diff --git a/book/chapters/data.ipynb b/book/chapters/data.ipynb
index e7645c3..61f0387 100644
--- a/book/chapters/data.ipynb
+++ b/book/chapters/data.ipynb
@@ -223,6 +223,7 @@
     "***Tasks***\n",
     "\n",
     "\n",
+    "- Does the S3 Zarr source go down on Sunday evenings? Is there a way to test if it is down?\n",
     "- This section does not demonstrate profile use; but it should\n",
     "- This section effectively does the profile-from-data chart twice; once is enough\n",
     "- Mothball the ReformatDataFile() function in `data.py` which is part of the old Data Explorer procedure.\n",
@@ -811,9 +812,9 @@
    ],
    "source": [
     "# temperature and salinity\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['salinity']], [0], \n",
+    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['conductivity']], [0], \n",
     "                          d['temperature'][0], d['temperature'][1], 'Temperature', colors['temperature'],    'ascent',\n",
-    "                          d['salinity'][0],       d['salinity'][1],    'Salinity',  colors['salinity'], 'ascent', 6, 4)"
+    "                          d['conductivity'][0],       d['conductivity'][1],    'Conductivity',  colors['conductivity'], 'ascent', 6, 4)"
    ]
   },
   {
diff --git a/book/chapters/data/rca/sensors/data_explorer_osb/ctd_jan22_density.nc b/book/chapters/data/rca/sensors/data_explorer_osb/ctd_jan22_density.nc
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diff --git a/book/chapters/data/rca/sensors/data_explorer_osb/vel_jan22_up.nc b/book/chapters/data/rca/sensors/data_explorer_osb/vel_jan22_up.nc
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diff --git a/book/chapters/dataloader.ipynb b/book/chapters/dataloader.ipynb
index 7068876..27c8260 100644
--- a/book/chapters/dataloader.ipynb
+++ b/book/chapters/dataloader.ipynb
@@ -11,7 +11,11 @@
     "# Data Loader\n",
     "\n",
     "\n",
-    "In the event that the data referenced in this book is not available: This notebook has sections to load it in.\n",
+    "In the event that the data referenced in this book are not available: This notebook has sections to load it from the cloud.\n",
+    "In particular we are using the **interactive oceans** zarr file collections via the Python `s3sf` library. \n",
+    "\n",
+    "\n",
+    "For more on this see [Joe Duprey's gist on GitHub](https://gist.github.com/jdduprey/7d5735d6de9c0c46fd16b78ee865f612).\n",
     "\n",
     "\n",
     "## Shallow Profiler Oregon Slope Base January 2022"
@@ -20,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b36f6773-8465-41b0-a408-a8b8d9d0a13d",
+   "id": "e3021b6b-d5b8-4c06-9b2d-05e1ccceb286",
    "metadata": {
     "tags": []
    },
@@ -30,39 +34,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Jupyter Notebook running Python 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "import netCDF4\n",
-    "import xarray as xr\n",
-    "import s3fs\n",
-    "from   shallowprofiler import *\n",
-    "from   charts import *\n",
-    "\n",
-    "fs = s3fs.S3FileSystem(anon=True)\n",
-    "\n",
-    "streamlist = fs.listdir('ooi-data', detail = False)\n",
-    "\n",
-    "profiles = ReadProfileMetadata()\n",
-    "\n",
-    "# 'streamlist' as the last line of the cell will list out the streams; organized by site and instrument\n",
-    "# For more details on the nomenclature run the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ba1fb07e-5c79-4e38-969b-0e253f363c30",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Jupyter Notebook running Python 3\n",
       "List Oregon Slope Base Profiler streams:\n",
       "\n",
       "ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n",
@@ -107,11 +79,29 @@
       "Non-shallow-profiler examples:\n",
       "  DP O3 A is the Axial Base Deep Profiler\n",
       "  LJ 01 A is the Oregon Slope Base Seafloor\n",
-      "\n"
+      "\n",
+      "CTD: ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n",
+      "Current: ooi-data/RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data\n",
+      "There are 10 Oregon Slope Base profiler streams\n"
      ]
     }
    ],
    "source": [
+    "import netCDF4\n",
+    "import xarray as xr\n",
+    "import s3fs\n",
+    "from   shallowprofiler import *\n",
+    "from   charts import *\n",
+    "\n",
+    "fs = s3fs.S3FileSystem(anon=True)\n",
+    "\n",
+    "streamlist = fs.listdir('ooi-data', detail = False)\n",
+    "\n",
+    "profiles = ReadProfileMetadata()\n",
+    "\n",
+    "# 'streamlist' as the last line of the cell will list out the streams; organized by site and instrument\n",
+    "# For more details on the nomenclature run the following cell.\n",
+    "\n",
     "def InstrumentBreakout(s):\n",
     "    print('Field breakout:', s[9:11], s[11:13], s[13:15], s[15:17], s[18:20], s[20:22], s[22:23], s[24:26], \\\n",
     "          s[27:32], s[32:33], s[33:36], s[46:])\n",
@@ -155,18 +145,8 @@
     "print('Non-shallow-profiler examples:')\n",
     "print('  DP O3 A is the Axial Base Deep Profiler')\n",
     "print('  LJ 01 A is the Oregon Slope Base Seafloor')\n",
-    "print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "033ab2fe-0e44-43fe-8bcb-0fa6d7113bb5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
+    "print()\n",
+    "\n",
     "def loadData(stream_name):\n",
     "    fs = s3fs.S3FileSystem(anon=True)\n",
     "    zarr_store = fs.get_mapper(stream_name)\n",
@@ -175,17 +155,20 @@
     "\n",
     "def ShallowProfilerDataReduce(ds, t0, t1, keepers, rename):\n",
     "    \"\"\"\n",
-    "    From a zarr Dataset for a shallow profiler stream ('ds') write a NetCDF file with just \n",
-    "    time/sensor-value/depth over a time period typically no longer than a month.\n",
-    "      - check that the dimension and coordinate is 'time'\n",
-    "      - drop or rename data vars\n",
-    "      - drop attributes\n",
+    "    From an XArray Dataset for a shallow profiler instrument stream: Clean up and return a \n",
+    "    subset of the data also in Dataset format. \n",
+    "      - constrain the time range to the interval t0 - t1\n",
+    "      - verify one dimension/coordinate: 'time'\n",
+    "          - others are removed via .drop_dims()\n",
+    "      - from a list of keeper data variables: rename them\n",
+    "      - drop the other data variables\n",
+    "      - drop all the attributes\n",
     "    \"\"\"\n",
     "    if not len(ds.dims) == 1: \n",
     "        for dim in ds.dims:\n",
     "            if not dim == 'time':\n",
     "                ds = ds.drop_dims(dim)\n",
-    "        if not len(ds.dims) == 1: return False, \"Failed to drop non-time dimensions\"\n",
+    "        if not len(ds.dims) == 1: return False, \"time dimension only code failed\"\n",
     "    if not list(dict(ds.dims))[0] == 'time': \n",
     "        return False, \"Dataset dim is not 'time' (as assumed)\"\n",
     "    if not len(ds.coords) == 1: \n",
@@ -193,35 +176,15 @@
     "    if not list(dict(ds.coords))[0] == 'time': \n",
     "        return False, \"Dataset coord is not 'time' (as assumed)\"\n",
     "    \n",
-    "    ds = ds.sel(time=slice(t0, t1))\n",
+    "    ds = ds.sel(time=slice(t0, t1))               # time window\n",
     "    \n",
     "    for s in ds.data_vars:\n",
     "        if not s in keepers: ds = ds.drop(s)                              # drop extraneous data vars\n",
     "    for s in ds.data_vars: ds = ds.rename({s:rename[keepers.index(s)]})   # rename the others\n",
     "    a = [s for s in ds.attrs]\n",
     "    for p in a: ds.attrs.pop(p)\n",
-    "    return ds, 'looks ok'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "253326b9-2e9b-4630-a621-91859d9bec4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CTD: ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n",
-      "Current: ooi-data/RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data\n",
-      "There are 10 Oregon Slope Base profiler streams\n"
-     ]
-    }
-   ],
-   "source": [
+    "    return ds, 'looks ok'\n",
+    "\n",
     "osb_profiler_streams = [sname for sname in streamlist if 'SF01A' in sname]\n",
     "\n",
     "for s in osb_profiler_streams: \n",
@@ -270,11 +233,12 @@
     "        stream_ctd = s\n",
     "        break\n",
     "        \n",
-    "ds = loadData(stream_ctd)                             # lazy load\n",
-    "t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
-    "ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
-    "print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
-    "ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
+    "if False:\n",
+    "    ds = loadData(stream_ctd)                             # lazy load\n",
+    "    t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
+    "    ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
+    "    print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
+    "    ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
    ]
   },
   {
@@ -305,51 +269,69 @@
    },
    "outputs": [],
    "source": [
-    "t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
-    "ds_do, reply1           = ShallowProfilerDataReduce(ds, t0, t1, ['corrected_dissolved_oxygen', 'sea_water_pressure'], ['do', 'depth'])\n",
-    "ds_density, reply2      = ShallowProfilerDataReduce(ds, t0, t1, ['sea_water_density', 'sea_water_pressure'], ['density', 'depth'])\n",
-    "ds_conductivity, reply3 = ShallowProfilerDataReduce(ds, t0, t1, ['sea_water_electrical_conductivity', 'sea_water_pressure'], ['conductivity', 'depth'])\n",
-    "ds_salinity, reply4    = ShallowProfilerDataReduce(ds, t0, t1, ['sea_water_practical_salinity', 'sea_water_pressure'], ['salinity', 'depth'])\n",
-    "ds_temp, reply5        = ShallowProfilerDataReduce(ds, t0, t1, ['sea_water_temperature', 'sea_water_pressure'], ['temp', 'depth'])\n",
+    "if False:\n",
+    "    t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
+    "    ds_do, reply1           = ShallowProfilerDataReduce(ds, t0, t1, ['corrected_dissolved_oxygen', 'sea_water_pressure'], ['do', 'depth'])\n",
+    "    ds_density, reply2      = ShallowProfilerDataReduce(ds, t0, t1, ['sea_water_density', 'sea_water_pressure'], ['density', 'depth'])\n",
+    "    ds_conductivity, reply3 = ShallowProfilerDataReduce(ds, t0, t1, ['sea_water_electrical_conductivity', 'sea_water_pressure'], ['conductivity', 'depth'])\n",
+    "    ds_salinity, reply4    = ShallowProfilerDataReduce(ds, t0, t1, ['sea_water_practical_salinity', 'sea_water_pressure'], ['salinity', 'depth'])\n",
+    "    ds_temp, reply5        = ShallowProfilerDataReduce(ds, t0, t1, ['sea_water_temperature', 'sea_water_pressure'], ['temp', 'depth'])\n",
     "\n",
-    "if False: \n",
-    "    print(reply1)\n",
-    "    print(reply2)\n",
-    "    print(reply3)\n",
-    "    print(reply4)\n",
-    "    print(reply5)\n",
+    "    if False: \n",
+    "        print(reply1)\n",
+    "        print(reply2)\n",
+    "        print(reply3)\n",
+    "        print(reply4)\n",
+    "        print(reply5)\n",
     "\n",
-    "if False: \n",
-    "    print(ds_do)\n",
-    "    print(ds_density)\n",
-    "    print(ds_conductivity)\n",
-    "    print(ds_salinity)\n",
-    "    print(ds_temp)\n",
+    "    if False: \n",
+    "        print(ds_do)\n",
+    "        print(ds_density)\n",
+    "        print(ds_conductivity)\n",
+    "        print(ds_salinity)\n",
+    "        print(ds_temp)\n",
     "\n",
-    "ds_do.to_netcdf('./data/rca/sensors/osb/do_jan_2022.nc') \n",
-    "ds_density.to_netcdf('./data/rca/sensors/osb/density_jan_2022.nc') \n",
-    "ds_conductivity.to_netcdf('./data/rca/sensors/osb/conductivity_jan_2022.nc') \n",
-    "ds_salinity.to_netcdf('./data/rca/sensors/osb/salinity_jan_2022.nc') \n",
-    "ds_temp.to_netcdf('./data/rca/sensors/osb/temp_jan_2022.nc') "
+    "    ds_do.to_netcdf('./data/rca/sensors/osb/do_jan_2022.nc') \n",
+    "    ds_density.to_netcdf('./data/rca/sensors/osb/density_jan_2022.nc') \n",
+    "    ds_conductivity.to_netcdf('./data/rca/sensors/osb/conductivity_jan_2022.nc') \n",
+    "    ds_salinity.to_netcdf('./data/rca/sensors/osb/salinity_jan_2022.nc') \n",
+    "    ds_temp.to_netcdf('./data/rca/sensors/osb/temp_jan_2022.nc') "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "14ccbac3-2fbc-48cb-a969-f52ee75ff82b",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting 1 charts\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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UqZNK2ZEjR9CgQQM0adJEJe5u3bqpHBZVnJb66KOPVNYfNmxYmWLV5syZMzA3N8eAAQNUyhVXPCkOadeqVQuenp44deoUAG4fNWzYEMOHD8ezZ8/w5MkT5OTk4MKFC3jvvfdU2hsQEAAXFxeV9ir6qISGhqq8bt++fSEUCnWKXSwWY/fu3UhISECzZs3AGMOff/6p/PwBBf2iCn8+FMLDw9G3b1/Y2tqCz+dDKBRi5MiRyM/PVzsN+u2336J+/foICAjAuXPnsH37djg7OxcbX6tWrZCcnIyhQ4fif//7H16/fq21roODA+RyOeLi4ordZnp6Or788kvUrl0bAoEAAoEAFhYWyMjIwP3799Xqa/rMA1CeztP2ORs0aBAEgvLpnln0O4OV4jqZonV1/Tzp8t7/9ddfCAgIQL169bS+vq5/rwoBAQGwtLRUzjs6OsLBwUHt9GlpxMTEwN7eHjweT6U8NDQUDRo0UOtLN3ToUI3beZvvoyNHjoDH42H48OEq9ZycnNC4cWO198HJyQmtWrVSKWvUqNFbvQ+l0b9/f5iamirnLS0t0adPH5w/f17tlJe2z77ib+PEiRPIy8vDyJEjVdpuamoKf39/jadWP/zwQ51jHTRoECZMmIAZM2bg66+/xpw5c1S6exT3u5uXl4elS5fC19cXIpEIAoEAIpEIjx49Uvs+qF27Nn777TccPHgQvXv3Rvv27Uu8mEiX32sFoVAIa2vrMl0JXGWTpi1btkAoFKpMCoq+Rh988AGSk5ORnJwMqVSKdu3aYd++fcrLa3k8Hk6fPo1u3brhm2++QbNmzWBvb48pU6Yoz4UqzicX14Fc0edgwIABajGtWLECjDEkJiaqrGNra6syr+hgnJWVVab3Q7Fe4T/OwjT9qMbHx+PWrVtqMVtaWoIxpvxyT0hIgEAgUIvZycmpTLFqk5CQoBwiojAHBwcIBAIkJCQoyzp37qxMok6dOoUuXbqgYcOGcHR0xKlTp/DPP/8gKytLJWmKj4/H4cOH1dqr6B9V9MespESkqNq1a6N9+/bIzs7GRx99pLZ+586dVV5X0Y8jIiIC7du3R3R0NL7//nv8/fffuHr1Kn766ScA6p8JsViMYcOGITs7G02aNFHrw6bJiBEjlFeBffjhh3BwcMC7776rsb+V4jNU0mdx2LBh+PHHHzF27FicOHECV65cwdWrV2Fvb69x3ZI+84r9W/RzpemzV1ZF931prhJ88eIFxGIxatSoAUD3z5Mu7/2rV69KvEhF179XBU3vmVgsLvN3DMDtK03fMQkJCXB0dFQr11QGvN33UXx8PBhjcHR0VKt76dKlCnkfSkPT96STkxNyc3ORnp5ebF3FZ1/xt6H4rWnZsqVa23ft2qXWdjMzs1JdeQZww0nIZDIIBAJMmTJFZVloaKja6z5//hwAEBQUhHnz5qFfv344fPgwLl++jKtXr6Jx48Ya3+tevXrB0dER2dnZCAoKUvkHUxNdfq8LMzU1LdM+rpRXz+miT58+uHr1qlp5SkoK9u3bB4D7YGmyY8cOTJw4EQDg4eGhTLIePnyI3bt3Y8GCBcjNzcX69ethb28PAIiKitJ6NZGdnR0AYO3atVp7+2v78igvihiKJmcKRRMRxToSiUTl6Jumbdra2iIvLw8JCQkqX0AlHYkoLVtbW1y+fBmMMZV4X758iby8PGU8AJeAbNiwAVeuXMHly5fx1VdfAeAumT958iRevHgBCwsLlf1hZ2eHRo0aYcmSJRpf38XFRWVe03tWnN9//x1Hjx5Fq1at8OOPP2Lw4MF49913lct/+eUXlT9uRXsOHjyIjIwM7N+/Hx4eHsrlRTs3Kty5cwfz589Hy5YtcfXqVaxZswZBQUElxjd69GiMHj0aGRkZOH/+PIKDg9G7d288fPhQ5XUVn6HC73dRKSkpOHLkCIKDgzFr1ixleU5OjtbPYEkUn624uDi4uroqyxWfvfJQ9DvDy8tLp/Wio6Nx/fp1+Pv7K496lebzVNJ7b29vj6ioqGJj0PXvVZ/s7Ozw77//qpXb2tqqdFhX0PYd8TbfR3Z2duDxePj77781Xs2q7ytcTU1NVTpLK2g7eqvpPYiLi4NIJIKFhYVauabPvuJvQ/Ee7N27V+VvVpvSfodlZGRgxIgReOeddxAfH4+xY8fif//7n3J58+bN1f6GFJ/z7du3Y+TIkVi6dKnK8tevX8Pa2lrttT799FOkpaWhfv36mDJlCtq3bw8bG5ti4yvp97qwpKSkMv1NVNmkydbWVuN/EDt27EBWVhYWL16Mdu3aqS0fOHAgNm7cqEyaCnvnnXfw1VdfYd++fcovhq5du4LP52PdunXw8/PTGEvbtm1hbW2Ne/fuYfLkyW/ZsgKl+W/Iw8MDEokET5480Xn7vXv3xtKlS2Fra1vsj0dAQAC++eYb/PHHHyr/eezYsUPn19JF586dsXv3bhw8eBAffPCBsnzr1q3K5YXr8ng8zJs3DyYmJujQoQMA4L333sOMGTPw4sULdOjQQeUIZO/evXHs2DHUqlWrxD/O0rp9+zamTJmCkSNH4rfffkObNm0wePBghIeHK19L25ghii+2wl/2jDGNV7BlZGRg4MCB8PT0xNmzZzFr1izMmjULbdu2VUnQimNubo4ePXogNzcX/fr1w927d1W+gJ8+fQpbW9tiE30ejwfGmNoP1O+//17iVTbaKK5I+uOPP9C8eXNl+e7du9WuRiurslwmn5WVhbFjxyIvLw8zZ85Ulpfl86Ttve/Rowe2bduG//77T+vnRNe/19Io7RHuunXr4s8//0RKSgqkUqmy3N/fH6tWrVKOq6Owc+dOnWPRtX29e/fG8uXLER0djUGDBum8/fLi6emJPXv2ICcnR/n+JSQk4OLFixqP6uzfvx8rV65UHqFLS0vD4cOH0b59e7WjK9o++4q/jW7dukEgEODJkyelOu2mq08//RQRERG4cuUKHjx4gAEDBuDbb7/FtGnTAHCnFrX9DfF4PLXvg6NHjyI6Ohq1a9dWKf/999+xfft2bNy4Ef7+/mjWrBlGjx5dqsGcNf1eK8TExCA7O7tMQ69U2aRJmw0bNsDGxgZffPGFxsPII0eOxJo1a3Dz5k3lZbkDBw5EnTp1IBKJcObMGdy6dUv537OnpyfmzJmDxYsXIysrSzlMwL179/D69WssXLgQFhYWWLt2LQIDA5GYmIgBAwbAwcEBr169ws2bN/Hq1SusW7eu1G1p2LAhzp07h8OHD8PZ2RmWlpZav1BFIhH8/PzUBgYtztSpU7Fv3z506NAB06ZNQ6NGjSCXyxEREYGQkBBMnz4d7777Lrp27YoOHTpg5syZyMjIQIsWLfDPP/9g27ZtGrfL4/G0nl8vzsiRI/HTTz8hMDAQz58/R8OGDXHhwgUsXboUPXv2VDnV5uDggAYNGiAkJAQBAQHKsUfee+89JCYmIjExEWvWrFHZ/qJFi3Dy5Em0adMGU6ZMgY+PD7Kzs/H8+XMcO3YM69evL9M4XhkZGRg0aBC8vLzw888/QyQSYffu3Tp/EXTp0gUikQhDhw7FzJkzkZ2djXXr1iEpKUmtbuEvNXNzc6xevRphYWEYMmQIwsPDNf5HBwDjxo2DRCJB27Zt4ezsjLi4OCxbtgxSqVTtiOylS5fg7+9f7H+pVlZW6NChA1auXAk7Ozt4enoiNDQUGzZs0BpDSerVq4fhw4fju+++g1AoxHvvvYc7d+5g1apVpT7FUFYRERG4dOkS5HI5UlJSVAa3XL16Nbp27aqsq+vnSZf3ftGiRfjrr7/QoUMHzJkzBw0bNkRycjKOHz+OoKAg1K1bV+e/19Jo2LAhAOD7779HYGAghEIhfHx8VPpCFdaxY0cwxnD58mWV92Lq1KnYuHEjevTogUWLFsHR0RE7duzAgwcPAAAmJiX3FNG1fW3btsUnn3yC0aNH49q1a+jQoQPMzc0RGxuLCxcuoGHDhpgwYUKp3gdF20JDQ0vs5zZixAj88ssvGD58OMaNG4eEhAR88803Wj+jfD4fXbp0QVBQEORyOVasWIHU1FQsXLhQre7+/fshEAjQpUsX3L17F/PmzUPjxo2VyaGnpycWLVqEuXPn4unTp+jevTtsbGwQHx+v/E7QtF1dKBKZTZs2oX79+qhfvz4mT56ML7/8Em3btlXrF1ZU7969sXnzZtStWxeNGjXC9evXsXLlSrXvVMU/mIGBgRg9ejQA7nd7wIAB+O6777SO5n3r1q0Sf68VFL+DhQe21Vmpu44bIV2vnrt58yYDwKZOnaq1zoMHD5SX/8bHx7NRo0axunXrMnNzc2ZhYcEaNWrEvv32W7WrnrZu3cpatmzJTE1NmYWFBWvatCnbtGmTSp3Q0FDWq1cvVqNGDSYUCpmrqyvr1auXyhU8iqskCl+SzVjBFUaFr2K5ceMGa9u2LTMzM2MANF6xUdiGDRsYn8/XeCVV/fr1Na6Tnp7OvvrqK+bj48NEIhGTSqWsYcOGbNq0aSqX7iYnJ7MxY8Ywa2trZmZmxrp06aJ8LwtfPZeWlsYAsCFDhhQbqyKuom1KSEhgn376KXN2dmYCgYB5eHiw2bNns+zsbLX1p02bxgCwJUuWqJTXqVOHAWC3bt1SW+fVq1dsypQpzMvLiwmFQlajRg3WvHlzNnfuXOXVYrpcSVXY8OHDmZmZmdqVQ3v27GEA2LffflviNg4fPswaN27MTE1NmaurK5sxY4byqkXF1SO//fYbA6D2uXv8+LHyShSFou/tli1bWEBAAHN0dGQikYi5uLiwQYMGqb1HiisT9+3bV2LMUVFR7MMPP2Q2NjbM0tKSde/end25c4d5eHioXOmm+GwXvSpK0yXFOTk5bPr06czBwYGZmpqy1q1bs7CwMLVtlqSsV88pJj6fz2xsbFjz5s3Z1KlTtQ73ocvnSdf3PjIyko0ZM4Y5OTkxoVCorKe4Wo8x3f9eAbBJkyapxavpfZw9ezZzcXFhJiYmavujqPz8fObp6al2JSRjjN25c4e99957zNTUlNWoUYN9/PHHbMuWLQwAu3nzprJeeXwfMcbYxo0b2bvvvsvMzc2ZRCJhtWrVYiNHjmTXrl0r8bWKXunGGGPNmzdnTk5OWtte2JYtW1i9evWYqakp8/X1Zbt27dJ69dyKFSvYwoULWc2aNZlIJGJNmzZVu/Rf8btw/fp11qdPH2ZhYcEsLS3Z0KFDVfa/wsGDB1lAQACzsrJiYrGYeXh4sAEDBqgMb1Oaq85v3brFJBKJ2mcjOzubNW/enHl6eiqHB9AmKSmJffzxx8zBwYGZmZmxdu3asb///lvluyg9PZ3VrVuX+fr6soyMDJX1J02axIRCofJKx6LfD6X5vR4xYgRr2LChTm0vqkokTUQ3WVlZzN7eXuOwAxXl6NGjjMfjaUxYiPH76quvmLu7u3IsIkKKWrVqFbOxsWGZmZkl1h03bhyzsLBgOTk5FRBZ2aWmpjKBQMB+/PHHcttmaf750vbPNCm9lJQUZm5uzn799dcyrV9lr54j6kxNTbFw4UKsWbNGbYiDinL27FkMGTJEedifVB7Jycn46aefsHTp0nK7xJ9UPZMmTYJUKlVe3amwaNEi/P777zhz5gwOHTqETz75BL///jumTZsGkUhkoGh1c/78ebi6umLcuHGGDoW8pW+//Rbu7u7KU3+lRd981cwnn3yC5ORkPH361CCJS9HbTJDK49mzZ5g9e3a5j79FqhZTU1Ns27ZN7bYditvMREVFIS8vD3Xq1MGaNWvw+eefGyhS3fXq1Qu9evUydBikHFhZWWHz5s1l/sePx1gpRm8jhBBCCKmm6PQcIYQQQogOKGkihBBCCNEBJU2EEEIIITqgpIkQQgghRAeUNBFCCCGE6KDSJ02enp7g8Xhq06RJk7SuExoaiubNm8PU1BTe3t5qN/KrLErb9nPnzmmsr7iVQWWTl5eHr776Cl5eXpBIJPD29saiRYsgl8uLXa8q7P+ytL0q7f+0tDRMnTpVeU/FNm3aaLxBd2FVYb8DpW97Zd7v58+fR58+feDi4gIej6d2yyHGGBYsWAAXFxdIJBJ07NgRd+/eLXG7+/btg6+vL8RiMXx9fXHgwAE9taDs9NH2zZs3a/wsZGdn67ElZVNS+/fv349u3bopb9Cs7QbmRb31vi/HgTYN4uXLlyw2NlY5nTx5stih/p8+fcrMzMzY559/zu7du8d+++03JhQK2d69eys28HJQ2rYrhp3/77//VNYrOsR8ZfH1118zW1tbduTIEfbs2TO2Z88eZmFhwb777jut61SV/V+Wtlel/T9o0CDm6+vLQkND2aNHj1hwcDCzsrJiUVFRGutXlf3OWOnbXpn3+7Fjx9jcuXPZvn37GAB24MABleXLly9nlpaWbN++fez27dts8ODBzNnZmaWmpmrd5sWLFxmfz2dLly5l9+/fZ0uXLmUCgYBdunRJz60pHX20fdOmTczKykrlcxAbG6vnlpRNSe3funUrW7hwofL2UeHh4SVuszz2faVPmor6/PPPWa1atZhcLte4fObMmaxu3boqZePHj2etW7euiPD0qqS2K748S7pHUGXRq1cvNmbMGJWy/v37s+HDh2tdp6rs/7K0vars/8zMTMbn89mRI0dUyhs3bszmzp2rcZ2qst/L0vaqst+L/nDK5XLm5OSkcluo7OxsJpVK2fr167VuZ9CgQax79+4qZd26ddPpfpiGUl5t37RpE5NKpXqMVD80JU0KitvR6JI0lce+r/Sn5wrLzc3F9u3bMWbMGK13YA8LC1O5+zYAdOvWDdeuXYNMJquIMPVCl7YrNG3aFM7OzujcuTPOnj1bQRGWv3bt2uH06dN4+PAhAODmzZu4cOECevbsqXWdqrL/y9J2hcq+//Py8pCfnw9TU1OVcolEggsXLmhcp6rs97K0XaGy7/einj17hri4OJX9KhaL4e/vj4sXL2pdT9tnobh1jE1Z2w4A6enp8PDwQM2aNdG7d2+1kdursvLY91UqaTp48CCSk5MxatQorXXi4uLg6OioUubo6Ii8vDy8fv1azxHqjy5td3Z2xq+//op9+/Zh//798PHxQefOnXH+/PmKC7Qcffnllxg6dCjq1q0LoVCIpk2bYurUqRg6dKjWdarK/i9L26vK/re0tISfnx8WL16MmJgY5OfnY/v27bh8+TJiY2M1rlNV9ntZ2l5V9ntRcXFxAKBxvyqWaVuvtOsYm7K2vW7duti8eTMOHTqEP//8E6ampmjbti0ePXqk13iNRXns+yp177kNGzagR48ecHFxKbZe0SMx7M2dZEo6QmPMdGm7j48PfHx8lPN+fn6IjIzEqlWr0KFDh4oIs1zt2rUL27dvx44dO1C/fn3cuHEDU6dOhYuLCwIDA7WuVxX2f1naXpX2/7Zt2zBmzBi4urqCz+ejWbNmGDZsGP7991+t61SF/Q6Uvu1Vab9romm/lrRPy7KOMSptO1q3bo3WrVsr59u2bYtmzZph7dq1+OGHH/QWpzF5231fZY40vXjxAqdOncLYsWOLrefk5KSWVb58+RICgQC2trb6DFFvdG27Jq1bt660/2XMmDEDs2bNwpAhQ9CwYUOMGDEC06ZNw7Jly7SuU1X2f1narkll3f+1atVCaGgo0tPTERkZiStXrkAmk8HLy0tj/aqy34HSt12TyrrfC3NycgIAjfu16NGEouuVdh1jU9a2F2ViYoKWLVtW+s+Crspj31eZpGnTpk1wcHAo8U7Ufn5+OHnypEpZSEgIWrRoAaFQqM8Q9UbXtmsSHh4OZ2dnPUSlf5mZmTAxUf0I8/n8Yi+7ryr7vyxt16Qy738AMDc3h7OzM5KSknDixAm8//77GutVlf1emK5t16Sy73cA8PLygpOTk8p+zc3NRWhoKNq0aaN1PW2fheLWMTZlbXtRjDHcuHGj0n8WdFUu+17nLuNGLD8/n7m7u7Mvv/xSbdmsWbPYiBEjlPOKS4+nTZvG7t27xzZs2FBpLz1mrHRt//bbb9mBAwfYw4cP2Z07d9isWbMYALZv376KDLncBAYGMldXV+Vl9/v372d2dnZs5syZyjpVdf+Xpe1Vaf8fP36c/fXXX+zp06csJCSENW7cmLVq1Yrl5uYyxqrufmes9G2vzPs9LS2NhYeHs/DwcAaArVmzhoWHh7MXL14wxrjL7qVSKdu/fz+7ffs2Gzp0qNpl9yNGjGCzZs1Szv/zzz+Mz+ez5cuXs/v377Ply5cb5ZAD+mj7ggUL2PHjx9mTJ09YeHg4Gz16NBMIBOzy5csV3r6SlNT+hIQEFh4ezo4ePcoAsJ07d7Lw8HCVIRT0se+rRNJ04sQJ5TgkRQUGBjJ/f3+VsnPnzrGmTZsykUjEPD092bp16yoo0vJXmravWLGC1apVi5mamjIbGxvWrl07dvTo0QqMtnylpqayzz//nLm7uzNTU1Pm7e3N5s6dy3JycpR1qur+L0vbq9L+37VrF/P29mYikYg5OTmxSZMmseTkZOXyqrrfGSt92yvzflcMl1B0CgwMZIxxl94HBwczJycnJhaLWYcOHdjt27dVtuHv76+sr7Bnzx7m4+PDhEIhq1u3rlEmkPpo+9SpU5m7uzsTiUTM3t6ede3alV28eLECW6W7ktq/adMmjcuDg4OV29DHvucx9qY3JCGEEEII0arK9GkihBBCCNEnSpoIIYQQQnRASRMhhBBCiA4oaSKEEEII0QElTYQQQgghOqCkiRBCCCFEB5Q0EUIIIYTooFokTTk5OViwYAFycnIMHUqFq85tB6p3+6nt1bPtQPVuP7Wd2q5PlWZwy59//hkrV65EbGws6tevj++++w7t27fXad3U1FRIpVKkpKTAyspKz5Eal+rcdqB6t5/aXj3bDlTv9lPbqe36bHulONK0a9cuTJ06FXPnzkV4eDjat2+PHj16ICIiwtChEUIIIaSaqBRJ05o1a/Dxxx9j7NixqFevHr777ju4ublh3bp1hg6NEEIIIdWEwNABlCQ3NxfXr1/HrFmzVMq7du2KixcvalwnJydH5bxmYmIiACAyMhJSqVR/wRqhtLQ0AEB0dDRSU1MNHE3Fq87tp7ZXz7YD1bv91Pbq2faUlBQAQF5enn5f6C1uQlwhoqOjGQD2zz//qJQvWbKEvfPOOxrXCQ4O1nj3Y5poookmmmiiqepOf//9t15zEqM/0qTA4/FU5hljamUKs2fPRlBQkHI+MjISDRo0wJUrV+Ds7KzXOAkhhBBSsWJjY9GqVSu4u7vr9XWMPmmys7MDn89HXFycSvnLly/h6OiocR2xWAyxWKycV5ySc3Z2Rs2aNfUXLCGEEEIMxsREv121jb4juEgkQvPmzXHy5EmV8pMnT6JNmzYGiooQQggh1Y3RH2kCgKCgIIwYMQItWrSAn58ffv31V0RERODTTz81dGiEEEIIqSaM/kgTAAwePBjfffcdFi1ahCZNmuD8+fM4duwYPDw8DB0aeUsvX77E+PHj4e7uDrFYDCcnJ3Tr1g1hYWHKOjweDwcPHiyX13v+/Dl4PB5u3LhRbL1z586Bx+MhOTlZbVmTJk2wYMECZZ3ips2bNwMA9u3bh44dO0IqlcLCwgKNGjXCokWLlFd26mL//v3o0qUL7O3tYWVlBT8/P5w4cUKt3r59++Dr6wuxWAxfX18cOHBAZfmyZcvQsmVLWFpawsHBAf369cN///2nXC6TyfDll1+iYcOGMDc3h4uLC0aOHImYmJgSY0xKSsKIESMglUohlUoxYsQItffw888/R/PmzSEWi9GkSROd2x8aGormzZvD1NQU3t7eWL9+vcryu3fv4sMPP4Snpyd4PB6+++47nbZ7+/Zt+Pv7QyKRwNXVFYsWLQIrMuZvSa9dVGJiIj777DP4+PjAzMwM7u7umDJlivIKH4D7LH788cfw8vKCRCJBrVq1EBwcjNzc3BJjLmkfA9yAwF5eXjA1NUXz5s3x999/6/R+EEK0qxRJEwBMnDgRz58/R05ODq5fv44OHToYOiRSDj788EPcvHkTW7ZswcOHD3Ho0CF07NixVMmErnT5MSqNNm3aIDY2VjkNGjQI3bt3VykbPHgw5s6di8GDB6Nly5b466+/cOfOHaxevRo3b97Etm3bdH698+fPo0uXLjh27BiuX7+OgIAA9OnTB+Hh4co6YWFhGDx4MEaMGIGbN29ixIgRGDRoEC5fvqysExoaikmTJuHSpUs4efIk8vLy0LVrV2RkZAAAMjMz8e+//2LevHn4999/sX//fjx8+BB9+/YtMcZhw4bhxo0bOH78OI4fP44bN25gxIgRKnUYYxgzZgwGDx6sc9ufPXuGnj17on379ggPD8ecOXMwZcoU7Nu3T1knMzMT3t7eWL58OZycnHTabmpqKrp06QIXFxdcvXoVa9euxapVq7BmzZpSvXZRMTExiImJwapVq3D79m1s3rwZx48fx8cff6ys8+DBA8jlcvzyyy+4e/cuvv32W6xfvx5z5swpNmZd9jENCEyInuj12jwjERkZyQCwyMhIQ4dCCklKSmIA2Llz57TW8fDwULmc1MPDgzHG2OPHj1nfvn2Zg4MDMzc3Zy1atGAnT55UW3fx4sUsMDCQWVlZsZEjR6pdnurv76/xdc+ePcsAsKSkJLVljRs3ZsHBwWrlgYGB7P3331cpu3z5MgPAvvvuO63vwdvw9fVlCxcuVM4PGjSIde/eXaVOt27d2JAhQ7Ru4+XLlwwACw0N1VrnypUrDAB78eKF1jr37t1jANilS5eUZWFhYQwAe/DggVr94OBg1rhxY63bK2zmzJmsbt26KmXjx49nrVu31ljfw8ODffvttyVu9+eff2ZSqZRlZ2cry5YtW8ZcXFyYXC4v02trs3v3biYSiZhMJtNa55tvvmFeXl7FbkeXfdyqVSv26aefqtSpW7cumzVrVqliJqSyqKjf+UpzpIlUPRYWFrCwsMDBgwe13mTx6tWrAIBNmzYhNjZWOZ+eno6ePXvi1KlTCA8PR7du3dCnTx+1/6RXrlyJBg0a4Pr165g3bx6uXLkCADh16hRiY2Oxf/9+PbYQ+OOPP2BhYYGJEydqXG5tbQ2g4LThuXPndN62XC5HWloaatSooSwLCwtD165dVep169ZN60CwQMGgcIW3o6kOj8dTxqtJWFgYpFIp3n33XWVZ69atIZVKi319XWhr17Vr1yCTyd5qu/7+/ipX23br1g0xMTF4/vy5zq+tOFWrWEcTxT2xBALtXUlTUlLU9oOnpycWLFigEnNx+1gxIHDROsUNCEwI0Q0lTcRgBAIBNm/ejC1btsDa2hpt27bFnDlzcOvWLWUde3t7AFxy4eTkpJxv3Lgxxo8fj4YNG6JOnTr4+uuv4e3tjUOHDqm8RqdOnfDFF1+gdu3aqF27tnJ9W1tbODk5FZsolIdHjx7B29sbQqGw2HpCoVDZ/0VXq1evRkZGBgYNGqQsi4uLUxuKw9HRUW3IDgXGGIKCgtCuXTs0aNBAY53s7GzMmjULw4YNK/ZGmHFxcXBwcFArd3Bw0Pr6utLWrry8PLx+/brct6tYputrm5mZwcfHR+t+TkhIwOLFizF+/HitsTx58gRr165Vu8ClVq1asLOzKzFmRbyvX79Gfn5+qT4HRF18erxa3zZCKGkiBvXhhx8iJiYGhw4dQrdu3XDu3Dk0a9ZM2YFam4yMDMycORO+vr6wtraGhYUFHjx4oHakqUWLFnqMvmSsmEFYC3N1dcWDBw/QqlUrnbb7559/YsGCBdi1a5daolKagWAnT56MW7du4c8//9S4XCaTYciQIZDL5fj555+V5Z9++qnySKGFhYXW1y7p9TUpvN3CCYSmdml7zdLQZbsl1WnVqhUePHgAV1dXte2npqaiV69e8PX1RXBwsMYYYmJi0L17dwwcOBBjx45VWXb69GlMnjy5xJiLlpXmc0BU9dvZD06rnTBgzwBDh0KMTKUYcoBUbaampujSpQu6dOmC+fPnY+zYsQgODsaoUaO0rjNjxgycOHECq1atQu3atSGRSDBgwAC1zt7m5uZliklxRCUlJUXtlFRycrLO9zB85513cOHCBchkshKPNulq165d+Pjjj7Fnzx689957KsucnJx0Hgj2s88+w6FDh3D+/HmNg77KZDIMGjQIz549w5kzZ1SOMi1atAhffPGF2mvHx8erbefVq1daB6LVpPCVjYrX1NYugUAAW1tbnbddlLbtAgVHnN7mtdPS0tC9e3dYWFjgwIEDGj8DMTExCAgIUA6nUtaYFfGWZUBgoup///0PALD/vn5P35PKh440EaPj6+urvJIL4E5d5efnq9T5+++/MWrUKHzwwQdo2LAhnJyciu1PoiASiQBAbXtF1alTByYmJso+VAqxsbGIjo6Gj4+PTm0ZNmwY0tPTVY7SFKZpSIPi/Pnnnxg1ahR27NiBXr16qS338/NTGwg2JCREZSBYxhgmT56M/fv348yZM/Dy8lLbjiJhevToEU6dOqWWHDg4OChPedauXVv52ikpKcp+YwBw+fJlpKSklGog2sLbVRxF09auFi1avFUy6ufnh/Pnz6sk2yEhIXBxcYGnp+dbvXZqaiq6du0KkUiEQ4cOwdTUVK1OdHQ0OnbsiGbNmmHTpk06jWZc0j6mAYEJ0SO9djM3EnT1nHF6/fo1CwgIYNu2bWM3b95kT58+Zbt372aOjo5szJgxynp16tRhEyZMYLGxsSwxMZExxli/fv1YkyZNWHh4OLtx4wbr06cPs7S0ZJ9//rlyPU1XUMlkMiaRSNjXX3/N4uLiWHJystb4JkyYwNzd3dmBAwfY06dP2YULF5i/vz9r2LChxiugNF09xxh39RWfz2czZsxgFy9eZM+fP2enTp1iAwYMUF5VFxUVxXx8fNjly5e1xrNjxw4mEAjYTz/9xGJjY5VT4Tb8888/jM/ns+XLl7P79++z5cuXM4FAoHJF24QJE5hUKmXnzp1T2U5mZqbyPerbty+rWbMmu3HjhkqdnJwcrfExxlj37t1Zo0aNWFhYGAsLC2MNGzZkvXv3Vqnz6NEjFh4ezsaPH8/eeecdFh4ezsLDw4vd9tOnT5mZmRmbNm0au3fvHtuwYQMTCoVs7969yjo5OTnKbTk7O7MvvviChYeHs0ePHmndbnJyMnN0dGRDhw5lt2/fZvv372dWVlZs1apVpXrty5cvMx8fHxYVFcUYYyw1NZW9++67rGHDhuzx48cq72FeXh5jjLsZee3atVmnTp1YVFSUSp3COnXqxNauXauc12Uf79y5kwmFQrZhwwZ27949NnXqVGZubs6eP3+u9b0gBbAAyolUDhX1O18tPhGUNBmn7OxsNmvWLNasWTMmlUqZmZkZ8/HxYV999ZXyB5wxxg4dOsRq167NBAKBcsiBZ8+esYCAACaRSJibmxv78ccfmb+/f4lJE2OM/fbbb8zNzY2ZmJhoHXJAEd+iRYtYvXr1mEQiYR4eHmzUqFFqP2oK2pImxhjbtWsX69ChA7O0tGTm5uasUaNGbNGiRcohB549e8YAsLNnz2qNx9/fX+NdvQMDA1Xq7dmzh/n4+DChUMjq1q3L9u3bp7Jc0zYAsE2bNqnEomkqLj7GGEtISGAfffQRs7S0ZJaWluyjjz5SG1ZBWzuePXtW7LbPnTvHmjZtykQiEfP09GTr1q1TWa4t7uL2MWOM3bp1i7Vv356JxWLm5OTEFixYoBxuQNfXVgxRoWiDYr64dm7atElrncI8PDzUhrgoaR8zxthPP/3EPDw8mEgkYs2aNSt2SAmiipKmyqeifud5jFX9ywOioqLg5uaGyMhIumEvIYQQrRhjMFlUcJqUBVf5n8gqoaJ+56lPEyGEEPJGPiu+vyOp3ihpIoQQQt5Iy0lTmX+e/NwwgRCjREkTIYQQ8kZ8huqwGd22dzNQJMQYUdJECCGEvGEmVB2V38lCt5s/k+qBkiZCCCHkjX9j/1WZP//ivIEiIcaIkiZCCCHkjUxZpqFDIEaMkiZCCCHkjUcJjwwdAjFilDQRQgghbwR4BRg6BGLEKGkihBBC3hDzxSrzwf7BBoqEGCNKmgghhJA3xALVpKmWTS0DRUKMESVNhBBCyBsivkhlftfdXQaKhBgjSpoIIYSQN4qenjv66KiBIiHGiJImQggh5A1xUqqhQyBGjJImQggh5I3LfZqpzE9uOdlAkRBjJDB0AIQQQohRSE1Fk7iC2UNDDqGPTx/DxUOMDh1pIoQQQgAgIwO1kgpmHcwdDBcLMUqUNBFCCCEA4OysMrs+7AcDBUKMlcGSpufPn+Pjjz+Gl5cXJBIJatWqheDgYOTm5qrU4/F4atP69esNFDUhhJAqbcQI5dPN93YYMBBijAzWp+nBgweQy+X45ZdfULt2bdy5cwfjxo1DRkYGVq1apVJ306ZN6N69u3JeKpVWdLiEEEKqgyVLgI3bCuZTUgD6zSFvGCxp6t69u0oi5O3tjf/++w/r1q1TS5qsra3h5ORU0SESQgipbtzc0Ps/4IjPm3lLS4OGQ4yLUfVpSklJQY0aNdTKJ0+eDDs7O7Rs2RLr16+HXC43QHSEEEKqg3YCr4KZv/82XCDE6BjNkANPnjzB2rVrsXr1apXyxYsXo3PnzpBIJDh9+jSmT5+O169f46uvvtK6rZycHOTk5Cjn09LS9BY3IYSQqqXpzG+Bff0AAHmdOkKQzwwbEDEa5X6kacGCBRo7bxeerl27prJOTEwMunfvjoEDB2Ls2LEqy7766iv4+fmhSZMmmD59OhYtWoSVK1cWG8OyZcsglUqVk6+vb3k3kxBCSBXV5kXB2Yxw6hlCCuExxso1hX79+jVev35dbB1PT0+YmpoC4BKmgIAAvPvuu9i8eTNMTIrP4/755x+0a9cOcXFxcHR01Fin6JGm6Oho+Pr6IjIyEjVr1ixliwghhFQrJibgBXM/jVvQDyODDxg4IFKSqKgouLm56f13vtxPz9nZ2cHOzk6nutHR0QgICEDz5s2xadOmEhMmAAgPD4epqSmsra211hGLxRCLC266mJpK9xIihBCimxRRwbGEOoMnGTASYmwM1qcpJiYGHTt2hLu7O1atWoVXr14plymulDt8+DDi4uLg5+cHiUSCs2fPYu7cufjkk09UkiJCCCGkvByc0x9g+wEAlv5dgHjq00Q4BkuaQkJC8PjxYzx+/FjtUJrijKFQKMTPP/+MoKAgyOVyeHt7Y9GiRZg0iTJ/Qggh+sH69AYOcUlT3eJ7m5BqxmBJ06hRozBq1Khi6xQdy4kQQgjRt2aJBWcy4prWAfWEJQpGNU4TIYQQYmgNPd9VPp88im7aSwpQ0kQIIYQUwrOzg3sy91yQnVtsXVK9UNJECCGEFBYaii8uck9znOlIEylASRMhhBBS2J9/olks9/Rq7LXi65JqhZImQgghpLAmTWCfyT2Nz4hHYlaiYeMhRoOSJkIIIaQwgQC1C+VJ556fM1goxLhQ0kQIIYQU1qwZcvkFs5Q0EQVKmgghhJDCAgJwqdDgTAN8BxguFmJUKGkihBBCClu3Ds+tC2avx1w3WCjEuFDSRAghhCjExwMTJ2LobYAv54qCQoIMGxMxGpQ0EUIIIQpWVoBEAnE+sG9XQfGjhEeGi4kYDUqaCCGEEAWJBEhPBwB0eFFQHJMWY6CAiDGhpIkQQggpLCcHACDNKSi6/fK2gYIhxoSSJkIIIaQwkQgAYMKALk+4IjOhmQEDIsaCkiZCCCGkMD4fmDwZAPCvM1c09fhUw8VDjAYlTYQQQkhRn34KAKiZys2m5aYZMBhiLChpIoQQQory9wcAjHszRBMNcEkASpoIIYQQdSkpAABJHjebKcs0YDDEWFDSRAghhBSWnw/kcdkSe1PEA89w8RCjQUkTIYQQUtgXXyifsje5kgmPfi4JJU2EEEKIqn37lE/lA7m+TJQ0EYCSJkIIIUTVzJnKp/J9ewFQ0kQ49CkghBBCCvvwQ+VT+ZvTczwe9WkilDQRQgghBTIzARcX5ayiIzgdaSIAJU2EEEJIgb59VWblP3wPgJImwqFPASGEEAIAUVHA6dMF83v24M6ruwBoyAHCoaSJEEIIAYCIiILnL17gV69E/PrvrwCAenb1DBQUMSYGTZo8PT3B4/FUplmzZqnUiYiIQJ8+fWBubg47OztMmTIFubm5BoqYEEJIlZVZaNRvPh/jj4xXzs5sO1PDCqS6ERg6gEWLFmHcuHHKeQsLC+Xz/Px89OrVC/b29rhw4QISEhIQGBgIxhjWrl1riHAJIYRUVYV+f+Dqiu61u+P44+PwtvGGRCgxXFzEaBg8abK0tISTk5PGZSEhIbh37x4iIyPh8uZqhtWrV2PUqFFYsmQJrKysKjJUQgghVdnNmwXP8/PxQd0PcPzxcXhIPQwXEzEqBu/TtGLFCtja2qJJkyZYsmSJyqm3sLAwNGjQQJkwAUC3bt2Qk5OD69evGyJcQgghVVWTJgXPmzVDw8vPAQDnX5zHlegrBgmJGBeDHmn6/PPP0axZM9jY2ODKlSuYPXs2nj17ht9//x0AEBcXB0dHR5V1bGxsIBKJEBcXp3W7OTk5yMnJUc6npaXppwGEEEKqDkmhU3C3bsFv0i28NwI4VSsfO+/sRCvXVoaLjRiFcj/StGDBArXO3UWna9euAQCmTZsGf39/NGrUCGPHjsX69euxYcMGJCQkKLenaRRWxlixo7MuW7YMUqlUOfn6+pZ3MwkhhFQ1jRoBhw+r3LDXL4p7lOVmGSgoYkzK/UjT5MmTMWTIkGLreHp6aixv3bo1AODx48ewtbWFk5MTLl++rFInKSkJMplM7QhUYbNnz0ZQUJByPjo6mhInQgghJevdm5tWrgR27ULIqTe/Z6z41Uj1UO5Jk52dHezs7Mq0bnh4OADA2dkZAODn54clS5YgNjZWWRYSEgKxWIzmzZtr3Y5YLIZYLFbOp6amlikeQggh1ZiDAy7X5J7eiL9h0FCIcTBYn6awsDBcunQJAQEBkEqluHr1KqZNm4a+ffvC3d0dANC1a1f4+vpixIgRWLlyJRITE/HFF19g3LhxdOUcIYQQ/cnLA1asgEc94IU10L9mV0NHRIyAwa6eE4vF2LVrFzp27AhfX1/Mnz8f48aNw59//qmsw+fzcfToUZiamqJt27YYNGgQ+vXrh1WrVhkqbEIIIdXBnTvAiROoncjNOtrRsAPEgEeamjVrhkuXLpVYz93dHUeOHKmAiAghhJA3GjcGAAjk3KxMyDdgMMRYGHycJkIIIcTo8HhA7dqwejN6TVoO9Y0llDQRQgghmrm4wDqbe5qUmVB8XVItUNJECCGEaHL+vPJIU0RatGFjIUaBkiZCCCFEEwsLZL3p+ZuYnWjYWIhRoKSJEEII0WT4cAjfdASvxbc3bCzEKFDSRAghhGhiawv+m6SJyeWGjYUYBUqaCCGEEE3++w8vrLmnTjxLg4ZCjAMlTYQQQogmiYm46sI9bWhZy7CxEKNASRMhhBBSVGwscOYM0kXcrKMtjQhOKGkihBBCOHI5sGUL0LMn4OKCZFMg2ZRbZOvuY9jYiFEw2G1UCCGEEKORkADUrAlkZyuL5DxA/ubQglTqaKDAiDGhI02EEEKqt+RkwM5OJWECAOuTfyufp9JtVAgoaSKEEFLdLV+uXrZqFUzatoONqQ0A4FXmqwoOihgjOj1HCCGk+pLJgMxM1TLGAAAhT0KQlJ0EAEjKSqroyIgRoqSJEEJI9XPuHPD115CfOQ2ZCSATgXvkA7LUaOTJ8/C/B/9TVm/p2tJwsRKjQUkTIYSQKoUxhuBzwVh5cSWEJkLwTfgQmggBADK5DHl5MsiyMyBrC8jba9jAtzVVZjt4dICFyKICIifGjpImQggxIMYYHrx+AB6PBz6PDwDIk+dpnPJZPhhjYGBlfpQz+Vtvo/CjnMnLbVtliU/O5JDJZcjNz0Vu5HPIXsfjSd4r/I0XAIBsZGt+44v59ePz+BDyhRCaCOFs6YwJLSaU924nlRQlTYQQYkC9dvTCX4//MnQY1cr9HwEzGSDMB4TygkfBqDEQ/vI7eDyeoUMkRoqSJkKI0ZEzOcIiw5CUnYRMWSYyZZlIzk5GTl5OmY5OlHQ0pMzrl7D8adJTXIm+otK2Fi4tkC/PVx45uvfqXoW/v+3c24EHHngM4AFvHhn3qJwYt0z+5pG9WS5n4F2+DJ6cwURRF+XzaFLGdURvkh5R/pvn+YWeywGBHMgWcANV9r8PeBfu0+3oyF0916AB0KJFBe0BUllR0kQIMToNfm6A+6/vGzoMvbgWc82gr//HPmDY7QsGjUHJ0hLg8wETE+5R8TwrC0gqxdVqEyYAH30EiEQFk1is+Tmfr7/2kCqPkiZCSNkxBuTlAbm53KXbubnK53FpsdjweDeYXM7VY+zNc27+cXoktsZyp6XcxY5w4lshOzYSt6Ra+qAU46MEV1jIeNxRELkcvHw5eHI5TOSsoEwuL/ScccsyMkt1tKO0y/JNgB/eBV5Yc3GuPQZ4JnNHPgRygK94ZKrzupYVnjfqE0qKRIUxwNQUGD8emDEDcHY2bFyElBIlTYRURbm5wP/+B7x+zSUzAMDjAfn5QGoqkJjIjX7s4qJMdNJy0/Ew/yWy87KRl5eDvJws5OVmI0+Wg7zcHO5RMeXlIE+WizyWj7w3l2nnmRRMmUJgmaarkrSIyIlHBOIBaemb2jwG2P5rdOlXrCBBYW+eeHkB7dsDZmaAuTk3FX4ukXBHWXg8zROgfVlJEwDcuwdERABCIXfERdOjUFj219D0mh06ANIy7FRCjBQlTYToUaYsE6efnoaQL4SIL4LQRAgejwdZvgx58jzu8md5HmT5MsiZXDkp+tnImRxyWS7Y//4H+YW/C8ogB5PLIc97sx4PYHhzryy7GpAnJnJlvDdlbyZlHR6X3HzjDED4ZjI36FtVZnaevsDvQQU/+oUngaBg4vNVHwVvvv6OH+du1CqRcEdBJBLuh97RkUtmFKd1hMKC00fapsLLCycPxqBTJ0NHQEilR0kTIXoiy5ehzto6iEmLefuNCQB01LVyYplfxhYS2MAMAhM+BDwBBCYCCPgCCPhCCPkiCPhCCAQi1UmlTAiBiZBb780kLDKvyyTkc+vweXyY8ExgwjMBj8cDDzzwTfgQ88UQC8QwFZiigUMDgPcWd4Rq0KDs6xJCqhVKmghRYEylX44sKwMvkp8jOzsdspxMyP4OheyntdyIwSZArpU5ZK2aQ5Yvgyw/F7L8XOTKZdy8XAaZPA8xDYpPmJrHcFf6KPqnKPrGmBTqI6Myr+vyzp1hIhSBxxfARCCAiYkAPJ4JTPh8mIjEMBGKwRMKcfv1XZx5dgYAsHvAbgysP1D/7zMhhFRSlDSRqu35cyA+nuu/k5VVMCnm584FUlIAAOc8gYN1gRw+sF7bHRM+LjyTAeB8mUO79yNQ73UpV/rzT6BlS+5UkalpwaToi0IIIURvKGkiVdfGjcDHH5dcD8BFNyBglPblzuk8CPOYctwXxWB4ImtbCK1sIHxzGkrZd+nN6Sw+X4gXuS8LTm8JuMe5jSaj3vjmgJNT+bSVEEKI3hksaTp37hwCAgI0Lrty5QpatuT+1dc0Muu6devw6aef6jU+UgXUqKFeVrcud5mzRMJNIhHw55+okyaCs9ACsTL1/kANHBrgdvDtCgiYEEKIMTNY0tSmTRvExsaqlM2bNw+nTp1CiyKjsm7atAndu3dXzkvpElaii379uFNzjo4FZYMGAQsXKmdz83Ox68seGHlwJKAhYZrSagqC/IIqIFhCCCHGzmBJk0gkglOhUxMymQyHDh3C5MmT1Y4uWVtbq9QlRGcODlz/JVNTbn71amDBAoDHw4yQGVgVtkptFXOhOSzFlpjdbjamvDulYuMlhBBitN7iOt3ydejQIbx+/RqjRo1SWzZ58mTY2dmhZcuWWL9+PeRyebHbysnJQWpqqnJKS0vTU9SkUhCLuWQJADIygO+/x5XoK2oJ07CGw5D7VS7S56QjdnosJUyEEEJUGE1H8A0bNqBbt25wc3NTKV+8eDE6d+4MiUSC06dPY/r06Xj9+jW++uorrdtatmwZFhY6BUMIRo4Epk8HADxePA3vphQssjezx+mRp9HQsaGBgiOEEFIZ8BhjrDw3uGDBghITlqtXr6r0W4qKioKHhwd2796NDz/8sNh1V69ejUWLFiElJUVrnZycHOTk5Cjno6Oj4evri8jISNSsWVPHlpAq581p32XtgDnvcUWfNPsEP/T4AWKB2ICBEUIIeRtRUVFwc3PT++98uR9pmjx5MoYMGVJsHU9PT5X5TZs2wdbWFn379i1x+61bt0Zqairi4+PhWLiDbyFisRhiccGPYGpqasmBk6rPxgZISoJNofvBPkp8RAkTIYQQnZR70mRnZwc7Ozud6zPGsGnTJowcORJCobDE+uHh4TA1NYW1tfVbREmqpW3bgN698Z9tQZHDvRfcSOA0MCQhhJASGLwj+JkzZ/Ds2TN8rGEQwsOHD+O3337DnTt38OTJE/z++++YO3cuPvnkE5UjSYTopFcvYOFCfBxeUPRv1lPu5qpnzxouLkIIIZWCwZOmDRs2oE2bNqhXr57aMqFQiJ9//hl+fn5o1KgRvv/+eyxatAirFVdCEVJa8+ejQUwetqd1BQA8sgWuuIK73QohhBBSjHLvCG6MKqqDGKk8cjt3hLhDKACgcRxwo9fhglHCFZOjI9cPihBCiFGrtB3BCakMRA8e4fp9oPl44KYTEPVRH9TUdL1Ahw7A++8D5uaAVMpNVlbcvL094Opa4bETQggxDEqaSPX0++9o1LuncrbhBODUVsA1DbDKASQygAcA589zkzbTpgGLF3NJFCGEkCqNkiZSPfXoAcHV62h4sBtu818jWQK0GF+wmMcAIUwgZCYQ5eZDmMcgygeEckCYD+VzUdq3uLvkW6S/uS6hAXPA3x8cgnXjdw3TLkIIIXpDSROpvpo1w8UGzzDn9Bxcjr6MiJQIxKXHAQAYD8iFHLk8OTJMdd/kHd5L2BxsDdsdgIWcD/N8bnLKFaF2thkc8yVwYGZwYGZw4VnCJd8cFqFhMMsDeKYS7pYviokxwM4O+PlnoFYtPb0JhBBCdEVJE6nWLEQW+KHHD8p5OZMjIzcDGbIMyPJlkMllyM3PhSz/zaNcBlluNnJD/oLsyUPkHj6IkR8AKUUSqwQzIAH5APLflGQB0DKKvQ/3YJYLmMu4I1kxVlxZn/+A7kNrw1xkDgu5ABZyAczlfFjIhXDLMYVtvggQCAomPl+35yYmQEoKIJcXLOPzgRYtuFOOIlF5vs2EEFIlUNJESCEmPBNYii1hKbYsvuKnHbnHFXIkv3oFMIasrDRkZCQhPSsFGVmpyMhKQXp2Kl5nJSAiIwYvcxK5SZaM+LxkROUl4SUruJl0poibCjvsw01AhsYwmscAzWIBcR4gzi/0KAPE2dx8qhh4aQ5MvArUSirhDdi7F0hKApYvL6EiIYRUP5Q0EfI2TEy4oQkASOAECQDdx8PnjmxlyjK5o1u56cjITEFGRhLWhf+Krf/txlDPPsjNz0V6bjoy8jKRLsvEnbTHyGPcEazrLtykizVtgMOW42GZC1j88Ass3hzZksgASR5gmgeYMABPnpTqLSCEkOqCxmkipBK6FnMNl6MuI0+eh7TcNOTk5SAnP6fg8c3zE09OIDErUeft3voZaPgSXDJobQ34+QHTpwMdO9KtZgghRovGaSKEaNXCpQVauLTQqe7rzNcIPhuMR4mPEJ0WjXuv7mmte6L2m6RJLgcSE4GjR7kJ4Po8CYXcJBCoPzc15QYFNTMrmCIjgRcvgA8+4Dq183hcQmZiUvC86GNJyxR9svj8gikrC/j3X6BrV6BvX0rwCCF6QUeaCKlG5EyOPn/2wbFHx4qt98MxYNQNwDK3YuIqd6amqkmVLlPRREzbxBhw5QqQl8c9l8mA7GzV13d1BY4fBxo0MEz7CalmKup3npImQqqpnLwcXIu5hnab2mmtU9+2HpraNUSTGr5wN3OCGUQwgxBmPCHMmODNJIQZ48NUxiDMyoVJVjaQmclN164B//wDdOrEHY2Sy7mJMdVHXZ4zBuTnc8/z8wue5+QAoaEV+M6VQv36BUfkkpKAiAjAxwe4cIEbWZ4QUi4oaSpHlDQRUrLErETsuL0Dc8/MRWqOpnvK6EZgIoCILyr7ZFL8clOBKRo7NUZLl5YQ8oUFL5yUxJ2mK5pYlXYqad3Ro8vh3QbQti1QuzZ3VMzEhEsKXVyAceMAJ6fyeQ1CqglKmsoRJU2ElA5jDDFpMQiPC0d4bDhuxt/E68zXyJRlqk0ZsgzImdyg8TpbOCN8fDgcLRwr7kVlMi5JUxxVy83lyhRT0fk+fXTf9saNQMuWgLMzUKMG9dEipASUNJUjSpoI0R/GGGRyGbJkWcrBQMt7WntlrU6xdK/dHfdf3Ud6bjoEJgJs/WArutbqqud3QEdZWcAvv3Ad7F1duaNWZ89yY2MVp25d4Pp1rmM9IUQjSprKESVNhFRu225uw8iDI8u07oa+GyAVS7lR1UUWMBeaw1xkDjFfDFOBKcQCMcR8McQCMUx4JuUcuQ5SUoBbt4AOHbTXOX8eaN++4mIipJKhIQcIIeSNEY1HYETjEQCAfHk+MmQZSMtJQ3puOtJyuceU7BT029VPbd2PD32s8+sITYTKJEqRUJkKTOFg7oCaVjVRp0YdNHBogJpWNdHYsTHEAvHbNezhQ6BfP+D+ffVlHTpwR6RatODGyyKEGBwlTYSQSoVvwoeV2ApWYvWrz+Tz5fjr8V+YdmIaHiY8xPs+7yv7XSnuKZiRm4FMWSZy8nOQnac6VAB3b0EZ0pGuUq5tbCsvay9ITaWwEltBKi54dLJwgrWpNfgmfABAc+fmaOXaCryifZP++ktzwjR6NNeXSSLhhjNYu5a7Ao8xIDoa6NWLjjwRYgB0eo4QUm0p+mMpRlLPzstWGV09Oy8bWXlZiEuPQ2RKJG7G38TDhIe4Hnu91K/lV9MP45uPh5nQDGZCM0iEEpjJ+TCbuwCSk+dgJgPMZIBNdsnbUtG9O7ByJY0JRao16tNUjihpIoSUJ1m+DBEpEYjPiMfWm1vxy/Vfym3bDeCIHswbn2U1gts3ZdzurFnc7W/sSnMnREIqL0qayhElTYQQffrfg//h1NNTYGBQfKUyMMiZHPnyfGTlZSFTlql8zJRlIktW6HleFjJyM5D/5kbMCqlfpsAy7DqQllYwYjljwOPHwOeflxzYlSvc0AWEVHGUNJUjSpoIIZVBZEok3L9zVykb12wc3KXucLNyQ32H+mju3Fy1b1R6OtfPadMmYMUK9Y126sQNa2Bjo+foCTEcSprKESVNhJDKgjGGej/Vw38J/2lcXrtGbSzrvAwDfAeoL8zL4zqNBwWpls+aBSxbpodoCTEOFfU7b4BBSQghhGjD4/HwYPID3J90H7/3+R3B/sEY02QM6trVBQA8TnyMgXsGYunfS9VXFgiAsWPVyy0s9Bw1IdUDDTlACCFGqK5dXWWiBAC8harDFcw9MxeRKZGoIamhnGwkNjDv9QGamgF2mW8qWlgAM2dWYOSEVF2UNBFCSCXQ16cvDv13SKVs/fX16hXfDJw+Ohzw8GoCm76DUetZCNq5t4PUVFoBkRJSdVHSRAghlcDBwQdxM/4mYtJikJiVWGhKQNLRffjTNhb5hTpcbGoKADeA8zeUZfsH7ccH9T6o4MgJqTooaSKEkEqAx+OhiVMTNHFqUlCYmAgMGQKcjMU2AHccgNDZw5DZsgkeJT7Cb//+prKNFykvKjRmQqoavXYEX7JkCdq0aQMzMzNYW1trrBMREYE+ffrA3NwcdnZ2mDJlCnJzc1Xq3L59G/7+/pBIJHB1dcWiRYtQDS76I4QQzRgDrl4FbG2BkyeVxQ3mfo9JU//AZ+9+Bk9rT2W5i6ULDgw+gM/f1WFsJ0KIVno90pSbm4uBAwfCz88PGzZsUFuen5+PXr16wd7eHhcuXEBCQgICAwPBGMPatWsBAKmpqejSpQsCAgJw9epVPHz4EKNGjYK5uTmmT5+uz/AJIcR4yOXcPeeOH1dfZm0N3LwJuLsjMSsRtt/YKhd523jj1qe3YC4yr7hYCami9Jo0LVy4EACwefNmjctDQkJw7949REZGwsXFBQCwevVqjBo1CkuWLIGVlRX++OMPZGdnY/PmzRCLxWjQoAEePnyINWvWICgoSP0GmIQQUpXs2sWdggMg5wGvzYHXZoWmwIF43aYJblz+AiE7QpCSk6KyetjHYZQwEVJODNqnKSwsDA0aNFAmTADQrVs35OTk4Pr16wgICEBYWBj8/f0hFotV6syePRvPnz+Hl5eX2nZzcnKQk5OjnE9LS9NvQwghpLQeP+Zuc5KVBWRmco9vnsuy0vGfLA63WRzuPbyIe4OA/+yAxzWAnKLf2tl7gDN7VIp87X0R2DgQgY0D4WDuUHFtIqSKM2jSFBcXB0dHR5UyGxsbiEQixMXFKet4enqq1FGsExcXpzFpWrZsmfIoFyGEGJXISMDdXeOiV2bAp72BI+8AuYpvZ2fVOjzwYCOxgZ2ZXcEksYOtmS1qWtVEv7r94C7VvH1CyNspddK0YMGCEhOSq1evokWLFjptT9PpNcaYSnnROopO4NpOzc2ePRtBhW4jEB0dDV9fX53iIYQQvdq/X2NxogToOxS45MbNW0GMBibOqC9yha91HdTt0B8+Tg3gJnWDwIQufCbEEEr9lzd58mQMeXN+XZuiR4a0cXJywuXLl1XKkpKSIJPJlEeTnJyclEedFF6+fAkAakepFMRiscrpvNTUVJ3iIYSQMmGMO8WWlgakpnI30c3OLphycgqeX7yotvrWxsDU7kCSBLDO5uHYuHNo7dWe+mwSYmRKnTTZ2dnBzs6uXF7cz88PS5YsQWxsLJyduWPQISEhEIvFaN68ubLOnDlzkJubC5FIpKzj4uKic3JGCCFl9uoV8OABEBsLfPQRd1NcAKhfn0uQUlO5ZEkuL/Wm83nA7vrAqH4A4wH1XwJ/oD8ae3co3zYQQsqFXo/xRkREIDExEREREcjPz8eNGzcAALVr14aFhQW6du0KX19fjBgxAitXrkRiYiK++OILjBs3DlZWVgCAYcOGYeHChRg1ahTmzJmDR48eYenSpZg/fz79F0YI0Z+bN4FmzbQnQ3fvqpfxeICVFbKk5kiUipBkwUeiBR9J5iZIlHBHkhIjHyLWHLjfpg5uy6KRLksHAPR5pw8OzDsAvglfj40ihLwNvSZN8+fPx5YtW5TzTZs2BQCcPXsWHTt2BJ/Px9GjRzFx4kS0bdsWEokEw4YNw6pVq5TrSKVSnDx5EpMmTUKLFi1gY2ODoKAglT5LhBBSbqKiADc39XInJ+4UXGoqMGsW0KULYGUFWFoix1yMj85PxcXYK0jMSkROfor6+gqKPtqZ/wEALEQWGOQ7CD/1+okSJkKMHI9Vg6G1o6Ki4ObmhsjISNSsWdPQ4RBCjEViIvD8OfDwIXf6TdM/Y507c+U9eyqLIlIicPThUZx+dhp3Xt7Bfwn/qa3G5/FhI7FBDUkN2Jhyj4rndmZ2qGtXF/Ud6qOeXT1Klgh5SxX1O0+XYBBCqq7kZODJE26KigISErhEKSEB2LOnxNXx0UfA9u0AuKt2/439F9NOTMPfEX+rVZWKpejr0xeLAxbDRmIDS5EldSEgpIqhpIkQUnllZABffgkcOsRdoaaYsrNLt53WrQEvLyA/H7hwAWjUCKxTJ1wa2BrHzsxDeFw4rsZcxcsM7spdHnho49YGver0QguXFqjvUB/OFs6UJBFSxVHSRAipvH7/Hfjpp7fbhlDInZp7/RqwtAT++gto1AgTjnyKX7bMVKkqEUjQ2bszvu32LWrXqP12r0sIqXQoaSKEVC75+UBMDHDmDDe9LZkMePGiYL5jRyAhAa8yX6lV/brT1wjyo4tQCKmuKGkihBgvuRz49Vdg61ZALAYiIrjbkMhk+nvN4GCAx8OklpOw/77q6N18HnXYJqQ6o6SJEGK8jhwBJkxQLxcIgHr1gG7dAFdXwMGBm6RSQCIBTE1VJ7GYW6cUfY4239isfD680XAM9B2IPu/0KYdGEUIqK0qaCCHG6/Vr1fnffuMSJRcXgK+/oz4JmQnYeWcnAOCH7j/gs3c/09trEUIqD0qaCCGGlZMDrFsHHD0K2NlxwwQkJHBTTIxqXSsrzQNPlpPc/FxsDN+ICUcLjm5pGoOJEFI9UdJECKl4jHHJUWwsMHUqcPKk9roSCVC7NjB0KPDhh6V6mfTcdNyIu4HHiY8RmRKJ5OxkpOSkcFN2SsF8NleWnac+VEFLl5alaxshpMqipIkQol85OcD06cCGDYC7O9eJOza2+LGU/vc/wNaWu3WJp2eZTsUN3TdUeYqtLNq4tcHegXvhbOlc5m0QQqoWSpoIIeVPcSQpJgb4+GPg8mWu/OFD1Xo2Nlz/JHt7rv9Ss2bc1XJisdZN58vzEZMWg9eZr5XTq8xXiE+Px7PkZ3iW/AxPk54qB6IEgPe834On1BM2EhtIxVJITaWwNrVWPi/8aCW2otuaEEI0oqSJEFJ2eXnA3bvApUvAjRvAo0fA06dcspSTo3mdlSu502zOztyVbTraFL4JYw6NKVV4fB4f2/tvx5AGQ0q1HiGEaEJJEyGk9F69Ar7+GtiyBUhJ0V7Pxgbw9uaSJH9/oFEjwMJCa3XGGO68vIPI1EjEpsUiJi0GsemxeJr0FCeenFDWE5gIYG9mDzszO+Vkb2YPT2tPeNt4w9vGG142XrA2tS7HRhNCqjtKmgghpRcYyN1uBOCuaGvZkpt8fLhO2zVrcv2RdDiSlC/Px7ILy3D88XH8E/lPifUXdlyIue3n0ik0QkiFo6SJEFI6r14BJ94c9dm/H+jb963GTDr26BjmnZ2nVt6zTk84WzjDxdIFzhbOcJO6oaNnR1iItB+pIoQQfaKkiRCim4wMYO5c7pScXM4dVfrgg7faZFhkGN7f+b5y3lJkiaigKFiJrd42WkIIKXeUNBFCSnb2LNCpU8G8kxPwww9vvVknCycwMOX8g8kPKGEihBgtE0MHQAgxcqdPFyRMHh7A2rXcTXO7dn3rTXvZeGFLvy0AuKNMLpYub71NQgjRF0qaCCHF++MP7rFxY+D2bWDyZO7mt+Wkf73+AIC03DTcf3W/3LZLCCHljU7PEUKK98+bK9pu3gS2beOujqtTh7sHXBmTp3x5PjaEb8CasDWITotWlrtauZZHxIQQoheUNBFS3WVlcYNSvngBpKVx85mZ3JSVpTqK96RJ6uvv31/qDuFXoq9g/JHxKmUNHBrgesx1NHNuBqmptCwtIYQQvaKkiZDqIDsbCA3lEqOUFGD9em7k7vJw/Hipk6aWri3xSbNP8Ou/vyrL7ry8g05bCzqb/9TzJ0xsObF8YiSEkHLAY4yxkqtVblFRUXBzc0NkZCRq1qxp6HAIqThxccA773BHkIpjYcGdcrOxAczMAImEe9T2XCzmki8PD6BHD0AkKlN4jDGcfHoSYw+NRWRqpMqyRo6NcPPTm2XaLiGkeqmo33k60kRIVfXqFXcLk6ws1fLhw7mr3xgDli4FfH0Ba2uAx6vwEKPTotF/V39kyDJUyt2s3HBk6JEKj4cQQopDSRMhVVFsLOCi5fL91asBB4eKjUcLoYkQJjz1i3g/bvoxUnJS4CJ3odulEEKMBg05QEhVdOiQ6vyIEdxpur17jSZhAgBHC0fcnnAbK95boVK+IHQBGq5rCMFiAQ4+OGiY4AghpAi9Jk1LlixBmzZtYGZmBmtra7XlN2/exNChQ+Hm5gaJRIJ69erh+++/V6nz/Plz8Hg8ten48eP6DJ2QyikzE5gzB1i4sKBs/Hhg61bgv/+ADz80XGxaeFh7YGbbmWDBDDFBMfil9y/o4t1FufyDXR+g67auqAbdLwkhRk6vp+dyc3MxcOBA+Pn5YcOGDWrLr1+/Dnt7e2zfvh1ubm64ePEiPvnkE/D5fEyePFml7qlTp1C/fn3lfI0aNfQZOiGVC2PcrU46d1Ytb9MG+Pxzw8RUBs6Wzvik+Sf4pPknuBl3E01+aQIAOPn0JA79dwjv132/+A0QQoge6TVpWvjmv93NmzdrXD5mzBiVeW9vb4SFhWH//v1qSZOtrS2cnJz0EichldrDh0Dr1kBSUkHZrFnAvHnclW6VVGOnxpDPl2PkwZHYfms79t3fR0kTIcSgjK5PU0pKisajSH379oWDgwPatm2LvXv3GiAyQowMY9yglD4+BQnTuHHcCN7LllXqhEmBx+OhtWtrAFC7wo4QQiqaUV09FxYWht27d+Po0aPKMgsLC6xZswZt27aFiYkJDh06hMGDB2PLli0YPny4xu3k5OQgJydHOZ9W0hg1hFQ2ubncWElF/fqrelklk5KdgpvxN/Ei+QUiUiKw8cZGAIBEIDFwZISQ6q7USdOCBQuUp920uXr1Klq0aFGq7d69exfvv/8+5s+fjy5dCjqB2tnZYdq0acr5Fi1aICkpCd98843WpGnZsmUlxkhIpXbxYsHzGjWAMWOAKVMMF89bepH8Ande3sGC0AW4FnNNY53CncMJIcQQSp00TZ48GUOGDCm2jqenZ6m2ee/ePXTq1Anjxo3DV199VWL91q1b4/fff9e6fPbs2QgKClLOR0dHw9fXt1QxEWLUbhYaKTs2tswjcleUhMwERKVGISYtRjlFp0XjRcoLPE58jMeJj9XW6eTVCR5SD3hIPdD7nd5o7tLcAJETQkiBUidNdnZ2sLOzK7cA7t69i06dOiEwMBBLlizRaZ3w8HA4OztrXS4WiyEudOoiNTX1reMkxKj8/DP3uHCh0SZMSVlJeJr0FL129EJ8Rnyxdfk8Pho4NICXjRfau7fHtNbTwDPACOWEEFIcvfZpioiIQGJiIiIiIpCfn48bN24AAGrXrg0LCwvcvXsXAQEB6Nq1K4KCghAXFwcA4PP5sLe3BwBs2bIFQqEQTZs2hYmJCQ4fPowffvgBK1as0PayhFR96encY9euFf7SRx4eQejzUDhaOCIjNwPpuelIyUlBcnYyUnJSkJKdgtj0WESkRKitW8+uHjytPeFq6QoXSxe4Sd1Qy6YWGjg0gL25fYW3hRBCSkOvSdP8+fOxZcsW5XzTpk0BAGfPnkXHjh2xZ88evHr1Cn/88Qf++OMPZT0PDw88f/5cOf/111/jxYsX4PP5eOedd7Bx40at/ZkIqRY8PYGYGODLL4EffgAaN9bryy04twALQ0vfT9DZwhn25va4FX8LOz/cicENBushOkIIqRg8Vg2G2a2oux8TUmHOnQO6dAHy8rj5zp2B//0PMDcv95fKl+dDsFjz/1fjm4+HhcgCUrEU1qbWkJq+eRRL0dipMaxNrcs9HkIIKaqifueNasgBQoiOOnYE/v0XWLoU2LkTOH0acHcHEhLK/aX4JnyYC81VxknaN2gfer/TGyK+cfanIoQQfaCkiZDKqmFDbpiBnTu5+cREQC4HTMp/zNqr465iyvEpOPX0FADgw93cPezedX0XzZybwc7MDvZm9hCYCOBo4Yj3fd4H34Rf7nEQQoghUdJESGV2/77qfOPG3BhOlpbl+jL17Ovh5IiTuBh5Eeuvrce2W9sAAJejL+Ny9GW1+os6LsI8/3nlGgMhhBia0d1GhRBSCqNHA598UjB/5w5gZQVEqF+5Vh7auLXB1g+2InVWKmpaae830N6jvV5enxBCDImSJkIqMx4PKDogbI0a3H3p9CQtJw3eP3gjKjVKbdnGvhvBghk6enbU2+sTQoih0Ok5QiqTK1e4U3KxsUBcHDdFFUle1q0DPDz0FkLX7V3xOvO1cv7zdz/H152+hoXIQm+vSQghxoCSJkIqi59+AiZPLr4Onw/UqaPXMC5FXVKZvxh5EZOPTYajuSN87X0xvNFw6gROCKmSKGkipLKwttZc/sUXQO/egJMTUKsWINDvn/WMNjOw8uJK5fzVmKu4GnNVOT/qf6PgV9MPXjZeyM7LhruVO5Z0XgIzoZle4yKEEH2jwS0JqUxSUoC//wbWrgVCQlTLrawqLozsFDxJeoL49HjEZ8QjLj0Ov//7O54nP0c+y9e4zvpe6zG04VBYiSsuTkJI9VBRv/OUNBFSWdy8yY3LFBYGyGQF5TwecOsW0KCB4WJ7Iz03HY8SHuFR4iM8TnyMuWfmqtWJmBoBN6mbAaIjhFRVFfU7T1fPEVJZ7NwJnD9fkDCZmQGjRgFnzgD16xs0NAULkQWaOjfFoPqDMKf9HKTPTsdfH/2lUsf9O3d02tIJd1/eNVCUhBBSNpQ0EVJZjBkDmJoWzGdmAps3AwEBXHnNmly/Jx4P6N+fu8JOLjdUtAAAc5E5utfujvz5+VjZpaAf1NnnZ/HR/o8MGBkhhJQedQQnpLKoU4fru3TiBLBrF3DtGhAfDyQnA7m5QHR0Qd0DB7jJxASwsQFsbblJKgUsLDRPUinXmbxJE+6xHGXKMnH44WGVsg/rfViur0EIIfpGSRMhlYlIBPTpw00K2dlc8vT6NTcswaZNXLmJCXekKSGh9DfyjY4GXFzKHGZKdgpWXVyFcy/OITo1Gs+Tn4OB6z7Zo3YP/N73d7hYln37hBBiCJQ0EVLZmZpyg1l6eAAbN3ITAOTlcYmUImlKSADS0rjp0SPg+++1bzMvr1Qh5Obn4nnyc1yNvopnyc8w76z6fedcLF0wqeUkzG43Gzwer1TbJ4QQY0BJEyFVlUDAnWYrcqqNMYZXvQPw2h5IEQMpViIkv+OO1Jr2yHSsgcxa7sh8/Cuy7mchU5aJzLxMZMoykSV7My/LRFZeoeeyLCRnJyuPJBUV7B+MSS0nwd7cviJaTQghekNJEyHVyMHbe/DB/kFAK3ATACAXwGNuygfw8M1USqYCU3jbeKO5c3M0dGiIRo6N0MChAVytXMspekIIMSxKmgipDq5fBz77DCM6hgHigmLvTFNIvetBaiqFVCyFucgcZgIzSIQSmAnNYCY0g0RQ6LmGcolQghqSGrA3s6fTboSQKo2SJkKqg3ffBfLzsfsV0HN4QfHV6Q9Qw0F/N/clhJCqhMZpIqSqyssDXrwA9uwB8rlbm/R4DGzpu0lZxcbe3VDREUJIpUNJEyFVzdOnQGAgN/aSpycwaFDBMj8/9PTprZy9+4pG5SaEEF3R6TlCqorERGDmTGDDhoIyoRBwcwNcXQEvL2D2bHxy+BMAAJ/Hp7GSCCGkFChpIsQYMQZkZXGjfScncyOBK8ZY0jS9fMmNAK4YX0kq5UYN79KFG+Tyjd13d+PAgwMAgPW916OGpEbFt40QQiopSpoIMZTnz7mb8N6+zY3onZJSkCQlJ5d6gEkAgLc3MG8ed3quyJVst+JvYfDewQCA0U1GY2yzsW/bAkIIqVYoaSLEECIiuNNlJeHzuZvwSqWApSU3WVgUPFfMW1lx94wLCOBOyRVxNfoqOm7pCACwEFlgZtuZ5dkaQgipFihpIqSiyOVATg433bpVfN1du4CePQFzc7UjRmWxOmw1MmWZAID9g/ajrl3dt94mIYRUN3T1HCFva88eoFEj7qhQ8+ZAgwZAnTpcB2x7e+4okEjELTczA2xsVG+4q8m773JHkMppsMgJLSbAXGgOAOi6vStWXVxVLtslhJDqRK9J05IlS9CmTRuYmZnB2tpaYx0ej6c2rV+/XqXO7du34e/vD4lEAldXVyxatAiMab7PFSF6d/488OOPwJdfAkOHcpf0377NHUn691/g7l3g8WMgKoq7YW5aGiCTqW6Dx+NutGttDTg6cjfbBbjkKi6uYL6c+Hv6Y+eAncr5GSdnlOv2CSGkOtDr6bnc3FwMHDgQfn5+2FD4MugiNm3ahO7duyvnpVKp8nlqaiq6dOmCgIAAXL16FQ8fPsSoUaNgbm6O6dOn6zN8QtTZ2nKX9mvj6cld8i8Wc0mR4rHwc7GY63dUQbccSclOwdQTU7Ht5jZlGV01RwghpafXpGnhwoUAgM2bNxdbz9raGk5F7sSu8McffyA7OxubN2+GWCxGgwYN8PDhQ6xZswZBQUF0rytSsYpLmADuKJSbW8XEooPHiY9RZ20dlbJlnZdhWutpBoqIEEIqL6Po0zR58mTY2dmhZcuWWL9+PeRyuXJZWFgY/P39IRYX3GW0W7duiImJwfPnzw0QLalWZDIuEVq8GOjXj+tnVJwdOyokrJIwxrD77m6VhOnDeh8if34+ZrWbBbFAXMzahBBCNDH41XOLFy9G586dIZFIcPr0aUyfPh2vX7/GV199BQCIi4uDp6enyjqOjo7KZV4aLtvOyclBTk6Ocj4tLU1/DSBVQ0YGsHAh1x9JJOL6Ib16BTx7xj0vytIScHHhJjs7biDKgABgzJiKj72IM8/OoPPWzsp5G1MbhIwIQQuXFgaMihBCKr9SJ00LFixQnnbT5urVq2jRQrcvaEVyBABNmjQBACxatEilvOgpOEUncG2n5pYtW1ZijKSayMkBYmK402qpqVwClJpaMCnmf/xR+zbs7ID33gNateKujmvUiOvAbYQO/3cYfXf2Vc43dGiIvz76C65WrgaMihBCqoZSJ02TJ0/GkCFDiq1T9MhQabRu3RqpqamIj4+Ho6MjnJycEBcXp1Ln5cuXAAqOOBU1e/ZsBAUFKeejo6Ph6+tb5phIJcAY8NtvwPjx3OX+EgkQHQ0kJJR9m05OwMmTgK+vyq1IjNmaS2sAAJ29OmNa62noWacn9fsjhJByUuqkyc7ODnZ2dvqIBQAQHh4OU1NT5RAFfn5+mDNnDnJzcyESiQAAISEhcHFx0ZqcicVilT5QqampeouXGEhuLpfQ3LwJ3L8PbN9esOzRI9W6YjF3tMjKSn2ytFSfZ4xb5/33NY6ubaxSslNw7vk5AMAvvX9BrRq1DBsQIYRUMXrt0xQREYHExEREREQgPz8fN27cAADUrl0bFhYWOHz4MOLi4uDn5weJRIKzZ89i7ty5+OSTT5RJz7Bhw7Bw4UKMGjUKc+bMwaNHj7B06VLMnz+f/oOuzsaPB4q7KvP4ca6/kasrN5hkNfisXIm+onxuwqscR8YIIaQy0WvSNH/+fGzZskU537RpUwDA2bNn0bFjRwiFQvz8888ICgqCXC6Ht7c3Fi1ahEmTJinXkUqlOHnyJCZNmoQWLVrAxsYGQUFBKqffSDX0998Fzxcv5gaITE4GnJ2BDz7gbj9STciZHPdf3cfqsNUAgC7eXeBlo8N97QghhJQKj1WDobWjoqLg5uaGyMhI1KxZ09DhkLcVHg40a8Y9b9sWuHDBsPEY0PWY62jxm+pFF0eHHUXPOj0NFBEhhFS8ivqdp2P4pPLp0aPg+fDhhovDgLJkWZh/dr5awrSl3xZKmAghRE8MPk4TIWqysoBz57hbjmRlAdnZ3KPieW5uQd0JE7jp77+Bdu0MFnJFuh5zHR02d0CmLFOl/OanN9HIsZGBoiKEkKqPkiZiGIwBt25x4yjJ5UBeHrB+PfDHH2Xb3ldfcYlWFReXHqdydOn77t+jqVNTNHJsBKmptJg1CSGEvC1Kmoh+HTsGDB7MXbpfuzZ3W5K8PODOndJt5733uLGXFDe/VTx/+RLIzAS++UY/8RuR5OxkuH/rrpz/uefPmNByggEjIoSQ6oWSJqJfvXoVPL96VXs9qRRISdG8bNUqYPr08o2rEsmUZWLp30uxMXwjZHIZAGCg70BKmAghpIJR0kT0SyDgjiwp1KsHrF3LHXkyMQHi4rgr4JydDRejkZtwdAK23twKAKhlUwvL31uO/vX6GzgqQgipfihpIvrz4IFqwgRwo3e/917B/Jw5gI73KayOjj06hq03t4IHHn7t8yuGNxoOU4GpocMihJBqiYYcIPpjZwe0aVN8naVLucEoiYrUnFSM/t9o9NrBnd4c12wcxjYbSwkTIYQYECVNRH/s7IB//uGulGOMO+qUnMxdNRcUBNSowdW7cQNITzdkpEaDMYZrMddQ/+f62Hxjs7J8+XvLDRcUIYQQAJQ0kYrE53Mdvhs2BFavBn75pWCZpSU39EA1FpMWg1a/t0LL31oiKjUK1qbW+KP/H8ifnw8biY2hwyOEkGqP+jQRw8jM5MZoUvD05OYlEoOFZAiyfBmuxVzD/vv7sSpsFQBAYCLAAN8BWNllJWpa0W1/CCHEWFDSRCre4sXA/PkF840bA6dPV6uEKTw2HNNOTEPoi1CVchFfhFuf3oKPnY+BIiOEEKINJU2k4oSEAFOmAP/9V1C2YAEwc2a1SZjy5HnYe28vhu4bqiyzldjC39MfHT06YmjDobAzszNghIQQQrShpInoV1YWsGcPMHUqkJSkumzgQCA42CBh6QtjDEnZSYhJi0FCZgJeZ75GQlaC8vmaS2vU1nk54yVMeNS9kBBCjB0lTaT8hYcDw4cD9+5pXt6jB/D990CdOhUbVzkKjw3Hk6QniEiJwPPk5ypTWm6aTtvY+eFODPAdQAkTIYRUEpQ0kfIXFKSeMAUFccmSnx9gbm6YuMrJr9d/xfgj44utYyuxhZ2ZHWzN3jxKbGErsYW9uT28bbzRs05PmAnNKihiQggh5YGSJlL+pk0Dzp1TLevbF/D3N0g4usiUZSImLYY7nZaZgISsBCRmJSIhk3tMzklGSnYKUnJScCHigsq6TZ2aYnzz8fC09oSntSfcpe6QCKtHHy1CCKlOKGki5a9PH2D2bGDZsoKyjh2BkSO5oQXc3bmpQYMKvedcRm4GwuPCcSPuBm7F30JUahSiUqMQnRaNxKzEMm1zTJMx+K3vb3SKjRBCqgFKmkj54/G426O4uQETJxaUb92qXrdzZ+7ec25uQP36QLt23E1+y1FydjJ+uPwDvr30LZKzk7XWMxOawcHcAbYSW9SQ1ICtma3yubWpNaxNrSEVSyE1lcLG1AZ1bOvASmxVrrESQggxXpQ0Ef2ZMIGbXr8GQkO5G/hGRgIREcCzZ9zQA6dPc5NCmzbAmTOAWFwuIXx36TssOLcAKTkpAABnC2c0cWqCFi4t4GntCVdLV9S0qglXK1dIxVLweLxyeV1CCCFVDyVNRP/s7IAPP1Qvf/YM2L8f+OKLgrKLF7kxm2rWBGrVAnx9gVatgPbtAW/vUr3svnv7MO3ENABAffv6mNdhHgb4DgDfhP82rSGEEFJNUdJEDMfLC5g+HXjnHWDFCu7mvmIxdzuVyEhuOncO+Pln7pTfzp3AoEEAgJy8HDxNeor4jHi8zHiJ+PQ3jxnxiEuPQ3hcOKJSowAAwxsNx5Z+W6jfESGEkLdCSRMxvD59uAkAGANevQKePAEePwZu3ADWrOHKBw8GBg3C+RfnMWTvEMSmxxa7WT6Pjx51euCnnj9RwkQIIeStUdJEjAuPBzg4cJOfH9CpE5c0vSH7bT16xgchIz8LEoEE7lJ3OFo4wsHcAQ5mDsrnde3qoqVLS5iLKveYUIQQQowHJU3EuDk7A126ACdPAgB4n05A5jwAPKC5fWNsGfgHvG1K19eJEEIIKQs6Z0GMm4kJcPw4cOQI8NlnEAR0xuy/Ab4cuBB7CY3XN8bjxMeGjpIQQkg1QEkTMX4mJkCvXsAPPwAHDmDJGeD4dm5Rem466qytg38i/jFsjIQQQqo8vSZNS5YsQZs2bWBmZgZra2u15Zs3bwaPx9M4vXz5EgDw/PlzjcuPHz+uz9CJsTp4EADQJE61uOv2rsjOy674eAghhFQbeu3TlJubi4EDB8LPzw8bNmxQWz548GB0795dpWzUqFHIzs6Gg4ODSvmpU6dQv3595XyNGjX0EzQxbl27Ap07w+70adhnAK/e9PO2TsqGaVQcd5sWQgghRA/0mjQtXLgQAHdESROJRAKJpODGpq9evcKZM2c0Jli2trZwcnLSS5ykEnF0BE6dAl68wJ/BI/Ce198AgCYxcuDaNUqaCCGE6I1R9WnaunUrzMzMMGDAALVlffv2hYODA9q2bYu9e/caIDpiVDw80HlfOI7+wc3+4yNB3gfvGzYmQgghVZpRJU0bN27EsGHDVI4+WVhYYM2aNdi7dy+OHTuGzp07Y/Dgwdi+fbvW7eTk5CA1NVU5paWlVUT4pCLl5QHp6ej2GKiRBaSwLFyOvmLoqAghhFRhpU6aFixYoLXztmK6du1aqQMJCwvDvXv38PHHH6uU29nZYdq0aWjVqhVatGiBRYsWYeLEifjmm2+0bmvZsmWQSqXKydfXt9TxECMnEACffgo+AzrFmgIAvjj5BRKzEg0cGCGEkKqKxxhjpVnh9evXeP36dbF1PD09YWpqqpzfvHkzpk6diuTkZK3rfPzxx/j3338RHh5eYgx//PEHxo4di6ysLI3Lc3JykJOTo5yPjo6Gr68vIiMjUbNmzRK3TyqJ2FjAxQVXXIGOYwXI4uXhw3ofYu8gOn1LCCHVSVRUFNzc3PT+O1/qjuB2dnaws7Mr1yDS09Oxe/duLFu2TKf64eHhcHZ21rpcLBZDLBYr51NTU986RmJEXrzgbq2ycSMAoFU0cIANQnfeDuy7vw/77u3Dh74fGjhIQgghVY1er56LiIhAYmIiIiIikJ+fjxs3bgAAateuDQsLC2W9Xbt2IS8vDx999JHaNrZs2QKhUIimTZvCxMQEhw8fxg8//IAVK1boM3RirJ48AZo0AdLTufl69YBPPkG3iRMx5bQdfrjyAyYcnYCedXpCIpQUuylCCCGkNPSaNM2fPx9btmxRzjdt2hQAcPbsWXTs2FFZvmHDBvTv3x82NjYat/P111/jxYsX4PP5eOedd7Bx40YMHz5cn6ETY3X4MJcwOTsDmzdz96Xj8QAA33T5Brvu7kJ8Rjz+ifwH73m/Z9hYCSGEVCl6TZo2b96sdYymwi5evKh1WWBgIAIDA8sxKlJpZWcD169zz/39uYEuCxELxKghqYH4jHjwwDNAgIQQQqoyoxpygBA1GRnAuXNAx46ARAIohppo2VJj9bh07v4qThY0ECohhJDypdcjTYSUSUwMcPcucPEi8P33QFKS6vK5c4EpUzSuKmdyAECePE/fURJCCKlmKGkixuW334BPPlEtq1ED6NABaNoUmDQJsLXVurqXjRduxN1AREoEGjs11nOwhBBCqhNKmojxuHlTNWHy9QXGjwcmTuQGs9SBuZC7g29ufq4+IiSEEFKNUdJEjMfJk9yjuTnw4AFQhgHKsvOyAQCmAtMSahJCCCGlQx3BifFQJE0DB5YpYQIKOoLbSDQPX0EIIYSUFR1pIobHGLBsGRASws2/+24ZNsEw+dhkRKdFAwA8pB7lGSEhhBBCSRMxsDNnuD5L//3HzQsEwLhxpd7Mk6Qn+PnazwCA0U1Gw8XSpTyjJIQQQuj0HDGQtDRg+HCgc2cuYTIxAfr3Bx4+BPj8Um1q/bX1qLO2TsF87/Xg8WhwS0IIIeWLjjSRipWfzw1QOWpUQdk77wBXrgBSaZk2OeHoBOXzH3v8CBFf9JZBEkIIIeroSBOpOBkZwIQJqglTjRrArVtlSpgYY/j8r8+V8yveW4FJrSaVQ6CEEEKIOjrSRCpGejpgaVkwP2QIsHgxUKuW8oa7pXUp6hJ+uPIDAGBQ/UGY8q7mUcIJIYSQ8kBJE6kY77yjOj93LlC79ltt8tB/hwBw95nbNWDXW22LEEIIKQmdniP6xxh3VKmw4cPLvLnUnFR8f+l7LP9nOQAgIzfjbaIjhBBCdEJHmkj5e/4cWLWKG9U7JgaIjOROzxU2YkSpN3vv1T0EnQjC6WenVW7Ie27UubeLlxBCCNEBJU2k/AUGAufPa17m4wMcPAjUrVvqza4JW4MTT04AAOra1cXUd6dieKPhMBeZv0WwhBBCiG4oaSLlz82t4HmrVsC2bVyZRFLmTTLGsCF8g3L+3sR7NBYTIYSQCkVJEylf778PHOI6aEMoBNasUe8EXgYLQxcqn//10V+UMBFCCKlw1BGclI8nT4DevQsSpho1gGvXgLZty2XzCZkJAAARX4TutbuXyzYJIYSQ0qAjTaTsPvsM+PFH9fKuXYG9e1XHZXpLK7qsQF27upjYcmK5bZMQQggpDTrSRMquaMJkbQ0cOwacOFGuCRMAmAnNMKnVJDotRwghxGAoaSJld/Uq9/jpp8CePUBcHNCjh2FjIoQQQvSETs+RsmvRghu4khBCCKkG6EgTIYQQQogOKGkihBBCCNEBJU2EEEIIITqgpIkQQgghRAd6S5qeP3+Ojz/+GF5eXpBIJKhVqxaCg4ORm5urUi8iIgJ9+vSBubk57OzsMGXKFLU6t2/fhr+/PyQSCVxdXbFo0SIw6oBMCCGEkAqkt6vnHjx4ALlcjl9++QW1a9fGnTt3MG7cOGRkZGDVqlUAgPz8fPTq1Qv29va4cOECEhISEBgYCMYY1q5dCwBITU1Fly5dEBAQgKtXr+Lhw4cYNWoUzM3NMX36dH2FTwghhBCigscq8JDNypUrsW7dOjx9+hQA8Ndff6F3796IjIyEi4sLAGDnzp0YNWoUXr58CSsrK6xbtw6zZ89GfHw8xGIxAGD58uVYu3YtoqKidBrsMCoqCm5uboiMjETNmjX110BCCCGEVLiK+p2v0HGaUlJSUKNGDeV8WFgYGjRooEyYAKBbt27IycnB9evXERAQgLCwMPj7+ysTJkWd2bNn4/nz5/Dy8lJ7nZycHOTk5Ki8LgDExsbqo1mEEEIIMSDF77tcLtfr61RY0vTkyROsXbsWq1evVpbFxcXB0dFRpZ6NjQ1EIhHi4uKUdTw9PVXqKNaJi4vTmDQtW7YMCxcuVCtv1arV2zaDEEIIIUYqMjIS7u7uett+qZOmBQsWaExICrt69SpatGihnI+JiUH37t0xcOBAjB07VqWuptNrjDGV8qJ1FGcUtZ2amz17NoKCgpTziYmJ8PLywp07dyCVSouNvapJS0uDr68v7t27B8tyvh9cZVCd209tr55tB6p3+6nt1bPtKSkpaNCgAerVq6fX1yl10jR58mQMGTKk2DqFjwzFxMQgICAAfn5++PXXX1XqOTk54fLlyyplSUlJkMlkyqNJTk5OyqNOCi9fvgQAtaNUCmKxWOV0noKbmxusrKyKjb2qSU1NBQC4urpWu7YD1bv91Pbq2Xageref2l49265or0Cg3xNopd66nZ0d7OzsdKobHR2NgIAANG/eHJs2bYKJieoIB35+fliyZAliY2Ph7OwMAAgJCYFYLEbz5s2VdebMmYPc3FyIRCJlHRcXF7XTdoQQQggh+qK3cZpiYmLQsWNHuLm5YdWqVXj16hXi4uJUjhp17doVvr6+GDFiBMLDw3H69Gl88cUXGDdunDJrHDZsGMRiMUaNGoU7d+7gwIEDWLp0KYKCgnS6co4QQgghpDzo7ThWSEgIHj9+jMePH6td/qfok8Tn83H06FFMnDgRbdu2hUQiwbBhw5TjOAGAVCrFyZMnMWnSJLRo0QI2NjYICgpS6bNUErFYjODgYI2n7Kq66tx2oHq3n9pePdsOVO/2U9up7fpUoeM0EUIIIYRUVnTvOUIIIYQQHVDSRAghhBCiA0qaCCGEEEJ0QEkTIYQQQogOKn3S5OnpCR6PpzZNmjRJ6zqhoaFo3rw5TE1N4e3tjfXr11dgxOWntG0/d+6cxvoPHjyo4MjLR15eHr766it4eXlBIpHA29sbixYtKvHeQ1Vh/5el7VVp/6elpWHq1Knw8PCARCJBmzZtcPXq1WLXqQr7HSh92yvzfj9//jz69OkDFxcX8Hg8HDx4UGU5YwwLFiyAi4sLJBIJOnbsiLt375a43X379sHX1xdisRi+vr44cOCAnlpQdvpo++bNmzV+FrKzs/XYkrIpqf379+9Ht27dYGdnBx6Phxs3bui03bfe96ySe/nyJYuNjVVOJ0+eZADY2bNnNdZ/+vQpMzMzY59//jm7d+8e++2335hQKGR79+6t2MDLQWnbfvbsWQaA/ffffyrr5eXlVWzg5eTrr79mtra27MiRI+zZs2dsz549zMLCgn333Xda16kq+78sba9K+3/QoEHM19eXhYaGskePHrHg4GBmZWXFoqKiNNavKvudsdK3vTLv92PHjrG5c+eyffv2MQDswIEDKsuXL1/OLC0t2b59+9jt27fZ4MGDmbOzM0tNTdW6zYsXLzI+n8+WLl3K7t+/z5YuXcoEAgG7dOmSnltTOvpo+6ZNm5iVlZXK5yA2NlbPLSmbktq/detWtnDhQvbbb78xACw8PLzEbZbHvq/0SVNRn3/+OatVqxaTy+Ual8+cOZPVrVtXpWz8+PGsdevWFRGeXpXUdsWXZ1JSUsUGpie9evViY8aMUSnr378/Gz58uNZ1qsr+L0vbq8r+z8zMZHw+nx05ckSlvHHjxmzu3Lka16kq+70sba8q+73oD6dcLmdOTk5s+fLlyrLs7GwmlUrZ+vXrtW5n0KBBrHv37ipl3bp1Y0OGDCn3mMtLebV906ZNTCqV6jFS/dCUNCk8e/ZM56SpPPZ9pT89V1hubi62b9+OMWPGaB0tPCwsDF27dlUp69atG65duwaZTFYRYeqFLm1XaNq0KZydndG5c2ecPXu2giIsf+3atcPp06fx8OFDAMDNmzdx4cIF9OzZU+s6VWX/l6XtCpV9/+fl5SE/Px+mpqYq5RKJBBcuXNC4TlXZ72Vpu0Jl3+9FPXv2DHFxcSr7VSwWw9/fHxcvXtS6nrbPQnHrGJuyth0A0tPT4eHhgZo1a6J3794IDw/Xd7hGozz2fZVKmg4ePIjk5GSMGjVKa524uDi1G/06OjoiLy8Pr1+/1nOE+qNL252dnfHrr79i37592L9/P3x8fNC5c2ecP3++4gItR19++SWGDh2KunXrQigUomnTppg6dSqGDh2qdZ2qsv/L0vaqsv8tLS3h5+eHxYsXIyYmBvn5+di+fTsuX76M2NhYjetUlf1elrZXlf1elOKWXJr2a9GbvBddr7TrGJuytr1u3brYvHkzDh06hD///BOmpqZo27YtHj16pNd4jUV57Hv93g64gm3YsAE9evSAi4tLsfWKHolhbwZFr8z3stOl7T4+PvDx8VHO+/n5ITIyEqtWrUKHDh0qIsxytWvXLmzfvh07duxA/fr1cePGDUydOhUuLi4IDAzUul5V2P9laXtV2v/btm3DmDFj4OrqCj6fj2bNmmHYsGH4999/ta5TFfY7UPq2V6X9romm/VrSPi3LOsaotO1o3bo1WrdurZxv27YtmjVrhrVr1+KHH37QW5zG5G33fZU50vTixQucOnUKY8eOLbaek5OTWlb58uVLCAQC2Nra6jNEvdG17Zq0bt260v6XMWPGDMyaNQtDhgxBw4YNMWLECEybNg3Lli3Tuk5V2f9labsmlXX/16pVC6GhoUhPT0dkZCSuXLkCmUwGLy8vjfWryn4HSt92TSrrfi/MyckJADTu16JHE4quV9p1jE1Z216UiYkJWrZsWek/C7oqj31fZZKmTZs2wcHBAb169Sq2np+fH06ePKlSFhISghYtWkAoFOozRL3Rte2ahIeHw9nZWQ9R6V9mZiZMTFQ/wnw+v9jL7qvK/i9L2zWpzPsfAMzNzeHs7IykpCScOHEC77//vsZ6VWW/F6Zr2zWp7PsdALy8vODk5KSyX3NzcxEaGoo2bdpoXU/bZ6G4dYxNWdteFGMMN27cqPSfBV2Vy77Xucu4EcvPz2fu7u7syy+/VFs2a9YsNmLECOW84tLjadOmsXv37rENGzZU2kuPGStd27/99lt24MAB9vDhQ3bnzh02a9YsBoDt27evIkMuN4GBgczV1VV52f3+/fuZnZ0dmzlzprJOVd3/ZWl7Vdr/x48fZ3/99Rd7+vQpCwkJYY0bN2atWrViubm5jLGqu98ZK33bK/N+T0tLY+Hh4Sw8PJwBYGvWrGHh4eHsxYsXjDHusnupVMr279/Pbt++zYYOHap22f2IESPYrFmzlPP//PMP4/P5bPny5ez+/fts+fLlRjnkgD7avmDBAnb8+HH25MkTFh4ezkaPHs0EAgG7fPlyhbevJCW1PyEhgYWHh7OjR48yAGznzp0sPDxcZQgFfez7KpE0nThxQjkOSVGBgYHM399fpezcuXOsadOmTCQSMU9PT7Zu3boKirT8labtK1asYLVq1WKmpqbMxsaGtWvXjh09erQCoy1fqamp7PPPP2fu7u7M1NSUeXt7s7lz57KcnBxlnaq6/8vS9qq0/3ft2sW8vb2ZSCRiTk5ObNKkSSw5OVm5vKrud8ZK3/bKvN8VwyUUnQIDAxlj3KX3wcHBzMnJiYnFYtahQwd2+/ZtlW34+/sr6yvs2bOH+fj4MKFQyOrWrWuUCaQ+2j516lTm7u7ORCIRs7e3Z127dmUXL16swFbprqT2b9q0SePy4OBg5Tb0se95jL3pDUkIIYQQQrSqMn2aCCGEEEL0iZImQgghhBAdUNJECCGEEKIDSpoIIYQQQnRASRMhhBBCiA4oaSKEEEII0QElTYQQQgghOqCkiRBCCCFEB5Q0EUIIIYTogJImQgghhBAdUNJECCGEEKKD/2+00TQKRsEoGAWjYBSMglFABAAAufGFB5Gc11UAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ds_temp = xr.open_dataset('./data/rca/sensors/osb/temp_jan_2022.nc')\n",
