Oceanomalies_chl_Hawaii.ipynb

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    "## Plotting Chl-a images and timeseries for a bloom north of Hawaii using PACE OCI data\n",
    "\n",
    "**Author**: Afonso Ferreira"
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    "# Import all the stuff!\n",
    "import earthaccess\n",
    "import xarray as xr\n",
    "from xarray.backends.api import open_datatree\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "import matplotlib as mpl\n",
    "from matplotlib.patches import Polygon\n",
    "from matplotlib.path import Path\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "import datetime\n",
    "#The function defined just creates a list of dates between two dates of choice (in this case)\n",
    "def date_range_list(start_date, end_date):\n",
    "    # Return list of datetime.date objects (inclusive) between start_date and end_date (inclusive).\n",
    "    date_list = []\n",
    "    curr_date = start_date\n",
    "    while curr_date <= end_date:\n",
    "        date_list.append(curr_date)\n",
    "        curr_date += datetime.timedelta(days=1)\n",
    "    return date_list"
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    "# Define our timespan and search the data\n",
    "tspan = (\"2024-07-07\", \"2024-08-5\")\n",
    "results = earthaccess.search_data(\n",
    "    short_name=\"PACE_OCI_L3M_CHL_NRT\",\n",
    "    temporal=tspan,\n",
    "    granule_name=\"*.DAY.*4km.*\",\n",
    ")"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4GB\n",
       "Dimensions:  (date: 30, lat: 4320, lon: 8640, rgb: 3, eightbitcolor: 256)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 17kB 89.98 89.94 89.9 89.85 ... -89.9 -89.94 -89.98\n",
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       "Attributes: (12/64)\n",
       "    product_name:                      PACE_OCI.20240707.L3m.DAY.CHL.V2_0.chl...\n",
       "    instrument:                        OCI\n",
       "    title:                             OCI Level-3 Standard Mapped Image\n",
       "    project:                           Ocean Biology Processing Group (NASA/G...\n",
       "    platform:                          PACE\n",
       "    source:                            satellite observations from OCI-PACE\n",
       "    ...                                ...\n",
       "    identifier_product_doi:            10.5067/PACE/OCI/L3M/CHL/2.0\n",
       "    keywords:                          Earth Science &gt; Oceans &gt; Ocean Chemist...\n",
       "    keywords_vocabulary:               NASA Global Change Master Directory (G...\n",
       "    data_bins:                         3310866\n",
       "    data_minimum:                      0.0009999999\n",
       "    data_maximum:                      98.42797</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-370c26c8-25b7-40eb-93a4-c1c1421564d3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-370c26c8-25b7-40eb-93a4-c1c1421564d3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>date</span>: 30</li><li><span class='xr-has-index'>lat</span>: 4320</li><li><span class='xr-has-index'>lon</span>: 8640</li><li><span>rgb</span>: 3</li><li><span>eightbitcolor</span>: 256</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d69b64a7-1b96-44f4-9fe0-3d3f744e9f29' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d69b64a7-1b96-44f4-9fe0-3d3f744e9f29' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.98 89.94 89.9 ... -89.94 -89.98</div><input id='attrs-fc9de7fc-bd10-4ca2-8738-675eb0dc9f1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc9de7fc-bd10-4ca2-8738-675eb0dc9f1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3c769ab-b75a-4baa-9eb9-fcbd67a570dd' class='xr-var-data-in' type='checkbox'><label for='data-d3c769ab-b75a-4baa-9eb9-fcbd67a570dd' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</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([ 89.979164,  89.9375  ,  89.895836, ..., -89.895836, -89.93751 ,\n",
       "       -89.97917 ], dtype=float32)</pre></div></li><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'>-180.0 -179.9 ... 179.9 180.0</div><input id='attrs-833aa1f1-d0e1-4ea8-88d7-48015d7b597f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-833aa1f1-d0e1-4ea8-88d7-48015d7b597f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ef72428-98a4-45f5-a892-558266a5930e' class='xr-var-data-in' type='checkbox'><label for='data-7ef72428-98a4-45f5-a892-558266a5930e' 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>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</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([-179.97917, -179.9375 , -179.89583, ...,  179.89584,  179.93752,\n",
