access_ocean_color.ipynb

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    "# NASA ocean color data \n",
    "\n",
    "\n",
    "This notebook demonstrates how we can access Ocean Color data from NASA.\n",
    "\n",
    "\n",
    "The main repository for NASAs ocean color data is: https://oceandata.sci.gsfc.nasa.gov/opendap/\n",
    "\n",
    "We will look at data from the MODIS-Aqua (MODIS-A) satellite, and in particular we will look at the level 3 product, which is data that has gone through the highest level of processing and nicely gridded.\n",
    "\n",
    "\n",
    "NASA organizes data by year and year day. You can see this structure by clicking through the OpenDAP server. The file used in this example contains the mapped chlorophyll-a data for July 28 (year day 210), 2019.\n",
    "\n",
    "\n",
    "### credit:\n",
    "this lession borrowed from Tom Connolly: https://github.com/tompc35/oceanography-notebooks/blob/master/ocean_color_netcdf.ipynb"
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    "# import statements\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "import xarray as xr\n"
   ]
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   "source": [
    "# use `xr.open_dataset()` to access the data\n",
    "\n",
    "the data for yearday 210 of 2019 is located at:\n",
    "```python\n",
    "url = 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/MODISA/L3SMI/2019/210/A2019210.L3m_DAY_CHL_chlor_a_4km.nc'\n",
    "```\n"
   ]
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   "cell_type": "code",
   "execution_count": 59,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 4320, lon: 8640, w: 1)\n",
       "Coordinates:\n",
       "  * w        (w) float64 1.1\n",
       "  * lat      (lat) float32 89.979164 89.9375 89.895836 ... -89.93751 -89.97917\n",
       "  * lon      (lon) float32 -179.97917 -179.9375 ... 179.93752 179.97917\n",
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       "    product_name:                     X2020102.L3m_DAY_CHL_chlor_a_4km.nc\n",
       "    project:                          Ocean Biology Processing Group (NASA/GS...\n",
       "    temporal_range:                   day\n",
       "    processing_version:               2018.1\n",
       "    date_created:                     2020-06-24T18:31:21.000Z\n",
       "    l2_flag_names:                    ATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,C...\n",
       "    time_coverage_start:              2020-04-10T21:20:01.000Z\n",
       "    time_coverage_end:                2020-04-12T02:59:59.000Z\n",
       "    start_orbit_number:               43806\n",
       "    end_orbit_number:                 108063\n",
       "    map_projection:                   Equidistant Cylindrical\n",
       "    latitude_units:                   degrees_north\n",
       "    longitude_units:                  degrees_east\n",
       "    northernmost_latitude:            90.0\n",
       "    southernmost_latitude:            -90.0\n",
       "    westernmost_longitude:            -180.0\n",
       "    easternmost_longitude:            180.0\n",
       "    geospatial_lat_max:               90.0\n",
       "    geospatial_lat_min:               -90.0\n",
       "    geospatial_lon_max:               180.0\n",
       "    geospatial_lon_min:               -180.0\n",
       "    latitude_step:                    0.041666668\n",
       "    longitude_step:                   0.041666668\n",
       "    sw_point_latitude:                -89.979164\n",
       "    sw_point_longitude:               -179.97917\n",
       "    spatialResolution:                4.64 km\n",
       "    geospatial_lon_resolution:        0.041666668\n",
       "    geospatial_lat_resolution:        0.041666668\n",
       "    geospatial_lat_units:             degrees_north\n",
       "    geospatial_lon_units:             degrees_east\n",
       "    number_of_lines:                  4320\n",
       "    number_of_columns:                8640\n",
       "    measure:                          Mean\n",
       "    suggested_image_scaling_minimum:  0.01\n",
       "    suggested_image_scaling_maximum:  20.0\n",
       "    suggested_image_scaling_type:     LOG\n",
       "    suggested_image_scaling_applied:  No\n",
       "    _lastModified:                    2020-06-24T18:31:21.000Z\n",
       "    Conventions:                      CF-1.6 ACDD-1.3\n",
       "    institution:                      NASA Goddard Space Flight Center, Ocean...\n",
       "    standard_name_vocabulary:         CF Standard Name Table v36\n",
       "    naming_authority:                 gov.nasa.gsfc.sci.oceandata\n",
       "    id:                               X2020102.L3b_DAY_CHL.nc/L3/X2020102.L3b...\n",
       "    license:                          https://science.nasa.gov/earth-science/...\n",
       "    creator_name:                     NASA/GSFC/OBPG\n",
       "    publisher_name:                   NASA/GSFC/OBPG\n",
       "    creator_email:                    data@oceancolor.gsfc.nasa.gov\n",
       "    publisher_email:                  data@oceancolor.gsfc.nasa.gov\n",
       "    creator_url:                      https://oceandata.sci.gsfc.nasa.gov\n",
       "    publisher_url:                    https://oceandata.sci.gsfc.nasa.gov\n",
       "    processing_level:                 L3 Mapped\n",
       "    cdm_data_type:                    grid\n",
       "    keywords:                         Earth Science &gt; Oceans &gt; Ocean Chemistr...\n",
       "    keywords_vocabulary:              NASA Global Change Master Directory (GC...\n",
       "    _NCProperties:                    version=2,netcdf=4.7.1,hdf5=1.10.5,\n",
       "    data_bins:                        Attribute edlided: Unsupported attribut...\n",
       "    data_minimum:                     0.001\n",