     "\n",
     "# temperature: ascent versus descent\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0], \n",
-    "                          ds_temp.temp, -ds_temp.depth,\n",
-    "                          'T-Ascent',   colors['temperature'],    'ascent',\n",
-    "                          ds_temp.temp, -ds_temp.depth,\n",
-    "                          'T-Descent',  'green',                  'descent', 6, 4)"
+    "fig,axs = ChartTwoSensors(profiles, [ranges['temp'], ranges['temp']], [0], \n",
+    "                          ds_temp.temp, -ds_temp.depth, 'T-Ascent',   colors['temp'],    'ascent',\n",
+    "                          ds_temp.temp, -ds_temp.depth, 'T-Descent',  'green',           'descent', 6, 4)"
    ]
   },
   {
@@ -362,25 +344,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "cfe26ea4-75e6-4778-b077-71b1ae8ca111",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "instrument_key = 'phsen'\n",
-    "for s in osb_profiler_streams: \n",
-    "    if instrument_key in s: \n",
-    "        print('Found this instrument stream:', s)\n",
-    "        instrument_stream = s\n",
-    "        break\n",
-    "        \n",
-    "ds = loadData(instrument_stream)                      # lazy load\n",
-    "t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
-    "ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
-    "print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
-    "ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
+    "if False:\n",
+    "    instrument_key = 'phsen'\n",
+    "    for s in osb_profiler_streams: \n",
+    "        if instrument_key in s: \n",
+    "            print('Found this instrument stream:', s)\n",
+    "            instrument_stream = s\n",
+    "            break\n",
+    "\n",
+    "    ds = loadData(instrument_stream)                      # lazy load\n",
+    "    t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
+    "    ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
+    "    print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
+    "    ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
    ]
   },
   {
@@ -397,29 +380,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "d023983f-ac29-44bb-b4ed-78bf758c25a4",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
-    "ds_ph, reply = ShallowProfilerDataReduce(ds, t0, t1, ['ph_seawater', 'int_ctd_pressure'], ['ph', 'depth'])\n",
-    "print(reply)\n",
-    "print(ds_ph)\n",
+    "if False:\n",
+    "    t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
+    "    ds_ph, reply = ShallowProfilerDataReduce(ds, t0, t1, ['ph_seawater', 'int_ctd_pressure'], ['ph', 'depth'])\n",
+    "    print(reply)\n",
+    "    print(ds_ph)\n",
     "\n",
-    "ds_ph.to_netcdf('./data/rca/sensors/osb/ph_jan_2022.nc') "
+    "    ds_ph.to_netcdf('./data/rca/sensors/osb/ph_jan_2022.nc') "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "25bb73e1-578b-4752-9816-c3d99cda4854",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting 4 charts\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x1600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ds_ph = xr.open_dataset('./data/rca/sensors/osb/ph_jan_2022.nc')\n",
     "\n",
@@ -437,25 +440,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "c887bf8c-facf-4400-8aa7-cafdd2947452",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "instrument_key = 'flort'\n",
-    "for s in osb_profiler_streams: \n",
-    "    if instrument_key in s: \n",
-    "        print('Found this instrument stream:', s)\n",
-    "        instrument_stream = s\n",
-    "        break\n",
-    "        \n",
-    "ds = loadData(instrument_stream)                      # lazy load\n",
-    "t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
-    "ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
-    "print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
-    "ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
+    "if False:\n",
+    "    instrument_key = 'flort'\n",
+    "    for s in osb_profiler_streams: \n",
+    "        if instrument_key in s: \n",
+    "            print('Found this instrument stream:', s)\n",
+    "            instrument_stream = s\n",
+    "            break\n",
+    "\n",
+    "    ds = loadData(instrument_stream)                      # lazy load\n",
+    "    t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
+    "    ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
+    "    print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
+    "    ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
    ]
   },
   {
@@ -477,36 +481,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0512238e-e884-4f06-9a62-dcfde0769a17",
+   "execution_count": 11,
+   "id": "9fcda352-1f28-4e2a-9bf2-d52597ac0ecd",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
-    "ds_fdom, reply        = ShallowProfilerDataReduce(ds, t0, t1, ['fluorometric_cdom', 'int_ctd_pressure'], ['fdom', 'depth'])\n",
-    "ds_chlora, reply      = ShallowProfilerDataReduce(ds, t0, t1, ['fluorometric_chlorophyll_a', 'int_ctd_pressure'], ['chlora', 'depth'])\n",
-    "ds_backscatter, reply = ShallowProfilerDataReduce(ds, t0, t1, ['optical_backscatter', 'int_ctd_pressure'], ['backscatter', 'depth'])\n",
-    "\n",
-    "ds_fdom.to_netcdf('./data/rca/sensors/osb/fdom_jan_2022.nc')\n",
-    "ds_chlora.to_netcdf('./data/rca/sensors/osb/chlora_jan_2022.nc')\n",
-    "ds_backscatter.to_netcdf('./data/rca/sensors/osb/backscatter_jan_2022.nc')\n",
+    "if False:\n",
+    "    t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
+    "    ds_fdom, reply        = ShallowProfilerDataReduce(ds, t0, t1, ['fluorometric_cdom', 'int_ctd_pressure'], ['fdom', 'depth'])\n",
+    "    ds_chlora, reply      = ShallowProfilerDataReduce(ds, t0, t1, ['fluorometric_chlorophyll_a', 'int_ctd_pressure'], ['chlora', 'depth'])\n",
+    "    ds_backscatter, reply = ShallowProfilerDataReduce(ds, t0, t1, ['optical_backscatter', 'int_ctd_pressure'], ['backscatter', 'depth'])\n",
     "\n",
-    "ds_fdom        = xr.open_dataset('./data/rca/sensors/osb/fdom_jan_2022.nc')\n",
-    "ds_chlora      = xr.open_dataset('./data/rca/sensors/osb/chlora_jan_2022.nc')\n",
-    "ds_backscatter = xr.open_dataset('./data/rca/sensors/osb/backscatter_jan_2022.nc')"
+    "    ds_fdom.to_netcdf('./data/rca/sensors/osb/fdom_jan_2022.nc')\n",
+    "    ds_chlora.to_netcdf('./data/rca/sensors/osb/chlora_jan_2022.nc')\n",
+    "    ds_backscatter.to_netcdf('./data/rca/sensors/osb/backscatter_jan_2022.nc')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8eff1269-6b7d-494d-980a-bb7f7e4c0f9f",
+   "execution_count": 12,
+   "id": "eb92f3ef-c4d5-424d-8fd5-8d81ab996266",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting 1 charts\n",
+      "\n",
+      "Attempting 1 charts\n",
+      "\n",
+      "Attempting 1 charts\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "ds_fdom        = xr.open_dataset('./data/rca/sensors/osb/fdom_jan_2022.nc')\n",
+    "ds_chlora      = xr.open_dataset('./data/rca/sensors/osb/chlora_jan_2022.nc')\n",
+    "ds_backscatter = xr.open_dataset('./data/rca/sensors/osb/backscatter_jan_2022.nc')\n",
+    "\n",
     "fig, axes = ChartSensor(profiles, ranges['fdom'],        [0], ds_fdom.fdom,               -ds_fdom.depth,        'profile fdom', 'black', 'ascent', 6, 4)\n",
     "fig, axes = ChartSensor(profiles, ranges['chlora'],      [0], ds_chlora.chlora,           -ds_chlora.depth,      'profile chlora', 'black', 'ascent', 6, 4)\n",
     "fig, axes = ChartSensor(profiles, ranges['backscatter'], [0], ds_backscatter.backscatter, -ds_backscatter.depth, 'profile backscatter', 'black', 'ascent', 6, 4)"
@@ -522,12 +570,2227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "6ffe45d8-0a2f-4a24-bf2e-89f3eeeed381",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found this instrument stream: ooi-data/RS01SBPS-SF01A-3B-OPTAAD101-streamed-optaa_sample\n",
+      "<xarray.DataArray 'time' ()>\n",
+      "array('2022-01-26T07:22:13.359541760', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-01-26T07:22:13.359541760\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time                <xarray.DataArray 'time' ()>\n",
+      "array('2022-12-31T21:45:01.696457216', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-12-31T21:45:01.696457216\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time\n"
+     ]
+    },
+    {
+     "data": {
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+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                       (time: 8894293, wavelength: 87)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 2022-01-26T07:22:13.3...\n",
+       "  * wavelength                    (wavelength) int32 0 1 2 3 4 ... 83 84 85 86\n",
+       "Data variables: (12/29)\n",
+       "    a_reference_counts            (time, wavelength) float32 dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;\n",
+       "    a_reference_dark_counts       (time) float32 dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;\n",
+       "    a_signal_counts               (time, wavelength) float32 dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;\n",
+       "    a_signal_dark_counts          (time) float32 dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;\n",
+       "    beam_attenuation              (time, wavelength) float64 dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;\n",
+       "    c_reference_counts            (time, wavelength) float32 dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;\n",
+       "    ...                            ...\n",
+       "    pressure_counts               (time) float32 dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;\n",
+       "    record_length                 (time) float32 dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;\n",
+       "    sea_water_practical_salinity  (time) float64 dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;\n",
+       "    sea_water_temperature         (time) float64 dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;\n",
+       "    wavelength_a                  (wavelength) float64 dask.array&lt;chunksize=(84,), meta=np.ndarray&gt;\n",
+       "    wavelength_c                  (wavelength) float64 dask.array&lt;chunksize=(84,), meta=np.ndarray&gt;\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:18.009000\n",
+       "    AssetUniqueID:                      ATOSU-58332-00007\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        Spectrophotometer: OPTAA Series D\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       WET Labs\n",
+       "    ...                                 ...\n",
+       "    stream:                             optaa_sample\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T08:25:02.519261696\n",
+       "    time_coverage_start:                2014-10-06T21:48:00.000000000\n",
+       "    title:                              Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d771af94-2172-4560-8d6f-0172809dd8e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d771af94-2172-4560-8d6f-0172809dd8e4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 8894293</li><li><span class='xr-has-index'>wavelength</span>: 87</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0ad08faf-4c2a-48e7-95c1-7bbe0c62f1f1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ad08faf-4c2a-48e7-95c1-7bbe0c62f1f1' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-26T07:22:13.359541760 .....</div><input id='attrs-d2a75a46-d7c6-4706-892a-1c4f84eda874' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2a75a46-d7c6-4706-892a-1c4f84eda874' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e5f03f4-ec11-4f61-bf41-b5b09e347261' class='xr-var-data-in' type='checkbox'><label for='data-5e5f03f4-ec11-4f61-bf41-b5b09e347261' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-26T07:22:13.359541760&#x27;, &#x27;2022-01-26T07:22:13.603157504&#x27;,\n",
+       "       &#x27;2022-01-26T07:22:14.104120832&#x27;, ..., &#x27;2022-12-31T21:45:01.197442560&#x27;,\n",
+       "       &#x27;2022-12-31T21:45:01.446963200&#x27;, &#x27;2022-12-31T21:45:01.696457216&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 81 82 83 84 85 86</div><input id='attrs-674dd2b2-a6d8-46f5-b3d0-01bcb2f766a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-674dd2b2-a6d8-46f5-b3d0-01bcb2f766a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e873b141-9518-49b0-a3c5-0afd1a661405' class='xr-var-data-in' type='checkbox'><label for='data-e873b141-9518-49b0-a3c5-0afd1a661405' 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([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f655d5f7-15ab-451b-9ed8-465f9507b800' class='xr-section-summary-in' type='checkbox'  ><label for='section-f655d5f7-15ab-451b-9ed8-465f9507b800' class='xr-section-summary' >Data variables: <span>(29)</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>a_reference_counts</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;</div><input id='attrs-1b02d1b2-2c1a-4741-be95-2479d6173253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b02d1b2-2c1a-4741-be95-2479d6173253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07948ff7-55b1-4910-80d8-f82fa042cfbc' class='xr-var-data-in' type='checkbox'><label for='data-07948ff7-55b1-4910-80d8-f82fa042cfbc' 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>Array of raw a-channel reference counts, used to compute the Optical Absorption Coefficient data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>OPTAREF_L0</dd><dt><span>long_name :</span></dt><dd>Reference Absorption</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.88 GiB </td>\n",
+       "                        <td> 92.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293, 87) </td>\n",
+       "                        <td> (290000, 84) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_reference_dark_counts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-041a1f7b-771e-4979-a413-14d390f31cf1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-041a1f7b-771e-4979-a413-14d390f31cf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38e4b29e-abbf-4135-96f4-20cf78f003ff' class='xr-var-data-in' type='checkbox'><label for='data-38e4b29e-abbf-4135-96f4-20cf78f003ff' 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>A-channel reference dark counts, used for diagnostic purposes</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorption Dark Reference</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_signal_counts</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;</div><input id='attrs-68b72d12-b763-4f8d-822e-347fcde9248b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68b72d12-b763-4f8d-822e-347fcde9248b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-479f425b-e908-4cac-b105-657d48cb383d' class='xr-var-data-in' type='checkbox'><label for='data-479f425b-e908-4cac-b105-657d48cb383d' 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>Array of raw a-channel signal counts. This is the uncorrected absorption measurement.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>OPTASIG_L0</dd><dt><span>long_name :</span></dt><dd>Signal Absorption</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.88 GiB </td>\n",
+       "                        <td> 92.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293, 87) </td>\n",
+       "                        <td> (290000, 84) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_signal_dark_counts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-cc4a5835-5cae-40a8-a9e5-09d7c7d10085' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc4a5835-5cae-40a8-a9e5-09d7c7d10085' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd7d10e3-7a57-42a5-a173-1ca778fa696f' class='xr-var-data-in' type='checkbox'><label for='data-dd7d10e3-7a57-42a5-a173-1ca778fa696f' 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>A-signal reference dark counts, used for diagnostic purposes</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorption Dark Signal</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam_attenuation</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;</div><input id='attrs-f5889be1-e481-4369-ace2-7353e7c6053e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5889be1-e481-4369-ace2-7353e7c6053e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-098b4dac-5455-4104-bb06-4989b3f29ab6' class='xr-var-data-in' type='checkbox'><label for='data-098b4dac-5455-4104-bb06-4989b3f29ab6' 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>ancillary_variables :</span></dt><dd>sea_water_practical_salinity internal_temp_raw sea_water_temperature c_signal_counts c_reference_counts</dd><dt><span>comment :</span></dt><dd>The Optical Beam Attenuation Coefficient is the rate that the intensity of a beam of light will decrease in response to the combined effects of absorption and scatter as a function of propagation distance. The Attenuation Coefficient results from the spectral beam attenuation of the combination of all seawater impurities including all particulate and dissolved matter of optical importance.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>OPTATTN_L2</dd><dt><span>long_name :</span></dt><dd>Optical Beam Attenuation Coefficient</dd><dt><span>precision :</span></dt><dd>6</dd><dt><span>units :</span></dt><dd>m-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.77 GiB </td>\n",
+       "                        <td> 89.72 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293, 87) </td>\n",
+       "                        <td> (140000, 84) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 128 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_reference_counts</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;</div><input id='attrs-03acdaef-77b8-401a-806e-72ff05b42b93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03acdaef-77b8-401a-806e-72ff05b42b93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d6e1350-6b46-4463-9ce5-30122a25c525' class='xr-var-data-in' type='checkbox'><label for='data-7d6e1350-6b46-4463-9ce5-30122a25c525' 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>Array of raw c-channel reference counts, used to compute the Optical Beam Attenuation Coefficient data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>OPTCREF_L0</dd><dt><span>long_name :</span></dt><dd>Reference Beam Attenuation</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.88 GiB </td>\n",
+       "                        <td> 92.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293, 87) </td>\n",
+       "                        <td> (290000, 84) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"25\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"25\" y2=\"9\" />\n",
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+       "  <line x1=\"0\" y1=\"21\" x2=\"25\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"29\" x2=\"25\" y2=\"29\" />\n",
+       "  <line x1=\"0\" y1=\"36\" x2=\"25\" y2=\"36\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"25\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n",
+       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"68\" x2=\"25\" y2=\"68\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"79\" x2=\"25\" y2=\"79\" />\n",
+       "  <line x1=\"0\" y1=\"87\" x2=\"25\" y2=\"87\" />\n",
+       "  <line x1=\"0\" y1=\"95\" x2=\"25\" y2=\"95\" />\n",
+       "  <line x1=\"0\" y1=\"99\" x2=\"25\" y2=\"99\" />\n",
+       "  <line x1=\"0\" y1=\"107\" x2=\"25\" y2=\"107\" />\n",
+       "  <line x1=\"0\" y1=\"115\" x2=\"25\" y2=\"115\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"120\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.41261651458248,0.0 25.41261651458248,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >87</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">8894293</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_reference_dark_counts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-7f5c7b9d-d599-4058-8ecf-e7b279da9a52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f5c7b9d-d599-4058-8ecf-e7b279da9a52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ce0d675-2668-4844-ae65-b17d902c960b' class='xr-var-data-in' type='checkbox'><label for='data-2ce0d675-2668-4844-ae65-b17d902c960b' 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>C-channel reference dark counts, used for diagnostic purposes</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Attenuation Dark Reference</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_signal_counts</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;</div><input id='attrs-d3668d5f-5fbc-4361-b6d3-6e5d85d44c45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3668d5f-5fbc-4361-b6d3-6e5d85d44c45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0690870e-13d3-46a4-a4cb-ed660caea210' class='xr-var-data-in' type='checkbox'><label for='data-0690870e-13d3-46a4-a4cb-ed660caea210' 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>Array of raw c-channel signal counts. This is the uncorrected beam attenuation measurement.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>OPTCSIG_L0</dd><dt><span>long_name :</span></dt><dd>Signal Beam Attenuation</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.88 GiB </td>\n",
+       "                        <td> 92.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293, 87) </td>\n",
+       "                        <td> (290000, 84) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"25\" y2=\"1\" />\n",
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+       "  <line x1=\"0\" y1=\"36\" x2=\"25\" y2=\"36\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"25\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n",
+       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n",
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+       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"79\" x2=\"25\" y2=\"79\" />\n",
+       "  <line x1=\"0\" y1=\"87\" x2=\"25\" y2=\"87\" />\n",
+       "  <line x1=\"0\" y1=\"95\" x2=\"25\" y2=\"95\" />\n",
+       "  <line x1=\"0\" y1=\"99\" x2=\"25\" y2=\"99\" />\n",
+       "  <line x1=\"0\" y1=\"107\" x2=\"25\" y2=\"107\" />\n",
+       "  <line x1=\"0\" y1=\"115\" x2=\"25\" y2=\"115\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"120\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >87</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_signal_dark_counts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-7967dbf3-2fe5-4f7c-9217-fa430fc6ffc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7967dbf3-2fe5-4f7c-9217-fa430fc6ffc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e85d66f9-57d1-442a-b467-7c938869e3eb' class='xr-var-data-in' type='checkbox'><label for='data-e85d66f9-57d1-442a-b467-7c938869e3eb' 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>C-signal reference dark counts, used for diagnostic purposes</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Attenuation Dark Signal</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>checksum</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-3cbef515-1011-4ef8-88b3-16251e838f0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cbef515-1011-4ef8-88b3-16251e838f0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40a8ad8b-71ad-4339-912b-52e4d04cb0cb' class='xr-var-data-in' type='checkbox'><label for='data-40a8ad8b-71ad-4339-912b-52e4d04cb0cb' 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>Contains a modulo 65535 checksum. Checksum is the calculated by summing all bytes in output buffer, excluding checksum</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Checksum</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deployment</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-0a456a79-0ed9-4d3d-aa05-e0081b4fb27e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a456a79-0ed9-4d3d-aa05-e0081b4fb27e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31bc75bf-f1ae-47a5-b029-bfa4f6e2336b' class='xr-var-data-in' type='checkbox'><label for='data-31bc75bf-f1ae-47a5-b029-bfa4f6e2336b' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>driver_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;</div><input id='attrs-8bd53a00-dffe-4e80-b950-01061eb095cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bd53a00-dffe-4e80-b950-01061eb095cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f633dce2-39bf-4a3b-a71d-4832b24348d8' class='xr-var-data-in' type='checkbox'><label for='data-f633dce2-39bf-4a3b-a71d-4832b24348d8' 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>Driver timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 67.86 MiB </td>\n",
+       "                        <td> 53.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (6980481,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elapsed_run_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;</div><input id='attrs-7b6df1c7-5b13-4e3f-b3d4-1f46f034468e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b6df1c7-5b13-4e3f-b3d4-1f46f034468e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1ffaa91-a420-4f33-b570-97f647f75186' class='xr-var-data-in' type='checkbox'><label for='data-c1ffaa91-a420-4f33-b570-97f647f75186' 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>Time is milliseconds since instrument was powered up</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Elapsed Run Time</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>ms</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 67.86 MiB </td>\n",
+       "                        <td> 53.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (6980481,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>external_temp_raw</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-ea6432d2-c31f-409c-b09e-8d210db3a77f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea6432d2-c31f-409c-b09e-8d210db3a77f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6afcc7e0-c5c3-4d44-94ad-00a3934f01c7' class='xr-var-data-in' type='checkbox'><label for='data-6afcc7e0-c5c3-4d44-94ad-00a3934f01c7' 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>Temperature external to the instrument measured in counts</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Raw External Temperature</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ingestion_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;</div><input id='attrs-952451ca-ba5b-46c0-b88e-7fbb3c30c2bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-952451ca-ba5b-46c0-b88e-7fbb3c30c2bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80426c7a-725d-48bd-9a77-14925dca0ce9' class='xr-var-data-in' type='checkbox'><label for='data-80426c7a-725d-48bd-9a77-14925dca0ce9' 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>The NTP Timestamp for when the granule was ingested</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 67.86 MiB </td>\n",
+       "                        <td> 53.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (6980481,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>int_ctd_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;</div><input id='attrs-30ffb133-7e69-4f16-b925-a195d24e707e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30ffb133-7e69-4f16-b925-a195d24e707e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6338d445-7a33-42d1-8952-ff8ada4e48a2' class='xr-var-data-in' type='checkbox'><label for='data-6338d445-7a33-42d1-8952-ff8ada4e48a2' 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>Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>units :</span></dt><dd>dbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 67.86 MiB </td>\n",
+       "                        <td> 53.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (6980481,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_temp_raw</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-b030cc41-dd63-4fcf-9588-d1baaac0c220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b030cc41-dd63-4fcf-9588-d1baaac0c220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17361004-fb60-4de1-9bd4-f13dd9ab239f' class='xr-var-data-in' type='checkbox'><label for='data-17361004-fb60-4de1-9bd4-f13dd9ab239f' 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>Internal Instrument Temperature of the Absorption Spectrophotometer. These data are used in the calculation of the optical absorption coefficient as changes in instrument internal temperature may alter absorption properties of the seawater.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>OPTTEMP_L0</dd><dt><span>long_name :</span></dt><dd>Internal Instrument Temperature Measurement</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;</div><input id='attrs-b6e7ede1-1fd9-4470-8690-43d95171dede' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6e7ede1-1fd9-4470-8690-43d95171dede' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4577395-ada9-43b2-8f56-7a6972ea5665' class='xr-var-data-in' type='checkbox'><label for='data-f4577395-ada9-43b2-8f56-7a6972ea5665' 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>Internal timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 67.86 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
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+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meter_type</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-fd8a99f8-740f-4385-a28e-7d4ea112608c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd8a99f8-740f-4385-a28e-7d4ea112608c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ceff0e2-da13-4153-a6ee-8b3fece9adb5' class='xr-var-data-in' type='checkbox'><label for='data-9ceff0e2-da13-4153-a6ee-8b3fece9adb5' 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>Meter type -- 53 indicates an ac-s</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Meter Type</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_wavelengths</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-74463fea-3c1d-43e8-bd25-57e389e731f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74463fea-3c1d-43e8-bd25-57e389e731f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7fc93f1-f1e8-4fa4-929f-ddee6fa5db57' class='xr-var-data-in' type='checkbox'><label for='data-b7fc93f1-f1e8-4fa4-929f-ddee6fa5db57' 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>Number of output wavelengths</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Number of Wavelengths</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
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+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>optical_absorption</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120481, 84), meta=np.ndarray&gt;</div><input id='attrs-a647261d-ab83-4450-8dcd-d544fafdb400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a647261d-ab83-4450-8dcd-d544fafdb400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63f08bf8-3346-4e1a-a03b-9295297daf47' class='xr-var-data-in' type='checkbox'><label for='data-63f08bf8-3346-4e1a-a03b-9295297daf47' 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>ancillary_variables :</span></dt><dd>a_reference_counts sea_water_practical_salinity a_signal_counts internal_temp_raw beam_attenuation sea_water_temperature</dd><dt><span>comment :</span></dt><dd>Optical Absorption Coefficient is the rate that the intensity of a beam of light will decrease in response to the absorption (removal) of light energy as a function of propagation distance. The Optical Absorption Coefficient reflects the absorption coefficient for the combination of all seawater impurities including all particulate and dissolved matter of optical importance.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>OPTABSN_L2</dd><dt><span>long_name :</span></dt><dd>Optical Absorption Coefficient</dd><dt><span>precision :</span></dt><dd>6</dd><dt><span>units :</span></dt><dd>m-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.77 GiB </td>\n",
+       "                        <td> 89.72 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293, 87) </td>\n",
+       "                        <td> (140000, 84) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 128 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>packet_type</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-8db61677-32eb-4c40-bd01-0ce07bc45f4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8db61677-32eb-4c40-bd01-0ce07bc45f4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ee69a6-c920-4157-8072-adb5d3a903cc' class='xr-var-data-in' type='checkbox'><label for='data-92ee69a6-c920-4157-8072-adb5d3a903cc' 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>Distinguishes ac-s versus Histar data packets</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Packet Type</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>port_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;</div><input id='attrs-6dc8c395-34a4-488f-8555-2aa5ce87219a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dc8c395-34a4-488f-8555-2aa5ce87219a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d50b422-bc09-4bcc-a3f1-d7140cbad7ad' class='xr-var-data-in' type='checkbox'><label for='data-4d50b422-bc09-4bcc-a3f1-d7140cbad7ad' 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>Port timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 67.86 MiB </td>\n",
+       "                        <td> 53.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (6980481,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_counts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-7e7b94dc-38d2-4f06-b0ab-ce2bc1cae4ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e7b94dc-38d2-4f06-b0ab-ce2bc1cae4ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaef2857-f228-41ef-8721-7781245b93cc' class='xr-var-data-in' type='checkbox'><label for='data-eaef2857-f228-41ef-8721-7781245b93cc' 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>Raw A/D counts from the pressure sensor</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>record_length</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8894293,), meta=np.ndarray&gt;</div><input id='attrs-abe9ad55-c08c-4378-a985-d8dbfa18d6d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abe9ad55-c08c-4378-a985-d8dbfa18d6d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-200994e1-8942-4284-a14c-4b8a18fc2bd9' class='xr-var-data-in' type='checkbox'><label for='data-200994e1-8942-4284-a14c-4b8a18fc2bd9' 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>Number of bytes sent in the packet</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Record Length</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                        <td> 33.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_practical_salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;</div><input id='attrs-0f80babe-e4a4-480a-ba2e-0197c68a7025' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f80babe-e4a4-480a-ba2e-0197c68a7025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2c17625-fd9a-4f86-b673-92c50aa63120' class='xr-var-data-in' type='checkbox'><label for='data-d2c17625-fd9a-4f86-b673-92c50aa63120' 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>alternate_parameter_name :</span></dt><dd>ctdpf_sbe43_sample-practical_salinity</dd><dt><span>comment :</span></dt><dd>Salinity is generally defined as the concentration of dissolved salt in a parcel of seawater. Practical Salinity is a more specific unitless quantity calculated from the conductivity of seawater and adjusted for temperature and pressure. It is approximately equivalent to Absolute Salinity (the mass fraction of dissolved salt in seawater) but they are not interchangeable.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRACSAL_L2</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>long_name :</span></dt><dd>Practical Salinity</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 67.86 MiB </td>\n",
+       "                        <td> 53.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (6980481,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6980481,), meta=np.ndarray&gt;</div><input id='attrs-f332918d-1abb-4e06-a955-f4a647cfede7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f332918d-1abb-4e06-a955-f4a647cfede7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71698ba8-2f48-46cc-80d0-ad2e703f413c' class='xr-var-data-in' type='checkbox'><label for='data-71698ba8-2f48-46cc-80d0-ad2e703f413c' 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>alternate_parameter_name :</span></dt><dd>ctdpf_sbe43_sample-seawater_temperature</dd><dt><span>comment :</span></dt><dd>Seawater temperature near the sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>TEMPWAT_L1</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>long_name :</span></dt><dd>Seawater Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 67.86 MiB </td>\n",
+       "                        <td> 53.26 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (8894293,) </td>\n",
+       "                        <td> (6980481,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wavelength_a</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(84,), meta=np.ndarray&gt;</div><input id='attrs-5ebca23d-76da-437b-bd72-0f806feb09e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ebca23d-76da-437b-bd72-0f806feb09e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f814a8cb-363f-4788-9fd1-8aaa88eefa47' class='xr-var-data-in' type='checkbox'><label for='data-f814a8cb-363f-4788-9fd1-8aaa88eefa47' 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>The wavelength at which optical absorption measurements were taken in nm</dd><dt><span>long_name :</span></dt><dd>Optical Absorption Wavelength</dd><dt><span>precision :</span></dt><dd>6</dd><dt><span>units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 696 B </td>\n",
+       "                        <td> 672 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (87,) </td>\n",
+       "                        <td> (84,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "  <text x=\"60.000000\" y=\"46.091766\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >87</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wavelength_c</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(84,), meta=np.ndarray&gt;</div><input id='attrs-3acc578f-d618-41a1-ae31-986feafc8c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3acc578f-d618-41a1-ae31-986feafc8c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-465960d7-15e1-476c-827d-6bc5ee27f233' class='xr-var-data-in' type='checkbox'><label for='data-465960d7-15e1-476c-827d-6bc5ee27f233' 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>The wavelength at which beam attenuation measurements were taken in nm</dd><dt><span>long_name :</span></dt><dd>Beam Attenuation Wavelength</dd><dt><span>precision :</span></dt><dd>6</dd><dt><span>units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 696 B </td>\n",
+       "                        <td> 672 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (87,) </td>\n",
+       "                        <td> (84,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "  <text x=\"60.000000\" y=\"46.091766\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >87</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8486181-05fe-444f-82d0-fb33e6154579' class='xr-section-summary-in' type='checkbox'  ><label for='section-d8486181-05fe-444f-82d0-fb33e6154579' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e70b27f-bdf1-4ff2-bc2f-37d3086cad46' class='xr-index-data-in' type='checkbox'/><label for='index-1e70b27f-bdf1-4ff2-bc2f-37d3086cad46' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-26 07:22:13.359541760&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:13.603157504&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:14.104120832&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:14.359901696&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:14.612194304&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:14.859116544&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:15.104157696&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:15.351991808&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:15.604581888&#x27;,\n",
+       "               &#x27;2022-01-26 07:22:15.859092480&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-12-31 21:44:59.447601152&#x27;,\n",
+       "               &#x27;2022-12-31 21:44:59.696876544&#x27;,\n",
+       "               &#x27;2022-12-31 21:44:59.946465792&#x27;,\n",
+       "               &#x27;2022-12-31 21:45:00.196649472&#x27;,\n",
+       "               &#x27;2022-12-31 21:45:00.446891520&#x27;,\n",
+       "               &#x27;2022-12-31 21:45:00.697331200&#x27;,\n",
+       "               &#x27;2022-12-31 21:45:00.947608064&#x27;,\n",
+       "               &#x27;2022-12-31 21:45:01.197442560&#x27;,\n",
+       "               &#x27;2022-12-31 21:45:01.446963200&#x27;,\n",
+       "               &#x27;2022-12-31 21:45:01.696457216&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=8894293, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>wavelength</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-12f0335d-a76c-461c-8616-c5fcd89289b4' class='xr-index-data-in' type='checkbox'/><label for='index-12f0335d-a76c-461c-8616-c5fcd89289b4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;wavelength&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6fa9a8e3-65f9-429d-a25b-8f3f9e2cdd1e' class='xr-section-summary-in' type='checkbox'  ><label for='section-6fa9a8e3-65f9-429d-a25b-8f3f9e2cdd1e' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:18.009000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATOSU-58332-00007</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>Spectrophotometer: OPTAA Series D</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>WET Labs</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>AC-S</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>ACS-223</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-08T11:14:28.125580</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-08T11:14:15.325477</dd><dt><span>date_modified :</span></dt><dd>2024-07-08T11:14:28.125582</dd><dt><span>date_processed :</span></dt><dd>2024-07-08T11:19:22.748267</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-08T11:14:28.125554 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-3B-OPTAAD101-streamed-optaa_sample</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>3B-OPTAAD101</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-3B-OPTAAD101-streamed-optaa_sample</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>optaa_sample</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T08:25:02.519261696</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-06T21:48:00.000000000</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-3B-OPTAAD101-streamed-optaa_sample</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                       (time: 8894293, wavelength: 87)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 2022-01-26T07:22:13.3...\n",
+       "  * wavelength                    (wavelength) int32 0 1 2 3 4 ... 83 84 85 86\n",
+       "Data variables: (12/29)\n",
+       "    a_reference_counts            (time, wavelength) float32 dask.array<chunksize=(120481, 84), meta=np.ndarray>\n",
+       "    a_reference_dark_counts       (time) float32 dask.array<chunksize=(8894293,), meta=np.ndarray>\n",
+       "    a_signal_counts               (time, wavelength) float32 dask.array<chunksize=(120481, 84), meta=np.ndarray>\n",
+       "    a_signal_dark_counts          (time) float32 dask.array<chunksize=(8894293,), meta=np.ndarray>\n",
+       "    beam_attenuation              (time, wavelength) float64 dask.array<chunksize=(120481, 84), meta=np.ndarray>\n",
+       "    c_reference_counts            (time, wavelength) float32 dask.array<chunksize=(120481, 84), meta=np.ndarray>\n",
+       "    ...                            ...\n",
+       "    pressure_counts               (time) float32 dask.array<chunksize=(8894293,), meta=np.ndarray>\n",
+       "    record_length                 (time) float32 dask.array<chunksize=(8894293,), meta=np.ndarray>\n",
+       "    sea_water_practical_salinity  (time) float64 dask.array<chunksize=(6980481,), meta=np.ndarray>\n",
+       "    sea_water_temperature         (time) float64 dask.array<chunksize=(6980481,), meta=np.ndarray>\n",
+       "    wavelength_a                  (wavelength) float64 dask.array<chunksize=(84,), meta=np.ndarray>\n",
+       "    wavelength_c                  (wavelength) float64 dask.array<chunksize=(84,), meta=np.ndarray>\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:18.009000\n",
+       "    AssetUniqueID:                      ATOSU-58332-00007\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        Spectrophotometer: OPTAA Series D\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       WET Labs\n",
+       "    ...                                 ...\n",
+       "    stream:                             optaa_sample\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T08:25:02.519261696\n",
+       "    time_coverage_start:                2014-10-06T21:48:00.000000000\n",
+       "    title:                              Data produced by Stream Engine versio..."
