tutorials/04_On-Prem_Cloud.ipynb

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   "source": [
    "# Pairing Cloud and non-Cloud Data\n",
    "\n",
    "## Accessing and harmonizing data located within and outside of the NASA Earthdata Cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "___\n",
    "\n",
    "This tutorial will combine several workflow steps and components from the previous days, demonstrating the process of using the geolocation of data available outside of the Earthdata Cloud to then access coincident variables of cloud-accessible data. This may be a common use case as NASA Earthdata continues to migrate to the cloud, producing a \"hybrid\" data archive across Amazon Web Services (AWS) and original on-premise data storage systems. Additionally, you may also want to combine field measurements with remote sensing data available on the Earthdata Cloud.\n",
    "\n",
    "This specific example explores the pairing of the ICESat-2 [ATL07 Sea Ice Height](https://nsidc.org/data/atl07) data product, currently (as of November 2021) available publicly via direct download at the NSIDC DAAC, along with Sea Surface Temperature (SST) from the GHRSST MODIS L2 dataset ([MODIS_A-JPL-L2P-v2019.0](https://cmr.earthdata.nasa.gov/search/concepts/C1693233348-PODAAC.html)) available from PO.DAAC on the Earthdata Cloud. \n",
    "\n",
    "The use case we're looking at today centers over an area north of Greenland for a single day in June, where a melt pond was observed using the [NASA OpenAltimetry](https://openaltimetry.org/data/icesat2/?start_date=2019-06-22&annoId=180) application. Melt ponds are an important feature of Arctic sea ice dynamics, leading to an decrease in sea ice albedo and other changes in heat balance. Many NASA Earthdata datasets produce variables including sea ice albedo, sea surface temperature, air temperature, and sea ice height, which can be used to better understand these dynamics. "
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Requirements\n",
    "___\n",
    "\n",
    "* AWS instance running in us-west 2\n",
    "\n",
    "* Earthdata Login\n",
    "\n",
    "* .netrc file\n",
    "\n",
    "## Learning Objectives\n",
    "___\n",
    "\n",
    "1. Search for data programmatically using the Common Metadata Repository (CMR), determining granule (file) coverage across two datasets over an area of interest.\n",
    "2. Download data from an on-premise storage system to our cloud environment.\n",
    "3. Read in 1-dimensional trajectory data (ICESat-2 ATL07) into `xarray` and perform attribute conversions.\n",
    "4. Select and read in sea surface temperature (SST) data (MODIS_A-JPL-L2P-v2019.0) from the Earthdata Cloud into `xarray`.\n",
    "5. Extract, resample, and plot coincident SST data based on ICESat-2 geolocation.\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "\n",
    "# Access via download\n",
    "import requests\n",
    "\n",
    "# Access AWS S3\n",
    "import s3fs\n",
    "\n",
    "# Read and work with datasets\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "from shapely.geometry import box\n",
    "\n",
    "# For resampling\n",
    "import pyresample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Specify data, time range, and area of interest \n",
    "\n",
    "We are going to focus on getting data for an area north of Greenland for a single day in June.\n",
    "\n",
    "These `bounding_box` and `temporal` variables will be used for data search, subset, and access below.\n",
    "\n",
    "The same search and access steps for both datasets can be performed via [Earthdata Search](https://search.earthdata.nasa.gov/search?sb[0]=-62.8%2C81.7%2C-53.4%2C83&qt=2019-06-22T00%3A00%3A00.000Z%2C2019-06-22T23%3A59%3A59.999Z&m=79.82854077875072!-70.03537326243078!3!0!0!0%2C2) using the same spatial and temporal filtering options. See the [Earthdata Search tutorial](https://nasa-openscapes.github.io/2021-Cloud-Workshop-AGU/tutorials/01_Earthdata_Search.html) for more information on how to use Earthdata Search to discover and access data from the Earthdata Cloud. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bounding Box spatial parameter in decimal degree 'W,S,E,N' format.\n",
    "bounding_box = '-62.8,81.7,-56.4,83'\n",
    "\n",
    "# Each date in yyyy-MM-ddTHH:mm:ssZ format; date range in start,end format\n",
    "temporal = '2019-06-22T00:00:00Z,2019-06-22T23:59:59Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "See the [Data Discovery with CMR tutorial](https://nasa-openscapes.github.io/2021-Cloud-Hackathon/tutorials/01_Data_Discovery_CMR.html) for more details on how to navigate the NASA Common Metadata Repository (CMR) Application Programming Interface, or API. For some background, the CMR catalogs all data for NASA’s Earth Observing System Data and Information System (EOSDIS). The CMR API allows applications (software, services, etc) to send information to each other. A helpful analogy is a waiter in a restaurant. The waiter takes your drink or food order that you select from the menu, often translated into short-hand, to the bar or kitchen, and then returns (hopefully) with what you ordered when it is ready.\n",
    "\n",
    "The CMR API accepts search terms such as collection name, keywords, datetime range, and location, queries the CMR database and returns the results.\n",
    "\n",
    "For this tutorial, we have already identified the unique identifier, or `concept_id` for each dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "modis_concept_id = 'C1940473819-POCLOUD'\n",
    "icesat2_concept_id = 'C2003771980-NSIDC_ECS'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This [Earthdata Search Project](https://search.earthdata.nasa.gov/projects?projectId=6596763247) also provides the same data access links that we will identify in the following steps for each dataset (note that you will need an Earthdata Login account to access this project). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Search and download ICESat-2 ATL07 files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a granule search over our time and area of interest. How many granules are returned?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "granule_url = 'https://cmr.earthdata.nasa.gov/search/granules'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "response = requests.get(granule_url,\n",
    "                       params={\n",
    "                           'concept_id': icesat2_concept_id,\n",
    "                           'temporal': temporal,\n",
    "                           'bounding_box': bounding_box,\n",
    "                           'page_size': 200,\n",
    "                       },\n",
    "                       headers={\n",
    "                           'Accept': 'application/json'\n",
    "                       }\n",
    "                      )\n",
    "print(response.headers['CMR-Hits'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the file names, size, and links:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATL07-01_20190622055317_12980301_004_01.h5 237.0905504227 https://n5eil01u.ecs.nsidc.org/DP9/ATLAS/ATL07.004/2019.06.22/ATL07-01_20190622055317_12980301_004_01.h5\n",
      "ATL07-01_20190622200154_13070301_004_01.h5 230.9151573181 https://n5eil01u.ecs.nsidc.org/DP9/ATLAS/ATL07.004/2019.06.22/ATL07-01_20190622200154_13070301_004_01.h5\n"
     ]
    }
   ],
   "source": [
    "granules = response.json()['feed']['entry']\n",
    "for granule in granules:\n",
    "    print(f'{granule[\"producer_granule_id\"]} {granule[\"granule_size\"]} {granule[\"links\"][0][\"href\"]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download ATL07 files\n",
    "\n",
    "Although several services are supported for ICESat-2 data, we are demonstrating direct access through the “on-prem” file system at NSIDC for simplicity. \n",
    "\n",
    "Some of these services include: \n",
    "- [icepyx](https://icepyx.readthedocs.io/en/latest/) \n",
    "    - From the icepyx documentation: “icepyx is both a software library and a community composed of ICESat-2 data users, developers, and the scientific community. We are working together to develop a shared library of resources - including existing resources, new code, tutorials, and use-cases/examples - that simplify the process of querying, obtaining, analyzing, and manipulating ICESat-2 datasets to enable scientific discovery.” \n",
    "- [NSIDC DAAC Data Access and Service API](https://nsidc.org/support/how/how-do-i-programmatically-request-data-services#API) \n",
    "    - The API provided by the NSIDC DAAC allows you to access data programmatically using specific temporal and spatial filters. The same subsetting, reformatting, and reprojection services available on select data sets through NASA Earthdata Search can also be applied using this API. \n",
    "- [IceFlow](https://github.com/nsidc/NSIDC-Data-Tutorials/tree/main/notebooks/iceflow) \n",
    "    - The IceFlow python library simplifies accessing and combining data from several of NASA’s cryospheric altimetry missions, including ICESat/GLAS, Operation IceBridge, and ICESat-2. In particular, IceFlow harmonizes the various file formats and georeferencing parameters across several of the missions’ data sets, allowing you to analyze data across the multi-decadal time series.\n",
    "\n",
    "We’ve found 2 granules. We’ll download the first one and write it to a file with the same name as the `producer_granule_id`.\n",
    "\n",
    "We need the url for the granule as well. This is href links we printed out above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "icesat_id = granules[0]['producer_granule_id']\n",
    "icesat_url = granules[0]['links'][0]['href']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To retrieve the granule data, we use the `requests.get()` method, which will utilize the .netrc file on the backend to authenticate the request against Earthdata Login.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get(icesat_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The response returned by requests has the same structure as all the other responses: a header and contents.  The header information has information about the response, including the size of the data we downloaded in bytes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: Sun, 12 Dec 2021 01:52:31 GMT\n",
