{
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   "source": [
    "# EcoFOCI evaluation/application of Glider Tools for post processing\n",
    "\n",
    "**Summary**\n",
    "\n",
    "\n",
    "**Resources**\n",
    "\n",
    "- [https://github.com/GliderToolsCommunity/GliderTools](GliderTools Python Package) for secondary processing of data\n",
    "- [https://gliders.ioos.us/data/](IOOS Glider DAC)\n",
    "- [https://underwatergliders.org/index.php/data-management-tools/](UnderWaterGlider Users Group)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df0bb562-ed9d-483c-8a5c-2bd5f9da0887",
   "metadata": {},
   "source": [
    "## Oculus Glider Data Applicability\n",
    "\n",
    "There are pre-existing QC procedures built into both, SeaGlider data (what GliderTools works with) and Oculus data as developed by UW.\n",
    "\n",
    "There are Engineering parameters and science parameters (including platform instrumentation and additional science instrumentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba104266-ad4c-44a5-b85d-0ef7056b46f6",
   "metadata": {},
   "source": [
    "## Oculus Glider Data Visualization (PMEL local or FOCI NRT)\n",
    "\n",
    "- Kongsberg pilot webpage (immediate information but only for pilots and PI's)\n",
    "- internal plotly dashboard (NRT / NRT QC) - of binned profile data or timeseries data... quick to plot and look at\n",
    "- internal plotly dashboard (Delayed Mode QC), see gliderTools + IOOS QARTOD + PMEL QC\n",
    "- **GliderScope???** [https://imos.org.au/gliderscope](link)  (gui third party-matlab based runtime-needs admin to install on Mac but also is available for Win)\n",
    "\n",
    "## Other Services and gliders\n",
    "- [https://gliders.ioos.us/map/](IOOS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9feec81a-d670-49d4-a16d-67faa796a46f",
   "metadata": {},
   "source": [
    "## Oculus Glider Distribition\n",
    "\n",
    "Via Internal ERDDAP:\n",
    "- NRT (Pre-existing QC from timeseries files or profile files made by the basestation)\n",
    "    - profile files are binned by depth intervals (5m) and may be better hosted as a \"gridded\" dataset via erddap than tabular\n",
    "    - timeseries is one long datastream\n",
    "- Processed and Cleaned\n",
    "    - initial qc with improved dials?\n",
    "    - GliderTools\n",
    "    - IOOS QARTOD QC\n",
    "- Modified Science Routines\n",
    "    - BeringSea interface tools\n",
    "    \n",
    "Via IOOS GliderDAC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef29f84-37b5-408a-8e51-5a9bdd2db2b8",
   "metadata": {},
   "source": [
    "## Examples from BS long mission (2017) \n",
    "\n",
    "Local erddap has two datastreams available for simplicity... the timeseries merged profiles data and the upanddown merged profiles data (from the base station).\n",
    "\n",
    "### Start with gridded (5m) basestation data (NRT level quicklooks with some on base station QC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb4a3075-a517-4ad7-b915-d596f9ae05d2",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>griddap</th>\n",
       "      <th>Subset</th>\n",
       "      <th>tabledap</th>\n",
       "      <th>Make A Graph</th>\n",
       "      <th>wms</th>\n",
       "      <th>files</th>\n",
       "      <th>Title</th>\n",
       "      <th>Summary</th>\n",
       "      <th>FGDC</th>\n",
       "      <th>ISO 19115</th>\n",
       "      <th>Info</th>\n",
       "      <th>Background Info</th>\n",
       "      <th>RSS</th>\n",
       "      <th>Email</th>\n",
       "      <th>Institution</th>\n",
       "      <th>Dataset ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/tabled...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/tabled...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/tabled...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/files/...</td>\n",
       "      <td>Physical data collected from Seaglider SG401 d...</td>\n",
       "      <td>PMEL SG401 Bering Sea Test Apr 26, 2017. Up an...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/metada...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/metada...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/info/d...</td>\n",
       "      <td>https://data.nodc.noaa.gov/accession/0092291</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/rss/da...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/subscr...</td>\n",
       "      <td>NOAA/PMEL</td>\n",
       "      <td>datasets_sg401_2017_upanddown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/tabled...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/tabled...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/tabled...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/files/...</td>\n",
       "      <td>Physical data collected from Seaglider SG401 d...</td>\n",
       "      <td>PMEL SG401 Bering Sea Test Apr 26, 2017. Times...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/metada...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/metada...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/info/d...</td>\n",
       "      <td>https://data.nodc.noaa.gov/accession/0092291</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/rss/da...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/subscr...</td>\n",
       "      <td>NOAA/PMEL - EcoFOCI</td>\n",
       "      <td>datasets_sg401_2017_timeseries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/gridda...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/gridda...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/files/...</td>\n",
       "      <td>Physical, chemical, and biological data collec...</td>\n",
       "      <td>SG401 Bering Sea. Applied Physics Lab\\n       ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/info/d...</td>\n",
       "      <td>https://data.nodc.noaa.gov/accession/0092291</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/rss/da...</td>\n",
       "      <td>http://akutan.pmel.noaa.gov:8080/erddap/subscr...</td>\n",
       "      <td>Applied Physics Lab University of Washington S...</td>\n",
       "      <td>datasets_sg401_2017_upanddown_gridded</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             griddap  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2  http://akutan.pmel.noaa.gov:8080/erddap/gridda...   \n",
       "\n",
       "                                              Subset  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/tabled...   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/tabled...   \n",
       "2                                                NaN   \n",
       "\n",
       "                                            tabledap  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/tabled...   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/tabled...   \n",
       "2                                                NaN   \n",
       "\n",
       "                                        Make A Graph  wms  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/tabled...  NaN   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/tabled...  NaN   \n",
       "2  http://akutan.pmel.noaa.gov:8080/erddap/gridda...  NaN   \n",
       "\n",
       "                                               files  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/files/...   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/files/...   \n",
       "2  http://akutan.pmel.noaa.gov:8080/erddap/files/...   \n",
       "\n",
       "                                               Title  \\\n",
       "0  Physical data collected from Seaglider SG401 d...   \n",
       "1  Physical data collected from Seaglider SG401 d...   \n",
       "2  Physical, chemical, and biological data collec...   \n",
       "\n",
       "                                             Summary  \\\n",
       "0  PMEL SG401 Bering Sea Test Apr 26, 2017. Up an...   \n",
       "1  PMEL SG401 Bering Sea Test Apr 26, 2017. Times...   \n",
       "2  SG401 Bering Sea. Applied Physics Lab\\n       ...   \n",
       "\n",
       "                                                FGDC  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/metada...   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/metada...   \n",
       "2                                                NaN   \n",
       "\n",
       "                                           ISO 19115  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/metada...   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/metada...   \n",
       "2                                                NaN   \n",
       "\n",
       "                                                Info  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/info/d...   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/info/d...   \n",
       "2  http://akutan.pmel.noaa.gov:8080/erddap/info/d...   \n",
       "\n",
       "                                Background Info  \\\n",
       "0  https://data.nodc.noaa.gov/accession/0092291   \n",
       "1  https://data.nodc.noaa.gov/accession/0092291   \n",
       "2  https://data.nodc.noaa.gov/accession/0092291   \n",
       "\n",
       "                                                 RSS  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/rss/da...   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/rss/da...   \n",
       "2  http://akutan.pmel.noaa.gov:8080/erddap/rss/da...   \n",
       "\n",
       "                                               Email  \\\n",
       "0  http://akutan.pmel.noaa.gov:8080/erddap/subscr...   \n",
       "1  http://akutan.pmel.noaa.gov:8080/erddap/subscr...   \n",
       "2  http://akutan.pmel.noaa.gov:8080/erddap/subscr...   \n",
       "\n",
       "                                         Institution  \\\n",
       "0                                          NOAA/PMEL   \n",
       "1                                NOAA/PMEL - EcoFOCI   \n",
       "2  Applied Physics Lab University of Washington S...   \n",
       "\n",
       "                              Dataset ID  \n",
       "0          datasets_sg401_2017_upanddown  \n",
       "1         datasets_sg401_2017_timeseries  \n",
       "2  datasets_sg401_2017_upanddown_gridded  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from erddapy import ERDDAP\n",
    "import pandas as pd\n",
    "\n",
    "server_url = 'http://akutan.pmel.noaa.gov:8080/erddap'\n",
    "\n",
    "e = ERDDAP(server=server_url)\n",
    "# timeseries or upanddown (profiles)\n",
    "df = pd.read_csv(e.get_search_url(response='csv', search_for='datasets_sg401_2017_'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5e38e7f-982f-47df-ad94-c8ca23f909f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#thin convenience wrapper for loading FOCI erddap glider data\n",
    "import GliderDataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75065008-27c8-4cd2-9c81-d5e3a78b0940",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            datasets_sg401_2017_upanddown\n",
      "1    datasets_sg401_2017_upanddown_gridded\n",
      "Name: Dataset ID, dtype: object\n"
     ]
    }
   ],
   "source": [
    "sg401=GliderDataLoader.erddap_glider('datasets_sg401_2017_upanddown')\n",
    "sg401.list_data(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a4f05e0-d84d-4530-8cb0-99b675c8d33c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets_sg401_2017_upanddown\n",
      "datasets_sg401_2017_upanddown_gridded\n",
      "Failed to generate url datasets_sg401_2017_upanddown_gridded\n"
     ]
    }
   ],
   "source": [
    "variables = {'datasets_sg401_2017_upanddown':['profile_id',\n",
    "                     'latitude', \n",
    "                     'depth', \n",
    "                     'longitude', \n",
    "                     'salinity',\n",
    "                     'density', \n",
    "                     'time', \n",
    "                     'temperature']}\n",
    "sg401.constrain_data(variables=variables,start_date='2017-01-01T00:00:00Z')\n",
    "dfs = sg401.load_data(year='2017')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "822124c4-dc75-4a4b-b7ea-11ea81079874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1224x144 with 2 Axes>, <AxesSubplot:ylabel='Depth (m)'>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg401.plot_timeseries(dfs['datasets_sg401_2017_upanddown'].sort_index(), 'temperature (degree_C)','depth (m)','temperature (DegC)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "868a997c-d335-4620-84cd-d273be57e047",
   "metadata": {},
   "source": [
    "### Use Gridded ERRDAP Dataset if Available for binned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1d596e7a-3c3a-4ea4-b812-f2534a257df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#all\n",
    "import cmocean\n",
    "e.protocol = 'griddap'\n",
    "e.dataset_id='datasets_sg401_2017_upanddown_gridded'\n",
    "e.variables = ['temperature','ctd_time']\n",
    "e.response = 'ncCF'\n",
    "ds = e.to_xarray(decode_times=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8ac4b3b8-7544-4e24-a68f-68ba2f87efc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fb4dbbc5fa0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.temperature.T.plot(yincrease=False,cmap=cmocean.cm.thermal,vmin=-2,vmax=12,figsize=(18,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67b3132-5c4e-40c4-a58a-122669ebabb2",
   "metadata": {},
   "source": [
    "### Now using larger archived timeseries data ((NRT level quicklooks with some on base station QC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e480de0-54f4-4f5e-80b4-a8e501f8e4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    datasets_sg401_2017_timeseries\n",
      "Name: Dataset ID, dtype: object\n"
     ]
    }
   ],
   "source": [
    "sg401=GliderDataLoader.erddap_glider('datasets_sg401_2017_timeseries')\n",
    "sg401.list_data(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5819f313-746e-44ed-9040-ccbecda0f72e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets_sg401_2017_timeseries\n"
     ]
    }
   ],
   "source": [
    "variables = {'datasets_sg401_2017_timeseries':['profile_id',\n",
    "                     'latitude', \n",
    "                     'depth', \n",
    "                     'longitude', \n",
    "                     'salinity',\n",
    "                     'density', \n",
    "                     'time', \n",
    "                     'temperature']}\n",
    "sg401.constrain_data(variables=variables,start_date='2017-01-01T00:00:00Z')\n",
    "dfs = sg401.load_data(year='2017')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5ad59fbc-9641-4c85-bb48-f165ddc9a09c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1224x144 with 2 Axes>, <AxesSubplot:ylabel='Depth (m)'>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg401.plot_timeseries(dfs['datasets_sg401_2017_timeseries'].sort_index(), 'temperature (degree_C)','depth (m)','temperature (DegC)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed4fb338-da6e-41c8-b3ca-3682311097b5",
   "metadata": {},
   "source": [
    "## Loading individual dive datasets and running GliderTools\n",
    "\n",
    "This isn't truly Delayed mode data (just delayed mode processing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e50ef8ad-5f64-4f5c-8939-548f67e37fed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T14:47:06.205695Z",
     "start_time": "2019-08-13T14:47:05.268632Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "# pylab for more MATLAB like environment and inline displays plots below cells \n",
    "%pylab inline\n",
    "\n",
    "# if gsw Warning shows, manually install gsw if possible - will still work without\n",
    "import glidertools as gt\n",
    "from cmocean import cm as cmo  # we use this for colormaps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8491f3-835b-43c6-8cb4-8798ce2e22ce",
   "metadata": {},
   "source": [
    "# Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71936db4-f7e9-44ed-a6f4-824eab837193",
   "metadata": {},
   "source": [
    "## Working with Seaglider base station files\n",
    "\n",
    "GliderTools supports loading Seaglider files, including `scicon` data (different sampling frequencies).  \n",
    "There is a function that makes it easier to find variable names that you'd like to load: `gt.load.seaglider_show_variables`  \n",
    "\n",
    "This function is demonstrated in the cell below.\n",
    "The function accepts a **list of file names** and can also receive a string with a wildcard placeholder (`*`) and basic regular expressions are also supported. In the example below we use a simple asterisk placeholder for all the files. \n",
    "\n",
    "Note that the function chooses only one file from the passed list or glob string - this file name will be shown. The returned table shows the variable name, dimensions, units and brief comment if it is available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "15cb31e1-b737-4a1a-9fb3-111bb5c52745",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T14:47:06.505548Z",
     "start_time": "2019-08-13T14:47:06.208113Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "information is based on file: test_data/sg401_2017/p4012283.nc\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c6d29_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >dims</th>\n",
       "      <th class=\"col_heading level0 col1\" >units</th>\n",
       "      <th class=\"col_heading level0 col2\" >comment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row0\" class=\"row_heading level0 row0\" >gc_data_pts</th>\n",
       "      <td id=\"T_c6d29_row0_col0\" class=\"data row0 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row1\" class=\"row_heading level0 row1\" >gc_depth</th>\n",
       "      <td id=\"T_c6d29_row1_col0\" class=\"data row1 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row1_col1\" class=\"data row1 col1\" >meters</td>\n",
       "      <td id=\"T_c6d29_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row2\" class=\"row_heading level0 row2\" >gc_end_secs</th>\n",
       "      <td id=\"T_c6d29_row2_col0\" class=\"data row2 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row2_col1\" class=\"data row2 col1\" >seconds  since 1970-1-1 00:00:00</td>\n",
       "      <td id=\"T_c6d29_row2_col2\" class=\"data row2 col2\" >End of GC time in GMT epoch format</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row3\" class=\"row_heading level0 row3\" >gc_gcphase</th>\n",
       "      <td id=\"T_c6d29_row3_col0\" class=\"data row3 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row4\" class=\"row_heading level0 row4\" >gc_humidity</th>\n",
       "      <td id=\"T_c6d29_row4_col0\" class=\"data row4 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row4_col1\" class=\"data row4 col1\" >percent</td>\n",
       "      <td id=\"T_c6d29_row4_col2\" class=\"data row4 col2\" >Internal relative humidity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row5\" class=\"row_heading level0 row5\" >gc_int_press</th>\n",
       "      <td id=\"T_c6d29_row5_col0\" class=\"data row5 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row5_col1\" class=\"data row5 col1\" >psia</td>\n",
       "      <td id=\"T_c6d29_row5_col2\" class=\"data row5 col2\" >Internal pressure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row6\" class=\"row_heading level0 row6\" >gc_ob_vertv</th>\n",
       "      <td id=\"T_c6d29_row6_col0\" class=\"data row6 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row6_col1\" class=\"data row6 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row7\" class=\"row_heading level0 row7\" >gc_pitch_ad</th>\n",
       "      <td id=\"T_c6d29_row7_col0\" class=\"data row7 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row7_col1\" class=\"data row7 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row8\" class=\"row_heading level0 row8\" >gc_pitch_ad_start</th>\n",
       "      <td id=\"T_c6d29_row8_col0\" class=\"data row8 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row8_col1\" class=\"data row8 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row9\" class=\"row_heading level0 row9\" >gc_pitch_ctl</th>\n",
       "      <td id=\"T_c6d29_row9_col0\" class=\"data row9 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row9_col1\" class=\"data row9 col1\" >cm</td>\n",
       "      <td id=\"T_c6d29_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row10\" class=\"row_heading level0 row10\" >gc_pitch_errors</th>\n",
       "      <td id=\"T_c6d29_row10_col0\" class=\"data row10 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row10_col1\" class=\"data row10 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row10_col2\" class=\"data row10 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row11\" class=\"row_heading level0 row11\" >gc_pitch_i</th>\n",
       "      <td id=\"T_c6d29_row11_col0\" class=\"data row11 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row11_col1\" class=\"data row11 col1\" >A</td>\n",
       "      <td id=\"T_c6d29_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row12\" class=\"row_heading level0 row12\" >gc_pitch_retries</th>\n",
       "      <td id=\"T_c6d29_row12_col0\" class=\"data row12 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row12_col1\" class=\"data row12 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row12_col2\" class=\"data row12 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row13\" class=\"row_heading level0 row13\" >gc_pitch_secs</th>\n",
       "      <td id=\"T_c6d29_row13_col0\" class=\"data row13 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row13_col1\" class=\"data row13 col1\" >seconds</td>\n",
       "      <td id=\"T_c6d29_row13_col2\" class=\"data row13 col2\" >Elapsed seconds since start of this pitch change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row14\" class=\"row_heading level0 row14\" >gc_pitch_volts</th>\n",
       "      <td id=\"T_c6d29_row14_col0\" class=\"data row14 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row14_col1\" class=\"data row14 col1\" >V</td>\n",
       "      <td id=\"T_c6d29_row14_col2\" class=\"data row14 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row15\" class=\"row_heading level0 row15\" >gc_roll_ad</th>\n",
       "      <td id=\"T_c6d29_row15_col0\" class=\"data row15 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row15_col1\" class=\"data row15 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row15_col2\" class=\"data row15 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row16\" class=\"row_heading level0 row16\" >gc_roll_ad_start</th>\n",
       "      <td id=\"T_c6d29_row16_col0\" class=\"data row16 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row16_col1\" class=\"data row16 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row16_col2\" class=\"data row16 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row17\" class=\"row_heading level0 row17\" >gc_roll_errors</th>\n",
       "      <td id=\"T_c6d29_row17_col0\" class=\"data row17 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row17_col1\" class=\"data row17 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row17_col2\" class=\"data row17 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row18\" class=\"row_heading level0 row18\" >gc_roll_i</th>\n",
       "      <td id=\"T_c6d29_row18_col0\" class=\"data row18 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row18_col1\" class=\"data row18 col1\" >A</td>\n",
       "      <td id=\"T_c6d29_row18_col2\" class=\"data row18 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row19\" class=\"row_heading level0 row19\" >gc_roll_retries</th>\n",
       "      <td id=\"T_c6d29_row19_col0\" class=\"data row19 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row19_col1\" class=\"data row19 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row19_col2\" class=\"data row19 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row20\" class=\"row_heading level0 row20\" >gc_roll_secs</th>\n",
       "      <td id=\"T_c6d29_row20_col0\" class=\"data row20 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row20_col1\" class=\"data row20 col1\" >seconds</td>\n",
       "      <td id=\"T_c6d29_row20_col2\" class=\"data row20 col2\" >Elapsed seconds since start of this roll change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row21\" class=\"row_heading level0 row21\" >gc_roll_volts</th>\n",
       "      <td id=\"T_c6d29_row21_col0\" class=\"data row21 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row21_col1\" class=\"data row21 col1\" >V</td>\n",
       "      <td id=\"T_c6d29_row21_col2\" class=\"data row21 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row22\" class=\"row_heading level0 row22\" >gc_st_secs</th>\n",
       "      <td id=\"T_c6d29_row22_col0\" class=\"data row22 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row22_col1\" class=\"data row22 col1\" >seconds since 1970-1-1 00:00:00</td>\n",
       "      <td id=\"T_c6d29_row22_col2\" class=\"data row22 col2\" >Start of GC time in GMT epoch format</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row23\" class=\"row_heading level0 row23\" >gc_vbd_ad</th>\n",
       "      <td id=\"T_c6d29_row23_col0\" class=\"data row23 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row23_col1\" class=\"data row23 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row23_col2\" class=\"data row23 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row24\" class=\"row_heading level0 row24\" >gc_vbd_ctl</th>\n",
       "      <td id=\"T_c6d29_row24_col0\" class=\"data row24 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row24_col1\" class=\"data row24 col1\" >cm</td>\n",
       "      <td id=\"T_c6d29_row24_col2\" class=\"data row24 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row25\" class=\"row_heading level0 row25\" >gc_vbd_errors</th>\n",
       "      <td id=\"T_c6d29_row25_col0\" class=\"data row25 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row25_col1\" class=\"data row25 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row25_col2\" class=\"data row25 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row26\" class=\"row_heading level0 row26\" >gc_vbd_i</th>\n",
       "      <td id=\"T_c6d29_row26_col0\" class=\"data row26 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row26_col1\" class=\"data row26 col1\" >A</td>\n",
       "      <td id=\"T_c6d29_row26_col2\" class=\"data row26 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row27\" class=\"row_heading level0 row27\" >gc_vbd_pot1_ad</th>\n",
       "      <td id=\"T_c6d29_row27_col0\" class=\"data row27 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row27_col1\" class=\"data row27 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row27_col2\" class=\"data row27 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row28\" class=\"row_heading level0 row28\" >gc_vbd_pot1_ad_start</th>\n",
