1_extract_box_edmw.ipynb

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    "<p style=\"font-size:14px; text-align: right\">CoastWatch Python Exercises</p>  \n",
    "\n",
    "# Python Basics: A tutorial for the NOAA Satellite Workshop\n",
    "> history | updated August 2021  \n",
    "> owner | NOAA CoastWatch"
   ]
  },
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    "## In this exercise, you will use Python to download data and metadata from ERDDAP.\n",
    "\n",
    "### The exercise demonstrates the following skills: \n",
    "\n",
    "* Using Python to retrieve information about a dataset from ERDDAP\n",
    "* Downloading satellite data from ERDDAP in netCDF format\n",
    "* Extracting data with Python\n",
    "\n",
    "### About the dataset used in this exercise\n",
    "* For the examples in this exercise we will use the NOAA GeoPolar Sea Surface Temperature  dataset from the CoastWatch West Coast Node \n",
    "* ERDDAP ID = nesdisGeoPolarSSTN5SQNRT\n",
    "* https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph  \n",
    "* The dataset contains monthly composites of SST\n",
    "* The low spatial resolution (0.05 degrees) will allow for small download size and help prevent overloading the internet bandwidth during the class  \n",
    "\n",
    " "
   ]
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    "### Look for python modules you might not have installed\n",
    "* Use the pkg_resources module to check for installed modules\n",
    "* We will be using the xarray, numpy, and pandas modules for this exercise. \n",
    "* Make sure that they are installed in your Python 3 environment. \n",
    "* A quick way to do this is with the script below"
   ]
  },
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     "text": [
      "All modules are installed\n"
     ]
    }
   ],
   "source": [
    "import pkg_resources\n",
    "\n",
    "# Create a set 'curly brackets' of the modules to look for\n",
    "# You can put any modules that you want to in the set\n",
    "required = {'xarray', 'numpy', 'pandas'}\n",
    "\n",
    "# look for the installed packages\n",
    "installed = {pkg.key for pkg in pkg_resources.working_set}\n",
    "\n",
    "# Find which modules are missing\n",
    "missing = required - installed\n",
    "if len(missing)==0:\n",
    "    print('All modules are installed')\n",
    "else:\n",
    "    print('These modules are missing', ', '.join(missing))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the primary modules for this tutorial\n",
    "* numpy is used for matrix operations\n",
    "* numpy.ma specifically is used for masked arrays\n",
    "* pandas is used for tabular data\n",
    "* xarray is used for opening the gridded dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
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   "source": [
    "import numpy as np  \n",
    "import numpy.ma as ma  \n",
    "import pandas as pd  \n",
    "import xarray as xr  "
   ]
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    "## Get information about a dataset from ERDDAP\n",
    "\n",
    "We will use the xarray `open_dataset` function to access data and metadata from ERDDAP. Here we describe how to create the url for the `open_dataset` function and demonstrate a function to generate the ERDDAP url.\n",
    "\n",
    "### Open a pointer to an ERDDAP dataset, using the xarray `open_dataset` function \n",
    "> xr.open_dataset('full_url_to_erddap_dataset')  \n",
    "\n",
    "Where, the `full_url_to_erddap_dataset` is the base url to the ERDDAP you are using and the ERDDAP dataset id. So, for our dataset:    \n",
    "* base_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
    "* dataset_id = 'nesdisGeoPolarSSTN5SQNRT'\n",
    "* full_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT'\n",
    "\n",
    "__A simple `open_dataset` example__   \n",
    "```python\n",
    "base_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
    "dataset_id = 'nesdisGeoPolarSSTN5SQNRT'\n",
    "full_URL = '/'.join([base_URL, dataset_id])\n",
    "print(full_URL)\n",
    "da = xr.open_dataset(full_URL)\n",
    "```\n",
    "\n",
    "__Make this more versatile by putting it into a function__\n",
    "\n",
    "```python\n",
    "def point_to_dataset(dataset_id, base_url='https://coastwatch.pfeg.noaa.gov/erddap/griddap'):\n",
    "    base_url = base_url.rstrip('/')\n",
    "    full_url = '/'.join([base_url, dataset_id])\n",
    "    return xr.open_dataset(full_url)\n",
    "```  \n",
    "* __dataset_id__ is the ERDDAP id for the dataset of interest. For this example: 'nesdisGeoPolarSSTN5SQNRT'\n",
    "* __base_url__ is the url of the ERDDAP you are pulling data from. For this example, the West Coast Node ERDDAP at 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
    "* __full_url__ is the full URL to the ERDDAP dataset created by joining base_url and dataset_id\n",
    "* The pointer to the dataset is returned\n",
    "* The default base_url is the West Coast Node ERDDAP 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'. \n",
    "* Examples of passing dataset_id and base_url to the function:\n",
    "```python\n",
    "point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
    "```\n",
    "```python\n",
    "point_to_dataset(dataset_id = 'nesdisGeoPolarSSTN5SQNRT')\n",
    "```\n",
    "```python\n",
    "point_to_dataset('nesdisGeoPolarSSTN5SQNRT', 'https://upwell.pfeg.noaa.gov/erddap/griddap')\n",
    "```\n",
    "```python\n",
