2_extract_at_ship_stations_edmw.ipynb

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    "<p style=\"font-size:14px; text-align: right\">CoastWatch Python Exercises</p>  \n",
    "\n",
    "# Extracting data at ship stations tutorial\n",
    "> history | updated August 2021  \n",
    "> owner | NOAA CoastWatch"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this exercise, you will import a ship track and extract satellite SST values at locations along the track.\n",
    "The exercise demonstrates the following skills:\n",
    "\n",
    "*    Getting data from a remote CSV file\n",
    "*    Downloading satellite data from ERDDAP in netCDF format\n",
    "*    Extracting data with Python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look for python modules you might not have installed\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. \n",
    "* If you find missing modules, please go to the appendix at the bottom of the page for installation instructions."
   ]
  },
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   "execution_count": 26,
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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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the primary modules used in 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,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import numpy.ma as ma \n",
    "import pandas as pd  \n",
    "import xarray as xr  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate means for a ship station track \n",
    "\n",
    "In the notebook repo you will see a .csv file file in the /data directory named my_monterey_track.csv. Load a station location file (.csv) with the time, latitude, and longitude coordinates for ship station locations.\n",
    "\n",
    "For this example the CSV file has three columns, with each row locating a station in time and space:  \n",
    "`sample_date\tsample_time\tsample_lat\tsample_lon`  \n",
    "`6/3/20\t        18:00\t    37.3\t    -123.2`\n",
    "\n",
    "### Load the CSV file data using the \"read_csv\" function from the Pandas module.  \n",
    "To use the date and time, we will need to create a Pandas date object. Pandas has a nice option (parse_dates) to create a column date object from the date and time fields as the CSV file is loaded. We will also keep the original sample_date and sample_time by setting the keep_date_col to True.\n",
    "\n",
    "### The Xarray module requires dates be formatted in a ISO 8601 date format, so we will also create an iso_date column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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       "  sample_date_sample_time sample_date sample_time  sample_lat  sample_lon  \\\n",
       "0     2020-06-03 18:00:00      6/3/20       18:00        37.3       236.8   \n",
       "1     2020-06-04 19:34:00      6/4/20       19:34        36.1       237.5   \n",
       "2     2020-06-05 20:15:00      6/5/20       20:15        34.4       238.2   \n",
       "3     2020-06-06 18:45:00      6/6/20       18:45        32.2       239.8   \n",
       "4     2020-06-07 12:52:00      6/7/20       12:52        31.9       240.9   \n",
       "5     2020-06-08 14:23:00      6/8/20       14:23        31.9       241.8   \n",
       "\n",
       "     iso_date  \n",
       "0  2020-06-03  \n",
       "1  2020-06-04  \n",
       "2  2020-06-05  \n",
       "3  2020-06-06  \n",
       "4  2020-06-07  \n",
       "5  2020-06-08  "
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   "source": [
    "# Load the file into a Pandas data frame\n",
    "# This creates a date object column named 'sample_date_sample_time' \n",
    "track_DF = pd.read_csv('../data/my_monterey_track.csv', parse_dates=[['sample_date', 'sample_time']], keep_date_col=True) \n",
    "\n",
    "\n",
    "# track_DF['iso_date'] = track_DF.sample_date_sample_time.dt.strftime('%Y-%m-%dT12:%M:%S')\n",
    "track_DF['iso_date'] = track_DF.sample_date_sample_time.dt.strftime('%Y-%m-%d')\n",
    "# Display the data\n",
    "display(track_DF)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the satellite SST data from ERDDAP \n",
    "* We use two functions to get the data, these are described in greater detail in Tutotial 1\n",
    "* point_to_dataset sets up the request url and returns a dataset array\n",
    "* get_data makes a subset request and returns the satellite data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
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   "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",
    "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"
   ]
  },
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   "cell_type": "code",
   "execution_count": 30,