       "      <td id=\"T_c6d29_row28_col0\" class=\"data row28 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row28_col1\" class=\"data row28 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row28_col2\" class=\"data row28 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row29\" class=\"row_heading level0 row29\" >gc_vbd_pot2_ad</th>\n",
       "      <td id=\"T_c6d29_row29_col0\" class=\"data row29 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row29_col1\" class=\"data row29 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row29_col2\" class=\"data row29 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row30\" class=\"row_heading level0 row30\" >gc_vbd_pot2_ad_start</th>\n",
       "      <td id=\"T_c6d29_row30_col0\" class=\"data row30 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row30_col1\" class=\"data row30 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row30_col2\" class=\"data row30 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row31\" class=\"row_heading level0 row31\" >gc_vbd_retries</th>\n",
       "      <td id=\"T_c6d29_row31_col0\" class=\"data row31 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row31_col1\" class=\"data row31 col1\" >1</td>\n",
       "      <td id=\"T_c6d29_row31_col2\" class=\"data row31 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row32\" class=\"row_heading level0 row32\" >gc_vbd_secs</th>\n",
       "      <td id=\"T_c6d29_row32_col0\" class=\"data row32 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row32_col1\" class=\"data row32 col1\" >seconds</td>\n",
       "      <td id=\"T_c6d29_row32_col2\" class=\"data row32 col2\" >Elapsed seconds since start of this VBD change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row33\" class=\"row_heading level0 row33\" >gc_vbd_volts</th>\n",
       "      <td id=\"T_c6d29_row33_col0\" class=\"data row33 col0\" >gc_event</td>\n",
       "      <td id=\"T_c6d29_row33_col1\" class=\"data row33 col1\" >V</td>\n",
       "      <td id=\"T_c6d29_row33_col2\" class=\"data row33 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row34\" class=\"row_heading level0 row34\" >log_gps_lat</th>\n",
       "      <td id=\"T_c6d29_row34_col0\" class=\"data row34 col0\" >gps_info</td>\n",
       "      <td id=\"T_c6d29_row34_col1\" class=\"data row34 col1\" >degrees_north</td>\n",
       "      <td id=\"T_c6d29_row34_col2\" class=\"data row34 col2\" >GPS latitudes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row35\" class=\"row_heading level0 row35\" >log_gps_lon</th>\n",
       "      <td id=\"T_c6d29_row35_col0\" class=\"data row35 col0\" >gps_info</td>\n",
       "      <td id=\"T_c6d29_row35_col1\" class=\"data row35 col1\" >degrees_east</td>\n",
       "      <td id=\"T_c6d29_row35_col2\" class=\"data row35 col2\" >GPS longitudes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row36\" class=\"row_heading level0 row36\" >log_gps_time</th>\n",
       "      <td id=\"T_c6d29_row36_col0\" class=\"data row36 col0\" >gps_info</td>\n",
       "      <td id=\"T_c6d29_row36_col1\" class=\"data row36 col1\" >seconds since 1970-1-1 00:00:00</td>\n",
       "      <td id=\"T_c6d29_row36_col2\" class=\"data row36 col2\" >GPS times in GMT epoch format</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row37\" class=\"row_heading level0 row37\" >eng_mag_x</th>\n",
       "      <td id=\"T_c6d29_row37_col0\" class=\"data row37 col0\" >magnetometer_data_point</td>\n",
       "      <td id=\"T_c6d29_row37_col1\" class=\"data row37 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row37_col2\" class=\"data row37 col2\" >magnetometer x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row38\" class=\"row_heading level0 row38\" >eng_mag_y</th>\n",
       "      <td id=\"T_c6d29_row38_col0\" class=\"data row38 col0\" >magnetometer_data_point</td>\n",
       "      <td id=\"T_c6d29_row38_col1\" class=\"data row38 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row38_col2\" class=\"data row38 col2\" >magnetometer y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row39\" class=\"row_heading level0 row39\" >eng_mag_z</th>\n",
       "      <td id=\"T_c6d29_row39_col0\" class=\"data row39 col0\" >magnetometer_data_point</td>\n",
       "      <td id=\"T_c6d29_row39_col1\" class=\"data row39 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row39_col2\" class=\"data row39 col2\" >magnetometer z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row40\" class=\"row_heading level0 row40\" >eng_satd_PARuV</th>\n",
       "      <td id=\"T_c6d29_row40_col0\" class=\"data row40 col0\" >sat1000_data_point</td>\n",
       "      <td id=\"T_c6d29_row40_col1\" class=\"data row40 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row40_col2\" class=\"data row40 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row41\" class=\"row_heading level0 row41\" >eng_satd_Temp</th>\n",
       "      <td id=\"T_c6d29_row41_col0\" class=\"data row41 col0\" >sat1000_data_point</td>\n",
       "      <td id=\"T_c6d29_row41_col1\" class=\"data row41 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row41_col2\" class=\"data row41 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row42\" class=\"row_heading level0 row42\" >eng_satd_Timer</th>\n",
       "      <td id=\"T_c6d29_row42_col0\" class=\"data row42 col0\" >sat1000_data_point</td>\n",
       "      <td id=\"T_c6d29_row42_col1\" class=\"data row42 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row42_col2\" class=\"data row42 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row43\" class=\"row_heading level0 row43\" >eng_satd_pitch</th>\n",
       "      <td id=\"T_c6d29_row43_col0\" class=\"data row43 col0\" >sat1000_data_point</td>\n",
       "      <td id=\"T_c6d29_row43_col1\" class=\"data row43 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row43_col2\" class=\"data row43 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row44\" class=\"row_heading level0 row44\" >eng_satd_roll</th>\n",
       "      <td id=\"T_c6d29_row44_col0\" class=\"data row44 col0\" >sat1000_data_point</td>\n",
       "      <td id=\"T_c6d29_row44_col1\" class=\"data row44 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row44_col2\" class=\"data row44 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row45\" class=\"row_heading level0 row45\" >eng_satu_PARuV</th>\n",
       "      <td id=\"T_c6d29_row45_col0\" class=\"data row45 col0\" >sat1001_data_point</td>\n",
       "      <td id=\"T_c6d29_row45_col1\" class=\"data row45 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row45_col2\" class=\"data row45 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row46\" class=\"row_heading level0 row46\" >eng_satu_Temp</th>\n",
       "      <td id=\"T_c6d29_row46_col0\" class=\"data row46 col0\" >sat1001_data_point</td>\n",
       "      <td id=\"T_c6d29_row46_col1\" class=\"data row46 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row46_col2\" class=\"data row46 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row47\" class=\"row_heading level0 row47\" >eng_satu_Timer</th>\n",
       "      <td id=\"T_c6d29_row47_col0\" class=\"data row47 col0\" >sat1001_data_point</td>\n",
       "      <td id=\"T_c6d29_row47_col1\" class=\"data row47 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row47_col2\" class=\"data row47 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row48\" class=\"row_heading level0 row48\" >eng_satu_pitch</th>\n",
       "      <td id=\"T_c6d29_row48_col0\" class=\"data row48 col0\" >sat1001_data_point</td>\n",
       "      <td id=\"T_c6d29_row48_col1\" class=\"data row48 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row48_col2\" class=\"data row48 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row49\" class=\"row_heading level0 row49\" >eng_satu_roll</th>\n",
       "      <td id=\"T_c6d29_row49_col0\" class=\"data row49 col0\" >sat1001_data_point</td>\n",
       "      <td id=\"T_c6d29_row49_col1\" class=\"data row49 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row49_col2\" class=\"data row49 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row50\" class=\"row_heading level0 row50\" >aanderaa4330_dissolved_oxygen</th>\n",
       "      <td id=\"T_c6d29_row50_col0\" class=\"data row50 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row50_col1\" class=\"data row50 col1\" >micromoles/kg</td>\n",
       "      <td id=\"T_c6d29_row50_col2\" class=\"data row50 col2\" >Dissolved oxygen concentration, calculated from optode tcphase corrected for salininty and depth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row51\" class=\"row_heading level0 row51\" >aanderaa4330_dissolved_oxygen_qc</th>\n",
       "      <td id=\"T_c6d29_row51_col0\" class=\"data row51 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row51_col1\" class=\"data row51 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row51_col2\" class=\"data row51 col2\" >Whether to trust each optode dissolved oxygen value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row52\" class=\"row_heading level0 row52\" >aanderaa4330_instrument_dissolved_oxygen</th>\n",
       "      <td id=\"T_c6d29_row52_col0\" class=\"data row52 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row52_col1\" class=\"data row52 col1\" >micromoles/kg</td>\n",
       "      <td id=\"T_c6d29_row52_col2\" class=\"data row52 col2\" >Dissolved oxygen concentration reported from optode corrected for salinity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row53\" class=\"row_heading level0 row53\" >aanderaa4330_results_time</th>\n",
       "      <td id=\"T_c6d29_row53_col0\" class=\"data row53 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row53_col1\" class=\"data row53 col1\" >seconds since 1970-1-1 00:00:00</td>\n",
       "      <td id=\"T_c6d29_row53_col2\" class=\"data row53 col2\" >time for Aanderaa 4330 in GMT epoch format</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row54\" class=\"row_heading level0 row54\" >buoyancy</th>\n",
       "      <td id=\"T_c6d29_row54_col0\" class=\"data row54 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row54_col1\" class=\"data row54 col1\" >g</td>\n",
       "      <td id=\"T_c6d29_row54_col2\" class=\"data row54 col2\" >Buoyancy of vehicle, corrected for compression effects</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row55\" class=\"row_heading level0 row55\" >conductivity</th>\n",
       "      <td id=\"T_c6d29_row55_col0\" class=\"data row55 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row55_col1\" class=\"data row55 col1\" >S/m</td>\n",
       "      <td id=\"T_c6d29_row55_col2\" class=\"data row55 col2\" >Conductivity corrected for anomalies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row56\" class=\"row_heading level0 row56\" >conductivity_qc</th>\n",
       "      <td id=\"T_c6d29_row56_col0\" class=\"data row56 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row56_col1\" class=\"data row56 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row56_col2\" class=\"data row56 col2\" >Whether to trust each corrected conductivity value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row57\" class=\"row_heading level0 row57\" >conductivity_raw</th>\n",
       "      <td id=\"T_c6d29_row57_col0\" class=\"data row57 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row57_col1\" class=\"data row57 col1\" >S/m</td>\n",
       "      <td id=\"T_c6d29_row57_col2\" class=\"data row57 col2\" >Uncorrected conductivity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row58\" class=\"row_heading level0 row58\" >conductivity_raw_qc</th>\n",
       "      <td id=\"T_c6d29_row58_col0\" class=\"data row58 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row58_col1\" class=\"data row58 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row58_col2\" class=\"data row58 col2\" >Whether to trust each raw conductivity value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row59\" class=\"row_heading level0 row59\" >ctd_depth</th>\n",
       "      <td id=\"T_c6d29_row59_col0\" class=\"data row59 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row59_col1\" class=\"data row59 col1\" >meters</td>\n",
       "      <td id=\"T_c6d29_row59_col2\" class=\"data row59 col2\" >CTD thermistor depth corrected for average latitude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row60\" class=\"row_heading level0 row60\" >ctd_pressure</th>\n",
       "      <td id=\"T_c6d29_row60_col0\" class=\"data row60 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row60_col1\" class=\"data row60 col1\" >dbar</td>\n",
       "      <td id=\"T_c6d29_row60_col2\" class=\"data row60 col2\" >Pressure at CTD thermistor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row61\" class=\"row_heading level0 row61\" >ctd_time</th>\n",
       "      <td id=\"T_c6d29_row61_col0\" class=\"data row61 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row61_col1\" class=\"data row61 col1\" >seconds since 1970-1-1 00:00:00</td>\n",
       "      <td id=\"T_c6d29_row61_col2\" class=\"data row61 col2\" >Time of CTD sample in GMT epoch format</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row62\" class=\"row_heading level0 row62\" >density</th>\n",
       "      <td id=\"T_c6d29_row62_col0\" class=\"data row62 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row62_col1\" class=\"data row62 col1\" >g/m^3</td>\n",
       "      <td id=\"T_c6d29_row62_col2\" class=\"data row62 col2\" >Sea water potential density</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row63\" class=\"row_heading level0 row63\" >density_insitu</th>\n",
       "      <td id=\"T_c6d29_row63_col0\" class=\"data row63 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row63_col1\" class=\"data row63 col1\" >g/m^3</td>\n",
       "      <td id=\"T_c6d29_row63_col2\" class=\"data row63 col2\" >Sea water in-situ density based on pressure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row64\" class=\"row_heading level0 row64\" >depth</th>\n",
       "      <td id=\"T_c6d29_row64_col0\" class=\"data row64 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row64_col1\" class=\"data row64 col1\" >meters</td>\n",
       "      <td id=\"T_c6d29_row64_col2\" class=\"data row64 col2\" >Depth below the surface, corrected for average latitude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row65\" class=\"row_heading level0 row65\" >dissolved_oxygen_sat</th>\n",
       "      <td id=\"T_c6d29_row65_col0\" class=\"data row65 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row65_col1\" class=\"data row65 col1\" >micromoles/kg</td>\n",
       "      <td id=\"T_c6d29_row65_col2\" class=\"data row65 col2\" >Calculated saturation value for oxygen given measured presure and corrected temperature, and salinity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row66\" class=\"row_heading level0 row66\" >east_displacement</th>\n",
       "      <td id=\"T_c6d29_row66_col0\" class=\"data row66 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row66_col1\" class=\"data row66 col1\" >meters</td>\n",
       "      <td id=\"T_c6d29_row66_col2\" class=\"data row66 col2\" >Eastward displacement from hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row67\" class=\"row_heading level0 row67\" >east_displacement_gsm</th>\n",
       "      <td id=\"T_c6d29_row67_col0\" class=\"data row67 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row67_col1\" class=\"data row67 col1\" >meters</td>\n",
       "      <td id=\"T_c6d29_row67_col2\" class=\"data row67 col2\" >Eastward displacement from gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row68\" class=\"row_heading level0 row68\" >eng_GC_phase</th>\n",
       "      <td id=\"T_c6d29_row68_col0\" class=\"data row68 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row68_col1\" class=\"data row68 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row68_col2\" class=\"data row68 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row69\" class=\"row_heading level0 row69\" >eng_aa4330_AirSat</th>\n",
       "      <td id=\"T_c6d29_row69_col0\" class=\"data row69 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row69_col1\" class=\"data row69 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row69_col2\" class=\"data row69 col2\" >As reported by the instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row70\" class=\"row_heading level0 row70\" >eng_aa4330_CalPhase</th>\n",
       "      <td id=\"T_c6d29_row70_col0\" class=\"data row70 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row70_col1\" class=\"data row70 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row70_col2\" class=\"data row70 col2\" >As reported by the instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row71\" class=\"row_heading level0 row71\" >eng_aa4330_O2</th>\n",
       "      <td id=\"T_c6d29_row71_col0\" class=\"data row71 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row71_col1\" class=\"data row71 col1\" >micromoles/L</td>\n",
       "      <td id=\"T_c6d29_row71_col2\" class=\"data row71 col2\" >Dissolved oxygen as reported by the instument, based on on-board calibration data, assuming optode temperature but without depth or salinity correction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row72\" class=\"row_heading level0 row72\" >eng_aa4330_TCPhase</th>\n",
       "      <td id=\"T_c6d29_row72_col0\" class=\"data row72 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row72_col1\" class=\"data row72 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row72_col2\" class=\"data row72 col2\" >As reported by the instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row73\" class=\"row_heading level0 row73\" >eng_aa4330_Temp</th>\n",
       "      <td id=\"T_c6d29_row73_col0\" class=\"data row73 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row73_col1\" class=\"data row73 col1\" >degrees_Celsius</td>\n",
       "      <td id=\"T_c6d29_row73_col2\" class=\"data row73 col2\" >As reported by the instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row74\" class=\"row_heading level0 row74\" >eng_depth</th>\n",
       "      <td id=\"T_c6d29_row74_col0\" class=\"data row74 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row74_col1\" class=\"data row74 col1\" >cm</td>\n",
       "      <td id=\"T_c6d29_row74_col2\" class=\"data row74 col2\" >Measured vertical distance below the surface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row75\" class=\"row_heading level0 row75\" >eng_elaps_t</th>\n",
       "      <td id=\"T_c6d29_row75_col0\" class=\"data row75 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row75_col1\" class=\"data row75 col1\" >seconds</td>\n",
       "      <td id=\"T_c6d29_row75_col2\" class=\"data row75 col2\" >Elapsed seconds since start of dive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row76\" class=\"row_heading level0 row76\" >eng_elaps_t_0000</th>\n",
       "      <td id=\"T_c6d29_row76_col0\" class=\"data row76 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row76_col1\" class=\"data row76 col1\" >seconds</td>\n",
       "      <td id=\"T_c6d29_row76_col2\" class=\"data row76 col2\" >Elapsed seconds since start of mission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row77\" class=\"row_heading level0 row77\" >eng_head</th>\n",
       "      <td id=\"T_c6d29_row77_col0\" class=\"data row77 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row77_col1\" class=\"data row77 col1\" >degrees</td>\n",
       "      <td id=\"T_c6d29_row77_col2\" class=\"data row77 col2\" >Vehicle heading (magnetic)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row78\" class=\"row_heading level0 row78\" >eng_pitchAng</th>\n",
       "      <td id=\"T_c6d29_row78_col0\" class=\"data row78 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row78_col1\" class=\"data row78 col1\" >degrees</td>\n",
       "      <td id=\"T_c6d29_row78_col2\" class=\"data row78 col2\" >Vehicle pitch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row79\" class=\"row_heading level0 row79\" >eng_pitchCtl</th>\n",
       "      <td id=\"T_c6d29_row79_col0\" class=\"data row79 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row79_col1\" class=\"data row79 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row79_col2\" class=\"data row79 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row80\" class=\"row_heading level0 row80\" >eng_rec</th>\n",
       "      <td id=\"T_c6d29_row80_col0\" class=\"data row80 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row80_col1\" class=\"data row80 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row80_col2\" class=\"data row80 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row81\" class=\"row_heading level0 row81\" >eng_rollAng</th>\n",
       "      <td id=\"T_c6d29_row81_col0\" class=\"data row81 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row81_col1\" class=\"data row81 col1\" >degrees</td>\n",
       "      <td id=\"T_c6d29_row81_col2\" class=\"data row81 col2\" >Vehicle roll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row82\" class=\"row_heading level0 row82\" >eng_rollCtl</th>\n",
       "      <td id=\"T_c6d29_row82_col0\" class=\"data row82 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row82_col1\" class=\"data row82 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row82_col2\" class=\"data row82 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row83\" class=\"row_heading level0 row83\" >eng_sbect_condFreq</th>\n",
       "      <td id=\"T_c6d29_row83_col0\" class=\"data row83 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row83_col1\" class=\"data row83 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row83_col2\" class=\"data row83 col2\" >As reported by the instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row84\" class=\"row_heading level0 row84\" >eng_sbect_tempFreq</th>\n",
       "      <td id=\"T_c6d29_row84_col0\" class=\"data row84 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row84_col1\" class=\"data row84 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row84_col2\" class=\"data row84 col2\" >As reported by the instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row85\" class=\"row_heading level0 row85\" >eng_vbdCC</th>\n",
       "      <td id=\"T_c6d29_row85_col0\" class=\"data row85 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row85_col1\" class=\"data row85 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row85_col2\" class=\"data row85 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row86\" class=\"row_heading level0 row86\" >eng_wlbb2fl_sig470nm</th>\n",
       "      <td id=\"T_c6d29_row86_col0\" class=\"data row86 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row86_col1\" class=\"data row86 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row86_col2\" class=\"data row86 col2\" >blue scattering as reported by instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row87\" class=\"row_heading level0 row87\" >eng_wlbb2fl_sig695nm</th>\n",
       "      <td id=\"T_c6d29_row87_col0\" class=\"data row87 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row87_col1\" class=\"data row87 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row87_col2\" class=\"data row87 col2\" >chlorophyll fluorescence as reported by instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row88\" class=\"row_heading level0 row88\" >eng_wlbb2fl_sig700nm</th>\n",
       "      <td id=\"T_c6d29_row88_col0\" class=\"data row88 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row88_col1\" class=\"data row88 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row88_col2\" class=\"data row88 col2\" >red scattering as reported by instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row89\" class=\"row_heading level0 row89\" >eng_wlbb2fl_temp</th>\n",
       "      <td id=\"T_c6d29_row89_col0\" class=\"data row89 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row89_col1\" class=\"data row89 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row89_col2\" class=\"data row89 col2\" >temperature as reported by instrument</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row90\" class=\"row_heading level0 row90\" >glide_angle</th>\n",
       "      <td id=\"T_c6d29_row90_col0\" class=\"data row90 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row90_col1\" class=\"data row90 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row90_col2\" class=\"data row90 col2\" >Glide angle based on hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row91\" class=\"row_heading level0 row91\" >glide_angle_gsm</th>\n",
       "      <td id=\"T_c6d29_row91_col0\" class=\"data row91 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row91_col1\" class=\"data row91 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row91_col2\" class=\"data row91 col2\" >Glide angle based on gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row92\" class=\"row_heading level0 row92\" >horz_speed</th>\n",
       "      <td id=\"T_c6d29_row92_col0\" class=\"data row92 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row92_col1\" class=\"data row92 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row92_col2\" class=\"data row92 col2\" >Vehicle horizontal speed based on hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row93\" class=\"row_heading level0 row93\" >horz_speed_gsm</th>\n",
       "      <td id=\"T_c6d29_row93_col0\" class=\"data row93 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row93_col1\" class=\"data row93 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row93_col2\" class=\"data row93 col2\" >Vehicle horizontal speed based on gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row94\" class=\"row_heading level0 row94\" >latitude</th>\n",
       "      <td id=\"T_c6d29_row94_col0\" class=\"data row94 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row94_col1\" class=\"data row94 col1\" >degrees_north</td>\n",
       "      <td id=\"T_c6d29_row94_col2\" class=\"data row94 col2\" >Latitude of the sample based on hdm DAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row95\" class=\"row_heading level0 row95\" >latitude_gsm</th>\n",
       "      <td id=\"T_c6d29_row95_col0\" class=\"data row95 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row95_col1\" class=\"data row95 col1\" >degrees_north</td>\n",
       "      <td id=\"T_c6d29_row95_col2\" class=\"data row95 col2\" >Latitude of the sample based on gsm DAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row96\" class=\"row_heading level0 row96\" >longitude</th>\n",
       "      <td id=\"T_c6d29_row96_col0\" class=\"data row96 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row96_col1\" class=\"data row96 col1\" >degrees_east</td>\n",
       "      <td id=\"T_c6d29_row96_col2\" class=\"data row96 col2\" >Longitude of the sample based on hdm DAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row97\" class=\"row_heading level0 row97\" >longitude_gsm</th>\n",
       "      <td id=\"T_c6d29_row97_col0\" class=\"data row97 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row97_col1\" class=\"data row97 col1\" >degrees_east</td>\n",