      "Server: Apache\n",
      "Vary: User-Agent\n",
      "Content-Disposition: attachment\n",
      "Content-Length: 248607461\n",
      "Keep-Alive: timeout=15, max=100\n",
      "Connection: Keep-Alive\n"
     ]
    }
   ],
   "source": [
    "for k, v in r.headers.items():\n",
    "    print(f'{k}: {v}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The contents needs to be saved to a file.  To keep the directory clean, we will create a `downloads` directory to store the file.  We can use a shell command to do this or use the `makedirs` method from the `os` package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(\"downloads\", exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see a `downloads` directory in the file browser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To write the data to a file, we use `open` to open a file.  We need to specify that the file is open for writing by using the _write-mode_ `w`.  We also need to specify that we want to write bytes by setting the _binary-mode_ `b`.  This is important because the response contents are bytes.  The default mode for `open` is `text-mode`. So make sure you use `b`.\n",
    "\n",
    "We'll use the `with` statement _context-manager_ to open the file, write the contents of the response, and then close the file.  Once the data in `r.content` is written sucessfully to the file, or if there is an error, the file is closed by the _context-manager_.\n",
    "\n",
    "We also need to prepend the `downloads` path to the filename.  We do this using `Path` from the `pathlib` package in the standard library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "outfile = Path('downloads', icesat_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not outfile.exists():\n",
    "    with open(outfile, 'wb') as f:\n",
    "        f.write(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ATL07-01_20190622055317_12980301_004_01.h5` is an HDF5 file.  `xarray` can open this but you need to tell it which group to read the data from.  In this case we read the sea ice segment height data for ground-track 1 left-beam. You can explore the variable hierarchy in [Earthdata Search](https://search.earthdata.nasa.gov/projects?p=C2003771980-NSIDC_ECS!C2003771980-NSIDC_ECS&pg[1][a]=2027648674!NSIDC_ECS&pg[1][v]=t&pg[1][id]=ATL07-01_20190622055317_12980301_004_01.h5&pg[1][gsk]=-start_date&pg[1][m]=esi0&q=atl07&tl=1637194666!3!!&m=13.8515625!-43.8046875!2!1!0!0%2C2), by selecting the Customize option under Download Data.\n",
    "\n",
    "This code block performs the following operations: \n",
    "- Extracts the `height_segment_height` variable from the heights group, along with the dimension variables contained in the higher level `sea_ice_segments` group,\n",
    "- Convert attributes from bytestrings to strings,\n",
    "- Drops the HDF attribute DIMENSION_LIST,\n",
    "- Sets _FillValue to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-var-name span,\n",
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       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (segment: 235584)\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    latitude               (segment) float64 82.38 82.38 82.38 ... 72.61 72.61\n",
       "    longitude              (segment) float64 -55.11 -55.11 ... 145.1 145.1\n",
       "    delta_time             (segment) float64 4.642e+07 4.642e+07 ... 4.642e+07\n",
       "    height_segment_height  (segment) float32 nan nan nan ... -0.4335 -0.4463</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-dec7d581-d0a1-41bb-b815-85401133972e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dec7d581-d0a1-41bb-b815-85401133972e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>segment</span>: 235584</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6acf333d-17ac-4c00-be54-8c856da5c8aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6acf333d-17ac-4c00-be54-8c856da5c8aa' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5bd03019-a376-43e3-b0ff-5a5bd3d1544b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5bd03019-a376-43e3-b0ff-5a5bd3d1544b' 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>latitude</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>82.38 82.38 82.38 ... 72.61 72.61</div><input id='attrs-435f9890-ec6c-4abc-9998-88e2d34f77b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-435f9890-ec6c-4abc-9998-88e2d34f77b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-804fb402-dd71-47d2-a815-a5bd10c1a6d5' class='xr-var-data-in' type='checkbox'><label for='data-804fb402-dd71-47d2-a815-a5bd10c1a6d5' 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>contentType :</span></dt><dd>referenceInformation</dd><dt><span>coordinates :</span></dt><dd>delta_time longitude</dd><dt><span>description :</span></dt><dd>Latitude, WGS84, North=+, Lat of segment center</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>source :</span></dt><dd>ATBD, section 4.4</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([82.38431982, 82.38431982, 82.38431982, ..., 72.60984638,\n",
       "       72.60977493, 72.60970985])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.11 -55.11 ... 145.1 145.1</div><input id='attrs-1e1280e6-0885-4acd-879d-aab2e466b902' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e1280e6-0885-4acd-879d-aab2e466b902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe83c840-4be7-4213-adb6-5df291f01f9c' class='xr-var-data-in' type='checkbox'><label for='data-fe83c840-4be7-4213-adb6-5df291f01f9c' 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>contentType :</span></dt><dd>referenceInformation</dd><dt><span>coordinates :</span></dt><dd>delta_time latitude</dd><dt><span>description :</span></dt><dd>Longitude, WGS84, East=+,Lon of segment center</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>source :</span></dt><dd>ATBD, section 4.4</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([-55.10896068, -55.10896068, -55.10896068, ..., 145.05396164,\n",
       "       145.05392851, 145.05389832])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_time</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.642e+07 4.642e+07 ... 4.642e+07</div><input id='attrs-af57142a-1fdc-481f-a59d-c40618c5aef9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af57142a-1fdc-481f-a59d-c40618c5aef9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-680306d7-3be6-4d71-8bee-e5838b45f5e3' class='xr-var-data-in' type='checkbox'><label for='data-680306d7-3be6-4d71-8bee-e5838b45f5e3' 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>CLASS :</span></dt><dd>DIMENSION_SCALE</dd><dt><span>NAME :</span></dt><dd>gt1l/sea_ice_segments/delta_time</dd><dt><span>REFERENCE_LIST :</span></dt><dd>[(&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)]</dd><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>coordinates :</span></dt><dd>latitude longitude</dd><dt><span>description :</span></dt><dd>Number of GPS seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch offset is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By adding the offset contained within atlas_sdp_gps_epoch to delta time parameters, the time in gps_seconds relative to the GPS epoch can be computed.</dd><dt><span>long_name :</span></dt><dd>Elapsed GPS seconds</dd><dt><span>source :</span></dt><dd>telemetry</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>seconds since 2018-01-01</dd></dl></div><div class='xr-var-data'><pre>array([46419293.64266939, 46419293.64266939, 46419293.64266939, ...,\n",
       "       46419681.87646231, 46419681.87759533, 46419681.87862704])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_segment_height</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... -0.4335 -0.4463</div><input id='attrs-ca84d809-c180-48cf-a3be-be33f173ab6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca84d809-c180-48cf-a3be-be33f173ab6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d8bf861-8b9b-4d97-af54-8d9195553513' class='xr-var-data-in' type='checkbox'><label for='data-4d8bf861-8b9b-4d97-af54-8d9195553513' 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>_FillValue :</span></dt><dd>3.4028235e+38</dd><dt><span>contentType :</span></dt><dd>referenceInformation</dd><dt><span>coordinates :</span></dt><dd>../delta_time ../latitude ../longitude</dd><dt><span>description :</span></dt><dd>Mean height from along-track segment fit detremined by the sea ice algorithm. The sea ice height is relative to the tide-free MSS.</dd><dt><span>long_name :</span></dt><dd>height of segment surface</dd><dt><span>source :</span></dt><dd>ATBD, section 4.2.2.4</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([        nan,         nan,         nan, ..., -0.46550068,\n",
       "       -0.43347716, -0.4462675 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0289a675-9517-4e8e-96be-d84ef992250b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0289a675-9517-4e8e-96be-d84ef992250b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (segment: 235584)\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    latitude               (segment) float64 82.38 82.38 82.38 ... 72.61 72.61\n",
       "    longitude              (segment) float64 -55.11 -55.11 ... 145.1 145.1\n",
       "    delta_time             (segment) float64 4.642e+07 4.642e+07 ... 4.642e+07\n",
       "    height_segment_height  (segment) float32 nan nan nan ... -0.4335 -0.4463"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_names = [\n",
    "    '/gt1l/sea_ice_segments/latitude',\n",
    "    '/gt1l/sea_ice_segments/longitude',\n",
    "    '/gt1l/sea_ice_segments/delta_time',\n",
    "    '/gt1l/sea_ice_segments/heights/height_segment_height'\n",
    "    ]\n",
    "with h5py.File(outfile, 'r') as h5:\n",
    "    data_vars = {}\n",
    "    for varname in variable_names:\n",
    "        var = h5[varname]\n",
    "        name = varname.split('/')[-1]\n",
    "        # Convert attributes\n",
    "        attrs = {}\n",
    "        for k, v in var.attrs.items():\n",
    "            if k != 'DIMENSION_LIST':\n",
    "                if isinstance(v, bytes):\n",
    "                    attrs[k] = v.decode('utf-8')\n",
    "                else:\n",
    "                    attrs[k] = v\n",
    "        data = var[:]\n",
    "        if '_FillValue' in attrs:\n",