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "instrument_key = 'optaa'\n",
     "for s in osb_profiler_streams: \n",
@@ -574,37 +2837,30 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found this instrument stream: ooi-data/RS01SBPS-SF01A-3C-PARADA101-streamed-parad_sa_sample\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "instrument_key = 'parad'\n",
-    "for s in osb_profiler_streams: \n",
-    "    if instrument_key in s: \n",
-    "        print('Found this instrument stream:', s)\n",
-    "        instrument_stream = s\n",
-    "        break\n",
-    "        \n",
-    "ds = loadData(instrument_stream)                      # lazy load\n",
-    "t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
-    "ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
-    "print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
-    "ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
+    "if False:\n",
+    "    instrument_key = 'parad'\n",
+    "    for s in osb_profiler_streams: \n",
+    "        if instrument_key in s: \n",
+    "            print('Found this instrument stream:', s)\n",
+    "            instrument_stream = s\n",
+    "            break\n",
+    "\n",
+    "    ds = loadData(instrument_stream)                      # lazy load\n",
+    "    t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
+    "    ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
+    "    print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
+    "    ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "629f4ef7-fbb5-41ca-86ef-57d890bdf5db",
+   "cell_type": "markdown",
+   "id": "a85cfc79-50dd-42a0-a7e6-315bb4c4f123",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "***seems to fail: kernel restart (timeout?)***"
+   ]
   },
   {
    "cell_type": "code",
@@ -629,29 +2885,38 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found this instrument stream: ooi-data/RS01SBPS-SF01A-3D-SPKIRA101-streamed-spkir_data_record\n"
+     ]
+    }
+   ],
    "source": [
-    "instrument_key = 'spkir'\n",
-    "for s in osb_profiler_streams: \n",
-    "    if instrument_key in s: \n",
-    "        print('Found this instrument stream:', s)\n",
-    "        instrument_stream = s\n",
-    "        break\n",
-    "        \n",
-    "ds = loadData(instrument_stream)                      # lazy load\n",
-    "t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
-    "ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
-    "print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
-    "ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
+    "if False:\n",
+    "    instrument_key = 'spkir'\n",
+    "    for s in osb_profiler_streams: \n",
+    "        if instrument_key in s: \n",
+    "            print('Found this instrument stream:', s)\n",
+    "            instrument_stream = s\n",
+    "            break\n",
+    "\n",
+    "    ds = loadData(instrument_stream)                      # lazy load\n",
+    "    t0, t1 = '2022-01-01T00', '2022-12-31T23'             # January 2022\n",
+    "    ds = ds.sel(time=slice(t0, t1))                       # Subset the full time range to one month\n",
+    "    print(ds.time[0], '              ', ds.time[-1])      # verify selected one month time range\n",
+    "    ds                                                    # get a 'data variable' list of sensors/metadata for this instrument"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9c1f9945-d3e1-4575-95a4-48a6bc068d12",
+   "cell_type": "markdown",
+   "id": "d849a077-1f7a-45c2-9252-ba18fd9be430",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "***seems to fail: kernel restart (timeout?)***"
+   ]
   },
   {
    "cell_type": "code",
@@ -671,12 +2936,2499 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "8344cfa7-362c-4522-818c-788e7a639d48",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found this instrument stream: ooi-data/RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_dark_sample\n",
+      "<xarray.DataArray 'time' ()>\n",
+      "array('2022-01-01T07:22:05.457853440', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-01-01T07:22:05.457853440\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time                <xarray.DataArray 'time' ()>\n",
+      "array('2022-12-31T21:48:35.696192512', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-12-31T21:48:35.696192512\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time\n"
+     ]
+    },
+    {
+     "data": {
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+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                                (time: 1650111, wavelength: 256)\n",
+       "Coordinates:\n",
+       "  * time                                   (time) datetime64[ns] 2022-01-01T0...\n",
+       "  * wavelength                             (wavelength) int32 0 1 2 ... 254 255\n",
+       "Data variables: (12/36)\n",
+       "    aux_fitting_1                          (time) float32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    aux_fitting_2                          (time) float32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    checksum                               (time) float32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    date_of_sample                         (time) float64 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    deployment                             (time) int32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    driver_timestamp                       (time) datetime64[ns] dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    ...                                     ...\n",
+       "    temp_interior                          (time) float32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    temp_lamp                              (time) float32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    temp_spectrometer                      (time) float32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    time_of_sample                         (time) float64 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    voltage_lamp                           (time) float32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "    voltage_main                           (time) float32 dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:15.338000\n",
+       "    AssetUniqueID:                      ATOSU-68020-00005\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        Nitrate: NUTNR Series A\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       Satlantic\n",
+       "    ...                                 ...\n",
+       "    stream:                             nutnr_a_dark_sample\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T11:15:25.654908928\n",
+       "    time_coverage_start:                2014-10-06T22:38:13.329600000\n",
+       "    title:                              Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-945071ef-0665-4780-84fb-69e9927faab3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-945071ef-0665-4780-84fb-69e9927faab3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1650111</li><li><span class='xr-has-index'>wavelength</span>: 256</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-99d63850-f0b7-4e19-8b83-d6d08fd4ab63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-99d63850-f0b7-4e19-8b83-d6d08fd4ab63' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T07:22:05.457853440 .....</div><input id='attrs-649cf17c-b523-4d43-a8d8-37dad8bde908' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-649cf17c-b523-4d43-a8d8-37dad8bde908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c9ca2e0-64a8-4736-931b-f112fc822819' class='xr-var-data-in' type='checkbox'><label for='data-7c9ca2e0-64a8-4736-931b-f112fc822819' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T07:22:05.457853440&#x27;, &#x27;2022-01-01T07:22:10.089222656&#x27;,\n",
+       "       &#x27;2022-01-01T07:22:11.168378368&#x27;, ..., &#x27;2022-12-31T21:48:33.536158208&#x27;,\n",
+       "       &#x27;2022-12-31T21:48:34.606148608&#x27;, &#x27;2022-12-31T21:48:35.696192512&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 251 252 253 254 255</div><input id='attrs-591dc376-2f8b-443a-bc31-9df6c3cff47b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-591dc376-2f8b-443a-bc31-9df6c3cff47b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a618187b-f4bb-4ee6-b8fe-95a2ed7bec7a' class='xr-var-data-in' type='checkbox'><label for='data-a618187b-f4bb-4ee6-b8fe-95a2ed7bec7a' 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([  0,   1,   2, ..., 253, 254, 255], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17f05ea8-6d63-4b74-97b4-3349f50e26f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-17f05ea8-6d63-4b74-97b4-3349f50e26f7' class='xr-section-summary' >Data variables: <span>(36)</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>aux_fitting_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-01e5bdd4-659f-49f5-b96b-42ea33007671' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01e5bdd4-659f-49f5-b96b-42ea33007671' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf503320-835b-4932-b0f8-d85b5ef309d2' class='xr-var-data-in' type='checkbox'><label for='data-bf503320-835b-4932-b0f8-d85b5ef309d2' 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>Aux Fitting 1</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Aux Fitting 1</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aux_fitting_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-5322a320-7cc3-489f-961f-4f5b66e0d423' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5322a320-7cc3-489f-961f-4f5b66e0d423' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73fc1c07-d135-49a7-9d72-09ef9fdded11' class='xr-var-data-in' type='checkbox'><label for='data-73fc1c07-d135-49a7-9d72-09ef9fdded11' 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>Aux Fitting 2</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Aux Fitting 2</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>checksum</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-e7ef18f7-a914-4c46-8435-6ebf27b2baeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7ef18f7-a914-4c46-8435-6ebf27b2baeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a70a288-e2a7-4818-9d02-df082f96f9fb' class='xr-var-data-in' type='checkbox'><label for='data-0a70a288-e2a7-4818-9d02-df082f96f9fb' 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>Data checksum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Data Checksum</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_of_sample</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-65f58e02-5e9d-4a2c-8696-197141a89eda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65f58e02-5e9d-4a2c-8696-197141a89eda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aba9e9c8-51e6-4510-b567-cd93fef1bdaa' class='xr-var-data-in' type='checkbox'><label for='data-aba9e9c8-51e6-4510-b567-cd93fef1bdaa' 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>Date of Sample</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Date of Sample</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deployment</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-6d6655aa-af3f-46d7-b191-0dfba16a3812' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d6655aa-af3f-46d7-b191-0dfba16a3812' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da7e0e8-3b63-430b-babf-a84bd5f0f8a5' class='xr-var-data-in' type='checkbox'><label for='data-8da7e0e8-3b63-430b-babf-a84bd5f0f8a5' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>driver_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-597f0314-9d8e-4534-976e-8d2be160cd79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-597f0314-9d8e-4534-976e-8d2be160cd79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1d15e21-1d89-494b-adb0-7d33a99629ee' class='xr-var-data-in' type='checkbox'><label for='data-d1d15e21-1d89-494b-adb0-7d33a99629ee' 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>Driver timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>humidity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-c96c0cb2-1aa5-4575-bd52-00e549349306' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c96c0cb2-1aa5-4575-bd52-00e549349306' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fbb2fd5-60f9-4cb3-af1a-0f9e248bf4a3' class='xr-var-data-in' type='checkbox'><label for='data-5fbb2fd5-60f9-4cb3-af1a-0f9e248bf4a3' 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>Humidity</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Humidity</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ingestion_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-593e9d1e-6dad-4643-8dab-d964adb60b22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-593e9d1e-6dad-4643-8dab-d964adb60b22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f441974-dc94-45b8-870b-7dd89f990d97' class='xr-var-data-in' type='checkbox'><label for='data-9f441974-dc94-45b8-870b-7dd89f990d97' 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>The NTP Timestamp for when the granule was ingested</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>int_ctd_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-2628ba42-5490-4fdc-8d15-4b4dbbb579c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2628ba42-5490-4fdc-8d15-4b4dbbb579c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecbdb829-56f4-4d44-ad8d-c445e592cbb1' class='xr-var-data-in' type='checkbox'><label for='data-ecbdb829-56f4-4d44-ad8d-c445e592cbb1' 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>Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>units :</span></dt><dd>dbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-21c598fa-a19d-47f2-b94e-64fd1f364c36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21c598fa-a19d-47f2-b94e-64fd1f364c36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b5c118e-e34a-4f8c-a956-e6a0ad936ce7' class='xr-var-data-in' type='checkbox'><label for='data-6b5c118e-e34a-4f8c-a956-e6a0ad936ce7' 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>Internal timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                        <td> 12.59 MiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lamp_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-ce68c985-f14f-4ccb-b057-c29aa71d97a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce68c985-f14f-4ccb-b057-c29aa71d97a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4f9a750-00b5-4499-9ab6-0af514859dde' class='xr-var-data-in' type='checkbox'><label for='data-a4f9a750-00b5-4499-9ab6-0af514859dde' 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>Elapsed time lamp has been on in seconds</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Time</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-2e4f4e61-ec89-4313-a153-97979042dd9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e4f4e61-ec89-4313-a153-97979042dd9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74ea89e1-63a5-4fb7-bdae-33bd81080086' class='xr-var-data-in' type='checkbox'><label for='data-74ea89e1-63a5-4fb7-bdae-33bd81080086' 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>Dissolved Nitrate Concentration, uncorrected for temperature and salinity effects.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITRDIS_L1</dd><dt><span>long_name :</span></dt><dd>Dissolved Nitrate Concentration</dd><dt><span>units :</span></dt><dd>umol L-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-9f95c50b-7f04-48a9-a7b6-73aadfea9e4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f95c50b-7f04-48a9-a7b6-73aadfea9e4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecdbe1a0-6e78-4b25-8924-def05b6d2a50' class='xr-var-data-in' type='checkbox'><label for='data-ecdbe1a0-6e78-4b25-8924-def05b6d2a50' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.57 MiB </td>\n",
+       "                        <td> 1.57 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-b779965e-66b0-4878-929b-0fa7459946c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b779965e-66b0-4878-929b-0fa7459946c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9993322-05f1-4407-9899-63fdaf9c2bbd' class='xr-var-data-in' type='checkbox'><label for='data-c9993322-05f1-4407-9899-63fdaf9c2bbd' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.57 MiB </td>\n",
+       "                        <td> 1.57 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_absorbance_at_254_nm</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-a9ddbde8-4230-4e28-a995-d8b16bf7558d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9ddbde8-4230-4e28-a995-d8b16bf7558d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a411662-dfdd-4c58-bbf9-0e8235372e85' class='xr-var-data-in' type='checkbox'><label for='data-4a411662-dfdd-4c58-bbf9-0e8235372e85' 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>Absorbance at 254 nm</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorbance at 254 Nm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_absorbance_at_350_nm</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-8c0a5a61-28ef-475c-9342-e9afa4cb031c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c0a5a61-28ef-475c-9342-e9afa4cb031c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a46785c-6532-4cc7-b727-54d940e196f5' class='xr-var-data-in' type='checkbox'><label for='data-0a46785c-6532-4cc7-b727-54d940e196f5' 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>Absorbance at 350 nm</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorbance at 350 Nm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_bromide_trace</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-5435a10c-cb7e-4ab8-8378-d499c4a881b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5435a10c-cb7e-4ab8-8378-d499c4a881b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-956a957c-53c1-4ce8-9688-21e9e61e1c01' class='xr-var-data-in' type='checkbox'><label for='data-956a957c-53c1-4ce8-9688-21e9e61e1c01' 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>Bromide Trace</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Bromide Trace</dd><dt><span>units :</span></dt><dd>mg/l</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_current_main</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-fc163e06-a812-49e7-9093-bc977463a103' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc163e06-a812-49e7-9093-bc977463a103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e062ad91-da46-44e9-a8d2-2703f5fae3b2' class='xr-var-data-in' type='checkbox'><label for='data-e062ad91-da46-44e9-a8d2-2703f5fae3b2' 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>Main Current</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Main Current</dd><dt><span>units :</span></dt><dd>mA</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_dark_value_used_for_fit</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-1407cc2e-798e-40e6-b087-6fdbe29dcf7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1407cc2e-798e-40e6-b087-6fdbe29dcf7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e46c58be-1a62-4ab8-879f-e6ce11330cd6' class='xr-var-data-in' type='checkbox'><label for='data-e46c58be-1a62-4ab8-879f-e6ce11330cd6' 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>Dark Value Used for Fit</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Dark Value Used for Fit</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_base_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-520f7736-2200-4d32-9808-26d161018c2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-520f7736-2200-4d32-9808-26d161018c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-252edef9-8db1-44f5-9213-b8b717cef835' class='xr-var-data-in' type='checkbox'><label for='data-252edef9-8db1-44f5-9213-b8b717cef835' 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>Fit Base 1</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit Base 1</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_base_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-ad3ed49a-0287-4834-a36a-ed437208585f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad3ed49a-0287-4834-a36a-ed437208585f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf05d8b2-ff5c-41d0-9f70-b07647ee80f7' class='xr-var-data-in' type='checkbox'><label for='data-cf05d8b2-ff5c-41d0-9f70-b07647ee80f7' 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>Fit Base 2</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit Base 2</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_rmse</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-74124ab2-c3f5-4ecc-be2a-f9b0c9cdbb45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74124ab2-c3f5-4ecc-be2a-f9b0c9cdbb45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d6fa314-e393-4b17-abcc-dfab6fe4d8e9' class='xr-var-data-in' type='checkbox'><label for='data-1d6fa314-e393-4b17-abcc-dfab6fe4d8e9' 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>Fit RMSE</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit RMSE</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_integration_time_factor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-3a5145db-b5ec-40f5-9545-cdb132188da1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a5145db-b5ec-40f5-9545-cdb132188da1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-968a0db4-55fe-4ab1-ba9b-4fe2c8dc541f' class='xr-var-data-in' type='checkbox'><label for='data-968a0db4-55fe-4ab1-ba9b-4fe2c8dc541f' 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>Integration Time Factor</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Integration Time Factor</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-e9665a17-e14d-4a50-98ed-5976f74d371e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9665a17-e14d-4a50-98ed-5976f74d371e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d4e7029-7507-468c-b9da-389e47314e57' class='xr-var-data-in' type='checkbox'><label for='data-2d4e7029-7507-468c-b9da-389e47314e57' 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>Nitrogen in Nitrate</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Nitrogen in Nitrate</dd><dt><span>units :</span></dt><dd>mg/l</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-90924918-cb9c-4245-8de3-db905529f70c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90924918-cb9c-4245-8de3-db905529f70c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8df18c0b-ba99-4f50-9383-b724ccb23c8e' class='xr-var-data-in' type='checkbox'><label for='data-8df18c0b-ba99-4f50-9383-b724ccb23c8e' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.57 MiB </td>\n",
+       "                        <td> 1.57 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-390f3d0b-a808-4268-86e2-af0b45e28b23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-390f3d0b-a808-4268-86e2-af0b45e28b23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c5251fd-d26b-4bfd-9b1c-1a12a325654c' class='xr-var-data-in' type='checkbox'><label for='data-1c5251fd-d26b-4bfd-9b1c-1a12a325654c' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.57 MiB </td>\n",
+       "                        <td> 1.57 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_spectrum_average</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-00fe445e-0cb9-4f63-b6be-6bc596c5f4f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00fe445e-0cb9-4f63-b6be-6bc596c5f4f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08657a3f-8f27-48e8-925e-14525d02ff84' class='xr-var-data-in' type='checkbox'><label for='data-08657a3f-8f27-48e8-925e-14525d02ff84' 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>Spectrum Average</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Spectrum Average</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_voltage_int</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-c107a297-3491-4978-93ff-27ae053bc4e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c107a297-3491-4978-93ff-27ae053bc4e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5e6861e-b84f-4e39-89b1-748e4ecbb127' class='xr-var-data-in' type='checkbox'><label for='data-e5e6861e-b84f-4e39-89b1-748e4ecbb127' 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>Internal Power Supply Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Power Supply Voltage</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>port_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-735b68cf-d7d9-45cb-9531-06d92a90728e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-735b68cf-d7d9-45cb-9531-06d92a90728e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eac3c8e-75eb-4aa4-93fd-898dac3fb66b' class='xr-var-data-in' type='checkbox'><label for='data-6eac3c8e-75eb-4aa4-93fd-898dac3fb66b' 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>Port timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectral_channels</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96286, 256), meta=np.ndarray&gt;</div><input id='attrs-4cfa39b0-6054-497f-9aa4-13accec9dc11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cfa39b0-6054-497f-9aa4-13accec9dc11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b24754d-3a4c-4ca4-b404-6048e7f6db3d' class='xr-var-data-in' type='checkbox'><label for='data-0b24754d-3a4c-4ca4-b404-6048e7f6db3d' 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>UV Absorption Spectra is an array of values produced by the NUTNR instrument class and is based on the ultraviolet (UV) absorption characteristics of the components of seawater, including dissolved nitrate.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITROPT_L0</dd><dt><span>long_name :</span></dt><dd>UV Absorption Spectra</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.57 GiB </td>\n",
+       "                        <td> 94.73 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111, 256) </td>\n",
+       "                        <td> (97000, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 18 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_interior</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-6479aafd-5c20-418a-a2a2-dcd655570560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6479aafd-5c20-418a-a2a2-dcd655570560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1857fff9-e20e-4be7-9104-529914f3a12d' class='xr-var-data-in' type='checkbox'><label for='data-1857fff9-e20e-4be7-9104-529914f3a12d' 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>Interior Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Interior Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_lamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-9bc3ac34-d60a-494d-85bf-36539b3b2345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bc3ac34-d60a-494d-85bf-36539b3b2345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a9e1746-7ded-4281-b4b7-023152ce2206' class='xr-var-data-in' type='checkbox'><label for='data-0a9e1746-7ded-4281-b4b7-023152ce2206' 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>Lamp Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_spectrometer</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-5c899b18-a8ec-4c18-a872-328aa72de3f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c899b18-a8ec-4c18-a872-328aa72de3f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bdf444d-7b4a-431b-a993-59c2b2a9f927' class='xr-var-data-in' type='checkbox'><label for='data-8bdf444d-7b4a-431b-a993-59c2b2a9f927' 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>Spectrometer Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Spectrometer Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_of_sample</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-62b5dcdc-2693-4ca7-8517-e11de04adfe4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62b5dcdc-2693-4ca7-8517-e11de04adfe4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcd4186e-8043-46cb-9feb-ca452b85cbec' class='xr-var-data-in' type='checkbox'><label for='data-bcd4186e-8043-46cb-9feb-ca452b85cbec' 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>Time of Sample</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Time of Sample</dd><dt><span>units :</span></dt><dd>h</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                        <td> 12.59 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_lamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-70f979f5-fcd7-408e-847b-8dcf06188ef3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70f979f5-fcd7-408e-847b-8dcf06188ef3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fca06393-a777-4702-9a87-a49ca1fd44e7' class='xr-var-data-in' type='checkbox'><label for='data-fca06393-a777-4702-9a87-a49ca1fd44e7' 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>Lamp Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                        <td> 6.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                        <td> (1650111,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_main</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1650111,), meta=np.ndarray&gt;</div><input id='attrs-90750697-e382-4b7f-b422-f8327386c958' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90750697-e382-4b7f-b422-f8327386c958' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d361957-8e3b-4bfc-97e8-e79bd3946f60' class='xr-var-data-in' type='checkbox'><label for='data-9d361957-8e3b-4bfc-97e8-e79bd3946f60' 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>Main Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Main Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.29 MiB </td>\n",
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+       "                        <td> (1650111,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-aac535a0-ef1f-40a2-bcc4-19691764cf01' class='xr-section-summary-in' type='checkbox'  ><label for='section-aac535a0-ef1f-40a2-bcc4-19691764cf01' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e4601af-8ebe-44ec-96dd-d22d0ee68571' class='xr-index-data-in' type='checkbox'/><label for='index-0e4601af-8ebe-44ec-96dd-d22d0ee68571' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 07:22:05.457853440&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:10.089222656&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:11.168378368&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:12.265265152&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:13.336309248&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:14.425379328&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:15.531576320&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:16.613574656&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:17.711744512&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:18.782683648&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-12-31 21:48:25.919754240&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:27.003810816&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:28.101828608&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:29.176862208&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:30.267947008&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:31.334020608&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:32.436094464&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:33.536158208&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:34.606148608&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:35.696192512&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1650111, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>wavelength</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-18949928-62d0-47e6-8664-19e75addd423' class='xr-index-data-in' type='checkbox'/><label for='index-18949928-62d0-47e6-8664-19e75addd423' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       246, 247, 248, 249, 250, 251, 252, 253, 254, 255],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;wavelength&#x27;, length=256))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e53e6f6c-b54f-46f0-97a2-587b87f657bf' class='xr-section-summary-in' type='checkbox'  ><label for='section-e53e6f6c-b54f-46f0-97a2-587b87f657bf' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:15.338000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATOSU-68020-00005</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>Nitrate: NUTNR Series A</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Satlantic</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>Deep SUNA</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>344</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-08T11:16:04.174989</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-08T11:15:27.957591</dd><dt><span>date_modified :</span></dt><dd>2024-07-08T11:16:04.174991</dd><dt><span>date_processed :</span></dt><dd>2024-07-08T11:20:35.646657</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-08T11:16:04.174961 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_dark_sample</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>4A-NUTNRA101</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_dark_sample</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>nutnr_a_dark_sample</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T11:15:25.654908928</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-06T22:38:13.329600000</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_dark_sample</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                                (time: 1650111, wavelength: 256)\n",
+       "Coordinates:\n",
+       "  * time                                   (time) datetime64[ns] 2022-01-01T0...\n",
+       "  * wavelength                             (wavelength) int32 0 1 2 ... 254 255\n",
+       "Data variables: (12/36)\n",
+       "    aux_fitting_1                          (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    aux_fitting_2                          (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    checksum                               (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    date_of_sample                         (time) float64 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    deployment                             (time) int32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    driver_timestamp                       (time) datetime64[ns] dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    ...                                     ...\n",
+       "    temp_interior                          (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    temp_lamp                              (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    temp_spectrometer                      (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    time_of_sample                         (time) float64 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    voltage_lamp                           (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "    voltage_main                           (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:15.338000\n",
+       "    AssetUniqueID:                      ATOSU-68020-00005\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        Nitrate: NUTNR Series A\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       Satlantic\n",
+       "    ...                                 ...\n",
+       "    stream:                             nutnr_a_dark_sample\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T11:15:25.654908928\n",
+       "    time_coverage_start:                2014-10-06T22:38:13.329600000\n",
+       "    title:                              Data produced by Stream Engine versio..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "instrument_key = 'nutnr_a_dark_sample'\n",
     "for s in osb_profiler_streams: \n",
@@ -718,12 +5470,2978 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "69f59bc3-04c9-4dff-9950-b10a3bffd5fa",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found this instrument stream: ooi-data/RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample\n",
+      "<xarray.DataArray 'time' ()>\n",
+      "array('2022-01-01T07:22:07.611640320', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-01-01T07:22:07.611640320\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time                <xarray.DataArray 'time' ()>\n",
+      "array('2022-12-31T21:48:02.217944576', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-12-31T21:48:02.217944576\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time\n"
+     ]
+    },
+    {
+     "data": {
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+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                                     (time: 91592, wavelength: 256)\n",
+       "Coordinates:\n",
+       "  * time                                        (time) datetime64[ns] 2022-01...\n",
+       "  * wavelength                                  (wavelength) int32 0 1 ... 255\n",
+       "Data variables: (12/45)\n",
+       "    aux_fitting_1                               (time) float32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    aux_fitting_2                               (time) float32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    checksum                                    (time) float32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    date_of_sample                              (time) float64 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    deployment                                  (time) int32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    driver_timestamp                            (time) datetime64[ns] dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    ...                                          ...\n",
+       "    temp_interior                               (time) float32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    temp_lamp                                   (time) float32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    temp_spectrometer                           (time) float32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    time_of_sample                              (time) float64 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    voltage_lamp                                (time) float32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "    voltage_main                                (time) float32 dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:15.338000\n",
+       "    AssetUniqueID:                      ATOSU-68020-00005\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        Nitrate: NUTNR Series A\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       Satlantic\n",
+       "    ...                                 ...\n",
+       "    stream:                             nutnr_a_sample\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T11:13:21.608314368\n",
+       "    time_coverage_start:                2014-10-06T22:38:15.226800128\n",
+       "    title:                              Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-56c86e94-f5d3-454c-b65d-da1eb4a239be' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-56c86e94-f5d3-454c-b65d-da1eb4a239be' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 91592</li><li><span class='xr-has-index'>wavelength</span>: 256</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-87310478-d960-4dad-b9ae-de229c4ce81e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87310478-d960-4dad-b9ae-de229c4ce81e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T07:22:07.611640320 .....</div><input id='attrs-4b37282b-3170-406b-b516-6a7723a7910d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b37282b-3170-406b-b516-6a7723a7910d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f659c96-57a1-4155-858a-062c58eea583' class='xr-var-data-in' type='checkbox'><label for='data-8f659c96-57a1-4155-858a-062c58eea583' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T07:22:07.611640320&#x27;, &#x27;2022-01-01T07:22:08.877734912&#x27;,\n",
+       "       &#x27;2022-01-01T07:22:48.130280960&#x27;, ..., &#x27;2022-12-31T21:47:22.890671616&#x27;,\n",
+       "       &#x27;2022-12-31T21:48:01.261864960&#x27;, &#x27;2022-12-31T21:48:02.217944576&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 251 252 253 254 255</div><input id='attrs-29ca499b-95c4-47b8-b2d6-39b258c98efb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29ca499b-95c4-47b8-b2d6-39b258c98efb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8a77ad5-179d-45c1-bec7-6360004c6d3b' class='xr-var-data-in' type='checkbox'><label for='data-e8a77ad5-179d-45c1-bec7-6360004c6d3b' 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([  0,   1,   2, ..., 253, 254, 255], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa50c9dc-ce6a-4a9e-8d7a-cd4954ff6cd0' class='xr-section-summary-in' type='checkbox'  ><label for='section-aa50c9dc-ce6a-4a9e-8d7a-cd4954ff6cd0' class='xr-section-summary' >Data variables: <span>(45)</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>aux_fitting_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-838198f4-28ee-47df-90c6-f7a266927dec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-838198f4-28ee-47df-90c6-f7a266927dec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b608748-12cd-45b5-a5f5-c81b6e07461f' class='xr-var-data-in' type='checkbox'><label for='data-1b608748-12cd-45b5-a5f5-c81b6e07461f' 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>Aux Fitting 1</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Aux Fitting 1</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aux_fitting_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-6b9b54fb-44f9-4b38-b12a-df15c754d0f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b9b54fb-44f9-4b38-b12a-df15c754d0f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe78e95-e01e-42b0-a473-7ecc02ae157f' class='xr-var-data-in' type='checkbox'><label for='data-dbe78e95-e01e-42b0-a473-7ecc02ae157f' 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>Aux Fitting 2</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Aux Fitting 2</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>checksum</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-1286c87e-b1a1-4dcd-a664-9fa71d178d79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1286c87e-b1a1-4dcd-a664-9fa71d178d79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77cf101a-2886-4161-b739-483fddaa5565' class='xr-var-data-in' type='checkbox'><label for='data-77cf101a-2886-4161-b739-483fddaa5565' 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>Data checksum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Data Checksum</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_of_sample</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-be0ff96e-c3f1-4edb-9ef8-0cca8b455199' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be0ff96e-c3f1-4edb-9ef8-0cca8b455199' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e282cf8d-74e9-4c67-a77f-71fcb46895da' class='xr-var-data-in' type='checkbox'><label for='data-e282cf8d-74e9-4c67-a77f-71fcb46895da' 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>Date of Sample</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Date of Sample</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deployment</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-b5472698-08f7-4b67-8d83-c4526dac8f0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5472698-08f7-4b67-8d83-c4526dac8f0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4c79348-88ee-4c1b-9e29-c8f9ca65ec43' class='xr-var-data-in' type='checkbox'><label for='data-b4c79348-88ee-4c1b-9e29-c8f9ca65ec43' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>driver_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-08009524-4014-415d-a68e-6b2dc22e6b4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08009524-4014-415d-a68e-6b2dc22e6b4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98810c52-0a2b-459b-80ac-c2607e31c02e' class='xr-var-data-in' type='checkbox'><label for='data-98810c52-0a2b-459b-80ac-c2607e31c02e' 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>Driver timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>humidity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-aa00d493-f682-496f-8527-c7bcadae4fb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa00d493-f682-496f-8527-c7bcadae4fb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c01d8ce-48a4-4c75-b360-afa236c6f6e7' class='xr-var-data-in' type='checkbox'><label for='data-3c01d8ce-48a4-4c75-b360-afa236c6f6e7' 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>Humidity</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Humidity</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ingestion_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-bbc7b0ce-438f-4f85-90b9-4d21aac0fa4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbc7b0ce-438f-4f85-90b9-4d21aac0fa4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c270b8b-b016-4a21-a682-a3e2d92f9df9' class='xr-var-data-in' type='checkbox'><label for='data-6c270b8b-b016-4a21-a682-a3e2d92f9df9' 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>The NTP Timestamp for when the granule was ingested</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>int_ctd_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-fe808bbd-b2aa-401e-a6e5-15874c6af837' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe808bbd-b2aa-401e-a6e5-15874c6af837' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1f3d549-b558-45b8-81f2-530addeb7be4' class='xr-var-data-in' type='checkbox'><label for='data-a1f3d549-b558-45b8-81f2-530addeb7be4' 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>Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>units :</span></dt><dd>dbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-8109488f-feec-4da1-92ec-b23fa73c029c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8109488f-feec-4da1-92ec-b23fa73c029c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06d7bc0a-64f4-403d-89ae-7e64e81e5c5d' class='xr-var-data-in' type='checkbox'><label for='data-06d7bc0a-64f4-403d-89ae-7e64e81e5c5d' 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>Internal timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lamp_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-dbcf3df8-14f9-4511-b521-28c074eef006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbcf3df8-14f9-4511-b521-28c074eef006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b009693-a338-4237-9fc4-00ac0de7f175' class='xr-var-data-in' type='checkbox'><label for='data-7b009693-a338-4237-9fc4-00ac0de7f175' 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>Elapsed time lamp has been on in seconds</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Time</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-7b916977-5f8a-4bb5-949a-38031aad7f81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b916977-5f8a-4bb5-949a-38031aad7f81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5aca0634-58a6-4852-b929-d7da429833cd' class='xr-var-data-in' type='checkbox'><label for='data-5aca0634-58a6-4852-b929-d7da429833cd' 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>ancillary_variables :</span></dt><dd>nitrate_concentration_qartod_results nitrate_concentration_qartod_executed</dd><dt><span>comment :</span></dt><dd>Dissolved Nitrate Concentration, uncorrected for temperature and salinity effects.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITRDIS_L1</dd><dt><span>long_name :</span></dt><dd>Dissolved Nitrate Concentration</dd><dt><span>units :</span></dt><dd>umol L-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_qartod_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-bb089f1e-9661-41c5-bb0a-9b84400efdcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb089f1e-9661-41c5-bb0a-9b84400efdcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04c79a6c-9516-473d-92a8-fd5dfa43f564' class='xr-var-data-in' type='checkbox'><label for='data-04c79a6c-9516-473d-92a8-fd5dfa43f564' 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>Individual QARTOD test flags. For each datum, flags are listed in a string matching the order of the tests_executed attribute. Flags should be interpreted using the standard QARTOD mapping: [1: pass, 2: not_evaluated, 3: suspect_or_of_high_interest, 4: fail, 9: missing_data].</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Dissolved Nitrate Concentration Individual QARTOD Flags</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>nitrate_concentration status_flag</dd><dt><span>tests_executed :</span></dt><dd>gross_range_test, climatology_test</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U2 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_qartod_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-82a92a87-1074-4d38-84d0-396d5fa9970e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82a92a87-1074-4d38-84d0-396d5fa9970e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c63e6bc7-c527-4a4a-96c0-b80fc20fef68' class='xr-var-data-in' type='checkbox'><label for='data-c63e6bc7-c527-4a4a-96c0-b80fc20fef68' 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>Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>flag_meanings :</span></dt><dd>pass not_evaluated suspect_or_of_high_interest fail missing_data</dd><dt><span>flag_values :</span></dt><dd>1,2,3,4,9</dd><dt><span>long_name :</span></dt><dd>Dissolved Nitrate Concentration QARTOD Summary Flag</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>nitrate_concentration status_flag</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-49151d19-3ef3-4d47-ace4-0377e15f86f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49151d19-3ef3-4d47-ace4-0377e15f86f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2181b6aa-3467-407d-8af8-06cbb9f81a0f' class='xr-var-data-in' type='checkbox'><label for='data-2181b6aa-3467-407d-8af8-06cbb9f81a0f' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-9da523d5-ae92-471d-a14b-beeb0a5e1773' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9da523d5-ae92-471d-a14b-beeb0a5e1773' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a56805cc-9b4a-44ba-8e5f-5f8064b5c58a' class='xr-var-data-in' type='checkbox'><label for='data-a56805cc-9b4a-44ba-8e5f-5f8064b5c58a' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_absorbance_at_254_nm</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-d3079299-aa08-4bef-be08-c248a57b9e15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3079299-aa08-4bef-be08-c248a57b9e15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d2481bc-8d4f-4b7b-992b-c67729e2ddd3' class='xr-var-data-in' type='checkbox'><label for='data-4d2481bc-8d4f-4b7b-992b-c67729e2ddd3' 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>Absorbance at 254 nm</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorbance at 254 Nm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_absorbance_at_350_nm</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-bb2bd201-9631-414f-858a-66aac281fd21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb2bd201-9631-414f-858a-66aac281fd21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e16579e7-e9c3-43df-ace3-1c4db629188a' class='xr-var-data-in' type='checkbox'><label for='data-e16579e7-e9c3-43df-ace3-1c4db629188a' 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>Absorbance at 350 nm</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorbance at 350 Nm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_bromide_trace</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-41fc6ce9-2f50-418a-9cdc-ff8fb8fdf5f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41fc6ce9-2f50-418a-9cdc-ff8fb8fdf5f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cbd855d-e74d-4444-8b42-4791053082ea' class='xr-var-data-in' type='checkbox'><label for='data-0cbd855d-e74d-4444-8b42-4791053082ea' 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>Bromide Trace</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Bromide Trace</dd><dt><span>units :</span></dt><dd>mg/l</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_current_main</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-70e29e52-2031-4b18-a898-2ebc68b33906' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70e29e52-2031-4b18-a898-2ebc68b33906' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72fa52b2-e512-459a-be65-417cb069e40f' class='xr-var-data-in' type='checkbox'><label for='data-72fa52b2-e512-459a-be65-417cb069e40f' 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>Main Current</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Main Current</dd><dt><span>units :</span></dt><dd>mA</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_dark_value_used_for_fit</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-12ff93ba-04a4-4698-b064-8d67d12abd43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12ff93ba-04a4-4698-b064-8d67d12abd43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9711c155-041f-4422-bfce-2d8f74fc8b89' class='xr-var-data-in' type='checkbox'><label for='data-9711c155-041f-4422-bfce-2d8f74fc8b89' 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>Dark Value Used for Fit</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Dark Value Used for Fit</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_base_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-fa74d3bb-2412-494e-9a2e-0102cc5a68fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa74d3bb-2412-494e-9a2e-0102cc5a68fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bb69b61-5688-455b-a472-f4db62e742b4' class='xr-var-data-in' type='checkbox'><label for='data-7bb69b61-5688-455b-a472-f4db62e742b4' 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>Fit Base 1</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit Base 1</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_base_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-a395f72f-db83-487f-9b27-2530bd5cca45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a395f72f-db83-487f-9b27-2530bd5cca45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c46fd19-9b4e-4392-bd41-7deb1b426f32' class='xr-var-data-in' type='checkbox'><label for='data-0c46fd19-9b4e-4392-bd41-7deb1b426f32' 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>Fit Base 2</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit Base 2</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_rmse</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-45af3b2a-8879-4e10-8de6-2e3a5e171338' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45af3b2a-8879-4e10-8de6-2e3a5e171338' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1722d0aa-7396-4ec5-8101-23530b18def8' class='xr-var-data-in' type='checkbox'><label for='data-1722d0aa-7396-4ec5-8101-23530b18def8' 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>Fit RMSE</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit RMSE</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_integration_time_factor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-0c4d90a4-8928-4f94-b4a4-50c7e7180fe4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c4d90a4-8928-4f94-b4a4-50c7e7180fe4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-098d3696-dd7f-468b-9a6b-c1bec138b481' class='xr-var-data-in' type='checkbox'><label for='data-098d3696-dd7f-468b-9a6b-c1bec138b481' 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>Integration Time Factor</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Integration Time Factor</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-d6372e64-a31f-4919-b78f-eddaf820a94c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6372e64-a31f-4919-b78f-eddaf820a94c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86007c90-3731-4870-8b9d-baca0ede5e45' class='xr-var-data-in' type='checkbox'><label for='data-86007c90-3731-4870-8b9d-baca0ede5e45' 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>Nitrogen in Nitrate</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Nitrogen in Nitrate</dd><dt><span>units :</span></dt><dd>mg/l</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-61356b86-dc2e-4cc4-a4d2-a4cf32b32e86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61356b86-dc2e-4cc4-a4d2-a4cf32b32e86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b795e177-32ac-4e00-a2b4-9097c7f44caa' class='xr-var-data-in' type='checkbox'><label for='data-b795e177-32ac-4e00-a2b4-9097c7f44caa' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-57ca8c73-be28-4b66-83f6-dd58ca1854f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57ca8c73-be28-4b66-83f6-dd58ca1854f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c2aaddf-a039-4f90-8369-bd764624d65f' class='xr-var-data-in' type='checkbox'><label for='data-2c2aaddf-a039-4f90-8369-bd764624d65f' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_spectrum_average</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-423a146c-ce57-4d2b-ba9e-5816193de405' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-423a146c-ce57-4d2b-ba9e-5816193de405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b80bd98-b3ea-4485-b7e1-de8ce179ca03' class='xr-var-data-in' type='checkbox'><label for='data-3b80bd98-b3ea-4485-b7e1-de8ce179ca03' 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>Spectrum Average</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Spectrum Average</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_voltage_int</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-7283dc09-bc3a-4d7b-ad5f-e18573c5454c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7283dc09-bc3a-4d7b-ad5f-e18573c5454c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c83a7154-a453-41ab-b115-7f0213f35817' class='xr-var-data-in' type='checkbox'><label for='data-c83a7154-a453-41ab-b115-7f0213f35817' 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>Internal Power Supply Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Power Supply Voltage</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>port_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-e2c7bfa2-c31d-41e1-aa3a-a49cbb83fe94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2c7bfa2-c31d-41e1-aa3a-a49cbb83fe94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23941312-ba64-467b-9ad1-de9f95b96ba8' class='xr-var-data-in' type='checkbox'><label for='data-23941312-ba64-467b-9ad1-de9f95b96ba8' 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>Port timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-6bd12d1c-aed3-4297-bcb1-6539462818db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bd12d1c-aed3-4297-bcb1-6539462818db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15e83f94-7e61-4cd7-a2ec-acc21bc4bc65' class='xr-var-data-in' type='checkbox'><label for='data-15e83f94-7e61-4cd7-a2ec-acc21bc4bc65' 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>ancillary_variables :</span></dt><dd>salinity_corrected_nitrate_qartod_results salinity_corrected_nitrate_qartod_executed sea_water_temperature frame_type spectral_channels sea_water_practical_salinity nutnr_dark_value_used_for_fit</dd><dt><span>comment :</span></dt><dd>Temperature and salinity corrected dissolved nitrate concentration.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITRTSC_L2</dd><dt><span>long_name :</span></dt><dd>Nitrate Concentration - Temp and Sal Corrected</dd><dt><span>units :</span></dt><dd>umol L-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate_qartod_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-e06cd8e0-8cdc-48ef-909e-8815d335aa1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e06cd8e0-8cdc-48ef-909e-8815d335aa1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bed60cb5-5679-4682-964b-18c6a31c980f' class='xr-var-data-in' type='checkbox'><label for='data-bed60cb5-5679-4682-964b-18c6a31c980f' 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>Individual QARTOD test flags. For each datum, flags are listed in a string matching the order of the tests_executed attribute. Flags should be interpreted using the standard QARTOD mapping: [1: pass, 2: not_evaluated, 3: suspect_or_of_high_interest, 4: fail, 9: missing_data].</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Nitrate Concentration - Temp and Sal Corrected Individual QARTOD Flags</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>salinity_corrected_nitrate status_flag</dd><dt><span>tests_executed :</span></dt><dd>gross_range_test, climatology_test</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U2 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate_qartod_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-92511bf3-15dc-419e-a3b5-ae4f01b0ae90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92511bf3-15dc-419e-a3b5-ae4f01b0ae90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb976443-1beb-432b-adf4-6d7fb0e21806' class='xr-var-data-in' type='checkbox'><label for='data-eb976443-1beb-432b-adf4-6d7fb0e21806' 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>Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>flag_meanings :</span></dt><dd>pass not_evaluated suspect_or_of_high_interest fail missing_data</dd><dt><span>flag_values :</span></dt><dd>1,2,3,4,9</dd><dt><span>long_name :</span></dt><dd>Nitrate Concentration - Temp and Sal Corrected QARTOD Summary Flag</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>salinity_corrected_nitrate status_flag</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-7e2b170b-dabb-4894-aeef-9775b17e40c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e2b170b-dabb-4894-aeef-9775b17e40c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1941e13d-7c23-412e-a372-10a5293f83e3' class='xr-var-data-in' type='checkbox'><label for='data-1941e13d-7c23-412e-a372-10a5293f83e3' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 89.45 kiB </td>\n",
+       "                        <td> 89.45 kiB </td>\n",
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+       "                        <td> (91592,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-879c8562-5065-43cd-a3ee-8ccf96de0684' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-879c8562-5065-43cd-a3ee-8ccf96de0684' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca2ae642-8d58-40f9-8bc5-acffc2e327f8' class='xr-var-data-in' type='checkbox'><label for='data-ca2ae642-8d58-40f9-8bc5-acffc2e327f8' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                </thead>\n",
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+       "                    <tr>\n",
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+       "                        <td> 89.45 kiB </td>\n",
+       "                        <td> 89.45 kiB </td>\n",
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+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_practical_salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-09a0fd4d-c135-44c9-aae8-573d73cfe21a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09a0fd4d-c135-44c9-aae8-573d73cfe21a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13360542-5dbb-47c0-9cb7-fa206b012762' class='xr-var-data-in' type='checkbox'><label for='data-13360542-5dbb-47c0-9cb7-fa206b012762' 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>alternate_parameter_name :</span></dt><dd>ctdpf_sbe43_sample-practical_salinity</dd><dt><span>comment :</span></dt><dd>Salinity is generally defined as the concentration of dissolved salt in a parcel of seawater. Practical Salinity is a more specific unitless quantity calculated from the conductivity of seawater and adjusted for temperature and pressure. It is approximately equivalent to Absolute Salinity (the mass fraction of dissolved salt in seawater) but they are not interchangeable.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRACSAL_L2</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>long_name :</span></dt><dd>Practical Salinity</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> 715.56 kiB </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-2ec13ff0-220a-43b0-acc6-96c68420effb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ec13ff0-220a-43b0-acc6-96c68420effb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77977f66-7a1e-42f8-903a-c981029e9bad' class='xr-var-data-in' type='checkbox'><label for='data-77977f66-7a1e-42f8-903a-c981029e9bad' 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>alternate_parameter_name :</span></dt><dd>ctdpf_sbe43_sample-seawater_temperature</dd><dt><span>comment :</span></dt><dd>Seawater temperature near the sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>TEMPWAT_L1</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>long_name :</span></dt><dd>Seawater Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectral_channels</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(39491, 256), meta=np.ndarray&gt;</div><input id='attrs-5ec73f34-d7e7-49f2-a8c2-6df8612d9bec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ec73f34-d7e7-49f2-a8c2-6df8612d9bec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91af8800-8840-4930-ba56-6faf3d05b5c1' class='xr-var-data-in' type='checkbox'><label for='data-91af8800-8840-4930-ba56-6faf3d05b5c1' 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>UV Absorption Spectra is an array of values produced by the NUTNR instrument class and is based on the ultraviolet (UV) absorption characteristics of the components of seawater, including dissolved nitrate.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITROPT_L0</dd><dt><span>long_name :</span></dt><dd>UV Absorption Spectra</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 89.45 MiB </td>\n",
+       "                        <td> 50.88 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (91592, 256) </td>\n",
+       "                        <td> (52101, 256) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_interior</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-4566567c-008b-44b6-8c6f-1ad6f619ca96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4566567c-008b-44b6-8c6f-1ad6f619ca96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf5e54ce-b338-4554-9a2c-48a5341ba099' class='xr-var-data-in' type='checkbox'><label for='data-cf5e54ce-b338-4554-9a2c-48a5341ba099' 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>Interior Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Interior Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_lamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-04920cbd-63cb-49f5-a7cf-d411a4d4155c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04920cbd-63cb-49f5-a7cf-d411a4d4155c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-628f0b13-979c-4cfa-8271-5f604b72cb2d' class='xr-var-data-in' type='checkbox'><label for='data-628f0b13-979c-4cfa-8271-5f604b72cb2d' 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>Lamp Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_spectrometer</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-aef87c4e-d669-44ac-bff5-40d6cf08a3e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aef87c4e-d669-44ac-bff5-40d6cf08a3e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2d1e2e1-a1f8-4291-b61c-d60a9b7776f3' class='xr-var-data-in' type='checkbox'><label for='data-b2d1e2e1-a1f8-4291-b61c-d60a9b7776f3' 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>Spectrometer Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Spectrometer Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_of_sample</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-ac94d0ff-47e1-4f85-9fb1-09a356ce5740' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac94d0ff-47e1-4f85-9fb1-09a356ce5740' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51038e7c-dfeb-493c-ae7b-290ab284c585' class='xr-var-data-in' type='checkbox'><label for='data-51038e7c-dfeb-493c-ae7b-290ab284c585' 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>Time of Sample</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Time of Sample</dd><dt><span>units :</span></dt><dd>h</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                        <td> 715.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_lamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-3b7092f2-e8ba-477e-bb9a-5f7d706cfef8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b7092f2-e8ba-477e-bb9a-5f7d706cfef8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ab3f7ed-8cd4-4beb-b51b-a33158a1b2b7' class='xr-var-data-in' type='checkbox'><label for='data-7ab3f7ed-8cd4-4beb-b51b-a33158a1b2b7' 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>Lamp Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_main</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91592,), meta=np.ndarray&gt;</div><input id='attrs-4b5fe2c9-727a-4a6d-9204-df84f65b56af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b5fe2c9-727a-4a6d-9204-df84f65b56af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb4507a5-bafd-4dcc-9019-93ead13c8518' class='xr-var-data-in' type='checkbox'><label for='data-fb4507a5-bafd-4dcc-9019-93ead13c8518' 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>Main Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Main Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                        <td> 357.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91592,) </td>\n",
+       "                        <td> (91592,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-29b0bef8-f221-4968-bd5a-724fbdfd333b' class='xr-section-summary-in' type='checkbox'  ><label for='section-29b0bef8-f221-4968-bd5a-724fbdfd333b' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5c295036-3c8f-4140-9339-d2820ce90f1b' class='xr-index-data-in' type='checkbox'/><label for='index-5c295036-3c8f-4140-9339-d2820ce90f1b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 07:22:07.611640320&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:08.877734912&#x27;,\n",
+       "               &#x27;2022-01-01 07:22:48.130280960&#x27;,\n",
+       "               &#x27;2022-01-01 07:23:27.352933888&#x27;,\n",
+       "               &#x27;2022-01-01 07:23:28.655081984&#x27;,\n",
+       "               &#x27;2022-01-01 07:24:07.928694272&#x27;,\n",
+       "               &#x27;2022-01-01 07:24:09.222790656&#x27;,\n",
+       "               &#x27;2022-01-01 07:24:48.597636608&#x27;,\n",
+       "               &#x27;2022-01-01 07:24:49.888985600&#x27;,\n",
+       "               &#x27;2022-01-01 07:25:29.212340736&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-12-31 21:45:21.636364800&#x27;,\n",
+       "               &#x27;2022-12-31 21:45:22.591453696&#x27;,\n",
+       "               &#x27;2022-12-31 21:46:01.992573952&#x27;,\n",
+       "               &#x27;2022-12-31 21:46:02.951598080&#x27;,\n",
+       "               &#x27;2022-12-31 21:46:41.406718976&#x27;,\n",
+       "               &#x27;2022-12-31 21:46:42.353713664&#x27;,\n",
+       "               &#x27;2022-12-31 21:47:21.945701888&#x27;,\n",
+       "               &#x27;2022-12-31 21:47:22.890671616&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:01.261864960&#x27;,\n",
+       "               &#x27;2022-12-31 21:48:02.217944576&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=91592, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>wavelength</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c99124a-a8cc-4423-a2aa-860c6dd822f5' class='xr-index-data-in' type='checkbox'/><label for='index-6c99124a-a8cc-4423-a2aa-860c6dd822f5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       246, 247, 248, 249, 250, 251, 252, 253, 254, 255],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;wavelength&#x27;, length=256))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e58ae3f-4e5f-485a-9e2e-2e80219bf586' class='xr-section-summary-in' type='checkbox'  ><label for='section-7e58ae3f-4e5f-485a-9e2e-2e80219bf586' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:15.338000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATOSU-68020-00005</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>Nitrate: NUTNR Series A</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Satlantic</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>Deep SUNA</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>344</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-08T11:16:01.225709</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-08T11:15:44.922199</dd><dt><span>date_modified :</span></dt><dd>2024-07-08T11:16:01.225711</dd><dt><span>date_processed :</span></dt><dd>2024-07-08T11:20:52.479634</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-08T11:16:01.225682 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>4A-NUTNRA101</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>nutnr_a_sample</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T11:13:21.608314368</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-06T22:38:15.226800128</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                                     (time: 91592, wavelength: 256)\n",
+       "Coordinates:\n",
+       "  * time                                        (time) datetime64[ns] 2022-01...\n",
+       "  * wavelength                                  (wavelength) int32 0 1 ... 255\n",
+       "Data variables: (12/45)\n",
+       "    aux_fitting_1                               (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    aux_fitting_2                               (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    checksum                                    (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    date_of_sample                              (time) float64 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    deployment                                  (time) int32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    driver_timestamp                            (time) datetime64[ns] dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    ...                                          ...\n",
+       "    temp_interior                               (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    temp_lamp                                   (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    temp_spectrometer                           (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    time_of_sample                              (time) float64 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    voltage_lamp                                (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "    voltage_main                                (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:15.338000\n",
+       "    AssetUniqueID:                      ATOSU-68020-00005\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        Nitrate: NUTNR Series A\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       Satlantic\n",
+       "    ...                                 ...\n",
+       "    stream:                             nutnr_a_sample\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T11:13:21.608314368\n",
+       "    time_coverage_start:                2014-10-06T22:38:15.226800128\n",
+       "    title:                              Data produced by Stream Engine versio..."