       "      <td id=\"T_c6d29_row97_col2\" class=\"data row97 col2\" >Longitude of the sample based on gsm DAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row98\" class=\"row_heading level0 row98\" >north_displacement</th>\n",
       "      <td id=\"T_c6d29_row98_col0\" class=\"data row98 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row98_col1\" class=\"data row98 col1\" >meters</td>\n",
       "      <td id=\"T_c6d29_row98_col2\" class=\"data row98 col2\" >Northward displacement from hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row99\" class=\"row_heading level0 row99\" >north_displacement_gsm</th>\n",
       "      <td id=\"T_c6d29_row99_col0\" class=\"data row99 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row99_col1\" class=\"data row99 col1\" >meters</td>\n",
       "      <td id=\"T_c6d29_row99_col2\" class=\"data row99 col2\" >Northward displacement from gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row100\" class=\"row_heading level0 row100\" >pressure</th>\n",
       "      <td id=\"T_c6d29_row100_col0\" class=\"data row100 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row100_col1\" class=\"data row100 col1\" >dbar</td>\n",
       "      <td id=\"T_c6d29_row100_col2\" class=\"data row100 col2\" >Uncorrected sea-water pressure at pressure sensor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row101\" class=\"row_heading level0 row101\" >salinity</th>\n",
       "      <td id=\"T_c6d29_row101_col0\" class=\"data row101 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row101_col1\" class=\"data row101 col1\" >1e-3</td>\n",
       "      <td id=\"T_c6d29_row101_col2\" class=\"data row101 col2\" >Salinity corrected for thermal-inertia effects (PSU)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row102\" class=\"row_heading level0 row102\" >salinity_qc</th>\n",
       "      <td id=\"T_c6d29_row102_col0\" class=\"data row102 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row102_col1\" class=\"data row102 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row102_col2\" class=\"data row102 col2\" >Whether to trust each corrected salinity value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row103\" class=\"row_heading level0 row103\" >salinity_raw</th>\n",
       "      <td id=\"T_c6d29_row103_col0\" class=\"data row103 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row103_col1\" class=\"data row103 col1\" >1e-3</td>\n",
       "      <td id=\"T_c6d29_row103_col2\" class=\"data row103 col2\" >Uncorrected salinity derived from temperature_raw and conductivity_raw (PSU)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row104\" class=\"row_heading level0 row104\" >salinity_raw_qc</th>\n",
       "      <td id=\"T_c6d29_row104_col0\" class=\"data row104 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row104_col1\" class=\"data row104 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row104_col2\" class=\"data row104 col2\" >Whether to trust each raw salinity value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row105\" class=\"row_heading level0 row105\" >sigma_t</th>\n",
       "      <td id=\"T_c6d29_row105_col0\" class=\"data row105 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row105_col1\" class=\"data row105 col1\" >g/m^3</td>\n",
       "      <td id=\"T_c6d29_row105_col2\" class=\"data row105 col2\" >Sigma based on density</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row106\" class=\"row_heading level0 row106\" >sigma_theta</th>\n",
       "      <td id=\"T_c6d29_row106_col0\" class=\"data row106 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row106_col1\" class=\"data row106 col1\" >g/m^3</td>\n",
       "      <td id=\"T_c6d29_row106_col2\" class=\"data row106 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row107\" class=\"row_heading level0 row107\" >sound_velocity</th>\n",
       "      <td id=\"T_c6d29_row107_col0\" class=\"data row107 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row107_col1\" class=\"data row107 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row107_col2\" class=\"data row107 col2\" >Sound velocity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row108\" class=\"row_heading level0 row108\" >speed</th>\n",
       "      <td id=\"T_c6d29_row108_col0\" class=\"data row108 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row108_col1\" class=\"data row108 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row108_col2\" class=\"data row108 col2\" >Vehicle speed based on hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row109\" class=\"row_heading level0 row109\" >speed_gsm</th>\n",
       "      <td id=\"T_c6d29_row109_col0\" class=\"data row109 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row109_col1\" class=\"data row109 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row109_col2\" class=\"data row109 col2\" >Vehicle speed based on gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row110\" class=\"row_heading level0 row110\" >speed_qc</th>\n",
       "      <td id=\"T_c6d29_row110_col0\" class=\"data row110 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row110_col1\" class=\"data row110 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row110_col2\" class=\"data row110 col2\" >Whether to trust each hdm speed value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row111\" class=\"row_heading level0 row111\" >temperature</th>\n",
       "      <td id=\"T_c6d29_row111_col0\" class=\"data row111 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row111_col1\" class=\"data row111 col1\" >degrees_Celsius</td>\n",
       "      <td id=\"T_c6d29_row111_col2\" class=\"data row111 col2\" >Termperature (in situ) corrected for thermistor first-order lag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row112\" class=\"row_heading level0 row112\" >temperature_qc</th>\n",
       "      <td id=\"T_c6d29_row112_col0\" class=\"data row112 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row112_col1\" class=\"data row112 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row112_col2\" class=\"data row112 col2\" >Whether to trust each corrected temperature value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row113\" class=\"row_heading level0 row113\" >temperature_raw</th>\n",
       "      <td id=\"T_c6d29_row113_col0\" class=\"data row113 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row113_col1\" class=\"data row113 col1\" >degrees_Celsius</td>\n",
       "      <td id=\"T_c6d29_row113_col2\" class=\"data row113 col2\" >Uncorrected temperature (in situ)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row114\" class=\"row_heading level0 row114\" >temperature_raw_qc</th>\n",
       "      <td id=\"T_c6d29_row114_col0\" class=\"data row114 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row114_col1\" class=\"data row114 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row114_col2\" class=\"data row114 col2\" >Whether to trust each raw temperature value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row115\" class=\"row_heading level0 row115\" >theta</th>\n",
       "      <td id=\"T_c6d29_row115_col0\" class=\"data row115 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row115_col1\" class=\"data row115 col1\" >degrees_Celsius</td>\n",
       "      <td id=\"T_c6d29_row115_col2\" class=\"data row115 col2\" >Potential temperature based on corrected salinity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row116\" class=\"row_heading level0 row116\" >time</th>\n",
       "      <td id=\"T_c6d29_row116_col0\" class=\"data row116 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row116_col1\" class=\"data row116 col1\" >seconds since 1970-1-1 00:00:00</td>\n",
       "      <td id=\"T_c6d29_row116_col2\" class=\"data row116 col2\" >Time of the sample in GMT epoch format</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row117\" class=\"row_heading level0 row117\" >vert_speed</th>\n",
       "      <td id=\"T_c6d29_row117_col0\" class=\"data row117 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row117_col1\" class=\"data row117 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row117_col2\" class=\"data row117 col2\" >Vehicle vertical speed based on hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row118\" class=\"row_heading level0 row118\" >vert_speed_gsm</th>\n",
       "      <td id=\"T_c6d29_row118_col0\" class=\"data row118 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row118_col1\" class=\"data row118 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row118_col2\" class=\"data row118 col2\" >Vehicle vertical speed based on gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row119\" class=\"row_heading level0 row119\" >wlbb2fl_results_time</th>\n",
       "      <td id=\"T_c6d29_row119_col0\" class=\"data row119 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row119_col1\" class=\"data row119 col1\" >seconds since 1970-1-1 00:00:00</td>\n",
       "      <td id=\"T_c6d29_row119_col2\" class=\"data row119 col2\" >wlbb2fl result time in GMT epoch format</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row120\" class=\"row_heading level0 row120\" >wlbb2fl_sig470nm_adjusted</th>\n",
       "      <td id=\"T_c6d29_row120_col0\" class=\"data row120 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row120_col1\" class=\"data row120 col1\" >meter^-1 steradian^-1</td>\n",
       "      <td id=\"T_c6d29_row120_col2\" class=\"data row120 col2\" >total volume blue scattering coefficient using manufacturer-supplied dark counts and scaling factor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row121\" class=\"row_heading level0 row121\" >wlbb2fl_sig695nm_adjusted</th>\n",
       "      <td id=\"T_c6d29_row121_col0\" class=\"data row121 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row121_col1\" class=\"data row121 col1\" >micrograms/liter</td>\n",
       "      <td id=\"T_c6d29_row121_col2\" class=\"data row121 col2\" >chlorophyll-a concentration using manufacturer-supplied dark counts and scaling factor based on phytoplankton monoculture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row122\" class=\"row_heading level0 row122\" >wlbb2fl_sig700nm_adjusted</th>\n",
       "      <td id=\"T_c6d29_row122_col0\" class=\"data row122 col0\" >sg_data_point</td>\n",
       "      <td id=\"T_c6d29_row122_col1\" class=\"data row122 col1\" >meter^-1 steradian^-1</td>\n",
       "      <td id=\"T_c6d29_row122_col2\" class=\"data row122 col2\" >total volume red scattering coefficient using manufacturer-supplied dark counts and scaling factor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row123\" class=\"row_heading level0 row123\" >CTD_qc</th>\n",
       "      <td id=\"T_c6d29_row123_col0\" class=\"data row123 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row123_col1\" class=\"data row123 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row123_col2\" class=\"data row123 col2\" >Whether to trust the corrected CTD values</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row124\" class=\"row_heading level0 row124\" >GPS1_qc</th>\n",
       "      <td id=\"T_c6d29_row124_col0\" class=\"data row124 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row124_col1\" class=\"data row124 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row124_col2\" class=\"data row124 col2\" >Whether to trust the GPS1 information</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row125\" class=\"row_heading level0 row125\" >GPS2_qc</th>\n",
       "      <td id=\"T_c6d29_row125_col0\" class=\"data row125 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row125_col1\" class=\"data row125 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row125_col2\" class=\"data row125 col2\" >Whether to trust the GPS2 information</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row126\" class=\"row_heading level0 row126\" >GPSE_qc</th>\n",
       "      <td id=\"T_c6d29_row126_col0\" class=\"data row126 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row126_col1\" class=\"data row126 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row126_col2\" class=\"data row126 col2\" >Whether to trust the final GPS information</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row127\" class=\"row_heading level0 row127\" >aa4330</th>\n",
       "      <td id=\"T_c6d29_row127_col0\" class=\"data row127 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row127_col1\" class=\"data row127 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row127_col2\" class=\"data row127 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row128\" class=\"row_heading level0 row128\" >aanderaa4330_qc</th>\n",
       "      <td id=\"T_c6d29_row128_col0\" class=\"data row128 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row128_col1\" class=\"data row128 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row128_col2\" class=\"data row128 col2\" >Whether to trust the Aanderaa 3880 results</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row129\" class=\"row_heading level0 row129\" >avg_latitude</th>\n",
       "      <td id=\"T_c6d29_row129_col0\" class=\"data row129 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row129_col1\" class=\"data row129 col1\" >degrees_north</td>\n",
       "      <td id=\"T_c6d29_row129_col2\" class=\"data row129 col2\" >The average latitude of the dive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row130\" class=\"row_heading level0 row130\" >depth_avg_curr_east</th>\n",
       "      <td id=\"T_c6d29_row130_col0\" class=\"data row130 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row130_col1\" class=\"data row130 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row130_col2\" class=\"data row130 col2\" >Eastward component of the dive depth-average current based on hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row131\" class=\"row_heading level0 row131\" >depth_avg_curr_east_gsm</th>\n",
       "      <td id=\"T_c6d29_row131_col0\" class=\"data row131 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row131_col1\" class=\"data row131 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row131_col2\" class=\"data row131 col2\" >Eastward component of depth-average current based on gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row132\" class=\"row_heading level0 row132\" >depth_avg_curr_north</th>\n",
       "      <td id=\"T_c6d29_row132_col0\" class=\"data row132 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row132_col1\" class=\"data row132 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row132_col2\" class=\"data row132 col2\" >Northward component of the dive depth-average current based on hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row133\" class=\"row_heading level0 row133\" >depth_avg_curr_north_gsm</th>\n",
       "      <td id=\"T_c6d29_row133_col0\" class=\"data row133 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row133_col1\" class=\"data row133 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row133_col2\" class=\"data row133 col2\" >Northward component of depth-average current based on gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row134\" class=\"row_heading level0 row134\" >depth_avg_curr_qc</th>\n",
       "      <td id=\"T_c6d29_row134_col0\" class=\"data row134 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row134_col1\" class=\"data row134 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row134_col2\" class=\"data row134 col2\" >Whether to trust the dive depth-average current values and displacements</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row135\" class=\"row_heading level0 row135\" >flight_avg_speed_east</th>\n",
       "      <td id=\"T_c6d29_row135_col0\" class=\"data row135 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row135_col1\" class=\"data row135 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row135_col2\" class=\"data row135 col2\" >Eastward component of flight average speed based on hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row136\" class=\"row_heading level0 row136\" >flight_avg_speed_east_gsm</th>\n",
       "      <td id=\"T_c6d29_row136_col0\" class=\"data row136 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row136_col1\" class=\"data row136 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row136_col2\" class=\"data row136 col2\" >Eastward component of flight average speed based on gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row137\" class=\"row_heading level0 row137\" >flight_avg_speed_north</th>\n",
       "      <td id=\"T_c6d29_row137_col0\" class=\"data row137 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row137_col1\" class=\"data row137 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row137_col2\" class=\"data row137 col2\" >Northward component of flight average speed based on hdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row138\" class=\"row_heading level0 row138\" >flight_avg_speed_north_gsm</th>\n",
       "      <td id=\"T_c6d29_row138_col0\" class=\"data row138 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row138_col1\" class=\"data row138 col1\" >m/s</td>\n",
       "      <td id=\"T_c6d29_row138_col2\" class=\"data row138 col2\" >Northward component of flight average speed based on gsm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row139\" class=\"row_heading level0 row139\" >glider</th>\n",
       "      <td id=\"T_c6d29_row139_col0\" class=\"data row139 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row139_col1\" class=\"data row139 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row139_col2\" class=\"data row139 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row140\" class=\"row_heading level0 row140\" >hdm_qc</th>\n",
       "      <td id=\"T_c6d29_row140_col0\" class=\"data row140 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row140_col1\" class=\"data row140 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row140_col2\" class=\"data row140 col2\" >Whether corrected temperatures, salinities, and velocities from the hydrodynamic model converged on a consistent solution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row141\" class=\"row_heading level0 row141\" >latlong_qc</th>\n",
       "      <td id=\"T_c6d29_row141_col0\" class=\"data row141 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row141_col1\" class=\"data row141 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row141_col2\" class=\"data row141 col2\" >Whether to trust the dive estimated latitude and longitude estimates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row142\" class=\"row_heading level0 row142\" >log_10V_AH</th>\n",
       "      <td id=\"T_c6d29_row142_col0\" class=\"data row142 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row142_col1\" class=\"data row142 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row142_col2\" class=\"data row142 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row143\" class=\"row_heading level0 row143\" >log_24V_AH</th>\n",
       "      <td id=\"T_c6d29_row143_col0\" class=\"data row143 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row143_col1\" class=\"data row143 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row143_col2\" class=\"data row143 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row144\" class=\"row_heading level0 row144\" >log_AD7714Ch0Gain</th>\n",
       "      <td id=\"T_c6d29_row144_col0\" class=\"data row144 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row144_col1\" class=\"data row144 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row144_col2\" class=\"data row144 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row145\" class=\"row_heading level0 row145\" >log_AH0_10V</th>\n",
       "      <td id=\"T_c6d29_row145_col0\" class=\"data row145 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row145_col1\" class=\"data row145 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row145_col2\" class=\"data row145 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row146\" class=\"row_heading level0 row146\" >log_AH0_24V</th>\n",
       "      <td id=\"T_c6d29_row146_col0\" class=\"data row146 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row146_col1\" class=\"data row146 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row146_col2\" class=\"data row146 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row147\" class=\"row_heading level0 row147\" >log_ALTIM_BOTTOM_PING_RANGE</th>\n",
       "      <td id=\"T_c6d29_row147_col0\" class=\"data row147 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row147_col1\" class=\"data row147 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row147_col2\" class=\"data row147 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row148\" class=\"row_heading level0 row148\" >log_ALTIM_BOTTOM_TURN_MARGIN</th>\n",
       "      <td id=\"T_c6d29_row148_col0\" class=\"data row148 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row148_col1\" class=\"data row148 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row148_col2\" class=\"data row148 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row149\" class=\"row_heading level0 row149\" >log_ALTIM_FREQUENCY</th>\n",
       "      <td id=\"T_c6d29_row149_col0\" class=\"data row149 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row149_col1\" class=\"data row149 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row149_col2\" class=\"data row149 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row150\" class=\"row_heading level0 row150\" >log_ALTIM_PING_DELTA</th>\n",
       "      <td id=\"T_c6d29_row150_col0\" class=\"data row150 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row150_col1\" class=\"data row150 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row150_col2\" class=\"data row150 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row151\" class=\"row_heading level0 row151\" >log_ALTIM_PING_DEPTH</th>\n",
       "      <td id=\"T_c6d29_row151_col0\" class=\"data row151 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row151_col1\" class=\"data row151 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row151_col2\" class=\"data row151 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row152\" class=\"row_heading level0 row152\" >log_ALTIM_PULSE</th>\n",
       "      <td id=\"T_c6d29_row152_col0\" class=\"data row152 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row152_col1\" class=\"data row152 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row152_col2\" class=\"data row152 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row153\" class=\"row_heading level0 row153\" >log_ALTIM_SENSITIVITY</th>\n",
       "      <td id=\"T_c6d29_row153_col0\" class=\"data row153 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row153_col1\" class=\"data row153 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row153_col2\" class=\"data row153 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row154\" class=\"row_heading level0 row154\" >log_ALTIM_TOP_MIN_OBSTACLE</th>\n",
       "      <td id=\"T_c6d29_row154_col0\" class=\"data row154 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row154_col1\" class=\"data row154 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row154_col2\" class=\"data row154 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row155\" class=\"row_heading level0 row155\" >log_ALTIM_TOP_PING_RANGE</th>\n",
       "      <td id=\"T_c6d29_row155_col0\" class=\"data row155 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row155_col1\" class=\"data row155 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row155_col2\" class=\"data row155 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row156\" class=\"row_heading level0 row156\" >log_ALTIM_TOP_TURN_MARGIN</th>\n",
       "      <td id=\"T_c6d29_row156_col0\" class=\"data row156 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row156_col1\" class=\"data row156 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row156_col2\" class=\"data row156 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row157\" class=\"row_heading level0 row157\" >log_APOGEE_PITCH</th>\n",
       "      <td id=\"T_c6d29_row157_col0\" class=\"data row157 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row157_col1\" class=\"data row157 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row157_col2\" class=\"data row157 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row158\" class=\"row_heading level0 row158\" >log_CALL_NDIVES</th>\n",
       "      <td id=\"T_c6d29_row158_col0\" class=\"data row158 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row158_col1\" class=\"data row158 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row158_col2\" class=\"data row158 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row159\" class=\"row_heading level0 row159\" >log_CALL_TRIES</th>\n",
       "      <td id=\"T_c6d29_row159_col0\" class=\"data row159 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row159_col1\" class=\"data row159 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row159_col2\" class=\"data row159 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row160\" class=\"row_heading level0 row160\" >log_CALL_WAIT</th>\n",
       "      <td id=\"T_c6d29_row160_col0\" class=\"data row160 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row160_col1\" class=\"data row160 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row160_col2\" class=\"data row160 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row161\" class=\"row_heading level0 row161\" >log_CAPMAXSIZE</th>\n",
       "      <td id=\"T_c6d29_row161_col0\" class=\"data row161 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row161_col1\" class=\"data row161 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row161_col2\" class=\"data row161 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row162\" class=\"row_heading level0 row162\" >log_CAPUPLOAD</th>\n",
       "      <td id=\"T_c6d29_row162_col0\" class=\"data row162 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row162_col1\" class=\"data row162 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row162_col2\" class=\"data row162 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row163\" class=\"row_heading level0 row163\" >log_CAP_FILE_SIZE</th>\n",
       "      <td id=\"T_c6d29_row163_col0\" class=\"data row163 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row163_col1\" class=\"data row163 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row163_col2\" class=\"data row163 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row164\" class=\"row_heading level0 row164\" >log_CF8_MAXERRORS</th>\n",
       "      <td id=\"T_c6d29_row164_col0\" class=\"data row164 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row164_col1\" class=\"data row164 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row164_col2\" class=\"data row164 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row165\" class=\"row_heading level0 row165\" >log_CFSIZE</th>\n",