       "        179.97917], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0365cbdb-e645-4ff4-b731-3cfcd45069f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0365cbdb-e645-4ff4-b731-3cfcd45069f9' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>chlor_a</span></div><div class='xr-var-dims'>(date, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 512, 1024), meta=np.ndarray&gt;</div><input id='attrs-40160992-bda0-44c3-a8fe-f8c120863c93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40160992-bda0-44c3-a8fe-f8c120863c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-154bb5b9-0c77-46dc-b86f-cd4a1455208c' class='xr-var-data-in' type='checkbox'><label for='data-154bb5b9-0c77-46dc-b86f-cd4a1455208c' 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 Concentration, OCI Algorithm</dd><dt><span>units :</span></dt><dd>mg m^-3</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_chlorophyll_in_sea_water</dd><dt><span>valid_min :</span></dt><dd>0.001</dd><dt><span>valid_max :</span></dt><dd>100.0</dd><dt><span>reference :</span></dt><dd>Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.</dd><dt><span>display_scale :</span></dt><dd>log</dd><dt><span>display_min :</span></dt><dd>0.01</dd><dt><span>display_max :</span></dt><dd>20.0</dd></dl></div><div class='xr-var-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> 4.17 GiB </td>\n",
       "                        <td> 2.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30, 4320, 8640) </td>\n",
       "                        <td> (1, 512, 1024) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2430 chunks in 91 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>palette</span></div><div class='xr-var-dims'>(date, rgb, eightbitcolor)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3, 256), meta=np.ndarray&gt;</div><input id='attrs-66c99b00-5483-49f3-87a0-c1c66c21f346' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66c99b00-5483-49f3-87a0-c1c66c21f346' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-224d2110-edf8-404b-922c-e1b371c6c4c9' class='xr-var-data-in' type='checkbox'><label for='data-224d2110-edf8-404b-922c-e1b371c6c4c9' 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'><table>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 22.50 kiB </td>\n",
       "                        <td> 768 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 3, 256) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 30 chunks in 91 graph layers </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba4ca019-3fdc-428e-8753-4b16f23ba8aa' class='xr-section-summary-in' type='checkbox'  ><label for='section-ba4ca019-3fdc-428e-8753-4b16f23ba8aa' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4fe6b5dd-7fcf-4997-ad6f-2be9947c6931' class='xr-index-data-in' type='checkbox'/><label for='index-4fe6b5dd-7fcf-4997-ad6f-2be9947c6931' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 89.97916412353516,            89.9375,  89.89583587646484,\n",
       "        89.85416412353516,            89.8125,  89.77083587646484,\n",
       "        89.72916412353516,            89.6875,  89.64583587646484,\n",
       "        89.60416412353516,\n",
       "       ...\n",
       "       -89.60417175292969, -89.64583587646484, -89.68750762939453,\n",
       "       -89.72917175292969, -89.77083587646484, -89.81250762939453,\n",
       "       -89.85417175292969, -89.89583587646484, -89.93750762939453,\n",
       "       -89.97917175292969],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=4320))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-14b7f9d4-ae7e-4b52-8eaa-8a0488b93285' class='xr-index-data-in' type='checkbox'/><label for='index-14b7f9d4-ae7e-4b52-8eaa-8a0488b93285' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-179.9791717529297,          -179.9375, -179.8958282470703,\n",
       "       -179.8541717529297,          -179.8125, -179.7708282470703,\n",
       "       -179.7291717529297,          -179.6875, -179.6458282470703,\n",
       "       -179.6041717529297,\n",
       "       ...\n",
       "        179.6041717529297, 179.64584350585938, 179.68751525878906,\n",
       "        179.7291717529297, 179.77084350585938, 179.81251525878906,\n",
       "        179.8541717529297, 179.89584350585938, 179.93751525878906,\n",