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "instrument_key = 'nutnr_a_sample'\n",
     "for s in osb_profiler_streams: \n",
@@ -765,12 +8483,2733 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "5b764c28-ae36-4066-9d3e-64873c13962a",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found this instrument stream: ooi-data/RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data\n",
+      "<xarray.DataArray 'time' ()>\n",
+      "array('2022-01-01T00:00:00.527429120', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-01-01T00:00:00.527429120\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time                <xarray.DataArray 'time' ()>\n",
+      "array('2022-12-31T23:59:59.845964288', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-12-31T23:59:59.845964288\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time\n"
+     ]
+    },
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+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                                 (time: 29325960)\n",
+       "Coordinates:\n",
+       "  * time                                    (time) datetime64[ns] 2022-01-01T...\n",
+       "Data variables: (12/40)\n",
+       "    amplitude_beam1                         (time) float32 dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;\n",
+       "    amplitude_beam2                         (time) float32 dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;\n",
+       "    amplitude_beam3                         (time) float32 dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;\n",
+       "    analog1                                 (time) float32 dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;\n",
+       "    battery_voltage_dv                      (time) float32 dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;\n",
+       "    ctdpf_sbe43_sample-depth                (time) float64 dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;\n",
+       "    ...                                      ...\n",
+       "    velpt_d_northward_velocity              (time) float64 dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;\n",
+       "    velpt_d_northward_velocity_qc_executed  (time) uint8 dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;\n",
+       "    velpt_d_northward_velocity_qc_results   (time) uint8 dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;\n",
+       "    velpt_d_upward_velocity                 (time) float64 dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;\n",
+       "    velpt_d_upward_velocity_qc_executed     (time) uint8 dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;\n",
+       "    velpt_d_upward_velocity_qc_results      (time) uint8 dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:19.204000\n",
+       "    AssetUniqueID:                      ATAPL-70114-00004\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        Single Point Velocity Meter: VELPT Se...\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       Nortek\n",
+       "    ...                                 ...\n",
+       "    stream:                             velpt_velocity_data\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T11:16:08.537853952\n",
+       "    time_coverage_start:                2014-10-06T23:32:22.570285568\n",
+       "    title:                              Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b2b9882f-6d13-4166-9183-6c4449d210e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2b9882f-6d13-4166-9183-6c4449d210e9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 29325960</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d7976ad3-ace6-41c0-b90c-ac7e35937a9e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7976ad3-ace6-41c0-b90c-ac7e35937a9e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T00:00:00.527429120 .....</div><input id='attrs-553fe417-2835-4c5d-8363-db882a9ccce2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-553fe417-2835-4c5d-8363-db882a9ccce2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a204a4b9-ec22-4a49-8bbd-975e78bcd20f' class='xr-var-data-in' type='checkbox'><label for='data-a204a4b9-ec22-4a49-8bbd-975e78bcd20f' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.527429120&#x27;, &#x27;2022-01-01T00:00:01.527426048&#x27;,\n",
+       "       &#x27;2022-01-01T00:00:02.527426048&#x27;, ..., &#x27;2022-12-31T23:59:57.845967360&#x27;,\n",
+       "       &#x27;2022-12-31T23:59:58.845957120&#x27;, &#x27;2022-12-31T23:59:59.845964288&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-05c63500-8c0e-4526-80a2-1e457e764c5e' class='xr-section-summary-in' type='checkbox'  ><label for='section-05c63500-8c0e-4526-80a2-1e457e764c5e' class='xr-section-summary' >Data variables: <span>(40)</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>amplitude_beam1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-03c5d5f3-662d-4736-9ab7-773aa4322635' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03c5d5f3-662d-4736-9ab7-773aa4322635' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbfdb8fe-8260-48f9-b405-3ea8641cc1d4' class='xr-var-data-in' type='checkbox'><label for='data-dbfdb8fe-8260-48f9-b405-3ea8641cc1d4' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Amplitude, Beam 1</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amplitude_beam2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-8e091fc4-ce02-4a46-9379-49ad1e7e665e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e091fc4-ce02-4a46-9379-49ad1e7e665e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f47eb4a9-496e-4f8f-943e-0bab4c67e1e0' class='xr-var-data-in' type='checkbox'><label for='data-f47eb4a9-496e-4f8f-943e-0bab4c67e1e0' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Amplitude, Beam 2</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amplitude_beam3</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-33c89c32-e4e0-4059-b4de-cb1a221be2d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33c89c32-e4e0-4059-b4de-cb1a221be2d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88e81613-e985-49d5-82bb-94f56e240751' class='xr-var-data-in' type='checkbox'><label for='data-88e81613-e985-49d5-82bb-94f56e240751' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Amplitude, Beam 3</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analog1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-638c9404-34b7-4dd2-b7b4-6581d134b741' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-638c9404-34b7-4dd2-b7b4-6581d134b741' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2133796c-ebe1-415d-b02c-9f07469441eb' class='xr-var-data-in' type='checkbox'><label for='data-2133796c-ebe1-415d-b02c-9f07469441eb' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Analog 1</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>battery_voltage_dv</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-43fc80d3-adba-4805-841f-b0c2d4181a1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43fc80d3-adba-4805-841f-b0c2d4181a1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f59c6e88-e4d0-4759-bcac-c3b397b20a7e' class='xr-var-data-in' type='checkbox'><label for='data-f59c6e88-e4d0-4759-bcac-c3b397b20a7e' 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>Instrument battery voltage reported in decivolts.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>units :</span></dt><dd>dV</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctdpf_sbe43_sample-depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-3369e166-89b7-4350-9216-081d4eb6b5a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3369e166-89b7-4350-9216-081d4eb6b5a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebbc63a1-1ed3-43ff-ba4c-46a9731b315c' class='xr-var-data-in' type='checkbox'><label for='data-ebbc63a1-1ed3-43ff-ba4c-46a9731b315c' 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>Depth (m) calculated from pressure (dbar) and latitude.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>long_name :</span></dt><dd>Depth calculated from pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deployment</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-d95d8ce2-63a3-49dc-834d-75cc9db0bf80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d95d8ce2-63a3-49dc-834d-75cc9db0bf80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c45d972-8cc7-4fa9-85e8-19d3eef00c5a' class='xr-var-data-in' type='checkbox'><label for='data-1c45d972-8cc7-4fa9-85e8-19d3eef00c5a' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>driver_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-9b25f25b-e2fe-4812-8f6a-8840575c07ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b25f25b-e2fe-4812-8f6a-8840575c07ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74d9a2f3-1964-4b80-b0e8-86404e2561e4' class='xr-var-data-in' type='checkbox'><label for='data-74d9a2f3-1964-4b80-b0e8-86404e2561e4' 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>Driver timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_code</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-cd540f24-c958-48ad-bb51-28733cda176e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd540f24-c958-48ad-bb51-28733cda176e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-746cdd1c-4619-48a8-b3a9-deb83b8d8f29' class='xr-var-data-in' type='checkbox'><label for='data-746cdd1c-4619-48a8-b3a9-deb83b8d8f29' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Error Code</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_decidegree</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-47e99991-3ef8-444a-89ad-64ef7e7e4f54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47e99991-3ef8-444a-89ad-64ef7e7e4f54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f412ce2-1afd-4d8b-a701-e12661f5d485' class='xr-var-data-in' type='checkbox'><label for='data-1f412ce2-1afd-4d8b-a701-e12661f5d485' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Compass Heading</dd><dt><span>precision :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ingestion_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-f8ba9ccd-0cae-453d-9876-73b1d1b3358e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8ba9ccd-0cae-453d-9876-73b1d1b3358e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b08c7b9f-41e8-4a99-a0cf-5eb9837b3fc5' class='xr-var-data-in' type='checkbox'><label for='data-b08c7b9f-41e8-4a99-a0cf-5eb9837b3fc5' 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>The NTP Timestamp for when the granule was ingested</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >29325960</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>int_ctd_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-5521d7f3-d472-48be-88ea-239cb957923c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5521d7f3-d472-48be-88ea-239cb957923c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c47f1859-59dd-4bf1-8d3d-f45b5569b522' class='xr-var-data-in' type='checkbox'><label for='data-c47f1859-59dd-4bf1-8d3d-f45b5569b522' 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>Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>units :</span></dt><dd>dbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-e0c9f25c-d74e-4b3d-bb82-8e6435446eeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0c9f25c-d74e-4b3d-bb82-8e6435446eeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d74f3ca6-1735-4281-8eeb-f3031a94eb79' class='xr-var-data-in' type='checkbox'><label for='data-d74f3ca6-1735-4281-8eeb-f3031a94eb79' 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>Internal timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch_decidegree</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-5e0fbc8a-69d7-4719-97ae-d22978921e43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e0fbc8a-69d7-4719-97ae-d22978921e43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-018f431f-17b4-4fa1-ac36-5c3ca17e314c' class='xr-var-data-in' type='checkbox'><label for='data-018f431f-17b4-4fa1-ac36-5c3ca17e314c' 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>The rotated angle about the pitch-axis relative to the horizontal plane. Rotation follows the right hand rule designation; i.e. with the r.h. thumb pointing in the rotation axis direction, positive rotation is in the direction of the curled fingers.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Compass Pitch</dd><dt><span>precision :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>platform_pitch_angle</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>port_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-d2331faf-f29d-4ca8-be52-5132d98933ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2331faf-f29d-4ca8-be52-5132d98933ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63f03986-0770-48d1-92ee-12cb5c984886' class='xr-var-data-in' type='checkbox'><label for='data-63f03986-0770-48d1-92ee-12cb5c984886' 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>Port timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll_decidegree</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-ed2b29f4-8185-4374-8d65-1f7176520498' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2b29f4-8185-4374-8d65-1f7176520498' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34c00e87-7abb-4734-9416-b5522a3da5d5' class='xr-var-data-in' type='checkbox'><label for='data-34c00e87-7abb-4734-9416-b5522a3da5d5' 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>The rotated angle about the roll-axis relative to the horizontal plane. Rotation follows the right hand rule designation; i.e. with the r.h. thumb pointing in the rotation axis direction, positive rotation is in the direction of the curled fingers.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Compass Roll</dd><dt><span>precision :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>platform_roll_angle</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"83\" y1=\"0\" x2=\"83\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_pressure_mbar</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-ac973da5-c29d-4a00-807d-f9bd13d66f16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac973da5-c29d-4a00-807d-f9bd13d66f16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b1d23c6-3cf3-4e6a-98e3-73398c588e78' class='xr-var-data-in' type='checkbox'><label for='data-0b1d23c6-3cf3-4e6a-98e3-73398c588e78' 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>alternate_parameter_name :</span></dt><dd>pressure_mbar</dd><dt><span>comment :</span></dt><dd>Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_pressure_mbar_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-07d03e54-871b-4222-b4b5-cc6f9d32936e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07d03e54-871b-4222-b4b5-cc6f9d32936e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81971ec3-4529-473b-8cde-9a9a9310d8c1' class='xr-var-data-in' type='checkbox'><label for='data-81971ec3-4529-473b-8cde-9a9a9310d8c1' 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>alternate_parameter_name :</span></dt><dd>pressure_mbar_qc_executed</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_pressure_mbar_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-7fbc0a4e-9df8-450a-b93c-61dd34f44961' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fbc0a4e-9df8-450a-b93c-61dd34f44961' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a503bc7-3ef7-4e04-ac0f-fd2cd17dd185' class='xr-var-data-in' type='checkbox'><label for='data-1a503bc7-3ef7-4e04-ac0f-fd2cd17dd185' 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>alternate_parameter_name :</span></dt><dd>pressure_mbar_qc_results</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <th> Chunk </th>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
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+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (29325960,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sound_speed_dms</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-01bde914-c488-4305-b7a7-ed2326ec7b23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01bde914-c488-4305-b7a7-ed2326ec7b23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2abeaccd-f95b-4fce-ac7b-5dc88a190ad6' class='xr-var-data-in' type='checkbox'><label for='data-2abeaccd-f95b-4fce-ac7b-5dc88a190ad6' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>units :</span></dt><dd>dm s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-a85b6302-24ef-4e65-8f7b-4a455b92f0a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a85b6302-24ef-4e65-8f7b-4a455b92f0a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1eaf31c-bb7d-4349-b143-412cd20bdbba' class='xr-var-data-in' type='checkbox'><label for='data-b1eaf31c-bb7d-4349-b143-412cd20bdbba' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Status</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                <thead>\n",
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+       "                        <th> Array </th>\n",
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+       "                </thead>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
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+       "                    \n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_centidegree</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-cb6a7273-5aa0-4818-8ea6-c99b72dc30ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb6a7273-5aa0-4818-8ea6-c99b72dc30ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-409c8a6e-6351-459f-b24d-e71289873c00' class='xr-var-data-in' type='checkbox'><label for='data-409c8a6e-6351-459f-b24d-e71289873c00' 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>Seawater temperature near the sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Seawater Temperature</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>units :</span></dt><dd>cdegrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-a6026fe3-96b4-4bb9-8558-78f543c550c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6026fe3-96b4-4bb9-8558-78f543c550c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bed0249e-7535-4f5e-b29d-5837718b3376' class='xr-var-data-in' type='checkbox'><label for='data-bed0249e-7535-4f5e-b29d-5837718b3376' 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>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single &quot;point&quot; of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the eastward component of mean point seawater velocity in earth coordinates relative to true north (accounted for magnetic variation).</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLE_L0</dd><dt><span>long_name :</span></dt><dd>Eastward Mean Point Seawater Velocity</dd><dt><span>units :</span></dt><dd>mm s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
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+       "                    </tr>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam1_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-800ed664-9dc6-44ed-bcf5-b29ae6bf8546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-800ed664-9dc6-44ed-bcf5-b29ae6bf8546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-022d9981-8a8f-4b04-bd50-7987278e963c' class='xr-var-data-in' type='checkbox'><label for='data-022d9981-8a8f-4b04-bd50-7987278e963c' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam1_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-e68f5b2b-9a8a-490e-b811-d5e36785b301' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e68f5b2b-9a8a-490e-b811-d5e36785b301' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3ecc14f-6e0b-454b-b4bc-95932b565d4a' class='xr-var-data-in' type='checkbox'><label for='data-b3ecc14f-6e0b-454b-b4bc-95932b565d4a' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-1743c280-2d4a-411e-943e-4ba46e7aae6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1743c280-2d4a-411e-943e-4ba46e7aae6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06f114f8-e74c-4957-a3f6-fb97dcb5b393' class='xr-var-data-in' type='checkbox'><label for='data-06f114f8-e74c-4957-a3f6-fb97dcb5b393' 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>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single &quot;point&quot; of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the northward component of mean point seawater velocity in earth coordinates relative to true north (accounted for magnetic variation).</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLN_L0</dd><dt><span>long_name :</span></dt><dd>Northward Mean Point Seawater Velocity</dd><dt><span>units :</span></dt><dd>mm s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam2_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-beeb6567-8f86-483b-ba90-2fb219f968b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beeb6567-8f86-483b-ba90-2fb219f968b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-195060ed-0f0d-4b35-92bf-c66bc1da3e06' class='xr-var-data-in' type='checkbox'><label for='data-195060ed-0f0d-4b35-92bf-c66bc1da3e06' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam2_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-2d86c46e-04c0-4f69-b3bf-b809eae76b6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d86c46e-04c0-4f69-b3bf-b809eae76b6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a56cd0b1-d0f3-4d41-8b36-d97d4c982fcd' class='xr-var-data-in' type='checkbox'><label for='data-a56cd0b1-d0f3-4d41-8b36-d97d4c982fcd' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam3</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20384956,), meta=np.ndarray&gt;</div><input id='attrs-7c7626ce-a460-4935-b670-4ae04281d0af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c7626ce-a460-4935-b670-4ae04281d0af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bfe46d0-7c85-4846-a489-e2ed141b6c9f' class='xr-var-data-in' type='checkbox'><label for='data-4bfe46d0-7c85-4846-a489-e2ed141b6c9f' 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>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single &quot;point&quot; of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the upward component of mean point seawater velocity.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLU_L0</dd><dt><span>long_name :</span></dt><dd>Upward Mean Point Seawater Velocity</dd><dt><span>units :</span></dt><dd>mm s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 111.87 MiB </td>\n",
+       "                        <td> 77.76 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (20384956,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "    </tr>\n",
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+       "                    <tr>\n",
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+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam3_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-1c5c466d-b7fc-4e6e-ac3d-64e41662971c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c5c466d-b7fc-4e6e-ac3d-64e41662971c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89751a9c-d079-4757-832c-59777c846104' class='xr-var-data-in' type='checkbox'><label for='data-89751a9c-d079-4757-832c-59777c846104' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (29325960,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_eastward_velocity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-8a2fae3d-b91d-4e8f-b995-d2f39f9c4184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a2fae3d-b91d-4e8f-b995-d2f39f9c4184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cb016f6-6d17-4844-8a63-02b62ceb7243' class='xr-var-data-in' type='checkbox'><label for='data-1cb016f6-6d17-4844-8a63-02b62ceb7243' 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>ancillary_variables :</span></dt><dd>velocity_beam1 time velocity_beam2</dd><dt><span>comment :</span></dt><dd>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single &quot;point&quot; of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the eastward component of mean point seawater velocity in earth coordinates relative to true north (accounted for magnetic variation).</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLE_L1</dd><dt><span>long_name :</span></dt><dd>Eastward Mean Point Seawater Velocity</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
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+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_eastward_velocity_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-fad0aa77-535d-4a8e-948d-3d7901281826' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fad0aa77-535d-4a8e-948d-3d7901281826' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42ea0cc8-79e6-4ac5-a245-9b9931531a52' class='xr-var-data-in' type='checkbox'><label for='data-42ea0cc8-79e6-4ac5-a245-9b9931531a52' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                    <tr>\n",
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+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
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+       "                        <td> (29325960,) </td>\n",
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+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_eastward_velocity_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-48515edd-4b6e-4d71-9ef4-869b794d6012' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48515edd-4b6e-4d71-9ef4-869b794d6012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b20b50d-1d1f-45b9-9873-d216923091b4' class='xr-var-data-in' type='checkbox'><label for='data-8b20b50d-1d1f-45b9-9873-d216923091b4' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <th> Bytes </th>\n",
+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_northward_velocity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-1b3e16d0-89fe-4f51-a3b7-62ec231a1c55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b3e16d0-89fe-4f51-a3b7-62ec231a1c55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9065d2c-ee1f-422a-8254-e45d4c2b633a' class='xr-var-data-in' type='checkbox'><label for='data-c9065d2c-ee1f-422a-8254-e45d4c2b633a' 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>ancillary_variables :</span></dt><dd>velocity_beam1 time velocity_beam2</dd><dt><span>comment :</span></dt><dd>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single &quot;point&quot; of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the northward component of mean point seawater velocity in earth coordinates relative to true north (accounted for magnetic variation).</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLN_L1</dd><dt><span>long_name :</span></dt><dd>Northward Mean Point Seawater Velocity</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_northward_velocity_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-b8e7c550-ba86-4216-9ddf-aae688bb0bb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8e7c550-ba86-4216-9ddf-aae688bb0bb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3990df29-6812-4824-929f-a60b74c236e4' class='xr-var-data-in' type='checkbox'><label for='data-3990df29-6812-4824-929f-a60b74c236e4' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
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+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_northward_velocity_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-0cb59884-3e1e-4d4f-87a8-a3cddf41d0c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cb59884-3e1e-4d4f-87a8-a3cddf41d0c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff7f9a5a-7d9c-43b4-a7c6-ba24899087d4' class='xr-var-data-in' type='checkbox'><label for='data-ff7f9a5a-7d9c-43b4-a7c6-ba24899087d4' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_upward_velocity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9384956,), meta=np.ndarray&gt;</div><input id='attrs-32f4fe06-1071-477e-8e19-517fbf434562' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32f4fe06-1071-477e-8e19-517fbf434562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bee11138-ee4c-4e7e-858a-38f1be008e00' class='xr-var-data-in' type='checkbox'><label for='data-bee11138-ee4c-4e7e-858a-38f1be008e00' 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>ancillary_variables :</span></dt><dd>velocity_beam3</dd><dt><span>comment :</span></dt><dd>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single &quot;point&quot; of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the upward component of mean point seawater velocity.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLU_L1</dd><dt><span>long_name :</span></dt><dd>Upward Mean Point Seawater Velocity</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>upward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 223.74 MiB </td>\n",
+       "                        <td> 91.55 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (12000000,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3 chunks in 3 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_upward_velocity_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-07b39cd5-8487-413a-917f-68f30eb6ebf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07b39cd5-8487-413a-917f-68f30eb6ebf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b761cf6-460e-47fc-81da-e2bbce8673f6' class='xr-var-data-in' type='checkbox'><label for='data-5b761cf6-460e-47fc-81da-e2bbce8673f6' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <td> 27.97 MiB </td>\n",
+       "                        <td> 27.97 MiB </td>\n",
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+       "                        <td> (29325960,) </td>\n",
+       "                        <td> (29325960,) </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_upward_velocity_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29325960,), meta=np.ndarray&gt;</div><input id='attrs-e0b56190-db43-40bf-91a4-6d1eab0e89f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0b56190-db43-40bf-91a4-6d1eab0e89f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79ad0c3a-bbe1-40e9-8bcb-73a769125775' class='xr-var-data-in' type='checkbox'><label for='data-79ad0c3a-bbe1-40e9-8bcb-73a769125775' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> 27.97 MiB </td>\n",
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+       "                        <td> (29325960,) </td>\n",
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+       "                    <tr>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-59b8226f-2e2b-48ff-9f70-ea0b2d2197dc' class='xr-section-summary-in' type='checkbox'  ><label for='section-59b8226f-2e2b-48ff-9f70-ea0b2d2197dc' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08744700-36c5-4e83-bd90-cdee2d9a019d' class='xr-index-data-in' type='checkbox'/><label for='index-08744700-36c5-4e83-bd90-cdee2d9a019d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 00:00:00.527429120&#x27;,\n",
+       "               &#x27;2022-01-01 00:00:01.527426048&#x27;,\n",
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+       "               &#x27;2022-01-01 00:00:08.527418368&#x27;,\n",
+       "               &#x27;2022-01-01 00:00:09.527418368&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-12-31 23:59:50.845976576&#x27;,\n",
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+       "               &#x27;2022-12-31 23:59:58.845957120&#x27;,\n",
+       "               &#x27;2022-12-31 23:59:59.845964288&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=29325960, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-08f810dd-9cb6-49f1-abea-e25b12e9911c' class='xr-section-summary-in' type='checkbox'  ><label for='section-08f810dd-9cb6-49f1-abea-e25b12e9911c' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:19.204000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATAPL-70114-00004</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>Single Point Velocity Meter: VELPT Series D</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Nortek</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>Aquadopp</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>AQS-6802/AQD 11930</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-08T11:16:28.711813</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-08T11:16:11.889081</dd><dt><span>date_modified :</span></dt><dd>2024-07-08T11:16:28.711815</dd><dt><span>date_processed :</span></dt><dd>2024-07-08T11:21:20.215762</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-08T11:16:28.711786 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>4B-VELPTD102</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>velpt_velocity_data</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T11:16:08.537853952</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-06T23:32:22.570285568</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                                 (time: 29325960)\n",
+       "Coordinates:\n",
+       "  * time                                    (time) datetime64[ns] 2022-01-01T...\n",
+       "Data variables: (12/40)\n",
+       "    amplitude_beam1                         (time) float32 dask.array<chunksize=(20384956,), meta=np.ndarray>\n",
+       "    amplitude_beam2                         (time) float32 dask.array<chunksize=(20384956,), meta=np.ndarray>\n",
+       "    amplitude_beam3                         (time) float32 dask.array<chunksize=(20384956,), meta=np.ndarray>\n",
+       "    analog1                                 (time) float32 dask.array<chunksize=(20384956,), meta=np.ndarray>\n",
+       "    battery_voltage_dv                      (time) float32 dask.array<chunksize=(20384956,), meta=np.ndarray>\n",
+       "    ctdpf_sbe43_sample-depth                (time) float64 dask.array<chunksize=(9384956,), meta=np.ndarray>\n",
+       "    ...                                      ...\n",
+       "    velpt_d_northward_velocity              (time) float64 dask.array<chunksize=(9384956,), meta=np.ndarray>\n",
+       "    velpt_d_northward_velocity_qc_executed  (time) uint8 dask.array<chunksize=(29325960,), meta=np.ndarray>\n",
+       "    velpt_d_northward_velocity_qc_results   (time) uint8 dask.array<chunksize=(29325960,), meta=np.ndarray>\n",
+       "    velpt_d_upward_velocity                 (time) float64 dask.array<chunksize=(9384956,), meta=np.ndarray>\n",
+       "    velpt_d_upward_velocity_qc_executed     (time) uint8 dask.array<chunksize=(29325960,), meta=np.ndarray>\n",
+       "    velpt_d_upward_velocity_qc_results      (time) uint8 dask.array<chunksize=(29325960,), meta=np.ndarray>\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:19.204000\n",
+       "    AssetUniqueID:                      ATAPL-70114-00004\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        Single Point Velocity Meter: VELPT Se...\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       Nortek\n",
+       "    ...                                 ...\n",
+       "    stream:                             velpt_velocity_data\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T11:16:08.537853952\n",
+       "    time_coverage_start:                2014-10-06T23:32:22.570285568\n",
+       "    title:                              Data produced by Stream Engine versio..."
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "instrument_key = 'velpt'\n",
     "for s in osb_profiler_streams: \n",
@@ -787,20 +11226,84 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f759c852-2b10-401f-8829-6f7eccdb5fac",
+   "cell_type": "markdown",
+   "id": "79f86ba8-a9f0-4eef-a8b9-78014e01bb1a",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "For the current sensor: \n",
+    "depth: `int_ctd_pressure`. Velocities: `velpt_d_upward_velocity`, `velpt_d_northward_velocity`, `velpt_d_eastward_velocity`.\n",
+    "Respectively `depth`, `up`, `north`, `east`"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "065f320a-a80c-420b-8440-cfef842a599a",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting 1 charts\n",
+      "\n",
+      "Attempting 1 charts\n",
+      "\n",
+      "Attempting 1 charts\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if True:\n",
+    "    t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
+    "    ds_up, reply    = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_upward_velocity',    'int_ctd_pressure'], ['up', 'depth'])\n",
+    "    ds_north, reply = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_northward_velocity', 'int_ctd_pressure'], ['north', 'depth'])\n",
+    "    ds_east, reply  = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_eastward_velocity',  'int_ctd_pressure'], ['east', 'depth'])\n",
+    "\n",
+    "    ds_up.to_netcdf('./data/rca/sensors/osb/up_jan_2022.nc')\n",
+    "    ds_north.to_netcdf('./data/rca/sensors/osb/north_jan_2022.nc')\n",
+    "    ds_east.to_netcdf('./data/rca/sensors/osb/east_jan_2022.nc')\n",
+    "    \n",
+    "    \n",
+    "ds_up    = xr.open_dataset('./data/rca/sensors/osb/up_jan_2022.nc')\n",
+    "ds_north = xr.open_dataset('./data/rca/sensors/osb/north_jan_2022.nc')\n",
+    "ds_east  = xr.open_dataset('./data/rca/sensors/osb/east_jan_2022.nc')\n",
+    "\n",
+    "fig, axes = ChartSensor(profiles, ranges['up'],     [0], ds_up.up,       -ds_up.depth,    'current up', 'black', 'ascent', 6, 4)\n",
+    "fig, axes = ChartSensor(profiles, ranges['north'],  [0], ds_north.north, -ds_north.depth, 'current north', 'black', 'ascent', 6, 4)\n",
+    "fig, axes = ChartSensor(profiles, ranges['east'],   [0], ds_east.east,   -ds_east.depth,  'current east', 'black', 'ascent', 6, 4)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -812,12 +11315,2204 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "129d7553-1fbc-4bd3-aed5-a21583f02e29",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found this instrument stream: ooi-data/RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record\n",
+      "<xarray.DataArray 'time' ()>\n",
+      "array('2022-01-01T02:16:20.634201088', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-01-01T02:16:20.634201088\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time                <xarray.DataArray 'time' ()>\n",
+      "array('2022-12-31T23:26:46.404636160', dtype='datetime64[ns]')\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2022-12-31T23:26:46.404636160\n",
+      "Attributes:\n",
+      "    axis:           T\n",
+      "    long_name:      time\n",
+      "    standard_name:  time\n"
+     ]
+    },
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                                   (time: 5190, spectrum: 14)\n",
+       "Coordinates:\n",
+       "  * spectrum                                  (spectrum) int32 0 1 2 ... 12 13\n",
+       "  * time                                      (time) datetime64[ns] 2022-01-0...\n",
+       "Data variables: (12/31)\n",
+       "    absorbance_ratio_434                      (time) float32 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    absorbance_ratio_434_qc_executed          (time) uint8 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    absorbance_ratio_434_qc_results           (time) uint8 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    absorbance_ratio_620                      (time) float32 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    absorbance_ratio_620_qc_executed          (time) uint8 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    absorbance_ratio_620_qc_results           (time) uint8 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    ...                                        ...\n",
+       "    record_length                             (time) float32 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    record_time                               (time) datetime64[ns] dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    record_type                               (time) float32 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    thermistor_raw                            (time) float32 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    unique_id                                 (time) float32 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "    voltage_battery                           (time) float32 dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:20.856000\n",
+       "    AssetUniqueID:                      ATAPL-58336-00003\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        pCO2 Water: PCO2W Series A\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       Sunburst Sensors\n",
+       "    ...                                 ...\n",
+       "    stream:                             pco2w_a_sami_data_record\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T11:05:11.220713472\n",
+       "    time_coverage_start:                2014-10-07T01:05:10.333669376\n",
+       "    title:                              Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-80d5f558-74d8-40f0-8d3d-f20698cd8d9a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-80d5f558-74d8-40f0-8d3d-f20698cd8d9a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5190</li><li><span class='xr-has-index'>spectrum</span>: 14</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ac931073-1df5-44ab-8369-b7604d40b8f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac931073-1df5-44ab-8369-b7604d40b8f0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spectrum</span></div><div class='xr-var-dims'>(spectrum)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12 13</div><input id='attrs-3b1e9c72-1df2-41e4-9284-f54ddf5163f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b1e9c72-1df2-41e4-9284-f54ddf5163f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3dfa60f-a730-4fbf-b45c-f3c621ce6180' class='xr-var-data-in' type='checkbox'><label for='data-f3dfa60f-a730-4fbf-b45c-f3c621ce6180' 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([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T02:16:20.634201088 .....</div><input id='attrs-9a6b94a1-2a03-470d-8293-deeee85f17df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a6b94a1-2a03-470d-8293-deeee85f17df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef8acdfe-e597-42ef-b643-97d19671696a' class='xr-var-data-in' type='checkbox'><label for='data-ef8acdfe-e597-42ef-b643-97d19671696a' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T02:16:20.634201088&#x27;, &#x27;2022-01-01T03:43:32.948877312&#x27;,\n",
+       "       &#x27;2022-01-01T06:41:21.954166272&#x27;, ..., &#x27;2022-12-31T23:07:29.245393920&#x27;,\n",
+       "       &#x27;2022-12-31T23:16:27.824401408&#x27;, &#x27;2022-12-31T23:26:46.404636160&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2880e634-3158-4be2-aa7c-1828e890769b' class='xr-section-summary-in' type='checkbox'  ><label for='section-2880e634-3158-4be2-aa7c-1828e890769b' class='xr-section-summary' >Data variables: <span>(31)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_434</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-e7fdf735-1384-4518-9cbe-3efcb759ace2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7fdf735-1384-4518-9cbe-3efcb759ace2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46ade819-3fc6-4a66-b75b-fad7529e47d4' class='xr-var-data-in' type='checkbox'><label for='data-46ade819-3fc6-4a66-b75b-fad7529e47d4' 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>ancillary_variables :</span></dt><dd>light_measurements</dd><dt><span>comment :</span></dt><dd>The Optical Absorbance ratio at 434 nm collected during the measurement cycle and used to calculate the PCO2WAT data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2ABS1-SAMP_L0</dd><dt><span>long_name :</span></dt><dd>Optical Absorbance Ratio at 434 Nm</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_434_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-4698f758-88ba-4dd5-bd0b-c5b5938b7970' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4698f758-88ba-4dd5-bd0b-c5b5938b7970' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bcc38ba-3938-4184-968d-e9b448933a3e' class='xr-var-data-in' type='checkbox'><label for='data-7bcc38ba-3938-4184-968d-e9b448933a3e' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_434_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-5e2d36a6-72e5-48f6-9ec7-13e19824a1e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e2d36a6-72e5-48f6-9ec7-13e19824a1e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96ea35a4-bf70-4bc0-acfb-250cc4968259' class='xr-var-data-in' type='checkbox'><label for='data-96ea35a4-bf70-4bc0-acfb-250cc4968259' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_620</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-5167b9ef-9806-420b-9fd1-b18611afb088' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5167b9ef-9806-420b-9fd1-b18611afb088' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38e4ccbb-fd17-4755-86d2-cecc49f6217f' class='xr-var-data-in' type='checkbox'><label for='data-38e4ccbb-fd17-4755-86d2-cecc49f6217f' 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>ancillary_variables :</span></dt><dd>light_measurements</dd><dt><span>comment :</span></dt><dd>The Optical Absorbance ratio at 620 nm collected during the measurement cycle and used to calculate the PCO2WAT data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2ABS2-SAMP_L0</dd><dt><span>long_name :</span></dt><dd>Optical Absorbance Ratio at 620 Nm</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_620_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-3897cab7-cb1b-4e19-a619-2add7735be9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3897cab7-cb1b-4e19-a619-2add7735be9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09c9bbef-0a48-445d-be03-0713cec25a24' class='xr-var-data-in' type='checkbox'><label for='data-09c9bbef-0a48-445d-be03-0713cec25a24' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_620_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-e983b5b0-4463-4573-a94c-16bfeb5712b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e983b5b0-4463-4573-a94c-16bfeb5712b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7aeb1902-9b6f-4361-a90d-a252eb458606' class='xr-var-data-in' type='checkbox'><label for='data-7aeb1902-9b6f-4361-a90d-a252eb458606' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>checksum</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-54f5b2cb-5a4f-4d8c-836e-b64f716b8dad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54f5b2cb-5a4f-4d8c-836e-b64f716b8dad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7d5e8b4-19c2-46c7-9dee-b86d1ae0eab4' class='xr-var-data-in' type='checkbox'><label for='data-c7d5e8b4-19c2-46c7-9dee-b86d1ae0eab4' 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>Data checksum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Data Checksum</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deployment</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-adcf7b85-08f6-496d-9b1e-03be6f07deaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-adcf7b85-08f6-496d-9b1e-03be6f07deaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3ee8d48-3f58-4ae9-a13f-0e66cf1011a9' class='xr-var-data-in' type='checkbox'><label for='data-b3ee8d48-3f58-4ae9-a13f-0e66cf1011a9' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>driver_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-e96f793c-1d70-408e-bcfe-2a212fe99d83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e96f793c-1d70-408e-bcfe-2a212fe99d83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50818250-8e30-4c9f-aeee-d96dddbbc870' class='xr-var-data-in' type='checkbox'><label for='data-50818250-8e30-4c9f-aeee-d96dddbbc870' 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>Driver timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ingestion_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-910ec651-ef70-4aae-914b-dfbab2b4224e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-910ec651-ef70-4aae-914b-dfbab2b4224e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-182a6a02-75d6-4ce8-b2a4-1a32150add32' class='xr-var-data-in' type='checkbox'><label for='data-182a6a02-75d6-4ce8-b2a4-1a32150add32' 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>The NTP Timestamp for when the granule was ingested</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>int_ctd_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-af7b8b87-5e6c-494b-8942-d93bdebbee4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af7b8b87-5e6c-494b-8942-d93bdebbee4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f79b39c-7642-4186-a83d-5b5e7caa04d5' class='xr-var-data-in' type='checkbox'><label for='data-5f79b39c-7642-4186-a83d-5b5e7caa04d5' 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>Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>units :</span></dt><dd>dbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-ea4adf54-59a2-4744-a9d4-da2d912e337a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea4adf54-59a2-4744-a9d4-da2d912e337a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72b88162-b24d-4f2d-ba4e-b0bdd65c0353' class='xr-var-data-in' type='checkbox'><label for='data-72b88162-b24d-4f2d-ba4e-b0bdd65c0353' 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>Internal timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>light_measurements</span></div><div class='xr-var-dims'>(time, spectrum)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190, 14), meta=np.ndarray&gt;</div><input id='attrs-ab8fb266-7ab8-49ba-b8ca-280d5c8858bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab8fb266-7ab8-49ba-b8ca-280d5c8858bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08693ea0-07c0-4cb0-8027-993219a038ba' class='xr-var-data-in' type='checkbox'><label for='data-08693ea0-07c0-4cb0-8027-993219a038ba' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Light Measurements</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <th> Chunk </th>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 283.83 kiB </td>\n",
+       "                        <td> 283.83 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (5190, 14) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_battery_volts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-8e17b1e8-0361-4de4-9517-b0db368bce18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e17b1e8-0361-4de4-9517-b0db368bce18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4224afdf-19c1-4ade-aad7-ac58884f0a77' class='xr-var-data-in' type='checkbox'><label for='data-4224afdf-19c1-4ade-aad7-ac58884f0a77' 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>ancillary_variables :</span></dt><dd>voltage_battery</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-8d56e9f6-e8d0-454f-ab5f-673144f5cc46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d56e9f6-e8d0-454f-ab5f-673144f5cc46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a50a29c-5151-4755-8053-9f4df9f0be67' class='xr-var-data-in' type='checkbox'><label for='data-9a50a29c-5151-4755-8053-9f4df9f0be67' 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>ancillary_variables :</span></dt><dd>pco2_seawater_qartod_results pco2_seawater_qartod_executed pco2w_a_absorbance_blank_434 pco2w_thermistor_temperature light_measurements record_type pco2w_a_absorbance_blank_620</dd><dt><span>comment :</span></dt><dd>Partial Pressure of CO2 in Seawater provides a measure of the amount of CO2 and HCO3 in seawater. Specifically, it refers to the pressure that would be exerted by CO2 if all other gases were removed. Partial pressure of a gas dissolved in seawater is understood as the partial pressure in air that the gas would exert in a hypothetical air volume in equilibrium with that seawater. NOTE: the following calibration coefficients in the parameterfunctionmap are deprecated: &quot;ea434&quot;, &quot;eb434&quot;, &quot;ea620&quot;, &quot;eb620&quot;. Those arguments are still present in the function declaration, but they are completely unused and can be supplied with an arbitrary fill value.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PCO2WAT_L1</dd><dt><span>long_name :</span></dt><dd>pCO2 Seawater</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>partial_pressure_of_carbon_dioxide_in_sea_water</dd><dt><span>units :</span></dt><dd>uatm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
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+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
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+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater_qartod_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-3b418b54-4d3c-40eb-8f75-142c4a6b041d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b418b54-4d3c-40eb-8f75-142c4a6b041d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-033bd536-cefb-4ec6-9600-3835d100b669' class='xr-var-data-in' type='checkbox'><label for='data-033bd536-cefb-4ec6-9600-3835d100b669' 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>Individual QARTOD test flags. For each datum, flags are listed in a string matching the order of the tests_executed attribute. Flags should be interpreted using the standard QARTOD mapping: [1: pass, 2: not_evaluated, 3: suspect_or_of_high_interest, 4: fail, 9: missing_data].</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>pCO2 Seawater Individual QARTOD Flags</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>partial_pressure_of_carbon_dioxide_in_sea_water status_flag</dd><dt><span>tests_executed :</span></dt><dd>gross_range_test, climatology_test</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
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+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U2 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater_qartod_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-9a02b029-9501-4bbe-990a-366939841513' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a02b029-9501-4bbe-990a-366939841513' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0354e784-cfbc-4367-a233-f17ae89cb5e9' class='xr-var-data-in' type='checkbox'><label for='data-0354e784-cfbc-4367-a233-f17ae89cb5e9' 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>Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>flag_meanings :</span></dt><dd>pass not_evaluated suspect_or_of_high_interest fail missing_data</dd><dt><span>flag_values :</span></dt><dd>1,2,3,4,9</dd><dt><span>long_name :</span></dt><dd>pCO2 Seawater QARTOD Summary Flag</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>partial_pressure_of_carbon_dioxide_in_sea_water status_flag</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-1b8ba09b-e659-40ea-83de-cd75dd4b7b45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b8ba09b-e659-40ea-83de-cd75dd4b7b45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccaa65d5-9916-43a9-90af-b6a73b194054' class='xr-var-data-in' type='checkbox'><label for='data-ccaa65d5-9916-43a9-90af-b6a73b194054' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-735b6937-b858-44a9-9fce-9d9f19ae8ecf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-735b6937-b858-44a9-9fce-9d9f19ae8ecf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0700f2ed-7ea8-487d-979f-f3bc09520fd8' class='xr-var-data-in' type='checkbox'><label for='data-0700f2ed-7ea8-487d-979f-f3bc09520fd8' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_a_absorbance_blank_434</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-9b7a1512-141e-48e5-8d22-e9b430e434d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b7a1512-141e-48e5-8d22-e9b430e434d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b403157-497e-4e21-9941-1b2ab8dd3584' class='xr-var-data-in' type='checkbox'><label for='data-8b403157-497e-4e21-9941-1b2ab8dd3584' 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>The Optical Absorbance ratio at 434 nm collected during the blank cycle and used to calculate the PCO2WAT data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2ABS1-BLNK_L0</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-4F-PCO2WA101</dd><dt><span>long_name :</span></dt><dd>Optical Absorbance Ratio at 434 Nm - Blank</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>stream :</span></dt><dd>pco2w_a_sami_data_record_cal</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_a_absorbance_blank_620</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-716537e4-bac8-4a76-8730-85eaf95f769f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-716537e4-bac8-4a76-8730-85eaf95f769f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15b7823b-4d59-493f-960f-5b158db11216' class='xr-var-data-in' type='checkbox'><label for='data-15b7823b-4d59-493f-960f-5b158db11216' 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>The Optical Absorbance ratio at 620 nm collected during the blank cycle and used to calculate the PCO2WAT data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2ABS2-BLNK_L0</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-4F-PCO2WA101</dd><dt><span>long_name :</span></dt><dd>Optical Absorbance Ratio at 620 Nm - Blank</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>stream :</span></dt><dd>pco2w_a_sami_data_record_cal</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_thermistor_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-2d5b600b-737b-4039-83e6-cc8de4ba72b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d5b600b-737b-4039-83e6-cc8de4ba72b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8875d200-a7b0-46c5-86a5-147c51e531df' class='xr-var-data-in' type='checkbox'><label for='data-8875d200-a7b0-46c5-86a5-147c51e531df' 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>ancillary_variables :</span></dt><dd>thermistor_raw</dd><dt><span>comment :</span></dt><dd>This is the Thermistor Temperature measurement, used to calculate the L1 PCO2WAT Partial Pressure of CO2 in Seawater data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2THRM_L1</dd><dt><span>long_name :</span></dt><dd>Thermistor Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_thermistor_temperature_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-7cb99233-e4ea-456a-9c0d-34bc061f5a37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cb99233-e4ea-456a-9c0d-34bc061f5a37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7286c0c-7825-437b-8124-a8ea8ce67382' class='xr-var-data-in' type='checkbox'><label for='data-d7286c0c-7825-437b-8124-a8ea8ce67382' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_thermistor_temperature_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-404807a4-653a-47f9-8da8-5bb64a98e721' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-404807a4-653a-47f9-8da8-5bb64a98e721' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b951a33a-c13a-43de-a1f3-6d0916702378' class='xr-var-data-in' type='checkbox'><label for='data-b951a33a-c13a-43de-a1f3-6d0916702378' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                        <td> 5.07 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>port_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-0063561f-5e98-4a54-8fb9-bf806f53180a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0063561f-5e98-4a54-8fb9-bf806f53180a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64b68f1a-ab6d-48e3-8d5e-7c462c74c314' class='xr-var-data-in' type='checkbox'><label for='data-64b68f1a-ab6d-48e3-8d5e-7c462c74c314' 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>Port timestamp, UTC</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>record_length</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-faf41fd6-6577-42e7-b66e-c8e890cad4a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faf41fd6-6577-42e7-b66e-c8e890cad4a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bf425a7-0e09-4ce3-a966-9041d7a6de7b' class='xr-var-data-in' type='checkbox'><label for='data-0bf425a7-0e09-4ce3-a966-9041d7a6de7b' 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>A 1 byte value indicating the length of the record. Records can be up to 255 bytes long</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Record Length</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>record_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-6fcf7fc5-981f-4b7b-8095-24d9f6f8e1e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fcf7fc5-981f-4b7b-8095-24d9f6f8e1e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f95d6476-f818-489e-b183-e823bbdac8b7' class='xr-var-data-in' type='checkbox'><label for='data-f95d6476-f818-489e-b183-e823bbdac8b7' 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>Time of control record in seconds since January 1, 1904.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Record Time, UTC</dd><dt><span>precision :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                        <td> 40.55 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>record_type</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-0d56e6ae-ea89-4f6f-be6a-30c785c5a2f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d56e6ae-ea89-4f6f-be6a-30c785c5a2f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eddab2a-0467-41d6-92f6-441fd237fb39' class='xr-var-data-in' type='checkbox'><label for='data-3eddab2a-0467-41d6-92f6-441fd237fb39' 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>Record type</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Record Type</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermistor_raw</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-00914a50-649e-47ee-bf75-7dbb7bffee29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00914a50-649e-47ee-bf75-7dbb7bffee29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74e56c90-5ac1-4a38-9a59-0a946b67e9e8' class='xr-var-data-in' type='checkbox'><label for='data-74e56c90-5ac1-4a38-9a59-0a946b67e9e8' 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>This is the raw measurement, used to calculate the Thermistor Temperature.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2THRM_L0</dd><dt><span>long_name :</span></dt><dd>Thermistor Temperature Raw</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>unique_id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-14959768-0ceb-4d61-9540-7aeb9cacde11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14959768-0ceb-4d61-9540-7aeb9cacde11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d745e48-d9b7-40dc-9685-cccb21a6e6b0' class='xr-var-data-in' type='checkbox'><label for='data-1d745e48-d9b7-40dc-9685-cccb21a6e6b0' 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>A 1 byte hash of the SAMI name and calibration.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Instrument Unique ID</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_battery</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5190,), meta=np.ndarray&gt;</div><input id='attrs-b7f25d32-d5ed-480a-bbc2-066ce3791b9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7f25d32-d5ed-480a-bbc2-066ce3791b9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f1951be-035e-4beb-9c51-b1ce82848ab4' class='xr-var-data-in' type='checkbox'><label for='data-1f1951be-035e-4beb-9c51-b1ce82848ab4' 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>Battery voltage in counts.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Voltage Battery</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                        <td> 20.27 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5190,) </td>\n",
+       "                        <td> (5190,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "\n",
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+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0be887b4-89c7-4237-8286-bcd6e79cd175' class='xr-section-summary-in' type='checkbox'  ><label for='section-0be887b4-89c7-4237-8286-bcd6e79cd175' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>spectrum</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-05f59eb5-9218-4a89-b24d-e2bd2174d2d9' class='xr-index-data-in' type='checkbox'/><label for='index-05f59eb5-9218-4a89-b24d-e2bd2174d2d9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype=&#x27;int32&#x27;, name=&#x27;spectrum&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2539b9f8-23e3-445f-a125-385db47338c3' class='xr-index-data-in' type='checkbox'/><label for='index-2539b9f8-23e3-445f-a125-385db47338c3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 02:16:20.634201088&#x27;,\n",
+       "               &#x27;2022-01-01 03:43:32.948877312&#x27;,\n",
+       "               &#x27;2022-01-01 06:41:21.954166272&#x27;,\n",
+       "               &#x27;2022-01-01 08:38:47.687980032&#x27;,\n",
+       "               &#x27;2022-01-01 08:45:48.267300864&#x27;,\n",
+       "               &#x27;2022-01-01 08:52:51.156151296&#x27;,\n",
+       "               &#x27;2022-01-01 08:59:51.750470656&#x27;,\n",
+       "               &#x27;2022-01-01 09:06:52.408842240&#x27;,\n",
+       "               &#x27;2022-01-01 09:13:53.112488960&#x27;,\n",
+       "               &#x27;2022-01-01 09:20:54.940146176&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-12-31 22:16:21.463670784&#x27;,\n",
+       "               &#x27;2022-12-31 22:23:22.184012288&#x27;,\n",
+       "               &#x27;2022-12-31 22:30:23.808183296&#x27;,\n",
+       "               &#x27;2022-12-31 22:37:25.636162560&#x27;,\n",
+       "               &#x27;2022-12-31 22:44:26.278152704&#x27;,\n",
+       "               &#x27;2022-12-31 22:51:27.935206912&#x27;,\n",
+       "               &#x27;2022-12-31 22:58:29.559876608&#x27;,\n",
+       "               &#x27;2022-12-31 23:07:29.245393920&#x27;,\n",
+       "               &#x27;2022-12-31 23:16:27.824401408&#x27;,\n",
+       "               &#x27;2022-12-31 23:26:46.404636160&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=5190, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c618da83-fc14-4877-9373-7e1c543d59cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-c618da83-fc14-4877-9373-7e1c543d59cc' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:20.856000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATAPL-58336-00003</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>pCO2 Water: PCO2W Series A</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Sunburst Sensors</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>SAMI-pCO2</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>C0076</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-08T11:17:01.393782</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-08T11:16:37.513113</dd><dt><span>date_modified :</span></dt><dd>2024-07-08T11:17:01.393784</dd><dt><span>date_processed :</span></dt><dd>2024-07-08T11:21:44.776799</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-08T11:17:01.393758 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>4F-PCO2WA101</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>pco2w_a_sami_data_record</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T11:05:11.220713472</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-07T01:05:10.333669376</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                                   (time: 5190, spectrum: 14)\n",
+       "Coordinates:\n",
+       "  * spectrum                                  (spectrum) int32 0 1 2 ... 12 13\n",
+       "  * time                                      (time) datetime64[ns] 2022-01-0...\n",
+       "Data variables: (12/31)\n",
+       "    absorbance_ratio_434                      (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    absorbance_ratio_434_qc_executed          (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    absorbance_ratio_434_qc_results           (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    absorbance_ratio_620                      (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    absorbance_ratio_620_qc_executed          (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    absorbance_ratio_620_qc_results           (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    ...                                        ...\n",
+       "    record_length                             (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    record_time                               (time) datetime64[ns] dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    record_type                               (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    thermistor_raw                            (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    unique_id                                 (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "    voltage_battery                           (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n",
+       "Attributes: (12/62)\n",
+       "    AssetManagementRecordLastModified:  2024-07-04T16:24:20.856000\n",
+       "    AssetUniqueID:                      ATAPL-58336-00003\n",
+       "    Conventions:                        CF-1.6\n",
+       "    Description:                        pCO2 Water: PCO2W Series A\n",
+       "    FirmwareVersion:                    Not specified.\n",
+       "    Manufacturer:                       Sunburst Sensors\n",
+       "    ...                                 ...\n",
+       "    stream:                             pco2w_a_sami_data_record\n",
+       "    subsite:                            RS01SBPS\n",
+       "    summary:                            Dataset Generated by Stream Engine fr...\n",
+       "    time_coverage_end:                  2024-07-08T11:05:11.220713472\n",
+       "    time_coverage_start:                2014-10-07T01:05:10.333669376\n",
+       "    title:                              Data produced by Stream Engine versio..."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "instrument_key = 'pco2w'\n",
     "for s in osb_profiler_streams: \n",
@@ -834,12 +13529,12 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5d4d7bba-308b-4289-abf6-84f12d3d0226",
+   "cell_type": "markdown",
+   "id": "5ad5cd00-cd39-44d9-9307-0ab51ed018e8",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "**descent, midnight and noon** `pco2_seawater` > `pco2` and `int_ctd_pressure` > `depth`."