    "point_to_dataset(dataset_id = 'nesdisGeoPolarSSTN5SQNRT', \n",
    "                 base_url = 'https://upwell.pfeg.noaa.gov/erddap/griddap')\n",
    "```"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (latitude: 3600, longitude: 7200, time: 6914)\n",
       "Coordinates:\n",
       "  * time              (time) object 2002-09-01 12:00:00 ... 2021-08-12 12:00:00\n",
       "  * latitude          (latitude) float32 -89.97 -89.93 -89.88 ... 89.93 89.97\n",
       "  * longitude         (longitude) float32 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
       "Data variables:\n",
       "    analysed_sst      (time, latitude, longitude) float32 ...\n",
       "    analysis_error    (time, latitude, longitude) float32 ...\n",
       "    sea_ice_fraction  (time, latitude, longitude) float32 ...\n",
       "    mask              (time, latitude, longitude) float32 ...\n",
       "Attributes: (12/50)\n",
       "    acknowledgement:            NOAA/NESDIS\n",
       "    cdm_data_type:              Grid\n",
       "    comment:                    The Geo-Polar Blended Sea Surface Temperature...\n",
       "    Conventions:                CF-1.6, Unidata Observation Dataset v1.0, COA...\n",
       "    creator_email:              john.sapper@noaa.gov\n",
       "    creator_name:               Office of Satellite Products and Operations\n",
       "    ...                         ...\n",
       "    summary:                    This dataset is an aggregation of Science Qua...\n",
       "    testOutOfDate:              now-17days\n",
       "    time_coverage_end:          2021-08-12T12:00:00Z\n",
       "    time_coverage_start:        2002-09-01T12:00:00Z\n",
       "    title:                      SST, NOAA Geo-Polar Blended Global Level 4, S...\n",
       "    Westernmost_Easting:        -179.975</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-c129902c-ae87-4943-92aa-d60361867c14' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c129902c-ae87-4943-92aa-d60361867c14' 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'>latitude</span>: 3600</li><li><span class='xr-has-index'>longitude</span>: 7200</li><li><span class='xr-has-index'>time</span>: 6914</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e323b0a3-1ccb-41c0-8a6c-8da9e4cc70a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e323b0a3-1ccb-41c0-8a6c-8da9e4cc70a8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2002-09-01 12:00:00 ... 2021-08-...</div><input id='attrs-a578b0bc-a0a7-4586-bb67-a536e3e2a4ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a578b0bc-a0a7-4586-bb67-a536e3e2a4ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd6deab1-1f70-4c25-a718-0c6bc8638103' class='xr-var-data-in' type='checkbox'><label for='data-cd6deab1-1f70-4c25-a718-0c6bc8638103' 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>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>actual_range :</span></dt><dd>[1.0308816e+09 1.6287696e+09]</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Nominal time of Level 4 analysis</dd><dt><span>ioos_category :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>01-JAN-1970 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2002, 9, 1, 12, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2002, 9, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2002, 9, 3, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeGregorian(2021, 8, 10, 12, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 8, 11, 12, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 8, 12, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.97 -89.93 ... 89.93 89.97</div><input id='attrs-e35bc6fa-0940-4bb5-ad1b-323fab44bcac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e35bc6fa-0940-4bb5-ad1b-323fab44bcac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69e99c36-e173-42d4-a04a-dfcbc6a3425d' class='xr-var-data-in' type='checkbox'><label for='data-69e99c36-e173-42d4-a04a-dfcbc6a3425d' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>actual_range :</span></dt><dd>[-89.975  89.975]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-89.975, -89.925, -89.875, ...,  89.875,  89.925,  89.975],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</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-f4b51ece-9c53-43dd-b796-37ef83f0e9b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4b51ece-9c53-43dd-b796-37ef83f0e9b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41427a3c-08b3-4327-98ac-f43e4dcbc465' class='xr-var-data-in' type='checkbox'><label for='data-41427a3c-08b3-4327-98ac-f43e4dcbc465' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>actual_range :</span></dt><dd>[-179.975  179.975]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-179.975, -179.925, -179.875, ...,  179.875,  179.925,  179.975],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1769052e-b2bc-4cdc-bc75-b5d97cfe89e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1769052e-b2bc-4cdc-bc75-b5d97cfe89e8' class='xr-section-summary' >Data variables: <span>(4)</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>analysed_sst</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fbbcafc3-082b-46d4-b1e2-4a9b71c46cfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbbcafc3-082b-46d4-b1e2-4a9b71c46cfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4dc822b-7c36-4aad-b2d8-e05e26ac31c2' class='xr-var-data-in' type='checkbox'><label for='data-d4dc822b-7c36-4aad-b2d8-e05e26ac31c2' 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>colorBarMaximum :</span></dt><dd>32.