sim_swot_subset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c8c560e-d41a-49a9-965f-220607468e42",
   "metadata": {
    "tags": []
   },
   "source": [
    "## **Using Harmony to Subset SWOT Simulated Data**\n",
    "### **Objectives of this Jupyter notebook**\n",
    "1. Use a Harmony request to obtain a temporal and spatial subset of L2 altimetric data.\n",
    "2. Construct and submit the Harmony request by using the harmony-py library.\n",
    "3. Open the data in xarray and make a simple plot to visually confirm the download and subset.\n",
    "\n",
    "### **Why are we using Harmony here?** https://harmony.earthdata.nasa.gov/\n",
    "1. The advantage of reducing data size for access/download, especially for long global time series.\n",
    "2. Harmony allows us to access data from different NASA DAACs in a consistent way (not DAAC-specific).\n",
    "3. A number of different services like spatial subset, variable subset, etc., can all be called from Harmony\n",
    "\n",
    "### **Datasets of interest in this notebook**\n",
    "- Nadir GLORYS CalVal: https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1\n",
    "- Nadir GLORYS Science: https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1\n",
    "- Nadir ECCO CalVal: https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1\n",
    "- Nadir ECCO Science: https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1\n",
    "- KaRIn GLORYS CalVal: https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1\n",
    "- KaRIn GLORYS Science: https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1\n",
    "- KaRIn ECCO CalVal: https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1\n",
    "- KaRIn ECCO Science: https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0387625-8bbe-48af-8636-02dd83d15a77",
   "metadata": {},
   "source": [
    "#### **Import libraries**\n",
    "Note: Install harmony-py to your Python environment before you can import it for the first time. See https://github.com/nasa/harmony-py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dfdd7f20-8c99-4efb-88c2-812a5d14c526",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from IPython.display import display, JSON\n",
    "from datetime import datetime, timedelta, time\n",
    "\n",
    "# highlight the harmony-py library\n",
    "from harmony import BBox, Client, Collection, Request, Environment, LinkType \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a145ea4-aea6-4857-b9bc-f3c027769a0b",
   "metadata": {},
   "source": [
    "#### **Let's start up the client from the harmony-py library.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a42e431d-8acd-485d-8026-df425bb94dab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Start the Harmony Client.\n",
    "\n",
    "harmony_client = Client(env=Environment.PROD)\n",
    "\n",
    "# \"PROD\" stands for production. This is the environment for users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ae0bfa-d1b7-4185-8cf8-cb53803a4dc2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## **Temporal and spatial subset using a bounding box around CA Crossover region**\n",
    "1. Define the **collection** of interest by calling Collection(id = YourCollection), where YourCollection is a collection short name or concept-id.\n",
    "2. Set **time bounds**.\n",
    "3. Set **spatial bounding box**.\n",
    "4. There are also other options such as variables, granules, and concatenation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d917d15-89a5-43a3-bbc1-c1d0d5fb2c0e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection = Collection(id='SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1')\n",
    "\n",
    "start_day = datetime(2015,4,15,0,0,0)\n",
    "end_day = datetime(2015,4,20,0,0,0)\n",
    "\n",
    "request = Request(\n",
    "    collection=collection,\n",
    "    temporal={\n",
    "        'start': start_day,\n",
    "        'stop': end_day\n",
    "    },\n",
    "    spatial=BBox(-140, 20, -100, 50), # [20-50N], [140W-100W] CA Current crossover point (35N,125W)\n",
    "    # variables=[],\n",
    "    # granule_id=granuleIDs,\n",
    "    # concatenate = True,\n",
    ")\n",
    "\n",
    "request.is_valid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9ee57a7c-8667-4e86-a3e2-e0ed42625618",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate, br' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; token=*****; _urs-gui_session=4f208576cd57e635f4649bb4cca9da9d' -H 'User-Agent: CPython/3.9.10 Linux/5.4.156-83.273.amzn2.x86_64 python-requests/2.27.1 harmony-py/0.4.1' 'https://harmony.earthdata.nasa.gov/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%2820%3A50%29&subset=lon%28-140%3A-100%29&subset=time%28%222015-04-15T00%3A00%3A00%22%3A%222015-04-20T00%3A00%3A00%22%29'\n",
      "Job ID: afb341e0-84ac-4c8b-9eee-eca0e2cedec2\n"
     ]
    }
   ],
   "source": [
    "print(harmony_client.request_as_curl(request))\n",
    "job_id = harmony_client.submit(request)\n",
    "print(f'Job ID: {job_id}') # This job id is shareable:show how to do this"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fcfb037-1a3c-4dd0-9292-216ab6a94b4f",
   "metadata": {},
   "source": [
    "#### **View the job status**\n",
    "A Harmony request is limited to 200 granules. The limit is there to prevent users from accidentally make huge requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60bdcdee-65d2-4ddc-a542-d7e50f211b49",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'running',\n",
       " 'message': 'The job is being processed',\n",
       " 'progress': 0,\n",
       " 'created_at': datetime.datetime(2022, 3, 15, 0, 45, 29, 808000, tzinfo=tzlocal()),\n",
       " 'updated_at': datetime.datetime(2022, 3, 15, 0, 45, 33, 443000, tzinfo=tzlocal()),\n",
       " 'created_at_local': '2022-03-15T00:45:29+00:00',\n",
       " 'updated_at_local': '2022-03-15T00:45:33+00:00',\n",