+   ]
   },
   {
    "cell_type": "code",
diff --git a/book/chapters/epipelargosy.ipynb b/book/chapters/epipelargosy.ipynb
index 0a4d626..b17f9b8 100644
--- a/book/chapters/epipelargosy.ipynb
+++ b/book/chapters/epipelargosy.ipynb
@@ -10,6 +10,8 @@
     "\n",
     "# Epipelargosy\n",
     "\n",
+    "## (επιπελαργοση)\n",
+    "\n",
     "\n",
     "> Wavering between the profit and the loss /\n",
     "In this brief transit where the dreams cross... <br><br>-T.S.Eliot\n",
@@ -78,52 +80,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "0dc9ce86-ece0-4a8e-b016-b8604c1c30e2",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Jupyter Notebook running Python 3\n"
+     ]
+    }
+   ],
    "source": [
     "from matplotlib import pyplot as plt\n",
     "from shallowprofiler import *\n",
     "from data import *\n",
     "from charts import *\n",
+    "from os import path\n",
     "\n",
-    "profiles = ReadProfileMetadata()\n",
     "\n",
-    "d = {}\n",
+    "def AssembleDataFilename(base_path, site, sensor, month, year): \n",
+    "    return base_path + '/' + site + '/' + sensor + '_' + month + '_' + year + '.nc'\n",
     "\n",
-    "relative_path = './data/rca/sensors/'\n",
-    "sitestring = 'osb'                    # available: osb = Oregon Slope Base\n",
-    "monthstring = 'jan22'                 # available: apr21, jul21, jan22\n",
-    "\n",
-    "# error: apr21 and jul21 seem to fail on pCO2 despite data files present\n",
-    "\n",
-    "def AssembleDataFilename(relative_path, site, instrument, time, sensor): \n",
-    "    return relative_path + site + '/' + instrument + '_' + time + '_' + sensor + '.nc'\n",
     "\n",
     "def GetSensorTuple(s, f):\n",
     "    '''\n",
     "    s is the sensor identifier string like 'temperature'\n",
     "    f is the source filename like './../data/osb_ctd_jan22_temperature.nc' \n",
     "    '''\n",
-    "    df_sensor    = xr.open_dataset(f)[s]\n",
-    "    df_z         = xr.open_dataset(f)['z']\n",
-    "    range_lo     = ranges[s][0]\n",
-    "    range_hi     = ranges[s][1]\n",
-    "    sensor_color = colors[s]\n",
-    "    return (df_sensor, df_z, range_lo, range_hi, sensor_color)\n",
+    "    DA_sensor    = xr.open_dataset(f)[s]                # DataArray\n",
+    "    DA_depth     = xr.open_dataset(f)['depth']          # DataArray\n",
+    "    range_lo     = ranges[s][0]                         # expected numerical range of this sensor data\n",
+    "    range_hi     = ranges[s][1]                         #   lo and high\n",
+    "    sensor_color = colors[s]                            #   default chart color for this sensor\n",
+    "    return (DA_sensor, DA_depth, range_lo, range_hi, sensor_color)\n",
+    "\n",
+    "\n",
+    "profiles = ReadProfileMetadata()\n",
+    "\n",
     "\n",
+    "d = {}                # d is an empty dictionary that will be populated with 5-tuples based on \n",
+    "                      # what data files are resident in the base path. Default is January 2022,\n",
+    "                      # Oregon Slope Base\n",
+    "\n",
+    "base_path   = './data/rca/sensors'\n",
+    "sitestring  = 'osb'\n",
+    "monthstring = 'jan'               \n",
+    "yearstring  = '2022'\n",
     "\n",
     "for sensor in sensors:          # sensor is a list of 2 strings [sensor_str, instrument_str]\n",
-    "    f = AssembleDataFilename(relative_path, sitestring, sensor[1], monthstring, sensor[0])\n",
-    "    d[sensor[0]] = GetSensorTuple(sensor[0], f)    # create a dictionary entry for this sensor"
+    "    f = AssembleDataFilename(base_path, sitestring, sensor[0], monthstring, yearstring)\n",
+    "    if path.isfile(f):\n",
+    "        d[sensor[0]] = GetSensorTuple(sensor[0], f)    # create a dictionary entry for this sensor\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "e4ddc842-33a2-4b31-b705-8a01ea9bb1e4",
    "metadata": {
     "tags": []
@@ -133,26 +150,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<xarray.DataArray 'pco2' (time: 998)>\n",
-      "[998 values with dtype=float64]\n",
+      "<xarray.DataArray 'ph' (time: 1003)>\n",
+      "[1003 values with dtype=float64]\n",
       "Coordinates:\n",
-      "  * time     (time) datetime64[ns] 2022-01-01T02:16:00 ... 2022-01-31T23:37:00 <xarray.DataArray 'z' (time: 998)>\n",
-      "[998 values with dtype=float64]\n",
+      "  * time     (time) datetime64[ns] 2022-01-01T02:12:11.470588928 ... 2022-01-... <xarray.DataArray 'depth' (time: 1003)>\n",
+      "[1003 values with dtype=float64]\n",
       "Coordinates:\n",
-      "  * time     (time) datetime64[ns] 2022-01-01T02:16:00 ... 2022-01-31T23:37:00\n"
+      "  * time     (time) datetime64[ns] 2022-01-01T02:12:11.470588928 ... 2022-01-...\n"
      ]
     }
    ],
    "source": [
-    "# Have a look at the pCO2 DataArray after removing all Attributes\n",
-    "for a in list(d['pco2'][0].attrs.keys()): del d['pco2'][0].attrs[a]\n",
-    "for a in list(d['pco2'][1].attrs.keys()): del d['pco2'][1].attrs[a]\n",
-    "print(d['pco2'][0], d['pco2'][1])"
+    "# Have a look at the pH DataArray after removing all Attributes\n",
+    "for a in list(d['ph'][0].attrs.keys()): del d['ph'][0].attrs[a]\n",
+    "for a in list(d['ph'][1].attrs.keys()): del d['ph'][1].attrs[a]\n",
+    "print(d['ph'][0], d['ph'][1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "8536b017-911e-4d74-ba57-1c4c90d91ba8",
    "metadata": {
     "tags": []
@@ -160,7 +177,7 @@
    "outputs": [],
    "source": [
     "# Start with CTD temperature and salinity\n",
-    "how_many_charts = 3\n",
+    "how_many_charts = 1\n",
     "\n",
     "# quick check: use d['temperature'].z.plot()\n",
     "profile_list = [n for n in range(how_many_charts)]"
@@ -192,7 +209,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
+   "id": "292c9b27-c546-4361-858b-24bdd96720bf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['conductivity', 'ctd'],\n",
+       " ['density', 'ctd'],\n",
+       " ['pressure', 'ctd'],\n",
+       " ['salinity', 'ctd'],\n",
+       " ['temp', 'ctd'],\n",
+       " ['chlora', 'fluor'],\n",
+       " ['backscatter', 'fluor'],\n",
+       " ['fdom', 'fluor'],\n",
+       " ['spkir412nm', 'spkir'],\n",
+       " ['spkir443nm', 'spkir'],\n",
+       " ['spkir490nm', 'spkir'],\n",
+       " ['spkir510nm', 'spkir'],\n",
+       " ['spkir555nm', 'spkir'],\n",
+       " ['spkir620nm', 'spkir'],\n",
+       " ['spkir683nm', 'spkir'],\n",
+       " ['nitrate', 'nitrate'],\n",
+       " ['pco2', 'pco2'],\n",
+       " ['do', 'do'],\n",
+       " ['par', 'par'],\n",
+       " ['ph', 'ph'],\n",
+       " ['up', 'vel'],\n",
+       " ['east', 'vel'],\n",
+       " ['north', 'vel']]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sensors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "049d4cff-b1d2-4152-a4ba-41e2891b050d",
    "metadata": {
     "tags": []
@@ -202,15 +264,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Attempting 3 charts\n",
+      "Attempting 1 charts\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 600x1200 with 6 Axes>"
+       "<Figure size 600x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -219,14 +281,14 @@
    ],
    "source": [
     "# temperature and salinity\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['salinity']], profile_list, \n",
-    "                          d['temperature'][0], d['temperature'][1], 'Temperature', colors['temperature'],    'ascent',\n",
-    "                          d['salinity'][0],       d['salinity'][1],    'Salinity',  colors['salinity'], 'ascent', 6, 4)"
+    "fig,axs = ChartTwoSensors(profiles, [ranges['temp'], ranges['salinity']], profile_list, \n",
+    "                          d['temp'][0],     -d['temp'][1],     'Temperature', colors['temp'],     'ascent',\n",
+    "                          d['salinity'][0], -d['salinity'][1], 'Salinity',    colors['salinity'], 'ascent', 6, 4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "a10605ea-57a9-4d9e-a593-e36483d06eee",
    "metadata": {
     "tags": []
@@ -236,15 +298,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Attempting 3 charts\n",
+      "Attempting 1 charts\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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KIvX3tn379uTp6anV9r/88kuVhFobaWlplJycTA8fPlT7rCh/+ObNm6fymmHDhpGxsbFYvwMHDhAAlX+IiIQfbW0SwypVqpCDg4NW8aamplKZMmWoRo0a4nGWiCghIYHs7OyoUaNGBRq/tomhtseHatWqkY+Pj9p8TQnKmDFjSKFQqCRx169fJwC0ZMmSLGPJbjv37t0jPT09WrRokTjv3bt3ZG1trZI8a/L06VMCQHPnzlVb5uPjk+WxR09PT+XYldfEMCIiggwMDOibb75RKZeQkEAODg4aE9ac5CUxVCZASsp/XP766y9xnqurK8lkMrXfeD8/P7KwsKA3b94QkfCPoJ6enlo+oswR9u/frxKPpaWlePzLibbHIG3XlZycTMePHycAdOnSJZXlP/30EwGgnTt3Ut++fUmhUNDly5dVymT+Lil/vzL/k6LJH3/8QQDoxo0bWsec793VUIbLQnfv3sXNmzfx5ZdfAgBSUlLEoW3btoiKisKtW7dUXt+xY0eV6Zo1awIAHj58CADw9PSEkZERBg0ahHXr1uH+/fsfHfOoUaMQExODrVu3AhAua6xYsQLt2rXLsSXQkydPYGtrq/F0foUKFfDHH39g165daN++PZo0aZJli7J169ahbdu2GDBgALZs2QJjY2Nx2e3bt3Hv3j30799fZX5Gb968QWhoKDp37qxSxtzcHB06dFArP3nyZKSkpMDHx0dtWa9evVTq4+rqikaNGiE4OFitbJcuXTTGk9GBAwcAAMOHD8+xbGxsLJo3b46zZ8/i5MmT4uVpbdSsWROVKlVSmbd37174+vqiTJkyKp+/Nm3aAACOHz8OAAgODoa5ubna569Xr15ab18bR48ehYeHh1oDAX9/fxARjh49CgBo3LgxjI2NxcZcgYGBaNasGVq3bo1Tp07h7du3iIyMxJ07d/Dpp58CEG4BOHLkCD7//HOYmJiofd/ev3+P06dPq2xXm/2nZG1tjc2bN+PChQto1KgRypYtq3aJIjU1VWW7GS+PHz16FJ9++iksLS2hr68PQ0NDTJ48GbGxsSo9GshkMqxfvx7m5ubw8vLCgwcPsGXLFpiammYbX/369XHp0iUMGzYMhw4dQnx8fJZl7ezsEBMTo3Y7QWYxMTEYMmQIXFxcYGBgAENDQ7i6ugIQ7hnLTNPx6/3792L9lN8h5TFRKb8/ZwBw69YtPHnyBH369IGeXvqh3szMDF26dMHp06dVbr+RKv7cHB+01a9fP7x79w6bN28W5wUEBEAul+c51nLlyqF9+/ZYvny5+Du3ceNGxMbGYsSIEdm+Vnk7lJ2dncblWR170tLScOLEiTzFm9GhQ4eQkpKCr776SuX7aWxsDB8fHxw7duyjt6GNzJ+b7t27w8DAQO23pVq1aqhVq5bKvF69eiE+Pl5sZbt3715Ur14dnp6eKnVq1aoVZDKZWp2aN2+OUqVKaRWnNsegtLQ0le1mvOx///599OrVCw4ODuKxTvlbm/m48d1336Fdu3b44osvsG7dOixZsgQ1atTINr569eoBEN6/LVu24PHjx1mWVX7msiuTWb4mhm/evEFsbCzKlCkDQLj/DhBaYhkaGqoMw4YNAwC1e3Wsra1VpuVyOQChIQEg3IQZFBQEOzs7DB8+HOXLl0f58uXV7iPKjdq1a6NJkybifVh79+5FeHh4jl92ZVxZJWsA0K5dO9jb24v37enr62sst2nTJigUCgwYMEAtyVTeg5HdTdIvX75EWloaHBwc1JZpmpedrNYRGxurMs/ExAQWFhY5ru/Zs2fQ19fXKo7bt2/jzJkzaNOmjdZdOCg5OjqqzXv69Cn27Nmj9vmrVq0agPTPX2xsrMYGU7l973ISGxurMU7ld0b5HhsbG6Nx48ZiYnjkyBH4+fmhWbNmSE1Nxb///ovAwEAAEBPD2NhYpKSkYMmSJWr1bdu2rUp9lTTFkp0GDRqgWrVqeP/+PYYOHaqWrJUvX15lu9OnTwcgtKRt2bIlAOE+uf/++w+hoaGYOHEigPTvt5K1tTU6duyI9+/fo3Xr1jkeKAFgwoQJmD9/Pk6fPo02bdrA2toaLVq00NgVlLGxsXgDelbS0tLQsmVL7NixA99//z2OHDmCs2fPisl15piVcWeU+fgVGxsLAwMDtXLafs7Kli2LZ8+eadXdjvKzlNXnLS0tDS9fvizU+DXJzfFBW9WqVUO9evUQEBAAQPiH5a+//kKnTp1QunTpPK9X2R2Q8ru3bNkyeHt7o06dOtm+Tvn+ZfVbkd2xJ/NxNy+Uv8X16tVTOzZs3rxZ7bhQUDLvY+VnKXMds/sdU5Z9+vQpLl++rFYfc3NzENFHH+tyOgb169dPZbvKkxivX79GkyZNcObMGcycORPHjh1DaGgoduzYAUD9uCGTyeDv74/379/DwcFBrb2CJk2bNsWuXbvEZN/Z2RnVq1fH33//rVZW+ZnTdLzKSr42P9y3bx9SU1PRrFkzAICNjQ0A4YDduXNnja+pXLlyrrfTpEkTNGnSBKmpqTh37hyWLFmC0aNHw97eHj179sxT7CNHjkS3bt1w4cIFLF26FJUqVcryJuGMbGxssu0naMiQIUhISEC1atUwcuRINGnSRON/LRs2bMCkSZPg4+ODw4cPw9PTU1xma2sLAHj06FGW2ylVqhRkMpnGxhK5bUCR1Toy/xho0zciIMSfmpqK6OjoHL+c3t7e6NatG/r37w9AuGE749mO7GiKx8bGBjVr1sSsWbM0vkaZkFlbW+Ps2bNqy/O78Ym1tbXGhkfKMwrK7wwAtGjRApMnT8bZs2fx6NEj+Pn5wdzcHPXq1UNgYCCePHmCSpUqwcXFBYDwGdDX10efPn2yPPvi7u6uMq3tPlSaMmUKrly5grp162Ly5Mlo3749ypUrJy7fs2cPEhMTxWnl+7tp0yYYGhpi7969Kj+Ou3bt0ridwMBArFixAvXr18fOnTuxffv2HM9uGhgYYMyYMRgzZgxevXqFoKAg/PDDD2jVqhUiIyNVWiC/ePECcrkcZmZmWa7v6tWruHTpEtauXYu+ffuK87NqoKMNa2trpKSkIDY2VuX7pO3nrFWrVjh8+DD27NmT47FOuf6sPm96enpan0HJuE5t4zc2Nlb5LChl/sHOzfEhN77++msMGzYMN27cwP379xEVFYWvv/76o9bZvHlzVK9eHUuXLoWZmRkuXLiAv/76K8fXKb/XL1680LhcmbhlpHxPMx93M1J+lxITE8UkHlB/j5Xb37Ztm3jG+2Nl3HZG2SWy0dHRcHJyEqc1fZaU5TS9Fkh/P2xsbKBQKLBmzRqN28p4LAVyf6zL6Rg0depUlZNH5ubmAIQrI0+ePMGxY8dUrsi9evVK43aioqIwfPhweHp64tq1axg3bpzGhjaZderUCZ06dUJiYiJOnz6NOXPmoFevXnBzc1PpDkn5mcv8fmQn384YRkREYNy4cbC0tBRbblWuXBkVK1bEpUuX4OXlpXFQvpl5oa+vjwYNGohn+rJL0DL/55vZ559/jrJly2Ls2LEICgrCsGHDtPogValSBbGxsWot9wBg1apV+Ouvv7B06VLs3r0br169yvLAVLp0aQQFBaFq1arw9fVVueRXqVIllC9fHmvWrNF4oAUAU1NT1K9fHzt27FA5C5KQkKDWkjQnf//9t8otAQ8fPsSpU6fEhD+3lJdtV6xYoVX5vn37YtOmTQgICMBXX32VY8u87LRv3x5Xr15F+fLlNX7+lImLr68vEhIS1FpJb9y4Mc/b1qRFixa4fv262md1/fr1kMlk8PX1Fed9+umnSElJwaRJk+Ds7IwqVaqI84OCgsRLs0omJibw9fVFWFgYatasqbG+2f3I5CQwMBBz5szBjz/+iMDAQLHldlJSklimRo0aGt9fZTc4Gc+Yv3v3Dn/++afadqKiotC7d2/4+Pjg1KlT6NixI/r3748HDx5oHauVlRW6du2K4cOH48WLF2qtM+/fvw8PD49s16H8/mf8wQWg1utCbij374YNG1Tma/s569+/PxwcHPD9999neWlIeWaicuXKcHJywsaNG1W+z2/evMH27dvFlsoFFb+bmxtiYmJUkp6kpCQcOnRIpZy2xwe5XJ6rsx5ffPEFjI2NsXbtWqxduxZOTk7iWeuP2c7IkSOxb98+TJgwAfb29mKPF9lxdXWFQqHAvXv3NC7P6tijp6eHpk2bZrle5a1OylbWSpmP+a1atYKBgQHu3buX5W9xbtnb28PY2Fht2//880+Wr8n8udmyZQtSUlLUfluuXbuGS5cuqczbuHEjzM3NxbOz7du3x71792Btba2xPnl5yIKSNscgNzc3le0pT3Ll5riRmpqKL774AjKZDAcOHMCcOXOwZMkS8TusDblcDh8fH/z0008AoNbK/P79+9DT08vdSTit70ak9BtaAwICKCQkhP7991/avn07jR49miwtLal06dJ09OhRldccPXqU5HI5tWzZkjZu3EjHjx+nnTt30uzZs6lr165q6858I6nyJnllo5EVK1ZQt27dxJa5+/fvp65duxIAsVUskfrNr/fv3ycA9Nlnn9G///5LoaGhaq3IlDeBamp5lhXljdMZt01EdPnyZVIoFCo3xytvis1483Lmer99+5Zat25NZmZmKu+lslWyp6cnrVu3joKDg2ndunUqDQ8OHz5Menp69Mknn9DOnTtp27ZtVK9ePXJxcclV4xNlq+S9e/fShg0bqEKFCmRubk53794Vy/bt25dMTU01vifZtUoeNGgQ7d69mw4dOkRz586lX3/9VSyTsVUyEdG+fftIoVBQ586dVRoGaZL5tUpPnjwhV1dXqlKlCi1fvpyOHDlC+/bto2XLllG7du0oMjKSiITWYJUqVSJLS0taunQpHTp0iEaNGqWxVbLypuuM+za7uDS1SnZwcKDff/9dbIEok8nUGl6lpqZSqVKlCIDKze3Km5gB0I4dO1Rec+3aNSpVqhTVr1+fAgICKDg4mHbv3k0LFy5UabSi3Ndbt27NsQ7K99HOzo58fX3FhgwhISFkaGhIo0aNyvH1R44cIQDUtWtXOnz4MP39999Ut25dqlixospN8ikpKeTj40P29vZig7MXL15Q2bJlqV69eiqfA02NT8aPH0/btm2j48eP0/r168nNzY1cXV1VWj2mpqaSpaUljRkzJtuYk5KSqHz58uTq6kobN26kgwcP0vDhw6lSpUpZ3lyfsScBIvVGAKmpqdS0aVOSy+U0e/bsXLdKJiI6c+YM2drakq2tLU2bNo0OHz5Mx44doz/++IN8fHzIyspKLKu8ub9t27b0zz//0JYtW6hevXpZtkrOz/jv379PhoaG1KxZM9q3bx9t376dfHx8yN3dPctWydkdH/r27UtyuZw2bdpEZ8+eFW/Qz64RxBdffEF2dnZkZGREP/zwg9pyTY03stqO0tu3b8na2poAqDUmzE7z5s3J29tbbX7GVsnKHhFGjRql1lMBkfrvWlxcHJUuXZpq1KhBO3fupD179lCXLl3E9zhj48nZs2eTgYEBDR48mHbu3EnHjh2jzZs309ixY2ny5Mliudwc3wYMGEDGxsa0YMECCgoKotmzZ1P16tVzbJV8+PBhWrRoEZmZmVGtWrXUvtcZWyUfOHBAbJX8008/ieVev35NtWvXJmdnZ1qwYAEFBgbSoUOH6I8//qBu3brR6dOnVd63zC28s5KbY5Amz58/p1KlSlGtWrVox44dtGfPHurZs6d4rMv4OZ04cSLp6empNLLs0KEDWVlZ0f3798V5mX9TJ02aRF9//TX99ddfdOzYMdq1axf5+vqSoaGhWmvrDh06qLTE10aeEkPlYGRkRHZ2duTj40OzZ8+mmJgYja+7dOkSde/enezs7MjQ0JAcHByoefPmtHLlSrV155QYhoSE0Oeff06urq4kl8vJ2tqafHx8aPfu3aoV03CQXbx4Mbm7u5O+vr7GA0l4eDgBoCFDhmj9nqSmppKbm5vKj/rr16+pSpUq5OHhIbagUho+fDgZGhqKrVI11TsxMZG6dOlCxsbGtG/fPnF+SEgItWnThiwtLUkul1P58uXVWnnt3r2batasSUZGRlS2bFmaO3euxoNfdt3V/PnnnzRy5EiytbUluVxOTZo0oXPnzqm8PreJYWpqKi1atIiqV69ORkZGZGlpSd7e3rRnzx6xjKbkLjg4mMzMzKh169b09u1bjdvL6rVKz549o5EjR5K7uzsZGhpS6dKlqW7dujRx4kSVVmaPHj2iLl26kJmZGZmbm1OXLl3o1KlTap+VK1euEAAaP358lvFkjCvzAfbhw4fUq1cvsra2JkNDQ6pcuTL9/PPPKi1HlT7//HMCQBs2bBDnJSUlkampKenp6al00aT04MED6tevHzk5OZGhoSHZ2tpSo0aNaObMmWKZ3CSGmg6USj///LPYoi4na9asocqVK5NcLqdy5crRnDlzaPXq1So/YMoD5ZEjR1Ree+rUKTIwMFBJQjO/twsWLKBGjRqRjY2N+Pnv37+/2L2OkjJJPX/+fI4xX79+nfz8/Mjc3JxKlSpF3bp1o4iIiDwnhkREr169on79+pGVlRWZmJiQn58f3bx5U+vEkEjovuh///sfVatWjUxMTEgul1OFChVo8ODBdOXKFZWyu3btogYNGpCxsTGZmppSixYt6L///lMpU1Dx79+/nzw9PUmhUFC5cuVo6dKlGo9H2hwfwsPDqWXLlmRubi4mGUTZJ4aHDx8Wf69u376ttlxTLFltJyN/f38yMDCgR48eqS3LyurVq0lfX5+ePHmiMt/Hx4eqVatGx44dIy8vL5LL5eTo6Eg//PCDWm8emt7js2fPUqNGjcjU1JScnJxoypQptGrVKrX9RkRiAmFhYUFyuZxcXV2pa9euKolJbo5vcXFxNGDAALK3tydTU1Pq0KGD+Duq6ftx/vx56tChg3iM/eKLL+jp06cq61Qey7dt20bVqlUjIyMjcnNzo4ULF6pt//Xr1/Tjjz9S5cqVxc9NjRo16Ntvv6Xo6GiV903bxDA3x6CsnDp1iry9vcnExIRsbW1pwIABdOHCBZXPqfJETub9GRsbq5aEZv5N3bt3L7Vp04acnJzEPKxt27Yq/+wRCa3OTUxMaMGCBVrVXSlXiaGu+/XXXwlAtv0baTJ//nwqVapUtokL0x3Lli0jU1NTlQMPKz569+6t0lULY7mRmJhIjo6O1K1bt1y97t27d2Rra6uxy5qipCCOb1n946FJdv/ks9xZtWoVmZqaat1Nj1K+d1dTHIWFhWHHjh2YPn06OnXqJLZY1dbw4cNhaWmp9nQJppuCg4MxcuRIyR/7yHLv3r172Lx5s3g/DmPaevbsGU6ePImhQ4fi6dOnGD9+fK5er3wa08KFC7VqVS4VPr7phpSUFPz000+YMGFCrhuZFY+Hohawzz//HNHR0WjSpEnuHx0D4Qv/559/5vnRQqx4UfZ3yYqfiIgILF26FJ988onUobBiZt++ffj666/h6OiI5cuX59hFjSaDBg3Cq1evcP/+fa26YJICH990Q2RkJHr37o2xY8fm+rUyogzN1RhjjDHGWInFl5IZY4wxxhgATgwZY4wxxtgHnBgyxhhjjDEAnBgyxhhjjLEPODFkjDHGGGMAODEEIDyjs2bNmrCwsICFhQW8vb1x4MCBLMvv2LEDfn5+sLW1Fctnfv4ny5vc7ouTJ0+icePGsLa2hkKhQJUqVbBo0aJCjFi35XZ/ZPTff//BwMAAnp6eBRtkCZHbfXHs2DHIZDK14ebNm4UYte7Ky3cjMTEREydOhKurK+RyOcqXL481a9YUUsS6K7f7wt/fX+N3I7d9GOsq7scQgLOzM+bOnYsKFSoAANatW4dOnTohLCxM4wflxIkT8PPzw+zZs2FlZYWAgAB06NABZ86cQe3atQs7fJ2S231hamqKESNGoGbNmjA1NcXJkycxePBgmJqaYtCgQYUdvs7J7f5QiouLw1dffYUWLVrg6dOnhRWuTsvrvrh16xYsLCzEaVtb2wKPtSTIy/7o3r07nj59itWrV6NChQqIiYlBSkpKYYatk3K7L3755RfMnTtXnE5JSUGtWrXQrVu3Qou5SCuQ57DogFKlStGqVau0Lu/h4UHTpk0rwIhKrtzui88//5x69+5dgBGVbNrsjx49etCPP/5IU6ZMoVq1ahVOYCVQdvtC+TxsTc/TZgUju/1x4MABsrS0pNjY2EKOqmTKze/Gzp07SSaTqT1XvaTiS8mZpKamYtOmTXjz5g28vb21ek1aWhoSEhJQunTpAo6uZMnLvggLC8OpU6fg4+NTwNGVPNruj4CAANy7dw9TpkwpxOhKltx8N2rXrg1HR0e0aNECwcHBhRRhyaLN/ti9eze8vLwwb948ODk5oVKlShg3bhzevXtXyNHqtrz8bqxevRqffvopXF1dCzi6YkLqzLSouHz5MpmampK+vj5ZWlrSvn37tH7tvHnzqHTp0vT06dMCjLDkyMu+cHJyIiMjI9LT06Pp06cXQpQlR272x+3bt8nOzo5u3bpFRMRnDPNZbvbFzZs36ffff6fz58/TqVOnaOjQoSSTyej48eOFGLFuy83+aNWqFcnlcmrXrh2dOXOG9u3bR66urvT1118XYsS6K6+/4U+ePCF9fX3avHlzAUdYfHBi+EFiYiLduXOHQkNDafz48WRjY0PXrl3L8XUbN24kExMTCgwMLIQoS4a87Iv79+/T5cuX6ffff6fSpUvTxo0bCyla3aft/khJSSEvLy9asWKFOI8Tw/yV1+OUUvv27alDhw4FGGHJkpv94efnR8bGxvTq1Stx3vbt20kmk9Hbt28LK2SdldfvxuzZs8na2poSExMLIcrigRPDLLRo0YIGDRqUbZlNmzaRQqGgvXv3FlJUJZM2+yKjGTNmUKVKlQowopItq/3x8uVLAkD6+vriIJPJxHlHjhyRIFrdltvvxsyZM6lKlSoFGFHJlt3++Oqrr6h8+fIq865fv04A6Pbt24URXomizXcjLS2NKlSoQKNHjy6kqIoHbpWcBSJCYmJilsv//vtv9OvXD3///TfatWtXiJGVPDnti48tz3Inq/fXwsICV65cUZm3fPlyHD16FNu2bYO7u3thhVhi5PazHhYWBkdHxwKMqGTLbn80btwYW7duxevXr2FmZgYAuH37NvT09ODs7FyYYZYI2nw3jh8/jrt376J///6FFFXxwIkhgB9++AFt2rSBi4sLEhISsGnTJhw7dgwHDx4EAEyYMAGPHz/G+vXrAQhJ4VdffYVffvkFDRs2RHR0NABAoVDA0tJSsnrogtzui2XLlqFs2bKoUqUKAKFfw/nz5+Obb76RrA66JDf7Q09PD9WrV1d5vZ2dHYyNjdXms9zL7Xdj8eLFcHNzQ7Vq1ZCUlIS//voL27dvx/bt26Wshs7I7f7o1asXZsyYga+//hrTpk3D8+fP8d1336Ffv35QKBRSVqXYy+2+UFq9ejUaNGjAx6dMODEE8PTpU/Tp0wdRUVGwtLREzZo1cfDgQfj5+QEAoqKiEBERIZb/7bffkJKSguHDh2P48OHi/L59+2Lt2rWFHb5Oye2+SEtLw4QJE/DgwQMYGBigfPnymDt3LgYPHixVFXRKbvcHKzi53RdJSUkYN24cHj9+DIVCgWrVqmHfvn1o27atVFXQKbndH2ZmZggMDMQ333wDLy8vWFtbo3v37pg5c6ZUVdAZeTlOxcXFYfv27fjll1+kCLlIkxERSR0EY4wxxhiTHvdjyBhjjDHGAHBiyBhjjDHGPuDEkDHGGGOMAeDEkDHGGGOMfcCJIWOMMcYYA8CJIWOMMcYY+4ATQ8YYY4wxBoATQ60lJiZi6tSp/Ki1IoD3RdHC+6Po4H1RtPD+KDp4X2ivRHVwvXz5cvz888+IiopCtWrVsHjxYjRp0kSr18bHx8PS0hJxcXGwsLAo4EhZdnhfFC28P4oO3hdFC++PooP3hfZKzBnDzZs3Y/To0Zg4cSLCwsLQpEkTtGnThh/nxRhjjDH2QYlJDBcuXIj+/ftjwIABqFq1KhYvXgwXFxesWLFC6tAYY4wxxooEA6kDKAxJSUk4f/48xo8frzK/ZcuWOHXqlMbXJCYmqtyL8OLFCwBAZGQkLC0tCy5YlqOEhAQAwOPHjxEfHy9xNIz3R9HB+6Jo4f1RdMTFxQEAUlJSJI6k6CsRieHz58+RmpoKe3t7lfn29vaIjo7W+Jo5c+Zg2rRpavOrV69eIDGy3PPw8JA6BJYB74+ig/dF0cL7o+i4fv06PvnkE6nDKNJKRGKoJJPJVKaJSG2e0oQJEzBmzBhxOjIyEtWrV8fZs2fh6OhYoHEyxhhjLP9ERUWhfv36KFu2rNShFHklIjG0sbGBvr6+2tnBmJgYtbOISnK5HHK5XJxWXj52dHSEs7NzwQXLGGOMsQKhp1dimlbkWYl4h4yMjFC3bl0EBgaqzA8MDESjRo0kiooxxhhjrGgpEWcMAWDMmDHo06cPvLy84O3tjd9//x0REREYMmSI1KExxhhjjBUJJeKMIQD06NEDixcvxvTp0+Hp6YkTJ05g//79cHV1lTo0lkcxMTEYPHgwypYtC7lcDgcHB7Rq1QohISFiGZlMhl27duXL9sLDwyGTyXDx4sVsyx07dgwymQyvXr1SW+bp6YmpU6eKZbIb1q5dCwDYvn07mjVrBktLS5iZmaFmzZqYPn262FJeGzt27ICfnx9sbW1hYWEBb29vHDp0SK3c9u3b4eHhAblcDg8PD+zcuVNl+Zw5c1CvXj2Ym5vDzs4On332GW7duiUuT05Oxv/+9z/UqFEDpqamKFOmDL766is8efIkxxhfvnyJPn36wNLSEpaWlujTp4/aezhq1CjUrVsXcrkcnp6eWtf/+PHjqFu3LoyNjVGuXDmsXLlSZfm1a9fQpUsXuLm5QSaTYfHixVqt98qVK/Dx8YFCoYCTkxOmT5+OzM8MyGnbmb148QLffPMNKleuDBMTE5QtWxYjR44UW1UCwmexf//+cHd3h0KhQPny5TFlyhQkJSXlGHNO+xgQHgbg7u4OY2Nj1K1bF//++69W7wdjrPgrMYkhAAwbNgzh4eFITEzE+fPn0bRpU6lDYh+hS5cuuHTpEtatW4fbt29j9+7daNasWa4SJm1p84ObG40aNUJUVJQ4dO/eHa1bt1aZ16NHD0ycOBE9evRAvXr1cODAAVy9ehULFizApUuX8Oeff2q9vRMnTsDPzw/79+/H+fPn4evriw4dOiAsLEwsExISgh49eqBPnz64dOkS+vTpg+7du+PMmTNimePHj2P48OE4ffo0AgMDkZKSgpYtW+LNmzcAgLdv3+LChQuYNGkSLly4gB07duD27dvo2LFjjjH26tULFy9exMGDB3Hw4EFcvHgRffr0USlDROjXrx969Oihdd0fPHiAtm3bokmTJggLC8MPP/yAkSNHYvv27WKZt2/foly5cpg7dy4cHBy0Wm98fDz8/PxQpkwZhIaGYsmSJZg/fz4WLlyYq21n9uTJEzx58gTz58/HlStXsHbtWhw8eBD9+/cXy9y8eRNpaWn47bffcO3aNSxatAgrV67EDz/8kG3M2uxjfhgAYyUcMa1ERkYSAIqMjJQ6FEZEL1++JAB07NixLMu4uroSAHFwdXUlIqK7d+9Sx44dyc7OjkxNTcnLy4sCAwPVXjtjxgzq27cvWVhY0FdffaWyLgDk4+OjcbvBwcEEgF6+fKm2rFatWjRlyhS1+X379qVOnTqpzDtz5gwBoMWLF2f5HnwMDw8PmjZtmjjdvXt3at26tUqZVq1aUc+ePbNcR0xMDAGg48ePZ1nm7NmzBIAePnyYZZnr168TADp9+rQ4LyQkhADQzZs31cpPmTKFatWqleX6Mvr++++pSpUqKvMGDx5MDRs21Fje1dWVFi1alON6ly9fTpaWlvT+/Xtx3pw5c6hMmTKUlpaWp21nZcuWLWRkZETJyclZlpk3bx65u7tnux5t9nH9+vVpyJAhKmWqVKlC48ePz1XMjBUl/BuuvRJ1xpDpDjMzM5iZmWHXrl1ZPhQ9NDQUABAQEICoqChx+vXr12jbti2CgoIQFhaGVq1aoUOHDmpnRH7++WdUr14d58+fx6RJk3D27FkAQFBQEKKiorBjx44CrCGwYcMGmJmZYdiwYRqXW1lZAUi/xH3s2DGt152WloaEhASULl1anBcSEoKWLVuqlGvVqlWWncAD6Z3GZlyPpjIymUyMV5OQkBBYWlqiQYMG4ryGDRvC0tIy2+1rI6t6nTt3DsnJyR+1Xh8fH5XeC1q1aoUnT54gPDxc620rbytQvkYT5fNdDQyyvi08Li5ObT+4ublh6tSpKjFnt4+VDwPIXCa7hwEwxnQLJ4asWDIwMMDatWuxbt06WFlZoXHjxvjhhx9w+fJlsYytrS0AIYFycHAQp2vVqoXBgwejRo0aqFixImbOnIly5cph9+7dKtto3rw5xo0bhwoVKqBChQri662treHg4JBtMpQf7ty5g3LlysHQ0DDbcoaGhuL9aNpasGAB3rx5g+7du4vzoqOjc9UJPBFhzJgx+OSTT7Ls+P39+/cYP348evXqle2D66Ojo2FnZ6c2387OLsvtayureqWkpOD58+f5vl7lMm23bWJigsqVK2e5n2NjYzFjxgwMHjw4y1ju3buHJUuWqDWmK1++PGxsbHKMWRlvXh4GwBjTLZwYsmKrS5cuePLkCXbv3o1WrVrh2LFjqFOnjthoIytv3rzB999/Dw8PD1hZWcHMzAw3b95UO2Po5eVVgNHnjLLpgD0jJycn3Lx5E/Xr19dqvX///TemTp2KzZs3qyVjuekEfsSIEbh8+TL+/vtvjcuTk5PRs2dPpKWlYfny5eL8IUOGiGd8zczMstx2TtvXJON6MyZJmuqV1TZzQ5v15lSmfv36uHnzJpycnNTWHx8fj3bt2sHDwwNTpkzRGMOTJ0/QunVrdOvWDQMGDFBZduTIEYwYMSLHmDPPy83ngDGmW0pMdzVMNxkbG8PPzw9+fn6YPHkyBgwYgClTpsDf3z/L13z33Xc4dOgQ5s+fjwoVKkChUKBr165qDUxMTU3zFJPyzFhcXJza5dNXr15p/aztSpUq4eTJk0hOTs7xrKG2Nm/ejP79+2Pr1q349NNPVZY5ODho3Qn8N998g927d+PEiRMaO3xPTk5G9+7d8eDBAxw9elTlbOH06dMxbtw4tW0/ffpUbT3Pnj3LshN6TTK2GFduM6t6GRgYwNraWut1Z5bVeoH0M4cfs+2EhAS0bt0aZmZm2Llzp8bPwJMnT+Dr6yt2wZXXmJXx5uVhAIwx3cJnDJlO8fDwEFvIAsJl1tTUVJUy//77L/z9/fH555+jRo0acHBwyPb+LiUjIyMAUFtfZhUrVoSenp54T6NSVFQUHj9+jMqVK2tVl169euH169cqZ9sy0tQdTnb+/vtv+Pv7Y+PGjWjXrp3acm9vb7VO4A8fPqzSCTwRYcSIEdixYweOHj0Kd3d3tfUok8I7d+4gKChILQGys7MTL89XqFBB3HZcXJx4HycAnDlzBnFxcbnqhD7jepVnQ7Oql5eX10cl3N7e3jhx4oTKPxSHDx9GmTJl4Obm9lHbjo+PR8uWLWFkZITdu3fD2NhYrczjx4/RrFkz1KlTBwEBAVo90SGnfcwPA2CMcatkLXGLpqLl+fPn5OvrS3/++SddunSJ7t+/T1u2bCF7e3vq16+fWK5ixYo0dOhQioqKohcvXhAR0WeffUaenp4UFhZGFy9epA4dOpC5uTmNGjVKfJ2mlqnJycmkUCho5syZFB0dTa9evcoyvqFDh1LZsmVp586ddP/+fTp58iT5+PhQjRo1NLYs1dQqmUho1aqvr0/fffcdnTp1isLDwykoKIi6du0qtlZ+9OgRVa5cmc6cOZNlPBs3biQDAwNatmwZRUVFiUPGOvz333+kr69Pc+fOpRs3btDcuXPJwMBApaXw0KFDydLSko4dO6aynrdv34rvUceOHcnZ2ZkuXryoUiYxMTHL+IiIWrduTTVr1qSQkBAKCQmhGjVqUPv27VXK3Llzh8LCwmjw4MFUqVIlCgsLo7CwsGzXff/+fTIxMaFvv/2Wrl+/TqtXryZDQ0Patm2bWCYxMVFcl6OjI40bN47CwsLozp07Wa731atXZG9vT1988QVduXKFduzYQRYWFjR//vxcbfvMmTNUuXJlevToERERxcfHU4MGDahGjRp09+5dlfcwJSWFiIgeP35MFSpUoObNm9OjR49UymTUvHlzWrJkiTitzT7etGkTGRoa0urVq+n69es0evRoMjU1pfDw8CzfC8aKOv4N1x4nhlriD1XR8v79exo/fjzVqVOHLC0tycTEhCpXrkw//vijmKQQEe3evZsqVKhABgYGYnc1Dx48IF9fX1IoFOTi4kJLly4lHx+fHBNDIqI//viDXFxcSE9PL8vuapTxTZ8+napWrUoKhYJcXV3J399f7YdbKavEkIho8+bN1LRpUzI3NydTU1OqWbMmTZ8+Xeyu5sGDBwSAgoODs4zHx8dHrbsdANS3b1+Vclu3bqXKlSuToaEhValShbZv366yXNM6AFBAQIBKLJqG7OIjIoqNjaUvv/ySzM3NydzcnL788ku1LnmyqseDBw+yXfexY8eodu3aZGRkRG5ubrRixQqV5VnFnd0+JiK6fPkyNWnShORyOTk4ONDUqVPFrmq03bayeyNlHZTT2dUzICAgyzIZubq6qnWPlNM+JiJatmwZubq6kpGREdWpUyfb7ogYKw74N1x7MqJM3fQzjR49egQXFxdERkZqvKeKMcYYY0UT/4Zrj+8xZIwxxhhjADgxZIwxxhhjH3BiyBhjjDHGAHBiyBhjjDHGPuDEkDHGGGOMAeDEkDHGGGOMfcCJIWOMMcYYA8CJIWOMMcYY+4ATQ8YYY4wxBoATQ8YYY4wx9gEnhowxxhhjDAAnhowxxhhj7ANODJnOi75wFmu8KiDixBGpQ2GM5cGJE8Do0cC9e1JHwpjuM5A6AMYK2qZW3gCAHV1aYvSzVImjYYzllo+P8PeXXwAiaWNhTNfxGUNWosQ/ipA6BMZYLuzbJ3UEjJUsnBgynTfkbqw4vqa2u4SRMMZyS49/pRgrVPyVYzrP2NJK6hAYY3nUpo3UETBWshTrxDA8PBz9+/eHu7s7FAoFypcvjylTpiApKUmlnEwmUxtWrlwpUdRMas+uXZY6BMZYHj17JnUEjOm2Yp0Y3rx5E2lpafjtt99w7do1LFq0CCtXrsQPP/ygVjYgIABRUVHi0LdvXwkiZlL5bPN+cXxDs9oSRsIYy62Mh2s7O+niYKwkKNatklu3bo3WrVuL0+XKlcOtW7ewYsUKzJ8/X6WslZUVHBwcCjtEVkS4NW8ldQiMsTxauxZYt07qKBgrGYr1GUNN4uLiULp0abX5I0aMgI2NDerVq4eVK1ciLS1NguiYlMydXMTx59evSBgJY4wxVjTpVGJ47949LFmyBEOGDFGZP2PGDGzduhVBQUHo2bMnxo4di9mzZ2e7rsTERMTHx4tDQkJCQYbOCsHX59N7x/3Lx1O6QBhjH2X3bqkjYEx3FcnEcOrUqRobjGQczp07p/KaJ0+eoHXr1ujWrRsGDBigsuzHH3+Et7c3PD09MXbsWEyfPh0///xztjHMmTMHlpaW4uDh4ZHv9WSFS09fX2X6fdwraQJhjOVa797p4ydPShcHY7pORlT0+pF//vw5nj9/nm0ZNzc3GBsbAxCSQl9fXzRo0ABr166FXg4dX/3333/45JNPEB0dDXt7e41lEhMTkZiYKE4/fvwYHh4eiIyMhLOzcy5rxIqK65vX4/CIrwEA7i3bodMGPvXAWFFHpNqfYVoaIJNJFw8rfh49egQXFxf+DddCkWx8YmNjAxsbG63KPn78GL6+vqhbty4CAgJyTAoBICwsDMbGxrCyssqyjFwuh1wuF6fj4+O1iocVbVW79RYTwweH+ZEKjBUHmZNATgoZKzhFMjHU1pMnT9CsWTOULVsW8+fPx7MMHVwpWyDv2bMH0dHR8Pb2hkKhQHBwMCZOnIhBgwapJH6sZJBl+sch+c0bGJqaShQNYywvDh4EMnRIwRjLR0XyHkNtHT58GHfv3sXRo0fh7OwMR0dHcVAyNDTE8uXL4e3tjZo1a+KXX37B9OnTsWDBAgkjZ1L6OvSOOL6ptbeEkTDGtBUWlj7Oh2/GCk6xPmPo7+8Pf3//bMtk7uuQMUu3cuL4+1cvJYyEMaatNWvSx6dOlSwMxnResT5jyFhedVi/AwDwJvoJ4h9FSBwNYywnXl7p4w0bShcHY7qOE0NWIrm3bC+Or6ntLmEkjDFt3L2bPh7B/8sxVmA4MWQlUuY+DRljRdv69enjbm6ShcGYzuPEkJVYXXYGiePPrl2WMBLGWE4ydCvL3dUwVoA4MWQllssnvuL49s6fShgJYywnnTpJHQFjJQMnhowBeP8iFpSWJnUYjLEsvHqVPv7ihWRhMKbzODFkJZrPrMXi+GpuhMJYkeXjkz5ubS1dHIzpOk4MWYlWe9A34vjrJ49QBB8dzhgDMHSo1BEwVjJwYshKPHe/tuL45TUrJIyEMZaV+/fTx+vWlS4OxnQdJ4asxOvw5y5xPHj8N1kXZIxJIjUVKF8+ffqPP6SLhTFdx4khK/G4T0PGijZ9fdW+C5OTJQuFMZ3HiSFjAJr/vEwcv7njbwkjYYxpcudO+niDBtLFwZiu48SQMQA1vhokjh8c3FvCSBhjmvCJfcYKByeGjAGQ6al+FZLfvpUoEsaYJpmfdsIdCDBWMDgxZOyDXkfPi+NB3w6UMBLGmCbz5qWPL10qXRyM6TJODBn7wK6Gpzh+a8cm6QJhjGk0ZEj6+OrV0sXBmC7jxJCxDDx69hXH4x9FSBgJYyyzXbvSx3/5RbIwGNNpnBgylkHz+ekdXK/hR+QxVqQoFOnjtrbSxcGYLuPEkLEMDORyleln1y5LFAljLLOAgPTxatWki4MxXcaJIWOZeA4YIY7v+7qbhJEwxjLav1/qCBjTfZwYMpaJz+zF4virB3elC4QxJkpKUp1OSZEmDsZ0HSeGjGUiy9Rh2tvnzySKhDGmZGioOp2YKE0cjOk6TgwZ06DLjkBx/PeqDhJGwhhTytj4ZORI6eJgTJdxYsiYBi5NmksdAmMsgyVLgHfv0qddXaWLhTFdxokhY1nJcEk57LdfJQyEsZItLQ0YNSp9uk0bYNIk6eJhTJdxYshYFoY/iBPHj//4rYSRMFayLVmiOr1jhzRxMFYSGEgdAGNFlaGpqdQhMMYAxMamj6ekAPr60sXCmK4r9mcM3dzcIJPJVIbx48erlImIiECHDh1gamoKGxsbjBw5EkmZ+z5gTIMO69NPTfAj8hiThomJ8Nfbm5NCxgqaTpwxnD59OgYOHChOm5mZieOpqalo164dbG1tcfLkScTGxqJv374gIizJfH2CsUzKt+kkjm/r5It+5+9JGA1jJdP798LfkBBp42CsJNCJxNDc3BwODpq7FDl8+DCuX7+OyMhIlClTBgCwYMEC+Pv7Y9asWbCwsCjMUFkx5NSwCR6f/hfxEeFSh8JYiRQaKnUEjJUcxf5SMgD89NNPsLa2hqenJ2bNmqVymTgkJATVq1cXk0IAaNWqFRITE3H+/HkpwmXFTNOZC8TxiBNHJIyEsZLJyyt9vGtX4OJFyUJhTOcV+zOGo0aNQp06dVCqVCmcPXsWEyZMwIMHD7Bq1SoAQHR0NOzt7VVeU6pUKRgZGSE6OjrL9SYmJiIxQ9f6CQkJBVMBVuS9vHNTHN/RpSVGP0uVMBrGSp5GjdLHt28Xhq5dgc2bAT2dOL3BWNFRJL9SU6dOVWtQknk4d+4cAODbb7+Fj48PatasiQEDBmDlypVYvXo1YjM0Y8v8iDMAICKN85XmzJkDS0tLcfDw8Mj/irJiwdW3lcr0+7hX0gTCWAnVsiWwdCmQ8TC8bZt6NzaMsY8nIyKSOojMnj9/jufPn2dbxs3NDcbGxmrzHz9+DGdnZ5w+fRoNGjTA5MmT8c8//+DSpUtimZcvX6J06dI4evQofH19Na4/8xnDx48fw8PDA5GRkXB2ds5jzVhxRWlp+MVeeFirYz1v9Nh/UuKIGCuZkpIAuVwYd3EBIrizAKaFR48ewcXFhX/DtVAkLyXb2NjAxsYmT68NCwsDADg6OgIAvL29MWvWLERFRYnzDh8+DLlcjrp162a5HrlcDrny6AMgPj4+T/Ew3SDT00PNr4fgcsBKRIVy00jGpGJkBJibAwkJQKdOOZdnjOVOkbyUrK2QkBAsWrQIFy9exIMHD7BlyxYMHjwYHTt2RNmyZQEALVu2hIeHB/r06YOwsDAcOXIE48aNw8CBA7lFMsuVqt37iOMXVv4iYSSMlVyPHwtJIQDcuSNtLIzpomKdGMrlcmzevBnNmjWDh4cHJk+ejIEDB+Lvv/8Wy+jr62Pfvn0wNjZG48aN0b17d3z22WeYP3++hJGz4sihbgNx/MSkMRJGwljJNW9e+jhfyGEs/xXJS8naqlOnDk6fPp1jubJly2Lv3r2FEBHTZTKZDFbuFfDqwV0AQGpyMvQNDSWOirGS5bvvgF9/FcbT0qSNhTFdVKzPGDJW2L44kt7TbvD/RkgYCWMlU8Z2A2fOSBcHY7qKE0PGckFunn5f6tU/V0kYCWOMMZb/ODFkLJc6bdwjjseF35cwEsYYYyx/cWLIWC65+7UVx/cP/lLCSBgr2Ro2lDoCxnQPJ4aM5YGlW3kAwNMLZ0F8BzxjktCi7SFjLJc4MWQsD1qvWC+OHx7ZX8JIGCt5KlVKH09Oli4OxnQRJ4aM5YGjV/o1rBub12dTkjGW3ypWTB8veg91Zax448SQsTxqNudXcTz8yEEJI2GsZKlXL308Nla6OBjTRZwYMpZHtfoPE8d39WyHt8+fSRgNYyVHcHD6eEyMdHEwpos4MWQsj2Qymcr0AW6hzFiB2rIFaNYMOH48fV61apKFw5hO4sSQsY/Q9/QNcTzyxBG8fcanLxgrCG3aAD16qCaFFSoABsX6wa6MFT2cGDL2EUqVr6Qy/buHo0SRMKa7Jk0CDma6jffBA+DOHWniYUyXcWLI2Efy+3W11CEwptNmzlSdXrAAcHOTJBTGdB4nhox9pGpf+KtMP714TppAGCsBUlOBMWOkjoIx3cWJIWP5wHPgN+L4334NEDR2CFK5513GPsqbN+qPvUtMlCYWxkoKTgwZyweNflC91nV1/R84v2yBRNEwphv8/YEzZ1TnLVokSSiMlRjcnouxfGBkZobO2w5hR9dW4rxTsybi1KyJKF2pKoytSiM1OQmfTJoNlybNJYyUsaLl/Xvgn3+A+HggIUEYYmOBmzeBwED18h4ehR8jYyUJJ4aM5ZM3MdEa57+4nd6lzfbOfuK4wtoGHdbvhIHCBHoGBsKgbwAjcwuY2NoVeLyM5ZfUVCAlRXVITgaePAEGDQLOn9f8OoUCePcud9v67LOPDpcxlg1ODBnLJw516ueq/LvY59jSronGZfa166HbnuPQNzJS60ibsYJCJJzBe/dOGE6fBrp2Lbjt5SYp/OUXYPDggouFMSbgxJCxfFKqfCWMfpaqMu/u3h3Y+3W3XK/raVgoljqb5FdoGn266HdU792/QLfB1KWlCWfUEhOFJCw+Pn1ISABev06/pPr0KTBvXvpr27UDXFyEM3RpacLfjOOa5qWmAlevAo8eSVfn7OzaBZQqBZiapg9mZsJfIyOpo2Os5OHEkLECVKF9ZzFZPDZhFC6uWipxROmC/zcCdjVqw9qjBvQNDaUOp1AQpSdk79+rjmsaMi9PTBSGpCT18czzjh3L//j37cv/deanBg2AHTsABwdAj5s2MlYscWLIWCFpNucXVGjfGeFHD+Hh0UMgSoO5kwsqduwKmZ6ekLV8QGlpiLl0oUATydSkJGz8tB4AoGqPr2BoYorkt28ASkNaSgogk0GmpweZTAZ9uTEafjcZZo5OBRaPNpKTgeBg4a1KSkpPxuLjARMTICoKiIgAoqOFBgzK4e3b9MSuONPTAyZPBvT1hUFPT/VvduMnTwJXrgiPkDM0FP5mHFcoAAsLwNxc+KtQCPONjNLLGBqmj5cpA9SrJ/U7whjLbzKiDL9GLEuPHj2Ci4sLIiMj4ezsLHU4rIQgIiS+ein0iZiWBqI0UNqHITUVaampeHrpHJIS4pGamISUd2/h7tcWekZGoNRURBw/guMTR+dbPE2nz0edod/m2/oyO38e+Pbb9EuhyoYMysui167l7/YUCsDYGJDLhb/ZDXJ5+mBkpD6eeZ6+PnDihPBaZbKVMfHKOG5mJiS2hoZ8po2xgsC/4drjxFBL/KFiuiItNRV39+7AuxfPYWhiCplMhtfRUTizYAZS3r7Vah15vT8xKQk4ehRo0ybXL9VKz55A+fKAoyNgbQ3Y2AClSwuJV+ZEz9AQ4HY9jJUM/BuuPU4MtcQfKlYSJL95g/jIcPzZpGa25YiAZBgjiczwJs0ap98PwEW9wejUVQFLKz28fStcvn3zBuL448dAeHj+xPnLL4CTk3C2rUwZYbCy4kSPMaYZ/4Zrj+8xZIyJDE1NYV2lGroHXcaKL2cg7vFTvEm1wo3kNgh5PwgAYCx7hSQyQ5qGw8e69fkTR4MGwI8/Cmf+Ml7CtbERLrkyxhgrGJwYMsZUpKUBfr2q4fr1TRqXvyerbF/fQjEXctlrGOknwjDtNQxlb9F0/Hdwr1cdZmbCPXTlywO2tgUQPGOMsY9SrBPDY8eOwdfXV+Oys2fPot6HJnOaOghesWIFhgwZUqDxMVYcyWTCmbrr17MuM2oUMH06YGJCuLHxDxwZOxQKG1u8e/5MY/l3i//CdQCjniYLLbAZY4wVScX6HsOkpCS8ePFCZd6kSZMQFBSE+/fviwmhTCZDQEAAWrduLZaztLSEQqHQelt8fwIraZKThW5gTp7U/Biytm0196v3Pu4VEl+9xMt7t7GrR1u15f3CHsDCuWz+B8wYY1ng33DtFet/3Y2MjODg4CAO1tbW2L17N/r166d2ltDKykqlbG6SQsZKIkNDoWVvp05CY5M7d1SX798vnF0cMULoO1DJ2NIKlq7ucGveCqOfpaLT33tVXremtjsW2+oj/OghvH+p+o8dY4wxaRXrxDCz3bt34/nz5/D391dbNmLECNjY2KBevXpYuXIl0tLSsl1XYmIi4uPjxSEhIaGAomaseKhQQUgQo6IAb+/0+cuWAa6uQpIokwG1awNxcenL3T9tg1ExKej6T7DK+nb1aIuVlWyx2FYfi231cWPLnyjGFzAYY0wn6FRiuHr1arRq1QouLi4q82fMmIGtW7ciKCgIPXv2xNixYzF79uxs1zVnzhxYWlqKg4eHR0GGzlix4eAAnDoF/PknYGmpvvzixfSuY/r3F5JJmUwG50ZNMezBK7g0aQ4Hr4Zqrzs03B+/2Blgsa0+Hp06UeD1YIwxpq5I3mM4depUTJs2LdsyoaGh8PLyEqcfPXoEV1dXbNmyBV26dMn2tQsWLMD06dMRl/G0RiaJiYlIzPD8rMePH8PDw4PvT2AsEyJg927N9yECwn2K5ubq85PfvsX1TesQ/L8RasucP/FF522HoKevn7/BMsZKJL7HUHtFMjF8/vw5nj9/nm0ZNzc3GBsbi9MzZszAkiVL8PjxYxgaGmb72v/++w+ffPIJoqOjYW9vr1VM/KFiLGcJCUKn0xkZGAiPtQOER8XNmgW8ewd8/73QNyEgPI3lVwcjtfXpy+VI/fAPWt3hY0FE8BrxHUxs7QqyGowxHcO/4dorkt3V2NjYwMbGRuvyRISAgAB89dVXOSaFABAWFgZjY2NYWVl9RJSMsczMzYWnm7i5pc9TJoWA8Ei8774TxidPFsoLt+/qY8SIFFhdmgLZ1T9RWl9ozZKa4az9+WULAAAXli8U57VathbWVarDtnot7gaHMcbyQZFMDHPr6NGjePDgAfr3V3926549exAdHQ1vb28oFAoEBwdj4sSJGDRoEOTK0xWMsXzj6ipcXgaEM4MvXgAXLgC9ewN+fsD27ellM7bpWrpUBmD6hwGoXSsZu9ffx/3tq/D8+mVEHAtS29ah4f7iuGO9Ruix/9/8rxBjjJUgOpEYrl69Go0aNULVqlXVlhkaGmL58uUYM2YM0tLSUK5cOUyfPh3Dhw+XIFLGShaFQnimsZOTakvld++A2Fhh+PlnYO9e1eUAEHbJEFSqMppO+1mcl/Q6AfGRD3Fi8ljEhd9HXPh9cVlU6Ck8OfMfyjRoXNDVYowxnVUk7zEsivj+BMYK1uPHQOav1rZtgLu76vOSlYOJCZD2Lg4rypdWeY1dzTr4bPN+yC2toK/FrSWMMd3Hv+Ha04kzhoyx4s/JCbh5E6hSJX1e167Zv6ZuXUv0HxGGsIU/CM9nlr3B0wtvcKuSF6z0HkEmA5rPX46afQcXbPCMMaYjODFkjBUZlSsDDx8K9ylq4/x54Pz5mgD2qi0ra3AW/ubdcHTcMNhWqwVHDX0nMsYYU8WJIWOsSClbVmi9/McfQuIXHw8kJgrzkpOF8devgbQ04dLzgQOa1xORUh/TXz6ElV4E/ms2F81/8kKrNgYoV65w68MYY8UJJ4aMsSLH0BAYNky7sklJwNq1wJ0rsTj5VzBOv1K9/vwqrSy2vVmObR/60bbXv4YqVQ1g71ERhoZ6MDQU+lpU/lWOP3sGXL0KlCoFmJkJr5XJAGWf23XqAKNHp/fFyBhjuoAbn2iJb1xlrGgLHDUA1zYG4GFyfSyOCym07VasKCSLyqTyYwaZTEh0U1OFs6OAkJR27Qr4+AjLGWO5x7/h2uMzhowxneA18n+IOncauH0Wi2z0QQTEprkjhYyRDAUS08zxniyQSOZ4T+ZIhSHSYIBUMoB9vaZw8W2H5GShQ+6YGCAgQLvt3rlTsPUCgGXLhP4fO3cu+G0xxko2TgwZYzqhVPmK+Oq/qwCAl/fu4NgPo+AYH4dnVy/CwFgB+1p14FjPE7E3ruLuvo2qL742H58O+B3Ve6d3kr9mjdBR99u3wMuXwr2NKSl5H1JTs18+ZUr29QsN5cSQMVbw+FKylvg0NGO643X0E2zt0Axx4fdU5rs2bwW5hSWq9+6Psj6fFmpMaWnAxYtCYxtfX/XlCgVQvrzwV19fGORyYOBAoEcPvszMWHb4N1x7nBhqiT9UjOmGf6d+Lz53OSfWVauj3ujx0DcwhMzAQPxrXsYZ1lWqFViMn34KHDmSu9dUrpzzPYzKhDLzkN0ybQdt1/H4MeDgINw3yY+3ZoWFf8O1x5eSGWMlyttnMVqXjb1xFQcH99a4TKanh1oDRkDPwAAyPT2kJScj7uF93D+4B/pGRnDybgK7Wl5oMPZHGJqY5CrGoCDg9GnA21v719y6latNFAkuLkLjGgMDIUnMaZDJtCuXuXxSEvDqlfBs7vh4obsjovSzrMpya9cCzZpJ+IYwVgTwGUMt8X8bjOkGSkvD49MnkfzmNYjSkPLuHVLev0NUaAiurPs937dXf8xENJowPc+vT00VGsNocx+jsvFMTvc6ajPkpqy2Q0iIkKQVZXXrCo9bNDQU4n33Dli0CBg1ii/XF2f8G649Tgy1xB8qxnRf8tu3CF08B8+uXQIAyDKcUtIzMIS+XA4DuTEMTUxxP3Af4h8+yHGdnbcfRtmmLQoy7GIlLU1IdOPj08/epaYK87UdiLQvGxICbNqUP7GPGQOYmgoJYloaYGQkdCPUtGn+rJ8VHP4N1x4nhlriDxVjLCMiwqOTwXh57zb0DY3w9NJ5XA5YqbHs6GephRwdy+j1ayA6Or1/yMxnVzPPS04GunXTfv1duwK//AJYWQlnG1nRw7/h2uPEUEv8oWKMZSctJQXBE0biytrf1JbVGzUelT7vAevKHtAz4Fu7i4PISOCnn4RxCwuhNfjkyTm/ztAQWLIEGDy4YONjucO/4drjxFBL/KFijGnjzdNo/FHdKcvlfU/fhFW5CumXqVmxQQSEhQFxcULyt3On5nLlywN37xZubCx7/BuuPf7XlTHG8pGpvQNGP0tFanIynl29iAvLF+L2ri3i8nUNqwAAGv84G67N/GBq7wiFrR30lA9hZkWWTCY8IxtI72vy1CmgcWPVcs2bC63KbWwAa2vhEjP/H8CKCz5jqCX+b4Mxllcxly5g34Ceah1qZ9Z8/nLEhd9HxY5dYV+rLmTc0V+R17Wr8LjCnHz9NeDpKXRKbmQkDBnHU1KAY8eAPn2AWrUKOuqSh3/DtceJoZb4Q8UY+1hJr19jubul1uWHP0zIdR+IrHBFRwuXlffuBRwdgUOH8me9trbA+/fAzJnAsGFCX48s7/g3XHucGGqJP1SMsfzwIOgA9vT5DGkpKVq/xty5LL44fAYmtnYFGBnLD6dPA927C51pv3oFVKoEVKkCWFqmd7Sdl+TR0FBoLQ0AtWsLraaTkoC+fQE3t3ysgI7i33DtcWKoJf5QMcYKQvSFs7h/aC9AhNfRT3D977VZlvUcNBKNf5gJQ1PTwguQ5bvISGDNmvTLyEZGQuKnry/0j7h6NRAaqv36hg0T7n3s3Vu4PM3U8W+49jgx1BJ/qBhjheH9q5d4cfsG7h/ai3O//qSxTLnWHdDy1zUwLlW6kKNjhSkxEYiNBZ4/B86dA/r31+518+YB337Ll58z4t9w7XFiqCX+UDHGClvkv0exvUtLoZ8UDTpu+AeuzVpC38iokCNjUtq1C5g6Fbh0Kfty/Ouejn/DtcdN3hhjrIhyadIco2NSMPpZKoY9iIPPrMUqy3d/2QlLnBS4tmGNNAEySXz2GXDxopD4JSYKl53PnxfOFGb04oUU0bHijhNDxhgrBozMzFB70DcYFZOC9utU+0cJHD0QD48FShQZk5KREeDlJdxj+N13qsv4jCHLC04MGWOsGJHJZKjQ9jN88/gdPHr2Fefv7NYaDwL3SxgZk1rmhu6l+RZUlgecGDLGWDGkb2SElkvWwGfmInHeP7064OW92xJGxaRCJDyKT2ntWn7aCssbTgwZY6wYqz14JHoc+E+cXtewKt4+fyZhREwKkyYBERHC+IABQv+GjOVFkU4MZ82ahUaNGsHExARWVlYay0RERKBDhw4wNTWFjY0NRo4ciaSkJJUyV65cgY+PDxQKBZycnDB9+nRwY2zGmK5w9GoIC1d3cfr3qg4SRsMK240bwKxZ6dPjxkkXCyv+inRimJSUhG7dumHo0KEal6empqJdu3Z48+YNTp48iU2bNmH79u0YO3asWCY+Ph5+fn4oU6YMQkNDsWTJEsyfPx8LFy4srGowxliB63fursr0Ylt9iSJhhSEtDfD3Fy4Xe3ikzz95EqhcWbKwmA4o0t1fTps2DQCwdu1ajcsPHz6M69evIzIyEmXKlAEALFiwAP7+/pg1axYsLCywYcMGvH//HmvXroVcLkf16tVx+/ZtLFy4EGPGjIGMb8JgjOmIIbefYWUlW3F6sa0+Bl55BFMHRwmjYvlt5Ejh+cyZ/f470Lhx4cfDdEuRTgxzEhISgurVq4tJIQC0atUKiYmJOH/+PHx9fRESEgIfHx/IMzwnqFWrVpgwYQLCw8Ph7u6uadVITExEYmKiOJ2QkFBwFWGMsXxgXKo0Kn3WHbd3bRHn/VHDGVW790GTaT/DxMY2m1ezouTSJeD+feH5yAkJwOvXwt9du4Q+CzOLjAS432aWH4p1YhgdHQ17e3uVeaVKlYKRkRGio6PFMm6ZnjCufE10dHSWieGcOXPEM5aMMVZctP3jb5XEEABubPkTN7b8ieHh8fyc5SIuOhpwzMUJ3gkTgC+/5KSQ5Z9Cv8dw6tSpkMlk2Q7nzp3Ten2aLgUTkcr8zGWUDU+yu4w8YcIExMXFicP169e1jokxxqQ0KiYFrZatVZv/8Dh3gl3UrViheb6zs9DaeMwYYdi2TbjPcPZsoFq1wo2R6bZCP2M4YsQI9OzZM9symc/wZcXBwQFnzpxRmffy5UskJyeLZwUdHBzEs4dKMTExAKB2tjEjuVyucvk5Pj5eq5gYY0xqMplM7awhALyLfYabO/6GqZ0DnBs343usJZSSAjx/LlwiVl4mfv0668fYPXoEDBkC1K1buHGykqfQE0MbGxvY2Njky7q8vb0xa9YsREVFwfHDuffDhw9DLpej7odvj7e3N3744QckJSXB6MOD5g8fPowyZcponYAyxlhxU2fot2pPQjkyZojK9DdP3kPf0LAwwypR3r0DduwAevcWpmvXBuLigJcvgVevcvfIupo1gSzufGIsXxXp7moiIiJw8eJFREREIDU1FRcvXsTFixfx+vVrAEDLli3h4eGBPn36ICwsDEeOHMG4ceMwcOBAWFhYAAB69eoFuVwOf39/XL16FTt37sTs2bO5RTJjTKe5NGmO0c9S0f/SQ7g0bYFSFauolVlSxhivox5LEJ1uIwK++QYwMUlPCgEgLExoUPLypVBGJgPMzYV7CitWFBJHpYULhXJpaULZS5f4EXescMioCPf07O/vj3Xr1qnNDw4ORrNmzQAIyeOwYcNw9OhRKBQK9OrVC/Pnz1e5DHzlyhUMHz4cZ8+eRalSpTBkyBBMnjw5V4nho0eP4OLigsjISDjzXb6MsWLq7r6d2OvfVWXe6GepEkWje1JTAQMN1+I8PICffhKSu1KlAGtrwMYG0CvSp2d0B/+Ga69IJ4ZFCX+oGGO64n3cK6ysYK0yr83vG1H58x4SRVS8EQlnA4OCgP/9T3XZ/v1AmzbSxMXS8W+49op1dzWMMcZyz9jSCuXbdsK9/f+I8w4M6oUDg3rB/8wtWJWrIGF0RdubN8CZM8JziR89Eobffsu6PCeFrLjhxJAxxkqgtn9swv5BX+Devl0q89c2EJ6n1vznZajpP0TDK0uG5GTh0u+ePUBiIvD+PXDrVu7WERFRMLExVpD47gbGGCuB9I2MUHfY2CyXH/1uOOIiwgsvoCJmzRpg0iTg7Fmh4Yc2SaG+vnCGcM4cofsZF5eCj5Ox/MaJIWOMlVB7v+6W7fLo86cLKZKi5e1bofVwbqWmAgcOCE8jWbQo/+NirDBwYsgYYyVUq6UB2S4/MOhLHP9xTCFFI534eGDuXMDPD6heHTA1BebN+7h1du6cP7ExVtj4HkPGGCuhXH1bauyq5uqfqxA0ZjAAIOy3X1C9zwBYV/Yo7PAKTYsWQOYnsVpaAl27AnXqAG5ugJ2dkDCamAiDQiEM+vqShMxYgeHEkDHGmIrqfQbAQKHAwaFfAQBOzZmEDmu3SxxVwcmYFH79NTB5MlC2LPcxyEom/tgzxhhT4/ZpW3H83r5dWGyrj6t/rpIwovxz9KiQAPbuDTRporpMoRDOEHJSyEoq/ugzxhhTY2xVCk1nLFSZFzRmMNJSUiSK6OOlpgK//y5cOl67FtiwATh5Ulimpwf06wfMnClpiIxJjhNDxhhjGtUZMgoDLqt2xveroxx7+nZGalKSRFHlztatQIMGQN26gJkZMHiw6vKBA4Uuad6+BVavFh5Xx1hJxokhY4yxLJk5OuHzrQdhVa6iOO/e/n+wtmEVpLx/L2Fk2SMCrl0DuncXEr8LF4ROqhUKwNlZaEDy22/CGcR69QC5XOqIGSsaODFkjDGWLddmfvA/cxPtVm8W5yVEPsSK8qVwYPCXePciVsLo0j1+DCxdCnz2GeDoKHQ9k9Ht20LH05GRwqPtBg2SJEzGijRODBljjGmlYseuGPbgFby++R4mtvZITUrCrR2b8FtlO8RHPpQsrufPgb59AVdX4JtvgH/+AZ4+BQwMAC8vICBAOFtYsSJ3L8NYTjgxZIwxpjUjM3N8MnkOBl57jK7/BIvz93wlXY/O/v7A+vVC45JGjYTOqv/7T+i4OjRUWM6XihnTDieGjDHGck0mk8G5UVN8MnkuAODN0ygkvU6QJJaQEOHv9u1CQvi//wkJokIhSTiMFWucGDLGGMuz2oNHwczRCW+fPcXuPp8j6fXrQt3+8uXAixfCJeJPPy3UTTOmkzgxZIwxlmf6RkZo8/tGGJmZ49HJYOwf0AOUllYo2/77b2DECGF80iTAwqJQNsuYTuPEkDHG2EdxavgJPt92CPrGxgg/chChi+cW+DZfvBAanBABw4YJj7FjjH08TgwZY4x9NMe6DdB83jIAwrOVzyyYVaDbi4sDkpOF8TlzAJmsQDfHWInBiSFjjLF8Ue0Lf3E8ZG7BnsIzNU0f//xz4N9/hX4Mi8kDWRgrsjgxZIwxlm8a/ZD+sOHFtvoIHDUAG1vUw2JbfdzauTmbV2rn8WMgKAg4ejR93tGjQNOmwhNN5HLh7KGdHXDnzkdvjrEShxNDxhhj+abOkNEq09c2BiDm8gUAwIFBvfA45F+t13XmDLBuHdCsmZDsyWRC8ufnB3zxRfavffYMCA7OvgxjTJ2B1AEwxhjTHQYKBYaHx+PRqeN4HfUIz65ewuWAleLyrR2bYdD1KJjY2mW5jnv3gAoVst5G1aqAlRVgZqY6mJoCKSlAWhrQuDHQWbo+txkrtjgxZIwxlq8MTU3h7tdWnK49eDTWNawiTr999jTLxJBIPSls1gy4cgX47TfA1xcoXbogomaMAZwYMsYYK2BW7uVVpv/y8cSomBTINDQlztxJtbk5XxJmrDDxPYaMMcYKlExPD/0vhqvMexoWqrFsxkYlPj7AkSMFGBhjTE2RTgxnzZqFRo0awcTEBFZWVmrLL126hC+++AIuLi5QKBSoWrUqfvnlF5Uy4eHhkMlkasPBgwcLqRaMMcbMnVzwZfAFyPT1AQCbWnljc9smSEtNzfI1zZsD9eoVVoSMMaCIJ4ZJSUno1q0bhg4dqnH5+fPnYWtri7/++gvXrl3DxIkTMWHCBCxdulStbFBQEKKiosShefPmBR0+Y4yxDGyr10LHv/6BUyMfAEBU6CmcmDQWRAQA2LFDtXylSoUdIWNMRspvZBG2du1ajB49Gq9evcqx7PDhw3Hjxg0c/XA9Ijw8HO7u7ggLC4Onp2eeY3j06BFcXFwQGRkJZ2fnPK+HMcYYcHBoH9zctlGcVsxMxZAhwnjr1sCuXUKfhIzlB/4N116RPmOYF3FxcSitoclax44dYWdnh8aNG2Pbtm0SRMYYY0yp1bJ14vjbNCsxKQSALVs4KWRMKjqVGIaEhGDLli0YPHiwOM/MzAwLFy7Etm3bsH//frRo0QI9evTAX3/9le26EhMTER8fLw4JCQkFHT5jjJUYMj099Dh4ClcSO2Lii1hx/vK5ETAzTZMwMsZKtkJPDKdOnaqxMUjG4dy5c7le77Vr19CpUydMnjwZfn5+4nwbGxt8++23qF+/Pry8vDB9+nQMGzYM8+bNy3Z9c+bMgaWlpTh4eHjkOibGGGNZc6zbAKHWy1TmDRtfFj/Zlsa7DMkiY6zwFHpiOGLECNy4cSPboXr16rla5/Xr19G8eXMMHDgQP/74Y47lGzZsiDs5PERzwoQJiIuLE4fr16/nKibGGGM5O3K2DEZ+HYnqZW6L8/a+nY219Svh/uG9EkbGWMlU6B1c29jYwMbGJt/Wd+3aNTRv3hx9+/bFrFmztHpNWFgYHB0dsy0jl8shz3CTS3x8/EfFyRhjTJ2tLfDLGhcAwJIlwMiRwPnkvujw6n/Y3fszdNt9DE4NP5E4SsZKjiJ9j2FERAQuXryIiIgIpKam4uLFi7h48SJev34NQEgKfX194efnhzFjxiA6OhrR0dF49uyZuI5169Zh48aNuHHjBm7duoX58+fj119/xTfffCNVtRhjjGkwdCjg4gK8TzXFs2ozASKcWTADxaDzDMZ0RpF+JN7kyZOxbl16y7XatWsDAIKDg9GsWTNs3boVz549w4YNG7BhwwaxnKurK8LDw8XpmTNn4uHDh9DX10elSpWwZs0a9O7du9DqwRhjLGcGBkDTpsCGDcCCE6PgrTBF8yM/wXrSWDSeNAcG3FSZsQJXLPoxLAq4DyTGGCt4t24JyWFMjOr876xqYertQJjY2kkTGCvW+Ddce0X6UjJjjLGSpXJlICICWLRIGFf6+dUlfFehF1KTk6ULjrESgBNDxhhjRYpcDoweDdy8qTp/z9s52NHZT+NrGGP5gxNDxhhjxcLjlNoIOmHCjVEYK0CcGDLGGCuy/vwzfTwNBvg9fj9qGf+D65vXSxcUYzqME0PGGGNFVu/eABGwf1f6Y0mvJH2GwyO+Rsr79xJGxphuKtLd1TDGGCuZ7t8Hzp0D7t4F7t0Drl0zVyvze1UHDHvwqvCDY0yHcWLIGGOsSDlzBmjYMOvljvpXAABJrxPw5OwplKnfqJAiY0z38aVkxhhjRYqxMWBpqXnZyJHA8WMp4vSWdk2wf1AvvLp/t5CiY0y3cWLIGGOsSKlVC3j6VLiUvHIlMHBg+rJffwUqNamNiK5P4dGzLwDg9s7NWNugMoLGDEZaaqpEUTOmGzgxZIwxVuTI5UKCeOUKsH+/+vIDx2zQcskadNzwjzjv6p+r8OT0yUKMkjHdw/cYMsYYK5Ju3QKWLVOd5+4O1KwJTJ0qtFZ2bdZSZbm9p1fhBciYDuLEkDHGWJHk4SFcRv7jj/R5Dx4Iwz//AIaGgJ2dESB7icfPLGAqe46nzvXgYHQH/S7ch7mTi3TBM1ZMyYi7kNcKP4CbMcakce0asHcvcOyY0H1NTAwQH591eVPZc5jqPYd9RRdY2ZrAzEwGU1PA1BSwsABKlVIdSpcGqlUTxplu4t9w7fEZQ8YYY0VatWrC8L//pc97/15IEGNigN9+A1atSl/2hmzwJtUGMTcB3FRbXZbu3gXKl8+3sBkrljgxZIwxVuwYGwNlywqDl5dwuZkIiImMw8n1h3F6zWbEx75DIpkhRd8C3jP/wJs3wLNnwPz5mtf55AknhoxxYsgYY0wnyGSAfVlLdPmxG7r82A1rvCog/uEDAMDw4cKNiosWpZeXy4WOtKtXB7p0AZo0kSJqxooW7q6GMcaYTur2T7CQLQI4PmksUlMJf/6ZvvzcOeG+xaVLAV9faWJkrKjhxJAxxphOMndyQdXufQAAx5ZthoGBDGFh6cuNjSUKjLEijBNDxhhjOuvTRb/DrmYdHHn3P5X5hw4BFSpIFBRjRRjfY8gYY0xn6RsaomdgKEYYhovzYq5dha1HdemCYqwI4zOGjDHGdNK7d0D//oCDA/AyzU2cv6apN+7s3iZdYIwVYXzGkDHGmM65ehWoUSN92sQEqOASh6rPpkEue4vA0QNRrnVH6BsZSRckY0UQJ4aMMcaKlbQ04PVr4ekncXHCoBx//hwICwNWr04vv2oV0KcPYGRkiZf3hmBdw1+QlBCP9Y2rwaVpC3gNHwercnzDIWMAJ4aMMcaKoKtXgT17gCtXhE6pnz8HYmOFBDA+XujMOif29sIzlRs0SJ9XqnwlNJo4C6dmTURc+H3Ehd/H1fV/YETkGxhwM2XGODFkjDFWtOzfD7Rrl3M5AwPA0jJ9sLAQnntcrhxQs6bQabWpqfrr6o8eD5meHv6bMUGcp2domI81YKz44sSQMcZYkXLyZNbL/v4baNZMSASNjcX+q3Pt3v5dKtNvnkZBYW0LA7k8bytkTEdwYsgYY6zApaUJzyjesQMwMwMSE4VWw0lJwpCcnD6elJT1eho2FFoZf6ym0+djS7v0Z+CtruUqjtcZNgZNp/388RthrBgq0t3VzJo1C40aNYKJiQmsrKw0lpHJZGrDypUrVcpcuXIFPj4+UCgUcHJywvTp00Ha3KDCGGPso7x8Cfz1F/DVV8D//gecOQMcOSKcFTx/XriH8NYt4P594NEjICYGePVK87ouXADc3PInrjL1G6H38Ysal0WfP5s/G2GsGCrSZwyTkpLQrVs3eHt7Y3XGJmaZBAQEoHXr1uK0paWlOB4fHw8/Pz/4+voiNDQUt2/fhr+/P0xNTTF27NgCjZ8xxkqykyeBJk00L1uzRmgcYmSU/WBoCFhZCX/zm41HDQx/mIBlruYq8zv+tSv/N8ZYMVGkE8Np06YBANauXZttOSsrKzhkcW1hw4YNeP/+PdauXQu5XI7q1avj9u3bWLhwIcaMGQNZXm9QYYwxlq1ff816mVwOtG1beLFkxdDEBGXqN8aTs/+J827t2IRa/YZKGBVj0inSl5K1NWLECNjY2KBevXpYuXIl0tLSxGUhISHw8fGBPMMNxa1atcKTJ08QHh4uQbSMMabb3r8Hdu0SWghn5csvhXJFgVOjpirTwf8bgbMLZ0sUDWPSKtJnDLUxY8YMtGjRAgqFAkeOHMHYsWPx/Plz/PjjjwCA6OhouGW6KcXe3l5c5u7urnG9iYmJSExMFKcTEhIKpgKMMVaMvXkDLF4MPHwo9DN4/z5w+zbw9q1qOSOj9K5jUlKA338XWhUXBY0nzkSNrwYi6txpHBjUCwBwas4kmDk5w6PHVxJHx1jhKvTEcOrUqeIl4qyEhobCy8tLq/UpE0AA8PT0BABMnz5dZX7my8XKhifZXUaeM2dOjnEyxpiuS0tTfcKIcnj1Svg7YoTm15UtK/RF6O0tDOXL571rmcJg4eIKCxdXXA5YicchJwAAh0d8jTPzZ6Lal/1Qb+T3kOnpxEU2xrJV6InhiBEj0LNnz2zLZD7DlxsNGzZEfHw8nj59Cnt7ezg4OCA6OlqlTExMDID0M4eaTJgwAWPGjBGnHz9+DA8PjzzHxRhjxcXNm0C9esJj5/Li+nWgcmWgOOZRFdp3FhNDAIgLv4dTsyYi6ux/6LRxj4SRMVY4Cj0xtLGxgY2NTYGtPywsDMbGxmL3Nt7e3vjhhx+QlJQEow8PSz98+DDKlCmTbQIql8tV7kuMj48vsJgZY0xqUVHA4cPAsmVAaKj6crk8/QkjVlaqTxyxtARSU4UnkYweDbi4FHb0+af2oG/g2rwlLqxYhKvr/xDnPwjcj3exz6GwLrjfL8aKgiJ9j2FERARevHiBiIgIpKam4uLFiwCAChUqwMzMDHv27EF0dDS8vb2hUCgQHByMiRMnYtCgQWJS16tXL0ybNg3+/v744YcfcOfOHcyePRuTJ0/mFsmMMQYgIgLw8BDuF8zswAHhUnCGXsB0XukKldHi5+UqiSEAhP76E3d8zXRekU4MJ0+ejHXr1onTtWvXBgAEBwejWbNmMDQ0xPLlyzFmzBikpaWhXLlymD59OoYPHy6+xtLSEoGBgRg+fDi8vLxQqlQpjBkzRuUyMWOMlWSGhulJoYsL8PXXQkOR4cM1P2u4JJDp6WHwrRgcGvYVwo8cBAC8vHNT4qgYK3gy4keAaOXRo0dwcXFBZGQknJ2dpQ6HMcbyzfHjwvOHAWDYMOFyMkt3Z/c27OvfAwDQedshlPX5VOKIWG7xb7j2iuGtwYwxxvKTMikEgP79JQujyKrQoYs4fmDwlxJGwljBK9KXkhljjOUfIuDqVaG18bt3Ql+D796plqlbF1AohOcSV6kiTZxFTcb70d/FPse2zz+FTCaDS5PmqDtiHPQL4nl9jEmEE0PGGNNB8fFAdDSQmCh0KD1iBHDqlHavffcOmDwZ2LKlYGMsTgxNTJH8VrgR89HJYABA5L9HcXbRLNQaMAL2NeugYqdu3KiRFXucGDLGWDGWnCwkfb//DrRoIXQbc+WK8BQSbbVqBZiYCN3NhIYCdnZAhmcEMADDH8Zjsa2+2vyUd+9wfonQUvnThHhU7zOgsENjLF9xYsgYY8XY3btCUggAR47k/vU3bwqdUbOcddq4B//06pDlcvs69QsxGsYKBieGjDFWjGlK6nbuFJ468vixcOZw4EAgmwc9MS25+7VFhz93Yk+fz8V5Df83FXWHjoFhSe3Xh+kcbpXMGGPF2KpV6vM+/xzo1EnoeiY5Wbi8zPKHlVt5lenTP03lpJDpFE4MGWOsGCtVKvvl06cDXbsWTiwlgYGJehLI3QEzXcKJIWOMFWPduglnBF+/Bp4+Be7fFx5jVz/D7W4hIUBamnQx6hLLsm4YfPOpyrwLyxdKFA1j+Y8TQ8YYK+b09IRH19nZAe7uQOvWwJkzQmtjJX31BrUsjxTWNhgVkyJO/zv1ewmjYSx/cWLIGGM6atq09PFataSLQxfJZDI4NfIRp9+/fCFhNIzlH04MGWNMx7x9CzRuDDRsKEzb2gL790sbky7qujNIHF9ZyVbCSBjLP5wYMsaYDrl0CXBzU33KSWgoUKaMZCHpLJme6k/o9U3rJIqEsfzDiSFjjOmAN2+ANm0AT0/g2bP0+QsWAK6ukoWl89qtSX9u4OFv+iEtJSWb0owVfZwYMsZYMZWSAsyaJTzOzswMOHhQdfnt28CYMdLEVlJU7NBFZfrRf8ekCYSxfMKJIWOMFVP//CM80/jdu/R5LVsC0dEAEVCxonSxlSRD7jwXx3d0bYXb/2yVMBrGPg4nhowxVkz5+KjPCw4WHoPHCo+xVSl4DhghTu8f0BOpyckSRsRY3nFiyBhjxZSNDXD5suq85GSgWjVg8GDh/sJdu4CrV7mD64LmPWG6yvQ/vdpLFAljH8dA6gAYY4zlXY0awmXjNWuA/v3T5//+u2o5Z2chkezfH6hcGWjUSOgUm+UPuYUlvg69g4B6wvX7iGNBeBMdBVMHR4kjYyx3ZMQPedTKo0eP4OLigsjISDg7O0sdDmOMqSECHj0Cjh8Hbt4E7t4F7t0Dzp1TL1umjLDM2Ljw49Rlu3q2Q/iR9FZA/S+Gw9zJRcKIGMC/4bnBZwwZY0xHyGSAiwvQu7fq/HfvgPXrgSFD0uc9eSJ0Y+PqCjg5AXXqAN7ewCefcLL4MTr9vRe/2KX/tB79fjg6bdgtYUSM5Q7fY8gYYzpOoRDuObxxQ7iMrBQTI3R+vWsXMHky4OcHVKkCvOCnu+WZTCZDg3GTxOkHh/eBL8yx4oQTQ8YYKyGqVBEuMScmAhERwJkzQpc3ixcD3boJZR4+FPpGZHnX8PspaPi/qeJ00LeDpAuGsVzixJAxxkoYIyPhknP9+kDHjsCoUYCDQ/ryhQuBI0eA8HDhvkWWOzKZDA0znDW8tmGNhNEwljucGDLGGEPjxqrTn34KuLsLXd9wVzd5U7XHV+L4kXFDJYyEMe1xYsgYYww9egCRkcC0aUCnTunzb9wQWjCz3Gs67WcorG0AAFfW/Y6T0ydIHBFjOePEkDHGGAChr8PJk4XGKNeupc9/+lR4zB7LHYW1Db4MviBOn1syjxuisCKvSCeGs2bNQqNGjWBiYgIrKyu15WvXroVMJtM4xMTEAADCw8M1Lj+Y+WnzjDHGRHv2qE7fvy9NHMWdmaMTSpWvJE4nv3ktYTSM5axIJ4ZJSUno1q0bhg7VfG9Gjx49EBUVpTK0atUKPj4+sLOzUykbFBSkUq558+aFUQXGGCuWPvsM8PJKn27cWOgn0c1N9Wwiy1nnHYHi+HJ3K9zetUXCaBjLXpHu4HratGkAhDODmigUCigUCnH62bNnOHr0KFavXq1W1traGg4Zm90xxhjLUuXKQh+Hd+4AldJPeOHhQ2D7dqFRCtOOeRnVJ23sH/gFnD/xhYmNrUQRMZa1In3GMLfWr18PExMTdO3aVW1Zx44dYWdnh8aNG2Pbtm0SRMcYY8VPxYrqrZK//16aWIqzEY/e4rNN+8Tp36vyiQpWNBXpM4a5tWbNGvTq1UvlLKKZmRkWLlyIxo0bQ09PD7t370aPHj2wbt069M783KgMEhMTkZiYKE4nJCQUaOyMMVZUyWTp466u/Mi8vDCQy+HWorXKPCKCLOOby1gRUOhnDKdOnZplgxHlcE7TE99zEBISguvXr6N///4q821sbPDtt9+ifv368PLywvTp0zFs2DDMmzcv2/XNmTMHlpaW4uDh4ZHrmBhjTFd8uLMHcXFAVJS0sRRng28+hcGHkxfhR7gRJCt6Cj0xHDFiBG7cuJHtUL169Vyvd9WqVfD09ETdunVzLNuwYUPcuXMn2zITJkxAXFycOFy/fj3XMTHGmK6YMAGwtQVevQJ69ZI6muJLYW2Dar36AQCO/zAar6OfSBwRY6oK/VKyjY0NbGxs8nWdr1+/xpYtWzBnzhytyoeFhcHR0THbMnK5HHK5XJyOj4//qBgZY6y4IgJ+/hl49kyYDgsT5vFV0LypP2YiLq1ehlcP7mJVDReMfpYqdUiMiYr0PYYRERF48eIFIiIikJqaiosXLwIAKlSoADMzM7Hc5s2bkZKSgi+//FJtHevWrYOhoSFq164NPT097NmzB7/++it++umnwqoGY4wVaxMnAsr/u+vXB2bN4qTwY5ja2cNn1mIcnzgaAPAgcD/c/dpKGxRjHxTpxHDy5MlYt26dOF27dm0AQHBwMJo1aybOX716NTp37oxSpUppXM/MmTPx8OFD6Ovro1KlSlizZk22DU8YY4yl27pV+DtgAPDHH9LGoitqD/oGCY8e4sKKRTj983RODFmRISN+Po9WHj16BBcXF0RGRsLZ2TnnFzDGmI745BPgv/+ABQuAMWOkjkZ3vH3+DKtquiAtORmtV/6JKl345s2Cwr/h2tOpfgwZY4zln8ePgapVhaQQAMqUkTYeXWNiY4t6oycAAA4O6YPAUQMkjogxTgwZY4xlQATcugW0aAE4OwM3b6Yv69RJurh0VYMxE8XxaxsDEDR2iITRMMaJIWOMsQyaNgWqVAGOHhWma9cGvvsOePsWyPDsAJZP9AwMMOzBK3H66vo/8Hcrb+kCYiUeJ4aMMcYAAFevAidPpk9PmABcuADMm8dJYUEyMjNHr6BQcfrphbMSRsNKOk4MGWOMAQDOn08fT0wEZs+WLpaSxq5WHTSdsVCcfv/qpYTRsJKME0PGGGMAgJgY4W/r1oCRkbSxlES1B49E6UpVAQAX/1gicTSspOLEkDHGGJKTge+/F8ZNTKSNpaSSyWSo3PkLAMDpedOQGB8ncUSsJOLEkDHGSrirV4EMD5PiS8gSqjtinDi+56vOEkbCSipODBljrAS7fBmoUQNIShKmJ00CKleWNqaSzEAuR6MJMwAAj/47hrv7d0kbECtxODFkjLESat06oFat9OkbN4Dp06WLhwnqjR4vju/t24UvKbNCxYkhY4yVQCNHAv7+wri1NRARIfRfyKQn09ODU8Mm4nTo4rkSRsNKGk4MGWOshBkyBFjyodGrnx9w/Trg4iJtTExV193B4vi5JfNAaWkSRsNKEk4MGWOshPntt/Tx0aMBOzvJQmFZkMlkcPJuKk4/vXhOwmhYScKJIWOMlTCffJI+/sUXwvORWdHTbPZicXzXF+2lC4SVKJwYMsaYjouPFxqVtGsn3EeY8bF3DRsCMpl0sbGs2VZPbxn0/kUs/mpWR8JoWElhIHUAjDHGCla/fsD27erzJ00Cpk0r/HiY9oY/TMAyV3MAwPNrl0BEkHEmzwoQnzFkjDEdZ5DhFMCUKUIL5NRU4Swi5xhFm6GJCdqt3ixOx0c+lDAaVhJwYsgYYzrsxg0gMFAYNzMTWiS7uAB6fPQvNiq07wwjcwsAwIZmtSWOhuk6PjQwxpiOiowEPDyAFy+E6X/+ARwcpI2J5Z5MTw8d1u8AACQlxCPixBGJI2K6jBNDxhjTMWPGCJeIy5ZNn7d5M9C8uXQxsY+TseuaHV1aYkt7HwmjYbqME0PGGNMxixapTm/dCnTvLk0sLH/o6evj6/P3xOknZ05mU5qxvOPEkDHGdMzly4CtrXDm8MYNoGtXqSNi+cGyrBt6BYXCzNEJPQ+fljocpqO4uxrGGNMxNWoAMTFSR8EKgl2tOhhwOULqMJgO4zOGjDHGGGMMACeGjDHGGGPsA04MGWOMMcYYAE4MGWOMMcbYB0U2MQwPD0f//v3h7u4OhUKB8uXLY8qUKUhKSlIpFxERgQ4dOsDU1BQ2NjYYOXKkWpkrV67Ax8cHCoUCTk5OmD59OoioMKvDGGOMMVbkFdlWyTdv3kRaWhp+++03VKhQAVevXsXAgQPx5s0bzJ8/HwCQmpqKdu3awdbWFidPnkRsbCz69u0LIsKSJUsAAPHx8fDz84Ovry9CQ0Nx+/Zt+Pv7w9TUFGPHjpWyiowxxhhjRYqMitGps59//hkrVqzA/fv3AQAHDhxA+/btERkZiTJlygAANm3aBH9/f8TExMDCwgIrVqzAhAkT8PTpU8jlcgDA3LlzsWTJEjx69AgyLZ8g/+jRI7i4uCAyMhLOzs4FU0HGGGOM5Tv+DddekT1jqElcXBxKly4tToeEhKB69epiUggArVq1QmJiIs6fPw9fX1+EhITAx8dHTAqVZSZMmIDw8HC4u7tr3FZiYiISExNVtg0AUVFR+V0txhhjjBUg5W93WlqaxJEUfcUmMbx37x6WLFmCBQsWiPOio6Nhb2+vUq5UqVIwMjJCdHS0WMbNzU2ljPI10dHRWSaGc+bMwbRp09Tm169f/2OqwRhjjDGJREZGomzGh4gzNYWeGE6dOlVjwpVRaGgovLy8xOknT56gdevW6NatGwYMGKBSVtOlYCJSmZ+5jPLqeXaXkSdMmIAxY8aI0y9evIC7uzuuXr0KS0vLbOPXJQkJCfDw8MD169dhbm4udTiFhuvN9S4pSmrdud4lq95xcXGoXr06qlatKnUoRV6hJ4YjRoxAz549sy2T8QzfkydP4OvrC29vb/z+++8q5RwcHHDmzBmVeS9fvkRycrJ4VtDBwUE8e6gU8+FZUZnPNmYkl8tVLj8rubi4wMLCItv4dUl8fDwAwMnJietdAnC9S1a9gZJbd653yaq3sq4GBsXmQqlkCv0dsrGxgY2NjVZlHz9+DF9fX9StWxcBAQHQ01PtXcfb2xuzZs1CVFQUHB0dAQCHDx+GXC5H3bp1xTI//PADkpKSYGRkJJYpU6aM2iVmxhhjjLGSrMj2Y/jkyRM0a9YMLi4umD9/Pp49e4bo6GiVs38tW7aEh4cH+vTpg7CwMBw5cgTjxo3DwIEDxf8OevXqBblcDn9/f1y9ehU7d+7E7NmzMWbMGK1bJDPGGGOMlQRF9pzq4cOHcffuXdy9e1etabnyHkF9fX3s27cPw4YNQ+PGjaFQKNCrVy+xn0MAsLS0RGBgIIYPHw4vLy+UKlUKY8aMUbl/UBtyuRxTpkzReHlZl3G9ud4lQUmtN1By68715nozzYpVP4aMMcYYY6zgFNlLyYwxxhhjrHBxYsgYY4wxxgBwYsgYY4wxxj7gxJAxxhhjjAHQwcTwxIkT6NChA8qUKQOZTIZdu3apLCciTJ06FWXKlIFCoUCzZs1w7do1cfmLFy/wzTffoHLlyjAxMUHZsmUxcuRI8VnJmSUmJsLT0xMymQwXL17MNractv0xinK9/f39IZPJVIaGDRt+bJUBFF693dzc1Oowfvz4bGPThf2dl3oX5P4uzLoDwL59+9CgQQMoFArY2Nigc+fO2camC/s8L/Uu7t/xY8eOqcWvHEJDQ7OMrbjv77zWu7jvbwC4ffs2OnXqBBsbG1hYWKBx48YIDg7ONraC3N9Fic4lhm/evEGtWrWwdOlSjcvnzZuHhQsXYunSpQgNDYWDgwP8/PyQkJAAQOg/8cmTJ5g/fz6uXLmCtWvX4uDBg+jfv7/G9X3//fcoU6aMVrHltO2PUZTrDQCtW7dGVFSUOOzfvz/3ldSgMOs9ffp0lTr8+OOP2camK/s7t/UGCm5/A4VX9+3bt6NPnz74+uuvcenSJfz333/o1atXtrHpwj7PS72B4v0db9SokUrsUVFRGDBgANzc3FQez5rbbetqvYHivb8BoF27dkhJScHRo0dx/vx5eHp6on379mpPSsvNtnUG6TAAtHPnTnE6LS2NHBwcaO7cueK89+/fk6WlJa1cuTLL9WzZsoWMjIwoOTlZZf7+/fupSpUqdO3aNQJAYWFhWa4jr9vOi6JUbyKivn37UqdOnfJSlVwpyHq7urrSokWLtI5FV/Z3butNVHj7m6jg6p6cnExOTk60atUqrWPRhX2el3oT6cZ3PKOkpCSys7Oj6dOnZ7kOXdjfmWlTb6Liv7+fPXtGAOjEiRNimfj4eAJAQUFBGtdRmPtbajp3xjA7Dx48QHR0NFq2bCnOk8vl8PHxwalTp7J8XVxcHCwsLFSesfj06VMMHDgQf/75J0xMTAps2/lBynorHTt2DHZ2dqhUqRIGDhwoPq+6IOVnvQHgp59+grW1NTw9PTFr1iwkJSXl+7bzg5T1VpJifwP5V/cLFy7g8ePH0NPTQ+3ateHo6Ig2bdpke9lIF/Z5XuqtpAvfcaXdu3fj+fPn8Pf3z/dt5wcp661UnPe3tbU1qlativXr1+PNmzdISUnBb7/9Bnt7e/Fxuvm17eKoRCWGylPE9vb2KvPt7e2zPH0cGxuLGTNmYPDgweI8IoK/vz+GDBmS4+n2j9l2fpGy3gDQpk0bbNiwAUePHsWCBQsQGhqK5s2bIzExMQ+10V5+1RsARo0ahU2bNiE4OBgjRozA4sWLMWzYsHzddn6Rst6AdPsbyL+6379/HwAwdepU/Pjjj9i7dy9KlSoFHx8fvHjxIt+2nV+krDegG9/xjFavXo1WrVrBxcUlX7edX6SsN1D897dMJkNgYCDCwsJgbm4OY2NjLFq0CAcPHoSVlVW+bbu4KrKPxCtImZ+RTEQan5scHx+Pdu3awcPDA1OmTBHnL1myBPHx8ZgwYUKBbbsgSFXvHj16iOPVq1eHl5cXXF1dsW/fvhxvas8PH1tvAPj222/F8Zo1a6JUqVLo2rWreDbtY7ddEKSqt9T7G/j4uqelpQEAJk6ciC5dugAAAgIC4OzsjK1bt2b741qc93le6y31Ps+Pz7rSo0ePcOjQIWzZsiVft10QpKp3cd/fRIRhw4bBzs4O//77LxQKBVatWoX27dsjNDQUjo6OH73t4qxEnTF0cHAAALXsPiYmRu2/gISEBLRu3RpmZmbYuXMnDA0NxWVHjx7F6dOnIZfLYWBggAoVKgAAvLy80Ldv34/edn6Tst6aODo6wtXVFXfu3MlrlbSSX/XWRNkC7+7dux+97fwmZb01Kaz9DeRf3ZU/DB4eHuI8uVyOcuXKISIi4qO3nd+krLcmxfk7HhAQAGtra3Ts2DHftp3fpKy3JsVtfx89ehR79+7Fpk2b0LhxY9SpUwfLly+HQqHAunXrPnrbxV2JSgzd3d3h4OCAwMBAcV5SUhKOHz+ORo0aifPi4+PRsmVLGBkZYffu3TA2NlZZz6+//opLly7h4sWLuHjxotgaa/PmzZg1a9ZHbbsgSFlvTWJjYxEZGZntf2X5Ib/qrUlYWBgAZFkHXdjfmuRUb00Ka38D+Vf3unXrQi6X49atW+K85ORkhIeHw9XV9aO2XRCkrLcmxfU7TkQICAjAV199leM/Sbqwv5VyU29Nitv+fvv2LQBAT081BdLT0xPPmud12zqhkBu7FLiEhAQKCwujsLAwAkALFy6ksLAwevjwIRERzZ07lywtLWnHjh105coV+uKLL8jR0ZHi4+OJSGiZ1KBBA6pRowbdvXuXoqKixCElJUXjNh88eKCxdW7lypVpx44d4nRO29bFeickJNDYsWPp1KlT9ODBAwoODiZvb29ycnIqNvU+deqUuN779+/T5s2bqUyZMtSxY8cs663NtnWx3gW9vwur7kREo0aNIicnJzp06BDdvHmT+vfvT3Z2dvTixQuNdddm27pYb134jisFBQURALp+/brGWHRxf+e23rqwv589e0bW1tbUuXNnunjxIt26dYvGjRtHhoaGdPHiRY311mbbukLnEsPg4GACoDb07duXiIQm51OmTCEHBweSy+XUtGlTunLlSo6vB0APHjzQuM2sEiQAFBAQIE7ntG1drPfbt2+pZcuWZGtrS4aGhlS2bFnq27cvRUREFJt6nz9/nho0aECWlpZkbGxMlStXpilTptCbN2+yrLc229bFehf0/i6suhMJXXeMHTuW7OzsyNzcnD799FO6evVqlnXXZtu6WG9d+I4rffHFF9SoUaMsY9HF/Z3beuvK/g4NDaWWLVtS6dKlydzcnBo2bEj79+/Pst7abFtXyIiIwBhjjDHGSrwSdY8hY4wxxhjLGieGjDHGGGMMACeGjDHGGGPsA04MGWOMMcYYAE4MGWOMMcbYB5wYMsYYY4wxAJwYMsYYY4yxDzgxZIwxxhhjADgxZIwxxhhjH3BiyBhjjDHGAHBiyBhjjDHGPuDEkDHGGGOMAQD+D6Px410SG0hwAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 600x1200 with 6 Axes>"
+       "<Figure size 600x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -253,14 +315,14 @@
    ],
    "source": [
     "# density and pressure\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['density'], ranges['pressure']], profile_list, \n",
-    "                          d['density'][0], d['density'][1], 'Density', colors['density'],    'ascent',\n",
-    "                          d['pressure'][0],       d['pressure'][1],    'Pressure',  colors['pressure'], 'ascent', 6, 4)"
+    "fig,axs = ChartTwoSensors(profiles, [ranges['density'], ranges['conductivity']], profile_list, \n",
+    "                          d['density'][0],      -d['density'][1],      'Density',      colors['density'],    'ascent',\n",
+    "                          d['conductivity'][0], -d['conductivity'][1], 'Conductivity', 'blue', 'ascent', 6, 4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "ff9ad8cd-eb68-483b-a565-d0299beb91eb",
    "metadata": {
     "tags": []
@@ -270,15 +332,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Attempting 3 charts\n",
+      "Attempting 1 charts\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 600x1200 with 6 Axes>"
+       "<Figure size 600x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -288,13 +350,13 @@
    "source": [
     "# dissolved oxygen and chlorophyll-a\n",
     "fig,axs = ChartTwoSensors(profiles, [ranges['do'], ranges['chlora']], profile_list, \n",
-    "                          d['do'][0], d['do'][1], 'Dissolved Oxygen', colors['do'],    'ascent',\n",
-    "                          d['chlora'][0],       d['chlora'][1],    'Chlorophyll-A',  colors['chlora'], 'ascent', 6, 4)"
+    "                          d['do'][0],     -d['do'][1], 'Dissolved Oxygen', colors['do'],    'ascent',\n",
+    "                          d['chlora'][0], -d['chlora'][1],    'Chlorophyll-A',  colors['chlora'], 'ascent', 6, 4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "1003765c-b69f-4bb0-a67a-ff959ec56197",
    "metadata": {
     "tags": []
@@ -304,15 +366,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Attempting 3 charts\n",
+      "Attempting 1 charts\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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T2TqqTsHK7qz7888/kZGRgRUrVqB79+6wt7fXuP7Hjx9DEAS4ubmpXN65c2dYW1vLXzs4OKB9+/Y4fvx4npeJNPH333+jWrVqqFWrlsKZvJCQEJ3dGXfz5k08fvwYffv2VbgcaW9vjy5duuD06dPyvgzNmzfH3bt3ce/ePaSkpODkyZNo3bo1goOD5WcIw8PDIZFI0LhxYwDAgQMHkJGRgc8++0xhG6ytrREUFKRyG7p06aJx/N27d8fQoUPx9ddfq/zvh4jUngnNyMjAzJkz4efnBysrK1hYWMDKygq3b9+Wn6mUKV++PJYtW4Zdu3ahXbt2aNKkSZ53jri6uqJcuXKYM2cO5s+fj+joaLV3jVhaWsLZ2RmxsbF5bvORI0fQokULODk5wdzcHJaWlvjhhx/w4sULPHv2TKFsrVq1FPoaWltbo2LFinjw4IF83tGjR+Wn9mXMzc216gsISI99VFQUzp49i7///hstWrRAz549sXPnToVyW7duRaNGjWBvbw8LCwtYWlpixYoVCvt83759AIDhw4fn2e6LFy/QrFkznD17FidPnlS6vFuvXj28evUKn376Kf766y/Ex8cr1bFv3z4EBwejSpUqubalSez5cerUKSQkJKBfv34K79WsrCy0bt0aUVFRSpfnNf07kZ0NLlGihML8Y8eOAZD+DWX36aefqq0rZ5t///03BEFAnz59FOL28PBAzZo15X/f+dm+Dh06KLyuUaMGUlJSlN7j+lC1alXUrFlTYV6vXr2QmJiodLNDr169FL6DvL290bBhQ/klpzt37uDGjRvo3bs3AChsf5s2bfDkyROlG0O0+QzU5LMp52dg9u9bTd/TEokEW7ZswYsXL1CnTh2Nz+zXq1cP+/btw/jx4xEREYF3796pLVuiRAmNPgPVMbrEqUqVKggICFCYtCH7IPf09AQA+Yf9vXv31K4jO2Xv5eWlcvnnn3+O58+fY+bMmbhw4YJWl+kAaac8S0tLtW8MDw8PlfPS0tLw5s0brdpS5enTp7h06RIsLS0VJgcHBxCRyi8AbclOl6o6pevp6YmsrCz5pQrZ5bfw8HCcPHkS6enpaNasGVq0aCE/1R4eHo5GjRrBxsZGvg0AULduXaXt2Lx5s9I2yIac0MaAAQOQnp4OCwsLpTs5jx07ptSu7H0TGhqK77//Hp06dcKePXtw5swZREVFoWbNmir/uNu2bQt3d3ekpKQgNDQ0zw8MQRBw+PBhhISE4KeffkKdOnVQvHhxjBw5UmVfJmtr61w/VADg7NmzaNWqFQBg2bJl+OeffxAVFYVJkyYBgNL6xYoVU6pDIpEolHvx4oXa97I2atasiYCAANStWxdt27bF1q1bUb58eYXkZ8eOHejevTtKlSqFdevWITIyElFRURgwYIC8fyMAPH/+HObm5hrFcOvWLZw5cwYff/wxqlWrprS8b9++WLlyJR48eIAuXbqgRIkSqF+/vkK/jOfPn+fZoVXT2PND9nfStWtXpffrjz/+CCJCQkKCwjp5XYaRkR3r7P/kAdLjbmFhAVdXV4X52RPonHK2+fTpUxAR3N3dleI+ffq0/O87P9uX870rkUgUtkefcvt7yHmJSV1ZWTnZto8bN05p24cNGwYASp+Dmh5bmbw+m3K2K7t7Tdv3dPny5dGkSROkpKSgd+/eGsW5aNEifPvtt9i1axeCg4Ph6uqKTp064fbt20plNfkMzI3mp2pMABFhz549sLOzkydcLVu2xB9//IFdu3Zh/PjxKtfbtWsXLCwslDpUy3h5eaFFixaYMmUKKlWqhIYNG2oVl5ubG9LS0pCcnAw7Ozul5Tk7pcvmWVlZ5Xpmy9raWmVnv/j4eIWzW25ubrCxsVHZmVe2vKBkH06qOhE+fvwYZmZmcHFxAQCULl0aFStWRHh4OHx8fBAQEABnZ2c0b94cw4YNw5kzZ3D69GmFfkmyGLdt2wZvb+8841F19jA3ycnJ6Nu3LypWrIinT59i0KBBCh0b/f39ERUVpbCOLDlft24dPvvsM3m/E5n4+HilTrSAtCNmUlISqlatipEjR6JJkybyfaOOt7c3VqxYAUD6Bb9lyxaEhYUhLS1NqV/Ky5cv8zymmzZtgqWlJf7++2+FL0JVt75rqlixYmrfywVhZmaGqlWrYuvWrXj27BlKlCiBdevWwdfXF5s3b1Y41jn/HooXL47MzEzExcXl+eEcGBiIbt26yf8xWrp0qdLNHJ9//jk+//xzJCcn4/jx45g8eTLatWuHW7duwdvbG8WLF8ejR49ybUfT2PNDdtwXL16MBg0aqCyTM6HR9G9FVndCQoLCvixWrBgyMjKQkJCgkDzldtxztunm5gZBEHDixAl5YpOdbF5+tk+XZH8rqampCnGq++czt7+HnAmdurKycrJtnzBhgtq7sytVqqTwWtvPwbw+m3J+Bvr6+gLQ/j29fPly7N27F/Xq1cMvv/yCHj16oH79+rnGZmdnhylTpmDKlCl4+vSp/OxT+/btcePGDYWyCQkJBftey/dFvkImuyabsx9OTrn1cZJdf544caJ8XkZGBvn5+ZGTkxPdvHlTaZ1NmzYRABoyZIjCfLzv4yRz4MAB6tixI61bt04+T9M+TmvXriUA9O+//yrMz6uPU/PmzeXzVF1DDwkJIT8/P4U6b968SRYWFgp9nKZPn062trZ09+7dXOPUhqo+TqVKlaJatWopXKd/8+YNlShRgho1aqSw/rBhw8jNzY1q166tcB29TJky1KpVKwJAZ8+elc+/d+8eWVhYKPRDU6dfv35kZ2en1fb06dOHbG1t6cqVK/K+X/Pnz9do3Zx9fYiI/v77bwKgcByIiJYtWybvf/Xff/+Rk5MTdezYUaFMzn2rTq1atahu3boK82JjYwkA/fzzz7muGxoaSvb29pSWliaf9/btWypTpozKvhqq/uZy9qXTVR+n58+fK8yX1SGRSOR/J507d6ZKlSoplHvy5AnZ29sr9EGR9XH6/vvvc207+zZu3bqVLC0tqXfv3kr9LnLatWsXAaC9e/cS0Yc+Tjdu3FC7jqaxE0nfW9n778ns3r1bZf/EpKQkcnZ2pqFDh+YaN5H6/a2ObF/+9ddfCvNlfZyWLFmiMD+3Pk452zx58qRC31R1dLF9qj5LNbVx40alzyYioo8++kirPk4ODg5KfZz8/f1V9nEaOHCgfF6FChWoTZs2ecap6fdpdpp8NqmjzXv60qVLZGNjQ5999hmlpqaSv78/eXt7U0JCgkK5nO8dVUaPHk0A5PtSpkKFCvTJJ59oFLsqJpk4NWrUiCIjIykyMpLCw8Pp119/pSZNmhAA6t69u1LHuevXr1Pp0qXJ1dWVZsyYQUeOHKF9+/bRsGHD5ElGzh2fM3FSRdPE6eHDhwSAfv/9d4X5ssTJy8uLGjduTDt27KBt27ZR3bp1ycLCgk6ePKm0f7K3tW7dOgJAQ4cOpfDwcFqxYgVVqlSJSpYsqfCF9ubNG6pduzaVLl2a5s2bR4cOHaIDBw7QsmXLqFu3bgqdPWUfNnl9aav6cl+/fj0BoDZt2tBff/1FW7Zsobp165KVlRWdOHFCYf3t27fLO/8eO3ZMPv/zzz8nAOTi4qLQyZyIaObMmWRhYUGDBw+mnTt3UkREBG3evJnGjh2r0NlU28RJ9oGxatUq+bwRI0aQpaUlnTlzJs/1P/vsM5JIJLRgwQI6fPgw/fTTT1S8eHEqXbq0wnGQfWBk7+wrS9IWLFggn5dz3/7777/UpEkTWrRoEe3bt48OHz5MkyZNIjMzM4V/Eog+7NdLly7lGvPhw4cJAHXt2pUOHjxIGzduJH9/f6pQoUK+E6fLly+TjY0N+fn50aZNm2j37t0UEhJCXl5eWiVO+/fvl/9979q1izp06EAAFDrar1y5Uv7eP3z4MK1evZrKlSsnjz+7vn37kiAI9OWXX9Lu3bvpwIEDNHv2bFq0aJHabdy7dy/Z2NhQ586dKTU1lYiIBg0aRF999RVt2rSJjh07Rps3b6ZatWqRk5MTPXv2jIiIHj16RCVLlqQSJUrQwoUL6fDhw7R9+3b64osv6Pr161rHHhQURCVKlKDdu3dTVFSUPCG7e/cuAaBOnTrRiRMnKCoqiuLj44mI6M8//yQzMzPq0aMHbd26lY4dO0bbtm2j77//XuEfRG0Tp9TUVLKxsaEJEyYozM/MzKRGjRqRjY0NzZ49mw4dOkRTp06l8uXLEwCaMmWKRm1++eWXZGtrS19//TXt2bOHjhw5QuvXr6ehQ4cqJGUF3T5Vn6Wafu69fv2aXF1dqXr16rRz507as2cPdenShXx9fVX+3ZQqVYrKlClDK1eupH379lHv3r2VbkSS/b17eXlRx44d6e+//6b169dT+fLlycHBge7cuSMve+TIEZJIJNSqVSvasGEDHTt2jHbu3EkzZ86krl27Km2jpomTpp9N6mj6nn7z5g1VrlyZ/Pz86M2bN0REapO0nIlTvXr1aOrUqbRr1y46duwY/fbbb1SsWDEKDAxUWE92M032v29tmWTiJPvCFQSB7O3tqVKlStS3b186cOCA2vXi4+Np/PjxVLlyZbK2tiZ7e3uqV68e/fLLLwr/dcvoMnEiImrSpInSfwqyxOnHH3+kKVOmUOnSpcnKyopq166ttC2q/tizsrLop59+orJly5K1tTUFBATQkSNHlL7QiKRv2O+++44qVapEVlZW5OTkRNWrV6cxY8YonCEYO3YsCYIg/5BXR91ZkV27dlH9+vXJ2tqa7OzsqHnz5vTPP/8orf/y5UsyMzMjOzs7hf0vS746d+6sst1du3ZRcHAwOTo6kkQiIW9vb+ratavCHVDaJE6qPjCIpHd++Pv7k4+PD718+TLXOl6+fEkDBw6kEiVKkK2tLTVu3JhOnDihcByyf2DkTNKHDx+ukKTl3LdPnz6l/v37U+XKlcnOzo7s7e2pRo0atGDBAqUzIn379qXq1atrtO0rV66kSpUqkUQiobJly9KsWbNoxYoV+U6ciKR3QzZo0IAkEgl5eHjQ119/TX/88Ue+76pzdXWl+vXr08qVK5US6dmzZ5OPjw9JJBKqUqUKLVu2TOVdT5mZmbRgwQKqVq2a/L0fGBhIe/bsyXUbjx49Svb29tS6dWt6+/YtrVmzhoKDg8nd3Z2srKzI09OTunfvrpSkxsTE0IABA8jDw4MsLS3l5Z4+fap17BcvXqRGjRqRra2t0hnMhQsXkq+vL5mbmysl/seOHaO2bduSq6srWVpaUqlSpaht27a0detWpf2taeJEJH1/5TzLTUSUkJBAn3/+OTk7O5OtrS21bNmSTp8+rXT2M682V65cSfXr1yc7OzuysbGhcuXK0WeffUbnzp1TKFeQ7VP1Warp5x4R0dmzZ6lhw4ZkZ2dHpUqVosmTJ9Py5cvV/t1s27aNqlatSlZWVuTj46N0Jlv29/7nn3/SyJEjqXjx4iSRSKhJkyZK200k/Ueqe/fuVKJECbK0tCQPDw9q1qwZ/fbbb0rbqEnipM1nU240eU/LzuxfvXpVYV3ZXaLZk7ScidP48eMpICCAXFxc5J9ZY8aMkf/DILNixQqytLRUuotdG0aTOJm6bdu2kbm5OT169EjsUHJVt25dhf9cmPF4/fo12dnZ0R9//CF2KMxERUVFEaA8JIEqsn+CVP3jZGj08bmX19A5MrLEKXvSx/KvcePG1KtXrwLVUaQ6hxuyzp07o27dupg1a5bSqKmGIjExEf/++2+Bn/PDxLFgwQKUKVMGn3/+udihMBMVEBCA7t27Y9q0afj777/l8zdu3IjY2FhUr14dZmZmOH36NObMmYOPPvpI65tpCht/7pmO48ePIyoqqsDHkhMnAyEIApYtW4bdu3cjKyvLIB/06+joqJM7e5g4HB0dsXr1aq3GPWNMW/PmzcOKFSuQlJQkf9Cvg4MDNm3ahOnTpyM5ORklS5ZE//79tR41Xgz8uWc6Xrx4gbVr16Js2bIFqkcgMqKn+jHGGGOMicjwTmswxhhjjBkoTpwYY4wxxjTEiRNjjDHGmIY4cWKMMcYY0xAnTowxxhhjGjKKxGnJkiXw9fWFtbU1/P39ceLEiVzLHzt2DP7+/rC2tkbZsmWVHnIKANu3b4efnx8kEgn8/Pywc+fOArdrLMTYn8ePH0f79u3h6ekJQRAK9LBYQyLGvpw1axbq1q0LBwcHlChRAp06dcLNmzd1ul1iEWN/Ll26FDVq1ICjoyMcHR0RGBiIffv26XS7xCLWZ6fMrFmzIAgCRo8eXdBNEZ0Y+zIsLAyCIChMHh4eOt0usYj13oyNjUWfPn1QrFgx2NraolatWjh//rx2wetkKE492rRpE1laWtKyZcvo2rVrNGrUKLKzs6MHDx6oLH/37l2ytbWlUaNG0bVr12jZsmVkaWlJ27Ztk5c5deoUmZub08yZM+n69evyZ5xlH+1W23aNhVj783//+x9NmjRJ/qy0nTt36ntT9U6sfRkSEkKrVq2iK1eu0MWLF6lt27ZUpkwZ+bOdjJVY+3P37t20d+9eunnzJt28eZMmTpxIlpaWdOXKFb1vsz6JtT9lzp49Sz4+PlSjRg0aNWqUvjazUIi1LydPnkxVq1alJ0+eyCfZcw+NmVj7MyEhgby9val///505swZunfvHoWHhys8708TBp841atXT+HBjERElStXpvHjx6ss/80331DlypUV5g0ePJgaNGggf929e3dq3bq1QpmQkBDq2bNnvts1FmLtz+xMJXEyhH1JRPTs2TOlhyEbI0PZn0RELi4utHz5cm3CNzhi7s+kpCSqUKECHTp0iIKCgow+cRJrX06ePJlq1qxZwOgNj1j789tvv6XGjRsXNHwy6Et1aWlpOH/+PFq1aqUwv1WrVjh16pTKdSIjI5XKh4SE4Ny5c0hPT8+1jKzO/LRrDMTan6bIkPbl69evAQCurq5ab4ehMJT9mZmZiU2bNiE5ORmBgYH53RzRib0/hw8fjrZt26JFixYF3RTRib0vb9++DU9PT/j6+qJnz564e/duQTdJVGLuz927dyMgIADdunVDiRIlULt2bSxbtkzrbTDoxCk+Ph6ZmZlwd3dXmO/u7o64uDiV68TFxaksn5GRgfj4+FzLyOrMT7vGQKz9aYoMZV8SEUJDQ9G4cWNUq1Ytv5sjOrH35+XLl2Fvbw+JRIIhQ4Zg586d8PPzK+hmiUbM/blp0yZcuHABs2bN0sWmiE7MfVm/fn2sXbsWBw4cwLJlyxAXF4eGDRvixYsXutg0UYi5P+/evYulS5eiQoUKOHDgAIYMGYKRI0di7dq1Wm2DUTy0ShAEhddEpDQvr/I552tSp7btGgux9qcpEntfjhgxApcuXcLJkye1ittQibU/K1WqhIsXL+LVq1fYvn07+vXrh2PHjhl18gQU/v6MiYnBqFGjcPDgQVhbWxcodkMjxnvz448/lv9evXp1BAYGoly5clizZg1CQ0O13wgDIsb+zMrKQkBAAGbOnAkAqF27Nq5evYqlS5fis88+0zh2gz7j5ObmBnNzc6Us9NmzZ0qZpYyHh4fK8hYWFihWrFiuZWR15qddYyDW/jRFhrAvv/rqK+zevRtHjx5F6dKlC7I5ohN7f1pZWaF8+fIICAjArFmzULNmTfz8888F3SzRiLU/z58/j2fPnsHf3x8WFhawsLDAsWPHsGjRIlhYWCAzM1NXm1hoxH5vZmdnZ4fq1avj9u3b+dkUgyDm/ixZsqTSP0NVqlTBw4cPtdoGg06crKys4O/vj0OHDinMP3ToEBo2bKhyncDAQKXyBw8eREBAACwtLXMtI6szP+0aA7H2pykSc18SEUaMGIEdO3bgyJEj8PX11cUmicrQ3ptEhNTUVG03w2CItT+bN2+Oy5cv4+LFi/IpICAAvXv3xsWLF2Fubq6rTSw0hvTeTE1NxfXr11GyZMn8bIpBEHN/NmrUSGnollu3bsHb21u7jShw93I9k922uGLFCrp27RqNHj2a7Ozs6P79+0RENH78eOrbt6+8vOy2xTFjxtC1a9doxYoVSrct/vPPP2Rubk6zZ8+m69ev0+zZs9UOR6CuXWMl1v5MSkqi6Ohoio6OJgA0f/58io6ONurhHcTal0OHDiUnJyeKiIhQuE357du3hbfxeiDW/pwwYQIdP36c7t27R5cuXaKJEyeSmZkZHTx4sPA2Xg/E2p85mcJddWLty7Fjx1JERATdvXuXTp8+Te3atSMHBwf+Hsrn/jx79ixZWFjQjBkz6Pbt27R+/XqytbWldevWaRW/wSdORES//voreXt7k5WVFdWpU0fhtut+/fpRUFCQQvmIiAiqXbs2WVlZkY+PDy1dulSpzq1bt1KlSpXI0tKSKleuTNu3b9eqXWMmxv48evQoAVCa+vXrp49NLDRi7EtV+xEArVq1Sh+bWKjE2J8DBgyQt1m8eHFq3ry50SdNMmJ9dmZnCokTkTj7skePHlSyZEmytLQkT09P6ty5M129elUv21fYxHpv7tmzh6pVq0YSiYQqV65Mf/zxh9axC0Tve1gxxhhjjLFcGXQfJ8YYY4wxQ8KJE2OMMcaYhjhxYowxxhjTECdOjDHGGGMa4sSJMcYYY0xDnDgxxhhjjGmIEyfGGGOMMQ0VicQpNTUVYWFhRv0IBUPC+1N3eF/qFu9P3eL9qTu8L3VLzP1pNANgLlmyBHPmzMGTJ09QtWpVLFy4EE2aNNFo3cTERDg5OeH169dwdHTUc6Smj/en7vC+1C3en7rF+1N3eF/qlpj70yjOOG3evBmjR4/GpEmTEB0djSZNmuDjjz/W+onGjDHGGGMFYRSJ0/z58zFw4EAMGjQIVapUwcKFC+Hl5YWlS5eKHRpjjDHGihALsQPIS1paGs6fP4/x48crzG/VqhVOnTqlcp3U1FSF654JCQkAgJiYGDg5Oekv2CIiKSkJABAbG4vExESRozFuvC91i/enbvH+1B3el7r1+vVrAEBGRkaht23wiVN8fDwyMzPh7u6uMN/d3R1xcXEq15k1axamTJmiNL9atWp6ibGo8vPzEzsEk8H7Urd4f+oW70/d4X2pW9euXUPjxo0LtU2DT5xkBEFQeE1ESvNkJkyYgNDQUPnrmJgYVKtWDWfPnkXJkiX1GidjjDHG9OvJkyeoV68eypQpU+htG3zi5ObmBnNzc6WzS8+ePVM6CyUjkUggkUjkr2WX50qWLInSpUvrL1jGGGOMFRozs8Lvqm3wncOtrKzg7++PQ4cOKcw/dOgQGjZsKFJUjDHGGCuKDP6MEwCEhoaib9++CAgIQGBgIP744w88fPgQQ4YMETs0xhhjjBUhBn/GCQB69OiBhQsXYurUqahVqxaOHz+O//3vf/D29hY7NFYAz549w+DBg1GmTBlIJBJ4eHggJCQEkZGR8jKCIGDXrl06ae/+/fsQBAEXL17MtVxERAQEQcCrV6+UltWqVQthYWHyMrlNq1evBgBs374dTZs2hZOTE+zt7VGjRg1MnTpVfrenJnbs2IGWLVuiePHicHR0RGBgIA4cOKBUbvv27fDz84NEIoGfnx927typsHzWrFmoW7cuHBwcUKJECXTq1Ak3b96UL09PT8e3336L6tWrw87ODp6envjss8/w+PHjPGN8+fIl+vbtCycnJzg5OaFv375K+3DUqFHw9/eHRCJBrVq1NN7+Y8eOwd/fH9bW1ihbtix+++03heVXr15Fly5d4OPjA0EQsHDhQo3qvXz5MoKCgmBjY4NSpUph6tSpyDkmcF5t55SQkICvvvoKlSpVgq2tLcqUKYORI0fK7wICpO/FgQMHwtfXFzY2NihXrhwmT56MtLS0PGPO6xgD0gGDfX19YW1tDX9/f5w4cUKj/cEYy5tRJE4AMGzYMNy/fx+pqak4f/48PvroI7FDYgXUpUsX/Pvvv1izZg1u3bqF3bt3o2nTplolFJrS5AtJGw0bNsSTJ0/kU/fu3dG6dWuFeT169MCkSZPQo0cP1K1bF/v27cOVK1cwb948/Pvvv/jzzz81bu/48eNo2bIl/ve//+H8+fMIDg5G+/btER0dLS8TGRmJHj16oG/fvvj333/Rt29fdO/