       "    data_maximum:                     99.3876\n",
       "    history:                          l3mapgen par=X2020102.L3m_DAY_CHL_chlor...\n",
       "    title:                            Level-3 Mapped Data\n",
       "    instrument:                       MODIS,VIIRS\n",
       "    platform:                         Aqua,Terra,SNPP</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-db738ebc-3747-4779-911a-6a9dba206f3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-db738ebc-3747-4779-911a-6a9dba206f3a' 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'>lat</span>: 4320</li><li><span class='xr-has-index'>lon</span>: 8640</li><li><span class='xr-has-index'>w</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d76039e1-9b5b-45ba-8dae-a437c1234c50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d76039e1-9b5b-45ba-8dae-a437c1234c50' 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'>w</span></div><div class='xr-var-dims'>(w)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.1</div><input id='attrs-eedb56ba-41d0-4bb6-9e0c-7551d3a52583' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eedb56ba-41d0-4bb6-9e0c-7551d3a52583' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1cd575f-ba5c-4447-aab6-ee07ab4243f2' class='xr-var-data-in' type='checkbox'><label for='data-c1cd575f-ba5c-4447-aab6-ee07ab4243f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.1])</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'>89.979164 89.9375 ... -89.97917</div><input id='attrs-d160324f-1bfe-4e64-863c-c8cfc72ad2eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d160324f-1bfe-4e64-863c-c8cfc72ad2eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-853e8542-5962-4ba5-8048-32bdc40dc9e4' class='xr-var-data-in' type='checkbox'><label for='data-853e8542-5962-4ba5-8048-32bdc40dc9e4' 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'>-179.97917 -179.9375 ... 179.97917</div><input id='attrs-bb6c30d4-9893-4e69-8978-e84f1c544233' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb6c30d4-9893-4e69-8978-e84f1c544233' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb11178b-d27d-43ca-9c9d-e70112c7b8ce' class='xr-var-data-in' type='checkbox'><label for='data-fb11178b-d27d-43ca-9c9d-e70112c7b8ce' 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-8239d7da-6570-44a1-993a-61a8eb12c10f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8239d7da-6570-44a1-993a-61a8eb12c10f' 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>chlor_a</span></div><div class='xr-var-dims'>(w, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-804dcfa1-f3f2-4001-9d0d-4d0d4cedd481' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-804dcfa1-f3f2-4001-9d0d-4d0d4cedd481' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16a8e3dd-c03e-470b-abcf-6bb80a533765' class='xr-var-data-in' type='checkbox'><label for='data-16a8e3dd-c03e-470b-abcf-6bb80a533765' 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'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35a7036e-1281-43ed-a84e-ae8dcc2a442b' class='xr-section-summary-in' type='checkbox'  ><label for='section-35a7036e-1281-43ed-a84e-ae8dcc2a442b' class='xr-section-summary' >Attributes: <span>(62)</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>X2020102.L3m_DAY_CHL_chlor_a_4km.nc</dd><dt><span>project :</span></dt><dd>Ocean Biology Processing Group (NASA/GSFC/OBPG)</dd><dt><span>temporal_range :</span></dt><dd>day</dd><dt><span>processing_version :</span></dt><dd>2018.1</dd><dt><span>date_created :</span></dt><dd>2020-06-24T18:31:21.000Z</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>2020-04-10T21:20:01.000Z</dd><dt><span>time_coverage_end :</span></dt><dd>2020-04-12T02:59:59.000Z</dd><dt><span>start_orbit_number :</span></dt><dd>43806</dd><dt><span>end_orbit_number :</span></dt><dd>108063</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>4.64 km</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.041666668</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.041666668</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>2020-06-24T18:31:21.000Z</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>X2020102.L3b_DAY_CHL.nc/L3/X2020102.L3b_DAY_CHL.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>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Chemistry &gt; Pigments &gt; Chlorophyll; Earth Science &gt; Oceans &gt; Ocean Chemistry &gt; Chlorophyllr</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>_NCProperties :</span></dt><dd>version=2,netcdf=4.7.1,hdf5=1.10.5,</dd><dt><span>data_bins :</span></dt><dd>Attribute edlided: Unsupported attribute type (NC_INT64)</dd><dt><span>data_minimum :</span></dt><dd>0.001</dd><dt><span>data_maximum :</span></dt><dd>99.3876</dd><dt><span>history :</span></dt><dd>l3mapgen par=X2020102.L3m_DAY_CHL_chlor_a_4km.nc.param\n",
       "[2020-06-24T14:32:42] ncattredit.py X2020102.L3m_DAY_CHL_chlor_a_4km.nc ./L3MATTRIB.txt</dd><dt><span>title :</span></dt><dd>Level-3 Mapped Data</dd><dt><span>instrument :</span></dt><dd>MODIS,VIIRS</dd><dt><span>platform :</span></dt><dd>Aqua,Terra,SNPP</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 4320, lon: 8640, w: 1)\n",
       "Coordinates:\n",
       "  * w        (w) float64 1.1\n",
       "  * lat      (lat) float32 89.979164 89.9375 89.895836 ... -89.93751 -89.97917\n",
       "  * lon      (lon) float32 -179.97917 -179.9375 ... 179.93752 179.97917\n",
       "Data variables:\n",
       "    chlor_a  (w, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes:\n",
       "    product_name:                     X2020102.L3m_DAY_CHL_chlor_a_4km.nc\n",
       "    project:                          Ocean Biology Processing Group (NASA/GS...\n",
       "    temporal_range:                   day\n",
       "    processing_version:               2018.1\n",
       "    date_created:                     2020-06-24T18:31:21.000Z\n",