       " 'request': 'https://harmony.earthdata.nasa.gov/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(20%3A50)&subset=lon(-140%3A-100)&subset=time(%222015-04-15T00%3A00%3A00%22%3A%222015-04-20T00%3A00%3A00%22)',\n",
       " 'num_input_granules': 141}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "harmony_client.status(job_id) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd98a529-2d1b-40d4-aa9d-cd5a5cdebeef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [ Processing: 100% ] |###################################################| [|]\n"
     ]
    }
   ],
   "source": [
    "harmony_client.wait_for_processing(job_id, show_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e6ba5d-c806-4df0-8112-af66f905fc26",
   "metadata": {},
   "source": [
    "#### **Download subsetted files**\n",
    "\n",
    "Filenames that end with \"subsetted.nc4\" have been subsetted. \n",
    "\n",
    "The other filenames (that are un-altered) indicate that these were rounded up as relevant files during Harmony's search, but do not contain data in the actual region of interest, so the files downloaded here are empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72309e63-5b09-474b-ad3a-221dd5622c60",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./swot_ocean_basic_subset/SWOT_GPR_2PTP018_359_20150414_234202_20150415_003327.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_360_20150415_003328_20150415_012454_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_361_20150415_012455_20150415_021621.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_362_20150415_021622_20150415_030747_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_363_20150415_030748_20150415_035914.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_364_20150415_035915_20150415_045041.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_365_20150415_045042_20150415_054208.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_366_20150415_054209_20150415_063335.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_367_20150415_063336_20150415_072502.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_368_20150415_072503_20150415_081628.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_369_20150415_081629_20150415_090755.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_370_20150415_090756_20150415_095922.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_371_20150415_095923_20150415_105048.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_372_20150415_105049_20150415_114216.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_373_20150415_114217_20150415_123343.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_374_20150415_123344_20150415_132510.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_375_20150415_132511_20150415_141637_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_376_20150415_141638_20150415_150803.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_377_20150415_150804_20150415_155930_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_378_20150415_155931_20150415_165057.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_379_20150415_165058_20150415_174223.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_380_20150415_174224_20150415_183350.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_381_20150415_183351_20150415_192517.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_382_20150415_192518_20150415_201643.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_383_20150415_201644_20150415_210811.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_384_20150415_210812_20150415_215938.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_385_20150415_215939_20150415_225104.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_386_20150415_225105_20150415_234231.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_387_20150415_234232_20150416_003358.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_388_20150416_003359_20150416_012524_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_389_20150416_012525_20150416_021651.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_390_20150416_021652_20150416_030818_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_391_20150416_030819_20150416_035944.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_392_20150416_035945_20150416_045112.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_393_20150416_045113_20150416_054240.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_394_20150416_054241_20150416_063406.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_395_20150416_063407_20150416_072533.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_396_20150416_072534_20150416_081700.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_397_20150416_081701_20150416_090826.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_398_20150416_090827_20150416_095953.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_399_20150416_095954_20150416_105120.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_400_20150416_105121_20150416_114246.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_401_20150416_114247_20150416_123413.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_402_20150416_123414_20150416_132540.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_403_20150416_132541_20150416_141707_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_404_20150416_141708_20150416_150834.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_405_20150416_150835_20150416_160000_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_406_20150416_160001_20150416_165127.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_407_20150416_165128_20150416_174254.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_408_20150416_174255_20150416_183420.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_409_20150416_183421_20150416_192547.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_410_20150416_192548_20150416_201714.