eHWfOnJGXOXbsGIYPH47Tp0/j0KFDyMjIQKtWrZCcnAwAePv2LS5cuIDvv/8eFy5cwI4dO3Dr1i106NAhzxh79eqFixcvYv/+/di/fz8uXryIvn37KpQhIgwYMAA9evTQeNvv3buHNm3aoEmTJoiOjsbEiRMxcuRIbN++XV7m7du3KFu2LGbPng0PDw+N6k1MTETLli3h6emJqKgoLF68GHPnzsX8+fO1ajunx48f4/Hjx5g7dy4uX76M1atXY//+/Rg4cKC8zI0bN5CVlYXff/8dV69exYIFC/Dbb79h4sSJucasyTHmAYMZ0zMqAmJiYggAxcTEiB0Ke+/ly5cEgCIiItSW8fb2JgDyydvbm4iI7ty5Qx06dKASJUqQnZ0dBQQE0KFDh5TWnTZtGvXr148cHR3ps88+U6gLAAUFBals9+jRowSAXr58qbSsZs2aNHnyZKX5/fr1o44dOyrMO3PmDAGghQsXqt0HBeHn50dTpkyRv+7evTu1bt1aoUxISAj17NlTbR3Pnj0jAHTs2DG1Zc6ePUsA6MGDB2rLXLt2jQDQ6dOn5fMiIyMJAN24cUOp/OTJk6lmzZpq68vum2++ocqVKyvMGzx4MDVo0EBleW9vb1qwYEGe9S5ZsoScnJwoJSVFPm/WrFnk6elJWVlZ+WpbnS1btpCVlRWlp6erLfPTTz+Rr69vrvVocozr1atHQ4YMUShTuXJlGj9+vFYxM2bIxPxeN5ozTsy02Nvbw97eHrt27VL7kMaoqCgAwKpVq/DkyRP56zdv3qBNmzYIDw9HdHQ0QkJC0L59e6X/qOfMmYNq1arh/Pnz+P7773H27FkAQHh4OJ48eYIdO3bocQuB9evXw97eHsOGDVO53NnZGcCHS4gREREa152VlYWkpCS4urrK50VGRqJVq1YK5UJCQtQOFAt8GEQuez2qygiCII9XlcjISDg5OaF+/fryeQ0aNICTk1Ou7WtC3XadO3cO6enpBao3KChI4Q7ckJAQPH78GPfv39e4bdllW9k6qsiep2Vhob5b6evXr5WOg4+PD8LCwhRizu0YywYMzlkmtwGDGWPa4cSJicLCwgKrV6/GmjVr4OzsjEaNGmHixIm4dOmSvEzx4sUBSBMMDw8P+euaNWti8ODBqF69OipUqIDp06ejbNmy2L17t0IbzZo1w7hx41C+fHmUL19evn6xYsXg4eGRa7KgC7dv30bZsmVhaWmZazlLS0t5fxhNzZs3D8nJyejevbt8XlxcnFYDxRIRQkND0bhxY7WDw6akpGD8+PHo1atXrg/SjIuLQ4kSJZTmlyhRQm37mlK3XRkZGYiPj9d5vbJlmrZta2uLSpUqqT3OL168wLRp0zB48GC1sfz3339YvHix0g0v5cqVg5ubW54xy+LNz4DBjDHtcOLERNOlSxc8fvwYu3fvRkhICCIiIlCnTh15p2p1kpOT8c0338DPzw/Ozs6wt7fHjRs3lM44BQQE6DH6vFEug7RmV6pUKdy4cQP16tXTqN6NGzciLCwMmzdvVkpWtBkodsSIEbh06RI2btyocnl6ejp69uyJrKwsLFmyRD5/yJAh8jOG9vb2atvOq31VstebPYlQtV3q2tSGJvXmVaZevXq4ceMGSpUqpVR/YmIi2rZtCz8/P0yePFllDI8fP0br1q3RrVs3DBo0SGHZ4cOHMWLEiDxjzjlPm/cBY0w7RjEcATNd1tbWaNmyJVq2bIkffvgBgwYNwuTJk9G/f3+163z99dc4cOAA5s6di/Lly8PGxgZdu3ZV6gBuZ2eXr5hkZ1Zev36tdHnq1atXGj/vsGLFijh58iTS09PzPOukqc2bN2PgwIHYunUrWrRoobDMw8ND44Fiv/rqK+zevRvHjx9XOShseno6unfvjnv37uHIkSMKZ5umTp2KcePGKbX99OlTpXqeP3+udqBaVbLf8ShrU912WVhYoFixYhrXnZO6eoEPZ54K0nZSUhJat24Ne3t77Ny5U+V74PHjxwgODpYPs5LfmGXx5mfAYMaYdviMEzMofn5+8ju8AOllrMzMTIUyJ06cQP/+/fHJJ5+gevXq8PDwyLV/iYyVlRUAKNWXU4UKFWBmZibvUyXz5MkTxMbGolKlShptS69evfDmzRuFszXZqRruIDcbN25E//79sWHDBrRt21ZpeWBgoNJAsQcPHlQYKJaIMGLECOzYsQNHjhyBr6+vUj2ypOn27dsIDw9XShBKlCghv/xZvnx5eduvX7+W9yMDgDNnzuD169daDVSbvV7Z2TR12xUQEFCghDQwMBDHjx9XSLgPHjwIT09P+Pj4FKjtxMREtGrVClZWVti9ezesra2VysTGxqJp06aoU6cOVq1apdEIyHkdYx4wmLFCUOjd0UXAd9UZnvj4eAoODqY///yT/v33X7p79y5t2bKF3N3dacCAAfJyFSpUoKFDh9KTJ08oISGBiIg6depEtWrVoujoaLp48SK1b9+eHBwcaNSoUfL1VN1ZlZ6eTjY2NjR9+nSKi4ujV69eqY1v6NChVKZMGdq5cyfdvXuXTp48SUFBQVS9enWVd0apuquOSHpXlrm5OX399dd06tQpun//PoWHh1PXrl3ld9s9evSIKlWqRGfOnFEbz4YNG8jCwoJ+/fVXevLkiXzKvg3//PMPmZub0+zZs+n69es0e/ZssrCwULjTbejQoeTk5EQREREK9bx9+1a+jzp06EClS5emixcvKpRJTU1VGx8RUevWralGjRoUGRlJkZGRVL16dWrXrp1Cmdu3b1N0dDQNHjyYKlasSNHR0RQdHZ1r3Xfv3iVbW1saM2YMXbt2jVasWEGWlpa0bds2eZnU1FR5XSVLlqRx48ZRdHQ03b59W229r169Ind3d/r000/p8uXLtGPHDnJ0dKS5c+dq1faZM2eoUqVK9OjRIyIiSkxMpPr161P16tXpzp07CvswIyODiIhiY2OpfPny1KxZM3r06JFCmeyaNWtGixcvlr/W5Bhv2rSJLC0tacWKFXTt2jUaPXo02dnZ0f3799XuC8aMjZjf65w4MVGkpKTQ+PHjqU6dOuTk5ES2trZUqVIl+u677+Rf4kREu3fvpvLly5OFhYV8OIJ79+5RcHAw2djYkJeXF/3yyy8UFBSUZ+JERLRs2TLy8vIiMzMztcMRyOKbOnUqValShWxsbMjb25v69++v9MUmoy5xIiLavHkzffTRR+Tg4EB2dnZUo0YNmjp1qnw4gnv37hEAOnr0qNp4goKClIZTAED9+vVTKLd161aqVKkSWVpaUuXKlWn79u0Ky1XVAYBWrVqlEIuqKbf4iIhevHhBvXv3JgcHB3JwcKDevXsrDbmgbjvu3buXa90RERFUu3ZtsrKyIh8fH1q6dKnCcnVx53aMiYguXbpETZo0IYlEQh4eHhQWFiYfikDTtmXDV8i2QfY6t+1ctWqV2jLZeXt7Kw1/kdcxJiL69ddfydvbm6ysrKhOnTq5DjfBmDES83tdIMoxTK4JevToEby8vBATE8MP+WWMMcaMnJjf69zHiTHGGGNMQ5w4McYYY4xpiBMnxhhjjDENceLEGGOMMaYhTpwYY4wxxjTEiRNjjDHGmIY4cWKMMcYY0xAnTowxxhhjGuLEiTHGGGNMQ5w4McYYY4xpiBMnxhhjjDENceLEGGOMMaYhC7EDYPqRkAD8/TcgCIqTmZny7//8AyxYoIcgHB8BHT8HypwELFOAFCdg93LgeRXpcst3gGUy8DgASLfTuvpBg4Cvv1aeTwRkZSlOquapm+/iAtSpU8BtZ4wxZpI4cTJBKSmAm5s0KRBV/Z+BcuEfXlu/Brp301n1ywEs36hmIQnvJzPphGyvZb+fGQlETAGylP8MwsKAyZN1FipjjDETwZfqTJCVFdCkichBlDoDNJorXvsCAWZZgHkGYJEGWKRKz3pZvQWskgHJG+CjmUCZEypX9/Ep3HAZY4wZBz7jZILMzIBjx/RTtyBoWLDqFv0EoENtKrTB1lv1YWspdiSMMcaMBSdOTD8iwgD3S4qX6gpJKaEOiqESiAhZ7yfXYlnw8CAQCETSn1mUhZ7beirMk66TpTRvctBkNPEW+zQeY4wxsYmWON2/fx/Tpk3DkSNHEBcXB09PT/Tp0weTJk2ClZWVvJyg4hTH0qVLMWTIkMIM16R16QLs2KHDCkueBwYH6LBC7cTSBcTigvSF8H56+X7Kp8P3DuPVt6/gZO2kgwgZY4wZK9ESpxs3biArKwu///47ypcvjytXruCLL75AcnIy5s5V7BuzatUqtG7dWv7ayYm/vHRJp0kTALT/UscVim9g7YFwlDiKHQZjjDGRiZY4tW7dWiEZKlu2LG7evImlS5cqJU7Ozs7w8PAo7BCN2oYNQO/eIjVOOrjnYMda4FLfgteTT0OGAEuXitY8Y4wxA2VQd9W9fv0arq6uSvNHjBgBNzc31K1bF7/99huysrJEiM54vHsnYtLk9Q9Q6lzB6zFPK3gdKtjaAnZ2gL29dHJwABwdAScnwNlZOoZTcDDw/fd6aZ4xxpiRM5jO4f/99x8WL16MefPmKcyfNm0amjdvDhsbGxw+fBhjx45FfHw8vvvuO7V1paamIjU1Vf46KSlJb3GzHIKm6qaejoOkk469VTP/ytArqFqiqs7bY4wxZlp0fsYpLCwMgiDkOp07p3hG4vHjx2jdujW6deuGQYMUvyy/++47BAYGolatWhg7diymTp2KOXPm5BrDrFmz4OTkJJ/8/Px0vZkGzcYGWLFCpMb3/yxSwwXTtO9plC0L+PoC7dsDT5+KHRFjjDFDJBDpdnzp+Ph4xMfH51rGx8cH1tbWAKRJU3BwMOrXr4/Vq1fDzCz3XO6ff/5B48aNERcXB3d3d5Vlcp5xio2NhZ+fH2JiYlC6dGktt8j4vHsnvSQlms8/ArxVDyypF9c/AV755FIgj7f4E3/gci+Fvlncx4kxxgzXo0eP4OXlJcr3us4v1bm5ucHNzU2jsrGxsQgODoa/vz9WrVqVZ9IEANHR0bC2toazs7PaMhKJBBKJRP46MTFRo3hMhY2NtI/OtGkiBZBYSrf1nR4FCJmAWSYgZL2f3r++2B+431S37QH40vRuDGSMMaYDovVxevz4MZo2bYoyZcpg7ty5eP78uXyZ7A66PXv2IC4uDoGBgbCxscHRo0cxadIkfPnllwqJEVM2dap0yg91D8TNypJ2pM6T5bv8NazKhYHAgXkAmRe4qmfPgOLFdRATY4yxIku0xOngwYO4c+cO7ty5o3SaTXb10NLSEkuWLEFoaCiysrJQtmxZTJ06FcOHDxcjZKMiS35kv6v6KfPNN8DPuuyaJOjwrsc6K4B3LsCh3Pu1aeLgQRHvNmSMMWYSdN7HyRCJeS1UDBkZQMWKwL17IgXQ8XOg9mqRGjdNZwedRd1SdcUOgzHGDIKY3+sGNY4T042sLOD1a5Eat0kAMvkyqq69SXsjdgiMMcZgQOM4Md2xsgLi4oC7dz/Mkz3yT8Wj/1Cxog4b/6QvUPF/+Vs30xIgQXp328PGQORYINNKsQxl3wAh7/kar6O4rEsXAWND38/NttOEHHWrW5bzGYvqlmmyjoe9B9ztVd9ByhhjrHBx4mSiLC2BSpU0K/vVV8DixTpq+N/P8p84mad/+L1cuHQSyXYA24MPAP+1yrPskSPS0cYZY4yZPr5Ux7BoEZCaKh3/6c0b6WW+Fy+kd6E9fgzExEj7S925o0FlhTl+k761+FajYsOG6TkOxhhjBoPPODEA0st7mujaFdi2LZcC9X7VSTx4Xln6UyAA9H7sJtnvef3UsKyQBfngmNlfCwSkOAO7NRt+/fffdbPJjDHGDB8nTkwruSZNH4/UXUPFb+iurvywfwoM9teoaNBRAEf1G44q4X3D0bxs88JvmDHGijC+VMcwe7a007gmU66cHhZKvExq2nGxhoZnjLGii884MZw8qaOKEsrpqCLj527nDnp/GVCAIP89J9kwaoIgQN2QaqrWdbdzx2/tftNRtIwxxjTFiRPDtm1Av37SDuGAdGRx2SQj+/3YsVwqcruptxgNTvKHZ7c4OUn7iAmCADtLO2zvvh21S9YWMTjGGGP6wokTQ7FiwNu3Oqho41/AuJKA3fO8yxqp1uVbY1ePXZBY8CCfjDFWFHHixHSTNAHSB/Fe6gMELtBRhVq4/THwqL40hizzDz9THaVjS2VKUKcO0L27Yp8tMzPln56eQKdO0t8ZY4yx7DhxYrh6FejSBYiPV51MZJ8e5tX/u/i1QolZSYV90kmVDl8C6/bhQiJwYfn7YQee+ANvPNRWV706cOmSnmJljDFmtDhxYvDzA65f16zsvXtA2bK5FIgMBcof0ElcOtXnY+V5C+4Dr71VFn/1Sq/RMMYYM1J8MYLhl180H44g16QJ0OgRJQYj1VHtoo0bCzEOxhhjRoPPODHs2KHDyoRMHVYG4FQoEBEmffAv3j8AmAQVv7//qSPr1wONGumsOsYYYyaCEycGc3MdVta5r27qSfQEbnQCjk0G0hx0UyeAjz4CsrI+DLeg6vfKlYFpPLYkY4wxFThxYvjoIyA8XEeVPakDVNfBda50OyDFBWg45/0MNWeTKPt8Ic9lxwHAPOcyxTLnY4D1n6g7e5Vt/vs62raVnp0Ssg2tLuSIRVAx7Hr2MqqWqyuTs+6c66tanlcZbeMTICCkfAjKuuR17ZYxxkyLQOqGKzYhjx49gpeXF2JiYlC6dGmxwzE4794BK1cCyckf5sm+J7N/XwoCMG6cJjUS4HMM8DwHtPpal6EyAxMbGgtPB0+xw2CMFTFifq/zGScGW1sdV1h3KdB2uI4rZYamuG1xuFi7iB0GY4wVKk6cTNSJE8DYsdJ+O7IpM/NDPx7Z71lZemi8jK4efieesZWXYG6PoWKHwRhjzMBw4mSC3r2T9lsSRVgB72w7Nxj4ryVwvTN0eZectt4NA9BDtOYZY4wZKE6cTJCNDTB+PDB7ttiRaGl+DJBoGH3Q6tUTOwLGGGOGiBMnIxEbCxhFv/bFN4CvKudv3VAv3cVxrylwrxkg5Lj3QeXddMqW3QQeHtOuSdmdZ5rcb6HqLjZ166m7407VenmVza/sd9gRct8+dXf1qVtPXXlV6+VVNr+y77e8jp+6faxqPX0fj7yORc7yMrmtl9s+zrmepsejTsk6aFWuld72B2OFiRMnIxEbK3YEGnpRSewIpHwjpFM+/QPgn/yvzhjLYVOXTehRja9/M+PHiZORqFcPuHwZOHNG+jrnP26yR6Jkf63r5QDwxRfAmze5BGrxLpeFjLGiqlqJamKHwJhOcOJkRKpVk066FhEBBAfrqLKun+qoogKIqwH5Y1jkj2rJ8bu6R7a8n1fcyRHnpv2MMk5lxNwSxhhjBoYTJ6a7pAkA0nU9KFQ+eFwqcBXPAXTeHINzX54reDyMMcZMhpmYjfv4+EAQBIVp/PjxCmUePnyI9u3bw87ODm5ubhg5ciTS0tJEipjlSRePWzEQo+qPEjsExhhjBkb0M05Tp07FF198IX9tb28v/z0zMxNt27ZF8eLFcfLkSbx48QL9+vUDEWHx4sVihMtyEzRF/22cHgns/1nvzUybBvStqfdmGGOMGRnREycHBwd4eHioXHbw4EFcu3YNMTEx8PSUPg9r3rx56N+/P2bMmAFHR8fCDNVkVakCXL+ug4q8j+ugEjX+Wg7c+Vj6u8Nj1WVkQw1k2AApzgVq7tGjAq3OGGPMRImeOP3444+YNm0avLy80K1bN3z99dewsrICAERGRqJatWrypAkAQkJCkJqaivPnzyNYp51zTAuR4kN7ZUPMqPpZoKTJbyvQYRBA5oBZRgEqykPHQdqV370MuKDlOtno5VE0jDHGjJ6oidOoUaNQp04duLi44OzZs5gwYQLu3buH5cuXAwDi4uLg7u6usI6LiwusrKwQFxentt7U1FSkpqbKXyclJelnAwxURgZQrhzw8KGeG7JKArp313Mj+UQF674XEKCjOBhjjJkUnSdOYWFhmDIl974uUVFRCAgIwJgxY+TzatSoARcXF3Tt2hU//vgjihUrBkD9CMu5jUA7a9asPGMwZUTS59XpXZoDcHg60Py7Qmgsm+TiwOKbkD7LTsUIyJlWQLpdvqsvVQpo1izfqzPGGDNhOk+cRowYgZ49e+ZaxsfHR+X8Bg0aAADu3LmDYsWKwcPDA2dkIz6+9/LlS6SnpyudicpuwoQJCA0Nlb+OjY2Fn5+fhltg/CwtgcePFUcbl+WZqn42bgzcv5/Pxk5Mkk4yvdoBFffmszINXekBpLjorfrYWGDuXOC33/TWBGOMMSOl88TJzc0Nbm5u+Vo3OjoaAFCyZEkAQGBgIGbMmIEnT57I5x08eBASiQT+/v5q65FIJJBIJPLXiYmJ+YrHmFlYAN7empX9/ntg4EAdNfzvZ/pJnBbdAhIq6L5eFcqVA775plCaYowxZmRE6+MUGRmJ06dPIzg4GE5OToiKisKYMWPQoUMHlCkjHa25VatW8PPzQ9++fTFnzhwkJCRg3Lhx+OKLL/iOujz8/jswZIgIDV/tDiSWBgY20l2d/7XQSdI0bhwwZ44O4mGMMVZkiTYApkQiwebNm9G0aVP4+fnhhx9+wBdffIGNGz8MoGhubo69e/fC2toajRo1Qvfu3dGpUyfMnTtXrLCNQmKiSEmTTMBS3daXVEon1WzerJNqGGOMFWGinXGqU6cOTp8+nWe5MmXK4O+//y6EiEyHrdhPPTk0B6i5Tjd1xTQAjkzXSVVhYTqphjHGWBEm+jhOTPcsLKTjEGW8H1aJSHfT69fSS15RUerLAB5Ijh6JtNqLtA8+aqj056P6wOVeQJZlrsXLlQOsraW/Z7/RMmcn+E6dgD59tA+HMcYYy44TJxMlCNK763TNwwPYsyfvcomp01B6/iokpSXhf73+h48rfKxR/UTAuXPAjh3A9rfA7dsflpmbS4cJ6NIF6NhRGgtjjDFWmDhxYnrhKHHEF3W+wPzT8zH/9PxcE6fMTODUKWmytGOH4sCdEgnQqpU0WWrfHnB1LYTgGWOMMTU4cWJ6M7L+SCw8sxDhd8Nx6ekl1HCvIV+Wng4cOwZs3w7s3Ak8ffphPTs7oE0babLUpg3g4CBC8IwxxpgKnDgxvfF29kZXv67YcnULFpxegKUhqxAeLk2Wdu8GEhI+lHVyAjp0ADp3BkJCABsb8eJmjDHG1OHEielFFkmfkjuk5hhsuboFa6PXY+uQmUiOKykv4+Ym7avUubO079L7ZztL18/68CBimZyvzc0VO4Qzxhhj+saJE8P48cCPP+qoMrN0YLA/4H5ZYXaWkI7kIZ4K8+IBrACwIgpAFIAbHYCtW4BMCQrK3Fx6d6G5uXTq21f6GBU+k8UYY6wgRBsAkxmOe/d0WFmfj5WSJo1V3g0Uv66TMDIzgdRU4O1bICkJWLIE+OsvnVTNGGOsCOMzTgybNgGffCJNMDIzpZfJZFPO1+PHq6hAyAQ+awn4Hi14MENq5758/d/A7bb5qjozM1+rMcYYY3KcODEMGACsXp3PlYUs6aU5j391GZJ6LcbnO3Hq00c/g2AuXAiMGqX7ehljjBkevlTHsH17AVYmAUjVw3gBqfZAiiOQ4vRhelUG2Pmn7tsqoJUrxY6AMcZYYeEzTgxJSQVZWwBWHQfcbgIjqugqJEDyRnme9eu8L+WJ4BIAYYrYUajWpEwTbOu+DSXsSogdCmOMmQQ+48Tg6Zl3GbX8/wDCzHSbNDGdOfHwBFZG8ykxxhjTFU6cGCpWzOeKkkSg/WCdxsJ0q5ZHLfSr2U/sMBhjzGTwpTqGbduAGTOkl+zMzJTHQMo+TZ+ebUXScd49MxFI09/zVXbtkg64yRhjjOUXJ04M164BCxbkY8U0e2DRbWBkBd0E8rU7AAIu9QH2/wyk2+qm3vcGD+bEiTHGWMFw4sTw0Uf5XFGSqLukCQAs30l/+i+X/n75UwCC9M69dFvgYZMCneV6+lT6jDzZ41yIPoxPJXtNBBQvDjRvzo9zYYwxpowTJxMVEwMsXy699CYIH37Kpuyv1Sp9GhgUWGgxK6ixXjpld/lTYPuGAlWr6RmnCROAmTML1BRjjDETxImTCXr3DihTpoCVVNgL9G6nk3h0Jr5SoTVVo0ahNcUYY8yIcOJUFFm+BcwypA/kbTQHaKyrJ/xqIOIH4NQ4vXYCV+fBAx0klIwxxoo0TpxMkLU10KH7S+yOWQU4PQQa/Fy4ARyfJO2XJBAAkv586Qtc/BzI0u1brkYNaX8kMzPlKftlyMBAwMtLp00zxhgrgjhxMkGELEQGVAL8nuumwkOzgReVpMnP05q6qVNHLl2STpr44QdgioGO8M0YY8w4cOJkAogI269vx6Yrm/Aq5RUO3zuss7o7PrwFp3IVQGWlr/80vEfFaezBA7EjYIwxZuw4cTJiF+Mu4tB/h/BN+De6rfj2x8CBeUB8Ffyl25pFNXSo2BEwxhgzdpw4GaklUUsw/H/D9VP53iXAKx/91K1jZcsClSsr9mnKPuyCbF7btkDduuLFyRhjzDRw4mSkElMT9Vf54NrATy90/0gVPbh7VzrlZedOwMEB6NpVg/GrGGOMMTUM/5uRqZSclqy/yqOGGUXSpK0ePaTP28s5GKiqadUqsaNljDFmiEQ74xQREYHg4GCVy86ePYu676+rCCpODSxduhRDhgzRa3yGbljdYZh+YnreBbP7LRp4XgXyx5jg/b6V/Z59XhH322/A55+LHQVjjDFDI1ri1LBhQzx58kRh3vfff4/w8HAEBAQozF+1ahVat24tf+3k5FQoMRqyjpvy8bTaIbU1K3ex34ffSZZICTlev59HORKtsyMMbsiC/GjcWOwIGGOMGSLREicrKyt4eHjIX6enp2P37t0YMWKE0lkmZ2dnhbIMqFisIqIeR+mn8lpr8r+u/3LdxbH4hnT8KB2RSIAZMxQf6KtqKlUK6NtXZ80yxhgzIQbTOXz37t2Ij49H//79lZaNGDECgwYNgq+vLwYOHIgvv/wSZmbq++CkpqYiNTVV/jopKUkfIYvqz0/+hJ2lHf648IfYoeiP93GdJk5duwJjx+qsOsYYY0WQwSROK1asQEhICLxyPBdj2rRpaN68OWxsbHD48GGMHTsW8fHx+O6779TWNWvWLEwx8SGiBUFAj2o99Js43W0OpNlBod9Tzn5QOS/l5SyT89KeJmVIAB41kD6iRYfatNFpdYwxxoognSdOYWFheSYtUVFRCv2YHj16hAMHDmDLli1KZbMnSLVq1QIATJ06NdfEacKECQgNDZW/jo2NhZ+fn6abYDROPDih3wbKajkC+T/jgENz9BOLDri7ix0BY4wxY6fzxGnEiBHo2bNnrmV8fHwUXq9atQrFihVDhw4d8qy/QYMGSExMxNOnT+Gu5ptQIpFAIpHIXycm6nHMIxGdjDkpdgiKGs2VTur8EQU8DlC/XM+yvSUYY4yxfNF54uTm5gY3NzeNyxMRVq1ahc8++wyWlpZ5lo+Ojoa1tTWcnZ0LEKXx23J1C8LvhosdhnaqbBc1cUrW49BXjDHGigbR+zgdOXIE9+7dw8CBA5WW7dmzB3FxcQgMDISNjQ2OHj2KSZMm4csvv1Q4o1QUvU55XaD1W5VrBTNB2sE+Nha4fJkUC+QcZiAngTQrJ/OsGnBc/eXVwtC6tfSuOcYYYyy/RE+cVqxYgYYNG6JKlSpKyywtLbFkyRKEhoYiKysLZcuWxdSpUzF8uJ6e0WZEXqW8KtD6n1b7FP1r9QcABAcDiChoRMZBk0et1KoF7NoFeHvrOxrGGGPGRiAy/f/BHz16BC8vL8TExKB06dJih6MTB+4cQOv1rfMuqA9LLkvPIJmwwYOlo4czxhgzPGJ+r5veA8mKCEvzvPuD6U2V7eK1XUjKlBE7AsYYY4ZI9Et1LH/OPDqj0/rql6oPczNzhXlCjufWCYKArEf+OBVp+qNITpoETJwodhSMMcYMDSdORurbxt+iYrGKiI6LxowTMwpc35lYaSK2v/d+hJQPUVsuKwvYXB64e1f1ckH40AH7/Hlg584Ch8YYY4wZDO7jZAJOPDiBj1Z/pLP6DvQ5AEB6xkkQBPg6+6KEXQmlcjmfKaiw7P3ZqsePgdt3PnTKFgTA7P2L7PNu3waGDUMed+nlsiw/6+WxTnq6uiW5bLcG+0Tb9RhjjCkS83udzziZgLIuZXVaX8g69Wec9GqSOM2qYzlN7AgY068LX15A7ZK1xQ6DMaPCiZMJKOVYCjRZeuIwJSMFT5Ke4J+Yf9B3Z1+RI2OMGbIHrx9w4sSYljhxMjHWFtbwdfGFr4svJ04mYnXH1XC2dgYhf1fVc16Nz35ZML9X6lXFIrsUmVucsvZUXZpUF4sgCLnGmb29nJdD9bHPVC3PXs6QYs0tlkpulVDLo1a+2mSsKOPEyYRVdquMG/E3xA6DFZCPsw+CfILEDoMxxhg4cTJpl4Zcgu1MW2RkZYgXRJY5cLe54jyNHteSfZmq/5rz6CguqPlPW12bWsf0IS4XF6B+g7w7f+d2lkKdbn7d0MS7SS4xMcYYK0ycOJkwS3NLzGg2A9+Gf6v3tmR9rPLr1i2gUiUdBVPIXgLYt1jsKBhjjBUGHo6gCMvMykRqZipSM1KRlpmm8PuB/w5g7MH8D3TZomwL/NTiJ5RyLKVyKIOcEhMBJ6d8Nyc60/8rYowxw8HDETBRmJuZw9bMFraWtkrLqpaoCh9nH3TZ0iVfdYffDUedP+oAkI4L1apcq1zLv3iRr2YMQuXKYkfAGGOssPCz6phanat0Bk0m0GRC1g9ZCCmXv/Gdfjn7C9Iy03ItY8xnm+ztxY6AMcZYYeHEiWlEEAQsbL0wX+vuubUHksa/QhCgdipWTLfxFqZz58SOgDHGWGHhS3VMY5XdKuPvT/9Gu43ttF+5dah00rVDs4F/vkGud9kVAk2fmNKrF7BuneblGWOMGRY+48S0kp6l5gFuYmk5HnC+L3YUGtuwAYiPFzsKxhhj+cVnnJjGzj0+h082fyJ2GIr+GQe88hE7Cnh6qj6LJLvbTrZsyRKgePHCi4sxxphuceLENPY8+XmB60j4JgEuNi5K81NSABubAlcvmsePNSvXsSPw+jXg6KjfeBhjjOkHJ05MYx9X+Bh3vrqD6LhodNvaLV91uP7kqn5hmAYVrD4C3A/OV9uGIiGBEyfGGDNW3MeJaaWcazl09esKmkzI/CETzXybFW4ArccUbnt6YGcndgSMMcbyi884sXwzE8xw+LPD8teZWZlw/ckViamJ+mkwzQ7Y84d+6i6grl0VRw8nUn4NAGPGcB8nxhgzZpw4MZ0xNzPHy29fouS8kniW/Cx/lUT3B263BV6WBZ7U0Wl8+tK7t3SIAcYYY6aPn1XHdO7+q/vw/dm34BXdCwYu9QGyLIAUJ+BWO4DMC16vHpj+XxFjjBkOflYdMyk+zj6gyYTjx4Ggdo8BmwTALEM6eVwEOnyhWUW+R6WTzNVuwNYteomZMcYY0wQnTkxvli0DkOQpnWQeBwBWSfkbRbzqVqBqtsGSrncCNu8saJgFxo9cYYyxooMv1TG9efAA8PFRs9DqDVD6NFBhLxC4MP+NLD8FJHoBJAAQdPwTivMAIMscOR/v4uoKvHiR/01gjDGmHb5Ux4zGkSNA8+Y6qCjNHrjbAmg7rGD1DGqog2C0NPcx8Kak/GVCgubPnvvuO2DaND3FxRhjTO/0Oo7TjBkz0LBhQ9ja2sLZ2VllmYcPH6J9+/aws7ODm5sbRo4cibS0NIUyly9fRlBQEGxsbFCqVClMnToVReBEmUE6c0bHFUYa4bhM5ml5l1Fj+nTgWT5vOGSMMSY+vZ5xSktLQ7du3RAYGIgVK1YoLc/MzETbtm1RvHhxnDx5Ei9evEC/fv1ARFi8eDEAIDExES1btkRwcDCioqJw69Yt9O/fH3Z2dhg7dqw+w2cqjB8PVKgA3Lun/By2nIiA69c/9AGSjW0kW48IwJuhwKahyDJ/hzsdPZBllY8xoLZtBGIC37/IFozscpsgS7JJ+bXaZVBdJqmU9GxZATx/DpQoUaAqGGOMiUSvidOUKVMAAKtXr1a5/ODBg7h27RpiYmLg6SntQDxv3jz0798fM2bMgKOjI9avX4+UlBSsXr0aEokE1apVw61btzB//nyEhoZC0PQaCVNr5Upg4ECRgwizzf+6zSYBqdmeYfI4ADg4F0h1KnhcelClitgRMMYYyy9RH7kSGRmJatWqyZMmAAgJCUFqairOnz8vLxMUFASJRKJQ5vHjx7h//77KelNTU5GYmCifkpKS9Lodxk70wRsDlhZsfde7QMmLHyb/5UCjn3QQmOayn03LazLjBx0xxpjRErVzeFxcHNzd3RXmubi4wMrKCnFxcfIyPjluzZKtExcXB19f5YEWZ82aJT/bxfK2fbu0701i4ofLboKg+Hv2nznnqfodALZuBZ480SCA6hsKFL9KVz7VfZ25+O8/aUJkZibdB7Lfc5unbuKTqIwxZri0TpzCwsLyTEqioqIQEBCgUX2qLrURkcL8nGVkHcPVXaabMGECQkM/jBMUGxsLPz8/jeIpilxcgHnzdF9vs2ZAp065lSCg+STA+6TuGn1bDPj9PPDaW3d1aqB8ef3Ue+ECULu2fupmjDGmPa0TpxEjRqBnz565lsl5hkgdDw8PnMlxm9bLly+Rnp4uP6vk4eEhP/sk8+z9bUk5z1bJSCQShUt7iYl6eugsy1XNmtleWLwDOvUHqulp5O9frwDPq+qnbhG9eSN2BIwxxrLTOnFyc3ODm5ubThoPDAzEjBkz8OTJE5QsKR0X5+DBg5BIJPD395eXmThxItLS0mBlZSUv4+npqXGCxsTx9Gm2F16n9Jc0AcDwavqrOztScZaTBJjleISeANVnQ1XOFwT5TX05Z7c4JgDH8q5D3dlXbcrqqu5aHrWwofMGlHIspbYdxhgzVnrt4/Tw4UMkJCTg4cOHyMzMxMWLFwEA5cuXh729PVq1agU/Pz/07dsXc+bMQUJCAsaNG4cvvvgCjo7Su6R69eqFKVOmoH///pg4cSJu376NmTNn4ocffuA76gxc/frA1avA+fNABjXBphcjcTBxkdhhFYzScAXSeVkFGVZM3bpGOlTZ8QfHMf34dCxtV8BO/4wxZoD0+siV/v37Y82aNUrzjx49iqZNmwKQJlfDhg3DkSNHYGNjg169emHu3LkKl9ouX76M4cOH4+zZs3BxccGQIUO0Spz4kSuGZcPlDei9o7fYYTA9MRPM8O+Qf1GtRCGdBWSMFTlifq/zs+qYVhISgCFDgFOnVC/P/m56/hxIT3//wu4pMNYTMMvKf+M71gI3OgFkBvkz5AQCMq2ALON4elC9esA//wAWxhEuY4wZJH5WHTMa8+dLhxnQmuudgiVNAND5M+B5FUhH884CDiwAbrcpWJ06Yvr/fjDGGAM4cWJa6tsX+OmnbGeSNPWiom4CKH79w++92wJXekhHDScByNlZWdU8ADg3GHhaU3l+AWja3W7SJOmYWYwxxowTX6pjOnf03lE0W9tM7DBy92M88K6YKE0/fcrPqmOMsYLgS3XM6BERFp1ZhNEHRosdSt7uNgdSXHRapbm56vk5z0T98gsnTYwxZsw4cWI60Xp9axz876DYYShKdQBWngSe1tB7UzVzXPlTdx43PBw4qeFA6S1bAn368LPtGGPMkHDixAoki7JgPlXN6RYxHZ4BnJhYaM1duKBZuehozetctw5wdMzrsTWMMcYKEydOTGsxr2NwO+E2zAQzpGWmFV7D/4wDknM+Zkd2akf48PqVL3CtS+HFpUcaPvKRMcZYIeHEiWnl9KPTCFwRKE7jjebmvjxisvTynNNDIHDBh/mqHpOi8g48FcvVzc+x3sKfPyyRDcwqCLI+TgIEATDL0eFJgAAnayd09esKCzP+U2SMMWPAn9ZMK2aCAXe4aTpFtKZHF6B719+3/sa6zut0FwxjjDG9MeBvQWZoHrx6gPrL64sdhslpXKax2CEwxhjTEJ9xYhrrs7NPgeto7T4A4yqsUJr/9i3QoUOBqxdF5crA9et5l2OMMWb8OHFiGruTcKfAdex/uhL7hyonTsbsxg3NRw7v2hXYskXz8owxxgwLX6pjGtvba69uKnK/BJinAmYZ0mfOFSHbtgHx8WJHwRhjLL/4jBPTmKDquW/5MTSP58QtuA+89tZNWwYoq2jliowxZlI4cWK5WnxmMUbuH1m4jUoSC7e9Qvbff4B7zuGoGGOMGQVOnFiuCjVp2rwNiGkEvPEovDZF4O8vdgSMMcbyixMnlqt9vffh4/Uf670dRytHJJIZYJGi97bEZGsLfP/9h8Exc05mZh9+b9RI+rw6xhhjhoMTJ5arLVe3FEo7iWmJQM/OeZb7ukQ4/Gya4/PPCyEoPXj7FpgzR/Pye/YA7drpLx7GGGPa4cSJ5WpEvRFYdXGV2GHIzXnWAti1CqilamnO59apmkfZlqmbl/21unn59LwqEFtP4+J+frppljHGmG5w4sRyVadkHazquAqf/2VAp3g6GVAs+bCt23Z08cv77BpjjDHDw+M4sVxtvbrVsJImE+AilBU7BMYYY/nEZ5xYrk48PKHbCv/tA+z8U7d1GplZJ4EVJT68VjWKuCBIO4b36SPtMM4YY8wwcOLEcrUgZAEAYPHZxbqpsMI+3dRjxMLDNSu3bh3g6Ah06qTXcBhjjGmBEyeWq/9e/odlF5bprL7Bjn/BZ9aHsyyyW+9TU6W36TNFAQFiR8AYYyw7TpyYHBEhiz48D8RzvieeJT8rUJ2eDp74rsl3kFhIYCaYwUz4D2bCPZgJZrC3soejxBHmgjlevbQAfjMHyAzIspAOgmlEA2GSjm66Y4wxZtg4cWIAgIysDFRcXBH3Xt3Tab2Pkx5j2P+GaVb4ixyvN+wBbvEgRowxxgyHXrudzpgxAw0bNoStrS2cnZ2Vlv/777/49NNP4eXlBRsbG1SpUgU///yzQpn79+9DEASlaf/+/foMvcjJzMrEkzdPxA5DUZq92BFoTN1I4Dmnhg2BzEyxo2WMMZZfej3jlJaWhm7duiEwMBArVqxQWn7+/HkUL14c69atg5eXF06dOoUvv/wS5ubmGDFihELZ8PBwVK1aVf7a1dVVn6EXKclpybCfpYckJbk48Fs08NYN8sEmSXYLmaD+dxMWGQk8fgx4eYkdCWOMsfzQa+I0ZcoUAMDq1atVLh8wYIDC67JlyyIyMhI7duxQSpyKFSsGDw/j6fNiTLL3a9Ipu+eA+2XgTmv91G+kVJx8ZYwxZiQMro/T69evVZ5N6tChA1JSUlChQgWMGTMGXbt2FSE60/Mk6QmO3j+qk7pKpTeFAAGAAAFm8Ejogqj/WumkblNRrRpgYyN2FIwxxvLLoBKnyMhIbNmyBXv37pXPs7e3x/z589GoUSOYmZlh9+7d6NGjB9asWYM+ffqorCc1NRWpqany10lJSXqP3RhdenoJNX+rqZO6IgdGokHpBgrzIiKA4KU6qd5kXLkCvH4NFCsmdiSMMcbyQ+vEKSwsTH4JTp2oqCgEaDkAzdWrV9GxY0f88MMPaNmypXy+m5sbxowZI38dEBCAly9f4qefflKbOM2aNSvPGBngKHHUST00WfW9+EFBwK5dwP/+9+F2/ey37Wef9+4dsHGjTsIRxaefqt7G7KOCEwETJ3LSxBhjxkwg0m4Emvj4eMTHx+daxsfHB9bW1vLXq1evxujRo/Hq1SuV5a9du4bg4GAMGjQIM2bMyDOG9evXY9CgQXj37p3K5TnPOMXGxsLPzw8xMTEoXbp0nvUXNbo88wQAYxqMwbxW8yCoepaIGllZQKNGwOnTOgvDYL16BTg5iR0FY4wZr0ePHsHLy0uU73Wtzzi5ubnBzc1NZwFcvXoVzZo1Q79+/TRKmgAgOjoaJUuWVLtcIpFAIpHIXycmJhY4TlNWvUR1lHIohdikWJ3Ut+D0AgyvOxzlXMtpvE5aGnDunE6aN3h37gD+/mJHwRhjLD/02sfp4cOHSEhIwMOHD5GZmYmLFy8CAMqXLw97e3tcvXoVwcHBaNWqFUJDQxEXFwcAMDc3R/HixQEAa9asgaWlJWrXrg0zMzPs2bMHixYtwo8//qjP0IsUQRCwocsGBK0O0lmd5ReX136l797/nB8DJJrmmcFmzThpYowxY6bXxOmHH37AmjVr5K9r164NADh69CiaNm2KrVu34vnz51i/fj3Wr18vL+ft7Y379+/LX0+fPh0PHjyAubk5KlasiJUrV6rt38S09/TNU50mTQVmG2+yiZOHh7SvkxZXMRljjBkQrfs4GSMxr4Uag8ysTLTd0BYH/jug87rHNBiDj7w/UrlMyDbYZWYm0KULgKc1gFe+Oo/DkDx7Brw/ocoYYywfjKqPEzM95mbm2N9nP4gIZlN19xSe9Z3Xo1f1XhqVTU4GcFNnTRs0M70+6Igxxpg+8Uc4k0vLTEP04Gid1deibAuNy755o7NmDZq/P99RxxhjxozPODFkZGXA/w9/XHp6SWd1Dqo9CFMipkAQBJgJZhAgyH/P+XpowFCYp3vrrO3CFhQkHeyTMcaY6ePEieHlu5c6TZoAYHn0co3L/vjP+zskw7RoYOdq4N9+2oSkN+bmYkfAGGOssPClOobidsVxffh1TGw8ET2r9RQ7HM20/xIQMsWOAgBw5IjYETDGGCssfMaJAQDKupTFHxf+QPzb3EeFF12GBAABG3cDxKd6GGOMFS5OnBgAwEwwg4OVg+EnThbvH6XTt3X+1r/SA9i2EYDuBlIqVUpnVTHGGDNwfKmOAQAszCzw38j/kP59OsL7hosdjv74bQPM03VaZaxunlTDGGPMCPAZJyYnCAIsBAs0L9scNFk6Luq9l/dAIETFRqHndt31fxoeMFI+AKYAAVlZAn7/XUBGunQOSID8rJDsd5KdJVK1XMP1LgwEMq10th2MMcaKFk6cWK58XaSjeJd1KYse1XoAAN6lv8O7jHewMLMAESGLspCUloRyi8ohIysDALCv9z5YmUsTlOzDD9hZ2qFOyToQVDxz5Oc2QLyaK4U5i4eHA9OmATduqC7frh1QooR0sElByDb55XitYtq9G7h7V7P9Y2kJXL6sWVnGGGPGjxMnpjUbSxvYWNoozHOxcUG9UvVwKuYUwoLC0Lq89n2QzM0Bd3fNyvbqJZ1iY4GtW4HNm4HTpz8s37cPaN4c6NED+OQTwMVF8zgWLNAubsYYY0UH93FiOnHhyQWcijkFCzMLfOn/ZaG1W6oUMHo0EBkJ3L8P/PQTUKeO9Nl3Bw8CAwdKk7H27YF164DExEILjTHGmAnixInpxK9nfwUAdPXripIOJUWJwdsb+Ppr4Px54NYtYPp0oFo1ID0d+PtvoG9f6eW7Ll2ALVuAt29FCZMxxpgR48SJFdiLty+w4coGAMCIuiNEjkaqQgVg0iRp/6OrV4EffgAqVQJSU4EdO6SX8EqUAD79FNi1C0hJETtixhhjxoATJ1ZgK6NXIiUjBbU8aqGhV0Oxw1Hi5wdMmQJcvw5ERwPjxwO+vkByMrBpk7QPlLs70K8f8L//Sc9QMcYYY6pw53CmsXsv76HsorJql1+MuwizqR9ycVcbVxzvfxzO1s5q1/F08FR5h11ezp0Dhg+XnlGS3Q2ndAddtunFi9zrS0wE1q6VTtr63/+Ajz/Wfj3GGGPGhxMnprGTD09qVT7hXQKqLa2WZ7mhAUMhQMDlywJOHH+fRD1qAFz5FCDDPynapg3QujVApHq5bH7O/FBVeUH4ML9tW2lyyA8RZowxwyEQqfu4Nx2PHj2Cl5cXYmJiULp0abHDMVoZWRn45ewvGHNgjNihqPe0GvDnIeCNh9iR6MT27UDnzmJHwRhjhkXM73XD/3eeGQwLMwuUcykndhi5c78CVNskdhQ64eICBAaKHQVjjLHs+FId04rbm2Dg9sdAhX1ih6Je6zHSSZXzg4A9ywo3nhyePpXe0ccYY8z48BknppV9f9kD6/8HhJF02rEWSLMFMiRih6YZ/+WAeZpozc+cyUkTY4wZMz7jxLTyzTfSO9Cio2V3rPUFTvWFIADHmlgjyyxVd409aAw8q674sF4yg9LDfAEABAg5u+vlnEfA6TF6eciv6fcUZIwxBnDixLRkbw8sXKh62Yzj3+O7o9/prjHvk9KpoI6GAccmF7yeXGg6osK4cdLHwuRjBAbGGGMGgC/VMZ2Z9NEk0GRCxvcZSPsuDSmTUvDq21cIKRcibmAlrojbfjZz5wLPn4sdBWOMsfziM04s3/be2ot2G9uJ1v70yvuQnmYGIkIWCCAgKwvIIgK9/2lOEsyeEixajKo8eMD9nBhjzFhx4sS09vTNU3jME3+cpO9uaDhcd/ardJd7Ats3QLF/VOGqVEm0phljjBUQX6pjWnua/FTsEPKv6lbAXNyH0b18KWrzjDHGCkCvZ5xmzJiBvXv34uLFi7CyssKrV6+Uyqh6TtnSpUsxZMgQ+evLly9jxIgROHv2LFxdXTF48GB8//33+XrGGcufm/E3Ef82Hi9TXqL9xvZih6PeqbFAuq30d5K9P4QPry8M0stdddqwsRG1ecYYYwWg18QpLS0N3bp1Q2BgIFasWKG23KpVq9C6dWv5aycnJ/nviYmJaNmyJYKDgxEVFYVbt26hf//+sLOzw9ixY/UZPntv0ZlFGLV/lNhhaOZJbeByb7GjyBUnTowxZrz0mjhNmTIFALB69epcyzk7O8PDQ3WfmfXr1yMlJQWrV6+GRCJBtWrVcOvWLcyfPx+hoaF81qkQlLAzkp7MsQHAndZ5lxORtTVgayt2FIwxxvLLIDqHjxgxAoMGDYKvry8GDhyIL7/8EmZm0u5XkZGRCAoKgkTyYWTqkJAQTJgwAffv34evr69YYRcJRAQXaxcsa78MX+z5QuxwpF6VARY+EDuKfElJAapUAfz8FOeryv9btwYGDQLMzQsnNsYYY3kTPXGaNm0amjdvDhsbGxw+fBhjx45FfHw8vvtOOpBiXFwcfHx8FNZxd3eXL1OVOKWmpiI19cMI1klJSfrbABNz+O5htPizhdhh5M75IRAmAP98DRz6SexotHb7tnTKy65d0gFHexv2lUfGGCtStL6rLiwsDIIg5DqdO3dO4/q+++47BAYGolatWhg7diymTp2KOXPmKJTJeTmO3j/fQt1lulmzZsHJyUk++eX8954pICL029UPwhTB8JOm7BrNAaxMOymOjxc7AsYYY9lpfcZpxIgR6NmzZ65lcp4h0kaDBg2QmJiIp0+fwt3dHR4eHoiLi1Mo8+zZMwAfzjzlNGHCBISGhspfx8bGcvKkwoNXD+Dzs494AcQGvH+W3PsHveV8rpx8Xrb58jIEHJwLpDnoP06RNG0K9O8vdhSMMcay0zpxcnNzg5ubmz5iAQBER0fD2toazs7OAIDAwEBMnDgRaWlpsLKS3kZ+8OBBeHp6qk3QJBKJQp+oxMREvcVrzBLeJYjX+N9LgXND8i5ngp4+5ZHDGWPMWOm1j9PDhw+RkJCAhw8fIjMzExcvXgQAlC9fHvb29tizZw/i4uIQGBgIGxsbHD16FJMmTcKXX34pT3x69eqFKVOmoH///pg4cSJu376NmTNn4ocffuA76gogJSMFpRxLoZxLOfz38j+9tTO97D/472hDCAJgZibtBC0IgFkAINT9MA8AfvlFb2EYlFevOHFijDFjpdfE6YcffsCaNWvkr2vXrg0AOHr0KJo2bQpLS0ssWbIEoaGhyMrKQtmyZTF16lQMHz5cvo6TkxMOHTqE4cOHIyAgAC4uLggNDVW4FMe0s/bftei3q1+htPVpOw+U7Zt3uXfvik7ixOM4McaY8RJI1tPahD169AheXl6IiYlB6dKlxQ5HNLde3EKlX/T/oLTX41/DUeKo9XrnzgGdOyvOy/7uFIQPrx89KkCAImrRAjh0SOwoGGPMuIn5vS76cASs8Og7aXK1ccW/Q/7NV9L09i1Qt64egjIw4eHA9evSsZwYY4wZH37IbxGw7/Y+CFP03x8sKTUJHvaqR4DPS1aWjoMxYO/ve2CMMWaE+IxTETD/9PxCaSc9Kx2W0yxR0r4kCIQBtQYgrGkYLM0t81zXrIik8B99BMyd+6GTvKxjvLzTfLYO9I0bAyEhqkcVZ4wxJg7u41QEPHz9EN4LvUVpe2+vvWhToY1GZe/fB1atUkwmZD9zzvv+e93Gaah27wbatxc7CsYYMyzcx4npxbpL69B3pwa3tOlJQ6+GaFymscblfXyA98+FzlNRSZyqVRM7AsYYY9lx4mRiniU/Q2RMJLIoS9SkCQBOxZyC02wn3Va6dRNwtYdu6yxkcXGAmkHvGWOMGThOnEzI0zdP4TEvf52zjUbDeUafOHGfJcYYM16cOJmQlykvdVpfKYdSqO5eHTm7wdH7Z8fJ5hNyLM8xP/v66tYlIsTHA7duvR+rKfsz6d6XE1IdYXFgKcgSSE/X0UbqSLNmH8aYIvpwlyCR4vTddzxqOGOMGTNOnEyIr7MvvJ288eD1A53UF5sUi9/a/YYSdtJvegEfTpVkf9xN9vnZl2tSXtv5mJsMQbiGqlXfv6bsbWf7Pef83Mq9cwVSXJS2QRuHDxdodcYYY0aCEycTIrGQ4P7o+7j09BIWnVkEAFh9cTUyKTPfdbbfaKC3dA3Pu4hW/jwA/NdKx5UyxhgzNZw4maAa7jWwvMNyAMCwusPg/4e/yBEZgQxJgVY/d06x75KqMZpyG14hr7J51W1nBxQvXqBNYIwxpgFOnEzYuIPjMC9ynt7baVG2hfx3dZftsi9ztnbGjGYzUNzuwzd9ZoYAV9cPtchRzvpyuRyX13wlsk5J5kBWwf4UDOFxMQsXAqNGiR0FY4yZNk6cTFhhJE0AEH43XOt1niY/xdF+R+WvE5IBpOkwqCLIykrsCBhjzPRx4mSiMrPy36+pMETcj4DQqz0Uzgj1zOXskoxGZ5k0WZ73urKzSIIgoJ3TBJSR1ADw4bEogDRZadECsMjxl5RzyIG8Xmu6juzuPEDxbj0LC8DGRsVmMMYY0ylOnExQZlYmavxWQ+ww8lbpb7EjyFXUuw+/n327CZiRDKTbqixr+g8uYowxBnDiZDKICAf/O4gtV7cgC1m49vya2CGZluj+QLr6UzoLFmhWzSefSB8twxhjzDhx4mQi/n36L1qvby12GPq1fi9wW7MHBhe20FDNy714gWwd4RljjBkTTpxMRFmXsvBx9sH9V/cLrc2hAUOxpO0SndSVkQHY2wOpqTqpzqClpIgdAWOMsfzixMlEOEoccW/UPbxOeQ3nH5311s7IeiNR3rU8XGxcEFIuBC/evlBYLqjq9ZyDAAGCIMDZ2lk+7927opE0AYClpdgRMMYYyy9OnEyMk7UTjnx2BM3WNtNL/YvOLtJZXd38umFLty0AgEzDvglQp4oVEzsCxhhj+SVQzie4mqBHjx7By8sLMTExKF26tNjhFDphSt5ngYwKCcDVblA7uKXK4QWQz/KU43XB2+v8iQB7B1WlNTtO6s7qqVtfNt/D3gPjG4+Hg0RF44wxZkTE/F7nM05FwMKQhRh9YLTYYeiOQEC1LWJHkW877orX9n8v/8OmrpvEC4AxxoycmdgBMP0b1WAUaDKhsltlsUNhIutetbvYITDGmFHjM05FSGOvxrgRf0PsMNC7em+s67xOaf733wPTp4sQUCFydQXi41WPHM4YY8zwceJUhHzb+Fvsu7MPsUmxem2nY6WO6ObXTWl+FmVBYiHBJ5U/UbnetGnSSRMtWwLh2j8iT3QJCcCpU0CjRmJHwhhjLD84cSoC9t/Zj4/Xf1xo7f118y/8dfMv/TbS+P2kjc3bgOtd9BGNVry9xY6AMcZYfnEfpyJgXuQ8sUMwDG2HQfEuOXF4eUkv1el6unBB7C1jjDHTp9fEacaMGWjYsCFsbW3h7OystHz16tUQBEHl9OzZMwDA/fv3VS7fv3+/PkM3KcvbL0fLsi1R070manvUFjucwkcCkGkJbNsMtUMKmIBVq8SOgDHGTJ9eL9WlpaWhW7duCAwMxIoVK5SW9+jRA61bKz5frX///khJSUGJEiUU5oeHh6Nq1ary1678sC+NeTt742Dfg/LX807Nw7hD40SMSIXbrYENewEy/ZOg1asDsv8jiICsLOlPTV+rmqpVAyZPLvRNYYyxIkevidOUKVMASM8sqWJjYwMbmw9PnH/+/DmOHDmiMskqVqwYPDw89BJnUXMn4Y7YISirsB+wSQDeuokdid7Nng0UL/4hGQI+JEDZX3t7A56ehR8fY4wx9Qyqc/jatWtha2uLrl27Ki3r0KEDUlJSUKFCBYwZM0ZlGaYZLyevQmnHXDBHVpYAygIA4f0I2wKQbgPcagdkZXv7nRtaJJImAGjbVvOyERFAUJDeQmGMMaYlg0qcVq5ciV69eimchbK3t8f8+fPRqFEjmJmZYffu3ejRowfWrFmDPn36qKwnNTUVqdmeGJuUlKT32I2Jk8SpUNrJpExplyLzHAssUtGheyL+6vnhzrusLMA8ZzkGp8I5VIwxxjSkdeIUFhYmvwSnTlRUFAICArSqNzIyEteuXcPatWsV5ru5uWHMmDHy1wEBAXj58iV++ukntYnTrFmz8oyxKOtQqQPCjoUh/m28aDHU9ayr8NrMTPFSVV4MbQDJUaOk2yC7ww1Qfedb9vk5tzfnNjVuDNSsqf/YGWOMaU7rh/zGx8cjPj73L1wfHx9YW1vLX69evRqjR4/Gq1ev1K4zcOBAXLhwAdHR0XnGsH79egwaNAjv3r1TuTznGafY2Fj4+fkV2Yf8qrM0aimG/W9YobX396d/o1GZRhAgyB9Um/P37GTzzQVzSCwkOZYVQsAGYPduoH17saNgjDHDYlQP+XVzc4Obm277orx58wZbtmzBrFmzNCofHR2NkiVLql0ukUggkXz4ok1MTCxwjMbuyL0jaL62uagxtNvYLt/rzmk5B+MaGtidgIVg3z5OnBhjzJDotY/Tw4cPkZCQgIcPHyIzMxMXL14EAJQvXx729vbycps3b0ZGRgZ69+6tVMeaNWtgaWmJ2rVrw8zMDHv27MGiRYvw448/6jN0k7MiWvlORWPy9cRkfH1M7CgKX+0iOOwWY4wZMr0mTj/88APWrFkjf137/bfA0aNH0bRpU/n8FStWoHPnznBxcVFZz/Tp0/HgwQOYm5ujYsWKWLlypdr+TUy1n1v/DAszCzx49QCCICDifoRe27s27BocJY4gEHJeDab3o3fL5hMIycmEatWyFRJIXhoZ1kBSKb3GW5i+/RaoV0+5/1P2PlJmZkCFCkC5cmJHyxhjLDut+zgZIzGvhRqq1IxUWM+wzrtgPj0b9wzF7Yprtc4ffwCDB+spIANSogRgaancWTz774IA9OgBTJkCWFmJFytjjBkio+rjxIwbEeH3879j6N6h+a4jpFyI/KxR9g7dI+uPREn7kijvWh4OEget6/3yS+mkCWPuHP7+aUJ5mj0b8PcHeMgyxhgzHJw4FTE9tvXA1mtbC1TH0ftHYS6Yw0wwk0/mZubov6u/wh1yA2sPRFjTMFiaW+oi9CLHzw8IDhY7CsYYY9mZ/oPBmIKktIIPBpqWmYZ3Ge+QnJ6MpLQkvE59jYR3CXj+9jmeJT/Ds+RneJr8FDNPzoTVdCucjT2rg8gVFYVO09euAW5uqseDUjWd1f1uZowxlgMnTkXMnk/34Pd2v6N/rf6F1ubLdy91Xuevv+q8SqO3bp3YETDGmOnjS3VFjIWZBXycfTD4b/33wl7TaQ2a+jRFGacyGpU/dQqYOPHDA2+zT4Di6zNn9Bi4AXF0BOrUUdwHsp/ZJz8/YOpU8eJkjLGighOnIuTF2xf4at9X2HhlY6G016JsC3g6eGpUNjkZaNRIzwEZiNu3AXd31UMRAB9+mpvz8/sYY8zQcOJUhIRFhBVO0jTrFZDqhFJh+m/KGDk6Ag7a33TIGGPMAHAfpyLk89qf67eBu82AVRFAqpN+2zFyxbUb3ooxxpgB4TNORUidknVAkwkR9yMQvEb397kL7ldgU/w5rF6rXi7rn5N9DKbsw6++VrOeqTHT4t+V//4DypbVXyyMMca0w4lTEfTw9UO91Et2z/C2XTf8/dkROEocpfPeP3Il52NWci57+xZo2ZKAhApAcgm9xGeMrl3jxIkxxgwJJ05FUGpGql7rb7a2Wf5WHPj+569XgOdVdRaPofnmG2k/J0DxjJuMbF5gINCiReHFxRhjLG+cOBVBZoKBd21Lsxc7Ar3asAGwfv+YQFXDDMh+vn0LNGkCSCSFHyNjjDHVOHEqgtKz0gu1vROfn0A5l3IwN5M+psVcMIcgCAqPbBEgYNs2AQMHCkCWBQQdPdiWCEgv3M3N06NHmpWbPVs66ZqDA3DoEFC/vu7rZowxU8eJUxHU3Ld5obbXZFUTzQt/K/1x/svzqFOyjk7aN+YHAutDUhIwcCBw5YrYkTDGmPHhxKkIqlCsAmgy4eB/B3Eq5hQA4G36W8w5NUfkyD5Iy0zLdfmbN9LHrrx5o3p59jv4ypYF7t7VcYBGbv58sSNgjDHjxIlTEdaqXCs09WmKdhva4dDdQ/pp5E0JIL4yAAEgARAIgCyrkfWMpg/zBQLeFkPgqF8A/KJcNtMSODIDSCytn3gNzNatQNeuYkfBGGNMhhOnIu5m/E39JU0AYP9MOulSmX+ARXd0W2chsrAAateW/p7zWXzZ533yCdCxozgxMsYYU40TpyLOzspO7BC0d3a42BEUSFwcUKyY2FEwxhjLD06cijhna2e9t7G121a0KtcKAgQIgqDy59u3AlxdBMgv6WX/aWIuXwaaNhU7CsYYY/nBiVMRcv7xeQQsCyj0drtt7aZZwR/0G4dOJZQDlkcCb7V/8Fyw7p92AwA4exaoW1c/dTPGGJMy8JEQmS6t+XeN2CGYDtf/AJ8IsaNQsHat2BEwxpjp4zNORUhY0zAkvEvA2dizuJ1wu9DaFcgcApnLX5P8LjrpK/n87M8fEVT8Tu8v2z2pA2SZK5ZRU6dSXVDThrbzr38C3NR/z+3q1QErNYOBZh+fys8PmDJF7+EwxliRx4lTEeJq44p1ndeJHQZjjDFmtPhSHWOMMcaYhjhxYowxxhjTECdOjDHGGGMa4sSJMcYYY0xDekuc7t+/j4EDB8LX1xc2NjYoV64cJk+ejLQ0xYe3Pnz4EO3bt4ednR3c3NwwcuRIpTKXL19GUFAQbGxsUKpUKUydOlXxDizGGGOMsUKgt7vqbty4gaysLPz+++8oX748rly5gi+++ALJycmYO3cuACAzMxNt27ZF8eLFcfLkSbx48QL9+vUDEWHx4sUAgMTERLRs2RLBwcGIiorCrVu30L9/f9jZ2WHs2LH6Cp8xxhhjTIlAhXjqZs6cOVi6dCnu3r0LANi3bx/atWuHmJgYeHp6AgA2bdqE/v3749mzZ3B0dMTSpUsxYcIEPH36FBKJBAAwe/ZsLF68GI8ePYIg5P1IjkePHsHLywsxMTEoXbq0/jaQMcYYY3on5vd6oY7j9Pr1a7i6uspfR0ZGolq1avKkCQBCQkKQmpqK8+fPIzg4GJGRkQgKCpInTbIyEyZMwP379+Hr66vUTmpqKlJTUxXaBYAnT57oY7MYY4wxVohk3+dZWVmF3nahJU7//fcfFi9ejHnz5snnxcXFwd3dXaGci4sLrKysEBcXJy/j4+OjUEa2TlxcnMrEadasWZiiYhjlevXqFXQzGGOMMWYgYmJiUKZMmUJtU+vEKSwsTGVSkl1UVBQCAj48TPbx48do3bo1unXrhkGDBimUVXWpjYgU5ucsI7u6qO4y3YQJExAaGip/nZCQAF9fX1y5cgVOTk65xm5qkpKS4Ofnh2vXrsHBwUHscApNUd1ugLedt523vagoqtsNSK8kVatWDVWqVCn0trVOnEaMGIGePXvmWib7GaLHjx8jODgYgYGB+OOPPxTKeXh44MyZMwrzXr58ifT0dPlZJQ8PD/nZJ5lnz54BgNLZKhmJRKJwaU/Gy8sLjo6OucZuahITEwEApUqVKlLbXlS3G+BtB3jbeduLhqK63QDk22thUfhPjtO6RTc3N7i5uWlUNjY2FsHBwfD398eqVatgZqY4+kFgYCBmzJiBJ0+eoGTJkgCAgwcPQiKRwN/fX15m4sSJSEtLg9X7p50ePHgQnp6eSpfwGGOMMcb0SW/jOD1+/BhNmzaFl5cX5s6di+fPnyMuLk7h7FGrVq3g5+eHvn37Ijo6GocPH8a4cePwxRdfyLPJXr16QSKRoH///rhy5Qp27tyJmTNnIjQ0VKM76hhjjDHGdEVv57gOHjyIO3fu4M6dO0q3Csr6KJmbm2Pv3r0YNmwYGjVqBBsbG/Tq1Us+zhMAODk54dChQxg+fDgCAgLg4uKC0NBQhT5MeZFIJJg8ebLKy3emrqhue1HdboC3nbedt72oKKrbDYi77YU6jhNjjDHGmDHjZ9UxxhhjjGmIEyfGGGOMMQ1x4sQYY4wxpiFOnBhjjDHGNGQyidOSJUvg6+sLa2tr+Pv748SJE2rLRkREQBAEpenGjRuFGHHBHT9+HO3bt4enpycEQcCuXbvyXOfYsWPw9/eHtbU1ypYti99++03/geqBtttuKsd81qxZqFu3LhwcHFCiRAl06tQJN2/ezHM9Uzju+dl2UznuS5cuRY0aNeDo6AhHR0cEBgZi3759ua5jCscc0H7bTeWY5zRr1iwIgoDRo0fnWs5Ujnt2mmx7YR53k0icNm/ejNGjR2PSpEmIjo5GkyZN8PHHH+Phw4e5rnfz5k08efJEPlWoUKGQItaN5ORk1KxZE7/88otG5e/du4c2bdqgSZMmiI6OxsSJEzFy5Ehs375dz5HqnrbbLmPsx/zYsWMYPnw4Tp8+jUOHDiEjIwOtWrVCcnKy2nVM5bjnZ9tljP24ly5dGrNnz8a5c+dw7tw5NGvWDB07dsTVq1dVljeVYw5ov+0yxn7Ms4uKisIff/yBGjVq5FrOlI67jKbbLlMox51MQL169WjIkCEK8ypXrkzjx49XWf7o0aMEgF6+fFkI0RUOALRz585cy3zzzTdUuXJlhXmDBw+mBg0a6DEy/dNk203xmBMRPXv2jADQsWPH1JYx1eOuybab6nEnInJxcaHly5erXGaqx1wmt203tWOelJREFSpUoEOHDlFQUBCNGjVKbVlTO+7abHthHnejP+OUlpaG8+fPo1WrVgrzW7VqhVOnTuW6bu3atVGyZEk0b94cR48e1WeYBiEyMlJpP4WEhODcuXNIT08XKarCZWrH/PXr1wAAV1dXtWVM9bhrsu0ypnTcMzMzsWnTJiQnJyMwMFBlGVM95ppsu4ypHPPhw4ejbdu2aNGiRZ5lTe24a7PtMoVx3Av/6Xg6Fh8fj8zMTKUH/rq7uys9HFimZMmS+OOPP+Dv74/U1FT8+eefaN68OSIiIvDRRx8VRtiiiIuLU7mfMjIyEB8fL39eoCkyxWNORAgNDUXjxo1RrVo1teVM8bhruu2mdNwvX76MwMBApKSkwN7eHjt37oSfn5/KsqZ2zLXZdlM65ps2bcKFCxcQFRWlUXlTOu7abnthHnejT5xkcj63jojUPsuuUqVKqFSpkvx1YGAgYmJiMHfuXKP7w9KWqv2kar6pMcVjPmLECFy6dAknT57Ms6ypHXdNt92UjnulSpVw8eJFvHr1Ctu3b0e/fv1w7NgxtQmEKR1zbbbdVI55TEwMRo0ahYMHD8La2lrj9UzhuOdn2wvzuBv9pTo3NzeYm5srnV169uyZUuadmwYNGuD27du6Ds+geHh4qNxPFhYWKFasmEhRiceYj/lXX32F3bt34+jRo0rPgszJ1I67NtuuirEedysrK5QvXx4BAQGYNWsWatasiZ9//lllWVM75tpsuyrGeMzPnz+PZ8+ewd/fHxYWFrCwsMCxY8ewaNEiWFhYIDMzU2kdUznu+dl2VfR13I3+jJOVlRX8/f1x6NAhfPLJJ/L5hw4dQseOHTWuJzo62qhOY+ZHYGAg9uzZozDv4MGDCAgIgKWlpUhRiccYjzkR4auvvsLOnTsREREBX1/fPNcxleOen21XxRiPuypEhNTUVJXLTOWYq5PbtqtijMe8efPmuHz5ssK8zz//HJUrV8a3334Lc3NzpXVM5bjnZ9tV0dtx13v380KwadMmsrS0pBUrVtC1a9do9OjRZGdnR/fv3yciovHjx1Pfvn3l5RcsWEA7d+6kW7du0ZUrV2j8+PEEgLZv3y7WJuRLUlISRUdHU3R0NAGg+fPnU3R0ND148ICIlLf77t27ZGtrS2PGjKFr167RihUryNLSkrZt2ybWJuSbtttuKsd86NCh5OTkRBEREfTkyRP59PbtW3kZUz3u+dl2UznuEyZMoOPHj9O9e/fo0qVLNHHiRDIzM6ODBw8SkekecyLtt91UjrkqOe8sM+XjnlNe216Yx90kEiciol9//ZW8vb3JysqK6tSpo3CLcr9+/SgoKEj++scff6Ry5cqRtbU1ubi4UOPGjWnv3r0iRF0wstsvc079+vUjIuXtJiKKiIig2rVrk5WVFfn4+NDSpUsLP3Ad0HbbTeWYq9pmALRq1Sp5GVM97vnZdlM57gMGDJB/vhUvXpyaN28uTxyITPeYE2m/7aZyzFXJmTyY8nHPKa9tL8zjLhC97znGGGOMMcZyZfSdwxljjDHGCgsnTowxxhhjGuLEiTHGGGNMQ5w4McYYY4xpiBMnxhhjjDENceLEGGOMMaYhTpwYY4wxxjTEiRNjjDHGmIY4cWKMMcYY0xAnTowxxhhjGuLEiTHGGGNMQ5w4McYYY4xp6P9ISTkmJVXMBgAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 600x1200 with 6 Axes>"
+       "<Figure size 600x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -321,74 +383,36 @@
    ],
    "source": [
     "# fdom and backscatter\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['fdom'], ranges['bb']], profile_list, \n",
-    "                          d['fdom'][0], d['fdom'][1], 'FDOM', colors['do'], 'ascent',\n",
-    "                          d['bb'][0], d['bb'][1], 'Backscatter', colors['chlora'], 'ascent', 6, 4)"