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>nighttime analysed SST for each ocean grid point</dd><dt><span>ioos_category :</span></dt><dd>Temperature</dd><dt><span>long_name :</span></dt><dd>analysed sea surface temperature</dd><dt><span>references :</span></dt><dd>Fieguth,P.W. et al. &quot;Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm&quot;, J. Atmos. Ocean Tech, 15 (2): 535-546, 1998.     Fieguth, P. Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10(11), 1676-1686, 2001.     Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, &quot;Statistical Processing of Large Image Sequences&quot;, IEEE Transactions on Geoscience and Remote Sensing, 12 (1), 80-93, 2005.</dd><dt><span>source :</span></dt><dd>OSPO-ACSPO_VIIRS, OSPO-ACSPO_METOPB_FRAC, OSPO-GOES16_SST_L3,OSPO-GOES15_SST_L3, OSPO-METEOSAT11_SST_L3, OSPO-HIMA8_SST_L3</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>valid_max :</span></dt><dd>39.999992</dd><dt><span>valid_min :</span></dt><dd>-2.0000062</dd></dl></div><div class='xr-var-data'><pre>[179210880000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analysis_error</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2199d90a-eca4-49a1-a843-e23ca4843689' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2199d90a-eca4-49a1-a843-e23ca4843689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-179bf6f9-9efa-4100-b55e-b5bfbf52337a' class='xr-var-data-in' type='checkbox'><label for='data-179bf6f9-9efa-4100-b55e-b5bfbf52337a' 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>colorBarMaximum :</span></dt><dd>1.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>Estimate of internal analysis accuracy</dd><dt><span>ioos_category :</span></dt><dd>Statistics</dd><dt><span>long_name :</span></dt><dd>estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>valid_max :</span></dt><dd>5.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[179210880000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_fraction</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6da8e249-a2b5-4a77-b401-c5fefbf16c0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6da8e249-a2b5-4a77-b401-c5fefbf16c0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1296bf6-78ac-4bd9-8ed4-6bb3c56b75a9' class='xr-var-data-in' type='checkbox'><label for='data-c1296bf6-78ac-4bd9-8ed4-6bb3c56b75a9' 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>colorBarMaximum :</span></dt><dd>1.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>Percentage of ice</dd><dt><span>ioos_category :</span></dt><dd>Ice Distribution</dd><dt><span>long_name :</span></dt><dd>sea ice fraction</dd><dt><span>source :</span></dt><dd>NCEP 1/12th degree ice mask</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[179210880000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1bc9338a-7f28-457c-9413-01417bfb0ee3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bc9338a-7f28-457c-9413-01417bfb0ee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94f19fa5-e224-4c55-bd94-9c1ac73f865e' class='xr-var-data-in' type='checkbox'><label for='data-94f19fa5-e224-4c55-bd94-9c1ac73f865e' 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>colorBarMaximum :</span></dt><dd>5.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>b0: 1=grid cell is water, b1: 1=grid cell is land, b2: 1=grid cell is ice</dd><dt><span>flag_meanings :</span></dt><dd>water land ice</dd><dt><span>flag_values :</span></dt><dd>[1 2 4]</dd><dt><span>ioos_category :</span></dt><dd>Ice Distribution</dd><dt><span>long_name :</span></dt><dd>sea/land/ice bit mask</dd><dt><span>source :</span></dt><dd>NCEP 1/12th degree ice mask, OSTIA land mask</dd><dt><span>standard_name :</span></dt><dd>sea_land_ice_bit_mask</dd><dt><span>valid_max :</span></dt><dd>4</dd><dt><span>valid_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[179210880000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55d5fa80-5012-414f-b658-4122e704d9a5' class='xr-section-summary-in' type='checkbox'  ><label for='section-55d5fa80-5012-414f-b658-4122e704d9a5' class='xr-section-summary' >Attributes: <span>(50)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>acknowledgement :</span></dt><dd>NOAA/NESDIS</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis includes only nighttime data.</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, Unidata Observation Dataset v1.0, COARDS, ACDD-1.3</dd><dt><span>creator_email :</span></dt><dd>john.sapper@noaa.gov</dd><dt><span>creator_name :</span></dt><dd>Office of Satellite Products and Operations</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>www.osdpd.nesdis.noaa.gov</dd><dt><span>date_created :</span></dt><dd>2019-04-07T12:46:39Z</dd><dt><span>Easternmost_Easting :</span></dt><dd>179.975</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.975</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.975</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.049999999999999996</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>179.975</dd><dt><span>geospatial_lon_min :</span></dt><dd>-179.975</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.049999999999999996</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>NESDIS geo-SST L1 to L2 processor, NESDIS Advanced Clear-Sky Processor for Oceans (ACSPO), NESDIS Geo-Polar 1/20th degree Blended SST Analysis\n",
       "2021-08-13T22:18:46Z https://www.star.nesdis.noaa.gov/thredds/dodsC/BlendedSST5kmNightAggGHRSSTLoM\n",