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_411_20150416_201715_20150416_210840.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_412_20150416_210841_20150416_220009.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_413_20150416_220010_20150416_225136.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_414_20150416_225137_20150416_234302.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_415_20150416_234303_20150417_003429.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_416_20150417_003430_20150417_012556_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_417_20150417_012557_20150417_021722.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_418_20150417_021723_20150417_030849_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_419_20150417_030850_20150417_040016.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_420_20150417_040017_20150417_045142.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_421_20150417_045143_20150417_054310.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_422_20150417_054311_20150417_063437.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_423_20150417_063438_20150417_072603.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_424_20150417_072604_20150417_081730.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_425_20150417_081731_20150417_090857.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_426_20150417_090858_20150417_100023.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_427_20150417_100024_20150417_105150.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_428_20150417_105151_20150417_114317.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_429_20150417_114318_20150417_123443.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_430_20150417_123444_20150417_132610.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_431_20150417_132611_20150417_141737_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_432_20150417_141739_20150417_150905.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_433_20150417_150906_20150417_160032_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_434_20150417_160033_20150417_165158.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_435_20150417_165159_20150417_174325.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_436_20150417_174326_20150417_183452.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_437_20150417_183453_20150417_192618.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_438_20150417_192619_20150417_201745.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_439_20150417_201746_20150417_210912.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_440_20150417_210913_20150417_220039.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_441_20150417_220040_20150417_225206.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_442_20150417_225207_20150417_234333.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_443_20150417_234334_20150418_003459.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_444_20150418_003500_20150418_012626_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_445_20150418_012627_20150418_021753.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_446_20150418_021754_20150418_030919_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_447_20150418_030920_20150418_040046.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_448_20150418_040047_20150418_045213.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_449_20150418_045214_20150418_054340.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_450_20150418_054341_20150418_063507.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_451_20150418_063508_20150418_072635.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_452_20150418_072636_20150418_081801.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_453_20150418_081802_20150418_090928.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_454_20150418_090929_20150418_100055.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_455_20150418_100056_20150418_105221.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_456_20150418_105222_20150418_114348.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_457_20150418_114349_20150418_123514.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_458_20150418_123515_20150418_132641.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_459_20150418_132642_20150418_141809_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_460_20150418_141810_20150418_150935.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_461_20150418_150936_20150418_160102_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_462_20150418_160103_20150418_165229.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_463_20150418_165230_20150418_174355.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_464_20150418_174356_20150418_183522.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_465_20150418_183523_20150418_192649.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_466_20150418_192650_20150418_201815.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_467_20150418_201816_20150418_210942.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_468_20150418_210943_20150418_220110.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_469_20150418_220111_20150418_225236.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_470_20150418_225237_20150418_234403.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_471_20150418_234404_20150419_003531.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_472_20150419_003532_20150419_012657_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_473_20150419_012658_20150419_021824.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_474_20150419_021825_20150419_030951_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_475_20150419_030952_20150419_040117.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_476_20150419_040118_20150419_045244.