       "      <td id=\"T_c6d29_row165_col0\" class=\"data row165 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row165_col1\" class=\"data row165 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row165_col2\" class=\"data row165 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row166\" class=\"row_heading level0 row166\" >log_COMM_SEQ</th>\n",
       "      <td id=\"T_c6d29_row166_col0\" class=\"data row166 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row166_col1\" class=\"data row166 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row166_col2\" class=\"data row166 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row167\" class=\"row_heading level0 row167\" >log_COMPASS2_DEVICE</th>\n",
       "      <td id=\"T_c6d29_row167_col0\" class=\"data row167 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row167_col1\" class=\"data row167 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row167_col2\" class=\"data row167 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row168\" class=\"row_heading level0 row168\" >log_COMPASS_DEVICE</th>\n",
       "      <td id=\"T_c6d29_row168_col0\" class=\"data row168 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row168_col1\" class=\"data row168 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row168_col2\" class=\"data row168 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row169\" class=\"row_heading level0 row169\" >log_COMPASS_USE</th>\n",
       "      <td id=\"T_c6d29_row169_col0\" class=\"data row169 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row169_col1\" class=\"data row169 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row169_col2\" class=\"data row169 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row170\" class=\"row_heading level0 row170\" >log_COURSE_BIAS</th>\n",
       "      <td id=\"T_c6d29_row170_col0\" class=\"data row170 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row170_col1\" class=\"data row170 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row170_col2\" class=\"data row170 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row171\" class=\"row_heading level0 row171\" >log_C_PITCH</th>\n",
       "      <td id=\"T_c6d29_row171_col0\" class=\"data row171 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row171_col1\" class=\"data row171 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row171_col2\" class=\"data row171 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row172\" class=\"row_heading level0 row172\" >log_C_ROLL_CLIMB</th>\n",
       "      <td id=\"T_c6d29_row172_col0\" class=\"data row172 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row172_col1\" class=\"data row172 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row172_col2\" class=\"data row172 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row173\" class=\"row_heading level0 row173\" >log_C_ROLL_DIVE</th>\n",
       "      <td id=\"T_c6d29_row173_col0\" class=\"data row173 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row173_col1\" class=\"data row173 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row173_col2\" class=\"data row173 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row174\" class=\"row_heading level0 row174\" >log_C_VBD</th>\n",
       "      <td id=\"T_c6d29_row174_col0\" class=\"data row174 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row174_col1\" class=\"data row174 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row174_col2\" class=\"data row174 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row175\" class=\"row_heading level0 row175\" >log_DATA_FILE_SIZE</th>\n",
       "      <td id=\"T_c6d29_row175_col0\" class=\"data row175 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row175_col1\" class=\"data row175 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row175_col2\" class=\"data row175 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row176\" class=\"row_heading level0 row176\" >log_DBDW</th>\n",
       "      <td id=\"T_c6d29_row176_col0\" class=\"data row176 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row176_col1\" class=\"data row176 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row176_col2\" class=\"data row176 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row177\" class=\"row_heading level0 row177\" >log_DEEPGLIDER</th>\n",
       "      <td id=\"T_c6d29_row177_col0\" class=\"data row177 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row177_col1\" class=\"data row177 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row177_col2\" class=\"data row177 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row178\" class=\"row_heading level0 row178\" >log_DEVICE1</th>\n",
       "      <td id=\"T_c6d29_row178_col0\" class=\"data row178 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row178_col1\" class=\"data row178 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row178_col2\" class=\"data row178 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row179\" class=\"row_heading level0 row179\" >log_DEVICE2</th>\n",
       "      <td id=\"T_c6d29_row179_col0\" class=\"data row179 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row179_col1\" class=\"data row179 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row179_col2\" class=\"data row179 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row180\" class=\"row_heading level0 row180\" >log_DEVICE3</th>\n",
       "      <td id=\"T_c6d29_row180_col0\" class=\"data row180 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row180_col1\" class=\"data row180 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row180_col2\" class=\"data row180 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row181\" class=\"row_heading level0 row181\" >log_DEVICE4</th>\n",
       "      <td id=\"T_c6d29_row181_col0\" class=\"data row181 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row181_col1\" class=\"data row181 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row181_col2\" class=\"data row181 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row182\" class=\"row_heading level0 row182\" >log_DEVICE5</th>\n",
       "      <td id=\"T_c6d29_row182_col0\" class=\"data row182 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row182_col1\" class=\"data row182 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row182_col2\" class=\"data row182 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row183\" class=\"row_heading level0 row183\" >log_DEVICE6</th>\n",
       "      <td id=\"T_c6d29_row183_col0\" class=\"data row183 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row183_col1\" class=\"data row183 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row183_col2\" class=\"data row183 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row184\" class=\"row_heading level0 row184\" >log_DEVICES</th>\n",
       "      <td id=\"T_c6d29_row184_col0\" class=\"data row184 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row184_col1\" class=\"data row184 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row184_col2\" class=\"data row184 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row185\" class=\"row_heading level0 row185\" >log_DEVICE_MAMPS</th>\n",
       "      <td id=\"T_c6d29_row185_col0\" class=\"data row185 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row185_col1\" class=\"data row185 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row185_col2\" class=\"data row185 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row186\" class=\"row_heading level0 row186\" >log_DEVICE_SECS</th>\n",
       "      <td id=\"T_c6d29_row186_col0\" class=\"data row186 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row186_col1\" class=\"data row186 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row186_col2\" class=\"data row186 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row187\" class=\"row_heading level0 row187\" >log_DIVE</th>\n",
       "      <td id=\"T_c6d29_row187_col0\" class=\"data row187 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row187_col1\" class=\"data row187 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row187_col2\" class=\"data row187 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row188\" class=\"row_heading level0 row188\" >log_D_ABORT</th>\n",
       "      <td id=\"T_c6d29_row188_col0\" class=\"data row188 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row188_col1\" class=\"data row188 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row188_col2\" class=\"data row188 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row189\" class=\"row_heading level0 row189\" >log_D_BOOST</th>\n",
       "      <td id=\"T_c6d29_row189_col0\" class=\"data row189 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row189_col1\" class=\"data row189 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row189_col2\" class=\"data row189 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row190\" class=\"row_heading level0 row190\" >log_D_CALL</th>\n",
       "      <td id=\"T_c6d29_row190_col0\" class=\"data row190 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row190_col1\" class=\"data row190 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row190_col2\" class=\"data row190 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row191\" class=\"row_heading level0 row191\" >log_D_FINISH</th>\n",
       "      <td id=\"T_c6d29_row191_col0\" class=\"data row191 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row191_col1\" class=\"data row191 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row191_col2\" class=\"data row191 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row192\" class=\"row_heading level0 row192\" >log_D_FLARE</th>\n",
       "      <td id=\"T_c6d29_row192_col0\" class=\"data row192 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row192_col1\" class=\"data row192 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row192_col2\" class=\"data row192 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row193\" class=\"row_heading level0 row193\" >log_D_GRID</th>\n",
       "      <td id=\"T_c6d29_row193_col0\" class=\"data row193 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row193_col1\" class=\"data row193 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row193_col2\" class=\"data row193 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row194\" class=\"row_heading level0 row194\" >log_D_NO_BLEED</th>\n",
       "      <td id=\"T_c6d29_row194_col0\" class=\"data row194 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row194_col1\" class=\"data row194 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row194_col2\" class=\"data row194 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row195\" class=\"row_heading level0 row195\" >log_D_OFFGRID</th>\n",
       "      <td id=\"T_c6d29_row195_col0\" class=\"data row195 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row195_col1\" class=\"data row195 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row195_col2\" class=\"data row195 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row196\" class=\"row_heading level0 row196\" >log_D_PITCH</th>\n",
       "      <td id=\"T_c6d29_row196_col0\" class=\"data row196 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row196_col1\" class=\"data row196 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row196_col2\" class=\"data row196 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row197\" class=\"row_heading level0 row197\" >log_D_SAFE</th>\n",
       "      <td id=\"T_c6d29_row197_col0\" class=\"data row197 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row197_col1\" class=\"data row197 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row197_col2\" class=\"data row197 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row198\" class=\"row_heading level0 row198\" >log_D_SURF</th>\n",
       "      <td id=\"T_c6d29_row198_col0\" class=\"data row198 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row198_col1\" class=\"data row198 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row198_col2\" class=\"data row198 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row199\" class=\"row_heading level0 row199\" >log_D_TGT</th>\n",
       "      <td id=\"T_c6d29_row199_col0\" class=\"data row199 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row199_col1\" class=\"data row199 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row199_col2\" class=\"data row199 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row200\" class=\"row_heading level0 row200\" >log_ERRORS</th>\n",
       "      <td id=\"T_c6d29_row200_col0\" class=\"data row200 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row200_col1\" class=\"data row200 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row200_col2\" class=\"data row200 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row201\" class=\"row_heading level0 row201\" >log_ESCAPE_HEADING</th>\n",
       "      <td id=\"T_c6d29_row201_col0\" class=\"data row201 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row201_col1\" class=\"data row201 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row201_col2\" class=\"data row201 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row202\" class=\"row_heading level0 row202\" >log_ESCAPE_HEADING_DELTA</th>\n",
       "      <td id=\"T_c6d29_row202_col0\" class=\"data row202 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row202_col1\" class=\"data row202 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row202_col2\" class=\"data row202 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row203\" class=\"row_heading level0 row203\" >log_FERRY_MAX</th>\n",
       "      <td id=\"T_c6d29_row203_col0\" class=\"data row203 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row203_col1\" class=\"data row203 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row203_col2\" class=\"data row203 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row204\" class=\"row_heading level0 row204\" >log_FG_AHR_10V</th>\n",
       "      <td id=\"T_c6d29_row204_col0\" class=\"data row204 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row204_col1\" class=\"data row204 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row204_col2\" class=\"data row204 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row205\" class=\"row_heading level0 row205\" >log_FG_AHR_10Vo</th>\n",
       "      <td id=\"T_c6d29_row205_col0\" class=\"data row205 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row205_col1\" class=\"data row205 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row205_col2\" class=\"data row205 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row206\" class=\"row_heading level0 row206\" >log_FG_AHR_24V</th>\n",
       "      <td id=\"T_c6d29_row206_col0\" class=\"data row206 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row206_col1\" class=\"data row206 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row206_col2\" class=\"data row206 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row207\" class=\"row_heading level0 row207\" >log_FG_AHR_24Vo</th>\n",
       "      <td id=\"T_c6d29_row207_col0\" class=\"data row207 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row207_col1\" class=\"data row207 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row207_col2\" class=\"data row207 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row208\" class=\"row_heading level0 row208\" >log_FILEMGR</th>\n",
       "      <td id=\"T_c6d29_row208_col0\" class=\"data row208 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row208_col1\" class=\"data row208 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row208_col2\" class=\"data row208 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row209\" class=\"row_heading level0 row209\" >log_FIX_MISSING_TIMEOUT</th>\n",
       "      <td id=\"T_c6d29_row209_col0\" class=\"data row209 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row209_col1\" class=\"data row209 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row209_col2\" class=\"data row209 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row210\" class=\"row_heading level0 row210\" >log_GLIDE_SLOPE</th>\n",
       "      <td id=\"T_c6d29_row210_col0\" class=\"data row210 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row210_col1\" class=\"data row210 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row210_col2\" class=\"data row210 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row211\" class=\"row_heading level0 row211\" >log_GPS</th>\n",
       "      <td id=\"T_c6d29_row211_col0\" class=\"data row211 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row211_col1\" class=\"data row211 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row211_col2\" class=\"data row211 col2\" >String reported in logfile for GPS fix (first surface position after dive)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row212\" class=\"row_heading level0 row212\" >log_GPS1</th>\n",
       "      <td id=\"T_c6d29_row212_col0\" class=\"data row212 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row212_col1\" class=\"data row212 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row212_col2\" class=\"data row212 col2\" >String reported in logfile for GPS1 fix (first surface position before dive)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row213\" class=\"row_heading level0 row213\" >log_GPS2</th>\n",
       "      <td id=\"T_c6d29_row213_col0\" class=\"data row213 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row213_col1\" class=\"data row213 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row213_col2\" class=\"data row213 col2\" >String reported in logfile for GPS2 fix (last surface position before dive)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row214\" class=\"row_heading level0 row214\" >log_GPS_DEVICE</th>\n",
       "      <td id=\"T_c6d29_row214_col0\" class=\"data row214 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row214_col1\" class=\"data row214 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row214_col2\" class=\"data row214 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row215\" class=\"row_heading level0 row215\" >log_HD_A</th>\n",
       "      <td id=\"T_c6d29_row215_col0\" class=\"data row215 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row215_col1\" class=\"data row215 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row215_col2\" class=\"data row215 col2\" >Hydrodynamic lift factor for given hull shape (1/degrees of attack angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row216\" class=\"row_heading level0 row216\" >log_HD_B</th>\n",
       "      <td id=\"T_c6d29_row216_col0\" class=\"data row216 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row216_col1\" class=\"data row216 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row216_col2\" class=\"data row216 col2\" >Hydrodynamic drag factor for given hull shape (Pa^(-1/4))</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row217\" class=\"row_heading level0 row217\" >log_HD_C</th>\n",
       "      <td id=\"T_c6d29_row217_col0\" class=\"data row217 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row217_col1\" class=\"data row217 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row217_col2\" class=\"data row217 col2\" >Hydrodynamic induced drag factor for given hull shape (1/degrees^2 of attack angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row218\" class=\"row_heading level0 row218\" >log_HEADING</th>\n",
       "      <td id=\"T_c6d29_row218_col0\" class=\"data row218 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row218_col1\" class=\"data row218 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row218_col2\" class=\"data row218 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row219\" class=\"row_heading level0 row219\" >log_HEAD_ERRBAND</th>\n",
       "      <td id=\"T_c6d29_row219_col0\" class=\"data row219 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row219_col1\" class=\"data row219 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row219_col2\" class=\"data row219 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row220\" class=\"row_heading level0 row220\" >log_HEAPDBG</th>\n",
       "      <td id=\"T_c6d29_row220_col0\" class=\"data row220 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row220_col1\" class=\"data row220 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row220_col2\" class=\"data row220 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row221\" class=\"row_heading level0 row221\" >log_HUMID</th>\n",
       "      <td id=\"T_c6d29_row221_col0\" class=\"data row221 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row221_col1\" class=\"data row221 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row221_col2\" class=\"data row221 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row222\" class=\"row_heading level0 row222\" >log_ICE_FREEZE_MARGIN</th>\n",
       "      <td id=\"T_c6d29_row222_col0\" class=\"data row222 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row222_col1\" class=\"data row222 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row222_col2\" class=\"data row222 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row223\" class=\"row_heading level0 row223\" >log_ID</th>\n",
       "      <td id=\"T_c6d29_row223_col0\" class=\"data row223 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row223_col1\" class=\"data row223 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row223_col2\" class=\"data row223 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row224\" class=\"row_heading level0 row224\" >log_INTERNAL_PRESSURE</th>\n",
       "      <td id=\"T_c6d29_row224_col0\" class=\"data row224 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row224_col1\" class=\"data row224 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row224_col2\" class=\"data row224 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row225\" class=\"row_heading level0 row225\" >log_INT_PRESSURE_SLOPE</th>\n",
       "      <td id=\"T_c6d29_row225_col0\" class=\"data row225 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row225_col1\" class=\"data row225 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row225_col2\" class=\"data row225 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row226\" class=\"row_heading level0 row226\" >log_INT_PRESSURE_YINT</th>\n",
       "      <td id=\"T_c6d29_row226_col0\" class=\"data row226 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row226_col1\" class=\"data row226 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row226_col2\" class=\"data row226 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row227\" class=\"row_heading level0 row227\" >log_IRIDIUM_FIX</th>\n",
       "      <td id=\"T_c6d29_row227_col0\" class=\"data row227 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row227_col1\" class=\"data row227 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row227_col2\" class=\"data row227 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row228\" class=\"row_heading level0 row228\" >log_KALMAN_CONTROL</th>\n",
       "      <td id=\"T_c6d29_row228_col0\" class=\"data row228 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row228_col1\" class=\"data row228 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row228_col2\" class=\"data row228 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row229\" class=\"row_heading level0 row229\" >log_KALMAN_USE</th>\n",
       "      <td id=\"T_c6d29_row229_col0\" class=\"data row229 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row229_col1\" class=\"data row229 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row229_col2\" class=\"data row229 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row230\" class=\"row_heading level0 row230\" >log_KALMAN_X</th>\n",
       "      <td id=\"T_c6d29_row230_col0\" class=\"data row230 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row230_col1\" class=\"data row230 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row230_col2\" class=\"data row230 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row231\" class=\"row_heading level0 row231\" >log_KALMAN_Y</th>\n",
       "      <td id=\"T_c6d29_row231_col0\" class=\"data row231 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row231_col1\" class=\"data row231 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row231_col2\" class=\"data row231 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row232\" class=\"row_heading level0 row232\" >log_LOGGERDEVICE1</th>\n",
       "      <td id=\"T_c6d29_row232_col0\" class=\"data row232 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row232_col1\" class=\"data row232 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row232_col2\" class=\"data row232 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row233\" class=\"row_heading level0 row233\" >log_LOGGERDEVICE2</th>\n",
       "      <td id=\"T_c6d29_row233_col0\" class=\"data row233 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row233_col1\" class=\"data row233 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row233_col2\" class=\"data row233 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row234\" class=\"row_heading level0 row234\" >log_LOGGERDEVICE3</th>\n",
       "      <td id=\"T_c6d29_row234_col0\" class=\"data row234 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row234_col1\" class=\"data row234 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row234_col2\" class=\"data row234 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row235\" class=\"row_heading level0 row235\" >log_LOGGERDEVICE4</th>\n",
       "      <td id=\"T_c6d29_row235_col0\" class=\"data row235 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row235_col1\" class=\"data row235 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row235_col2\" class=\"data row235 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row236\" class=\"row_heading level0 row236\" >log_LOGGERS</th>\n",
       "      <td id=\"T_c6d29_row236_col0\" class=\"data row236 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row236_col1\" class=\"data row236 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row236_col2\" class=\"data row236 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row237\" class=\"row_heading level0 row237\" >log_MASS</th>\n",
       "      <td id=\"T_c6d29_row237_col0\" class=\"data row237 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row237_col1\" class=\"data row237 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row237_col2\" class=\"data row237 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row238\" class=\"row_heading level0 row238\" >log_MASS_COMP</th>\n",
       "      <td id=\"T_c6d29_row238_col0\" class=\"data row238 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row238_col1\" class=\"data row238 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row238_col2\" class=\"data row238 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row239\" class=\"row_heading level0 row239\" >log_MAXI_10V</th>\n",