       "2021-08-13T22:18:46Z http://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.das</dd><dt><span>id :</span></dt><dd>Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0</dd><dt><span>infoUrl :</span></dt><dd>https://podaac.jpl.nasa.gov/dataset/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0</dd><dt><span>institution :</span></dt><dd>Office of Satellite Products and Operations</dd><dt><span>keywords :</span></dt><dd>analysed, analysed_sst, analysis_error, area, bit, blended, cryosphere, data, deviation, distribution, earth, Earth Science &gt; Cryosphere &gt; Sea Ice &gt; Ice Extent, Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature, Earth Science &gt; Oceans &gt; Sea Ice &gt; Ice Extent, error, estimated, extent, foundation, fraction, global, ice, ice distribution, infrared, input, land, latitude, longitude, mask, night, ocean, oceans, office, only, operations, over, products, satellite, science, sea, sea/land/ice, sea_ice_area_fraction, sea_ice_fraction, sea_land_ice_bit_mask, sea_surface_foundation_temperature, sst, standard, statistics, surface, temperature, time, using</dd><dt><span>keywords_vocabulary :</span></dt><dd>GCMD Science Keywords</dd><dt><span>license :</span></dt><dd>GHRSST protocol describes data use as free and open</dd><dt><span>metadata_link :</span></dt><dd>https://podaac.jpl.nasa.gov/ws/metadata/dataset?format=iso&amp;shortName=Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>Northernmost_Northing :</span></dt><dd>89.975</dd><dt><span>platform :</span></dt><dd>Suomi NPP, MetOp-B, GOESE (GOES-16), GOESW (GOES-15), Meteosat-11, Meteosat-08, Himawari-8</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>1.0</dd><dt><span>project :</span></dt><dd>Group for High Resolution Sea Surface Temperature</dd><dt><span>publisher_email :</span></dt><dd>ghrsst-po@nceo.ac.uk</dd><dt><span>publisher_name :</span></dt><dd>The GHRSST Project Office</dd><dt><span>publisher_type :</span></dt><dd>group</dd><dt><span>publisher_url :</span></dt><dd>https://www.ghrsst.org</dd><dt><span>references :</span></dt><dd>Fieguth,P.W. et al. &quot;Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm&quot;, J. Atmos. Ocean Tech, 15 (2): 535-546, 1998.     Fieguth, P. Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10(11), 1676-1686, 2001.     Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, &quot;Statistical Processing of Large Image Sequences&quot;, IEEE Transactions on Geoscience and Remote Sensing, 12 (1), 80-93, 2005.</dd><dt><span>sensor :</span></dt><dd>VIIRS, AVHRR, ABI, Imager, SEVIRI, SEVIRI, AHI</dd><dt><span>source :</span></dt><dd>OSPO-ACSPO_VIIRS, OSPO-ACSPO_METOPB_FRAC, OSPO-GOES16_SST_L3, OSPO-GOES15_SST_L3, OSPO-METEOSAT11_SST_L3, OSPO-HIMA8_SST_L3</dd><dt><span>sourceUrl :</span></dt><dd>https://www.star.nesdis.noaa.gov/thredds/dodsC/BlendedSST5kmNightAggGHRSSTLoM</dd><dt><span>Southernmost_Northing :</span></dt><dd>-89.975</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degree</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v70</dd><dt><span>summary :</span></dt><dd>This dataset is an aggregation of Science Quality STAR data (2002-2016) and Near Real Time OSPO data (2017-present). Analysed blended sea surface temperature over the global ocean using night only input data. An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary InfraRed (IR) and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05� spatial resolution.</dd><dt><span>testOutOfDate :</span></dt><dd>now-17days</dd><dt><span>time_coverage_end :</span></dt><dd>2021-08-12T12:00:00Z</dd><dt><span>time_coverage_start :</span></dt><dd>2002-09-01T12:00:00Z</dd><dt><span>title :</span></dt><dd>SST, NOAA Geo-Polar Blended Global Level 4, STAR (Science Quality, 2002-2016) + OSPO (Near Real Time, 2017-present), 0.05�, Nighttime</dd><dt><span>Westernmost_Easting :</span></dt><dd>-179.975</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (latitude: 3600, longitude: 7200, time: 6914)\n",
       "Coordinates:\n",
       "  * time              (time) object 2002-09-01 12:00:00 ... 2021-08-12 12:00:00\n",
       "  * latitude          (latitude) float32 -89.97 -89.93 -89.88 ... 89.93 89.97\n",
       "  * longitude         (longitude) float32 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
       "Data variables:\n",
       "    analysed_sst      (time, latitude, longitude) float32 ...\n",
       "    analysis_error    (time, latitude, longitude) float32 ...\n",
       "    sea_ice_fraction  (time, latitude, longitude) float32 ...\n",
       "    mask              (time, latitude, longitude) float32 ...\n",
       "Attributes: (12/50)\n",
       "    acknowledgement:            NOAA/NESDIS\n",
       "    cdm_data_type:              Grid\n",
       "    comment:                    The Geo-Polar Blended Sea Surface Temperature...\n",
       "    Conventions:                CF-1.6, Unidata Observation Dataset v1.0, COA...\n",
       "    creator_email:              john.sapper@noaa.gov\n",
       "    creator_name:               Office of Satellite Products and Operations\n",
       "    ...                         ...\n",
       "    summary:                    This dataset is an aggregation of Science Qua...\n",
       "    testOutOfDate:              now-17days\n",
       "    time_coverage_end:          2021-08-12T12:00:00Z\n",
       "    time_coverage_start:        2002-09-01T12:00:00Z\n",
       "    title:                      SST, NOAA Geo-Polar Blended Global Level 4, S...\n",