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_477_20150419_045245_20150419_054411.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_478_20150419_054412_20150419_063538.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_479_20150419_063539_20150419_072705.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_480_20150419_072706_20150419_081831.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_481_20150419_081832_20150419_090958.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_482_20150419_090959_20150419_100125.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_483_20150419_100126_20150419_105251.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_484_20150419_105252_20150419_114418.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_485_20150419_114419_20150419_123545.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_486_20150419_123546_20150419_132711.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_487_20150419_132712_20150419_141839_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_488_20150419_141840_20150419_151006.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_489_20150419_151007_20150419_160132_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_490_20150419_160133_20150419_165259.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_491_20150419_165300_20150419_174427.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_492_20150419_174428_20150419_183553.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_493_20150419_183554_20150419_192720.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_494_20150419_192721_20150419_201847.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_495_20150419_201848_20150419_211013.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_496_20150419_211014_20150419_220141.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_497_20150419_220142_20150419_225308.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_498_20150419_225309_20150419_234434.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_499_20150419_234435_20150420_003601.nc4']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futures = harmony_client.download_all(job_id, directory='./swot_ocean_basic_subset', overwrite=True)\n",
    "file_names = [f.result() for f in futures]\n",
    "sorted(file_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2006a11-bfbb-4789-90ff-b8c5e4346425",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./swot_ocean_basic_subset/SWOT_GPR_2PTP018_360_20150415_003328_20150415_012454_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_459_20150418_132642_20150418_141809_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_461_20150418_150936_20150418_160102_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_472_20150419_003532_20150419_012657_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_474_20150419_021825_20150419_030951_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_487_20150419_132712_20150419_141839_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_489_20150419_151007_20150419_160132_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_375_20150415_132511_20150415_141637_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_377_20150415_150804_20150415_155930_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_388_20150416_003359_20150416_012524_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_362_20150415_021622_20150415_030747_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_390_20150416_021652_20150416_030818_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_403_20150416_132541_20150416_141707_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_405_20150416_150835_20150416_160000_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_416_20150417_003430_20150417_012556_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_418_20150417_021723_20150417_030849_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_431_20150417_132611_20150417_141737_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_433_20150417_150906_20150417_160032_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_444_20150418_003500_20150418_012626_subsetted.nc4',\n",
       " './swot_ocean_basic_subset/SWOT_GPR_2PTP018_446_20150418_021754_20150418_030919_subsetted.nc4']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "data_files = [ f for f in file_names if \"subsetted\" in f]\n",
    "data_files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9bbceb-d9ab-4481-a199-94bb96276152",
   "metadata": {},
   "source": [
    "#### **Take a look at the subset data**\n",
    "Note: xarray is a little clunky with variables in groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b46c5c60-0a93-4f40-84dc-f531eb8a4e31",
   "metadata": {},
   "outputs": [
    {
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\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:                            (time: 9147)\n",
       "Coordinates:\n",
       "    latitude                           (time) float64 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    longitude                          (time) float64 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "  * time                               (time) datetime64[ns] 2015-04-15T00:44...\n",
       "Data variables: (12/31)\n",
       "    time_tai                           (time) datetime64[ns] dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    surface_classification_flag        (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    rad_side_1_surface_type_flag       (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    rad_side_2_surface_type_flag       (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    alt_qual                           (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    rad_qual                           (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    ...                                 ...\n",