       "        179.9791717529297],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=8640))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f20a4ad4-4991-487e-a194-1e3f9fb5b46d' class='xr-section-summary-in' type='checkbox'  ><label for='section-f20a4ad4-4991-487e-a194-1e3f9fb5b46d' class='xr-section-summary' >Attributes: <span>(64)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>product_name :</span></dt><dd>PACE_OCI.20240707.L3m.DAY.CHL.V2_0.chlor_a.4km.NRT.nc</dd><dt><span>instrument :</span></dt><dd>OCI</dd><dt><span>title :</span></dt><dd>OCI Level-3 Standard Mapped Image</dd><dt><span>project :</span></dt><dd>Ocean Biology Processing Group (NASA/GSFC/OBPG)</dd><dt><span>platform :</span></dt><dd>PACE</dd><dt><span>source :</span></dt><dd>satellite observations from OCI-PACE</dd><dt><span>temporal_range :</span></dt><dd>day</dd><dt><span>processing_version :</span></dt><dd>2.0</dd><dt><span>date_created :</span></dt><dd>2024-07-08T13:55:31.000Z</dd><dt><span>history :</span></dt><dd>l3mapgen par=PACE_OCI.20240707.L3m.DAY.CHL.V2_0.chlor_a.4km.NRT.nc.param </dd><dt><span>l2_flag_names :</span></dt><dd>ATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,CLDICE,COCCOLITH,LOWLW,CHLWARN,CHLFAIL,NAVWARN,MAXAERITER,ATMWARN,HISOLZEN,NAVFAIL,FILTER,HIGLINT</dd><dt><span>time_coverage_start :</span></dt><dd>2024-07-07T00:01:11.000Z</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T01:59:03.000Z</dd><dt><span>start_orbit_number :</span></dt><dd>0</dd><dt><span>end_orbit_number :</span></dt><dd>0</dd><dt><span>map_projection :</span></dt><dd>Equidistant Cylindrical</dd><dt><span>latitude_units :</span></dt><dd>degrees_north</dd><dt><span>longitude_units :</span></dt><dd>degrees_east</dd><dt><span>northernmost_latitude :</span></dt><dd>90.0</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.0</dd><dt><span>westernmost_longitude :</span></dt><dd>-180.0</dd><dt><span>easternmost_longitude :</span></dt><dd>180.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>latitude_step :</span></dt><dd>0.041666668</dd><dt><span>longitude_step :</span></dt><dd>0.041666668</dd><dt><span>sw_point_latitude :</span></dt><dd>-89.979164</dd><dt><span>sw_point_longitude :</span></dt><dd>-179.97917</dd><dt><span>spatialResolution :</span></dt><dd>4.638312 km</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>4.638312 km</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>4.638312 km</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>number_of_lines :</span></dt><dd>4320</dd><dt><span>number_of_columns :</span></dt><dd>8640</dd><dt><span>measure :</span></dt><dd>Mean</dd><dt><span>suggested_image_scaling_minimum :</span></dt><dd>0.01</dd><dt><span>suggested_image_scaling_maximum :</span></dt><dd>20.0</dd><dt><span>suggested_image_scaling_type :</span></dt><dd>LOG</dd><dt><span>suggested_image_scaling_applied :</span></dt><dd>No</dd><dt><span>_lastModified :</span></dt><dd>2024-07-08T13:55:31.000Z</dd><dt><span>Conventions :</span></dt><dd>CF-1.6 ACDD-1.3</dd><dt><span>institution :</span></dt><dd>NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v36</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.gsfc.sci.oceandata</dd><dt><span>id :</span></dt><dd>2.0/L3/PACE_OCI.20240707.L3b.DAY.CHL.V2_0.NRT.nc</dd><dt><span>license :</span></dt><dd>https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/</dd><dt><span>creator_name :</span></dt><dd>NASA/GSFC/OBPG</dd><dt><span>publisher_name :</span></dt><dd>NASA/GSFC/OBPG</dd><dt><span>creator_email :</span></dt><dd>data@oceancolor.gsfc.nasa.gov</dd><dt><span>publisher_email :</span></dt><dd>data@oceancolor.gsfc.nasa.gov</dd><dt><span>creator_url :</span></dt><dd>https://oceandata.sci.gsfc.nasa.gov</dd><dt><span>publisher_url :</span></dt><dd>https://oceandata.sci.gsfc.nasa.gov</dd><dt><span>processing_level :</span></dt><dd>L3 Mapped</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/PACE/OCI/L3M/CHL/2.0</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Chemistry &gt; Pigments &gt; Chlorophyll; Earth Science &gt; Oceans &gt; Ocean Chemistry &gt; Chlorophyll</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>data_bins :</span></dt><dd>3310866</dd><dt><span>data_minimum :</span></dt><dd>0.0009999999</dd><dt><span>data_maximum :</span></dt><dd>98.42797</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 4GB\n",
       "Dimensions:  (date: 30, lat: 4320, lon: 8640, rgb: 3, eightbitcolor: 256)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 17kB 89.98 89.94 89.9 89.85 ... -89.9 -89.94 -89.98\n",
       "  * lon      (lon) float32 35kB -180.0 -179.9 -179.9 ... 179.9 179.9 180.0\n",
       "Dimensions without coordinates: date, rgb, eightbitcolor\n",
       "Data variables:\n",