       "    Westernmost_Easting:        -179.975"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def point_to_dataset(dataset_id, base_url='https://coastwatch.pfeg.noaa.gov/erddap/griddap'):\n",
    "    base_url = base_url.rstrip('/')\n",
    "    full_url = '/'.join([base_url, dataset_id])\n",
    "    return xr.open_dataset(full_url)\n",
    "\n",
    "da = point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine the metadata\n",
    "### Examine the coordinate variables and dimensions\n",
    "* The code below lists the coordinate variables and their sizes.   \n",
    "* The dataset is a 3D array with:\n",
    "    * 6793 values in the time dimension (as of 4/15/2021 but that increases each day)  \n",
    "    * 3600 values in the latitude dimension\n",
    "    * 7200 values in the longitude dimension\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * time       (time) object 2002-09-01 12:00:00 ... 2021-08-12 12:00:00\n",
       "  * latitude   (latitude) float32 -89.97 -89.93 -89.88 ... 89.88 89.93 89.97\n",
       "  * longitude  (longitude) float32 -180.0 -179.9 -179.9 ... 179.9 179.9 180.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Frozen(SortedKeysDict({'time': 6914, 'latitude': 3600, 'longitude': 7200}))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(da.coords)\n",
    "display(da.dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine the data variables\n",
    "* The code below lists the data variables. \n",
    "* There are several variables in the dataset. We are interested in \"analysed_sst\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data variables ['analysed_sst', 'analysis_error', 'sea_ice_fraction', 'mask']\n"
     ]
    }
   ],
   "source": [
    "print ('data variables', list(da.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine the Global Attributes\n",
    "Global attributes provide information about a dataset as a whole. A few of the global attributes are important for helping you to decide if the dataset will work for your application:  \n",
    "\n",
    "* `geospatial_lat_min`, `geospatial_lat_max`, `geospatial_lon_min` and `geospatial_lon_max` provide the geographical range of the dataset  \n",
    "\n",
    "\n",
    "* `time_coverage_start` and `time_coverage_end` provide the time range covered by the dataset \n",
    "\n",
    "\n",
    "* `geospatial_lat_resolution` and `geospatial_lon_resolution` povide the spatial resolution\n",
    "\n",
    "\n",
    "* Attributes like `comment`, `summary`, and `references` provide more information about:\n",
    "    * how the dataset was generated\n",
    "    * how you may use the data\n",
    "    * the people and organizations to acknowledge if you use the data\n",
    "\n",
    "The code below lists these global attributes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude range: -89.975 to 89.975\n",
      "Longitude range: -179.975 to 179.975\n",
      "Time range: 2002-09-01T12:00:00Z to 2021-08-12T12:00:00Z\n",
      "Spatial resolution (degrees): lat 0.05 lon 0.05\n",
      " \n",
      "Dataset summary:\n",
      "This dataset is an aggregation of Science Quality STAR data (2002-2016) and Near Real Time OSPO data (2017-present). Analysed blended sea surface temperature over the global ocean using night only input data. An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary InfraRed (IR) and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05� spatial resolution.\n"
     ]
    }
   ],
   "source": [
    "print('Latitude range:', da.geospatial_lat_min, 'to', da.geospatial_lat_max)\n",
    "print('Longitude range:', da.geospatial_lon_min, 'to', da.geospatial_lon_max)\n",
    "print('Time range:', da.time_coverage_start, 'to', da.time_coverage_end)\n",
    "print('Spatial resolution (degrees):', \n",
    "      'lat', np.around(da.geospatial_lat_resolution, decimals=3), \n",
    "      'lon', np.around(da.geospatial_lon_resolution, decimals=3) \n",
    "     )\n",
    "\n",
    "print(' ')\n",
    "print('Dataset summary:')\n",
    "print(da.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download data from ERDDAP\n",
    "\n",
    "For this exercise, the area we are interested in includes Monterey Bay, CA:  \n",
    "* Latitude range: 32N, 39N\n",
    "* Longitude range: -124E, -117E\n",
    "* Time range June 3, 2020 to June 7, 2020 \n",
    "\n",
    "The Xarray module makes it really easy to request a subset of a dataset using latitude, longitude, and time ranges. Here is an example using the `sel` method with the `slice` function.  \n",
    "```python\n",
    "sst = da['analysed_sst'].sel(  \n",
    "                  latitude=slice(32., 39.),  \n",
    "                  longitude=slice(-124, -117),  \n",
    "                  time=slice('2020-06-03T12:00:00', '2020-06-07T12:00:00')  \n",
    "                 )  \n",
    "```\n",
    "\n",
    ">Note: If the dataset has an altitude dimension, an altitude slice would need to be added, e.g.  \n",
    ">```python \n",
    "                  altitude=slice(0.0), \n",
    ">```\n",
    "\n",
    "### Create a subsetting function\n",
    "* Use these xarray features in a function to make it more versatile.  \n",
    "* `my_da` is the data array produced from `open_dataset`\n",
    "* `my_var` is the name of the variable\n",
    "* the other inputs are the geographic and time ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(my_da, my_var,\n",
    "             my_lt_min, my_lt_max, \n",
    "             my_ln_min, my_ln_max, \n",
    "             my_tm_min, my_tm_max\n",
