18459751e-04 9.09349018e-05]</dd><dt><span>descriptions :</span></dt><dd>[&#x27;B02&#x27;, &#x27;B03&#x27;, &#x27;B04&#x27;]</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>s2_data_lineage :</span></dt><dd>{&quot;Data origin&quot;: &quot;S3 bucket (ARN=arn:aws:s3:::sentinel-cogs-inventory)&quot;}</dd><dt><span>ulx, uly :</span></dt><dd>[ 2.16473355 41.39765899]</dd></dl></div></li></ul></div></div>"
            ],
            "text/plain": [
              "<xarray.Dataset>\n",
              "Dimensions:  (time: 1, y: 94, x: 93)\n",
              "Coordinates:\n",
              "  * time     (time) datetime64[ns] 2021-06-16\n",
              "  * y        (y) float32 41.4 41.4 41.4 41.4 41.4 ... 41.39 41.39 41.39 41.39\n",
              "  * x        (x) float32 2.165 2.165 2.165 2.165 ... 2.175 2.175 2.176 2.176\n",
              "Data variables:\n",
              "    S2_B02   (time, y, x) float32 ...\n",
              "    S2_B03   (time, y, x) float32 ...\n",
              "    S2_B04   (time, y, x) float32 ...\n",
              "Attributes:\n",
              "    transform:        [ 1.18459751e-04  0.00000000e+00  2.16473355e+00  0.000...\n",
              "    crs:              +init=epsg:4326\n",
              "    res:              [1.18459751e-04 9.09349018e-05]\n",
              "    descriptions:     ['B02', 'B03', 'B04']\n",
              "    AREA_OR_POINT:    Area\n",
              "    _FillValue:       nan\n",
              "    s2_data_lineage:  {\"Data origin\": \"S3 bucket (ARN=arn:aws:s3:::sentinel-c...\n",
              "    ulx, uly:         [ 2.16473355 41.39765899]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fuse_result = client.fuse(\n",
        "        catalogs_list=[s2_search_result]\n",
        "    )\n",
        "fuse_result.dataset"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "040c7be8",
      "metadata": {
        "id": "040c7be8"
      },
      "source": [
        "### 6 - Look at the RGB image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "e8d44230",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "e8d44230",
        "outputId": "5895aaf5-307a-4c66-e507-ba02b1099780"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fuse_result.plot_rgb(all_dates = True, brightness_factor = 2)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "55292f3a",
      "metadata": {
        "id": "55292f3a"
      },
      "source": [
        "### 7 - Cluster S2 data with the **cluster** function\n",
        "\n",
        "In this example, we only use a single image. However, the Cluster function can also accept multi-temporal datasets to perform its K-mean clustering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "0a738341",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0a738341",
        "outputId": "d994ccf5-c567-4c12-fc18-6785d0860143",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Select the number of clustering classes you want\n",
        "n_clusters = 3\n",
        "# What variables are to be taken into account in the clustering?\n",
        "variables_to_use = [ \"S2_B02\", \"S2_B03\", \"S2_B04\"]\n",
        "# In this example we take all the dates from fuse_result.dataset, but you can select a single date as well)\n",
        "clustered_dataset_RGB = geoutils.cluster(fuse_result.dataset, n_clusters, variables_to_use)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "16efb23c",
      "metadata": {
        "id": "16efb23c"
      },
      "source": [
        "### 8 - Plot the clustered function using the util function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "MP6KJW9bGmYw",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "MP6KJW9bGmYw",
        "outputId": "232b04d6-6b6e-43fc-b97a-5ddb2ea487e8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "geoutils.plot_clustered_dataset(clustered_dataset_RGB, n_clusters)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}