       "    l2_flag_names:                    ATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,C...\n",
       "    time_coverage_start:              2020-04-10T21:20:01.000Z\n",
       "    time_coverage_end:                2020-04-12T02:59:59.000Z\n",
       "    start_orbit_number:               43806\n",
       "    end_orbit_number:                 108063\n",
       "    map_projection:                   Equidistant Cylindrical\n",
       "    latitude_units:                   degrees_north\n",
       "    longitude_units:                  degrees_east\n",
       "    northernmost_latitude:            90.0\n",
       "    southernmost_latitude:            -90.0\n",
       "    westernmost_longitude:            -180.0\n",
       "    easternmost_longitude:            180.0\n",
       "    geospatial_lat_max:               90.0\n",
       "    geospatial_lat_min:               -90.0\n",
       "    geospatial_lon_max:               180.0\n",
       "    geospatial_lon_min:               -180.0\n",
       "    latitude_step:                    0.041666668\n",
       "    longitude_step:                   0.041666668\n",
       "    sw_point_latitude:                -89.979164\n",
       "    sw_point_longitude:               -179.97917\n",
       "    spatialResolution:                4.64 km\n",
       "    geospatial_lon_resolution:        0.041666668\n",
       "    geospatial_lat_resolution:        0.041666668\n",
       "    geospatial_lat_units:             degrees_north\n",
       "    geospatial_lon_units:             degrees_east\n",
       "    number_of_lines:                  4320\n",
       "    number_of_columns:                8640\n",
       "    measure:                          Mean\n",
       "    suggested_image_scaling_minimum:  0.01\n",
       "    suggested_image_scaling_maximum:  20.0\n",
       "    suggested_image_scaling_type:     LOG\n",
       "    suggested_image_scaling_applied:  No\n",
       "    _lastModified:                    2020-06-24T18:31:21.000Z\n",
       "    Conventions:                      CF-1.6 ACDD-1.3\n",
       "    institution:                      NASA Goddard Space Flight Center, Ocean...\n",
       "    standard_name_vocabulary:         CF Standard Name Table v36\n",
       "    naming_authority:                 gov.nasa.gsfc.sci.oceandata\n",
       "    id:                               X2020102.L3b_DAY_CHL.nc/L3/X2020102.L3b...\n",
       "    license:                          https://science.nasa.gov/earth-science/...\n",
       "    creator_name:                     NASA/GSFC/OBPG\n",
       "    publisher_name:                   NASA/GSFC/OBPG\n",
       "    creator_email:                    data@oceancolor.gsfc.nasa.gov\n",
       "    publisher_email:                  data@oceancolor.gsfc.nasa.gov\n",
       "    creator_url:                      https://oceandata.sci.gsfc.nasa.gov\n",
       "    publisher_url:                    https://oceandata.sci.gsfc.nasa.gov\n",
       "    processing_level:                 L3 Mapped\n",
       "    cdm_data_type:                    grid\n",
       "    keywords:                         Earth Science > Oceans > Ocean Chemistr...\n",
       "    keywords_vocabulary:              NASA Global Change Master Directory (GC...\n",
       "    _NCProperties:                    version=2,netcdf=4.7.1,hdf5=1.10.5,\n",
       "    data_bins:                        Attribute edlided: Unsupported attribut...\n",
       "    data_minimum:                     0.001\n",
       "    data_maximum:                     99.3876\n",
       "    history:                          l3mapgen par=X2020102.L3m_DAY_CHL_chlor...\n",
       "    title:                            Level-3 Mapped Data\n",
       "    instrument:                       MODIS,VIIRS\n",
       "    platform:                         Aqua,Terra,SNPP"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# url = 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/MODISA/L3SMI/2019/210/A2019210.L3m_DAY_CHL_chlor_a_4km.nc'\n",
    "url = 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2020/102/X2020102.L3m_DAY_CHL_chlor_a_4km.nc'\n",
    "\n",
    "# url = files[0]\n",
    "data = xr.open_dataset(url, drop_variables=['palette'])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subset the data\n",
    "\n",
    "Again, let's just grab the mid-atlantic bight.\n",
    "\n",
    "Note, that for some reason I don't understand, the `lat` `coords` are listed from high to low, so when you slice, you need to reverse the order, i.e. use `sel( lat=slice(41, 38))` *not* `sel( lat=slice( 38, 41)`. This is a mystery.\n",
    "\n",
    "fill in the blanks to get a subset of the data that covers the MAB (the lat boundaries at 38 to 41 degrees, and lon boundaries are -76 to -71):\n",
    "\n",
    "```python\n",
    "data_mab_nj = data.___( lat = ___, lon = ___)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.035328\n"
     ]
    },
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       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
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       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "\n",
       ".xr-section-item {\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
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       "  font-size: 11px;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
       ".xr-array-wrap > label {\n",
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       "\n",
       ".xr-array-preview,\n",
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       "\n",
       ".xr-array-data,\n",
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       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\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",
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       "\n",
       ".xr-has-index {\n",