       "      <td id=\"T_c6d29_row239_col0\" class=\"data row239 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row239_col1\" class=\"data row239 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row239_col2\" class=\"data row239 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row240\" class=\"row_heading level0 row240\" >log_MAXI_24V</th>\n",
       "      <td id=\"T_c6d29_row240_col0\" class=\"data row240 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row240_col1\" class=\"data row240 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row240_col2\" class=\"data row240 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row241\" class=\"row_heading level0 row241\" >log_MAX_BUOY</th>\n",
       "      <td id=\"T_c6d29_row241_col0\" class=\"data row241 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row241_col1\" class=\"data row241 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row241_col2\" class=\"data row241 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row242\" class=\"row_heading level0 row242\" >log_MEM</th>\n",
       "      <td id=\"T_c6d29_row242_col0\" class=\"data row242 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row242_col1\" class=\"data row242 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row242_col2\" class=\"data row242 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row243\" class=\"row_heading level0 row243\" >log_MHEAD_RNG_PITCHd_Wd</th>\n",
       "      <td id=\"T_c6d29_row243_col0\" class=\"data row243 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row243_col1\" class=\"data row243 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row243_col2\" class=\"data row243 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row244\" class=\"row_heading level0 row244\" >log_MINV_10V</th>\n",
       "      <td id=\"T_c6d29_row244_col0\" class=\"data row244 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row244_col1\" class=\"data row244 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row244_col2\" class=\"data row244 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row245\" class=\"row_heading level0 row245\" >log_MINV_24V</th>\n",
       "      <td id=\"T_c6d29_row245_col0\" class=\"data row245 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row245_col1\" class=\"data row245 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row245_col2\" class=\"data row245 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row246\" class=\"row_heading level0 row246\" >log_MISSION</th>\n",
       "      <td id=\"T_c6d29_row246_col0\" class=\"data row246 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row246_col1\" class=\"data row246 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row246_col2\" class=\"data row246 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row247\" class=\"row_heading level0 row247\" >log_MOTHERBOARD</th>\n",
       "      <td id=\"T_c6d29_row247_col0\" class=\"data row247 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row247_col1\" class=\"data row247 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row247_col2\" class=\"data row247 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row248\" class=\"row_heading level0 row248\" >log_NAV_MODE</th>\n",
       "      <td id=\"T_c6d29_row248_col0\" class=\"data row248 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row248_col1\" class=\"data row248 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row248_col2\" class=\"data row248 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row249\" class=\"row_heading level0 row249\" >log_NOCOMM_ACTION</th>\n",
       "      <td id=\"T_c6d29_row249_col0\" class=\"data row249 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row249_col1\" class=\"data row249 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row249_col2\" class=\"data row249 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row250\" class=\"row_heading level0 row250\" >log_N_DIVES</th>\n",
       "      <td id=\"T_c6d29_row250_col0\" class=\"data row250 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row250_col1\" class=\"data row250 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row250_col2\" class=\"data row250 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row251\" class=\"row_heading level0 row251\" >log_N_FILEKB</th>\n",
       "      <td id=\"T_c6d29_row251_col0\" class=\"data row251 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row251_col1\" class=\"data row251 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row251_col2\" class=\"data row251 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row252\" class=\"row_heading level0 row252\" >log_N_GPS</th>\n",
       "      <td id=\"T_c6d29_row252_col0\" class=\"data row252 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row252_col1\" class=\"data row252 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row252_col2\" class=\"data row252 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row253\" class=\"row_heading level0 row253\" >log_N_NOCOMM</th>\n",
       "      <td id=\"T_c6d29_row253_col0\" class=\"data row253 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row253_col1\" class=\"data row253 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row253_col2\" class=\"data row253 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row254\" class=\"row_heading level0 row254\" >log_N_NOSURFACE</th>\n",
       "      <td id=\"T_c6d29_row254_col0\" class=\"data row254 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row254_col1\" class=\"data row254 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row254_col2\" class=\"data row254 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row255\" class=\"row_heading level0 row255\" >log_PHONE_DEVICE</th>\n",
       "      <td id=\"T_c6d29_row255_col0\" class=\"data row255 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row255_col1\" class=\"data row255 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row255_col2\" class=\"data row255 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row256\" class=\"row_heading level0 row256\" >log_PHONE_SUPPLY</th>\n",
       "      <td id=\"T_c6d29_row256_col0\" class=\"data row256 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row256_col1\" class=\"data row256 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row256_col2\" class=\"data row256 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row257\" class=\"row_heading level0 row257\" >log_PITCH_ADJ_DBAND</th>\n",
       "      <td id=\"T_c6d29_row257_col0\" class=\"data row257 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row257_col1\" class=\"data row257 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row257_col2\" class=\"data row257 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row258\" class=\"row_heading level0 row258\" >log_PITCH_ADJ_GAIN</th>\n",
       "      <td id=\"T_c6d29_row258_col0\" class=\"data row258 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row258_col1\" class=\"data row258 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row258_col2\" class=\"data row258 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row259\" class=\"row_heading level0 row259\" >log_PITCH_AD_RATE</th>\n",
       "      <td id=\"T_c6d29_row259_col0\" class=\"data row259 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row259_col1\" class=\"data row259 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row259_col2\" class=\"data row259 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row260\" class=\"row_heading level0 row260\" >log_PITCH_CNV</th>\n",
       "      <td id=\"T_c6d29_row260_col0\" class=\"data row260 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row260_col1\" class=\"data row260 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row260_col2\" class=\"data row260 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row261\" class=\"row_heading level0 row261\" >log_PITCH_DBAND</th>\n",
       "      <td id=\"T_c6d29_row261_col0\" class=\"data row261 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row261_col1\" class=\"data row261 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row261_col2\" class=\"data row261 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row262\" class=\"row_heading level0 row262\" >log_PITCH_GAIN</th>\n",
       "      <td id=\"T_c6d29_row262_col0\" class=\"data row262 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row262_col1\" class=\"data row262 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row262_col2\" class=\"data row262 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row263\" class=\"row_heading level0 row263\" >log_PITCH_MAX</th>\n",
       "      <td id=\"T_c6d29_row263_col0\" class=\"data row263 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row263_col1\" class=\"data row263 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row263_col2\" class=\"data row263 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row264\" class=\"row_heading level0 row264\" >log_PITCH_MAXERRORS</th>\n",
       "      <td id=\"T_c6d29_row264_col0\" class=\"data row264 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row264_col1\" class=\"data row264 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row264_col2\" class=\"data row264 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row265\" class=\"row_heading level0 row265\" >log_PITCH_MIN</th>\n",
       "      <td id=\"T_c6d29_row265_col0\" class=\"data row265 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row265_col1\" class=\"data row265 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row265_col2\" class=\"data row265 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row266\" class=\"row_heading level0 row266\" >log_PITCH_TIMEOUT</th>\n",
       "      <td id=\"T_c6d29_row266_col0\" class=\"data row266 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row266_col1\" class=\"data row266 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row266_col2\" class=\"data row266 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row267\" class=\"row_heading level0 row267\" >log_PITCH_VBD_SHIFT</th>\n",
       "      <td id=\"T_c6d29_row267_col0\" class=\"data row267 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row267_col1\" class=\"data row267 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row267_col2\" class=\"data row267 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row268\" class=\"row_heading level0 row268\" >log_PITCH_W_DBAND</th>\n",
       "      <td id=\"T_c6d29_row268_col0\" class=\"data row268 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row268_col1\" class=\"data row268 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row268_col2\" class=\"data row268 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row269\" class=\"row_heading level0 row269\" >log_PITCH_W_GAIN</th>\n",
       "      <td id=\"T_c6d29_row269_col0\" class=\"data row269 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row269_col1\" class=\"data row269 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row269_col2\" class=\"data row269 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row270\" class=\"row_heading level0 row270\" >log_PRESSURE_SLOPE</th>\n",
       "      <td id=\"T_c6d29_row270_col0\" class=\"data row270 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row270_col1\" class=\"data row270 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row270_col2\" class=\"data row270 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row271\" class=\"row_heading level0 row271\" >log_PRESSURE_YINT</th>\n",
       "      <td id=\"T_c6d29_row271_col0\" class=\"data row271 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row271_col1\" class=\"data row271 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row271_col2\" class=\"data row271 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row272\" class=\"row_heading level0 row272\" >log_PROTOCOL</th>\n",
       "      <td id=\"T_c6d29_row272_col0\" class=\"data row272 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row272_col1\" class=\"data row272 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row272_col2\" class=\"data row272 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row273\" class=\"row_heading level0 row273\" >log_P_OVSHOOT</th>\n",
       "      <td id=\"T_c6d29_row273_col0\" class=\"data row273 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row273_col1\" class=\"data row273 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row273_col2\" class=\"data row273 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row274\" class=\"row_heading level0 row274\" >log_P_OVSHOOT_WITHG</th>\n",
       "      <td id=\"T_c6d29_row274_col0\" class=\"data row274 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row274_col1\" class=\"data row274 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row274_col2\" class=\"data row274 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row275\" class=\"row_heading level0 row275\" >log_RAFOS_CORR_THRESH</th>\n",
       "      <td id=\"T_c6d29_row275_col0\" class=\"data row275 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row275_col1\" class=\"data row275 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row275_col2\" class=\"data row275 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row276\" class=\"row_heading level0 row276\" >log_RAFOS_DEVICE</th>\n",
       "      <td id=\"T_c6d29_row276_col0\" class=\"data row276 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row276_col1\" class=\"data row276 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row276_col2\" class=\"data row276 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row277\" class=\"row_heading level0 row277\" >log_RAFOS_HIT_WINDOW</th>\n",
       "      <td id=\"T_c6d29_row277_col0\" class=\"data row277 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row277_col1\" class=\"data row277 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row277_col2\" class=\"data row277 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row278\" class=\"row_heading level0 row278\" >log_RAFOS_MMODEM</th>\n",
       "      <td id=\"T_c6d29_row278_col0\" class=\"data row278 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row278_col1\" class=\"data row278 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row278_col2\" class=\"data row278 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row279\" class=\"row_heading level0 row279\" >log_RAFOS_PEAK_OFFSET</th>\n",
       "      <td id=\"T_c6d29_row279_col0\" class=\"data row279 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row279_col1\" class=\"data row279 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row279_col2\" class=\"data row279 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row280\" class=\"row_heading level0 row280\" >log_RELAUNCH</th>\n",
       "      <td id=\"T_c6d29_row280_col0\" class=\"data row280 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row280_col1\" class=\"data row280 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row280_col2\" class=\"data row280 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row281\" class=\"row_heading level0 row281\" >log_RHO</th>\n",
       "      <td id=\"T_c6d29_row281_col0\" class=\"data row281 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row281_col1\" class=\"data row281 col1\" >gram/cc</td>\n",
       "      <td id=\"T_c6d29_row281_col2\" class=\"data row281 col2\" >Expected density at deepest point over the deployment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row282\" class=\"row_heading level0 row282\" >log_ROLL_ADJ_DBAND</th>\n",
       "      <td id=\"T_c6d29_row282_col0\" class=\"data row282 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row282_col1\" class=\"data row282 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row282_col2\" class=\"data row282 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row283\" class=\"row_heading level0 row283\" >log_ROLL_ADJ_GAIN</th>\n",
       "      <td id=\"T_c6d29_row283_col0\" class=\"data row283 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row283_col1\" class=\"data row283 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row283_col2\" class=\"data row283 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row284\" class=\"row_heading level0 row284\" >log_ROLL_AD_RATE</th>\n",
       "      <td id=\"T_c6d29_row284_col0\" class=\"data row284 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row284_col1\" class=\"data row284 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row284_col2\" class=\"data row284 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row285\" class=\"row_heading level0 row285\" >log_ROLL_CNV</th>\n",
       "      <td id=\"T_c6d29_row285_col0\" class=\"data row285 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row285_col1\" class=\"data row285 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row285_col2\" class=\"data row285 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row286\" class=\"row_heading level0 row286\" >log_ROLL_DEG</th>\n",
       "      <td id=\"T_c6d29_row286_col0\" class=\"data row286 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row286_col1\" class=\"data row286 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row286_col2\" class=\"data row286 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row287\" class=\"row_heading level0 row287\" >log_ROLL_MAX</th>\n",
       "      <td id=\"T_c6d29_row287_col0\" class=\"data row287 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row287_col1\" class=\"data row287 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row287_col2\" class=\"data row287 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row288\" class=\"row_heading level0 row288\" >log_ROLL_MAXERRORS</th>\n",
       "      <td id=\"T_c6d29_row288_col0\" class=\"data row288 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row288_col1\" class=\"data row288 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row288_col2\" class=\"data row288 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row289\" class=\"row_heading level0 row289\" >log_ROLL_MIN</th>\n",
       "      <td id=\"T_c6d29_row289_col0\" class=\"data row289 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row289_col1\" class=\"data row289 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row289_col2\" class=\"data row289 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row290\" class=\"row_heading level0 row290\" >log_ROLL_TIMEOUT</th>\n",
       "      <td id=\"T_c6d29_row290_col0\" class=\"data row290 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row290_col1\" class=\"data row290 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row290_col2\" class=\"data row290 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row291\" class=\"row_heading level0 row291\" >log_R_PORT_OVSHOOT</th>\n",
       "      <td id=\"T_c6d29_row291_col0\" class=\"data row291 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row291_col1\" class=\"data row291 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row291_col2\" class=\"data row291 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row292\" class=\"row_heading level0 row292\" >log_R_STBD_OVSHOOT</th>\n",
       "      <td id=\"T_c6d29_row292_col0\" class=\"data row292 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row292_col1\" class=\"data row292 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row292_col2\" class=\"data row292 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row293\" class=\"row_heading level0 row293\" >log_SEABIRD_C_G</th>\n",
       "      <td id=\"T_c6d29_row293_col0\" class=\"data row293 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row293_col1\" class=\"data row293 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row293_col2\" class=\"data row293 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row294\" class=\"row_heading level0 row294\" >log_SEABIRD_C_H</th>\n",
       "      <td id=\"T_c6d29_row294_col0\" class=\"data row294 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row294_col1\" class=\"data row294 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row294_col2\" class=\"data row294 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row295\" class=\"row_heading level0 row295\" >log_SEABIRD_C_I</th>\n",
       "      <td id=\"T_c6d29_row295_col0\" class=\"data row295 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row295_col1\" class=\"data row295 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row295_col2\" class=\"data row295 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row296\" class=\"row_heading level0 row296\" >log_SEABIRD_C_J</th>\n",
       "      <td id=\"T_c6d29_row296_col0\" class=\"data row296 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row296_col1\" class=\"data row296 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row296_col2\" class=\"data row296 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row297\" class=\"row_heading level0 row297\" >log_SEABIRD_C_Z</th>\n",
       "      <td id=\"T_c6d29_row297_col0\" class=\"data row297 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row297_col1\" class=\"data row297 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row297_col2\" class=\"data row297 col2\" >Conductivity zero frequency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row298\" class=\"row_heading level0 row298\" >log_SEABIRD_T_G</th>\n",
       "      <td id=\"T_c6d29_row298_col0\" class=\"data row298 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row298_col1\" class=\"data row298 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row298_col2\" class=\"data row298 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row299\" class=\"row_heading level0 row299\" >log_SEABIRD_T_H</th>\n",
       "      <td id=\"T_c6d29_row299_col0\" class=\"data row299 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row299_col1\" class=\"data row299 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row299_col2\" class=\"data row299 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row300\" class=\"row_heading level0 row300\" >log_SEABIRD_T_I</th>\n",
       "      <td id=\"T_c6d29_row300_col0\" class=\"data row300 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row300_col1\" class=\"data row300 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row300_col2\" class=\"data row300 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row301\" class=\"row_heading level0 row301\" >log_SEABIRD_T_J</th>\n",
       "      <td id=\"T_c6d29_row301_col0\" class=\"data row301 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row301_col1\" class=\"data row301 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row301_col2\" class=\"data row301 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row302\" class=\"row_heading level0 row302\" >log_SENSORS</th>\n",
       "      <td id=\"T_c6d29_row302_col0\" class=\"data row302 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row302_col1\" class=\"data row302 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row302_col2\" class=\"data row302 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row303\" class=\"row_heading level0 row303\" >log_SENSOR_MAMPS</th>\n",
       "      <td id=\"T_c6d29_row303_col0\" class=\"data row303 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row303_col1\" class=\"data row303 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row303_col2\" class=\"data row303 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row304\" class=\"row_heading level0 row304\" >log_SENSOR_SECS</th>\n",
       "      <td id=\"T_c6d29_row304_col0\" class=\"data row304 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row304_col1\" class=\"data row304 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row304_col2\" class=\"data row304 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row305\" class=\"row_heading level0 row305\" >log_SIM_PITCH</th>\n",
       "      <td id=\"T_c6d29_row305_col0\" class=\"data row305 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row305_col1\" class=\"data row305 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row305_col2\" class=\"data row305 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row306\" class=\"row_heading level0 row306\" >log_SIM_W</th>\n",
       "      <td id=\"T_c6d29_row306_col0\" class=\"data row306 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row306_col1\" class=\"data row306 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row306_col2\" class=\"data row306 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row307\" class=\"row_heading level0 row307\" >log_SM_CC</th>\n",
       "      <td id=\"T_c6d29_row307_col0\" class=\"data row307 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row307_col1\" class=\"data row307 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row307_col2\" class=\"data row307 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row308\" class=\"row_heading level0 row308\" >log_SM_CCo</th>\n",
       "      <td id=\"T_c6d29_row308_col0\" class=\"data row308 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row308_col1\" class=\"data row308 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row308_col2\" class=\"data row308 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row309\" class=\"row_heading level0 row309\" >log_SM_GC</th>\n",
       "      <td id=\"T_c6d29_row309_col0\" class=\"data row309 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row309_col1\" class=\"data row309 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row309_col2\" class=\"data row309 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row310\" class=\"row_heading level0 row310\" >log_SPEED_FACTOR</th>\n",
       "      <td id=\"T_c6d29_row310_col0\" class=\"data row310 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row310_col1\" class=\"data row310 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row310_col2\" class=\"data row310 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row311\" class=\"row_heading level0 row311\" >log_SPEED_LIMITS</th>\n",
       "      <td id=\"T_c6d29_row311_col0\" class=\"data row311 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row311_col1\" class=\"data row311 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row311_col2\" class=\"data row311 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row312\" class=\"row_heading level0 row312\" >log_STOP_T</th>\n",
       "      <td id=\"T_c6d29_row312_col0\" class=\"data row312 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row312_col1\" class=\"data row312 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row312_col2\" class=\"data row312 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row313\" class=\"row_heading level0 row313\" >log_STROBE</th>\n",
