coverage-thredds/coverage-thredds.ipynb

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   "cell_type": "markdown",
   "id": "7b1194f4",
   "metadata": {},
   "source": [
    "# COVERAGE THREDDS Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbfe5a6d",
   "metadata": {},
   "source": [
    "The following examples demonstrate how to access datasets via the COVERAGE THREDDS Data Server."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f21ff3",
   "metadata": {},
   "source": [
    "### THREDDS NCSS Data Subsetting and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55952e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import metpy\n",
    "import xarray as xr\n",
    "from siphon.catalog import TDSCatalog\n",
    "from datetime import datetime\n",
    "from xarray.backends import NetCDF4DataStore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc7c872d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CHLA_analysis', 'analysis_error', 'dt_data', 'mask'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up access via NCSS\n",
    "dataset_catalog_url = ('https://coverage.ceos.org/thredds/catalog/TDS_aws.xml'\n",
    "                       '?dataset=JPL-L4-MRVA-CHLA-GLOB-v3.0')\n",
    "dataset_catalog = TDSCatalog(dataset_catalog_url)\n",
    "dataset = dataset_catalog.datasets[0].subset()\n",
    "dataset.variables # show variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e11ce265",
   "metadata": {},
   "outputs": [
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       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF3_CLASSIC data model, file format NETCDF3):\n",
       "    Conventions: CF-1.7, ACDD-1.3\n",
       "    standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata Convention\n",
       "    title: Daily global Chlorophyll-A map by MRVA\n",
       "    summary: A 0.25-degree resolution JPL CHL-A analysis, a merged, multi-sensor, L4 CHL-A analysis production from JPL based on the MRVA algorithm\n",
       "    keywords: Oceans > Ocean Optics > Ocean Color\n",
       "    keywords_vocabulary: NASA Global Change Master Directory (GCMD) Science Keywords\n",
       "    history: created at nominal 4-day latency\n",
       "    processing_level: L4\n",
       "    cdm_data_type: grid\n",
       "    creator_name: NASA COVERAGE project\n",
       "    creator_email: coverage@jpl.nasa.gov\n",
       "    creator_url: http://coverage.jpl.nasa.gov\n",
       "    creator_institution: Jet Propulsion Laboratory\n",
       "    institution: Jet Propulsion Laboratory\n",
       "    project: NASA COVERAGE\n",
       "    program: NASA Physical Oceanography Program and NASA Earth Science Data Information and System (ESDIS)\n",
       "    geospatial_lat_units: degrees north\n",
       "    geospatial_lon_units: degrees east\n",
       "    time_coverage_resolution: P1D\n",
       "    license: These data are available free of charge under the NASA ESDIS data policy.\n",
       "    id: CHL25-MRVA-JPL-L4-GLOB-v02\n",
       "    uuid: 27665bc0-d5fc-11e1-9b23-0800200c9a66\n",
       "    comment: MRVA=Multi-Resolution Variational Analysis (interpolation algorithm)\n",
       "    naming_authority: gov.nasa.earthdata\n",
       "    netcdf_version_id: 04.2\n",
       "    publisher_name: NASA COVERAGE Project\n",
       "    publisher_url: http://www.coverage-project.org\n",
       "    publisher_email: coverage@jpl.nasa.gov\n",
       "    acknowledgment: Please acknowledge the use of these data with the following statement:  These data were provided by JPL under support by NASA to the COVERAGE project.\n",
       "    product_version: 8.0.0.783 (R2012b)\n",
       "    geospatial_lat_min: 10.0\n",
       "    geospatial_lat_max: 50.25\n",
       "    geospatial_lon_min: -110.0\n",
       "    geospatial_lon_max: -44.75\n",
       "    geospatial_lat_resolution: 0.25\n",
       "    geospatial_lon_resolution: 0.25\n",
       "    date_created: 20220223T072924Z\n",
       "    start_time: 20220223T120000Z\n",
       "    stop_time: 20220223T120000Z\n",
       "    time_coverage_start: 20220223T000000Z\n",
       "    time_coverage_end: 20220223T235959Z\n",
       "    History: Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridCoverageWriter)\n",
       "Original Dataset = JPL-L4-MRVA-CHLA-GLOB-v3.0; Translation Date = 2022-02-25T10:02:11.243Z\n",
       "    dimensions(sizes): lon(261), time(2), lat(161)\n",
       "    variables(dimensions): float32 lon(lon), float64 time(time), float32 lat(lat), float32 CHLA_analysis(time, lat, lon)\n",
       "    groups: "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new NCSS dataset query\n",