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       "\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\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-589ddb05-84ee-4b9b-9af8-9577fdcc4c2d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-589ddb05-84ee-4b9b-9af8-9577fdcc4c2d' 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-b0cae917-bd41-4277-915e-717031c429b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0cae917-bd41-4277-915e-717031c429b4' 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-7d262cfc-e3eb-488f-bda6-c662cdeb0d6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d262cfc-e3eb-488f-bda6-c662cdeb0d6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9357b865-02d8-4e9a-af85-6a2c3e6863f3' class='xr-var-data-in' type='checkbox'><label for='data-9357b865-02d8-4e9a-af85-6a2c3e6863f3' 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-7e3577f3-3a1f-4a6a-9dbf-9333250e1449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e3577f3-3a1f-4a6a-9dbf-9333250e1449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01cb7113-de97-48db-bc83-4085e4b5ed1b' class='xr-var-data-in' type='checkbox'><label for='data-01cb7113-de97-48db-bc83-4085e4b5ed1b' 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-5c4cc05b-2a1e-4616-ba51-90ba903def7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c4cc05b-2a1e-4616-ba51-90ba903def7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f5d7044-4264-465e-b8f1-ab24e36885f5' class='xr-var-data-in' type='checkbox'><label for='data-8f5d7044-4264-465e-b8f1-ab24e36885f5' 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-977a85f7-071c-4846-bc6f-d7eb0c89b189' class='xr-section-summary-in' type='checkbox'  checked><label for='section-977a85f7-071c-4846-bc6f-d7eb0c89b189' 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-54688a70-d5e5-4993-acf2-5244012bb146' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54688a70-d5e5-4993-acf2-5244012bb146' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-170987ed-3490-4e97-9f7c-bf19f85dee01' class='xr-var-data-in' type='checkbox'><label for='data-170987ed-3490-4e97-9f7c-bf19f85dee01' 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-6f4d3cb7-1836-4b30-bc06-0c1d4f5767fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f4d3cb7-1836-4b30-bc06-0c1d4f5767fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87ab7dc8-729a-4a34-94d5-932d54552655' class='xr-var-data-in' type='checkbox'><label for='data-87ab7dc8-729a-4a34-94d5-932d54552655' 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-b5a3556e-ba9e-4968-bfdb-ce2149a57d2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5a3556e-ba9e-4968-bfdb-ce2149a57d2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41456ed6-2218-4a0b-8f78-0931f42b00bf' class='xr-var-data-in' type='checkbox'><label for='data-41456ed6-2218-4a0b-8f78-0931f42b00bf' 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-f626e1cc-a366-4474-b536-7a1e743df354' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f626e1cc-a366-4474-b536-7a1e743df354' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-729e7644-0005-4a4d-b657-1cde53780d60' class='xr-var-data-in' type='checkbox'><label for='data-729e7644-0005-4a4d-b657-1cde53780d60' 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-cb0b3efb-b79e-4ad2-ac6a-1f6a5b9cf131' class='xr-section-summary-in' type='checkbox'  ><label for='section-cb0b3efb-b79e-4ad2-ac6a-1f6a5b9cf131' 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",
       "2021-08-13T23:49:55Z https://www.star.nesdis.noaa.gov/thredds/dodsC/BlendedSST5kmNightAggGHRSSTLoM\n",
       "2021-08-13T23:49:55Z 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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the track data\n",
    "### Check that longitudes are in the same format\n",
    "\n",
    "Ensure the longitude format of the track data is compatible with the SST dataset. Some datasets in ERDDAP use a -180°E to +180°E format, others use 0-360°.\n",
    "\n",
    "* We can see from the metadata printed above that the satellite SST data product has longitudes in a -180 to +180 format.  \n",
    "    longitude range: -179.975 to 179.975\n",
    "\n",
    "We can use the following function to check the longitude formats in the satellite and ship track datasets and adjust the track longitudes if needed. Here is how the function works:  \n",
    "* If both longitude lists have the same format, then the station location longitudes are returned unchanged.\n",
    "* If the SST longitudes are -180 to 180 and the station location longitudes are 0-360, then 360 is subtracted from any station location longitude > 180\n",
    "* If the SST longitudes are 0 to 360 and the station location longitudes are -180 to 180, then 360 is added to any station location longitude < 0    \n",
    "* `dataset_lon` is the longitude column from the xarray data array ( sst['longitude'] )  \n",