       "      <td id=\"T_c6d29_row313_col0\" class=\"data row313 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row313_col1\" class=\"data row313 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row313_col2\" class=\"data row313 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row314\" class=\"row_heading level0 row314\" >log_SURFACE_URGENCY</th>\n",
       "      <td id=\"T_c6d29_row314_col0\" class=\"data row314 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row314_col1\" class=\"data row314 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row314_col2\" class=\"data row314 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row315\" class=\"row_heading level0 row315\" >log_SURFACE_URGENCY_FORCE</th>\n",
       "      <td id=\"T_c6d29_row315_col0\" class=\"data row315 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row315_col1\" class=\"data row315 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row315_col2\" class=\"data row315 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row316\" class=\"row_heading level0 row316\" >log_SURFACE_URGENCY_TRY</th>\n",
       "      <td id=\"T_c6d29_row316_col0\" class=\"data row316 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row316_col1\" class=\"data row316 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row316_col2\" class=\"data row316 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row317\" class=\"row_heading level0 row317\" >log_TCM_PITCH_OFFSET</th>\n",
       "      <td id=\"T_c6d29_row317_col0\" class=\"data row317 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row317_col1\" class=\"data row317 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row317_col2\" class=\"data row317 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row318\" class=\"row_heading level0 row318\" >log_TCM_ROLL_OFFSET</th>\n",
       "      <td id=\"T_c6d29_row318_col0\" class=\"data row318 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row318_col1\" class=\"data row318 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row318_col2\" class=\"data row318 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row319\" class=\"row_heading level0 row319\" >log_TCM_TEMP</th>\n",
       "      <td id=\"T_c6d29_row319_col0\" class=\"data row319 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row319_col1\" class=\"data row319 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row319_col2\" class=\"data row319 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row320\" class=\"row_heading level0 row320\" >log_TGT_AUTO_DEFAULT</th>\n",
       "      <td id=\"T_c6d29_row320_col0\" class=\"data row320 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row320_col1\" class=\"data row320 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row320_col2\" class=\"data row320 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row321\" class=\"row_heading level0 row321\" >log_TGT_DEFAULT_LAT</th>\n",
       "      <td id=\"T_c6d29_row321_col0\" class=\"data row321 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row321_col1\" class=\"data row321 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row321_col2\" class=\"data row321 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row322\" class=\"row_heading level0 row322\" >log_TGT_DEFAULT_LON</th>\n",
       "      <td id=\"T_c6d29_row322_col0\" class=\"data row322 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row322_col1\" class=\"data row322 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row322_col2\" class=\"data row322 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row323\" class=\"row_heading level0 row323\" >log_TGT_LATLONG</th>\n",
       "      <td id=\"T_c6d29_row323_col0\" class=\"data row323 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row323_col1\" class=\"data row323 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row323_col2\" class=\"data row323 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row324\" class=\"row_heading level0 row324\" >log_TGT_NAME</th>\n",
       "      <td id=\"T_c6d29_row324_col0\" class=\"data row324 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row324_col1\" class=\"data row324 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row324_col2\" class=\"data row324 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row325\" class=\"row_heading level0 row325\" >log_TGT_RADIUS</th>\n",
       "      <td id=\"T_c6d29_row325_col0\" class=\"data row325 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row325_col1\" class=\"data row325 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row325_col2\" class=\"data row325 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row326\" class=\"row_heading level0 row326\" >log_TT8_MAMPS</th>\n",
       "      <td id=\"T_c6d29_row326_col0\" class=\"data row326 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row326_col1\" class=\"data row326 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row326_col2\" class=\"data row326 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row327\" class=\"row_heading level0 row327\" >log_T_ABORT</th>\n",
       "      <td id=\"T_c6d29_row327_col0\" class=\"data row327 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row327_col1\" class=\"data row327 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row327_col2\" class=\"data row327 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row328\" class=\"row_heading level0 row328\" >log_T_BOOST</th>\n",
       "      <td id=\"T_c6d29_row328_col0\" class=\"data row328 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row328_col1\" class=\"data row328 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row328_col2\" class=\"data row328 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row329\" class=\"row_heading level0 row329\" >log_T_DIVE</th>\n",
       "      <td id=\"T_c6d29_row329_col0\" class=\"data row329 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row329_col1\" class=\"data row329 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row329_col2\" class=\"data row329 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row330\" class=\"row_heading level0 row330\" >log_T_EPIRB</th>\n",
       "      <td id=\"T_c6d29_row330_col0\" class=\"data row330 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row330_col1\" class=\"data row330 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row330_col2\" class=\"data row330 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row331\" class=\"row_heading level0 row331\" >log_T_GPS</th>\n",
       "      <td id=\"T_c6d29_row331_col0\" class=\"data row331 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row331_col1\" class=\"data row331 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row331_col2\" class=\"data row331 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row332\" class=\"row_heading level0 row332\" >log_T_LOITER</th>\n",
       "      <td id=\"T_c6d29_row332_col0\" class=\"data row332 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row332_col1\" class=\"data row332 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row332_col2\" class=\"data row332 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row333\" class=\"row_heading level0 row333\" >log_T_MISSION</th>\n",
       "      <td id=\"T_c6d29_row333_col0\" class=\"data row333 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row333_col1\" class=\"data row333 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row333_col2\" class=\"data row333 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row334\" class=\"row_heading level0 row334\" >log_T_NO_W</th>\n",
       "      <td id=\"T_c6d29_row334_col0\" class=\"data row334 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row334_col1\" class=\"data row334 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row334_col2\" class=\"data row334 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row335\" class=\"row_heading level0 row335\" >log_T_RSLEEP</th>\n",
       "      <td id=\"T_c6d29_row335_col0\" class=\"data row335 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row335_col1\" class=\"data row335 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row335_col2\" class=\"data row335 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row336\" class=\"row_heading level0 row336\" >log_T_SLOITER</th>\n",
       "      <td id=\"T_c6d29_row336_col0\" class=\"data row336 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row336_col1\" class=\"data row336 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row336_col2\" class=\"data row336 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row337\" class=\"row_heading level0 row337\" >log_T_TURN</th>\n",
       "      <td id=\"T_c6d29_row337_col0\" class=\"data row337 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row337_col1\" class=\"data row337 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row337_col2\" class=\"data row337 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row338\" class=\"row_heading level0 row338\" >log_T_TURN_SAMPINT</th>\n",
       "      <td id=\"T_c6d29_row338_col0\" class=\"data row338 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row338_col1\" class=\"data row338 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row338_col2\" class=\"data row338 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row339\" class=\"row_heading level0 row339\" >log_T_WATCHDOG</th>\n",
       "      <td id=\"T_c6d29_row339_col0\" class=\"data row339 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row339_col1\" class=\"data row339 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row339_col2\" class=\"data row339 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row340\" class=\"row_heading level0 row340\" >log_UNCOM_BLEED</th>\n",
       "      <td id=\"T_c6d29_row340_col0\" class=\"data row340 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row340_col1\" class=\"data row340 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row340_col2\" class=\"data row340 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row341\" class=\"row_heading level0 row341\" >log_UPLOAD_DIVES_MAX</th>\n",
       "      <td id=\"T_c6d29_row341_col0\" class=\"data row341 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row341_col1\" class=\"data row341 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row341_col2\" class=\"data row341 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row342\" class=\"row_heading level0 row342\" >log_USE_BATHY</th>\n",
       "      <td id=\"T_c6d29_row342_col0\" class=\"data row342 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row342_col1\" class=\"data row342 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row342_col2\" class=\"data row342 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row343\" class=\"row_heading level0 row343\" >log_USE_ICE</th>\n",
       "      <td id=\"T_c6d29_row343_col0\" class=\"data row343 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row343_col1\" class=\"data row343 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row343_col2\" class=\"data row343 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row344\" class=\"row_heading level0 row344\" >log_VBD_BLEED_AD_RATE</th>\n",
       "      <td id=\"T_c6d29_row344_col0\" class=\"data row344 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row344_col1\" class=\"data row344 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row344_col2\" class=\"data row344 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row345\" class=\"row_heading level0 row345\" >log_VBD_CNV</th>\n",
       "      <td id=\"T_c6d29_row345_col0\" class=\"data row345 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row345_col1\" class=\"data row345 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row345_col2\" class=\"data row345 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row346\" class=\"row_heading level0 row346\" >log_VBD_DBAND</th>\n",
       "      <td id=\"T_c6d29_row346_col0\" class=\"data row346 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row346_col1\" class=\"data row346 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row346_col2\" class=\"data row346 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row347\" class=\"row_heading level0 row347\" >log_VBD_LP_IGNORE</th>\n",
       "      <td id=\"T_c6d29_row347_col0\" class=\"data row347 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row347_col1\" class=\"data row347 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row347_col2\" class=\"data row347 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row348\" class=\"row_heading level0 row348\" >log_VBD_MAX</th>\n",
       "      <td id=\"T_c6d29_row348_col0\" class=\"data row348 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row348_col1\" class=\"data row348 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row348_col2\" class=\"data row348 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row349\" class=\"row_heading level0 row349\" >log_VBD_MAXERRORS</th>\n",
       "      <td id=\"T_c6d29_row349_col0\" class=\"data row349 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row349_col1\" class=\"data row349 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row349_col2\" class=\"data row349 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row350\" class=\"row_heading level0 row350\" >log_VBD_MIN</th>\n",
       "      <td id=\"T_c6d29_row350_col0\" class=\"data row350 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row350_col1\" class=\"data row350 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row350_col2\" class=\"data row350 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row351\" class=\"row_heading level0 row351\" >log_VBD_PUMP_AD_RATE_APOGEE</th>\n",
       "      <td id=\"T_c6d29_row351_col0\" class=\"data row351 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row351_col1\" class=\"data row351 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row351_col2\" class=\"data row351 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row352\" class=\"row_heading level0 row352\" >log_VBD_PUMP_AD_RATE_SURFACE</th>\n",
       "      <td id=\"T_c6d29_row352_col0\" class=\"data row352 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row352_col1\" class=\"data row352 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row352_col2\" class=\"data row352 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row353\" class=\"row_heading level0 row353\" >log_VBD_TIMEOUT</th>\n",
       "      <td id=\"T_c6d29_row353_col0\" class=\"data row353 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row353_col1\" class=\"data row353 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row353_col2\" class=\"data row353 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row354\" class=\"row_heading level0 row354\" >log_W_ADJ_DBAND</th>\n",
       "      <td id=\"T_c6d29_row354_col0\" class=\"data row354 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row354_col1\" class=\"data row354 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row354_col2\" class=\"data row354 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row355\" class=\"row_heading level0 row355\" >log_XPDR_DEVICE</th>\n",
       "      <td id=\"T_c6d29_row355_col0\" class=\"data row355 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row355_col1\" class=\"data row355 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row355_col2\" class=\"data row355 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row356\" class=\"row_heading level0 row356\" >log_XPDR_INHIBIT</th>\n",
       "      <td id=\"T_c6d29_row356_col0\" class=\"data row356 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row356_col1\" class=\"data row356 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row356_col2\" class=\"data row356 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row357\" class=\"row_heading level0 row357\" >log_XPDR_PINGS</th>\n",
       "      <td id=\"T_c6d29_row357_col0\" class=\"data row357 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row357_col1\" class=\"data row357 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row357_col2\" class=\"data row357 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row358\" class=\"row_heading level0 row358\" >log_XPDR_VALID</th>\n",
       "      <td id=\"T_c6d29_row358_col0\" class=\"data row358 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row358_col1\" class=\"data row358 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row358_col2\" class=\"data row358 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row359\" class=\"row_heading level0 row359\" >log__CALLS</th>\n",
       "      <td id=\"T_c6d29_row359_col0\" class=\"data row359 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row359_col1\" class=\"data row359 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row359_col2\" class=\"data row359 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row360\" class=\"row_heading level0 row360\" >log__SM_ANGLEo</th>\n",
       "      <td id=\"T_c6d29_row360_col0\" class=\"data row360 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row360_col1\" class=\"data row360 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row360_col2\" class=\"data row360 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row361\" class=\"row_heading level0 row361\" >log__SM_DEPTHo</th>\n",
       "      <td id=\"T_c6d29_row361_col0\" class=\"data row361 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row361_col1\" class=\"data row361 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row361_col2\" class=\"data row361 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row362\" class=\"row_heading level0 row362\" >log__XMS_NAKs</th>\n",
       "      <td id=\"T_c6d29_row362_col0\" class=\"data row362 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row362_col1\" class=\"data row362 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row362_col2\" class=\"data row362 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row363\" class=\"row_heading level0 row363\" >log__XMS_TOUTs</th>\n",
       "      <td id=\"T_c6d29_row363_col0\" class=\"data row363 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row363_col1\" class=\"data row363 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row363_col2\" class=\"data row363 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row364\" class=\"row_heading level0 row364\" >magnetic_variation</th>\n",
       "      <td id=\"T_c6d29_row364_col0\" class=\"data row364 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row364_col1\" class=\"data row364 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row364_col2\" class=\"data row364 col2\" >The magnetic variance from true north (degrees)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row365\" class=\"row_heading level0 row365\" >magnetometer</th>\n",
       "      <td id=\"T_c6d29_row365_col0\" class=\"data row365 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row365_col1\" class=\"data row365 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row365_col2\" class=\"data row365 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row366\" class=\"row_heading level0 row366\" >reviewed</th>\n",
       "      <td id=\"T_c6d29_row366_col0\" class=\"data row366 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row366_col1\" class=\"data row366 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row366_col2\" class=\"data row366 col2\" >Whether a scientist has reviewed and approved this profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row367\" class=\"row_heading level0 row367\" >sat1000</th>\n",
       "      <td id=\"T_c6d29_row367_col0\" class=\"data row367 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row367_col1\" class=\"data row367 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row367_col2\" class=\"data row367 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row368\" class=\"row_heading level0 row368\" >sat1001</th>\n",
       "      <td id=\"T_c6d29_row368_col0\" class=\"data row368 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row368_col1\" class=\"data row368 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row368_col2\" class=\"data row368 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row369\" class=\"row_heading level0 row369\" >sbe41</th>\n",
       "      <td id=\"T_c6d29_row369_col0\" class=\"data row369 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row369_col1\" class=\"data row369 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row369_col2\" class=\"data row369 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row370\" class=\"row_heading level0 row370\" >sg_cal_abs_compress</th>\n",
       "      <td id=\"T_c6d29_row370_col0\" class=\"data row370 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row370_col1\" class=\"data row370 col1\" >cc/dbar</td>\n",
       "      <td id=\"T_c6d29_row370_col2\" class=\"data row370 col2\" >SG vehicle compressibility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row371\" class=\"row_heading level0 row371\" >sg_cal_c_g</th>\n",
       "      <td id=\"T_c6d29_row371_col0\" class=\"data row371 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row371_col1\" class=\"data row371 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row371_col2\" class=\"data row371 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row372\" class=\"row_heading level0 row372\" >sg_cal_c_h</th>\n",
       "      <td id=\"T_c6d29_row372_col0\" class=\"data row372 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row372_col1\" class=\"data row372 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row372_col2\" class=\"data row372 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row373\" class=\"row_heading level0 row373\" >sg_cal_c_i</th>\n",
       "      <td id=\"T_c6d29_row373_col0\" class=\"data row373 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row373_col1\" class=\"data row373 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row373_col2\" class=\"data row373 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row374\" class=\"row_heading level0 row374\" >sg_cal_c_j</th>\n",
       "      <td id=\"T_c6d29_row374_col0\" class=\"data row374 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row374_col1\" class=\"data row374 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row374_col2\" class=\"data row374 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row375\" class=\"row_heading level0 row375\" >sg_cal_calibcomm</th>\n",
       "      <td id=\"T_c6d29_row375_col0\" class=\"data row375 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row375_col1\" class=\"data row375 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row375_col2\" class=\"data row375 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row376\" class=\"row_heading level0 row376\" >sg_cal_calibcomm_optode</th>\n",
       "      <td id=\"T_c6d29_row376_col0\" class=\"data row376 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row376_col1\" class=\"data row376 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row376_col2\" class=\"data row376 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row377\" class=\"row_heading level0 row377\" >sg_cal_calibcomm_wetlabs</th>\n",
       "      <td id=\"T_c6d29_row377_col0\" class=\"data row377 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row377_col1\" class=\"data row377 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row377_col2\" class=\"data row377 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row378\" class=\"row_heading level0 row378\" >sg_cal_comm_oxy_type</th>\n",
       "      <td id=\"T_c6d29_row378_col0\" class=\"data row378 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row378_col1\" class=\"data row378 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row378_col2\" class=\"data row378 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row379\" class=\"row_heading level0 row379\" >sg_cal_cpcor</th>\n",
       "      <td id=\"T_c6d29_row379_col0\" class=\"data row379 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row379_col1\" class=\"data row379 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row379_col2\" class=\"data row379 col2\" >Nominal compression factor of conductivity tube with pressure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row380\" class=\"row_heading level0 row380\" >sg_cal_ctcor</th>\n",
       "      <td id=\"T_c6d29_row380_col0\" class=\"data row380 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row380_col1\" class=\"data row380 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row380_col2\" class=\"data row380 col2\" >Nominal thermal expansion factor of a cube of boro-silicate glass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row381\" class=\"row_heading level0 row381\" >sg_cal_hd_a</th>\n",
       "      <td id=\"T_c6d29_row381_col0\" class=\"data row381 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row381_col1\" class=\"data row381 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row381_col2\" class=\"data row381 col2\" >Hydrodynamic lift factor for given hull shape (1/degrees of attack angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row382\" class=\"row_heading level0 row382\" >sg_cal_hd_b</th>\n",
       "      <td id=\"T_c6d29_row382_col0\" class=\"data row382 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row382_col1\" class=\"data row382 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row382_col2\" class=\"data row382 col2\" >Hydrodynamic drag factor for given hull shape (Pa^(-1/4))</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row383\" class=\"row_heading level0 row383\" >sg_cal_hd_c</th>\n",
       "      <td id=\"T_c6d29_row383_col0\" class=\"data row383 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row383_col1\" class=\"data row383 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row383_col2\" class=\"data row383 col2\" >Hydrodynamic induced drag factor for given hull shape (1/radians^2 of attack angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row384\" class=\"row_heading level0 row384\" >sg_cal_id_str</th>\n",
       "      <td id=\"T_c6d29_row384_col0\" class=\"data row384 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row384_col1\" class=\"data row384 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row384_col2\" class=\"data row384 col2\" >Three digit vehicle identification string</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row385\" class=\"row_heading level0 row385\" >sg_cal_mass</th>\n",
       "      <td id=\"T_c6d29_row385_col0\" class=\"data row385 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row385_col1\" class=\"data row385 col1\" >kg</td>\n",
       "      <td id=\"T_c6d29_row385_col2\" class=\"data row385 col2\" >Mass of the glider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row386\" class=\"row_heading level0 row386\" >sg_cal_mass_comp</th>\n",
       "      <td id=\"T_c6d29_row386_col0\" class=\"data row386 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row386_col1\" class=\"data row386 col1\" >kg</td>\n",