    "query = dataset.query()\n",
    "\n",
    "# Request data in netCDF format\n",
    "query.accept('netcdf')\n",
    "\n",
    "# Ask for our variable\n",
    "query.variables('CHLA_analysis')\n",
    "\n",
    "# Set the time range of data we want\n",
    "start = datetime(2021, 8, 1)\n",
    "end = datetime(2021, 8, 2)\n",
    "query.time_range(start, end)\n",
    "\n",
    "# Set the spatial limits\n",
    "query.lonlat_box(west=-110, east=-45, north=50, south=10)\n",
    "\n",
    "# get the data!\n",
    "data = dataset.get_data(query)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87ed2953",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (lon: 261, time: 2, lat: 161)\n",
       "Coordinates:\n",
       "  * lon            (lon) float32 -109.9 -109.6 -109.4 ... -45.38 -45.12 -44.88\n",
       "  * time           (time) datetime64[ns] 2021-08-01 2021-08-02\n",
       "  * lat            (lat) float32 10.12 10.38 10.62 10.88 ... 49.62 49.88 50.12\n",
       "Data variables:\n",
       "    CHLA_analysis  (time, lat, lon) float32 0.09808 0.09937 ... 0.6078 0.4362\n",
       "Attributes: (12/42)\n",
       "    Conventions:                CF-1.7, ACDD-1.3\n",
       "    standard_name_vocabulary:   NetCDF Climate and Forecast (CF) Metadata Con...\n",
       "    title:                      Daily global Chlorophyll-A map by MRVA\n",
       "    summary:                    A 0.25-degree resolution JPL CHL-A analysis, ...\n",
       "    keywords:                   Oceans &gt; Ocean Optics &gt; Ocean Color\n",
       "    keywords_vocabulary:        NASA Global Change Master Directory (GCMD) Sc...\n",
       "    ...                         ...\n",
       "    date_created:               20220223T072924Z\n",
       "    start_time:                 20220223T120000Z\n",
       "    stop_time:                  20220223T120000Z\n",
       "    time_coverage_start:        20220223T000000Z\n",
       "    time_coverage_end:          20220223T235959Z\n",
       "    History:                    Translated to CF-1.0 Conventions by Netcdf-Ja...</pre><div class='xr-wrap' hidden><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-c6e16c8c-80a7-4cb9-9e95-7bf431147b36' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c6e16c8c-80a7-4cb9-9e95-7bf431147b36' 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'>lon</span>: 261</li><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>lat</span>: 161</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0a674aee-8895-4b7e-aa6f-610e0b119306' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0a674aee-8895-4b7e-aa6f-610e0b119306' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-109.9 -109.6 ... -45.12 -44.88</div><input id='attrs-ab846a7f-c47e-47c8-bf49-ba75c7568341' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab846a7f-c47e-47c8-bf49-ba75c7568341' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76dda783-551b-413c-9b1c-50b245cf090a' class='xr-var-data-in' type='checkbox'><label for='data-76dda783-551b-413c-9b1c-50b245cf090a' 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>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>array([-109.875, -109.625, -109.375, ...,  -45.375,  -45.125,  -44.875],\n",
       "      dtype=float32)</pre></div></li><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'>2021-08-01 2021-08-02</div><input id='attrs-6d1229cd-2e20-4609-a3dd-66abc8fb4d77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d1229cd-2e20-4609-a3dd-66abc8fb4d77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f8e30dd-b4dc-459b-84a0-2ce503eb85f6' class='xr-var-data-in' type='checkbox'><label for='data-7f8e30dd-b4dc-459b-84a0-2ce503eb85f6' 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>long_name :</span></dt><dd>time coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-08-02T00:00:00.000000000&#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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.12 10.38 10.62 ... 49.88 50.12</div><input id='attrs-f92b89b6-9aac-4911-bf49-c6a61d8d0a15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f92b89b6-9aac-4911-bf49-c6a61d8d0a15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4466beb3-63eb-4177-8e0a-c6438fe56ccf' class='xr-var-data-in' type='checkbox'><label for='data-4466beb3-63eb-4177-8e0a-c6438fe56ccf' 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>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([10.125, 10.375, 10.625, 10.875, 11.125, 11.375, 11.625, 11.875, 12.125,\n",
       "       12.375, 12.625, 12.875, 13.125, 13.375, 13.625, 13.875, 14.125, 14.375,\n",