    "            data = np.where(data < attrs['_FillValue'], data, np.nan)\n",
    "        data_vars[name] = (['segment'], data, attrs)\n",
    "    is2_ds = xr.Dataset(data_vars)\n",
    "    \n",
    "is2_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "is2_ds.height_segment_height.plot() ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine the GHRSST MODIS L2 granules returned from our time and area of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "response = requests.get(granule_url, \n",
    "                        params={\n",
    "                            'concept_id': modis_concept_id,\n",
    "                            'temporal': temporal,\n",
    "                            'bounding_box': bounding_box,\n",
    "                            'page_size': 200,\n",
    "                            },\n",
    "                        headers={\n",
    "                            'Accept': 'application/json'\n",
    "                            }\n",
    "                       )\n",
    "print(response.headers['CMR-Hits'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20190622000501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 20.71552562713623 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622000501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622014501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 9.34600830078125E-5 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622014501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622032501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 21.307741165161133 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622032501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622050001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 9.34600830078125E-5 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622050001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622050501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 20.065649032592773 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622050501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622064001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 9.34600830078125E-5 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622064001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622064501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0 18.602201461791992 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622064501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc\n",
      "20190622064501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 18.665077209472656 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622064501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622082001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 19.782299995422363 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622082001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622100001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 19.13440227508545 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622100001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622113501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 20.3239164352417 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622113501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622114001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 9.34600830078125E-5 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622114001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622163001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 19.257243156433105 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622163001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n",
      "20190622181001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0 19.93498420715332 s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622181001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc\n"
     ]
    }
   ],
   "source": [
    "granules = response.json()['feed']['entry']\n",
    "for granule in granules:\n",
    "    print(f'{granule[\"title\"]} {granule[\"granule_size\"]} {granule[\"links\"][0][\"href\"]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load data into xarray via S3 direct access"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our CMR granule search returned 14 files for our time and area of interest.  However, not all granules will be suitable for analysis.\n",
    "\n",
    "I've identified the image with granule id G1956158784-POCLOUD as a good candidate, this is the 9th granule.  In this image, our area of interest is close to nadir.  This means that the instantaneous field of view over the area of interest cover a smaller area than at the edge of the image.\n",
    "\n",
    "We are looking for the link for _direct download access via s3_.  This is a url but with a prefix `s3://`.  This happens to be the first href link in the metadata.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a single granule we can cut and paste the s3 link.  If we have several granules, the s3 links can be extracted with some simple code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20190622100001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "granule = granules[9]\n",
    "\n",
    "for link in granule['links']:\n",
    "    if link['href'].startswith('s3://'):\n",
    "        s3_link = link['href']\n",
    "        \n",
    "s3_link"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Get S3 credentials\n",
    "As with the previous S3 download tutorials we need credentials to access data from s3: access keys and tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_credentials = requests.get('https://archive.podaac.earthdata.nasa.gov/s3credentials').json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essentially, what we are doing in this step is to \"mount\" the s3 bucket as a file system.  This allows us to treat the S3 bucket in a similar way to a local file system.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s3_fs = s3fs.S3FileSystem(\n",
    "    key=s3_credentials[\"accessKeyId\"],\n",
    "    secret=s3_credentials[\"secretAccessKey\"],\n",
    "    token=s3_credentials[\"sessionToken\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Open a s3 file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the `S3FileSystem` set up, we can access the granule.  `xarray` cannot open a `S3File` directly, so we use the `open` method for the `S3FileSystem` to open the granule using the endpoint url we extracted from the metadata.  We also have to set the `mode='rb'`.  This opens the granule in read-only mode and in byte-mode.  Byte-mode is important.  By default, `open` opens a file as text - in this case it would just be a string of characters - and `xarray` doesn't know what to do with that.\n",
    "\n",
    "We then pass the `S3File` object `f` to `xarray.open_dataset`.  For this dataset, we also have to set `decode_cf=False`.  This switch tells `xarray` not to use information contained in variable attributes to generate human readable coordinate variables.  Normally, this should work for netcdf files but for this particular cloud-hosted dataset, variable attribute data is not in the form expected by `xarray`.  We'll fix this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = s3_fs.open(s3_link, mode='rb')\n",
    "modis_ds = xr.open_dataset(f, decode_cf=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you click on the `Show/Hide Attributes` icon (the first document-like icon to the right of coordinate variable metadata) you can see that attributes are one-element arrays containing bytestrings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (nj: 2030, ni: 1354, time: 1)\n",
       "Coordinates:\n",
       "  * time                     (time) int32 1214042401\n",
       "Dimensions without coordinates: nj, ni\n",
       "Data variables:\n",
       "    lat                      (nj, ni) float32 ...\n",
       "    lon                      (nj, ni) float32 ...\n",
       "    sea_surface_temperature  (time, nj, ni) int16 ...\n",
       "    sst_dtime                (time, nj, ni) int16 ...\n",
       "    quality_level            (time, nj, ni) int8 ...\n",
       "    sses_bias                (time, nj, ni) int8 ...\n",
       "    sses_standard_deviation  (time, nj, ni) int8 ...\n",
       "    l2p_flags                (time, nj, ni) int16 ...\n",
       "    chlorophyll_a            (time, nj, ni) float32 ...\n",
       "    K_490                    (time, nj, ni) int16 ...\n",
       "    wind_speed               (time, nj, ni) int8 ...\n",
       "    dt_analysis              (time, nj, ni) int8 ...\n",
       "Attributes: (12/49)\n",
       "    Conventions:                [b&#x27;CF-1.7, ACDD-1.3&#x27;]\n",
       "    title:                      [b&#x27;MODIS Aqua L2P SST&#x27;]\n",
       "    summary:                    [b&#x27;Sea surface temperature retrievals produce...\n",
       "    references:                 [b&#x27;GHRSST Data Processing Specification v2r5&#x27;]\n",
       "    institution:                [b&#x27;NASA/JPL/OBPG/RSMAS&#x27;]\n",
       "    history:                    [b&#x27;MODIS L2P created at JPL PO.DAAC&#x27;]\n",
       "    ...                         ...\n",
       "    publisher_email:            [b&#x27;ghrsst-po@nceo.ac.uk&#x27;]\n",
       "    processing_level:           [b&#x27;L2P&#x27;]\n",
       "    cdm_data_type:              [b&#x27;swath&#x27;]\n",
       "    startDirection:             [b&#x27;Ascending&#x27;]\n",
       "    endDirection:               [b&#x27;Descending&#x27;]\n",