       "      <td id=\"T_c6d29_row386_col2\" class=\"data row386 col2\" >Mass of the compressee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row387\" class=\"row_heading level0 row387\" >sg_cal_mission_title</th>\n",
       "      <td id=\"T_c6d29_row387_col0\" class=\"data row387 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row387_col1\" class=\"data row387 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row387_col2\" class=\"data row387 col2\" >Description of mission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row388\" class=\"row_heading level0 row388\" >sg_cal_optode_ConcCoef0</th>\n",
       "      <td id=\"T_c6d29_row388_col0\" class=\"data row388 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row388_col1\" class=\"data row388 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row388_col2\" class=\"data row388 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row389\" class=\"row_heading level0 row389\" >sg_cal_optode_ConcCoef1</th>\n",
       "      <td id=\"T_c6d29_row389_col0\" class=\"data row389 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row389_col1\" class=\"data row389 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row389_col2\" class=\"data row389 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row390\" class=\"row_heading level0 row390\" >sg_cal_optode_FoilCoefA0</th>\n",
       "      <td id=\"T_c6d29_row390_col0\" class=\"data row390 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row390_col1\" class=\"data row390 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row390_col2\" class=\"data row390 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row391\" class=\"row_heading level0 row391\" >sg_cal_optode_FoilCoefA1</th>\n",
       "      <td id=\"T_c6d29_row391_col0\" class=\"data row391 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row391_col1\" class=\"data row391 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row391_col2\" class=\"data row391 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row392\" class=\"row_heading level0 row392\" >sg_cal_optode_FoilCoefA10</th>\n",
       "      <td id=\"T_c6d29_row392_col0\" class=\"data row392 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row392_col1\" class=\"data row392 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row392_col2\" class=\"data row392 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row393\" class=\"row_heading level0 row393\" >sg_cal_optode_FoilCoefA11</th>\n",
       "      <td id=\"T_c6d29_row393_col0\" class=\"data row393 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row393_col1\" class=\"data row393 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row393_col2\" class=\"data row393 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row394\" class=\"row_heading level0 row394\" >sg_cal_optode_FoilCoefA12</th>\n",
       "      <td id=\"T_c6d29_row394_col0\" class=\"data row394 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row394_col1\" class=\"data row394 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row394_col2\" class=\"data row394 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row395\" class=\"row_heading level0 row395\" >sg_cal_optode_FoilCoefA13</th>\n",
       "      <td id=\"T_c6d29_row395_col0\" class=\"data row395 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row395_col1\" class=\"data row395 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row395_col2\" class=\"data row395 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row396\" class=\"row_heading level0 row396\" >sg_cal_optode_FoilCoefA2</th>\n",
       "      <td id=\"T_c6d29_row396_col0\" class=\"data row396 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row396_col1\" class=\"data row396 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row396_col2\" class=\"data row396 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row397\" class=\"row_heading level0 row397\" >sg_cal_optode_FoilCoefA3</th>\n",
       "      <td id=\"T_c6d29_row397_col0\" class=\"data row397 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row397_col1\" class=\"data row397 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row397_col2\" class=\"data row397 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row398\" class=\"row_heading level0 row398\" >sg_cal_optode_FoilCoefA4</th>\n",
       "      <td id=\"T_c6d29_row398_col0\" class=\"data row398 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row398_col1\" class=\"data row398 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row398_col2\" class=\"data row398 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row399\" class=\"row_heading level0 row399\" >sg_cal_optode_FoilCoefA5</th>\n",
       "      <td id=\"T_c6d29_row399_col0\" class=\"data row399 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row399_col1\" class=\"data row399 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row399_col2\" class=\"data row399 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row400\" class=\"row_heading level0 row400\" >sg_cal_optode_FoilCoefA6</th>\n",
       "      <td id=\"T_c6d29_row400_col0\" class=\"data row400 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row400_col1\" class=\"data row400 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row400_col2\" class=\"data row400 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row401\" class=\"row_heading level0 row401\" >sg_cal_optode_FoilCoefA7</th>\n",
       "      <td id=\"T_c6d29_row401_col0\" class=\"data row401 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row401_col1\" class=\"data row401 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row401_col2\" class=\"data row401 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row402\" class=\"row_heading level0 row402\" >sg_cal_optode_FoilCoefA8</th>\n",
       "      <td id=\"T_c6d29_row402_col0\" class=\"data row402 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row402_col1\" class=\"data row402 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row402_col2\" class=\"data row402 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row403\" class=\"row_heading level0 row403\" >sg_cal_optode_FoilCoefA9</th>\n",
       "      <td id=\"T_c6d29_row403_col0\" class=\"data row403 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row403_col1\" class=\"data row403 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row403_col2\" class=\"data row403 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row404\" class=\"row_heading level0 row404\" >sg_cal_optode_FoilCoefB0</th>\n",
       "      <td id=\"T_c6d29_row404_col0\" class=\"data row404 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row404_col1\" class=\"data row404 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row404_col2\" class=\"data row404 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row405\" class=\"row_heading level0 row405\" >sg_cal_optode_FoilCoefB1</th>\n",
       "      <td id=\"T_c6d29_row405_col0\" class=\"data row405 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row405_col1\" class=\"data row405 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row405_col2\" class=\"data row405 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row406\" class=\"row_heading level0 row406\" >sg_cal_optode_FoilCoefB10</th>\n",
       "      <td id=\"T_c6d29_row406_col0\" class=\"data row406 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row406_col1\" class=\"data row406 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row406_col2\" class=\"data row406 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row407\" class=\"row_heading level0 row407\" >sg_cal_optode_FoilCoefB11</th>\n",
       "      <td id=\"T_c6d29_row407_col0\" class=\"data row407 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row407_col1\" class=\"data row407 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row407_col2\" class=\"data row407 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row408\" class=\"row_heading level0 row408\" >sg_cal_optode_FoilCoefB12</th>\n",
       "      <td id=\"T_c6d29_row408_col0\" class=\"data row408 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row408_col1\" class=\"data row408 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row408_col2\" class=\"data row408 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row409\" class=\"row_heading level0 row409\" >sg_cal_optode_FoilCoefB13</th>\n",
       "      <td id=\"T_c6d29_row409_col0\" class=\"data row409 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row409_col1\" class=\"data row409 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row409_col2\" class=\"data row409 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row410\" class=\"row_heading level0 row410\" >sg_cal_optode_FoilCoefB2</th>\n",
       "      <td id=\"T_c6d29_row410_col0\" class=\"data row410 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row410_col1\" class=\"data row410 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row410_col2\" class=\"data row410 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row411\" class=\"row_heading level0 row411\" >sg_cal_optode_FoilCoefB3</th>\n",
       "      <td id=\"T_c6d29_row411_col0\" class=\"data row411 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row411_col1\" class=\"data row411 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row411_col2\" class=\"data row411 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row412\" class=\"row_heading level0 row412\" >sg_cal_optode_FoilCoefB4</th>\n",
       "      <td id=\"T_c6d29_row412_col0\" class=\"data row412 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row412_col1\" class=\"data row412 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row412_col2\" class=\"data row412 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row413\" class=\"row_heading level0 row413\" >sg_cal_optode_FoilCoefB5</th>\n",
       "      <td id=\"T_c6d29_row413_col0\" class=\"data row413 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row413_col1\" class=\"data row413 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row413_col2\" class=\"data row413 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row414\" class=\"row_heading level0 row414\" >sg_cal_optode_FoilCoefB6</th>\n",
       "      <td id=\"T_c6d29_row414_col0\" class=\"data row414 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row414_col1\" class=\"data row414 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row414_col2\" class=\"data row414 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row415\" class=\"row_heading level0 row415\" >sg_cal_optode_FoilCoefB7</th>\n",
       "      <td id=\"T_c6d29_row415_col0\" class=\"data row415 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row415_col1\" class=\"data row415 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row415_col2\" class=\"data row415 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row416\" class=\"row_heading level0 row416\" >sg_cal_optode_FoilCoefB8</th>\n",
       "      <td id=\"T_c6d29_row416_col0\" class=\"data row416 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row416_col1\" class=\"data row416 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row416_col2\" class=\"data row416 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row417\" class=\"row_heading level0 row417\" >sg_cal_optode_FoilCoefB9</th>\n",
       "      <td id=\"T_c6d29_row417_col0\" class=\"data row417 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row417_col1\" class=\"data row417 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row417_col2\" class=\"data row417 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row418\" class=\"row_heading level0 row418\" >sg_cal_optode_PhaseCoef0</th>\n",
       "      <td id=\"T_c6d29_row418_col0\" class=\"data row418 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row418_col1\" class=\"data row418 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row418_col2\" class=\"data row418 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row419\" class=\"row_heading level0 row419\" >sg_cal_optode_PhaseCoef1</th>\n",
       "      <td id=\"T_c6d29_row419_col0\" class=\"data row419 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row419_col1\" class=\"data row419 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row419_col2\" class=\"data row419 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row420\" class=\"row_heading level0 row420\" >sg_cal_optode_PhaseCoef2</th>\n",
       "      <td id=\"T_c6d29_row420_col0\" class=\"data row420 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row420_col1\" class=\"data row420 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row420_col2\" class=\"data row420 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row421\" class=\"row_heading level0 row421\" >sg_cal_optode_PhaseCoef3</th>\n",
       "      <td id=\"T_c6d29_row421_col0\" class=\"data row421 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row421_col1\" class=\"data row421 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row421_col2\" class=\"data row421 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row422\" class=\"row_heading level0 row422\" >sg_cal_optode_SVUCoef0</th>\n",
       "      <td id=\"T_c6d29_row422_col0\" class=\"data row422 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row422_col1\" class=\"data row422 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row422_col2\" class=\"data row422 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row423\" class=\"row_heading level0 row423\" >sg_cal_optode_SVUCoef1</th>\n",
       "      <td id=\"T_c6d29_row423_col0\" class=\"data row423 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row423_col1\" class=\"data row423 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row423_col2\" class=\"data row423 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row424\" class=\"row_heading level0 row424\" >sg_cal_optode_SVUCoef2</th>\n",
       "      <td id=\"T_c6d29_row424_col0\" class=\"data row424 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row424_col1\" class=\"data row424 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row424_col2\" class=\"data row424 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row425\" class=\"row_heading level0 row425\" >sg_cal_optode_SVUCoef3</th>\n",
       "      <td id=\"T_c6d29_row425_col0\" class=\"data row425 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row425_col1\" class=\"data row425 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row425_col2\" class=\"data row425 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row426\" class=\"row_heading level0 row426\" >sg_cal_optode_SVUCoef4</th>\n",
       "      <td id=\"T_c6d29_row426_col0\" class=\"data row426 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row426_col1\" class=\"data row426 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row426_col2\" class=\"data row426 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row427\" class=\"row_heading level0 row427\" >sg_cal_optode_SVUCoef5</th>\n",
       "      <td id=\"T_c6d29_row427_col0\" class=\"data row427 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row427_col1\" class=\"data row427 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row427_col2\" class=\"data row427 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row428\" class=\"row_heading level0 row428\" >sg_cal_optode_SVUCoef6</th>\n",
       "      <td id=\"T_c6d29_row428_col0\" class=\"data row428 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row428_col1\" class=\"data row428 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row428_col2\" class=\"data row428 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row429\" class=\"row_heading level0 row429\" >sg_cal_optode_SVU_enabled</th>\n",
       "      <td id=\"T_c6d29_row429_col0\" class=\"data row429 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row429_col1\" class=\"data row429 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row429_col2\" class=\"data row429 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row430\" class=\"row_heading level0 row430\" >sg_cal_rho0</th>\n",
       "      <td id=\"T_c6d29_row430_col0\" class=\"data row430 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row430_col1\" class=\"data row430 col1\" >kg/m^3</td>\n",
       "      <td id=\"T_c6d29_row430_col2\" class=\"data row430 col2\" >Typical expected density of seawater for this deployment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row431\" class=\"row_heading level0 row431\" >sg_cal_sbect_modes</th>\n",
       "      <td id=\"T_c6d29_row431_col0\" class=\"data row431 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row431_col1\" class=\"data row431 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row431_col2\" class=\"data row431 col2\" >Number of modes to use for thermal-inertia correction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row432\" class=\"row_heading level0 row432\" >sg_cal_t_g</th>\n",
       "      <td id=\"T_c6d29_row432_col0\" class=\"data row432 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row432_col1\" class=\"data row432 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row432_col2\" class=\"data row432 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row433\" class=\"row_heading level0 row433\" >sg_cal_t_h</th>\n",
       "      <td id=\"T_c6d29_row433_col0\" class=\"data row433 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row433_col1\" class=\"data row433 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row433_col2\" class=\"data row433 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row434\" class=\"row_heading level0 row434\" >sg_cal_t_i</th>\n",
       "      <td id=\"T_c6d29_row434_col0\" class=\"data row434 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row434_col1\" class=\"data row434 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row434_col2\" class=\"data row434 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row435\" class=\"row_heading level0 row435\" >sg_cal_t_j</th>\n",
       "      <td id=\"T_c6d29_row435_col0\" class=\"data row435 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row435_col1\" class=\"data row435 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row435_col2\" class=\"data row435 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row436\" class=\"row_heading level0 row436\" >sg_cal_therm_expan</th>\n",
       "      <td id=\"T_c6d29_row436_col0\" class=\"data row436 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row436_col1\" class=\"data row436 col1\" >cc/degrees_Celsius</td>\n",
       "      <td id=\"T_c6d29_row436_col2\" class=\"data row436 col2\" >SG thermal expansion coeff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row437\" class=\"row_heading level0 row437\" >sg_cal_volmax</th>\n",
       "      <td id=\"T_c6d29_row437_col0\" class=\"data row437 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row437_col1\" class=\"data row437 col1\" >m^3</td>\n",
       "      <td id=\"T_c6d29_row437_col2\" class=\"data row437 col2\" >Maximum displaced volume of the glider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row438\" class=\"row_heading level0 row438\" >sg_cal_wlbb2fl_sig470nm_dark_counts</th>\n",
       "      <td id=\"T_c6d29_row438_col0\" class=\"data row438 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row438_col1\" class=\"data row438 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row438_col2\" class=\"data row438 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row439\" class=\"row_heading level0 row439\" >sg_cal_wlbb2fl_sig470nm_resolution_counts</th>\n",
       "      <td id=\"T_c6d29_row439_col0\" class=\"data row439 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row439_col1\" class=\"data row439 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row439_col2\" class=\"data row439 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row440\" class=\"row_heading level0 row440\" >sg_cal_wlbb2fl_sig470nm_scale_factor</th>\n",
       "      <td id=\"T_c6d29_row440_col0\" class=\"data row440 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row440_col1\" class=\"data row440 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row440_col2\" class=\"data row440 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row441\" class=\"row_heading level0 row441\" >sg_cal_wlbb2fl_sig695nm_dark_counts</th>\n",
       "      <td id=\"T_c6d29_row441_col0\" class=\"data row441 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row441_col1\" class=\"data row441 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row441_col2\" class=\"data row441 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row442\" class=\"row_heading level0 row442\" >sg_cal_wlbb2fl_sig695nm_resolution_counts</th>\n",
       "      <td id=\"T_c6d29_row442_col0\" class=\"data row442 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row442_col1\" class=\"data row442 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row442_col2\" class=\"data row442 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row443\" class=\"row_heading level0 row443\" >sg_cal_wlbb2fl_sig695nm_scale_factor</th>\n",
       "      <td id=\"T_c6d29_row443_col0\" class=\"data row443 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row443_col1\" class=\"data row443 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row443_col2\" class=\"data row443 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row444\" class=\"row_heading level0 row444\" >sg_cal_wlbb2fl_sig700nm_dark_counts</th>\n",
       "      <td id=\"T_c6d29_row444_col0\" class=\"data row444 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row444_col1\" class=\"data row444 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row444_col2\" class=\"data row444 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row445\" class=\"row_heading level0 row445\" >sg_cal_wlbb2fl_sig700nm_resolution_counts</th>\n",
       "      <td id=\"T_c6d29_row445_col0\" class=\"data row445 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row445_col1\" class=\"data row445 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row445_col2\" class=\"data row445 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row446\" class=\"row_heading level0 row446\" >sg_cal_wlbb2fl_sig700nm_scale_factor</th>\n",
       "      <td id=\"T_c6d29_row446_col0\" class=\"data row446 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row446_col1\" class=\"data row446 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row446_col2\" class=\"data row446 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row447\" class=\"row_heading level0 row447\" >start_of_climb_time</th>\n",
       "      <td id=\"T_c6d29_row447_col0\" class=\"data row447 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row447_col1\" class=\"data row447 col1\" >seconds</td>\n",
       "      <td id=\"T_c6d29_row447_col2\" class=\"data row447 col2\" >Elapsed seconds after dive start when second (positive) apogee pump starts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row448\" class=\"row_heading level0 row448\" >surface_curr_east</th>\n",
       "      <td id=\"T_c6d29_row448_col0\" class=\"data row448 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row448_col1\" class=\"data row448 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row448_col2\" class=\"data row448 col2\" >Eastward component of surface current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row449\" class=\"row_heading level0 row449\" >surface_curr_north</th>\n",
       "      <td id=\"T_c6d29_row449_col0\" class=\"data row449 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row449_col1\" class=\"data row449 col1\" >cm/s</td>\n",
       "      <td id=\"T_c6d29_row449_col2\" class=\"data row449 col2\" >Northward component of surface current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row450\" class=\"row_heading level0 row450\" >surface_curr_qc</th>\n",
       "      <td id=\"T_c6d29_row450_col0\" class=\"data row450 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row450_col1\" class=\"data row450 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row450_col2\" class=\"data row450 col2\" >Whether to trust the surface current values</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row451\" class=\"row_heading level0 row451\" >wlbb2fl</th>\n",
       "      <td id=\"T_c6d29_row451_col0\" class=\"data row451 col0\" >string</td>\n",
       "      <td id=\"T_c6d29_row451_col1\" class=\"data row451 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row451_col2\" class=\"data row451 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6d29_level0_row452\" class=\"row_heading level0 row452\" >trajectory</th>\n",
       "      <td id=\"T_c6d29_row452_col0\" class=\"data row452 col0\" >trajectory</td>\n",
       "      <td id=\"T_c6d29_row452_col1\" class=\"data row452 col1\" ></td>\n",
       "      <td id=\"T_c6d29_row452_col2\" class=\"data row452 col2\" >Dive number for observations</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe4bdee2d90>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#skipp first 1000 dives\n",
    "filenames = 'test_data/sg401_2017/p401[123]*.nc'\n",
    "\n",
    "gt.load.seaglider_show_variables(filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4e0c5b0f-c7c3-4ca0-86f2-f2f9ec5aeb6b",
   "metadata": {
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DIMENSION: sg_data_point\n",
      "{latitude, longitude, temperature, ctd_pressure, salinity_raw, temperature_qc, ctd_time, salinity, ctd_depth, temperature_raw}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2566/2566 [00:19<00:00, 129.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cannot merge data - not enough time indexed DataFrames\n",
      "Returning unmerged dataframes\n"
     ]
    }
   ],
   "source": [
    "names = [\n",
    "    'ctd_depth',\n",
    "    'ctd_time',\n",
    "    'ctd_pressure',\n",
    "    'salinity',\n",
    "    'salinity_raw',\n",
    "    'temperature',\n",
    "    'temperature_raw',\n",
    "    'temperature_qc',\n",
    "    'eng_wlbb2fl_sig470nm',\n",
    "    'eng_wlbb2fl_sig695nm',\n",
    "    'eng_wlbb2fl_sig700nm'\n",
    "]\n",
    "\n",
    "ds_dict = gt.load.seaglider_basestation_netCDFs(filenames, names, return_merged=True, keep_global_attrs=False)\n"
   ]
  },
  {
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   "id": "0621a18e-c6cf-4f43-ae9b-f85860ac6b35",
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   "source": [
    "some file lack enough information/details (or are incomplete dives):\n",
    "- early dive issues (2??)\n",
    "- p401067[34]"
   ]
  },
  {
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   "execution_count": 39,
   "id": "481866f2-fdb2-4ee0-87a3-e148a045eee3",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (sg_data_point: 349365)\n",
       "Coordinates:\n",
       "    latitude         (sg_data_point) float64 61.48 61.48 61.48 ... 57.93 57.93\n",
       "    longitude        (sg_data_point) float64 -173.8 -173.8 ... -169.1 -169.1\n",