    "* `location_lon` is the longitude column from the Pandas data frame, ( track_DF['sample_lon'] ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_it_180_to_180(dataset_lon, location_lon):\n",
    "    datasat_is = np.any((dataset_lon < 0))\n",
    "    location_is = np.any((location_lon < 0))\n",
    "    if datasat_is and location_is:\n",
    "        return location_lon    \n",
    "    elif not datasat_is and location_is:\n",
    "        return [ x if x>0 else x+360 for x in location_lon ]\n",
    "    elif datasat_is and not location_is:\n",
    "        return [ x if x<=180 else x-360 for x in location_lon ]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Call the `is_it_180_to_180` function__\n",
    "* Pass the dataset longitudes and the station location longitudes to the function \n",
    "* Add the results to the Pandas track data frame with the ship station information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_date_sample_time</th>\n",
       "      <th>sample_date</th>\n",
       "      <th>sample_time</th>\n",
       "      <th>sample_lat</th>\n",
       "      <th>sample_lon</th>\n",
       "      <th>iso_date</th>\n",
       "      <th>sample_lon_corrected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-03 18:00:00</td>\n",
       "      <td>6/3/20</td>\n",
       "      <td>18:00</td>\n",
       "      <td>37.3</td>\n",
       "      <td>236.8</td>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>-123.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-06-04 19:34:00</td>\n",
       "      <td>6/4/20</td>\n",
       "      <td>19:34</td>\n",
       "      <td>36.1</td>\n",
       "      <td>237.5</td>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>-122.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-05 20:15:00</td>\n",
       "      <td>6/5/20</td>\n",
       "      <td>20:15</td>\n",
       "      <td>34.4</td>\n",
       "      <td>238.2</td>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>-121.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-06-06 18:45:00</td>\n",
       "      <td>6/6/20</td>\n",
       "      <td>18:45</td>\n",
       "      <td>32.2</td>\n",
       "      <td>239.8</td>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>-120.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-07 12:52:00</td>\n",
       "      <td>6/7/20</td>\n",
       "      <td>12:52</td>\n",
       "      <td>31.9</td>\n",
       "      <td>240.9</td>\n",
       "      <td>2020-06-07</td>\n",
       "      <td>-119.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-08 14:23:00</td>\n",
       "      <td>6/8/20</td>\n",
       "      <td>14:23</td>\n",
       "      <td>31.9</td>\n",
       "      <td>241.8</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>-118.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sample_date_sample_time sample_date sample_time  sample_lat  sample_lon  \\\n",
       "0     2020-06-03 18:00:00      6/3/20       18:00        37.3       236.8   \n",
       "1     2020-06-04 19:34:00      6/4/20       19:34        36.1       237.5   \n",
       "2     2020-06-05 20:15:00      6/5/20       20:15        34.4       238.2   \n",
       "3     2020-06-06 18:45:00      6/6/20       18:45        32.2       239.8   \n",
       "4     2020-06-07 12:52:00      6/7/20       12:52        31.9       240.9   \n",
       "5     2020-06-08 14:23:00      6/8/20       14:23        31.9       241.8   \n",
       "\n",
       "     iso_date  sample_lon_corrected  \n",
       "0  2020-06-03                -123.2  \n",
       "1  2020-06-04                -122.5  \n",
       "2  2020-06-05                -121.8  \n",
       "3  2020-06-06                -120.2  \n",
       "4  2020-06-07                -119.1  \n",
       "5  2020-06-08                -118.2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_DF['sample_lon_corrected'] = is_it_180_to_180(\n",
    "                                                    sst['longitude'], \n",
    "                                                    track_DF['sample_lon']\n",
    "                                                   )\n",
    "\n",
    "track_DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select an area around each ship station to get matching satellite data\n",
    "Here we have a function for extracting satellite (or any gridded data) from ERDDAP at a ship track point. There are options to return only the exact match as a single point or to extract all satellite data within a distance from the ship location.\n",
    "\n",
    "* Use a single latitude and longitude pair to extract SST data, then the data from a single point will be returned. \n",
    "* To increase to chances of getting data (if there are gaps due to clouds for example) and improve your statistics, you can request a box of SST data around the station location, e.g. a latitude range and a longitude range.  \n",
    "\n",
    "* The box can increase by multiples of the spatial resolution of the dataset. \n",