       "    day_night_flag:             [b&#x27;Day&#x27;]</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-9e40d245-86ff-42f6-947f-cda565e9a56d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9e40d245-86ff-42f6-947f-cda565e9a56d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nj</span>: 2030</li><li><span>ni</span>: 1354</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-53b1ba44-207a-41b9-9ff9-21b203546442' class='xr-section-summary-in' type='checkbox'  checked><label for='section-53b1ba44-207a-41b9-9ff9-21b203546442' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1214042401</div><input id='attrs-32246dd8-fa6e-477e-99b6-04f644e11341' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32246dd8-fa6e-477e-99b6-04f644e11341' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58d6c25b-1b75-4d86-a933-5a7026cec86b' class='xr-var-data-in' type='checkbox'><label for='data-58d6c25b-1b75-4d86-a933-5a7026cec86b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;reference time of sst file&#x27;]</dd><dt><span>standard_name :</span></dt><dd>[b&#x27;time&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;seconds since 1981-01-01 00:00:00&#x27;]</dd><dt><span>comment :</span></dt><dd>[b&#x27;time of first sensor observation&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;coordinate&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([1214042401], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfb3fa95-c364-4547-a73e-41759029bd37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bfb3fa95-c364-4547-a73e-41759029bd37' class='xr-section-summary' >Data variables: <span>(12)</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>lat</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-15498efb-f602-4d70-911e-08e58d878d13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15498efb-f602-4d70-911e-08e58d878d13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c98d4c3a-f0c4-4d6d-b05b-788064e9ec83' class='xr-var-data-in' type='checkbox'><label for='data-c98d4c3a-f0c4-4d6d-b05b-788064e9ec83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;latitude&#x27;]</dd><dt><span>standard_name :</span></dt><dd>[b&#x27;latitude&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;degrees_north&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-999.]</dd><dt><span>valid_min :</span></dt><dd>[-90.]</dd><dt><span>valid_max :</span></dt><dd>[90.]</dd><dt><span>comment :</span></dt><dd>[b&#x27;geographical coordinates, WGS84 projection&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;coordinate&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2684d654-de4b-41a7-916a-214095418c82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2684d654-de4b-41a7-916a-214095418c82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9d40bd6-ec9c-4b1c-b948-f4e8545dfc4e' class='xr-var-data-in' type='checkbox'><label for='data-a9d40bd6-ec9c-4b1c-b948-f4e8545dfc4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;longitude&#x27;]</dd><dt><span>standard_name :</span></dt><dd>[b&#x27;longitude&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;degrees_east&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-999.]</dd><dt><span>valid_min :</span></dt><dd>[-180.]</dd><dt><span>valid_max :</span></dt><dd>[180.]</dd><dt><span>comment :</span></dt><dd>[b&#x27;geographical coordinates, WGS84 projection&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;coordinate&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_temperature</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbd518d1-96e3-4a6c-b902-3dc7ac841f50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbd518d1-96e3-4a6c-b902-3dc7ac841f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f3c4376-5593-4f85-974c-be57b82196e9' class='xr-var-data-in' type='checkbox'><label for='data-4f3c4376-5593-4f85-974c-be57b82196e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;sea surface temperature&#x27;]</dd><dt><span>standard_name :</span></dt><dd>[b&#x27;sea_surface_skin_temperature&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;kelvin&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-32767]</dd><dt><span>valid_min :</span></dt><dd>[-1000]</dd><dt><span>valid_max :</span></dt><dd>[10000]</dd><dt><span>comment :</span></dt><dd>[b&#x27;sea surface temperature from thermal IR (11 um) channels&#x27;]</dd><dt><span>scale_factor :</span></dt><dd>[0.005]</dd><dt><span>add_offset :</span></dt><dd>[273.15]</dd><dt><span>source :</span></dt><dd>[b&#x27;NASA and University of Miami&#x27;]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;physicalMeasurement&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_dtime</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4644d924-8334-4738-833c-3c7a5107c7f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4644d924-8334-4738-833c-3c7a5107c7f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bdf9478-98d6-478d-8aaf-1312fe3a6357' class='xr-var-data-in' type='checkbox'><label for='data-6bdf9478-98d6-478d-8aaf-1312fe3a6357' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;time difference from reference time&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;seconds&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-32768]</dd><dt><span>valid_min :</span></dt><dd>[-32767]</dd><dt><span>valid_max :</span></dt><dd>[32767]</dd><dt><span>comment :</span></dt><dd>[b&#x27;time plus sst_dtime gives seconds after 00:00:00 UTC January 1, 1981&#x27;]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;referenceInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quality_level</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-812dff36-8e88-494b-9536-054544443f39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-812dff36-8e88-494b-9536-054544443f39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cf34218-b372-4710-9fbf-a95ca67526f1' class='xr-var-data-in' type='checkbox'><label for='data-1cf34218-b372-4710-9fbf-a95ca67526f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;quality level of SST pixel&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-128]</dd><dt><span>valid_min :</span></dt><dd>[0]</dd><dt><span>valid_max :</span></dt><dd>[5]</dd><dt><span>comment :</span></dt><dd>[b&#x27;thermal IR SST proximity confidence value; signed byte array: WARNING Some applications are unable to properly handle signed byte values. If values are encountered &gt; 127, please subtract 256 from this reported value&#x27;]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3 4 5]</dd><dt><span>flag_meanings :</span></dt><dd>[b&#x27;no_data bad_data worst_quality low_quality acceptable_quality best_quality&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;qualityInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sses_bias</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df129cc5-59d8-4621-9dad-9999411d3c68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df129cc5-59d8-4621-9dad-9999411d3c68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84cad695-d9d6-4292-bac2-06cd5d29f48f' class='xr-var-data-in' type='checkbox'><label for='data-84cad695-d9d6-4292-bac2-06cd5d29f48f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;SSES bias error based on proximity confidence flags&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;kelvin&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-128]</dd><dt><span>valid_min :</span></dt><dd>[-127]</dd><dt><span>valid_max :</span></dt><dd>[127]</dd><dt><span>comment :</span></dt><dd>[b&#x27;thermal IR SST bias error; signed byte array: WARNING Some applications are unable to properly handle signed byte values. If values are encountered &gt; 127, please subtract 256 from this reported value&#x27;]</dd><dt><span>scale_factor :</span></dt><dd>[0.15748031]</dd><dt><span>add_offset :</span></dt><dd>[0.]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;auxiliaryInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sses_standard_deviation</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f41bf12a-d2e3-4b1b-89f6-85f557649932' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f41bf12a-d2e3-4b1b-89f6-85f557649932' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cbe66de-b3df-42c4-996e-7a1d57ea1864' class='xr-var-data-in' type='checkbox'><label for='data-8cbe66de-b3df-42c4-996e-7a1d57ea1864' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;SSES standard deviation error based on proximity confidence flags&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;kelvin&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-128]</dd><dt><span>valid_min :</span></dt><dd>[-127]</dd><dt><span>valid_max :</span></dt><dd>[127]</dd><dt><span>comment :</span></dt><dd>[b&#x27;thermal IR SST standard deviation error; signed byte array: WARNING Some applications are unable to properly handle signed byte values. If values are encountered &gt; 127, please subtract 256 from this reported value&#x27;]</dd><dt><span>scale_factor :</span></dt><dd>[0.07874016]</dd><dt><span>add_offset :</span></dt><dd>[10.]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;auxiliaryInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>l2p_flags</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e3742cc-ee26-41a1-b242-3ea61484c7b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e3742cc-ee26-41a1-b242-3ea61484c7b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef271e45-0eb0-4cc1-a428-26b3ca37f6c6' class='xr-var-data-in' type='checkbox'><label for='data-ef271e45-0eb0-4cc1-a428-26b3ca37f6c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;L2P flags&#x27;]</dd><dt><span>valid_min :</span></dt><dd>[0]</dd><dt><span>valid_max :</span></dt><dd>[16]</dd><dt><span>comment :</span></dt><dd>[b&#x27;These flags can be used to further filter data variables&#x27;]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>flag_meanings :</span></dt><dd>[b&#x27;microwave land ice lake river&#x27;]</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;qualityInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chlorophyll_a</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1282f416-9317-474c-947d-0a7b01e6d7ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1282f416-9317-474c-947d-0a7b01e6d7ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-900c440c-7f14-49f0-8483-63130d1237b6' class='xr-var-data-in' type='checkbox'><label for='data-900c440c-7f14-49f0-8483-63130d1237b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;Chlorophyll Concentration, OC3 Algorithm&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;mg m^-3&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-32767.]</dd><dt><span>valid_min :</span></dt><dd>[0.001]</dd><dt><span>valid_max :</span></dt><dd>[100.]