    "            ):\n",
    "    \n",
    "    my_data = my_da[my_var].sel(\n",
    "                                latitude=slice(my_lt_min, my_lt_max), \n",
    "                                longitude=slice(my_ln_min, my_ln_max), \n",
    "                                time=slice(my_tm_min, my_tm_max)\n",
    "                               )\n",
    "    return my_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the subsetting function with our geographical and time ranges\n",
    "The returned sst data array is a subset of `da`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('time', 'latitude', 'longitude')\n",
      "Dimension size: (5, 140, 140)\n"
     ]
    },
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       "array([[[16.879993, 16.889994, ..., 18.779993, 18.679995],\n",
       "        [16.819994, 16.829994, ..., 18.809994, 18.729994],\n",
       "        ...,\n",
       "        [11.529994, 11.449994, ...,       nan,       nan],\n",
       "        [11.449994, 11.419994, ...,       nan,       nan]],\n",
       "\n",
       "       [[16.929995, 16.969994, ..., 18.679995, 18.589994],\n",
       "        [16.899994, 16.949993, ..., 18.729994, 18.649994],\n",
       "        ...,\n",
       "        [11.319994, 11.179994, ...,       nan,       nan],\n",
       "        [11.229994, 11.139994, ...,       nan,       nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[16.979994, 16.979994, ..., 18.559994, 18.519995],\n",
       "        [16.979994, 16.989994, ..., 18.589994, 18.559994],\n",
       "        ...,\n",
       "        [10.599994, 10.519994, ...,       nan,       nan],\n",
       "        [10.459994, 10.389994, ...,       nan,       nan]],\n",
       "\n",
       "       [[17.059994, 17.059994, ..., 18.539993, 18.439995],\n",
       "        [17.069994, 17.059994, ..., 18.589994, 18.509995],\n",
       "        ...,\n",
       "        [11.369994, 11.189994, ...,       nan,       nan],\n",
       "        [11.109994, 10.949994, ...,       nan,       nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time       (time) object 2020-06-03 12:00:00 ... 2020-06-07 12:00:00\n",
       "  * latitude   (latitude) float32 32.03 32.08 32.12 32.17 ... 38.88 38.92 38.97\n",
       "  * longitude  (longitude) float32 -124.0 -123.9 -123.9 ... -117.1 -117.1 -117.0\n",
       "Attributes:\n",
       "    colorBarMaximum:  32.0\n",
       "    colorBarMinimum:  0.0\n",
       "    comment:          nighttime analysed SST for each ocean grid point\n",
       "    ioos_category:    Temperature\n",
       "    long_name:        analysed sea surface temperature\n",
       "    references:       Fieguth,P.W. et al. &quot;Mapping Mediterranean altimeter da...\n",
       "    source:           OSPO-ACSPO_VIIRS, OSPO-ACSPO_METOPB_FRAC, OSPO-GOES16_S...\n",
       "    standard_name:    sea_surface_foundation_temperature\n",
       "    units:            degree_C\n",
       "    valid_max:        39.999992\n",
       "    valid_min:        -2.0000062</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>latitude</span>: 140</li><li><span class='xr-has-index'>longitude</span>: 140</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2ba8cb3b-352e-4535-a43e-9942f1f1beb8' class='xr-array-in' type='checkbox' checked><label for='section-2ba8cb3b-352e-4535-a43e-9942f1f1beb8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>16.88 16.89 16.93 16.98 17.04 17.1 17.16 ... nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[16.879993, 16.889994, ..., 18.779993, 18.679995],\n",
       "        [16.819994, 16.829994, ..., 18.809994, 18.729994],\n",
       "        ...,\n",
       "        [11.529994, 11.449994, ...,       nan,       nan],\n",
       "        [11.449994, 11.419994, ...,       nan,       nan]],\n",
       "\n",
       "       [[16.929995, 16.969994, ..., 18.679995, 18.589994],\n",
       "        [16.899994, 16.949993, ..., 18.729994, 18.649994],\n",
       "        ...,\n",
       "        [11.319994, 11.179994, ...,       nan,       nan],\n",
       "        [11.229994, 11.139994, ...,       nan,       nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[16.979994, 16.979994, ..., 18.559994, 18.519995],\n",
       "        [16.979994, 16.989994, ..., 18.589994, 18.559994],\n",
       "        ...,\n",
       "        [10.599994, 10.519994, ...,       nan,       nan],\n",
       "        [10.459994, 10.389994, ...,       nan,       nan]],\n",
       "\n",
       "       [[17.059994, 17.059994, ..., 18.539993, 18.439995],\n",
       "        [17.069994, 17.059994, ..., 18.589994, 18.509995],\n",
       "        ...,\n",
       "        [11.369994, 11.189994, ...,       nan,       nan],\n",