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       "}\n",
       "\n",
       ".xr-var-list,\n",
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       "\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",
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       "\n",
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       "\n",
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       ".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",
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       "}\n",
       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "\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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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, 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",
       "  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:  (lat: 72, lon: 120)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 40.979164 40.9375 40.895832 ... 38.0625 38.020832\n",
       "  * lon      (lon) float32 -75.979164 -75.9375 -75.89583 ... -71.0625 -71.02083\n",
       "Data variables:\n",
       "    chlor_a  (lat, lon) float32 nan nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes:\n",
       "    product_name:                     X2020102.L3m_DAY_CHL_chlor_a_4km.nc\n",
       "    project:                          Ocean Biology Processing Group (NASA/GS...\n",
       "    temporal_range:                   day\n",
       "    processing_version:               2018.1\n",
       "    date_created:                     2020-06-24T18:31:21.000Z\n",
       "    l2_flag_names:                    ATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,C...\n",
       "    time_coverage_start:              2020-04-10T21:20:01.000Z\n",
       "    time_coverage_end:                2020-04-12T02:59:59.000Z\n",
       "    start_orbit_number:               43806\n",
       "    end_orbit_number:                 108063\n",
       "    map_projection:                   Equidistant Cylindrical\n",
       "    latitude_units:                   degrees_north\n",
       "    longitude_units:                  degrees_east\n",
       "    northernmost_latitude:            90.0\n",
       "    southernmost_latitude:            -90.0\n",
       "    westernmost_longitude:            -180.0\n",
       "    easternmost_longitude:            180.0\n",
       "    geospatial_lat_max:               90.0\n",
       "    geospatial_lat_min:               -90.0\n",
       "    geospatial_lon_max:               180.0\n",
       "    geospatial_lon_min:               -180.0\n",
       "    latitude_step:                    0.041666668\n",
       "    longitude_step:                   0.041666668\n",
       "    sw_point_latitude:                -89.979164\n",
       "    sw_point_longitude:               -179.97917\n",
       "    spatialResolution:                4.64 km\n",
       "    geospatial_lon_resolution:        0.041666668\n",
       "    geospatial_lat_resolution:        0.041666668\n",
       "    geospatial_lat_units:             degrees_north\n",
       "    geospatial_lon_units:             degrees_east\n",
       "    number_of_lines:                  4320\n",
       "    number_of_columns:                8640\n",
       "    measure:                          Mean\n",
       "    suggested_image_scaling_minimum:  0.01\n",
       "    suggested_image_scaling_maximum:  20.0\n",
       "    suggested_image_scaling_type:     LOG\n",
       "    suggested_image_scaling_applied:  No\n",
       "    _lastModified:                    2020-06-24T18:31:21.000Z\n",
       "    Conventions:                      CF-1.6 ACDD-1.3\n",
       "    institution:                      NASA Goddard Space Flight Center, Ocean...\n",
       "    standard_name_vocabulary:         CF Standard Name Table v36\n",
       "    naming_authority:                 gov.nasa.gsfc.sci.oceandata\n",
       "    id:                               X2020102.L3b_DAY_CHL.nc/L3/X2020102.L3b...\n",
       "    license:                          https://science.nasa.gov/earth-science/...\n",
       "    creator_name:                     NASA/GSFC/OBPG\n",
       "    publisher_name:                   NASA/GSFC/OBPG\n",
       "    creator_email:                    data@oceancolor.gsfc.nasa.gov\n",
       "    publisher_email:                  data@oceancolor.gsfc.nasa.gov\n",
       "    creator_url:                      https://oceandata.sci.gsfc.nasa.gov\n",
       "    publisher_url:                    https://oceandata.sci.gsfc.nasa.gov\n",
       "    processing_level:                 L3 Mapped\n",
       "    cdm_data_type:                    grid\n",
       "    keywords:                         Earth Science &gt; Oceans &gt; Ocean Chemistr...\n",
       "    keywords_vocabulary:              NASA Global Change Master Directory (GC...\n",
       "    _NCProperties:                    version=2,netcdf=4.7.1,hdf5=1.10.5,\n",
       "    data_bins:                        Attribute edlided: Unsupported attribut...\n",
       "    data_minimum:                     0.001\n",
       "    data_maximum:                     99.3876\n",
       "    history:                          l3mapgen par=X2020102.L3m_DAY_CHL_chlor...\n",
       "    title:                            Level-3 Mapped Data\n",
       "    instrument:                       MODIS,VIIRS\n",