    "    > For this dataset the spatial resolution was 0.05 degrees for the latitude and longitude. That is approximately 5 km resolution. So, a 5 pixel X 5 pixel box around each station location would result in a 25 x 25 km box (625 km^2) of data.  \n",
    "\n",
    "The following `data_box` function generates a latitude and longitude range box for each station location. The function asks for:\n",
    "\n",
    "* Station latitude and longitude\n",
    "* Spatial resolution of satellite dataset. (`da.geospatial_lat_resolution`, `da.geospatial_lon_resolution`)  \n",
    "* Number of pixels you want added to each side of the latitude and longitude center point  \n",
    "    * Choosing `2` for latitude, results in a range of five pixels: the station latitude for the center plus two pixels on each side. If you also choose `2` for the longitude, then you would get a 5x5 box around the station location. The default value for the function is `1`, which is a 3x3 box  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_box(\n",
    "             st_lat, st_lon, \n",
    "             lat_res, lon_res, \n",
    "             lat_pixels = 1, lon_pixels = 1\n",
    "            ):\n",
    "    \n",
    "    lt_min = st_lat - lat_res * lat_pixels\n",
    "    lt_max = st_lat + lat_res * lat_pixels\n",
    "    ln_min = st_lon - lon_res * lon_pixels\n",
    "    ln_max = st_lon + lon_res * lon_pixels\n",
    "\n",
    "    return lt_min, lt_max, ln_min, ln_max\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Call the data_box function  \n",
    "\n",
    "Add the satellite data request areas to the ship track data frame:\n",
    "\n",
    "```new_Pandas_dataframe_column = data_box(st_lat, st_lon, lat_res, lon_res, lat_pixels = 1, lon_pixels = 1)```\n",
    "\n",
    "Results in the following new columns in the pandas data frame:\n",
    "\n",
    "`track_DF['lat_min']`, `track_DF['lat_min']`, `track_DF['lon_min']`, `track_DF['lon_min']`\n",
    "\n",
    "Inputs:  \n",
    "* st_lat = the latitude column from the Pandas station location data frame, track_DF['sample_lat']\n",
    "* st_lon = the longitude column from the Pandas station location data frame, track_DF['sample_lon_corrected']\n",
    "    * make sure to use the corrected longitude column\n",
    "* lat_res = the spatial resolution for satellite latitude, da.geospatial_lat_resolution\n",
    "* lon_res = the spatial resolution for satellite longitude, da.geospatial_lon_resolution \n",
    "* lat_pixels = the number of pixels to add to each side of the latitude center point (default = 1) \n",
    "* lon_pixels = the number of pixels to add to each side of the longitude center point (default = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>sample_date_sample_time</th>\n",
       "      <th>sample_date</th>\n",
       "      <th>sample_time</th>\n",
       "      <th>sample_lat</th>\n",
       "      <th>sample_lon</th>\n",
       "      <th>iso_date</th>\n",
       "      <th>sample_lon_corrected</th>\n",
       "      <th>lat_min</th>\n",
       "      <th>lat_max</th>\n",
       "      <th>lon_min</th>\n",
       "      <th>lon_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-03 18:00:00</td>\n",
       "      <td>6/3/20</td>\n",
       "      <td>18:00</td>\n",
       "      <td>37.3</td>\n",
       "      <td>236.8</td>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>-123.2</td>\n",
       "      <td>37.2</td>\n",
       "      <td>37.4</td>\n",
       "      <td>-123.3</td>\n",
       "      <td>-123.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-06-04 19:34:00</td>\n",
       "      <td>6/4/20</td>\n",
       "      <td>19:34</td>\n",
       "      <td>36.1</td>\n",
       "      <td>237.5</td>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>-122.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>-122.6</td>\n",
       "      <td>-122.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-05 20:15:00</td>\n",
       "      <td>6/5/20</td>\n",
       "      <td>20:15</td>\n",
       "      <td>34.4</td>\n",
       "      <td>238.2</td>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>-121.8</td>\n",
       "      <td>34.3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>-121.9</td>\n",
       "      <td>-121.7</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-06-06 18:45:00</td>\n",
       "      <td>6/6/20</td>\n",
       "      <td>18:45</td>\n",
       "      <td>32.2</td>\n",
       "      <td>239.8</td>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>-120.2</td>\n",
       "      <td>32.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>-120.3</td>\n",
       "      <td>-120.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-07 12:52:00</td>\n",
       "      <td>6/7/20</td>\n",
       "      <td>12:52</td>\n",
       "      <td>31.9</td>\n",