       "    ctd_depth        (sg_data_point) float64 0.7374 0.2013 ... 1.294 -0.1832\n",
       "    ctd_time_dt64    (sg_data_point) datetime64[ns] 2017-08-13T21:07:40 ... 2...\n",
       "Dimensions without coordinates: sg_data_point\n",
       "Data variables:\n",
       "    temperature      (sg_data_point) float64 9.512 9.513 9.511 ... 7.004 7.024\n",
       "    ctd_pressure     (sg_data_point) float64 0.7451 0.2038 ... 1.306 -0.1844\n",
       "    salinity_raw     (sg_data_point) float64 31.16 31.15 31.15 ... 31.46 2.506\n",
       "    temperature_qc   (sg_data_point) int64 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1\n",
       "    ctd_time         (sg_data_point) float64 1.503e+09 1.503e+09 ... 1.506e+09\n",
       "    salinity         (sg_data_point) float64 nan nan nan nan ... 31.46 nan nan\n",
       "    temperature_raw  (sg_data_point) float64 9.512 9.513 9.511 ... 7.004 7.023\n",
       "    dives            (sg_data_point) float64 1e+03 1e+03 ... 3.612e+03 3.612e+03\n",
       "Attributes:\n",
       "    date_created:             2021-07-26 13:55:28\n",
       "    number_of_dives:          3612.0\n",
       "    files:                    [&#x27;p4011000.nc&#x27;, &#x27;p4011001.nc&#x27;, &#x27;p4011002.nc&#x27;, &#x27;...\n",
       "    time_coverage_start:      2017-08-13 21:07:40\n",
       "    time_coverage_end:        2017-09-26 16:05:42\n",
       "    geospatial_vertical_min:  -0.5030842835078432\n",
       "    geospatial_vertical_max:  67.57316616647842\n",
       "    geospatial_lat_min:       57.89913235353872\n",
       "    geospatial_lat_max:       62.14208166934556\n",
       "    geospatial_lon_min:       -174.5055983333333\n",
       "    geospatial_lon_max:       -168.8639759711956\n",
       "    processing:               [2021-07-26 13:55:28] imported data with Glider...</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-77550311-5c43-4c1a-81e3-ef9b4dc344c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-77550311-5c43-4c1a-81e3-ef9b4dc344c0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>sg_data_point</span>: 349365</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-90380c07-cbf2-4e44-9d30-5f19f6f8a234' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90380c07-cbf2-4e44-9d30-5f19f6f8a234' class='xr-section-summary' >Coordinates: <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'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>61.48 61.48 61.48 ... 57.93 57.93</div><input id='attrs-ef715ff3-52e2-4a23-b2db-25cb0e9a5996' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef715ff3-52e2-4a23-b2db-25cb0e9a5996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f33f51f-2815-44b6-8586-3feefe1f5560' class='xr-var-data-in' type='checkbox'><label for='data-2f33f51f-2815-44b6-8586-3feefe1f5560' 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>comment :</span></dt><dd>Latitude of the sample based on hdm DAC</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>platform :</span></dt><dd>glider</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([61.47664333, 61.47664333, 61.47664333, ..., 57.92881377,\n",
       "       57.92883207, 57.92884188])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-173.8 -173.8 ... -169.1 -169.1</div><input id='attrs-d988a906-ee7c-43a7-8c6a-039b8c941ba0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d988a906-ee7c-43a7-8c6a-039b8c941ba0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c34d9bae-27fe-43ae-9217-9015a31eccdf' class='xr-var-data-in' type='checkbox'><label for='data-c34d9bae-27fe-43ae-9217-9015a31eccdf' 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>comment :</span></dt><dd>Longitude of the sample based on hdm DAC</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>platform :</span></dt><dd>glider</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-173.81683   , -173.81683   , -173.81683   , ..., -169.11074467,\n",
       "       -169.11078933, -169.11081068])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctd_depth</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7374 0.2013 ... 1.294 -0.1832</div><input id='attrs-6434c960-3bc5-48a8-9ae5-d08c00715116' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6434c960-3bc5-48a8-9ae5-d08c00715116' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d469df63-8013-45af-8f56-da41a41ed104' class='xr-var-data-in' type='checkbox'><label for='data-d469df63-8013-45af-8f56-da41a41ed104' 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>comment :</span></dt><dd>CTD thermistor depth corrected for average latitude</dd><dt><span>platform :</span></dt><dd>glider</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([ 0.73742912,  0.20126934, -0.14697653, ...,  2.71163752,\n",
       "        1.29381673, -0.18323594])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctd_time_dt64</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-08-13T21:07:40 ... 2017-09-...</div><input id='attrs-dbd293fd-b7e3-4aa5-96ef-30d602dd0b2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbd293fd-b7e3-4aa5-96ef-30d602dd0b2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9cf3417-02cc-4d8e-a177-7819f16ce63b' class='xr-var-data-in' type='checkbox'><label for='data-d9cf3417-02cc-4d8e-a177-7819f16ce63b' 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([&#x27;2017-08-13T21:07:40.000000000&#x27;, &#x27;2017-08-13T21:07:54.000000000&#x27;,\n",
       "       &#x27;2017-08-13T21:08:02.000000000&#x27;, ...,\n",
       "       &#x27;2017-09-26T16:05:18.000000000&#x27;, &#x27;2017-09-26T16:05:30.000000000&#x27;,\n",
       "       &#x27;2017-09-26T16:05:42.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ce21da6-6836-408b-bc57-3072102554bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ce21da6-6836-408b-bc57-3072102554bd' class='xr-section-summary' >Data variables: <span>(8)</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>temperature</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.512 9.513 9.511 ... 7.004 7.024</div><input id='attrs-def8830f-8848-4e3a-bdff-49b2412da15c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-def8830f-8848-4e3a-bdff-49b2412da15c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b567818-1fd4-46b5-b708-93e5d53ec814' class='xr-var-data-in' type='checkbox'><label for='data-9b567818-1fd4-46b5-b708-93e5d53ec814' 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>units :</span></dt><dd>degrees_Celsius</dd><dt><span>comment :</span></dt><dd>Termperature (in situ) corrected for thermistor first-order lag</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>platform :</span></dt><dd>glider</dd></dl></div><div class='xr-var-data'><pre>array([9.51193002, 9.51302256, 9.51140929, ..., 7.00300615, 7.00403547,\n",
       "       7.02446412])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctd_pressure</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7451 0.2038 ... 1.306 -0.1844</div><input id='attrs-b8ab1b6b-4e13-46ee-b7e8-6ed106668eb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8ab1b6b-4e13-46ee-b7e8-6ed106668eb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46e018a4-0c51-4adc-a2a7-4d5d7316eb65' class='xr-var-data-in' type='checkbox'><label for='data-46e018a4-0c51-4adc-a2a7-4d5d7316eb65' 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>units :</span></dt><dd>dbar</dd><dt><span>comment :</span></dt><dd>Pressure at CTD thermistor</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>platform :</span></dt><dd>glider</dd></dl></div><div class='xr-var-data'><pre>array([ 0.74512522,  0.20382167, -0.14781317, ...,  2.73745652,\n",
       "        1.30637229, -0.18443319])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_raw</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>31.16 31.15 31.15 ... 31.46 2.506</div><input id='attrs-bd1f97b6-257d-4c3f-8bdf-3887ea20e37d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd1f97b6-257d-4c3f-8bdf-3887ea20e37d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-753dd9f1-aadf-4622-8d1b-634b00205501' class='xr-var-data-in' type='checkbox'><label for='data-753dd9f1-aadf-4622-8d1b-634b00205501' 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>units :</span></dt><dd>1e-3</dd><dt><span>comment :</span></dt><dd>Uncorrected salinity derived from temperature_raw and conductivity_raw (PSU)</dd><dt><span>platform :</span></dt><dd>glider</dd></dl></div><div class='xr-var-data'><pre>array([31.15784827, 31.15432947, 31.15488362, ..., 31.46198175,\n",
       "       31.46214907,  2.50609309])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_qc</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-636c38cc-2b17-4b71-9bd9-7adc552e5224' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-636c38cc-2b17-4b71-9bd9-7adc552e5224' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cff8f73-9fba-4dd0-86fe-46d1eb80cc7a' class='xr-var-data-in' type='checkbox'><label for='data-4cff8f73-9fba-4dd0-86fe-46d1eb80cc7a' 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>flag_meanings :</span></dt><dd>QC_NO_CHANGE QC_GOOD QC_PROBABLY_GOOD QC_PROBABLY_BAD QC_BAD QC_CHANGED QC_UNSAMPLED QC_INTERPOLATED QC_MISSING</dd><dt><span>comment :</span></dt><dd>Whether to trust each corrected temperature value</dd><dt><span>flag_values :</span></dt><dd>0 1 2 3 4 5 6 8 9</dd><dt><span>platform :</span></dt><dd>glider</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, ..., 1, 1, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctd_time</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.503e+09 1.503e+09 ... 1.506e+09</div><input id='attrs-32e98625-54e2-4f21-ad56-7be80f1f1c02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32e98625-54e2-4f21-ad56-7be80f1f1c02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a12a97c4-9990-493d-8705-757bb791b210' class='xr-var-data-in' type='checkbox'><label for='data-a12a97c4-9990-493d-8705-757bb791b210' 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>units :</span></dt><dd>seconds since 1970-1-1 00:00:00</dd><dt><span>comment :</span></dt><dd>Time of CTD sample in GMT epoch format</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>platform :</span></dt><dd>glider</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([1.50265846e+09, 1.50265847e+09, 1.50265848e+09, ...,\n",
       "       1.50644192e+09, 1.50644193e+09, 1.50644194e+09])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 31.46 nan nan</div><input id='attrs-097f8023-6e47-41e8-b0c3-02074ca98ef5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-097f8023-6e47-41e8-b0c3-02074ca98ef5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f479ae2-394d-44f9-9c43-f7f6f08b5b72' class='xr-var-data-in' type='checkbox'><label for='data-4f479ae2-394d-44f9-9c43-f7f6f08b5b72' 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>units :</span></dt><dd>1e-3</dd><dt><span>comment :</span></dt><dd>Salinity corrected for thermal-inertia effects (PSU)</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>platform :</span></dt><dd>glider</dd></dl></div><div class='xr-var-data'><pre>array([        nan,         nan,         nan, ..., 31.46201736,\n",
       "               nan,         nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_raw</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.512 9.513 9.511 ... 7.004 7.023</div><input id='attrs-ceaae36d-8b01-4cf2-acd5-2038c90a1de9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceaae36d-8b01-4cf2-acd5-2038c90a1de9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9de6a615-1a2e-4922-bc33-61680dc08413' class='xr-var-data-in' type='checkbox'><label for='data-9de6a615-1a2e-4922-bc33-61680dc08413' 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>units :</span></dt><dd>degrees_Celsius</dd><dt><span>comment :</span></dt><dd>Uncorrected temperature (in situ)</dd><dt><span>platform :</span></dt><dd>glider</dd></dl></div><div class='xr-var-data'><pre>array([9.51188082, 9.51303766, 9.51130812, ..., 7.00298831, 7.00352355,\n",
       "       7.02346695])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dives</span></div><div class='xr-var-dims'>(sg_data_point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 1e+03 ... 3.612e+03 3.612e+03</div><input id='attrs-bf601850-9322-4ed3-ab80-32769dc38869' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf601850-9322-4ed3-ab80-32769dc38869' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81f7f357-00cb-4771-9c04-82d5ac86999b' class='xr-var-data-in' type='checkbox'><label for='data-81f7f357-00cb-4771-9c04-82d5ac86999b' 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([1000. , 1000. , 1000. , ..., 3612.5, 3612.5, 3612.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-577cb157-c03a-4d8f-9c58-518dcbd5f648' class='xr-section-summary-in' type='checkbox'  ><label for='section-577cb157-c03a-4d8f-9c58-518dcbd5f648' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>date_created :</span></dt><dd>2021-07-26 13:55:28</dd><dt><span>number_of_dives :</span></dt><dd>3612.0</dd><dt><span>files :</span></dt><dd>[&#x27;p4011000.nc&#x27;, &#x27;p4011001.nc&#x27;, &#x27;p4011002.nc&#x27;, &#x27;p4011003.nc&#x27;, &#x27;p4011004.nc&#x27;, &#x27;p4011005.nc&#x27;, &#x27;p4011006.nc&#x27;, &#x27;p4011007.nc&#x27;, &#x27;p4011008.nc&#x27;, &#x27;p4011009.nc&#x27;, &#x27;p4011010.nc&#x27;, &#x27;p4011011.nc&#x27;, &#x27;p4011012.nc&#x27;, &#x27;p4011013.nc&#x27;, &#x27;p4011014.nc&#x27;, &#x27;p4011015.nc&#x27;, &#x27;p4011016.nc&#x27;, &#x27;p4011017.nc&#x27;, 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&#x27;p4013505.nc&#x27;, &#x27;p4013506.nc&#x27;, &#x27;p4013507.nc&#x27;, &#x27;p4013508.nc&#x27;, &#x27;p4013509.nc&#x27;, &#x27;p4013510.nc&#x27;, &#x27;p4013511.nc&#x27;, &#x27;p4013512.nc&#x27;, &#x27;p4013513.nc&#x27;, &#x27;p4013514.nc&#x27;, &#x27;p4013515.nc&#x27;, &#x27;p4013516.nc&#x27;, &#x27;p4013517.nc&#x27;, &#x27;p4013518.nc&#x27;, &#x27;p4013519.nc&#x27;, &#x27;p4013520.nc&#x27;, &#x27;p4013521.nc&#x27;, &#x27;p4013522.nc&#x27;, &#x27;p4013523.nc&#x27;, &#x27;p4013524.nc&#x27;, &#x27;p4013525.nc&#x27;, &#x27;p4013526.nc&#x27;, &#x27;p4013527.nc&#x27;, &#x27;p4013528.nc&#x27;, &#x27;p4013529.nc&#x27;, &#x27;p4013530.nc&#x27;, &#x27;p4013531.nc&#x27;, &#x27;p4013532.nc&#x27;, &#x27;p4013533.nc&#x27;, &#x27;p4013534.nc&#x27;, &#x27;p4013535.nc&#x27;, &#x27;p4013536.nc&#x27;, &#x27;p4013537.nc&#x27;, &#x27;p4013538.nc&#x27;, &#x27;p4013539.nc&#x27;, &#x27;p4013540.nc&#x27;, &#x27;p4013541.nc&#x27;, &#x27;p4013542.nc&#x27;, &#x27;p4013543.nc&#x27;, &#x27;p4013544.nc&#x27;, &#x27;p4013545.nc&#x27;, &#x27;p4013546.nc&#x27;, &#x27;p4013547.nc&#x27;, &#x27;p4013548.nc&#x27;, &#x27;p4013549.nc&#x27;, &#x27;p4013550.nc&#x27;, &#x27;p4013551.nc&#x27;, &#x27;p4013552.nc&#x27;, &#x27;p4013553.nc&#x27;, &#x27;p4013554.nc&#x27;, &#x27;p4013555.nc&#x27;, &#x27;p4013556.nc&#x27;, &#x27;p4013557.nc&#x27;, &#x27;p4013558.nc&#x27;, &#x27;p4013559.nc&#x27;, &#x27;p4013560.nc&#x27;, &#x27;p4013561.nc&#x27;, &#x27;p4013562.nc&#x27;, &#x27;p4013563.nc&#x27;, &#x27;p4013564.nc&#x27;, &#x27;p4013565.nc&#x27;, &#x27;p4013566.nc&#x27;, &#x27;p4013567.nc&#x27;, &#x27;p4013568.nc&#x27;, &#x27;p4013569.nc&#x27;, &#x27;p4013570.nc&#x27;, &#x27;p4013571.nc&#x27;, &#x27;p4013572.nc&#x27;, &#x27;p4013573.nc&#x27;, &#x27;p4013574.nc&#x27;, &#x27;p4013575.nc&#x27;, &#x27;p4013576.nc&#x27;, &#x27;p4013577.nc&#x27;, &#x27;p4013578.nc&#x27;, &#x27;p4013579.nc&#x27;, &#x27;p4013580.nc&#x27;, &#x27;p4013581.nc&#x27;, &#x27;p4013582.nc&#x27;, &#x27;p4013583.nc&#x27;, &#x27;p4013584.nc&#x27;, &#x27;p4013585.nc&#x27;, &#x27;p4013586.nc&#x27;, &#x27;p4013587.nc&#x27;, &#x27;p4013588.nc&#x27;, &#x27;p4013589.nc&#x27;, &#x27;p4013590.nc&#x27;, &#x27;p4013591.nc&#x27;, &#x27;p4013592.nc&#x27;, &#x27;p4013593.nc&#x27;, &#x27;p4013594.nc&#x27;, &#x27;p4013595.nc&#x27;, &#x27;p4013596.nc&#x27;, &#x27;p4013597.nc&#x27;, &#x27;p4013598.nc&#x27;, &#x27;p4013599.nc&#x27;, &#x27;p4013600.nc&#x27;, &#x27;p4013601.nc&#x27;, &#x27;p4013602.nc&#x27;, &#x27;p4013603.nc&#x27;, &#x27;p4013604.nc&#x27;, &#x27;p4013605.nc&#x27;, &#x27;p4013606.nc&#x27;, &#x27;p4013607.nc&#x27;, &#x27;p4013608.nc&#x27;, &#x27;p4013609.nc&#x27;, &#x27;p4013610.nc&#x27;, &#x27;p4013611.nc&#x27;, &#x27;p4013612.nc&#x27;]</dd><dt><span>time_coverage_start :</span></dt><dd>2017-08-13 21:07:40</dd><dt><span>time_coverage_end :</span></dt><dd>2017-09-26 16:05:42</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-0.5030842835078432</dd><dt><span>geospatial_vertical_max :</span></dt><dd>67.57316616647842</dd><dt><span>geospatial_lat_min :</span></dt><dd>57.89913235353872</dd><dt><span>geospatial_lat_max :</span></dt><dd>62.14208166934556</dd><dt><span>geospatial_lon_min :</span></dt><dd>-174.5055983333333</dd><dt><span>geospatial_lon_max :</span></dt><dd>-168.8639759711956</dd><dt><span>processing :</span></dt><dd>[2021-07-26 13:55:28] imported data with GliderTools.load.seaglider_basestation_netCDFs;\n",
       "</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:          (sg_data_point: 349365)\n",
       "Coordinates:\n",
       "    latitude         (sg_data_point) float64 61.48 61.48 61.48 ... 57.93 57.93\n",
       "    longitude        (sg_data_point) float64 -173.8 -173.8 ... -169.1 -169.1\n",
       "    ctd_depth        (sg_data_point) float64 0.7374 0.2013 ... 1.294 -0.1832\n",
       "    ctd_time_dt64    (sg_data_point) datetime64[ns] 2017-08-13T21:07:40 ... 2...\n",
       "Dimensions without coordinates: sg_data_point\n",
       "Data variables:\n",
       "    temperature      (sg_data_point) float64 9.512 9.513 9.511 ... 7.004 7.024\n",
       "    ctd_pressure     (sg_data_point) float64 0.7451 0.2038 ... 1.306 -0.1844\n",
       "    salinity_raw     (sg_data_point) float64 31.16 31.15 31.15 ... 31.46 2.506\n",
       "    temperature_qc   (sg_data_point) int64 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1\n",
       "    ctd_time         (sg_data_point) float64 1.503e+09 1.503e+09 ... 1.506e+09\n",
       "    salinity         (sg_data_point) float64 nan nan nan nan ... 31.46 nan nan\n",
       "    temperature_raw  (sg_data_point) float64 9.512 9.513 9.511 ... 7.004 7.023\n",
       "    dives            (sg_data_point) float64 1e+03 1e+03 ... 3.612e+03 3.612e+03\n",
       "Attributes:\n",
       "    date_created:             2021-07-26 13:55:28\n",
       "    number_of_dives:          3612.0\n",
       "    files:                    ['p4011000.nc', 'p4011001.nc', 'p4011002.nc', '...\n",
       "    time_coverage_start:      2017-08-13 21:07:40\n",
       "    time_coverage_end:        2017-09-26 16:05:42\n",
       "    geospatial_vertical_min:  -0.5030842835078432\n",
       "    geospatial_vertical_max:  67.57316616647842\n",
       "    geospatial_lat_min:       57.89913235353872\n",
       "    geospatial_lat_max:       62.14208166934556\n",
       "    geospatial_lon_min:       -174.5055983333333\n",
       "    geospatial_lon_max:       -168.8639759711956\n",
       "    processing:               [2021-07-26 13:55:28] imported data with Glider..."
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_dict['sg_data_point']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ad47b06b-ebea-4f0a-ad46-b444acc4902f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = ds_dict['sg_data_point'].rename({\n",
    "    'ctd_pressure': 'pressure',\n",
    "    'ctd_depth': 'depth',\n",
    "    'ctd_time_dt64': 'time',\n",
    "    'eng_wlbb2fl_sig695nm': 'flr_raw'\n",
    "})\n",
    "\n",
    "\n",
    "# name coordinates for quicker plotting\n",
    "x = dat.dives\n",
    "y = dat.depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "67a62f36-ac76-4f98-bce7-7c3c2e2cd53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x315 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gt.plot(x, y, dat.temperature_raw, shading='nearest', cmap=cmo.thermal, robust=True)\n",
    "title('Original Data')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7c001a79-0735-4496-b8a8-8de65fca237e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x315 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gt.plot(x, y, dat.temperature, shading='nearest', cmap=cmo.thermal, robust=True)\n",
    "title('Original Data - UW QC')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "22c12b27-fd45-4d93-9b98-7cd7965f5ae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "Physics Variable:\n",
      "\tRemoving outliers with IQR * 1.5: 0 obs\n",
      "\tRemoving spikes with rolling median (spike window=5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x765 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lots of options available, but lets filter (without much smoothing if any at all, lets do it in the verticle for sure and contemplate horizonatl filtering too)\n",
    "\n",
    "temp_qc = gt.calc_physics(dat.temperature, x, y, \n",
    "                          iqr=1.5, depth_threshold=0, #<-- depth \n",
    "                          spike_window=5, spike_method='median',\n",
    "                          savitzky_golay_window=0, savitzky_golay_order=2) #<--challenged by sharp interface, made it clearly wrong with window=11, order=2\n",
    "\n",
    "# PLOTTING\n",
    "fig, ax = plt.subplots(3, 1, figsize=[9, 8.5], sharex=True, dpi=90)\n",
    "\n",
    "gt.plot(x, y, dat.temperature, cmap=cmo.thermal, ax=ax[0])\n",
    "gt.plot(x, y, temp_qc, cmap=cmo.thermal, ax=ax[1])\n",
    "gt.plot(x, y, temp_qc - dat.temperature, cmap=cm.RdBu_r, vmin=-0.05, vmax=0.05, ax=ax[2])\n",
    "\n",
    "[a.set_xlabel('') for a in ax]\n",
    "\n",
    "ax[0].cb.set_label('Original Data')\n",
    "ax[1].cb.set_label('Cleaned Data')\n",
    "ax[2].cb.set_label('Difference from Original')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "71135d03-1456-4f89-b148-d7634190588c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "Physics Variable:\n",
      "\tRemoving outliers with IQR * 2.5: 313 obs\n",
      "\tRemoving spikes with rolling median (spike window=5)\n",
      "\tRemoving horizontal outliers (fraction=0.2, multiplier=2.5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x765 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lots of options available, but lets filter (without much smoothing if any at all, lets do it in the verticle for sure and contemplate horizonatl filtering too)\n",
    "# how does it do for despiking at interface?\n",
    "\n",
    "salt_qc = gt.calc_physics(dat.salinity, x, y, \n",
    "                          mask_frac=0.2, iqr=2.5, \n",
    "                          spike_window=5, spike_method='median', \n",
    "                          savitzky_golay_window=0, savitzky_golay_order=2)\n",
    "        \n",
    "# PLOTTING\n",
    "fig, ax = plt.subplots(3, 1, figsize=[9, 8.5], sharex=True, dpi=90)\n",
    "\n",
    "gt.plot(x, y, dat.salinity, cmap=cmo.haline, ax=ax[0])\n",
    "gt.plot(x, y, salt_qc, cmap=cmo.haline, ax=ax[1])\n",
    "gt.plot(x, y, salt_qc - dat.salinity, cmap=cm.RdBu_r, vmin=-0.02, vmax=0.02, ax=ax[2])\n",
    "\n",
    "[a.set_xlabel('') for a in ax]\n",
    "\n",
    "ax[0].cb.set_label('Original Data')\n",
    "ax[1].cb.set_label('Cleaned Data')\n",
    "ax[2].cb.set_label('Difference from Original')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "367462d0-614f-4f65-ac59-b79dfda1dfad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e1fa5609-0b56-4731-8876-7521cf4bc3f9",
   "metadata": {},
   "source": [
    "#### Insert data into original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2c046228-59ad-474c-aa4e-8408c9563089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T14:47:26.483963Z",
     "start_time": "2019-08-13T14:47:26.479824Z"
    }
   },
   "outputs": [],
   "source": [
    "dat['temp_gt_qc'] = temp_qc\n",
    "dat['salt_gt_qc'] = salt_qc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634434a7-3863-48af-8bf2-0248bc475250",
   "metadata": {},
   "source": [
    "## Derived Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "89d6adc2-eb9b-4f97-9afd-d555f9bcd6bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dens0 = gt.physics.potential_density(salt_qc, temp_qc, dat.pressure, dat.latitude, dat.longitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b0d79503-eba0-434b-8f23-31a2c79d6c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mld = gt.physics.mixed_layer_depth(x, y, dens0)\n",
    "mld_smoothed = mld.rolling(10, min_periods=3).mean()\n",
    "\n",
    "mld_mask = gt.physics.mixed_layer_depth(x, y, dens0, return_as_mask=True)\n",
    "mld_grid = gt.grid_data(x, y, mld_mask, verbose=False)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=[9, 3], dpi=100, sharey=True)\n",
    "\n",
    "mld_smoothed.plot(ax=ax[0])\n",
    "gt.plot(mld_grid, ax=ax[1])\n",
    "\n",
    "[a.set_ylim(100, 0) for a in ax]\n",
    "\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "[a.set_xlabel('Dives') for a in ax]\n",
    "xticks(rotation=0)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a3dc2e2-486a-4118-b5a5-48b8f676ff72",
   "metadata": {},
   "source": [
    "## Ancillary Instrumentation: Backscatter/Fluorometry , Oxygen , PAR\n",
    "\n",
    "- apply calibration curves from factor\n",
    "- despike in vertical\n",
    "- quenching and other fluorometric adjustments\n",
    "- oxygen equilibration in ML boundary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b2415a-089d-483c-ad5b-8e0000179878",
   "metadata": {},
   "source": [
    "# Gridding and interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5eeff26-20ec-493e-8a5c-5b9d50bd4c39",
   "metadata": {},
   "source": [
    "### Vertical gridding\n",
    "It is often more convenient and computationally efficient to work with data that has been gridded to a standard vertical grid (i.e. depths have been binned). \n",
    "GliderTools offers very easy to use and efficient tools to grid data once all the processing has been completed. \n",
    "\n",
    "The first task is to select the bin size of the data that will be gridded. \n",
    "GliderTools automatically selects bin sizes according to the sampling frequency of the dataset for every 50m.\n",
    "This is shown in the figure below, where the 2D histogram shows the sampling frequency (by depth) and the line shows the automatically selected bin size rounded up to the nearest 0.5m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8f81ebf3-6e99-43e2-bc00-877df765997e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = gt.plot.bin_size(dat.depth, cmap=mpl.cm.Blues)\n",
    "ax.set_xlim(0, 6)\n",
    "line = ax.get_children()[1]\n",
    "line.set_linewidth(6)\n",
    "line.set_color('orange')\n",
    "\n",
    "legend = ax.get_children()[-2]\n",
    "legend.set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229fadc7-90dc-483a-aa51-157a6f614652",
   "metadata": {},
   "source": [
    "1m seems the optimal gridding for the 2017 SG missions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98286511-647a-4511-b22f-90d3642bac90",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:glidertools]",
   "language": "python",
   "name": "conda-env-glidertools-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}