       "        [11.109994, 10.949994, ...,       nan,       nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e0fc2817-ba75-4c6c-880a-237a2371aed8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e0fc2817-ba75-4c6c-880a-237a2371aed8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2020-06-03 12:00:00 ... 2020-06-...</div><input id='attrs-0250a910-94cd-497d-a871-4e0d41ef07e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0250a910-94cd-497d-a871-4e0d41ef07e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c03e334-c8c0-4bbc-9dd2-19802ad2e487' class='xr-var-data-in' type='checkbox'><label for='data-5c03e334-c8c0-4bbc-9dd2-19802ad2e487' 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>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>actual_range :</span></dt><dd>[1.0308816e+09 1.6287696e+09]</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Nominal time of Level 4 analysis</dd><dt><span>ioos_category :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>01-JAN-1970 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2020, 6, 3, 12, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 4, 12, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 5, 12, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 6, 12, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 7, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>32.03 32.08 32.12 ... 38.92 38.97</div><input id='attrs-774399ff-8dbb-4fc3-8825-df6b2a01fbf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-774399ff-8dbb-4fc3-8825-df6b2a01fbf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d8896ad-b5c5-431b-bb21-dff115dc0a24' class='xr-var-data-in' type='checkbox'><label for='data-3d8896ad-b5c5-431b-bb21-dff115dc0a24' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>actual_range :</span></dt><dd>[-89.975  89.975]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([32.025, 32.075, 32.125, 32.175, 32.225, 32.275, 32.325, 32.375, 32.425,\n",
       "       32.475, 32.525, 32.575, 32.625, 32.675, 32.725, 32.775, 32.825, 32.875,\n",
       "       32.925, 32.975, 33.025, 33.075, 33.125, 33.175, 33.225, 33.275, 33.325,\n",
       "       33.375, 33.425, 33.475, 33.525, 33.575, 33.625, 33.675, 33.725, 33.775,\n",
       "       33.825, 33.875, 33.925, 33.975, 34.025, 34.075, 34.125, 34.175, 34.225,\n",
       "       34.275, 34.325, 34.375, 34.425, 34.475, 34.525, 34.575, 34.625, 34.675,\n",
       "       34.725, 34.775, 34.825, 34.875, 34.925, 34.975, 35.025, 35.075, 35.125,\n",
       "       35.175, 35.225, 35.275, 35.325, 35.375, 35.425, 35.475, 35.525, 35.575,\n",
       "       35.625, 35.675, 35.725, 35.775, 35.825, 35.875, 35.925, 35.975, 36.025,\n",
       "       36.075, 36.125, 36.175, 36.225, 36.275, 36.325, 36.375, 36.425, 36.475,\n",
       "       36.525, 36.575, 36.625, 36.675, 36.725, 36.775, 36.825, 36.875, 36.925,\n",
       "       36.975, 37.025, 37.075, 37.125, 37.175, 37.225, 37.275, 37.325, 37.375,\n",
       "       37.425, 37.475, 37.525, 37.575, 37.625, 37.675, 37.725, 37.775, 37.825,\n",
       "       37.875, 37.925, 37.975, 38.025, 38.075, 38.125, 38.175, 38.225, 38.275,\n",
       "       38.325, 38.375, 38.425, 38.475, 38.525, 38.575, 38.625, 38.675, 38.725,\n",
       "       38.775, 38.825, 38.875, 38.925, 38.975], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-124.0 -123.9 ... -117.1 -117.0</div><input id='attrs-a426463c-9134-4194-bc80-e136ec18388b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a426463c-9134-4194-bc80-e136ec18388b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df2f7f39-e9cf-47de-839f-9a60d057d0a8' class='xr-var-data-in' type='checkbox'><label for='data-df2f7f39-e9cf-47de-839f-9a60d057d0a8' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>actual_range :</span></dt><dd>[-179.975  179.975]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-123.975, -123.925, -123.875, -123.825, -123.775, -123.725, -123.675,\n",
       "       -123.625, -123.575, -123.525, -123.475, -123.425, -123.375, -123.325,\n",
       "       -123.275, -123.225, -123.175, -123.125, -123.075, -123.025, -122.975,\n",
       "       -122.925, -122.875, -122.825, -122.775, -122.725, -122.675, -122.625,\n",
       "       -122.575, -122.525, -122.475, -122.425, -122.375, -122.325, -122.275,\n",
       "       -122.225, -122.175, -122.125, -122.075, -122.025, -121.975, -121.925,\n",
       "       -121.875, -121.825, -121.775, -121.725, -121.675, -121.625, -121.575,\n",
       "       -121.525, -121.475, -121.425, -121.375, -121.325, -121.275, -121.225,\n",
       "       -121.175, -121.125, -121.075, -121.025, -120.975, -120.925, -120.875,\n",
       "       -120.825, -120.775, -120.725, -120.675, -120.625, -120.575, -120.525,\n",
       "       -120.475, -120.425, -120.375, -120.325, -120.275, -120.225, -120.175,\n",
       "       -120.125, -120.075, -120.025, -119.975, -119.925, -119.875, -119.825,\n",
       "       -119.775, -119.725, -119.675, -119.625, -119.575, -119.525, -119.475,\n",
       "       -119.425, -119.375, -119.325, -119.275, -119.225, -119.175, -119.125,\n",
       "       -119.075, -119.025, -118.975, -118.925, -118.875, -118.825, -118.775,\n",
       "       -118.725, -118.675, -118.625, -118.575, -118.525, -118.475, -118.425,\n",
       "       -118.375, -118.325, -118.275, -118.225, -118.175, -118.125, -118.075,\n",
       "       -118.025, -117.975, -117.925, -117.875, -117.825, -117.775, -117.725,\n",
       "       -117.675, -117.625, -117.575, -117.525, -117.475, -117.425, -117.375,\n",
       "       -117.325, -117.275, -117.225, -117.175, -117.125, -117.075, -117.025],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e94cfe5d-d01d-4a7e-b2a8-3b09cb310af3' class='xr-section-summary-in' type='checkbox'  ><label for='section-e94cfe5d-d01d-4a7e-b2a8-3b09cb310af3' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>colorBarMaximum :</span></dt><dd>32.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>nighttime analysed SST for each ocean grid point</dd><dt><span>ioos_category :</span></dt><dd>Temperature</dd><dt><span>long_name :</span></dt><dd>analysed sea surface temperature</dd><dt><span>references :</span></dt><dd>Fieguth,P.W. et al. &quot;Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm&quot;, J. Atmos. Ocean Tech, 15 (2): 535-546, 1998.     Fieguth, P. Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10(11), 1676-1686, 2001.     Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, &quot;Statistical Processing of Large Image Sequences&quot;, IEEE Transactions on Geoscience and Remote Sensing, 12 (1), 80-93, 2005.</dd><dt><span>source :</span></dt><dd>OSPO-ACSPO_VIIRS, OSPO-ACSPO_METOPB_FRAC, OSPO-GOES16_SST_L3,OSPO-GOES15_SST_L3, OSPO-METEOSAT11_SST_L3, OSPO-HIMA8_SST_L3</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>valid_max :</span></dt><dd>39.999992</dd><dt><span>valid_min :</span></dt><dd>-2.0000062</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'analysed_sst' (time: 5, latitude: 140, longitude: 140)>\n",