       "    platform:                         Aqua,Terra,SNPP</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-27050284-d975-4ba0-a8b8-cfd48f1cbfb7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-27050284-d975-4ba0-a8b8-cfd48f1cbfb7' 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'>lat</span>: 72</li><li><span class='xr-has-index'>lon</span>: 120</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5b63275a-8ba6-4989-9cc1-f639939aa36c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5b63275a-8ba6-4989-9cc1-f639939aa36c' 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'>40.979164 40.9375 ... 38.020832</div><input id='attrs-a6b565f8-de3c-48fd-bce2-6bcc8c836b8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6b565f8-de3c-48fd-bce2-6bcc8c836b8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f71058a-8d01-42b9-b4a4-4626c5a37cd2' class='xr-var-data-in' type='checkbox'><label for='data-7f71058a-8d01-42b9-b4a4-4626c5a37cd2' 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([40.979164, 40.9375  , 40.895832, 40.854164, 40.8125  , 40.770832,\n",
       "       40.729164, 40.6875  , 40.645832, 40.604164, 40.5625  , 40.520832,\n",
       "       40.479164, 40.4375  , 40.395832, 40.354164, 40.3125  , 40.270832,\n",
       "       40.229164, 40.1875  , 40.145832, 40.104164, 40.0625  , 40.020832,\n",
       "       39.979164, 39.9375  , 39.895832, 39.854164, 39.8125  , 39.770832,\n",
       "       39.729164, 39.6875  , 39.645832, 39.604164, 39.5625  , 39.520832,\n",
       "       39.479164, 39.4375  , 39.395832, 39.354164, 39.3125  , 39.270832,\n",
       "       39.229164, 39.1875  , 39.145832, 39.104164, 39.0625  , 39.020832,\n",
       "       38.979164, 38.9375  , 38.895832, 38.854164, 38.8125  , 38.770832,\n",
       "       38.729164, 38.6875  , 38.645832, 38.604164, 38.5625  , 38.520832,\n",
       "       38.479164, 38.4375  , 38.395832, 38.354164, 38.3125  , 38.270832,\n",
       "       38.229164, 38.1875  , 38.145832, 38.104164, 38.0625  , 38.020832],\n",
       "      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'>-75.979164 -75.9375 ... -71.02083</div><input id='attrs-01b9c480-ec26-43ad-91ad-36c0e725af99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01b9c480-ec26-43ad-91ad-36c0e725af99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d13a402e-25ac-419e-88b1-515865e7f3d2' class='xr-var-data-in' type='checkbox'><label for='data-d13a402e-25ac-419e-88b1-515865e7f3d2' 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([-75.979164, -75.9375  , -75.89583 , -75.854164, -75.8125  , -75.77083 ,\n",
       "       -75.729164, -75.6875  , -75.64583 , -75.604164, -75.5625  , -75.52083 ,\n",
       "       -75.479164, -75.4375  , -75.39583 , -75.354164, -75.3125  , -75.27083 ,\n",
       "       -75.229164, -75.1875  , -75.14583 , -75.104164, -75.0625  , -75.02083 ,\n",
       "       -74.979164, -74.9375  , -74.89583 , -74.854164, -74.8125  , -74.77083 ,\n",
       "       -74.729164, -74.6875  , -74.64583 , -74.604164, -74.5625  , -74.52083 ,\n",
       "       -74.479164, -74.4375  , -74.39583 , -74.354164, -74.3125  , -74.27083 ,\n",
       "       -74.229164, -74.1875  , -74.14583 , -74.104164, -74.0625  , -74.02083 ,\n",
       "       -73.979164, -73.9375  , -73.89583 , -73.854164, -73.8125  , -73.77083 ,\n",
       "       -73.729164, -73.6875  , -73.64583 , -73.604164, -73.5625  , -73.52083 ,\n",
       "       -73.479164, -73.4375  , -73.39583 , -73.354164, -73.3125  , -73.27083 ,\n",
       "       -73.229164, -73.1875  , -73.14583 , -73.104164, -73.0625  , -73.02083 ,\n",
       "       -72.979164, -72.9375  , -72.89583 , -72.854164, -72.8125  , -72.77083 ,\n",
       "       -72.729164, -72.6875  , -72.64583 , -72.604164, -72.5625  , -72.52083 ,\n",
       "       -72.479164, -72.4375  , -72.39583 , -72.354164, -72.3125  , -72.27083 ,\n",
       "       -72.229164, -72.1875  , -72.14583 , -72.104164, -72.0625  , -72.02083 ,\n",
       "       -71.979164, -71.9375  , -71.89583 , -71.854164, -71.8125  , -71.77083 ,\n",
       "       -71.729164, -71.6875  , -71.64583 , -71.604164, -71.5625  , -71.52083 ,\n",
       "       -71.479164, -71.4375  , -71.39583 , -71.354164, -71.3125  , -71.27083 ,\n",
       "       -71.229164, -71.1875  , -71.14583 , -71.104164, -71.0625  , -71.02083 ],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff607eef-7239-4bef-81c0-fc6fa7f21ad6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff607eef-7239-4bef-81c0-fc6fa7f21ad6' 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>chlor_a</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-27756acb-0f19-42ee-b215-60f535490789' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27756acb-0f19-42ee-b215-60f535490789' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a18d319-6cbf-4d69-bbc5-7beee5e639a9' class='xr-var-data-in' type='checkbox'><label for='data-1a18d319-6cbf-4d69-bbc5-7beee5e639a9' 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'><pre>array([[     nan,      nan,      nan, ..., 1.471052, 1.425075, 1.370474],\n",
       "       [     nan,      nan,      nan, ..., 1.558107, 1.530562, 1.515906],\n",
       "       [     nan,      nan,      nan, ..., 1.85512 , 1.890476, 1.795511],\n",
       "       ...,\n",
       "       [     nan, 5.923916, 5.923916, ...,      nan,      nan,      nan],\n",
       "       [6.396339, 6.396339, 6.396339, ...,      nan,      nan,      nan],\n",
       "       [     nan, 6.678375, 6.678375, ...,      nan,      nan,      nan]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4dc4bf58-0bca-4792-aa69-80c8abd8d543' class='xr-section-summary-in' type='checkbox'  ><label for='section-4dc4bf58-0bca-4792-aa69-80c8abd8d543' class='xr-section-summary' >Attributes: <span>(62)</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>X2020102.L3m_DAY_CHL_chlor_a_4km.nc</dd><dt><span>project :</span></dt><dd>Ocean Biology Processing Group (NASA/GSFC/OBPG)</dd><dt><span>temporal_range :</span></dt><dd>day</dd><dt><span>processing_version :</span></dt><dd>2018.1</dd><dt><span>date_created :</span></dt><dd>2020-06-24T18:31:21.000Z</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>2020-04-10T21:20:01.000Z</dd><dt><span>time_coverage_end :</span></dt><dd>2020-04-12T02:59:59.000Z</dd><dt><span>start_orbit_number :</span></dt><dd>43806</dd><dt><span>end_orbit_number :</span></dt><dd>108063</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>4.64 km</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.041666668</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.041666668</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>2020-06-24T18:31:21.000Z</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>X2020102.L3b_DAY_CHL.nc/L3/X2020102.L3b_DAY_CHL.