       "    chlor_a  (date, lat, lon) float32 4GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>\n",
       "    palette  (date, rgb, eightbitcolor) uint8 23kB dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n",
       "Attributes: (12/64)\n",
       "    product_name:                      PACE_OCI.20240707.L3m.DAY.CHL.V2_0.chl...\n",
       "    instrument:                        OCI\n",
       "    title:                             OCI Level-3 Standard Mapped Image\n",
       "    project:                           Ocean Biology Processing Group (NASA/G...\n",
       "    platform:                          PACE\n",
       "    source:                            satellite observations from OCI-PACE\n",
       "    ...                                ...\n",
       "    identifier_product_doi:            10.5067/PACE/OCI/L3M/CHL/2.0\n",
       "    keywords:                          Earth Science > Oceans > Ocean Chemist...\n",
       "    keywords_vocabulary:               NASA Global Change Master Directory (G...\n",
       "    data_bins:                         3310866\n",
       "    data_minimum:                      0.0009999999\n",
       "    data_maximum:                      98.42797"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nest all images along the date (29 satellite images)\n",
    "paths = earthaccess.open(results)\n",
    "dataset = xr.open_mfdataset(\n",
    "    paths,\n",
    "    combine=\"nested\",\n",
    "    concat_dim=\"date\",\n",
    ")\n",
    "# Print structure of the dataset\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3f94e4b-870e-4fef-9360-82c9d1e4f879",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slice the dataset to extract only the region that we want\n",
    "dataset = dataset.sel(lat=slice(34.0, 22.0), lon=slice(-174.0, -142))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8fd8e2d9-1f90-4f7f-8a27-5310777fdb71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract chla data\n",
    "chla = dataset[\"chlor_a\"]\n",
    "# plot the mean for the region\n",
    "chla_avg = chla.mean(\"date\")\n",
    "chla_avg.attrs.update(\n",
    "    {\n",
    "        \"long_name\": chla.attrs[\"long_name\"],\n",
    "        \"units\": f'lg({chla.attrs[\"units\"]})',\n",
    "    }\n",
    ")\n",
    "# extract longitude and latitude\n",
    "lon = dataset[\"lon\"]\n",
    "lat = dataset[\"lat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a15d2344-c71d-45e6-8c64-535f986656a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate basic statistics for chla\n",
    "chla_std = chla.std(\"date\") #standard deviation\n",
    "chla_min = chla.min(\"date\") # minimum\n",
    "chla_max = chla.max(\"date\") # maximum\n",
    "chla_amp = chla_max - chla_min # amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0cbe2fb-fb8f-4db1-bbb6-ea30986d53f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/numpy_compat.py:56: RuntimeWarning: invalid value encountered in divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    }
   ],
   "source": [
    "# Plot the average Chla during the study period\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.gridlines(draw_labels={\"left\": \"y\", \"bottom\": \"x\"}, alpha=0.3)\n",
    "f1 = ax.pcolormesh(lon, lat, chla_avg, norm=mpl.colors.LogNorm(vmin=0.01, vmax=.3))\n",
    "cbar = plt.colorbar(f1, fraction=0.02)\n",
    "cbar.set_label('Chla', fontsize=14)\n",
    "filename = 'chl_mean_07July02August.png'\n",
    "plt.savefig(filename,format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdf9415d-bc0c-4f85-b24b-437fbc9d3a04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the max Chla during the study period\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.gridlines(draw_labels={\"left\": \"y\", \"bottom\": \"x\"}, alpha=0.3)\n",
    "ax.pcolormesh(lon, lat, chla_max, norm=mpl.colors.LogNorm(vmin=0.01, vmax=.5))\n",
    "filename = 'chl_max_07July02August.png'\n",
    "plt.savefig(filename,format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13f2972c-ccbd-4081-85bc-bede6f5f891f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the standard deviation of Chla + a square around the core of the bloom\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.gridlines(draw_labels={\"left\": \"y\", \"bottom\": \"x\"}, alpha=0.3)\n",
    "f1 = ax.pcolormesh(lon, lat, chla_std, cmap=plt.cm.Reds, norm=mpl.colors.LogNorm(vmin=0.01, vmax=.5))\n",
    "cbar = plt.colorbar(f1, fraction=0.02)\n",
    "cbar.set_label('Chla', fontsize=14)\n",
    "ROI_verts = [(-157.5, 28), (-159, 28), (-159, 26), (-157.5, 26)]\n",