       "    pole_tide                          (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    internal_tide                      (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    wind_speed_alt                     (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    wind_speed_alt_mle3                (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    rad_water_vapor                    (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;\n",
       "    rad_cloud_liquid_water             (time) float32 dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1b7c0442-1341-4a74-8315-24b8ac303a95' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1b7c0442-1341-4a74-8315-24b8ac303a95' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 9147</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2a3bf4ef-4607-46b0-9acc-1c396eea7778' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a3bf4ef-4607-46b0-9acc-1c396eea7778' 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>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-3e6da95f-2b73-48eb-89b0-8b7795e62f5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e6da95f-2b73-48eb-89b0-8b7795e62f5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53c3996-7a71-45fa-a49d-d4d4da033a4d' class='xr-var-data-in' type='checkbox'><label for='data-a53c3996-7a71-45fa-a49d-d4d4da033a4d' 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>comment :</span></dt><dd>Positive latitude is North latitude, negative latitude is South latitude. See SWOT Nadir Altimeter User Handbook. Associated quality flag is orb_state_diode_flag for the OGDR products, orb_state_rest_flag for the IGDR and GDR products</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 71.46 kiB </td>\n",
       "                        <td> 4.11 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (9147,) </td>\n",
       "                        <td> (526,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 60 Tasks </td>\n",
       "                        <td> 20 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-cb696bd3-1366-4054-9854-c45f87cd1dfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb696bd3-1366-4054-9854-c45f87cd1dfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca221f29-92fc-4cae-9cb1-13713ffcdadb' class='xr-var-data-in' type='checkbox'><label for='data-ca221f29-92fc-4cae-9cb1-13713ffcdadb' 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>comment :</span></dt><dd>East longitude relative to Greenwich meridian. See SWOT Nadir Altimeter User Handbook. Associated quality flag is orb_state_diode_flag for the OGDR products, orb_state_rest_flag for the IGDR and GDR products</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 71.46 kiB </td>\n",
       "                        <td> 4.11 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (9147,) </td>\n",
       "                        <td> (526,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 60 Tasks </td>\n",
       "                        <td> 20 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></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'>2015-04-15T00:44:27.369390976 .....</div><input id='attrs-464c06d0-b1cb-40d6-8264-8b38e237c877' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-464c06d0-b1cb-40d6-8264-8b38e237c877' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f97ad970-cbe5-4479-873b-c4cd054a6bdd' class='xr-var-data-in' type='checkbox'><label for='data-f97ad970-cbe5-4479-873b-c4cd054a6bdd' 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 in UTC</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute [leap_second] is set to the UTC time at which the leap second occurs</dd><dt><span>leap_second :</span></dt><dd>0000-00-00T00:00:00</dd><dt><span>tai_utc_difference :</span></dt><dd>35.0</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-04-15T00:44:27.369390976&#x27;, &#x27;2015-04-15T00:44:28.386110976&#x27;,\n",
       "       &#x27;2015-04-15T00:44:29.402835968&#x27;, ..., &#x27;2015-04-19T15:50:30.859442944&#x27;,\n",
       "       &#x27;2015-04-19T15:50:31.876074944&#x27;, &#x27;2015-04-19T15:50:32.892718976&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad121a78-dd7a-4143-9054-181143683622' class='xr-section-summary-in' type='checkbox'  ><label for='section-ad121a78-dd7a-4143-9054-181143683622' class='xr-section-summary' >Data variables: <span>(31)</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>time_tai</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-6b213114-8b77-452d-9638-139d476b8d5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b213114-8b77-452d-9638-139d476b8d5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1ab62e9-d766-4c30-afd8-3444996d8225' class='xr-var-data-in' type='checkbox'><label for='data-a1ab62e9-d766-4c30-afd8-3444996d8225' 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 in TAI</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [time:tai_utc_difference]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 71.46 kiB </td>\n",
       "                        <td> 4.11 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (9147,) </td>\n",
       "                        <td> (526,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 60 Tasks </td>\n",