       "array([[[16.879993, 16.889994, ..., 18.779993, 18.679995],\n",
       "        [16.819994, 16.829994, ..., 18.809994, 18.729994],\n",
       "        ...,\n",
       "        [11.529994, 11.449994, ...,       nan,       nan],\n",
       "        [11.449994, 11.419994, ...,       nan,       nan]],\n",
       "\n",
       "       [[16.929995, 16.969994, ..., 18.679995, 18.589994],\n",
       "        [16.899994, 16.949993, ..., 18.729994, 18.649994],\n",
       "        ...,\n",
       "        [11.319994, 11.179994, ...,       nan,       nan],\n",
       "        [11.229994, 11.139994, ...,       nan,       nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[16.979994, 16.979994, ..., 18.559994, 18.519995],\n",
       "        [16.979994, 16.989994, ..., 18.589994, 18.559994],\n",
       "        ...,\n",
       "        [10.599994, 10.519994, ...,       nan,       nan],\n",
       "        [10.459994, 10.389994, ...,       nan,       nan]],\n",
       "\n",
       "       [[17.059994, 17.059994, ..., 18.539993, 18.439995],\n",
       "        [17.069994, 17.059994, ..., 18.589994, 18.509995],\n",
       "        ...,\n",
       "        [11.369994, 11.189994, ...,       nan,       nan],\n",
       "        [11.109994, 10.949994, ...,       nan,       nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time       (time) object 2020-06-03 12:00:00 ... 2020-06-07 12:00:00\n",
       "  * latitude   (latitude) float32 32.03 32.08 32.12 32.17 ... 38.88 38.92 38.97\n",
       "  * longitude  (longitude) float32 -124.0 -123.9 -123.9 ... -117.1 -117.1 -117.0\n",
       "Attributes:\n",
       "    colorBarMaximum:  32.0\n",
       "    colorBarMinimum:  0.0\n",
       "    comment:          nighttime analysed SST for each ocean grid point\n",
       "    ioos_category:    Temperature\n",
       "    long_name:        analysed sea surface temperature\n",
       "    references:       Fieguth,P.W. et al. \"Mapping Mediterranean altimeter da...\n",
       "    source:           OSPO-ACSPO_VIIRS, OSPO-ACSPO_METOPB_FRAC, OSPO-GOES16_S...\n",
       "    standard_name:    sea_surface_foundation_temperature\n",
       "    units:            degree_C\n",
       "    valid_max:        39.999992\n",
       "    valid_min:        -2.0000062"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_min  = 32.\n",
    "lat_max = 39.\n",
    "lon_min = -124.\n",
    "lon_max = -117.\n",
    "time_min = '2020-06-03T12:00:00'  # written in ISO format\n",
    "time_max = '2020-06-07T12:00:00'  # written in ISO format\n",
    "my_var = 'analysed_sst'\n",
    "\n",
    "sst = get_data(\n",
    "               da, my_var,\n",
    "               lat_min, lat_max,\n",
    "               lon_min, lon_max,\n",
    "               time_min, time_max\n",
    "              )\n",
    "\n",
    "\n",
    "print(sst.dims)\n",
    "print('Dimension size:', sst.shape)\n",
    "sst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the satellite SST data\n",
    "\n",
    "### Make a simple plot\n",
    "Xarray makes it easy to quickly visualize the data as a map.  \n",
    "* Use the isel method to pick a time slice by its index number \n",
    "* Use the imshow method to plot the data\n",
    "* We have 5 time steps so the index numbers are 0 to 4.  \n",
    "\n",
    "Plot the first time step:  \n",
    "```\n",
    "sst.isel(time=0).plot.imshow()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19aa7baf0>"
      ]
     },
     "execution_count": 34,
     "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": [
    "%matplotlib inline\n",
    "sst.isel(time=0).plot.imshow()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use a loop to plot all 5 time steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for i in range(0,5):\n",
    "    ax = plt.subplot()\n",
    "    sst.isel(time=i).plot.imshow()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the mean SST over the region for each day \n",
    "* Use the `numpy mean()` method to take the mean of the latitude longitude grid ( axis=(1,2) ) for each time slice.\n",
    "* Use the `matplotlib.pyplot.plot_date()` plot routine, which formats the x axis labels as dates\n",
    "* Use `sst.time` as the x axis values and the mean as the Y axis values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot_date(sst.time, sst.mean(axis=(1,2)), 'o')\n",
    "\n",
    "# auto format the date label positions on the x axis\n",
    "plt.gcf().autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation instructions for the modules used in this tutorial:\n",
    "pandas: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html   \n",
    "numpy: https://numpy.org/install/  \n",
    "xarray: http://xarray.pydata.org/en/latest/getting-started-guide/installing.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}