</dd><dt><span>comment :</span></dt><dd>[b&#x27;non L2P core field&#x27;]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;auxiliaryInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>K_490</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6bc97a4c-0c18-47e2-acb7-e26cf7f513ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bc97a4c-0c18-47e2-acb7-e26cf7f513ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d419179f-27c0-4447-b2ed-59015c8ce296' class='xr-var-data-in' type='checkbox'><label for='data-d419179f-27c0-4447-b2ed-59015c8ce296' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;Diffuse attenuation coefficient at 490 nm (OBPG)&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;m^-1&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-32767]</dd><dt><span>valid_min :</span></dt><dd>[50]</dd><dt><span>valid_max :</span></dt><dd>[30000]</dd><dt><span>comment :</span></dt><dd>[b&#x27;non L2P core field&#x27;]</dd><dt><span>scale_factor :</span></dt><dd>[0.0002]</dd><dt><span>add_offset :</span></dt><dd>[0.]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;auxiliaryInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a727a463-2b1b-457e-bc10-2b6b7ffbda5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a727a463-2b1b-457e-bc10-2b6b7ffbda5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5125e926-4d11-48f1-a650-519cf9d759b0' class='xr-var-data-in' type='checkbox'><label for='data-5125e926-4d11-48f1-a650-519cf9d759b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;10m wind speed&#x27;]</dd><dt><span>standard_name :</span></dt><dd>[b&#x27;wind_speed&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;m s-1&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-128]</dd><dt><span>valid_min :</span></dt><dd>[-127]</dd><dt><span>valid_max :</span></dt><dd>[127]</dd><dt><span>comment :</span></dt><dd>[b&#x27;Wind at 10 meters above the sea surface&#x27;]</dd><dt><span>scale_factor :</span></dt><dd>[0.2]</dd><dt><span>add_offset :</span></dt><dd>[25.]</dd><dt><span>source :</span></dt><dd>[b&#x27;TBD.  Placeholder.  Currently empty&#x27;]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>grid_mapping :</span></dt><dd>[b&#x27;TBD&#x27;]</dd><dt><span>time_offset :</span></dt><dd>[2.]</dd><dt><span>height :</span></dt><dd>[b&#x27;10 m&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;auxiliaryInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dt_analysis</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95a5761b-dfb8-4f64-9aa2-079d0ce0081b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95a5761b-dfb8-4f64-9aa2-079d0ce0081b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0d5fedb-711a-47e9-b9fa-92fe1cb95fb3' class='xr-var-data-in' type='checkbox'><label for='data-b0d5fedb-711a-47e9-b9fa-92fe1cb95fb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>[b&#x27;deviation from SST reference climatology&#x27;]</dd><dt><span>units :</span></dt><dd>[b&#x27;kelvin&#x27;]</dd><dt><span>_FillValue :</span></dt><dd>[-128]</dd><dt><span>valid_min :</span></dt><dd>[-127]</dd><dt><span>valid_max :</span></dt><dd>[127]</dd><dt><span>comment :</span></dt><dd>[b&#x27;TBD&#x27;]</dd><dt><span>scale_factor :</span></dt><dd>[0.1]</dd><dt><span>add_offset :</span></dt><dd>[0.]</dd><dt><span>source :</span></dt><dd>[b&#x27;TBD. Placeholder.  Currently empty&#x27;]</dd><dt><span>coordinates :</span></dt><dd>[b&#x27;lon lat&#x27;]</dd><dt><span>coverage_content_type :</span></dt><dd>[b&#x27;auxiliaryInformation&#x27;]</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int8]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-850d89dd-fa50-4efc-8a6a-361ab2956784' class='xr-section-summary-in' type='checkbox'  ><label for='section-850d89dd-fa50-4efc-8a6a-361ab2956784' class='xr-section-summary' >Attributes: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>[b&#x27;CF-1.7, ACDD-1.3&#x27;]</dd><dt><span>title :</span></dt><dd>[b&#x27;MODIS Aqua L2P SST&#x27;]</dd><dt><span>summary :</span></dt><dd>[b&#x27;Sea surface temperature retrievals produced at the NASA OBPG for the MODIS Aqua sensor.  These have been reformatted to GHRSST GDS specifications by the JPL PO.DAAC&#x27;]</dd><dt><span>references :</span></dt><dd>[b&#x27;GHRSST Data Processing Specification v2r5&#x27;]</dd><dt><span>institution :</span></dt><dd>[b&#x27;NASA/JPL/OBPG/RSMAS&#x27;]</dd><dt><span>history :</span></dt><dd>[b&#x27;MODIS L2P created at JPL PO.DAAC&#x27;]</dd><dt><span>comment :</span></dt><dd>[b&#x27;L2P Core without DT analysis or other ancillary fields; Day, Start Node:Ascending, End Node:Descending; WARNING Some applications are unable to properly handle signed byte values. If values are encountered &gt; 127, please subtract 256 from this reported value; Refined&#x27;]</dd><dt><span>license :</span></dt><dd>[b&#x27;GHRSST and PO.DAAC protocol allow data use as free and open.&#x27;]</dd><dt><span>id :</span></dt><dd>[b&#x27;MODIS_A-JPL-L2P-v2019.0&#x27;]</dd><dt><span>naming_authority :</span></dt><dd>[b&#x27;org.ghrsst&#x27;]</dd><dt><span>product_version :</span></dt><dd>[b&#x27;2019.0&#x27;]</dd><dt><span>uuid :</span></dt><dd>[b&#x27;f6e1f61d-c4a4-4c17-8354-0c15e12d688b&#x27;]</dd><dt><span>gds_version_id :</span></dt><dd>[b&#x27;2.0&#x27;]</dd><dt><span>netcdf_version_id :</span></dt><dd>[b&#x27;4.1&#x27;]</dd><dt><span>date_created :</span></dt><dd>[b&#x27;20200221T085224Z&#x27;]</dd><dt><span>file_quality_level :</span></dt><dd>[3]</dd><dt><span>spatial_resolution :</span></dt><dd>[b&#x27;1km&#x27;]</dd><dt><span>start_time :</span></dt><dd>[b&#x27;20190622T100001Z&#x27;]</dd><dt><span>time_coverage_start :</span></dt><dd>[b&#x27;20190622T100001Z&#x27;]</dd><dt><span>stop_time :</span></dt><dd>[b&#x27;20190622T100459Z&#x27;]</dd><dt><span>time_coverage_end :</span></dt><dd>[b&#x27;20190622T100459Z&#x27;]</dd><dt><span>northernmost_latitude :</span></dt><dd>[89.9862]</dd><dt><span>southernmost_latitude :</span></dt><dd>[66.2723]</dd><dt><span>easternmost_longitude :</span></dt><dd>[-45.9467]</dd><dt><span>westernmost_longitude :</span></dt><dd>[152.489]</dd><dt><span>source :</span></dt><dd>[b&#x27;MODIS sea surface temperature observations for the OBPG&#x27;]</dd><dt><span>platform :</span></dt><dd>[b&#x27;Aqua&#x27;]</dd><dt><span>sensor :</span></dt><dd>[b&#x27;MODIS&#x27;]</dd><dt><span>metadata_link :</span></dt><dd>[b&#x27;http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&amp;shortName=MODIS_A-JPL-L2P-v2019.0&#x27;]</dd><dt><span>keywords :</span></dt><dd>[b&#x27;Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature&#x27;]</dd><dt><span>keywords_vocabulary :</span></dt><dd>[b&#x27;NASA Global Change Master Directory (GCMD) Science Keywords&#x27;]</dd><dt><span>standard_name_vocabulary :</span></dt><dd>[b&#x27;NetCDF Climate and Forecast (CF) Metadata Convention&#x27;]</dd><dt><span>geospatial_lat_units :</span></dt><dd>[b&#x27;degrees_north&#x27;]</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>[0.01]</dd><dt><span>geospatial_lon_units :</span></dt><dd>[b&#x27;degrees_east&#x27;]</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>[0.01]</dd><dt><span>acknowledgment :</span></dt><dd>[b&#x27;The MODIS L2P sea surface temperature data are sponsored by NASA&#x27;]</dd><dt><span>creator_name :</span></dt><dd>[b&#x27;Ed Armstrong, JPL PO.DAAC&#x27;]</dd><dt><span>creator_email :</span></dt><dd>[b&#x27;edward.m.armstrong@jpl.nasa.gov&#x27;]</dd><dt><span>creator_url :</span></dt><dd>[b&#x27;http://podaac.jpl.nasa.gov&#x27;]</dd><dt><span>project :</span></dt><dd>[b&#x27;Group for High Resolution Sea Surface Temperature&#x27;]</dd><dt><span>publisher_name :</span></dt><dd>[b&#x27;The GHRSST Project Office&#x27;]</dd><dt><span>publisher_url :</span></dt><dd>[b&#x27;http://www.ghrsst.org&#x27;]</dd><dt><span>publisher_email :</span></dt><dd>[b&#x27;ghrsst-po@nceo.ac.uk&#x27;]</dd><dt><span>processing_level :</span></dt><dd>[b&#x27;L2P&#x27;]</dd><dt><span>cdm_data_type :</span></dt><dd>[b&#x27;swath&#x27;]</dd><dt><span>startDirection :</span></dt><dd>[b&#x27;Ascending&#x27;]</dd><dt><span>endDirection :</span></dt><dd>[b&#x27;Descending&#x27;]</dd><dt><span>day_night_flag :</span></dt><dd>[b&#x27;Day&#x27;]</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
       "Dimensions:                  (nj: 2030, ni: 1354, time: 1)\n",
       "Coordinates:\n",
       "  * time                     (time) int32 1214042401\n",
       "Dimensions without coordinates: nj, ni\n",
       "Data variables:\n",
       "    lat                      (nj, ni) float32 ...\n",
       "    lon                      (nj, ni) float32 ...\n",
       "    sea_surface_temperature  (time, nj, ni) int16 ...\n",
       "    sst_dtime                (time, nj, ni) int16 ...\n",
       "    quality_level            (time, nj, ni) int8 ...\n",
       "    sses_bias                (time, nj, ni) int8 ...\n",
       "    sses_standard_deviation  (time, nj, ni) int8 ...\n",
       "    l2p_flags                (time, nj, ni) int16 ...\n",
       "    chlorophyll_a            (time, nj, ni) float32 ...\n",
       "    K_490                    (time, nj, ni) int16 ...\n",
       "    wind_speed               (time, nj, ni) int8 ...\n",
       "    dt_analysis              (time, nj, ni) int8 ...\n",
       "Attributes: (12/49)\n",
       "    Conventions:                [b'CF-1.7, ACDD-1.3']\n",
       "    title:                      [b'MODIS Aqua L2P SST']\n",
       "    summary:                    [b'Sea surface temperature retrievals produce...\n",
       "    references:                 [b'GHRSST Data Processing Specification v2r5']\n",
       "    institution:                [b'NASA/JPL/OBPG/RSMAS']\n",
       "    history:                    [b'MODIS L2P created at JPL PO.DAAC']\n",
       "    ...                         ...\n",
       "    publisher_email:            [b'ghrsst-po@nceo.ac.uk']\n",
       "    processing_level:           [b'L2P']\n",
       "    cdm_data_type:              [b'swath']\n",
       "    startDirection:             [b'Ascending']\n",
       "    endDirection:               [b'Descending']\n",
       "    day_night_flag:             [b'Day']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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    "modis_ds"
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  {
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    "To fix this, we need to extract array elements as scalars, and convert those scalars from bytestrings to strings.  We use the `decode` method to do this.  The bytestrings are encoded as utf-8, which is a unicode character format.  This is the default encoding for `decode` but we've included it as an argument to be explicit.\n",