    "poly_ROI = Polygon(list(ROI_verts), facecolor=[1,1,1,0], edgecolor='k', linewidth=2, linestyle='-', zorder=2)\n",
    "plt.gca().add_patch(poly_ROI)\n",
    "filename = 'chl_bloommarked.png'\n",
    "plt.savefig(filename,format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2bdd5c2e-20cb-40d2-9e32-c0f51b6f343d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the gif with the daily images\n",
    "\n",
    "# create a list of dates and the frames\n",
    "frames = np.arange(np.size(chla, axis=0))\n",
    "date_list_gif = date_range_list(datetime.datetime(2024,7,7), datetime.datetime(2024,8,5))\n",
    "date_list = date_range_list(datetime.datetime(2024,7,7), datetime.datetime(2024,8,5))\n",
    "fig = plt.figure()\n",
    "def update(i):\n",
    "    ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "    ax.coastlines()\n",
    "    ax.gridlines(draw_labels={\"left\": \"y\", \"bottom\": \"x\"})\n",
    "    ax.pcolormesh(lon, lat, chla[i,:,:],norm=mpl.colors.LogNorm(vmin=0.01, vmax=.3))\n",
    "    poly_ROI = Polygon(list(ROI_verts), facecolor=[1,1,1,0], edgecolor='k', linewidth=2, linestyle='-', zorder=2)\n",
    "    plt.gca().add_patch(poly_ROI)\n",
    "    ax.text(-173, 23, date_list_gif[i].strftime('%Y-%m-%d'), weight='bold', fontsize=14)\n",
    "    #ax.set_extent([-174.0, -142.0, 22.0, 34])\n",
    "    return ax\n",
    "\n",
    "an = animation.FuncAnimation(fig=fig, func=update, frames=frames, interval=500)\n",
    "filename = f'chl_gif_07July02August.gif'\n",
    "an.save(filename, writer=\"pillow\", dpi = 300)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14baf288-1956-4be2-bedc-9f5b22ed080d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Slice the dataset to extract only the region that we want\n",
    "dataset = dataset.sel(lat=slice(28.0, 26.0), lon=slice(-159.0, -157.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a59d6614-8829-496d-815c-73ec801c3491",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract chla data\n",
    "chla = dataset[\"chlor_a\"]\n",
    "# plot the mean for the region\n",
    "chla_avg = chla.mean(\"date\")\n",
    "chla_avg.attrs.update(\n",
    "    {\n",
    "        \"long_name\": chla.attrs[\"long_name\"],\n",
    "        \"units\": f'lg({chla.attrs[\"units\"]})',\n",
    "    }\n",
    ")\n",
    "# extract longitude and latitude\n",
    "lon = dataset[\"lon\"]\n",
    "lat = dataset[\"lat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "075dabfe-ccc5-4f63-9c55-807d91365a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate basic statistics for chla\n",
    "chla_std = chla.std(\"date\") #standard deviation\n",
    "chla_min = chla.min(\"date\") # minimum\n",
    "chla_max = chla.max(\"date\") # maximum\n",
    "chla_amp = chla_max - chla_min # amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e98e6bdc-2d8e-4f92-a781-3f3d182922d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the average Chla during the study period\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.gridlines(draw_labels={\"left\": \"y\", \"bottom\": \"x\"}, alpha=0.3)\n",
    "f1 = ax.pcolormesh(lon, lat, chla_avg, norm=mpl.colors.LogNorm(vmin=0.01, vmax=.3))\n",
    "cbar = plt.colorbar(f1, fraction=0.02)\n",
    "cbar.set_label('Chla', fontsize=14)\n",
    "filename = 'chl_mean_07July02August_zoom.png'\n",
    "plt.savefig(filename,format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a405af4e-c292-4df5-815e-32413815703d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1075/92779025.py:3: RuntimeWarning: Mean of empty slice\n",
      "  chl_ROI_1D_mean = np.nanmean(chl_ROI_pixels, (1,2))\n"
     ]
    }
   ],
   "source": [
    "## Average over region\n",
    "chl_ROI_pixels = chla\n",
    "chl_ROI_1D_mean = np.nanmean(chl_ROI_pixels, (1,2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "454e2976-6768-4a4b-8b96-785313e7bfa1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Plot mean over time\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(date_list, chl_ROI_1D_mean, '-o')\n",
    "ax.set_xticks(ticks=date_list, labels=date_list, rotation=90)\n",
    "import matplotlib.dates as mdates\n",
    "myFmt = mdates.DateFormatter('%d/%m')\n",
    "ax.xaxis.set_major_formatter(myFmt)\n",
    "#ax.set_xticks(rotation=90)\n",
    "ax.set_ylabel('Chlor_a')\n",
    "ax.set_xlabel('Time')\n",
    "filename = 'chl_timeseries_Hawaai.png'\n",
    "plt.savefig(filename,format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c6a2caf-5fee-48fc-8a6a-3f4aa26e4870",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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