       "                        <td> 20 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> datetime64[ns] </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rad_side_2_surface_type_flag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-d2347ec0-1a60-487b-b3f2-06d530fd52df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2347ec0-1a60-487b-b3f2-06d530fd52df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-628ca64a-c43c-4c36-a339-6697569b7c49' class='xr-var-data-in' type='checkbox'><label for='data-628ca64a-c43c-4c36-a339-6697569b7c49' 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>radiometer surface type from second radiometer</dd><dt><span>flag_meanings :</span></dt><dd>open_ocean near_coast land</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_qual</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-e62b9f8a-27b7-4c40-97ba-c547ba21a98a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e62b9f8a-27b7-4c40-97ba-c547ba21a98a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecfb781c-9c66-4736-beaf-4e61dda130e6' class='xr-var-data-in' type='checkbox'><label for='data-ecfb781c-9c66-4736-beaf-4e61dda130e6' 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>altimeter quality flag</dd><dt><span>flag_meanings :</span></dt><dd>good bad</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>comment :</span></dt><dd>Compilation of all altimeter flags except altimeter echo type : Set to default in the current issue</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rad_qual</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-9f5e8ee3-1828-4940-a044-93a127592036' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f5e8ee3-1828-4940-a044-93a127592036' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78ce76b5-1b45-49f9-827f-75076a9960e7' class='xr-var-data-in' type='checkbox'><label for='data-78ce76b5-1b45-49f9-827f-75076a9960e7' 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>radiometer quality flag</dd><dt><span>flag_meanings :</span></dt><dd>good bad</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>comment :</span></dt><dd>Compilation of all radiometer flags except radiometer surface type : Set to default in the current issue</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geo_qual</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-78929dc2-2b8e-4c23-86e8-8a29fb730bda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78929dc2-2b8e-4c23-86e8-8a29fb730bda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89baa33a-795b-4655-8319-7e930d5ca009' class='xr-var-data-in' type='checkbox'><label for='data-89baa33a-795b-4655-8319-7e930d5ca009' 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>geophysical quality flag</dd><dt><span>flag_meanings :</span></dt><dd>good bad</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>comment :</span></dt><dd>Check on validity of all geophysical fields : Set to default in the current issue</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rad_wet_tropo_cor_interp_qual</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-b056f67f-1321-43b2-a17f-5218d4a4a9fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b056f67f-1321-43b2-a17f-5218d4a4a9fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca137a4a-f5e0-4413-83ee-b9b71eb5b69a' class='xr-var-data-in' type='checkbox'><label for='data-ca137a4a-f5e0-4413-83ee-b9b71eb5b69a' 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>radiometer wet tropospheric correction interpolation quality flag</dd><dt><span>flag_meanings :</span></dt><dd>good degraded bad</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>comment :</span></dt><dd>See SWOT Nadir Altimeter User Handbook</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_flag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-a0dd4e28-e57a-4fa4-a7dc-d8e974323df1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0dd4e28-e57a-4fa4-a7dc-d8e974323df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-982a9c41-6aca-4db4-b71e-f7d6ca541fb0' class='xr-var-data-in' type='checkbox'><label for='data-982a9c41-6aca-4db4-b71e-f7d6ca541fb0' 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>rain flag</dd><dt><span>flag_meanings :</span></dt><dd>no_rain rain high_rain_probability_from_altimeter high_probability_of_no_rain_from_altimeter ambiguous_situation_possibility_of_ice evaluation_not_possible</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3 4 5]</dd><dt><span>comment :</span></dt><dd>See SWOT Nadir Altimeter User Handbook</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rad_side_1_sea_ice_flag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-e52be737-8cad-42e6-87d1-5ba2797541f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e52be737-8cad-42e6-87d1-5ba2797541f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a2b1d0c-6081-4004-9c33-01d2c3e74d3e' class='xr-var-data-in' type='checkbox'><label for='data-3a2b1d0c-6081-4004-9c33-01d2c3e74d3e' 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>radiometer sea-ice flag from first radiometer</dd><dt><span>flag_meanings :</span></dt><dd>no_sea_ice sea_ice</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>comment :</span></dt><dd>See SWOT Nadir Altimeter User Handbook</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rad_side_2_sea_ice_flag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-028bd417-7b47-4f82-9312-f3b67845bec0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-028bd417-7b47-4f82-9312-f3b67845bec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6d79f59-a0f7-43f2-a656-3d42dc99d971' class='xr-var-data-in' type='checkbox'><label for='data-f6d79f59-a0f7-43f2-a656-3d42dc99d971' 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>radiometer sea-ice flag from second radiometer</dd><dt><span>flag_meanings :</span></dt><dd>no_sea_ice sea_ice</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>comment :</span></dt><dd>See SWOT Nadir Altimeter User Handbook</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-fdd6c199-6994-40f6-9d01-c5df6f943475' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdd6c199-6994-40f6-9d01-c5df6f943475' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0951899f-6c66-4293-80a1-959b60ddaa9a' class='xr-var-data-in' type='checkbox'><label for='data-0951899f-6c66-4293-80a1-959b60ddaa9a' 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>1 Hz altitude of satellite</dd><dt><span>standard_name :</span></dt><dd>height_above_reference_ellipsoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Altitude of satellite above the reference