       "      <td>240.9</td>\n",
       "      <td>2020-06-07</td>\n",
       "      <td>-119.1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-119.2</td>\n",
       "      <td>-119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-08 14:23:00</td>\n",
       "      <td>6/8/20</td>\n",
       "      <td>14:23</td>\n",
       "      <td>31.9</td>\n",
       "      <td>241.8</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>-118.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-118.3</td>\n",
       "      <td>-118.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sample_date_sample_time sample_date sample_time  sample_lat  sample_lon  \\\n",
       "0     2020-06-03 18:00:00      6/3/20       18:00        37.3       236.8   \n",
       "1     2020-06-04 19:34:00      6/4/20       19:34        36.1       237.5   \n",
       "2     2020-06-05 20:15:00      6/5/20       20:15        34.4       238.2   \n",
       "3     2020-06-06 18:45:00      6/6/20       18:45        32.2       239.8   \n",
       "4     2020-06-07 12:52:00      6/7/20       12:52        31.9       240.9   \n",
       "5     2020-06-08 14:23:00      6/8/20       14:23        31.9       241.8   \n",
       "\n",
       "     iso_date  sample_lon_corrected  lat_min  lat_max  lon_min  lon_max  \n",
       "0  2020-06-03                -123.2     37.2     37.4   -123.3   -123.1  \n",
       "1  2020-06-04                -122.5     36.0     36.2   -122.6   -122.4  \n",
       "2  2020-06-05                -121.8     34.3     34.5   -121.9   -121.7  \n",
       "3  2020-06-06                -120.2     32.1     32.3   -120.3   -120.1  \n",
       "4  2020-06-07                -119.1     31.8     32.0   -119.2   -119.0  \n",
       "5  2020-06-08                -118.2     31.8     32.0   -118.3   -118.1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\n",
    " track_DF['lat_min'], track_DF['lat_max'],\n",
    " track_DF['lon_min'], track_DF['lon_max']\n",
    "] = data_box(\n",
    "             track_DF['sample_lat'], track_DF['sample_lon_corrected'],\n",
    "             da.geospatial_lat_resolution, da.geospatial_lon_resolution,\n",
    "             2, 2\n",
    "            )\n",
    "                                                                                              \n",
    "track_DF                                                                                            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract SST data for each station location and generate some basic statistics\n",
    "* We now have the input parameters needed to obtain satellite data for the cruise track\n",
    "* Extract data for each station in the ship track\n",
    "   * Build a __for__ loop to use the `sample_date_sample_time`, `lat_min`, `lat_max`, `lon_min`, and `lon_max` columns from `track_DF` \n",
    "   * Use the `get_data()` function to download satellite data for each station.\n",
    "* Calculate the mean, standard deviation, and n stats, then add them to `track_DF`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "means [14.251244, 14.836244, 16.224995, 16.861244, 17.026245, 17.41437]\n",
      "stdevs [0.3615224, 0.12873788, 0.09499985, 0.048589565, 0.09873407, 0.10271109]\n",
      "ns [16, 16, 16, 16, 16, 16]\n"
     ]
    }
   ],
   "source": [
    "means = []\n",
    "stdevs = []\n",
    "ns = []\n",
    "\n",
    "for index, row in track_DF.iterrows():\n",
    "    \n",
    "    my_data = get_data(da, my_var,\n",
    "                       row['lat_min'], row['lat_max'], \n",
    "                       row['lon_min'], row['lon_max'], \n",
    "                       row['iso_date'], row['iso_date']\n",
    "                      )\n",
    "    \n",
    "    means.append(np.nanmean(my_data, axis=(1,2))[0])\n",
    "    stdevs.append(np.nanstd(my_data, axis=(1,2))[0])\n",
    "    ns.append(np.count_nonzero(my_data, axis=(1,2))[0])\n",
    "\n",
    "print('means', means)\n",
    "print('stdevs', stdevs)\n",
    "print('ns', ns)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">means = the average SST over the selected area at the ship location\n",
    ">\n",
    ">stdevs = An indicator of variability over the selected satellite data cells\n",
    ">\n",
    ">ns = The number of non-zero data points used to calculate the average\n",
    "\n",
    "\n",
    "Add the statistics to the track_DF dataframe and export as a csv\n",
    "* Uncomment the last line to save the dataframe as a CSV file called 'mbnms_results.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_date_sample_time</th>\n",
       "      <th>sample_date</th>\n",
       "      <th>sample_time</th>\n",
       "      <th>sample_lat</th>\n",
       "      <th>sample_lon</th>\n",
       "      <th>iso_date</th>\n",
       "      <th>sample_lon_corrected</th>\n",
       "      <th>lat_min</th>\n",
       "      <th>lat_max</th>\n",