    "\n",
    "Not all attributes are bytestrings.  Some are `floats`.  Take a look at `_FillValue`, and `valid_min` and `valid_max`.  To avoid an error, we use the `isinstance` function to check if the value of an attributes is type `bytes` - a bytestring.  If it is, then we decode it.  If not, we just extract the scalar and do nothing else.\n",
    "\n",
    "We also fix the global attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_attributes(da):\n",
    "    '''Decodes bytestring attributes to strings'''\n",
    "    for attr, value in da.attrs.items():\n",
    "        if isinstance(value[0], bytes):\n",
    "            da.attrs[attr] = value[0].decode('utf-8')\n",
    "        else:\n",
    "            da.attrs[attr] = value[0]\n",
    "    return\n",
    "\n",
    "# Fix variable attributes\n",
    "for var in modis_ds.variables:\n",
    "    da = modis_ds[var]\n",
    "    fix_attributes(da)\n",
    "            \n",
    "# Fix global attributes\n",
    "fix_attributes(modis_ds)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this done, we can use the `xarray` function `decode_cf` to convert the attributes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "modis_ds = xr.decode_cf(modis_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-var-name {\n",
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       "\n",
       ".xr-var-dims {\n",
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       "}\n",
       "\n",
       ".xr-var-dtype {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
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       ".xr-preview,\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (nj: 2030, ni: 1354, time: 1)\n",
       "Coordinates:\n",
       "    lat                      (nj, ni) float32 ...\n",
       "    lon                      (nj, ni) float32 ...\n",
       "  * time                     (time) datetime64[ns] 2019-06-22T10:00:01\n",
       "Dimensions without coordinates: nj, ni\n",
       "Data variables:\n",
       "    sea_surface_temperature  (time, nj, ni) float32 ...\n",
       "    sst_dtime                (time, nj, ni) timedelta64[ns] ...\n",
       "    quality_level            (time, nj, ni) float32 ...\n",
       "    sses_bias                (time, nj, ni) float32 ...\n",
       "    sses_standard_deviation  (time, nj, ni) float32 ...\n",
       "    l2p_flags                (time, nj, ni) int16 ...\n",
       "    chlorophyll_a            (time, nj, ni) float32 ...\n",
       "    K_490                    (time, nj, ni) float32 ...\n",
       "    wind_speed               (time, nj, ni) float32 ...\n",
       "    dt_analysis              (time, nj, ni) float32 ...\n",
       "Attributes: (12/49)\n",
       "    Conventions:                CF-1.7, ACDD-1.3\n",
       "    title:                      MODIS Aqua L2P SST\n",
       "    summary:                    Sea surface temperature retrievals produced a...\n",
       "    references:                 GHRSST Data Processing Specification v2r5\n",
       "    institution:                NASA/JPL/OBPG/RSMAS\n",
       "    history:                    MODIS L2P created at JPL PO.DAAC\n",
       "    ...                         ...\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L2P\n",
       "    cdm_data_type:              swath\n",
       "    startDirection:             Ascending\n",
       "    endDirection:               Descending\n",
       "    day_night_flag:             Day</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-496e66bb-6e30-4d32-94fe-e1e69a3aa00e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-496e66bb-6e30-4d32-94fe-e1e69a3aa00e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nj</span>: 2030</li><li><span>ni</span>: 1354</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4e228a93-bc22-4829-b4d5-38eb43e96dea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e228a93-bc22-4829-b4d5-38eb43e96dea' 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>lat</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3961e71d-f410-4b62-a309-b53e5fa35c0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3961e71d-f410-4b62-a309-b53e5fa35c0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b2709b4-3e56-4bd9-a4c1-05364cec3db6' class='xr-var-data-in' type='checkbox'><label for='data-7b2709b4-3e56-4bd9-a4c1-05364cec3db6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52fca852-dc81-4870-8f46-2e26048cd701' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52fca852-dc81-4870-8f46-2e26048cd701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37bf6c11-0ee0-4bab-9223-19dcb09cf3a8' class='xr-var-data-in' type='checkbox'><label for='data-37bf6c11-0ee0-4bab-9223-19dcb09cf3a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-06-22T10:00:01</div><input id='attrs-94999d9e-bab0-4075-b203-49f39e602097' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94999d9e-bab0-4075-b203-49f39e602097' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0beae798-1e53-466a-9781-665363d3bed2' class='xr-var-data-in' type='checkbox'><label for='data-0beae798-1e53-466a-9781-665363d3bed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reference time of sst file</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>time of first sensor observation</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-06-22T10:00:01.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ddb0abe-df46-43d8-b214-b24d0baf918a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ddb0abe-df46-43d8-b214-b24d0baf918a' class='xr-section-summary' >Data variables: <span>(10)</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>sea_surface_temperature</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c040bf00-9e53-445d-a09b-ddfff6ede44f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c040bf00-9e53-445d-a09b-ddfff6ede44f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9b75b04-6ae1-4cef-a383-b357d7c79f17' class='xr-var-data-in' type='checkbox'><label for='data-c9b75b04-6ae1-4cef-a383-b357d7c79f17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_skin_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-1000</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>comment :</span></dt><dd>sea surface temperature from thermal IR (11 um) channels</dd><dt><span>source :</span></dt><dd>NASA and University of Miami</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_dtime</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce800cfd-5ebe-4f2a-999f-703789456570' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce800cfd-5ebe-4f2a-999f-703789456570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09386c5c-6505-49a6-bcf7-d760ebc36ab9' class='xr-var-data-in' type='checkbox'><label for='data-09386c5c-6505-49a6-bcf7-d760ebc36ab9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time difference from reference time</dd><dt><span>valid_min :</span></dt><dd>-32767</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>comment :</span></dt><dd>time plus sst_dtime gives seconds after 00:00:00 UTC January 1, 1981</dd><dt><span>coverage_content_type :</span></dt><dd>referenceInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quality_level</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-222d6ac8-011d-41ee-9dae-2046ee674543' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-222d6ac8-011d-41ee-9dae-2046ee674543' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d170579-76fe-4980-bf7a-651339de53e9' class='xr-var-data-in' type='checkbox'><label for='data-1d170579-76fe-4980-bf7a-651339de53e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>quality level of SST pixel</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>5</dd><dt><span>comment :</span></dt><dd>thermal IR SST proximity confidence value; signed byte array: WARNING Some applications are unable to properly handle signed byte values. If values are encountered &gt; 127, please subtract 256 from this reported value</dd><dt><span>flag_values :</span></dt><dd>0</dd><dt><span>flag_meanings :</span></dt><dd>no_data bad_data worst_quality low_quality acceptable_quality best_quality</dd><dt><span>coverage_content_type :</span></dt><dd>qualityInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sses_bias</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e3091925-49b4-4245-9ea2-b03ba4a22950' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3091925-49b4-4245-9ea2-b03ba4a22950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49b5259c-8a54-4108-b43d-3d299cf8fd5e' class='xr-var-data-in' type='checkbox'><label for='data-49b5259c-8a54-4108-b43d-3d299cf8fd5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SSES bias error based on proximity confidence flags</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>comment :</span></dt><dd>thermal IR SST bias error; signed byte array: WARNING Some applications are unable to properly handle signed byte values. If values are encountered &gt; 127, please subtract 256 from this reported value</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sses_standard_deviation</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea1e47e5-36a2-46fd-b58c-737ad6142994' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea1e47e5-36a2-46fd-b58c-737ad6142994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adbef059-f946-4157-a17a-489a0b08557e' class='xr-var-data-in' type='checkbox'><label for='data-adbef059-f946-4157-a17a-489a0b08557e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SSES standard deviation error based on proximity confidence flags</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>comment :</span></dt><dd>thermal IR SST standard deviation error; signed byte array: WARNING Some applications are unable to properly handle signed byte values. If values are encountered &gt; 127, please subtract 256 from this reported value</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>l2p_flags</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cda0c7e4-9974-4c92-b749-7dd8cca0b794' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cda0c7e4-9974-4c92-b749-7dd8cca0b794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-507d5b33-4aa3-498b-819d-9b70c693b749' class='xr-var-data-in' type='checkbox'><label for='data-507d5b33-4aa3-498b-819d-9b70c693b749' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>L2P flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>16</dd><dt><span>comment :</span></dt><dd>These flags can be used to further filter data variables</dd><dt><span>flag_meanings :</span></dt><dd>microwave land ice lake river</dd><dt><span>flag_masks :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>qualityInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chlorophyll_a</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ae858591-0722-4e32-ad5e-297192905453' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae858591-0722-4e32-ad5e-297192905453' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2b89dd3-7e34-4143-b305-80cba3c2e165' class='xr-var-data-in' type='checkbox'><label for='data-f2b89dd3-7e34-4143-b305-80cba3c2e165' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Chlorophyll Concentration, OC3 Algorithm</dd><dt><span>units :</span></dt><dd>mg m^-3</dd><dt><span>valid_min :</span></dt><dd>0.001</dd><dt><span>valid_max :</span></dt><dd>100.0</dd><dt><span>comment :</span></dt><dd>non L2P core field</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>K_490</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e39a722d-2bc9-4132-abcc-693bb5d74f88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e39a722d-2bc9-4132-abcc-693bb5d74f88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-885bc47c-0792-4ce8-a79a-876aef7730a2' class='xr-var-data-in' type='checkbox'><label for='data-885bc47c-0792-4ce8-a79a-876aef7730a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse attenuation coefficient at 490 nm (OBPG)</dd><dt><span>units :</span></dt><dd>m^-1</dd><dt><span>valid_min :</span></dt><dd>50</dd><dt><span>valid_max :</span></dt><dd>30000</dd><dt><span>comment :</span></dt><dd>non L2P core field</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-371c0689-11ab-4d6d-a524-37f31eeee85a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-371c0689-11ab-4d6d-a524-37f31eeee85a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-426117b4-d0e6-4296-b8ea-1eec7fe92e21' class='xr-var-data-in' type='checkbox'><label for='data-426117b4-d0e6-4296-b8ea-1eec7fe92e21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10m wind speed</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>comment :</span></dt><dd>Wind at 10 meters above the sea surface</dd><dt><span>source :</span></dt><dd>TBD.  Placeholder.  Currently empty</dd><dt><span>grid_mapping :</span></dt><dd>TBD</dd><dt><span>time_offset :</span></dt><dd>2.0</dd><dt><span>height :</span></dt><dd>10 m</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dt_analysis</span></div><div class='xr-var-dims'>(time, nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad46fc85-fba9-40b3-a569-c1c0a14d2302' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad46fc85-fba9-40b3-a569-c1c0a14d2302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b265196-17d8-4c24-9045-460362d84bb9' class='xr-var-data-in' type='checkbox'><label for='data-8b265196-17d8-4c24-9045-460362d84bb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>deviation from SST reference climatology</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>comment :</span></dt><dd>TBD</dd><dt><span>source :</span></dt><dd>TBD. Placeholder.  Currently empty</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16d5140f-6659-4c80-ae09-90fcbe047a16' class='xr-section-summary-in' type='checkbox'  ><label for='section-16d5140f-6659-4c80-ae09-90fcbe047a16' class='xr-section-summary' >Attributes: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7, ACDD-1.3</dd><dt><span>title :</span></dt><dd>MODIS Aqua L2P SST</dd><dt><span>summary :</span></dt><dd>Sea surface temperature retrievals produced at the NASA OBPG for the MODIS Aqua sensor.  These have been reformatted to GHRSST GDS specifications by the JPL PO.DAAC</dd><dt><span>references :</span></dt><dd>GHRSST Data Processing Specification v2r5</dd><dt><span>institution :</span></dt><dd>NASA/JPL/OBPG/RSMAS</dd><dt><span>history :</span></dt><dd>MODIS L2P created at JPL PO.DAAC</dd><dt><span>comment :</span></dt><dd>L2P Core without DT analysis or other ancillary fields; Day, Start Node:Ascending, End Node:Descending; WARNING Some applications are unable to properly handle signed byte values. If values are encountered &gt; 127, please subtract 256 from this reported value; Refined</dd><dt><span>license :</span></dt><dd>GHRSST and PO.DAAC protocol allow data use as free and open.</dd><dt><span>id :</span></dt><dd>MODIS_A-JPL-L2P-v2019.0</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>product_version :</span></dt><dd>2019.0</dd><dt><span>uuid :</span></dt><dd>f6e1f61d-c4a4-4c17-8354-0c15e12d688b</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>netcdf_version_id :</span></dt><dd>4.1</dd><dt><span>date_created :</span></dt><dd>20200221T085224Z</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>spatial_resolution :</span></dt><dd>1km</dd><dt><span>start_time :</span></dt><dd>20190622T100001Z</dd><dt><span>time_coverage_start :</span></dt><dd>20190622T100001Z</dd><dt><span>stop_time :</span></dt><dd>20190622T100459Z</dd><dt><span>time_coverage_end :</span></dt><dd>20190622T100459Z</dd><dt><span>northernmost_latitude :</span></dt><dd>89.9862</dd><dt><span>southernmost_latitude :</span></dt><dd>66.2723</dd><dt><span>easternmost_longitude :</span></dt><dd>-45.9467</dd><dt><span>westernmost_longitude :</span></dt><dd>152.489</dd><dt><span>source :</span></dt><dd>MODIS sea surface temperature observations for the OBPG</dd><dt><span>platform :</span></dt><dd>Aqua</dd><dt><span>sensor :</span></dt><dd>MODIS</dd><dt><span>metadata_link :</span></dt><dd>http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&amp;shortName=MODIS_A-JPL-L2P-v2019.0</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.01</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.01</dd><dt><span>acknowledgment :</span></dt><dd>The MODIS L2P sea surface temperature data are sponsored by NASA</dd><dt><span>creator_name :</span></dt><dd>Ed Armstrong, JPL PO.DAAC</dd><dt><span>creator_email :</span></dt><dd>edward.m.armstrong@jpl.nasa.gov</dd><dt><span>creator_url :</span></dt><dd>http://podaac.jpl.nasa.gov</dd><dt><span>project :</span></dt><dd>Group for High Resolution Sea Surface Temperature</dd><dt><span>publisher_name :</span></dt><dd>The GHRSST Project Office</dd><dt><span>publisher_url :</span></dt><dd>http://www.ghrsst.org</dd><dt><span>publisher_email :</span></dt><dd>ghrsst-po@nceo.ac.uk</dd><dt><span>processing_level :</span></dt><dd>L2P</dd><dt><span>cdm_data_type :</span></dt><dd>swath</dd><dt><span>startDirection :</span></dt><dd>Ascending</dd><dt><span>endDirection :</span></dt><dd>Descending</dd><dt><span>day_night_flag :</span></dt><dd>Day</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                  (nj: 2030, ni: 1354, time: 1)\n",
       "Coordinates:\n",
       "    lat                      (nj, ni) float32 ...\n",
       "    lon                      (nj, ni) float32 ...\n",
       "  * time                     (time) datetime64[ns] 2019-06-22T10:00:01\n",
       "Dimensions without coordinates: nj, ni\n",
       "Data variables:\n",
       "    sea_surface_temperature  (time, nj, ni) float32 ...\n",
       "    sst_dtime                (time, nj, ni) timedelta64[ns] ...\n",
       "    quality_level            (time, nj, ni) float32 ...\n",
       "    sses_bias                (time, nj, ni) float32 ...\n",
       "    sses_standard_deviation  (time, nj, ni) float32 ...\n",
       "    l2p_flags                (time, nj, ni) int16 ...\n",
       "    chlorophyll_a            (time, nj, ni) float32 ...\n",
       "    K_490                    (time, nj, ni) float32 ...\n",
       "    wind_speed               (time, nj, ni) float32 ...\n",
       "    dt_analysis              (time, nj, ni) float32 ...\n",
       "Attributes: (12/49)\n",
       "    Conventions:                CF-1.7, ACDD-1.3\n",
       "    title:                      MODIS Aqua L2P SST\n",
       "    summary:                    Sea surface temperature retrievals produced a...\n",
       "    references:                 GHRSST Data Processing Specification v2r5\n",
       "    institution:                NASA/JPL/OBPG/RSMAS\n",
       "    history:                    MODIS L2P created at JPL PO.DAAC\n",
       "    ...                         ...\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L2P\n",
       "    cdm_data_type:              swath\n",
       "    startDirection:             Ascending\n",
       "    endDirection:               Descending\n",
       "    day_night_flag:             Day"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modis_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a quick plot to take a look at the `sea_surface_temperature` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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"
    }
   ],
   "source": [
    "modis_ds.sea_surface_temperature.plot() ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot MODIS and ICESat-2 data on a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb3944adb50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "map_proj = ccrs.NorthPolarStereo()\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(projection=map_proj)\n",
    "ax.coastlines()\n",
    "\n",
    "# Plot MODIS sst, save object as sst_img, so we can add colorbar\n",
    "sst_img = ax.pcolormesh(modis_ds.lon, modis_ds.lat, modis_ds.sea_surface_temperature[0,:,:], \n",
    "                        vmin=240, vmax=270,  # Set max and min values for plotting\n",
    "                        cmap='viridis', shading='auto',   # shading='auto' to avoid warning\n",
    "                        transform=ccrs.PlateCarree())  # coords are lat,lon but map if NPS \n",
    "\n",
    "# Plot IS2 surface height \n",
    "is2_img = ax.scatter(is2_ds.longitude, is2_ds.latitude,\n",
    "                     c=is2_ds.height_segment_height, \n",
    "                     vmax=1.5,  # Set max height to plot\n",
    "                     cmap='Reds', alpha=0.6, s=2,\n",
    "                     transform=ccrs.PlateCarree())\n",
    "\n",
    "# Add colorbars\n",
    "fig.colorbar(sst_img, label='MODIS SST (K)')\n",
    "fig.colorbar(is2_img, label='ATL07 Height (m)')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract SST coincident with ICESat-2 track\n",