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>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Chemistry &gt; Pigments &gt; Chlorophyll; Earth Science &gt; Oceans &gt; Ocean Chemistry &gt; Chlorophyllr</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>_NCProperties :</span></dt><dd>version=2,netcdf=4.7.1,hdf5=1.10.5,</dd><dt><span>data_bins :</span></dt><dd>Attribute edlided: Unsupported attribute type (NC_INT64)</dd><dt><span>data_minimum :</span></dt><dd>0.001</dd><dt><span>data_maximum :</span></dt><dd>99.3876</dd><dt><span>history :</span></dt><dd>l3mapgen par=X2020102.L3m_DAY_CHL_chlor_a_4km.nc.param\n",
       "[2020-06-24T14:32:42] ncattredit.py X2020102.L3m_DAY_CHL_chlor_a_4km.nc ./L3MATTRIB.txt</dd><dt><span>title :</span></dt><dd>Level-3 Mapped Data</dd><dt><span>instrument :</span></dt><dd>MODIS,VIIRS</dd><dt><span>platform :</span></dt><dd>Aqua,Terra,SNPP</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 72, lon: 120)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 40.979164 40.9375 40.895832 ... 38.0625 38.020832\n",
       "  * lon      (lon) float32 -75.979164 -75.9375 -75.89583 ... -71.0625 -71.02083\n",
       "Data variables:\n",
       "    chlor_a  (lat, lon) float32 ...\n",
       "Attributes:\n",
       "    product_name:                     X2020102.L3m_DAY_CHL_chlor_a_4km.nc\n",
       "    project:                          Ocean Biology Processing Group (NASA/GS...\n",
       "    temporal_range:                   day\n",
       "    processing_version:               2018.1\n",
       "    date_created:                     2020-06-24T18:31:21.000Z\n",
       "    l2_flag_names:                    ATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,C...\n",
       "    time_coverage_start:              2020-04-10T21:20:01.000Z\n",
       "    time_coverage_end:                2020-04-12T02:59:59.000Z\n",
       "    start_orbit_number:               43806\n",
       "    end_orbit_number:                 108063\n",
       "    map_projection:                   Equidistant Cylindrical\n",
       "    latitude_units:                   degrees_north\n",
       "    longitude_units:                  degrees_east\n",
       "    northernmost_latitude:            90.0\n",
       "    southernmost_latitude:            -90.0\n",
       "    westernmost_longitude:            -180.0\n",
       "    easternmost_longitude:            180.0\n",
       "    geospatial_lat_max:               90.0\n",
       "    geospatial_lat_min:               -90.0\n",
       "    geospatial_lon_max:               180.0\n",
       "    geospatial_lon_min:               -180.0\n",
       "    latitude_step:                    0.041666668\n",
       "    longitude_step:                   0.041666668\n",
       "    sw_point_latitude:                -89.979164\n",
       "    sw_point_longitude:               -179.97917\n",
       "    spatialResolution:                4.64 km\n",
       "    geospatial_lon_resolution:        0.041666668\n",
       "    geospatial_lat_resolution:        0.041666668\n",
       "    geospatial_lat_units:             degrees_north\n",
       "    geospatial_lon_units:             degrees_east\n",
       "    number_of_lines:                  4320\n",
       "    number_of_columns:                8640\n",
       "    measure:                          Mean\n",
       "    suggested_image_scaling_minimum:  0.01\n",
       "    suggested_image_scaling_maximum:  20.0\n",
       "    suggested_image_scaling_type:     LOG\n",
       "    suggested_image_scaling_applied:  No\n",
       "    _lastModified:                    2020-06-24T18:31:21.000Z\n",
       "    Conventions:                      CF-1.6 ACDD-1.3\n",
       "    institution:                      NASA Goddard Space Flight Center, Ocean...\n",
       "    standard_name_vocabulary:         CF Standard Name Table v36\n",
       "    naming_authority:                 gov.nasa.gsfc.sci.oceandata\n",
       "    id:                               X2020102.L3b_DAY_CHL.nc/L3/X2020102.L3b...\n",
       "    license:                          https://science.nasa.gov/earth-science/...\n",
       "    creator_name:                     NASA/GSFC/OBPG\n",
       "    publisher_name:                   NASA/GSFC/OBPG\n",
       "    creator_email:                    data@oceancolor.gsfc.nasa.gov\n",
       "    publisher_email:                  data@oceancolor.gsfc.nasa.gov\n",
       "    creator_url:                      https://oceandata.sci.gsfc.nasa.gov\n",
       "    publisher_url:                    https://oceandata.sci.gsfc.nasa.gov\n",
       "    processing_level:                 L3 Mapped\n",
       "    cdm_data_type:                    grid\n",
       "    keywords:                         Earth Science > Oceans > Ocean Chemistr...\n",
       "    keywords_vocabulary:              NASA Global Change Master Directory (GC...\n",
       "    _NCProperties:                    version=2,netcdf=4.7.1,hdf5=1.10.5,\n",
       "    data_bins:                        Attribute edlided: Unsupported attribut...\n",
       "    data_minimum:                     0.001\n",
       "    data_maximum:                     99.3876\n",
       "    history:                          l3mapgen par=X2020102.L3m_DAY_CHL_chlor...\n",
       "    title:                            Level-3 Mapped Data\n",
       "    instrument:                       MODIS,VIIRS\n",