       "       14.625, 14.875, 15.125, 15.375, 15.625, 15.875, 16.125, 16.375, 16.625,\n",
       "       16.875, 17.125, 17.375, 17.625, 17.875, 18.125, 18.375, 18.625, 18.875,\n",
       "       19.125, 19.375, 19.625, 19.875, 20.125, 20.375, 20.625, 20.875, 21.125,\n",
       "       21.375, 21.625, 21.875, 22.125, 22.375, 22.625, 22.875, 23.125, 23.375,\n",
       "       23.625, 23.875, 24.125, 24.375, 24.625, 24.875, 25.125, 25.375, 25.625,\n",
       "       25.875, 26.125, 26.375, 26.625, 26.875, 27.125, 27.375, 27.625, 27.875,\n",
       "       28.125, 28.375, 28.625, 28.875, 29.125, 29.375, 29.625, 29.875, 30.125,\n",
       "       30.375, 30.625, 30.875, 31.125, 31.375, 31.625, 31.875, 32.125, 32.375,\n",
       "       32.625, 32.875, 33.125, 33.375, 33.625, 33.875, 34.125, 34.375, 34.625,\n",
       "       34.875, 35.125, 35.375, 35.625, 35.875, 36.125, 36.375, 36.625, 36.875,\n",
       "       37.125, 37.375, 37.625, 37.875, 38.125, 38.375, 38.625, 38.875, 39.125,\n",
       "       39.375, 39.625, 39.875, 40.125, 40.375, 40.625, 40.875, 41.125, 41.375,\n",
       "       41.625, 41.875, 42.125, 42.375, 42.625, 42.875, 43.125, 43.375, 43.625,\n",
       "       43.875, 44.125, 44.375, 44.625, 44.875, 45.125, 45.375, 45.625, 45.875,\n",
       "       46.125, 46.375, 46.625, 46.875, 47.125, 47.375, 47.625, 47.875, 48.125,\n",
       "       48.375, 48.625, 48.875, 49.125, 49.375, 49.625, 49.875, 50.125],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00bd1d31-26e4-4055-9b23-9227201822fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00bd1d31-26e4-4055-9b23-9227201822fa' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>CHLA_analysis</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf6b3590-9164-469c-a088-7c2e86db7399' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf6b3590-9164-469c-a088-7c2e86db7399' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-904ebb29-3b86-4682-bbf0-5842a1c49671' class='xr-var-data-in' type='checkbox'><label for='data-904ebb29-3b86-4682-bbf0-5842a1c49671' 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>long_name :</span></dt><dd>Chlorophyll-a concentration analysis</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_chlorophyll_a_in_sea_water</dd><dt><span>units :</span></dt><dd>milligram m-3</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1000.0</dd></dl></div><div class='xr-var-data'><pre>array([[[0.098078, 0.099368, ..., 0.120863, 0.121468],\n",
       "        [0.083269, 0.114325, ..., 0.120998, 0.114963],\n",
       "        ...,\n",
       "        [     nan,      nan, ..., 0.946905, 0.583918],\n",
       "        [     nan,      nan, ..., 0.609604, 0.431455]],\n",
       "\n",
       "       [[0.09686 , 0.100888, ..., 0.12304 , 0.123573],\n",
       "        [0.082947, 0.114337, ..., 0.121869, 0.116965],\n",
       "        ...,\n",
       "        [     nan,      nan, ..., 0.959906, 0.58214 ],\n",
       "        [     nan,      nan, ..., 0.607818, 0.436175]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-158a4b20-f61e-44e3-a0bb-54a6137de268' class='xr-section-summary-in' type='checkbox'  ><label for='section-158a4b20-f61e-44e3-a0bb-54a6137de268' class='xr-section-summary' >Attributes: <span>(42)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7, ACDD-1.3</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>title :</span></dt><dd>Daily global Chlorophyll-A map by MRVA</dd><dt><span>summary :</span></dt><dd>A 0.25-degree resolution JPL CHL-A analysis, a merged, multi-sensor, L4 CHL-A analysis production from JPL based on the MRVA algorithm</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Optics &gt; Ocean Color</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>history :</span></dt><dd>created at nominal 4-day latency</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>creator_name :</span></dt><dd>NASA COVERAGE project</dd><dt><span>creator_email :</span></dt><dd>coverage@jpl.nasa.gov</dd><dt><span>creator_url :</span></dt><dd>http://coverage.jpl.nasa.gov</dd><dt><span>creator_institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>project :</span></dt><dd>NASA COVERAGE</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography Program and NASA Earth Science Data Information and System (ESDIS)</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees east</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>license :</span></dt><dd>These data are available free of charge under the NASA ESDIS data policy.