    "The MODIS SST is swath data, not a regularly-spaced grid of sea surface temperatures.  ICESat-2 sea surface heights are irregularly spaced segments along one ground-track traced by the ATLAS instrument on-board ICESat-2.  Fortunately, `pyresample` allows us to resample swath data.\n",
    "\n",
    "`pyresample` has many resampling methods.  We're going to use the nearest neighbour resampling method, which is implemented using a k-dimensional tree algorithm or [K-d tree](https://en.wikipedia.org/wiki/K-d_tree#Nearest_neighbour_search).  K-d trees are data structures that improve search efficiency for large data sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to define the _geometry_ of the ICESat-2 and MODIS data.  To do this we use the latitudes and longitudes of the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "is2_geometry = pyresample.SwathDefinition(lons=is2_ds.longitude,\n",
    "                                          lats=is2_ds.latitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "modis_geometry = pyresample.SwathDefinition(lons=modis_ds.lon, lats=modis_ds.lat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then implement the resampling method, passing the two geometries we have defined, the data array we want to resample - in this case sea surface temperature, and a search radius.  The resampling method expects a `numpy.Array` rather than an `xarray.DataArray`, so we use `values` to get the data as a `numpy.Array`.\n",
    "\n",
    "We set the search radius to 1000 m.  The MODIS data is nominally 1km spacing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_radius=1000.\n",
    "fill_value = np.nan\n",
    "is2_sst = pyresample.kd_tree.resample_nearest(\n",
    "    modis_geometry,\n",
    "    modis_ds.sea_surface_temperature.values,\n",
    "    is2_geometry,\n",
    "    search_radius,\n",
    "    fill_value=fill_value\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([263.375, 263.375, 263.375, ...,     nan,     nan,     nan],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is2_sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (segment: 235584)\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    latitude                 (segment) float64 82.38 82.38 82.38 ... 72.61 72.61\n",
       "    longitude                (segment) float64 -55.11 -55.11 ... 145.1 145.1\n",
       "    delta_time               (segment) float64 4.642e+07 4.642e+07 ... 4.642e+07\n",
       "    height_segment_height    (segment) float32 nan nan nan ... -0.4335 -0.4463\n",
       "    sea_surface_temperature  (segment) float32 263.4 263.4 263.4 ... nan nan nan</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-4e300b6c-664a-47a0-bbf8-b280e4a9155e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4e300b6c-664a-47a0-bbf8-b280e4a9155e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>segment</span>: 235584</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0e7bde29-df88-4e35-a152-17cd43e89b6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0e7bde29-df88-4e35-a152-17cd43e89b6f' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-521517a9-53aa-40fd-a860-d588911bc46c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-521517a9-53aa-40fd-a860-d588911bc46c' class='xr-section-summary' >Data variables: <span>(5)</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>latitude</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>82.38 82.38 82.38 ... 72.61 72.61</div><input id='attrs-5dc5e00b-5bcb-42a2-9c2c-b3db3ebe1f1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dc5e00b-5bcb-42a2-9c2c-b3db3ebe1f1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac404419-2081-4aa4-ab1b-3e4652607679' class='xr-var-data-in' type='checkbox'><label for='data-ac404419-2081-4aa4-ab1b-3e4652607679' 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>contentType :</span></dt><dd>referenceInformation</dd><dt><span>coordinates :</span></dt><dd>delta_time longitude</dd><dt><span>description :</span></dt><dd>Latitude, WGS84, North=+, Lat of segment center</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>source :</span></dt><dd>ATBD, section 4.4</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([82.38431982, 82.38431982, 82.38431982, ..., 72.60984638,\n",
       "       72.60977493, 72.60970985])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.11 -55.11 ... 145.1 145.1</div><input id='attrs-e106de78-efcf-43bc-a792-5d2e4e014699' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e106de78-efcf-43bc-a792-5d2e4e014699' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f8e6d29-dddb-43d6-bfc6-6744ecf6ade6' class='xr-var-data-in' type='checkbox'><label for='data-1f8e6d29-dddb-43d6-bfc6-6744ecf6ade6' 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>contentType :</span></dt><dd>referenceInformation</dd><dt><span>coordinates :</span></dt><dd>delta_time latitude</dd><dt><span>description :</span></dt><dd>Longitude, WGS84, East=+,Lon of segment center</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>source :</span></dt><dd>ATBD, section 4.4</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([-55.10896068, -55.10896068, -55.10896068, ..., 145.05396164,\n",
       "       145.05392851, 145.05389832])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_time</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.642e+07 4.642e+07 ... 4.642e+07</div><input id='attrs-90a6bd25-3112-4679-9e2b-5b0eb67d9b9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90a6bd25-3112-4679-9e2b-5b0eb67d9b9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cc0d8a2-78eb-46e4-9856-bc013322b88b' class='xr-var-data-in' type='checkbox'><label for='data-8cc0d8a2-78eb-46e4-9856-bc013322b88b' 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>CLASS :</span></dt><dd>DIMENSION_SCALE</dd><dt><span>NAME :</span></dt><dd>gt1l/sea_ice_segments/delta_time</dd><dt><span>REFERENCE_LIST :</span></dt><dd>[(&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)\n",
       " (&lt;HDF5 object reference&gt;, 0) (&lt;HDF5 object reference&gt;, 0)]</dd><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>coordinates :</span></dt><dd>latitude longitude</dd><dt><span>description :</span></dt><dd>Number of GPS seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch offset is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By adding the offset contained within atlas_sdp_gps_epoch to delta time parameters, the time in gps_seconds relative to the GPS epoch can be computed.</dd><dt><span>long_name :</span></dt><dd>Elapsed GPS seconds</dd><dt><span>source :</span></dt><dd>telemetry</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>seconds since 2018-01-01</dd></dl></div><div class='xr-var-data'><pre>array([46419293.64266939, 46419293.64266939, 46419293.64266939, ...,\n",
       "       46419681.87646231, 46419681.87759533, 46419681.87862704])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_segment_height</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... -0.4335 -0.4463</div><input id='attrs-317b4fdc-3d06-4b90-9d14-d722d5118103' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-317b4fdc-3d06-4b90-9d14-d722d5118103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99425a76-e471-4bd5-b833-1a5bc24c9bfb' class='xr-var-data-in' type='checkbox'><label for='data-99425a76-e471-4bd5-b833-1a5bc24c9bfb' 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>_FillValue :</span></dt><dd>3.4028235e+38</dd><dt><span>contentType :</span></dt><dd>referenceInformation</dd><dt><span>coordinates :</span></dt><dd>../delta_time ../latitude ../longitude</dd><dt><span>description :</span></dt><dd>Mean height from along-track segment fit detremined by the sea ice algorithm. The sea ice height is relative to the tide-free MSS.</dd><dt><span>long_name :</span></dt><dd>height of segment surface</dd><dt><span>source :</span></dt><dd>ATBD, section 4.2.2.4</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([        nan,         nan,         nan, ..., -0.46550068,\n",
       "       -0.43347716, -0.4462675 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_temperature</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>263.4 263.4 263.4 ... nan nan nan</div><input id='attrs-7a3e958a-cceb-4bb7-a4d8-a21b2815c2ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a3e958a-cceb-4bb7-a4d8-a21b2815c2ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2275027-c3a2-4efa-bfc2-46c957053739' class='xr-var-data-in' type='checkbox'><label for='data-e2275027-c3a2-4efa-bfc2-46c957053739' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([263.375, 263.375, 263.375, ...,     nan,     nan,     nan],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2115630-159f-4d0e-8325-70c07fd17a27' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2115630-159f-4d0e-8325-70c07fd17a27' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                  (segment: 235584)\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    latitude                 (segment) float64 82.38 82.38 82.38 ... 72.61 72.61\n",
       "    longitude                (segment) float64 -55.11 -55.11 ... 145.1 145.1\n",
       "    delta_time               (segment) float64 4.642e+07 4.642e+07 ... 4.642e+07\n",
       "    height_segment_height    (segment) float32 nan nan nan ... -0.4335 -0.4463\n",
       "    sea_surface_temperature  (segment) float32 263.4 263.4 263.4 ... nan nan nan"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is2_ds['sea_surface_temperature'] = xr.DataArray(is2_sst, dims='segment')\n",
    "is2_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot SST and Height along track\n",
    "This is a quick plot of the extracted data.  We're using `matplotlib` so we can use latitude as the x-value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb39fcd8040>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "is2_ds = is2_ds.set_coords(['latitude'])\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(15, 7))\n",
    "ax1.set_xlim(82.,88.)\n",
    "ax1.plot(is2_ds.latitude, is2_ds.sea_surface_temperature, \n",
    "         color='orange', label='SST', zorder=3)\n",
    "ax1.set_ylabel('SST (K)')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(is2_ds.latitude, is2_ds.height_segment_height, label='Height')\n",
    "ax2.set_ylabel('Height (m)')\n",
    "\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}