       "    platform:                         Aqua,Terra,SNPP"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mab_nj = data.sel( lat=slice(41, 38), lon=slice(-76,-71))\n",
    "\n",
    "print(data_mab_nj.nbytes/10**6)\n",
    "\n",
    "data_mab_nj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot the chlorophyll a\n",
    "\n",
    "for the mid atlantic bight subset using the built-in xarray plotting routine. i.e. fill in the blanks, and remember we want to just plot the variable `chlor_a`:\n",
    "```python\n",
    "data_mab_nj.___.___()\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fad6e3cf970>"
      ]
     },
     "execution_count": 43,
     "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": [
    "data_mab_nj.chlor_a.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chla should be plotted on a log scale\n",
    "\n",
    "Let's make the same plot with matplotlib, and use `np.log10()` to plot the data on a logarythmic scale:\n",
    "\n",
    "```python\n",
    "plt.pcolor( data_mab_nj.___, data_mab_nj.___, np.log10(data_mab_nj.___))\n",
    "# add a colorbar below:\n",
    "plt.___\n",
    "\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beaird/anaconda3/envs/fm2020/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1763: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  result = matplotlib.axes.Axes.pcolor(self, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7facffc1be80>"
      ]
     },
     "execution_count": 44,
     "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": [
    "ax = plt.axes(projection=ccrs.Mercator())\n",
    "\n",
    "plt.pcolor(data_mab_nj.lon, data_mab_nj.lat, np.log10(data_mab_nj.chlor_a), transform=ccrs.PlateCarree())\n",
    "plt.colorbar()\n",
    "ax.coastlines('50m')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# try to open multiple days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/010/X2019010.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/020/X2019020.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/030/X2019030.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/040/X2019040.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/050/X2019050.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/060/X2019060.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/070/X2019070.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/080/X2019080.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/090/X2019090.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/100/X2019100.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/110/X2019110.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/120/X2019120.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/130/X2019130.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/140/X2019140.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/150/X2019150.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/160/X2019160.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/170/X2019170.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/180/X2019180.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/190/X2019190.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/200/X2019200.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/210/X2019210.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/220/X2019220.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/230/X2019230.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/240/X2019240.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/250/X2019250.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/260/X2019260.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/270/X2019270.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/280/X2019280.L3m_DAY_CHL_chlor_a_4km.nc',\n",
       " 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019/290/X2019290.L3m_DAY_CHL_chlor_a_4km.nc']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "base_url = 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/MODISA/L3SMI/2019'\n",
    "base_url = 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/Merged_ATV/L3SMI/2019'\n",
    "\n",
    "# pick out days, the syntax is start day, stop day, steps between\n",
    "days = np.arange(10, 300, 10)\n",
    "\n",
    "# files = [f'{base_url}/{str(day).zfill(3)}/A2019{str(day).zfill(3)}.L3m_DAY_CHL_chlor_a_4km.nc' for day in days]\n",
    "\n",
    "files = [f'{base_url}/{str(day).zfill(3)}/X2019{str(day).zfill(3)}.L3m_DAY_CHL_chlor_a_4km.nc' for day in days]\n",
    "\n",
    "\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-01-09 21:50:00+0000', tz='UTC')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.to_datetime( ds.time_coverage_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only concat the chla, then only grab it, and subset to MAB:\n",
    "\n",
    "# need to add a dim to concatinate? I dont know why\n",
    "def preproc(ds):\n",
    "    ds = ds.expand_dims({'time': [ds.time_coverage_start]})\n",
    "    return ds\n",
    "\n",
    "ds = xr.open_mfdataset(files,  preprocess=preproc, drop_variables='palette', parallel=False).sel( \n",
    "    lat=slice(41, 38), lon=slice(-76,-71))\n",
    "# \n",
    "# ds = ds.rename({'concat_dim': 'days'})\n",