       "      <th>lon_min</th>\n",
       "      <th>lon_max</th>\n",
       "      <th>mean</th>\n",
       "      <th>stdev</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-03 18:00:00</td>\n",
       "      <td>6/3/20</td>\n",
       "      <td>18:00</td>\n",
       "      <td>37.3</td>\n",
       "      <td>236.8</td>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>-123.2</td>\n",
       "      <td>37.2</td>\n",
       "      <td>37.4</td>\n",
       "      <td>-123.3</td>\n",
       "      <td>-123.1</td>\n",
       "      <td>14.251244</td>\n",
       "      <td>0.361522</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-06-04 19:34:00</td>\n",
       "      <td>6/4/20</td>\n",
       "      <td>19:34</td>\n",
       "      <td>36.1</td>\n",
       "      <td>237.5</td>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>-122.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>-122.6</td>\n",
       "      <td>-122.4</td>\n",
       "      <td>14.836244</td>\n",
       "      <td>0.128738</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-05 20:15:00</td>\n",
       "      <td>6/5/20</td>\n",
       "      <td>20:15</td>\n",
       "      <td>34.4</td>\n",
       "      <td>238.2</td>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>-121.8</td>\n",
       "      <td>34.3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>-121.9</td>\n",
       "      <td>-121.7</td>\n",
       "      <td>16.224995</td>\n",
       "      <td>0.095000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-06-06 18:45:00</td>\n",
       "      <td>6/6/20</td>\n",
       "      <td>18:45</td>\n",
       "      <td>32.2</td>\n",
       "      <td>239.8</td>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>-120.2</td>\n",
       "      <td>32.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>-120.3</td>\n",
       "      <td>-120.1</td>\n",
       "      <td>16.861244</td>\n",
       "      <td>0.048590</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-07 12:52:00</td>\n",
       "      <td>6/7/20</td>\n",
       "      <td>12:52</td>\n",
       "      <td>31.9</td>\n",
       "      <td>240.9</td>\n",
       "      <td>2020-06-07</td>\n",
       "      <td>-119.1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-119.2</td>\n",
       "      <td>-119.0</td>\n",
       "      <td>17.026245</td>\n",
       "      <td>0.098734</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-08 14:23:00</td>\n",
       "      <td>6/8/20</td>\n",
       "      <td>14:23</td>\n",
       "      <td>31.9</td>\n",
       "      <td>241.8</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>-118.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-118.3</td>\n",
       "      <td>-118.1</td>\n",
       "      <td>17.414370</td>\n",
       "      <td>0.102711</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sample_date_sample_time sample_date sample_time  sample_lat  sample_lon  \\\n",
       "0     2020-06-03 18:00:00      6/3/20       18:00        37.3       236.8   \n",
       "1     2020-06-04 19:34:00      6/4/20       19:34        36.1       237.5   \n",
       "2     2020-06-05 20:15:00      6/5/20       20:15        34.4       238.2   \n",
       "3     2020-06-06 18:45:00      6/6/20       18:45        32.2       239.8   \n",
       "4     2020-06-07 12:52:00      6/7/20       12:52        31.9       240.9   \n",
       "5     2020-06-08 14:23:00      6/8/20       14:23        31.9       241.8   \n",
       "\n",
       "     iso_date  sample_lon_corrected  lat_min  lat_max  lon_min  lon_max  \\\n",
       "0  2020-06-03                -123.2     37.2     37.4   -123.3   -123.1   \n",
       "1  2020-06-04                -122.5     36.0     36.2   -122.6   -122.4   \n",
       "2  2020-06-05                -121.8     34.3     34.5   -121.9   -121.7   \n",
       "3  2020-06-06                -120.2     32.1     32.3   -120.3   -120.1   \n",
       "4  2020-06-07                -119.1     31.8     32.0   -119.2   -119.0   \n",
       "5  2020-06-08                -118.2     31.8     32.0   -118.3   -118.1   \n",
       "\n",
       "        mean     stdev   n  \n",
       "0  14.251244  0.361522  16  \n",
       "1  14.836244  0.128738  16  \n",
       "2  16.224995  0.095000  16  \n",
       "3  16.861244  0.048590  16  \n",
       "4  17.026245  0.098734  16  \n",
       "5  17.414370  0.102711  16  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_DF['mean'] = means\n",
    "track_DF['stdev'] = stdevs\n",
    "track_DF['n'] = ns\n",
    "display(track_DF)\n",
    "\n",
    "# uncomment to save the data\n",
    "# track_DF.to_csv('crusetrack_results.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation instruction are at these links\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 "
   ]
  }
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