</dd><dt><span>id :</span></dt><dd>CHL25-MRVA-JPL-L4-GLOB-v02</dd><dt><span>uuid :</span></dt><dd>27665bc0-d5fc-11e1-9b23-0800200c9a66</dd><dt><span>comment :</span></dt><dd>MRVA=Multi-Resolution Variational Analysis (interpolation algorithm)</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.earthdata</dd><dt><span>netcdf_version_id :</span></dt><dd>04.2</dd><dt><span>publisher_name :</span></dt><dd>NASA COVERAGE Project</dd><dt><span>publisher_url :</span></dt><dd>http://www.coverage-project.org</dd><dt><span>publisher_email :</span></dt><dd>coverage@jpl.nasa.gov</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement:  These data were provided by JPL under support by NASA to the COVERAGE project.</dd><dt><span>product_version :</span></dt><dd>8.0.0.783 (R2012b)</dd><dt><span>geospatial_lat_min :</span></dt><dd>10.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>50.25</dd><dt><span>geospatial_lon_min :</span></dt><dd>-110.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>-44.75</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>date_created :</span></dt><dd>20220223T072924Z</dd><dt><span>start_time :</span></dt><dd>20220223T120000Z</dd><dt><span>stop_time :</span></dt><dd>20220223T120000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20220223T000000Z</dd><dt><span>time_coverage_end :</span></dt><dd>20220223T235959Z</dd><dt><span>History :</span></dt><dd>Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridCoverageWriter)\n",
       "Original Dataset = JPL-L4-MRVA-CHLA-GLOB-v3.0; Translation Date = 2022-02-25T10:02:11.243Z</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (lon: 261, time: 2, lat: 161)\n",
       "Coordinates:\n",
       "  * lon            (lon) float32 -109.9 -109.6 -109.4 ... -45.38 -45.12 -44.88\n",
       "  * time           (time) datetime64[ns] 2021-08-01 2021-08-02\n",
       "  * lat            (lat) float32 10.12 10.38 10.62 10.88 ... 49.62 49.88 50.12\n",
       "Data variables:\n",
       "    CHLA_analysis  (time, lat, lon) float32 ...\n",
       "Attributes: (12/42)\n",
       "    Conventions:                CF-1.7, ACDD-1.3\n",
       "    standard_name_vocabulary:   NetCDF Climate and Forecast (CF) Metadata Con...\n",
       "    title:                      Daily global Chlorophyll-A map by MRVA\n",
       "    summary:                    A 0.25-degree resolution JPL CHL-A analysis, ...\n",
       "    keywords:                   Oceans > Ocean Optics > Ocean Color\n",
       "    keywords_vocabulary:        NASA Global Change Master Directory (GCMD) Sc...\n",
       "    ...                         ...\n",
       "    date_created:               20220223T072924Z\n",
       "    start_time:                 20220223T120000Z\n",
       "    stop_time:                  20220223T120000Z\n",
       "    time_coverage_start:        20220223T000000Z\n",
       "    time_coverage_end:          20220223T235959Z\n",
       "    History:                    Translated to CF-1.0 Conventions by Netcdf-Ja..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open the data in xarray\n",
    "ds = xr.open_dataset(NetCDF4DataStore(data))\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b23a54b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1bb8ea970>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data\n",
    "ds.CHLA_analysis.isel(time=0).plot(cmap='gist_ncar', vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd10bfc",
   "metadata": {},
   "source": [
    "### Interactive Web Map with THREDDS WMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "38125800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d86fedc03f484f55998f507cd9faeeed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[30, -30], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_te…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipyleaflet import Map, WMSLayer, basemaps\n",
    "\n",
    "# Make a WMS connection to a map layer\n",
    "wms_layer = WMSLayer(url='https://coverage.ceos.org/thredds/wms/scan-aggregation/JPL-L4-MRVA-CHLA-GLOB-v3.0.nc',\n",
    "                     layers='CHLA_analysis',\n",
    "                     format='image/png',\n",
    "                     time='2018-01-01T00:00:00.000Z',\n",
    "                     styles='default-scalar/x-Rainbow',\n",
    "                     colorscalerange='0,1',\n",
    "                     transparent=True)\n",
    "\n",
    "# Define map properties and add the WMS layer from above on top of basemap\n",
    "m = Map(basemap=basemaps.NASAGIBS.BlueMarble, center=(30, -30), zoom=3)\n",
    "m.add_layer(wms_layer)\n",
    "\n",
    "# Display interactive web map\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f69a15",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}