    "print('data size:',ds.nbytes/1e6, 'MB')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-83-6cf91cf67796>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchlor_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_da\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;31m# we can't use functools.wraps here since that also modifies the name / qualname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, rtol, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \"\"\"\n\u001b[0;32m--> 163\u001b[0;31m     \u001b[0mdarray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdarray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0mplot_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdarray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    838\u001b[0m         \"\"\"\n\u001b[1;32m    839\u001b[0m         \u001b[0mnew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 840\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    842\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpersist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m\"DataArray\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    812\u001b[0m         \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m         \"\"\"\n\u001b[0;32m--> 814\u001b[0;31m         \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    815\u001b[0m         \u001b[0mnew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m             \u001b[0;31m# evaluate all the dask arrays simultaneously\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 658\u001b[0;31m             \u001b[0mevaluated_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlazy_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    659\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    660\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlazy_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluated_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    450\u001b[0m         \u001b[0mpostcomputes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/dask/threaded.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(dsk, result, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[1;32m     74\u001b[0m                 \u001b[0mpools\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mthread\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m     results = get_async(\n\u001b[0m\u001b[1;32m     77\u001b[0m         \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/dask/local.py\u001b[0m in \u001b[0;36mget_async\u001b[0;34m(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)\u001b[0m\n\u001b[1;32m    473\u001b[0m             \u001b[0;31m# Main loop, wait on tasks to finish, insert new ones\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"waiting\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ready\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"running\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m                 \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfailed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfailed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m                     \u001b[0mexc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/site-packages/dask/local.py\u001b[0m in \u001b[0;36mqueue_get\u001b[0;34m(q)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    168\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_qsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'timeout' must be a non-negative number\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fm2020/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ds.chlor_a.mean(dim='time').plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mda_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mddof\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Compute covariance between two DataArray objects along a shared dimension.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "da_a: DataArray object\n",
       "    Array to compute.\n",
       "da_b: DataArray object\n",
       "    Array to compute.\n",
       "dim : str, optional\n",
       "    The dimension along which the covariance will be computed\n",
       "ddof: int, optional\n",
       "    If ddof=1, covariance is normalized by N-1, giving an unbiased estimate,\n",
       "    else normalization is by N.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "covariance: DataArray\n",
       "\n",
       "See also\n",
       "--------\n",
       "pandas.Series.cov: corresponding pandas function\n",
       "xr.corr: respective function to calculate correlation\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> da_a = DataArray(\n",
       "...     np.array([[1, 2, 3], [0.1, 0.2, 0.3], [3.2, 0.6, 1.8]]),\n",
       "...     dims=(\"space\", \"time\"),\n",
       "...     coords=[\n",
       "...         (\"space\", [\"IA\", \"IL\", \"IN\"]),\n",
       "...         (\"time\", pd.date_range(\"2000-01-01\", freq=\"1D\", periods=3)),\n",
       "...     ],\n",
       "... )\n",
       ">>> da_a\n",
       "<xarray.DataArray (space: 3, time: 3)>\n",
       "array([[1. , 2. , 3. ],\n",
       "       [0.1, 0.2, 0.3],\n",
       "       [3.2, 0.6, 1.8]])\n",
       "Coordinates:\n",
       "  * space    (space) <U2 'IA' 'IL' 'IN'\n",
       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03\n",
       ">>> da_b = DataArray(\n",
       "...     np.array([[0.2, 0.4, 0.6], [15, 10, 5], [3.2, 0.6, 1.8]]),\n",
       "...     dims=(\"space\", \"time\"),\n",
       "...     coords=[\n",
       "...         (\"space\", [\"IA\", \"IL\", \"IN\"]),\n",
       "...         (\"time\", pd.date_range(\"2000-01-01\", freq=\"1D\", periods=3)),\n",
       "...     ],\n",
       "... )\n",
       ">>> da_b\n",
       "<xarray.DataArray (space: 3, time: 3)>\n",
       "array([[ 0.2,  0.4,  0.6],\n",
       "       [15. , 10. ,  5. ],\n",
       "       [ 3.2,  0.6,  1.8]])\n",
       "Coordinates:\n",
       "  * space    (space) <U2 'IA' 'IL' 'IN'\n",
       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03\n",
       ">>> xr.cov(da_a, da_b)\n",
       "<xarray.DataArray ()>\n",
       "array(-3.53055556)\n",
       ">>> xr.cov(da_a, da_b, dim=\"time\")\n",
       "<xarray.DataArray (space: 3)>\n",
       "array([ 0.2, -0.5,  1.69333333])\n",
       "Coordinates:\n",
       "  * space    (space) <U2 'IA' 'IL' 'IN'\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/envs/fm2020/lib/python3.8/site-packages/xarray/core/computation.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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