ellipsoid. Associated quality flag is orb_state_diode_flag for the OGDR products, orb_state_rest_flag for the IGDR and GDR products</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rad_wet_tropo_cor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-8c6879f7-b48d-4e5a-be3e-77a3c423acb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c6879f7-b48d-4e5a-be3e-77a3c423acb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56b9dc84-5891-4fdf-8eff-66ae73bd6b15' class='xr-var-data-in' type='checkbox'><label for='data-56b9dc84-5891-4fdf-8eff-66ae73bd6b15' 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>radiometer wet tropospheric correction</dd><dt><span>standard_name :</span></dt><dd>altimeter_range_correction_due_to_wet_troposphere</dd><dt><span>source :</span></dt><dd>AMR</dd><dt><span>institution :</span></dt><dd>NASA/JPL</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>A wet tropospheric correction must be added (negative value) to the instrument range to correct this range measurement for wet tropospheric range delays of the radar pulse</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inv_bar_cor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-12bf3c27-f798-44ca-ac5f-b138ef98b39a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12bf3c27-f798-44ca-ac5f-b138ef98b39a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-988216de-aeaf-476b-bcad-e7d784afc0da' class='xr-var-data-in' type='checkbox'><label for='data-988216de-aeaf-476b-bcad-e7d784afc0da' 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>inverted barometer height correction</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_correction_due_to_air_pressure_at_low_frequency</dd><dt><span>source :</span></dt><dd>European Center for Medium Range Weather Forecasting</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Computed at the altimeter time-tag from the interpolation of 2 meteorological fields that surround the altimeter time-tag. See SWOT Nadir Altimeter User Handbook</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed_alt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(526,), meta=np.ndarray&gt;</div><input id='attrs-e99f7aac-55b0-4bac-8017-2967e6183f13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e99f7aac-55b0-4bac-8017-2967e6183f13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5199feb-8b9a-43da-9553-9bdfd7bce3f6' class='xr-var-data-in' type='checkbox'><label for='data-e5199feb-8b9a-43da-9553-9bdfd7bce3f6' 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>altimeter wind speed</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>comment :</span></dt><dd>Should not be used over land. See SWOT Nadir Altimeter User Handbook. A calibration bias of +0.06 dB has been added to the Ku band backscatter coefficient (/data_01/ku/sig0_ocean) before computing the wind speed</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                            (time: 9147)\n",
       "Coordinates:\n",
       "    latitude                           (time) float64 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    longitude                          (time) float64 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "  * time                               (time) datetime64[ns] 2015-04-15T00:44...\n",
       "Data variables: (12/31)\n",
       "    time_tai                           (time) datetime64[ns] dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    surface_classification_flag        (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    rad_side_1_surface_type_flag       (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    rad_side_2_surface_type_flag       (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    alt_qual                           (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    rad_qual                           (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    ...                                 ...\n",
       "    pole_tide                          (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    internal_tide                      (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    wind_speed_alt                     (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    wind_speed_alt_mle3                (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    rad_water_vapor                    (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>\n",
       "    rad_cloud_liquid_water             (time) float32 dask.array<chunksize=(526,), meta=np.ndarray>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_mfdataset(sorted(data_files),combine='nested',concat_dim='time',group='data_01')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d853d99-4bb3-4f44-bf9d-afdeb561e5c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip\n",
      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[11,7]) \n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.set_extent([-150, -90, 10, 60])\n",
    "plt.scatter(ds.longitude, ds.latitude, lw=1, c=ds.depth_or_elevation)\n",
    "plt.colorbar(label='Depth or elevation (m)')\n",
    "plt.clim(-4000,4000)\n",
    "plt.show()\n",
    "# ds.plot.scatter( y=\"latitude\",\n",
    "#                  x=\"longitude\", \n",
    "#                  hue=\"depth_or_elevation\",\n",
    "#                  s=1,\n",
    "#                  vmin=-4000,\n",
    "#                  vmax=4000,\n",
    "#                  levels=9, \n",
    "#                  cmap=\"jet\",\n",
    "#                  aspect=2.5,\n",
    "#                  size=9, )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962cf7a8-20fb-44eb-8813-d130a6bdbca9",
   "metadata": {},
   "source": [
    "#### **Next: Try subsetting by variable or granule IDs, or over a longer time period**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8097405-448e-48b0-b754-152cb6bd1bb4",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}