+    "fig,axs = ChartTwoSensors(profiles, [ranges['fdom'], ranges['backscatter']], profile_list, \n",
+    "                          d['fdom'][0],         -d['fdom'][1],        'FDOM', colors['do'], 'ascent',\n",
+    "                          d['backscatter'][0],  -d['backscatter'][1], 'Backscatter', colors['chlora'], 'ascent', 6, 4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "9ce31097-51b2-48d4-8692-bf69b13fa6ae",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attempting 3 charts\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x1200 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# pH and pCO2\n",
     "# Notice that pH and pCO2 are measured only on midnight and noon *descents*\n",
     "#   Profile 3 is the first midnight profile, 8 the first noon (but labels need fixing)\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['ph'], ranges['pco2']], [3, 8, 12],\n",
-    "                          d['ph'][0], d['ph'][1], 'pH', colors['ph'], 'descent',\n",
-    "                          d['pco2'][0], d['pco2'][1], 'pCO2', colors['pco2'], 'descent', 6, 4)"
+    "fig,axs = ChartTwoSensors(profiles, [ranges['ph'], ranges['pco2']], [3, 8, 12, 17],\n",
+    "                          d['ph'][0],    -d['ph'][1], 'pH', colors['ph'], 'descent',\n",
+    "                          d['pco2'][0],  -d['pco2'][1], 'pCO2', colors['pco2'], 'descent', 6, 4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "43af628f-3df3-4771-a105-440816f63949",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attempting 2 charts\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x800 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Two good representatives of noon spectral irradiance\n",
     "fig,axs = ChartTwoSensors(profiles, [ranges['spkir412nm'], ranges['spkir555nm']], [8, 80],\n",
@@ -398,31 +422,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "8ac0e077-f1fe-413a-873f-8e03dee8c524",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attempting 2 charts\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x800 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# PAR and longer-wavelength spectral irradiance (not a very strong signal; see above)\n",
     "fig,axs = ChartTwoSensors(profiles, [ranges['par'], ranges['spkir620nm']], [8, 80],\n",
@@ -433,31 +438,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "d24bdc33-b1a8-47ce-b82d-5cd2b41dfcec",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attempting 3 charts\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x1200 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Nitrate and current velocity 'up' (midnight and noon: ascent for nitrate)\n",
     "fig,axs = ChartTwoSensors(profiles, [ranges['nitrate'], ranges['up']], [3, 8, 12],\n",
@@ -467,31 +453,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "de6fd9ef-f269-403c-9831-4c183db18564",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attempting 1 charts\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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LcePGDWn5/fv3o2fPntDT04OmpiYEAgE2b96MiIgIaRlJc/moUaOwb98+xMfHqxqOUuPGjYNIJJLpTbp7924UFRVhypQptapLRfn5+bh58yZGjBgBPT096XoNDQ1MmDABcXFx0tM7J06cgL+/P9q0afPa9ZL44osvEBYWhmvXrgHgmuV37tyJSZMmycSjSEJCAiwsLOTWX7p0CX369IGZmZl0HZ/Px6hRoxTup0OHDnBzc5NZd/ToUfj7+8PGxkbm/XzrrbekryEpZ2RkhHfffVemnJeXF6ysrOROJXl5eaFVq1bSx1paWnBzc8PLly+rrKuqzp8/DwByp0FGjhwJXV1d6WkUHR0d+Pr64uzZswCAM2fOwMjICF9++SWKi4tx9epVAMDZs2fRr18/mfelXbt28PLykqnvwIEDFZ4669OnD4yNjVWKncfjYceOHdDX10fnzp3x4sUL7Nu3D7q6utIyYrFY5nUrnj5+/vw5xo4dCysrK2hoaEAgEMDPzw8AZL6nAHf6efDgwfjggw+wfft2rF27Fu3bt68yvpp8vyWfy+p+A0pLS/Hdd9/B09MTQqEQmpqaEAqFiIqKkosZAIYMGSLzuEOHDgAg/fwEBwejsLAQ48aNkynXo0cPODg4VBlLRf7+/tDX15c+trS0hIWFhfR1avK7IfH++++r/PoStf2+tG/fHj4+Pti6dat0XUREBG7duiVzurymn+eKzp8/D09PT3Tt2lVm/eTJk8EYk34XT5w4AS0tLbnT9K/jk08+AQBs3LhRuu73339H+/bt8eabb1a5bUJCAng8nszvY0XvvfcetLS0pI/19fXx7rvv4vLlyzLft9p6nfe8rrRt2xYdO3aUWTd27Fjk5OTI9VwfO3aszKldBwcH9OjRQ3oa8enTp3j8+LH0O1f5+JuYmPha3wUXFxds3LgRhw4dwjvvvINevXrJXbJV8TVLS0tlrltTJXcBuOvh9+3bh/T0dHTq1AmMMezevRsaGhpVxte1a1ecOHECCxYswMWLF6u8btrCwqLGeZHKSV16ejpKS0uxdu1aCAQCmeXtt98GwF1/AwBBQUEYNWoUbG1tsWvXLgQHByMkJARTp05FYWGhdJ9vvvkmDh06hNLSUkycOBF2dnZo166d3LnlmjAxMcGQIUOwY8cO6Rdq27Zt6Nq1q/Sas5rUpbLMzEwwxmBtbS33nI2NjXT/AJCamlrjCySrM3ToUDg6Okqv/du2bRvy8/Mxc+bMarctKCiQ+fGRSE9Ph6Wlpdx6ResAKKx7cnIyjhw5Ivd+St5zyfuZnJyMrKwsCIVCubJJSUly77upqanca4lEojrrQJCeng5NTU25jio8Hg9WVlbSvyUA9OvXDzdu3EB+fj7Onj2LPn36wNTUFD4+Pjh79ixevHiBFy9eyCR1ycnJuH//vlxd9fX1wRiTq6+i97YqpqamGDJkCAoLCzFo0CC5RGvq1Kkyr9u3b18AQF5eHnr16oWbN2/i22+/xcWLFxESEoKgoCAA8p1feDweJk+ejMLCQlhZWSm8/qqymny/JZ/L6v6uc+fOxeLFizFs2DAcOXIEN2/eREhICDp27Khw28qfH5FIJPM6kr+vlZWV3LaK1ilT3ee0Jr8bEjX9LKgSR1WmTp2K4OBgPH78GACwdetWiEQifPDBB9IyNf08V5Senq7y76aNjU2d9jK1tLTE6NGj8b///Q9lZWW4f/8+rly5glmzZlW7bUFBAQQCgdKDtbLPTnFxMfLy8l479td5z+tKVd+Pyp9bZWUl5STXk8+fP1+uTp9++ikA+eNvTb8LgwcPhqWlJQoLCzF37ly5v13l15Vcu6xq7iLh4uKCXr16Sf8xVCXO3377DV999RUOHToEf39/mJiYYNiwYYiKipIrq6WlVeNjnaaqBY2NjaX/VSpLIJycnAAAu3btgpOTE/bu3SuTsSu6uHHo0KEYOnQoioqKcOPGDaxcuRJjx46Fo6MjfH19a1QZiSlTpmD//v04c+YMWrVqhZCQEPzxxx+1qktlxsbG4PP5Ci+4lFxsK/mPztzcHHFxcbWqgzJ8Ph8zZ87E119/jTVr1mD9+vXo27cv3N3dq93WzMwMGRkZcutNTU2lX7SKkpKSFO5H0QW2ZmZm6NChA1asWKFwG8kPt5mZGUxNTXHy5EmF5Sq2djQEU1NTlJaWIjU1VSaxY4whKSlJ2toEcOP7LV68GJcvX8a5c+ewdOlS6frTp09LPzOSxAng6qutra30YvXK//0rem+rcubMGfzxxx/o2rUrDh48iH/++Ufmv9ply5bJHLgk7+/58+eRkJCAixcvSlvnACgdKiMxMREzZ86El5cXHj16hPnz58tdKK2Iqt9vyedSWWuIxK5duzBx4kR89913MuvT0tJgZGRUbTyVSZIgRZ/1pKSkOhvTsCa/GxI1/Sy8rg8++ABz587Ftm3bsGLFCuzcuRPDhg2TaTmu6ee5IlNTU5V/N69evQqxWFynid0XX3yBnTt34t9//8XJkydhZGQk10KriJmZGYqLi5Gfny/TCi6h7LMjFAqrPHuipaWl8JiYlpYm8z6+znte1WsD3DFZ8o+O5LUVUVZHQP4fCWVlJeUk8QYEBCjtTVz5eFbT78KMGTOQm5uLtm3b4vPPP0evXr1kPschISEy5WuTuwDApk2bcOzYMXTt2hW///47Ro8ejW7dulUZm66uLpYvX47ly5cjOTlZ2mr37rvvSv+hksjIyKj537cm52r79evHOnbsyIqKiqos99577zF3d3eZdYmJiUxPT0/hefyKwsLCZC76l1zrUfFaq+qUlpYyW1tbNmrUKDZ//nympaUld12PqnVRdM2br68vs7Kykrmov6ysjLVv317hNXUVL+BWZf+S6wKVXYuXmZnJdHV1mb+/PwPADh06VGUdJKZOncpMTEzk1tf0mrrBgwfL7WP69OnMxsam2uvBdu3axQCwGzduVBuvsteqfM1JTVS+7uvUqVMMAPvpp59kyu3fv1/uup7S0lJmYGDABgwYwACwp0+fMsbKL5Dt27cv8/T0lNnPt99+y3R0dNjz58+rjQ0Amzlzpsp1SUhIYBYWFszf35+VlpayIUOGMENDQ5Ve6/DhwzLXlkmMGDFC7vNYWlrK/Pz8mKWlJUtMTJReK1f5usjK760ilb/fEitWrGB8Pr/az4+i68OOHj0qd/2b5Pqe/fv3y5St/H3LyMiok2vqFP3dKl8zpervhuQ6osrXUDFW9W9DXXxfRo8ezaytrdmhQ4cYAHbq1CmZ52vyea5c/4CAAAaA3blzR6bczJkzFV5Tt3nz5hrtX5VjRY8ePVjXrl2Zjo4Omz17drV1qBhP5etMq7umrm/fvtJ1iq5VGzhwoNzvxZMnT5impqbM36sm77mqJNc237p1S2b9m2++WaNr6vT19eWuqfPx8VF4Td20adOk61xdXeWud1Wkqu+CMpLrc7ds2cKePXvGDA0N2dChQ1Xatia5y/3795m2tjabOHEiKyoqYj4+PszBwUHuNwyVrqlTZPbs2QyA9L2UcHV1lfttqk6Ne78aGxuzrl27sq1bt7ILFy6ww4cPs59++kmmx9yWLVukPZrOnTvHtm3bxlq3bs1cXV1l3pjFixezKVOmsF27drGLFy+yQ4cOMX9/fyYQCKS9/vLz85m2tjbr2bMnu3DhAgsJCWHx8fHVxhoQEMBEIhEzNzdnY8eOrXVdqur92q1bN7Z//37277//soEDByrt/WphYcF++eUXdu7cOfbPP/+wDz/8UGnvV8bKvxwff/wxu379OgsJCZG5CJsx7uJiAMzBwUF6UWl1JD9OlTs5hIWFSXu/7t27V9r71cHBgQGQufBa2YEjISGBOTg4MA8PD7Z+/Xp27tw5duzYMbZu3To2ePBgaY/l0tJS9tZbbzETExO2fPlyduLECXb27Fm2bds2NmnSJBYUFFTtayk6SDk4OFSbUDCmvPerQCBgy5YtY2fOnGFr1qxhenp6Mr1fJd59910GgDk5OUnXFRYWMm1tbQaAff755zLl8/LymLe3N7Ozs2Nr1qxhZ86cYadOnWIbN25kI0eOlElua5LUVU60GOMSlFatWrEuXbpU+89KWloaMzY2Zh07dmRBQUHsyJEjbMyYMdLvaMXP48KFCxmfz2dnz56VeR+MjIxkDjSV31tVvt8V99epU6dq6z1x4kQmEonYzz//zM6dO8d++OEHZm5uzuzs7GqV1DHG2KJFi6S9X0+ePMk2btxY496vqiR1qv5uVHUgq+q3oS6SOsk/OXZ2dszOzk7ut6Umn2dlvV+trKzYn3/+yU6dOsU+//xzxuPx2KeffiotJ+n9KhAI2P/93/+xEydOsGPHjrElS5Yo7f3KmGrHCknnCB6PxyIjI1V6T2JiYhgA9r///U9mfeXer0FBQezAgQOsS5cuTFNTk129elVaVlFSJ/kH95NPPmFnz55lmzdvZu7u7sza2lrm71WT91zSAeLChQtV1ik7O5uZmJiw9u3bs4MHD7IjR46w999/nzk5OVXb+/XEiRPS3q/ff/+9tFzl3q9Hjx5lf/31F3NxcWH6+vrSf4IZ43q/ikQiNmDAAPb3339Le7J/9913bMSIEXLvm6pJnSTRqvi5kHQo+fnnn6vdXtXcJS8vj3l4eDBPT09pxzRlCWTlpK5r164sMDCQHTp0iF26dIlt2LCBmZqaMl9fX5nt0tLSGAD222+/qVR36evVqDTjPshTp05ltra2TCAQMHNzc9ajRw/27bffypRbtWoVc3R0ZCKRiLVp04Zt3LhRrsfN0aNH2VtvvcVsbW2ZUChkFhYW7O2332ZXrlyR2dfu3buZh4cHEwgEKmW9jDEWGRnJADAA7MyZM7Wui6KDAGOMXblyhfXp04fp6uoybW1t1r17d3bkyBG514iNjWVTp05lVlZWTCAQMBsbGzZq1ChpLyxl+w8ICGA2NjaMz+cr/JJevHiRAWCrVq2q9r2QyM7OZnp6enI9PSX16datGxOJRMzKyop9+eWX0l5fFVs5lR04GGMsNTWVff7558zJyYkJBAJmYmLCfHx82MKFC2V6ZJaUlLDVq1ezjh07Mi0tLaanp8c8PDzYxx9/zKKioqp9LUUHKTMzM9a9e/dq3wNFrUkFBQXsq6++Yg4ODkwgEDBra2v2ySefSLvEV/Trr78yAOzDDz+UWd+/f38GgB0+fFhum7y8PLZo0SLm7u7OhEKhdCidOXPmyPSoq0lSJ0m0zp07J7P++vXrTFNTk33xxRfV7uP69evM19eX6ejoMHNzczZ9+nR29+5dmc/j6dOnGZ/Pl/vOpaenyyWQld9bVb/fubm5TEdHh61Zs6bamDMzM9m0adOYhYUF09HRYW+88Qa7cuWK3GeiJkmdWCxmK1euZPb29kwoFLIOHTqwI0eOqJwMqZrUMaba70Z1BzJlvw11kdSVlZUxe3t7BoAtXLhQYRlVP8+K6v/y5Us2duxYZmpqygQCAXN3d2c//vijXPJYUFDAlixZwlxdXZlQKGSmpqasT58+MsOEKNp/dceKoqIiJhKJ2KBBg1R6PyR69eol17Ik+Sx9//33bPny5czOzo4JhULm7e0t18KpKKkTi8Xshx9+YM7OzkxLS4t17tyZnT9/XuHfS9X3fN68eYzH46nUi/zWrVusR48eTFdXl9na2rKlS5eyTZs2KT07c+DAAda2bVsmFAqZo6Oj3NkNyXdu586d7PPPP2fm5uZMJBKxXr16sdu3b8u9/r1796RDjQkEAmZlZcX69OnDNmzYIPe+qZLUVUy0Krd4zZw5kwkEArme14qokruMHz+e6ejoSEd1kJCc4amYQFb+HC5YsIB17tyZGRsbM5FIxJydndmcOXNYWlqazL42b97MBAKBXK/r6tQ4qSONw9y5c5m2trbcB6E6s2bNYm3atFFpbKP+/fszV1fX2obYYCTj9B09elTdoZBa2LRpE9PV1VV5KBdCakty2cGxY8dqtN2BAweYhoYGi4uLq6fI6kaXLl1kWrrqQlX/yFek7B8pUjtvvPGGwrOM1VG5owRpHG7cuIHIyEisX78eH3/8scLeblVZtGgRduzYgX/++QcjRoyQrp87dy68vb1hb2+PjIwM/PXXXzhz5oxKMxOo24ULF+Dr6yszfy5pGkpLS/H9998jICBA5aFcCKmp8PBwvHz5UjpbgGSoJVW999576NKlC1auXCk3i0BjkZOTg3v37tVqvlDSuFy+fBkhISG1+ltSUtfE+Pr6QkdHB++88w6+/fbbGm9vaWmJv/76C5mZmTLry8rKsGTJEiQlJYHH48HT0xM7d+7E+PHj6yr0ejNz5kyVhnQhjU9sbCzGjx+PefPmqTsU0ox9+umnuHbtGjp16oTt27fXuDclj8fDxo0bcfjw4TrvlVtXDAwMlPbSJE1Leno6duzYAWdn5xpvy2OsCc2MSwghhBBCFGp8/24QQgghhJAao6SOEEIIIaQZoKSOEEIIIaQZoKSOEEIIIaQZoKSOEEIIIaQZaNZJXWZmJiZMmABDQ0MYGhpiwoQJSicsV+Tjjz8Gj8fDL7/8Um8x1qfa1H/ZsmXw8PCArq4ujI2N0a9fP9y8ebNhAq5jNa1/SUkJvvrqK7Rv3x66urqwsbHBxIkTpROONyW1+dsHBQVh4MCBMDMzA4/HQ1hYWIPEWhfWr18PJycnaGlpwcfHB1euXKmy/KVLl+Dj4wMtLS04Oztjw4YNDRRp/ahJ/RMTEzF27Fi4u7uDz+dj9uzZDRdoPalJ/YOCgtC/f3+Ym5vDwMAAvr6+OHXqVANGW/dqUv+rV6+iZ8+eMDU1hba2Njw8PPDzzz83YLR1r6bff4lr165BU1MTXl5e9RtgQ6rzYZAbkUGDBrF27dqx69evs+vXr7N27dqxd955R6VtDx48yDp27MhsbGxUmjOuMapN/f/66y925swZ9uzZM/bw4UM2bdo0ZmBgwFJSUhoo6rpT0/pnZWWxfv36sb1797LHjx+z4OBg1q1bN+bj49OAUdeN2vztd+zYwZYvXy6dEDs0NLRhgn1Ne/bsYQKBgG3cuJGFh4ezL774gunq6srMWVzR8+fPmY6ODvviiy9YeHg427hxIxMIBOzAgQMNHHndqGn9X7x4wT7//HO2fft25uXlpdKUco1ZTev/xRdfsO+//57dunWLRUZGsoCAACYQCNjdu3cbOPK6UdP63717l/3999/s4cOH7MWLF2znzp1MR0dHbm7bpqKm9ZfIyspizs7ObMCAAaxjx44NE2wDaLZJXXh4OAMgM9lxcHAwA8AeP35c5bZxcXHM1taWPXz4kDk4ODTJpO516l9RdnY2AyAzmXtTUFf1v3XrFgNQ7Q9EY/K6dZfMadlUkrquXbuyGTNmyKzz8PBgCxYsUFj+//7v/5iHh4fMuo8//liluYMbo5rWvyI/P78mn9S9Tv0lPD092fLly+s6tAZRF/UfPnw4Gz9+fF2H1iBqW//Ro0ezRYsWsaVLlzarpK7Znn4NDg6GoaEhunXrJl3XvXt3GBoa4vr160q3E4vFmDBhAr788ku0bdu2IUKtF7Wtf0XFxcX4888/YWhoiI4dO9ZXqPWiLuoPANnZ2eDxeDAyMqqHKOtHXdW9KSguLsadO3cwYMAAmfUDBgxQWtfg4GC58gMHDsTt27dRUlJSb7HWh9rUvzmpi/qLxWLk5ubCxMSkPkKsV3VR/9DQUFy/fh1+fn71EWK9qm39t27dimfPnmHp0qX1HWKDa7bThCUlJcHCwkJuvYWFBZKSkpRu9/3330NTUxOff/55fYZX72pbfwA4evQoxowZg1evXsHa2hpnzpyBmZlZfYVaL16n/hKFhYVYsGABxo4dCwMDg7oOsd7URd2birS0NJSVlcHS0lJmvaWlpdK6JiUlKSxfWlqKtLQ0WFtb11u8da029W9O6qL+a9asQX5+PkaNGlUfIdar16m/nZ0dUlNTUVpaimXLlmH69On1GWq9qE39o6KisGDBAly5cgWams0vBWpyLXXLli0Dj8ercrl9+zYAKJzfjzGmdN6/O3fu4Ndff8W2bdtqPDdgQ6nP+kv4+/sjLCwM169fx6BBgzBq1CikpKTUS31qqiHqD3CdJsaMGQOxWIz169fXeT1qo6Hq3hRVrld1dVVUXtH6pqKm9W9ualv/3bt3Y9myZdi7d6/Cf4SaitrU/8qVK7h9+zY2bNiAX375Bbt3767PEOuVqvUvKyvD2LFjsXz5cri5uTVUeA2qyaWps2bNwpgxY6os4+joiPv37yM5OVnuudTUVLmsXuLKlStISUlBq1atpOvKysowb948/PLLL4iOjn6t2OtCfdZfQldXFy4uLnBxcUH37t3h6uqKzZs3IyAg4LVirwsNUf+SkhKMGjUKL168wPnz5xtNK11D1L2pMTMzg4aGhtx/5SkpKUrramVlpbC8pqYmTE1N6y3W+lCb+jcnr1P/vXv3Ytq0adi/fz/69etXn2HWm9epv5OTEwCgffv2SE5OxrJly/DBBx/UW6z1oab1z83Nxe3btxEaGopZs2YB4E6/M8agqamJ06dPo0+fPg0Se31pckmdmZmZSqcCfX19kZ2djVu3bqFr164AgJs3byI7Oxs9evRQuM2ECRPkvtwDBw7EhAkTMGXKlNcPvg7UZ/2VYYyhqKioVvHWtfquvyShi4qKwoULFxrVQV4df/vGTigUwsfHB2fOnMHw4cOl68+cOYOhQ4cq3MbX1xdHjhyRWXf69Gl07twZAoGgXuOta7Wpf3NS2/rv3r0bU6dOxe7duzF48OCGCLVe1NXfvzH9xtdETetvYGCABw8eyKxbv349zp8/jwMHDkgT3SZNLd0zGsigQYNYhw4dWHBwMAsODmbt27eXG9bB3d2dBQUFKd1HU+39yljN65+Xl8cCAgJYcHAwi46OZnfu3GHTpk1jIpGIPXz4UB1VeC01rX9JSQkbMmQIs7OzY2FhYSwxMVG6FBUVqaMKtVabz356ejoLDQ1lx44dYwDYnj17WGhoKEtMTGzo8GtEMqTB5s2bWXh4OJs9ezbT1dVl0dHRjDHGFixYwCZMmCAtLxnSZM6cOSw8PJxt3ry5WQxpomr9GWMsNDSUhYaGMh8fHzZ27FgWGhrKHj16pI7wX1tN6//3338zTU1Ntm7dOpnveFZWlrqq8FpqWv/ff/+dHT58mEVGRrLIyEi2ZcsWZmBgwBYuXKiuKryW2nz+K2puvV+bdVKXnp7Oxo0bx/T19Zm+vj4bN24cy8zMlCkDgG3dulXpPppyUlfT+hcUFLDhw4czGxsbJhQKmbW1NRsyZAi7detWwwdfB2paf8lQHoqWCxcuNHj8r6M2n/2tW7cqrPvSpUsbNPbaWLduHXNwcGBCoZB16tSJXbp0SfrcpEmTmJ+fn0z5ixcvMm9vbyYUCpmjoyP7448/GjjiulXT+iv6Ozs4ODRs0HWoJvX38/NTWP9JkyY1fOB1pCb1/+2331jbtm2Zjo4OMzAwYN7e3mz9+vWsrKxMDZHXjZp+/itqbkkdj7H/rhAmhBBCCCFNVpPr/UoIIYQQQuRRUkcIIYQQ0gxQUkcIIYQQ0gxQUkcIIYQQ0gxQUkcIIYQQ0gxQUkcIIYQQ0gxQUkcIIYQQ0gy0mKSuqKgIy5Yta5JTodQFqj/Vn+pP9W+J9W/JdQeo/i2t/k1q8OH169fjxx9/RGJiItq2bYtffvkFvXr1UmnbnJwcGBoaIjs7u9FM0N6QqP5Uf6o/1b8l1r8l1x2g+re0+jeZlrq9e/di9uzZWLhwIUJDQ9GrVy+89dZbiImJUXdohBBCCCFq12SSup9++gnTpk3D9OnT0aZNG/zyyy+wt7fHH3/8oe7QCCGEEELUTlPdAaiiuLgYd+7cwYIFC2TWDxgwANevX1e4TVFRkcw59IyMDABAbGwsDA0N6y/YRio3NxcAEB8fj5ycHDVH0/Co/lR/gOrfEuvfkusOUP2zs7MBAKWlpWqOpIGwJiA+Pp4BYNeuXZNZv2LFCubm5qZwm6VLlzIAtNBCCy200EJLC1+uXLnSEOmK2jWJljoJHo8n85gxJrdOIiAgAHPnzpU+jo2NRbt27XDr1i1YW1vXa5yEEEIIUb/ExER07doVrVq1UncoDaJJJHVmZmbQ0NBAUlKSzPqUlBRYWloq3EYkEkEkEkkfS065Wltbw87Orv6CJYQQQkijwuc3mS4Er6VJ1FIoFMLHxwdnzpyRWX/mzBn06NFDTVERQgghhDQeTaKlDgDmzp2LCRMmoHPnzvD19cWff/6JmJgYzJgxQ92hEUIIIYSoXZNoqQOA0aNH45dffkFgYCC8vLxw+fJlHD9+HA4ODuoOjdSBlJQUfPzxx2jVqhVEIhGsrKwwcOBABAcHS8vweDwcOnSoTl4vOjoaPB4PYWFhVZa7ePEieDwesrKy5J7z8vLCsmXLpGWqWrZt2wYA+Oeff9C7d28YGhpCT08PHTp0QGBgoLR3tiqCgoLQv39/mJubw8DAAL6+vjh16pRcuX/++Qeenp4QiUTw9PTEwYMHZZ5fuXIlunTpAn19fVhYWGDYsGF48uSJ9PmSkhJ89dVXaN++PXR1dWFjY4OJEyciISGh2hgzMzMxYcIEGBoawtDQEBMmTJB7D7/44gv4+PhAJBLBy8urzuoeFBSEzp07w8jICLq6uvDy8sLOnTur3feDBw/g5+cHbW1t2NraIjAwEEzJ2OzXrl2DpqamynED3DXAb731ltznuKrPT0hISJX7rO5vDHCDtjs5OUFLSws+Pj64cuWKyjETQpqWJpPUAcCnn36K6OhoFBUV4c6dO3jzzTfVHRKpI++//z7u3buH7du3IzIyEocPH0bv3r1rlOyoqri4uE7316NHDyQmJkqXUaNGYdCgQTLrRo8ejYULF2L06NHo0qULTpw4gYcPH2LNmjW4d++eSkmHxOXLl9G/f38cP34cd+7cgb+/P959912EhoZKywQHB2P06NGYMGEC7t27hwkTJmDUqFG4efOmtMylS5cwc+ZM3LhxA2fOnEFpaSkGDBiA/Px8AMCrV69w9+5dLF68GHfv3kVQUBAiIyMxZMiQamMcO3YswsLCcPLkSZw8eRJhYWGYMGGCTBnGGKZOnYrRo0fXad1NTEywcOFCBAcH4/79+5gyZQqmTJmiMPGVyMnJQf/+/WFjY4OQkBCsXbsWq1evxk8//SRXNjs7GxMnTkTfvn1VjhsAfvnlF4Uduyp/fhITEzF9+nQ4Ojqic+fOSvenyt+YBm0npIVRb+fbhhMbG8sAsNjYWHWHQirJzMxkANjFixeVlnFwcJDpnu7g4MAYY+zp06dsyJAhzMLCgunq6rLOnTuzM2fOyG37zTffsEmTJjEDAwM2ceJEue7ufn5+Cl/3woULDADLzMyUe65jx45s6dKlcusnTZrEhg4dKrPu5s2bDAD75ZdflL4Hr8PT05MtX75c+njUqFFs0KBBMmUGDhzIxowZo3QfKSkpDAC7dOmS0jK3bt1iANjLly+VlgkPD2cA2I0bN6TrgoODGQD2+PFjufJLly5lHTt2VLq/6lSuuyLe3t5s0aJFSp9fv349MzQ0ZIWFhdJ1K1euZDY2NkwsFsuUHT16NFu0aFGN4g4LC2N2dnYsMTGRAWAHDx5UWra4uJhZWFiwwMDAKvepyt+4a9eubMaMGTJlPDw82IIFC1SKm5CmrqUd+5tUSx1pnvT09KCnp4dDhw4pnXRZchpq69atSExMlD7Oy8vD22+/jbNnzyI0NBQDBw7Eu+++K9cS8eOPP6Jdu3a4c+cOFi9ejFu3bgEAzp49i8TERAQFBdVjDYG//voLenp6+PTTTxU+b2RkBKD8tPDFixdV3rdYLEZubi5MTEyk64KDgzFgwACZcgMHDlQ6WDdQPkhnxf0oKsPj8aTxKhIcHAxDQ0N069ZNuq579+4wNDSs8vVrQ1HdK2KM4dy5c3jy5EmVLfvBwcHw8/OT6TE/cOBAJCQkIDo6Wrpu69atePbsGZYuXapwP5JTqRW3efXqFT744AP8/vvvsLKyqrZOhw8fRlpaGiZPniyz3tHREcuWLZOJuaq/sWTQ9splqhq0nRDStDWZjhKk+dLU1MS2bdvw4YcfYsOGDejUqRP8/PwwZswYdOjQAQBgbm4OgEt+Kh4YO3bsiI4dO0off/vttzh48CAOHz6MWbNmSdf36dMH8+fPlz6WHHRNTU1VOtC+rqioKDg7O0MgEFRZTiAQwN3dHTo6Oirve82aNcjPz8eoUaOk65KSkuSG+7G0tJQbFkiCMYa5c+fijTfeQLt27RSWKSwsxIIFCzB27NgqJ8ZOSkqChYWF3HoLCwulr19biuoOcMmnra0tioqKoKGhgfXr16N///5Vxuzo6CizTvL+JSUlwcnJCVFRUViwYAGuXLkCTU3FP506Ojpwd3eX+TvPmTMHPXr0wNChQ1Wq0+bNmzFw4EDY29vLrG/dujXMzMxkYq7qb5yWloaysrIafQ4IIU0bJXWkUXj//fcxePBgXLlyBcHBwTh58iR++OEHbNq0Sa7FoqL8/HwsX74cR48eRUJCAkpLS1FQUCDXUlfVtUkNgVUxUHZFtra2ePz4scr73b17N5YtW4Z///1XLpGqyWDds2bNwv3793H16lWFz5eUlGDMmDEQi8VYv369dP2MGTOwa9cu6eO8vDyFr13d6yuip6cnvT9+/Hhs2LBB5vmq6q6vr4+wsDDk5eXh3LlzmDt3LpydndG7d2+lr6fo/ZKsLysrw9ixY7F8+XK4ubkp3UfXrl1l/n6HDx/G+fPnZa75q0pcXBxOnTqFffv2yT137tw5lWKuvK4mnwNCSNNGSR1pNLS0tNC/f3/0798fS5YswfTp07F06dIqk7ovv/wSp06dwurVq+Hi4gJtbW2MGDFCrjOErq5urWKStEhlZ2fLnXLMyspSeR5hNzc3XL16FSUlJdW21qlq7969mDZtGvbv349+/frJPGdlZaXyYN2fffYZDh8+jMuXLyscmLukpASjRo3CixcvcP78eZlWusDAQJkWUMlrJycny+0nNTVV6WDhilTsmVy5ZbCqugPcQKMuLi4AuF7KERERWLlypdKkTtn7BXAtW7m5ubh9+zZCQ0OlLcBisRiMMWhqauL06dPo06eP3H7Pnz+PZ8+eyX123n//ffTq1UvuNPvWrVthamqqUmeU6v7GtRm0nRDStNE1daTR8vT0lPbEBLhTk2VlZTJlrly5gsmTJ2P48OFo3749rKysZK5nUkYoFAKA3P4qc3V1BZ/PlxtaIjExEfHx8XB3d1epLmPHjkVeXp5MK1dFioZMqcru3bsxefJk/P333xg8eLDc876+vnKDdZ8+fVpmsG7GGGbNmoWgoCCcP38eTk5OcvuRJHRRUVE4e/YsTE1NZZ63sLCAi4uLdJG8dnZ2tvS6RQC4efMmsrOzazRYeMX9VmyJq67uijDGlF6vKYn58uXLMv8MnD59GjY2NnB0dISBgQEePHiAsLAw6TJjxgy4u7sjLCxM5vrBihYsWID79+/LbAcAP//8M7Zu3SoX49atWzFx4kSVEv/q/sY0aDshLZB6+mc0vJbWA6YpSUtLY/7+/mznzp3s3r177Pnz52zfvn3M0tKSTZ06VVrO1dWVffLJJywxMZFlZGQwxhgbNmwY8/LyYqGhoSwsLIy9++67TF9fn33xxRfS7RwcHNjPP/8s85olJSVMW1ubffvttywpKYllZWUpje+TTz5hrVq1YgcPHmTPnz9nV69eZX5+fqx9+/aspKRErryi3q+MMfZ///d/TENDg3355Zfs+vXrLDo6mp09e5aNGDFC2is2Li6Oubu7s5s3byqN5++//2aampps3bp1LDExUbpUrMO1a9eYhoYGW7VqFYuIiGCrVq1impqaMj1SP/nkE2ZoaMguXrwos59Xr15J36MhQ4YwOzs7FhYWJlOmqKhIaXyMMTZo0CDWoUMHFhwczIKDg1n79u3ZO++8I1MmKiqKhYaGso8//pi5ubmx0NBQFhoaWuW+Van7d999x06fPs2ePXvGIiIi2Jo1a5impibbuHGj0v1mZWUxS0tL9sEHH7AHDx6woKAgZmBgwFavXq10G0W9X2/evMnc3d1ZXFyc0u2gpPfr2bNnGQAWHh6ucLs+ffqwtWvXSh+r8jfes2cPEwgEbPPmzSw8PJzNnj2b6erqsujoaKXxEdKctLRjPyV1RO0KCwvZggULWKdOnZihoSHT0dFh7u7ubNGiRdIEgzHGDh8+zFxcXJimpqZ0SJMXL14wf39/pq2tzezt7dnvv//O/Pz8qk3qGGNs48aNzN7envH5fKVDmkjiCwwMZG3atGHa2trMwcGBTZ48mSUmJiosryypY4yxvXv3sjfffJPp6+szXV1d1qFDBxYYGCgd0uTFixcMALtw4YLSePz8/OSGZAHAJk2aJFNu//79zN3dnQkEAubh4cH++ecfmecV7QMA27p1q0wsipaq4mOMsfT0dDZu3Dimr6/P9PX12bhx4+SGbVFWjxcvXrxW3RcuXMhcXFyYlpYWMzY2Zr6+vmzPnj1VxssYY/fv32e9evViIpGIWVlZsWXLlskNZ1KRoqROMgROVXVQltR98MEHrEePHkq3c3BwkBtCp7q/MWOMrVu3jjk4ODChUMg6depU5ZA1hDQ3Le3Yz2NMyZDpzUxcXBzs7e0RGxur8LohQgghhDQvLe3YT9fUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5TUEUIIIYQ0A5rqDoAQotj+R/vRxrwN2lm0U3cohNSJggIgK0t28fAAnJzUGxchzQUldYQ0Qmefn8UH/3wAA5EBTow7gW523dQdEiEoLgays4HMTPnkrPI6RWWKiuT3+dtvwGefNVAFCGnmKKkjpBHqZN0JXWy74EbcDfTd0Rf/jvkXfZ37qjss0sSVlXFJWU2TMcm6V69ePwYeDzAyKl8MDV9/n4QQDiV1hDRCJtomODPhDIbvHY6zz8/i7b/fxt4RezHMY5i6QyNqJBYDeXmqtZQpWpeTUzdxGBjIJmZGRoCxsWrr9PUBPl3NTUi9oKSOkEZKT6iHox8cxdigsQiKCMKIfSOwZegWTOw4Ud2hkVpijGvtqm1LWXY2l9i9Lh0d1ZOwyusMDABNOnIQ0ijRV5OQRkykKcK2odsQnxOPm/E3MenQJACgxK4JKCkBZswAHjyQTdBKSl5/30Ihl2zVJjEzNOS2J4Q0P2pP6qKjo/HNN9/g/PnzSEpKgo2NDcaPH4+FCxdCWOGXh8fjyW37xx9/YMaMGQ0ZLiH1LjkvGddir+FazDVcjb2Ku4l3USoulT4fFBFESV0TEB8PbNlSd/szMQHs7bnFwoI7jWlgwN1Wd19bm7uWjRDSvKk9qXv8+DHEYjH+97//wcXFBQ8fPsSHH36I/Px8rF69Wqbs1q1bMWjQIOljQ7rCljRxjDE8SX+CqzFXcS32Gq7GXMXTjKdy5Wz0bfBGqzfQ074nxncYr4ZISU05OgI3bwKPHgG5udz1bLm51d/Pz1e8v4wMbrl3r+axaGiUJ3nKEkBVE0RdXUoQCWms1J7UDRo0SCZRc3Z2xpMnT/DHH3/IJXVGRkawsrJq6BAJqXMp+SmYfXI2Tj87jfSCdJnneOChnUU79LTviZ6teuKNVm/AwdBBYWs1ady6duWWmigr4zpDqJIAVvd8Xh53HV9ZWfnp39fF48kniAYGgLc30K8f8MYb3DV7hJCGp/akTpHs7GyYmJjIrZ81axamT58OJycnTJs2DR999BH41I2KNDEp+Snos70PHqU+AgBoaWqhm2039LTnEjhfe18YaRmpN0iiNhoa3HVvdXEiQizmOmaokgCqkiyKxVySmJMj35P27Fngxx+56/XeeINL8Pr1Azp14upECKl/jS6pe/bsGdauXYs1a9bIrP/mm2/Qt29faGtr49y5c5g3bx7S0tKwaNEihfspKipCUYWRLnNzc+s1bkJUkZyXjD47+iA8NRw2+jb4+72/4WvvC6EGXblO6h6fD+jpccvrYoybEUJRspeWBly9Cpw5A8TGAufPc8vXX3OdM/r0Afr355K81q3p9C0h9YXHGGP1seNly5Zh+fLlVZYJCQlB586dpY8TEhLg5+cHPz8/bNq0qcpt16xZg8DAQGRnZ9fo9WNjY2FnZ6dCDQipW0l5SeizvQ8i0iJgq2+LC5MuwNXUVd1hEVJnGAOiorhWuzNngAsXuGFYKnJwKG/F69sXMDdXT6ykZYiLi4O9vX2LOfbXW1KXlpaGtLS0Kss4OjpCS0sLAJfQ+fv7o1u3bti2bVu1p1WvXbuGN954A0lJSbC0tJR7vnJLXXx8PDw9PVvMH5Y0Lom5ieizow8epz2GnYEdLky6ABcTF3WHRUi9Ki0F7tzhEryzZ4Hr1+WHdPHyKk/y+vWjU7WkbrW0pK7eTr+amZnBzMxMpbLx8fHw9/eHj48Ptm7dqtJ1cqGhodDS0oKRkZHC50UiEUQikfRxTl0NpU5ILUw9PBWP0x4DAMa0HQNjLWM1R0RI/dPUBLp145ZFi7ievVeulCd59+8DYWHcsno1MHcuUOnKG0JIDai9l0FCQgJ69+4Ne3t7rF69GqmpqUhKSkJSUpK0zJEjR7Bx40Y8fPgQz549w6ZNm7Bw4UJ89NFHMokbIY1VN9tu4PO4r9vq4NWwWmOFoXuG4kD4ARSWFqo5OkIahq4uMGgQl7jdu8eN4yf5H57HA9q0UW98hDR19Xb6VVXbtm3DlClTFD4nCe3kyZMICAjA06dPIRaL4ezsjOnTp2PmzJnQVHG+mpbWBEsan6S8JOx+sBs77+9EaFKodL2hyBAjPUdiQscJ6NWqFw1dQpq1ggIgMREIDAS2b+fWubpyCd4bb6g3NtL8tLRjv9qTuobS0v6wpHF7lPIIu+7vwl8P/kJsTqx0/Tf+32DRm4p7dBPSGL16BaSmli8pKcofp6TIDq7M43GnXAMDaWw7Uj9a2rGfkjpC1EjMxLj88jK2hG7Bzvs7IdQQ4uEnD6lXLFEbSZJWXXImua9sBoyqCIVAhw7Ab78Bvr51XwdCJFrasb/RjVNHSEvC5/HR27E3/Bz8kPoqFSefnsTnJz/H8bHH6TQsqRP5+aolZ5LHr17V/DWEQm4+WnNzbqnuvr4+jVVHSH2gpI6QRoDH4+HXQb+i3fp2OPn0JA4/OYyhHkPVHRZphCRJmqotabVJ0kQixUkZJWmENG6U1BHSSLiZumF+j/lYeXUlZp+ajQGtB0BboK3usIia5OQAS5YAkZGyyVpBQc33pSxJU3afkjRCmiZK6ghpRBb2Woifb/yM6KxohKeGw8fGR90hETW5fBn49VfVy+vpcfOs+vhwty4ulKQR0tJQUkdII5KYl4jC0kII+AJ4mHmoOxyiRgMHAps3Aw8fcvOpxsRwt0lJ3HRcleXlcYng5cvcYwMDwN6+fGnVSvaxvT3w34Q+hJBmgpI6QhqRM8/OAAC62HaBrlBXzdEQdRIIgKlT5dcXFwPx8VyCJ1kkCZ9kycjgTt8+esQtypiblyd4bdoA/fsDPXtyp2sJIU0PJXWENCLPMp8BAK7HXseAnQPwda+v4efgRz1hiZRQCDg5cYsy+flVJ30xMbLjy929C/z7L7BqFTfrQ58+3MwPAwcCrVs3XN0IIa+HxqkjpBFJzU/F/DPz8df9v1DGygAAPex74Os3vsbbrm9TckfqBGNAZqZsknfrFnDyJJCcLFvWxYVL8AYNAnr35pI+QpqKlnbsp6SOkEYoOisaP177EZtDN6OorAgA0NGyIwLeCMAIzxHQ4GuoOULSHInFwP37XHJ36hRw9SpQWlr+vFAI9OpV3orXrh11wCCNW0s79lNSR0gjlpSXhJ+Cf8Ift/9AXnEeAMDVxBUL3liA8R3GQ6ghVHOEpDnLzQXOn+cSvBMngOho2edtbMoTvP79AWNjtYRJiFIt7dhPSR0hTUBGQQZ+v/U7fr35KzIKMgAAfZ364syEM3RKljQIxoCoKC7BO3kSuHBBdsw8kYi7Ju+LL6j1jjQeLe3Yz1d3AISQ6plom2CJ3xK8nP0SawasgVBDiHMvziE8NVzdoZEWgscD3NyAzz4Djh3jetiePg3Mm8f1nC0qAubMAQYPlr8ujxDSMCipI6QJ0RPqYa7vXAxoPQAAEBQRpOaISEulpcWdcl29mhs2Zf16bt2JE0DHjlyLHiGkYVFSR0gT9H6b9wEA/z75V82REMK14n3yCRASwnWeSE7mrrU7c0bdkRHSslBSR0gTVFDCXcykyaehJknj4eoKtG1b/phPRxhCGhQdEQhpgv568BcAYFTbUWqOhBBOdjbw3ntcb1lNTWDLFqBvX3VHRUjLQkkdIU1MdFY0rsVeAw88jG47Wt3hEIKEBOCtt7gx7vT0gH/+AQYMUHdUhLQ81DhOSBOz+8FuAIC3tTcsdC3UHA1pqcRi4MkTYOdOwNeXS+gsLYFLlyihI0RdqKWOkCYmJCEEAHA38S5MfjBBb8fe6OfUD/2c+8HT3JPGrSP1IiGBm0rs1i2uQ0RICHfKVcLVlRu/ztlZfTES0tJRUkdIExPoHwiBhgDnX5xH2qs0HI08iqORRwEA1nrW6OfMJXh9nfrC1sBWzdGSpigrC7h9uzyBu3WLS+oq09ICvL2Bnj2Br74CzMwaPFRCSAU0owQhTZSYiXEv6R7OPj+Lsy/O4vLLyygsLZQp08asDfo790dArwBY6VmpKVLSFBw6xF0Ld+sWEBkp/zyfzw1X0qUL0LUrd9uuHSAQNHiohKispR37qaWOkCaKz+PD29ob3tbe+LLnlygsLURwbDDOPD+DdSHrkFOUg4i0CESkRUDMxFj79lp1h0waqeJiYMQIoKysfJ2zs2wC16kToKurvhgJIdWjpI6QZqKkrAThqeE4+PggcopypOv7OffDF92/UGNkpLETCoGBA4Hjx7nTqadP06lUQpoiSuoIaeKepD3BupB12Ba2DbnFuQC46cQmd5yMT7t8ijbmbdQcIWkKfvyR6+gQGgpERAC9eqk7IkJITVFSR0gTVCYuw/Go41h7ay3OPC+fi8nd1B2zus7CxI4TYSAyUGOEpKnx9AQ+/BD43/+AefOAGzdoRghCmhpK6ghpYkrFpXhz65sIjguWWT/cYzimeU+Dk7ETNHgaaoqONGXLlwN//cX1eP3uO2DhQm5eV0JI09Ao/g9zdHQEj8eTWRYsWCBTJiYmBu+++y50dXVhZmaGzz//HMXFxWqKmBD14fP4SMxLlFt/8PFBvLP7HbRd3xZ6K/Vg8aMFumzsgpH7R+LL01/i91u/42jkUTxMeYjcolw1RE4aO0tLYOlS7v7ixcDIkUBOTtXbEEIaj0bTUhcYGIgPP/xQ+lhPT096v6ysDIMHD4a5uTmuXr2K9PR0TJo0CYwxrF1LPfpIy8Ln8fHo00eISI3Ay+yXiM6KRnRWtMz9nKIcpL5KReqrVNxOuK1wPybaJnA0coSDoQMcjRxl7jsYOcBIy6hhK0YahXnzuF6uX3zBDXHy4AF3266duiMjhFSn0SR1+vr6sLJSPI7W6dOnER4ejtjYWNjY2AAA1qxZg8mTJ2PFihUwMKBrh0jLoiPQgY+ND3xsfBQ+n1WYxSV6WfJJ38vsl8goyJAudxPvKtyHtZ41Tow7gY5WHeuzKqSR4fGATz7hhjAZOZIbs65bN+DOHcDDQ93REUKq0miSuu+//x7ffPMN7O3tMXLkSHz55ZcQCoUAgODgYLRr106a0AHAwIEDUVRUhDt37sDf319dYRPSKBlpGcHLygteVl4Kn88pysHLrJfliV7WS0RnR0vvp75KRWJeIvaH76ekroXq1g2YMIG7tu7VKyA1lZI6Qhq7RpHUffHFF+jUqROMjY1x69YtBAQE4MWLF9i0aRMAICkpCZaWljLbGBsbQygUIikpSeE+i4qKUFRUJH2cm0vXEBEiYSAyQHvL9mhv2V7h87/f+h2fnfgMYUlhDRsYaTTWrOESOgBYtYqGOCGkKai3jhLLli2T6/xQebl9m7vWZ86cOfDz80OHDh0wffp0bNiwAZs3b0Z6erp0f4omKWeMKZ28fOXKlTA0NJQunp6e9VNRQpqhTtadAAChSaFqjoSow2+/AfPnc/cDA7l5XQkhjV+9tdTNmjULY8aMqbKMo6OjwvXdu3cHADx9+hSmpqawsrLCzZs3ZcpkZmaipKRErgVPIiAgAHPnzpU+jo+Pp8SOEBW1t2gPHnhIyE1ASn4KLHQt1B0SaSAbNnCdJABg0SKuFywhpGmot6TOzMwMZrWcZyY0lGsdsLa2BgD4+vpixYoVSExMlK47ffo0RCIRfHwUXyguEokgEomkj3OoXz4hKjvz/Aw0+BooFZciKj2KkroWoqioPKEDgH37gMRE4I03uKV1axq3jpDGTO3X1AUHB+PGjRvw9/eHoaEhQkJCMGfOHAwZMgStWrUCAAwYMACenp6YMGECfvzxR2RkZGD+/Pn48MMPqecrIXWoTFyGJReW4Lur3MVU/o7+SnvYkuZHJOJa5nbvBsLDuZ6vkZHA5s3c85aWXHLXsyd36+UFCARqDZkQUgGPMcbUGcDdu3fx6aef4vHjxygqKoKDgwPGjBmD//u//4OOjo60XExMDD799FOcP38e2traGDt2LFavXi3TGleVuLg42NvbIzY2FnZ2dvVVHUKarMyCTIwNGouTT08CAOZ0n4Mf+v8ATb7a//cjapCeDly/Dly9Cly7xs0yUXm8dx0doHv38pa87t0BfX31xEuIIi3t2K/2pK6htLQ/LCE18SD5AYbtHYbnmc+hramNTUM2YWz7seoOizQihYXA7dtckidJ9LKyZMvw+UDHjuVJXs+egK2tWsIlBEDLO/bTv+CEtCAlZSWIzopGZHokojKiEJkeicj0SFyPvY6C0gI4Gjni4OiDSse3Iy2XllZ5sgYAYjF3ivbaNS7JO38eSEgAQkO5RTLZT4cOwNGjgL29+mInpKWgpI6QZoYxhvjceESllydtkRnc7fPM5ygVlyrcrp9zP+x5fw9MdUwbOGLSVJSWAi9fll9rJ1miorgOFYo8egQkJVFSR0hDoKSOkCYqoyCjPGmrsERlROFVySul22lrasPV1BVupm5wM3GDm6kbPMw80MW2C/i8ehu6kjQRjHEJmqLE7dkzoKRE+bZGRoCbm+zSqRPg6tpg4RPSolFSR0gTcj32OtYEr8HF6IvIKMhQWk6DpwFnY2cucau02OjbUPJG5JSWAu+/D5w7B+TnKy+npcUlaZWTN1dXwMyMhjwhRJ0oqSOkkRMzMY5GHsUP137AtdhrMs/ZGdjJtLi5mbrB1dQVTkZOEGjQWBNEdQ8fAocPc/c1NAAnJ/mkzc0NsLPjOkQQQhofSuoIaaSKSovw94O/8eP1HxGRFgEAEGoIMaHDBHzk8xHamreFrlBXzVGS5kIyjXbbtsDdu4BQqN54CCE1R0kdIY1MdmE2/rzzJ365+QsSchMAAAYiA3zS+RN83u1z2OjbqDlC0hxJkjo7O0roCGmqKKkjpJHIKMjA91e/x4Y7G5BTxE1rZ6Nvgznd5+Ajn49gIKLZU0j9kSR1VlbqjYMQUnuU1BHSSHxx8gvsur9LZp2XlRfSX6UjKCIIHmYecDd1h7G2sZoiJM3Z/fvc7X/TaxNCmiBK6ghpJEZ6jsTDlId4kvYEBaUFAIDjUcdxPOq4TDkLXQu4m7rD3dSdS/TMuFtHI0ea0ovUSmgosGcPd3/YMLWGQgh5DXQEIKSRGOI+BEPch0DMxIjLicPjtMd4kvaEu03nbuNz45GSn4KU/BRcibkis72AL4CrqWt5smfqjrYWbdHOoh20NLXUVCvS2DEGzJ7N3Y4dC3Trpu6ICCG1RUkdIY0Mn8dHK8NWaGXYCgNaD5B5LrcoF5HpkdIkT3IbmR6JwtJChKeGIzw1XGYbDZ4GPMw80NGqI7wsvbhbKy9Y6Fo0ZLVII3X7NnD5Mnd/6lT1xkIIeT08xhhTdxANoaVN6ktaFjETIyY7RqZlLyItAg+SHyC9IF3hNtZ61nKJnquJKzT4Gg0cPVGn7Gygc2fg6VPA1BQ4doxa60jz0dKO/ZTUEdKMMcaQkJuAsKQw3Eu+J72NSo8Cg/xXX1tTG+0t28skeh0sO0BPqKeG6ElDSUkBBg/mWu10dIADB4C33lJ3VIS8vpZ27KekjpAWKK84Dw+SH8gkeveT7yucM5YHHlqbtIaXlRc6WnKJnpeVF2z1bcGjOaGajbw8YMQI4NQpbkaJzZuBSZPUHRUhr6elHfspqSOEAADKxGV4lvkMYUlhMi17kgGQKzPRNoGfgx+2D9sOfZF+A0dL6sOrV0DfvsCNG9zjjRuB6dPVGxMhr6OlHfspqSOEVCk1PxX3ku/hXtI9hCVzCV9EagTKWBkA4PaHt+Fj46PmKEltlJVxw5lcuMAtV65wLXYS775bPh8sIU1RSzv2U+9XQkiVzHXN0c+5H/o59wMAvCp5BZ8/ffA47TFGeI5AJ+tOao6QqEosBh48KE/iLl3iOkpUZGIC+Ptzy9ix6omTEFI7lNQRQmrk/878Hx6nPYa1njU2DN5A19U1YowBjx8D589zSdzFi0B6pc7QhoaAn195Ite+PcDnqyVcQshroqSOEKKyE1EnsC5kHQBg27BtMNUxVXNEpLKUFODff7lE7uLF8jldJXR1gV69gD59uCTO25vrGEEIafooqSOEqCT9VTqmHuZGp/286+dyAyOTxqFHD+DZM9l1AgEwYQI3uHDXrtxjQkjzQ43shBCVzDw+E0l5SWhj1gar+q1SdzhEiYEDAW1t2XUlJcCWLVzHh8GDga+/BoKCgJgY7hQtIaR5oJY6QkiVSspK8PeDv7H30V5o8DSwY/gOaAu0q9+QqMW6dcCvvwLh4dxgwpLl3j0gMxM4c4ZbJCwsuBklJMubb3LX2RFCmh5K6gghEDMxEnITEJkeKV2iMqIQmR6J55nPUSouBQAs7LUQnW06qzlaUh1NTaBDB26RzOdaXAw8fMgleCEh3O3Dh9w1eMePcwsA6OsDM2YAs2cDNjZqqwIhpBZonDpCWpD0V+kyiVtkBnf7NOOpwtkkJLQ1tTG8zXBsG7oNAg26IKu5KCgA7t8vb827epWbAxYAhELuOrwvvwTc3dUbJyG11dKO/dRSR0gzk1ech6cZT2WTt/+WzMJMpdtp8jXhbOwMN1M3uJm4wc3UDa6mrnAzdYONvg34PLoEt7nR1ga6deMWgLu+7sQJYNUqbiDizZu5a/GGDwe++orrZEEIabwoqSOkCSouK8bzzOfcadL0KJlWN2XTeknYG9hzidt/i6sJl7g5GjlSK1wLx+MBb7/NLdevA99/z80oERTELb17c8ndwIFcWUJI46L2pO7ixYvw9/dX+NytW7fQpUsXAFA4wOkff/yBGTNm1Gt8hDQWdxPvYsmFJXic9hgvsl5AzMRKy5rpmJUnbiblLW4uJi7QEeg0YNSkqRGLgawswNwcWLAA6NkTWLmSW3fxIrd07AgcOAC4uKg3VkKILLUndT169EBiYqLMusWLF+Ps2bPo3Fn2guytW7di0KBB0seG1EWLtCDbwrbhWNQxufXWetaY0GEC2lm0k54yNdE2UUOEpLEpK+N6vKalcTNJqHKbkcEldlW5dw/YvRtYvLhh6kEIUY3akzqhUAgrKyvp45KSEhw+fBizZs2Sa50zMjKSKUtIS/Jtn29hrmOOE09P4Gb8TWlLXWJeItaFrEMfpz4Y5DIIFroWlNQ1QyUlXMKlanKWns4ldLXtCqenB5iZAaam8re2tjQvLCGNUaPr/frPP/9g1KhRiI6Ohr29vXQ9j8eDra0tCgsL4eTkhGnTpuGjjz4CX8kkhUVFRSgqKpI+jo+Ph6enZ4vpAUOat4yCDJx9fhYnnp7AyacnkZQnOxeUm6kbBrUehLdc34Kfgx+NK9fIFBfXLDlLSwOys2v/eoaGyhM0ZetEorqrLyHq0tJ6vza6pO7tt98GAByXDJr0n2+//RZ9+/aFtrY2zp07hyVLliAgIACLFi1SuJ9ly5Zh+fLlcutbyh+WtByMMdxPvi9N8K7FXpOOKwcAWppa8HPww1sub2Gw22C4mNCFUHWtoACIjFQ9ScvNrd3r8HiAsbFqiZnk1sSEpgUjLRcldXVEWVJVUUhIiMx1c3FxcXBwcMC+ffvw/vvvV7ntmjVrEBgYiGwl/75SSx1pqXKKchAUEYQfrv2AiLQImec0+Zq4NPkSetj3UFN0zU9ZGeDmBjx/Xrvtzc25DgcWFsoTM8mtsTGgoVG38RPSnLW0pK7erqmbNWsWxowZU2UZR0dHmcdbt26FqakphgwZUu3+u3fvjpycHCQnJ8PS0lLueZFIBFGF8wc5OTmqBU5IE8IYQ1xOHMKSwrglOQyhiaF4kfVCYXkrPSsYaxk3cJTNG58PtG3LzaNaWlp9+cpSU7kWPCOj8lY3E5Py+5WXis/p6tLQIoSQcvWW1JmZmcHMzEzl8owxbN26FRMnToRAhXMFoaGh0NLSgpGR0WtESUjTwRhDRFoE7ibeLU/iksKQXpCusHwrw1bwsvKCt5U3vKy84GXlBQdDB4XDA5Ha4/G4sdwY406rpqcrXiSdHCovOTnctpmZ3CKZ0UEVQmHVSV/lxcKCe54+AoQ0T2rv/Spx/vx5vHjxAtOmTZN77siRI0hKSoKvry+0tbVx4cIFLFy4EB999JFMaxwhzVngpUAsu7RM6fNdbLpguMdwdLPrBi8rL+oB28B4PMDAgFucnFTfrmKvVmWJn6KlpITrcJGYyC2q0tLi5nS1tVW+2NhwCSMhpGlpNEnd5s2b0aNHD7Rp00buOYFAgPXr12Pu3LkQi8VwdnZGYGAgZs6cqYZICVEPWwNbCDWEKC4rVvh8SEIIbifchp2BHZyNndHauDVam7SWuTXWplOvjY1AAFhacouqGAPy86tP/ConiVlZQGEhd/1fddcAmptXnfjZ2nLX+FGrHyGNR6Pr/VpfWtrFkqR5KhWXIjY7Fs8yn+FZxjPutsL9vOK8Krc31jJGa5PW5UlfhYTP1sCW5ndt5goLgYQEID5e+ZKQwLUAqkJbu/pWP2travUj6tPSjv2U1BHSTDDGkPoqFc8ynuF55nO5hK/yWHaViTREcDJ2UpjwORk7QUtTq4FqQtSJMa7jRlWJX3w81wqoKguL6lv9jIyo1Y/UvZZ27KekjpAWIr84vzzZq9DK9zzzOaKzomXGtquMBx5sDWzhbOwMd1N3fNnjS7iaujZg9KSxKShQrdWvpES1/WlryyZ5rq5A587cQhMJkdpqacf+RnNNHSGk/hSUFCC9IB3FZcXQ0tSCha4FeDwejLSM0MqgFZLMknAn4Q4S8xRfcc/ADZ0SlxOHyy8vQ0+oh58G/tTAtSCNibY20Lo1tygjFsu2+ilLAjMyuCTx6VPFvX9tbcsTvM6dAR8f7po/QogsSuoIaWIYY8gpykHqq1Sk5qdKb9NepXH3FazPL8mv8evweXyYapvCXNccZjpmMNcxh7mOOaz1rTG90/R6qBlpbvh87tSrhQXg7a28XOVWv7g44OFD4PZtICKifP2//5Zv4+Agn+gZUz8g0sJRUkdII5WUl4Sfg39GTE6MNElLe5WG1PxUlIhVPKdVgYAvgLkul5hJbyvcN9Mxk3neWMsYGnyavoDUv6pa/fLygNBQLsGTLJGRwMuX3PLPP+VlW7fmErzhw4GRI7mkkpCWhJI6QhqpHfd24IfrP9RoG3sDe3S06oiOltxib2gvTdL0hfo08DBpcvT0gF69uEUiOxu4e1c20Xv+HHj2jFv27gV+/BFYvRro3VttoRPS4KijBCGNVNqrNPx+63e8yHqBhNwExOfEIyE3AdlFiuc7VsRE2wQ2+jaw0beBrb6twvuWepbQ5NP/d6Rpy8gA7twBLl4EfvuNa+EDgHfeAb7/HvD0VGt4RE1a2rGfkjpCmpj84nwk5CbILPG58XL3C0sLVdofn8eHpa6l4uTPoPy+qbYptfSRJiElBVi+HPjf/4CyMu407PTp3DrqSduytLRjPyV1hDRDjDFkFWbJJ3w58UjIK08GE3MTUcbKVNqnUEMoTfCUtfzZGthCT6hXz7UjRDWPHwMLFpR3sNDVBb78Epg/n7tPmr+Wduyncy6ENEM8Hg/G2sYw1jZGW4u2SsuVicuQ+ipV5vSuota/1FepKC4rRnRWNKKzopXuT4OngU1DNmGy1+S6rxQhKmCMa6mLjuYWX1+uQ0VYGDe12rJlwP79XOcLgUC9sRJS1yipI6QF0+BrwErPClZ6Vuhk3UlpuaLSIiTlJUmTvOux17ElbAuyCrNkypWxMqVz0xJSFyQzXkiSthcvyu9LloKCqvfB4wGadPQjzRB9rAkh1RJpivCq5BXOvziPoMdBuJt4V+b5rrZd8Z7HexjeZjjcTN3UFCVpDhgD0tPlE7WKydurV1Xvg8cD7OwAR8fyxcmp/L69PU1JRponSuoIIQoxxnAn8Q6CIoIQFBGEJ+lPpM/xeXy86fAm3vN4D8M8hsHe0F6NkZKmhDEgM1N5K1t0dHnPVWV4PMDGRj5Zq5i0CYX1WAlCGilK6gghMrIKsxB4KRD/RPyDmOwY6XqhhhD9nPvhPY/3MMR9CMx1aZ4molxJCfDgAXDzJtdhoWICl5tb/faSpE1Ra5u9PSAS1WPwhDRRlNQRQmSsu7UOP9/4Wfq4j1MfTPeejrdd34ahlqEaIyONWVwccOMGl8TduMGNGVfVtW1WVopPjTo6Aq1aAVpaDRI2Ic0KJXWEEBlTvKfg3ItzuBB9AQBwO+E2/B39acowIpWfzyVtFZO4hAT5coaGQLdugJeXbOLm4MBNDUYIqVs0Th0hRA5jDKefncaCcwsQlhQGALDUtcQP/X/AxI4T1RscUYvgYGD7di6Je/CAG9S3Ig0NoH17oHt3LpHr3h1wc6P5V4l6tbRjPyV1hBClxEyMvQ/3YsG5BYjJjgEPPGR+lUmnYVug1q25+VWVcXDgWuMsLQELC26R3K+4To/GpiYNqKUd++n0KyFEKT6PDz9HP5SUlQAA3nV/lxK6Fmr1am7Q3pQUIDmZu01LA8Ri7vmXL7mlOjo68omeskTQxIRrASSEqIaSOkKIUgUlBRi2ZxgS8xLR1rwtdg7fqe6QiJoMH84tFZWVcWPKVUz0JLeV1yUnA4WF3Bhzkl6w1eHzATMzxS1+iu7TdXqkpaOkjhCiEGMMHx75ECEJITDRNsHhDw7DQGSg7rBII6KhUZ5YtWtXdVnGuA4WypK+yuvS07lWQMl6VejrV9/6J1lnbEzX+5Hmh5I6QohUXE4cgmODcSPuBq7GXsWt+FvQ4GngwMgDcDZ2Vnd4pAnj8bjr6fT0uOvzqlNSwp3erS75k9wvLubGv8vNBZ49q37/mprAhAnAli2vXzdCGgtK6ghpoQpLC3E38S6XxMXfQHBsMOJz42XK8MDDurfXwd/JX01RkpZKIACsrbmlOowBOTmqtQImJwPZ2UBpKRAaWv/1IKQhUVJHSAvAGMPL7JfSVrjguGCEJYWhRFwiU06Dp4EOlh3ga+eL7nbd8UarN+Bk7KSmqAlRDY/HjYlnaMgNo1KVb74BlizhBjemVjrS3FBSR0gzlF+cjzuJd2Ra4ZLzk+XKWehawNfOV5rEdbbpDF2hrhoiJqT+nTwJLF3K3f/jD8DbW73xEFLXKKkjpJk4/+I8/gn/Bzfib+Be0j2UMdnRYTX5mvC28pYmcN3tusPRyBE8Hk9NERNSf8rKgIyM8lOviYnAZ59xp2o/+giYPFndERJS9yipI6QZSM1PRd8dfRU+Z6JtgvHtx2N8h/HoYNkBIk2aCZ00PZLr5iRJWkoKkJoq+7jiIuk9W1mXLsBvvzV8/IQ0hHpP6lasWIFjx44hLCwMQqEQWVlZcmViYmIwc+ZMnD9/Htra2hg7dixWr14NoVAoLfPgwQPMmjULt27dgomJCT7++GMsXryYWhkIAWCsbYzFby7GiacnEJEagfySfOlzGQUZ+O3Wb/jt1m/Q4GmgtUlreJp7oo1ZG3iae8LT3BMeZh7QEeiosQakJSooqDoxq/xccXHNX8PEpHwYE1dXIDAQENH/NaSZqvekrri4GCNHjoSvry82b94s93xZWRkGDx4Mc3NzXL16Fenp6Zg0aRIYY1i7di0AICcnB/3794e/vz9CQkIQGRmJyZMnQ1dXF/PmzavvKhDS6GnyNRHoH4hA/0CImRix2bEITw1HRFoEwlPDpUt2UTYi0yMRmR6JQzgk3Z4HHhyMHLgkz8wTbS3aoqttV3iYeYDPo8G8SO1ERACnTytP2vLyar5PPb3yJM3CAjA3l31ccTE15XrREtJSNNjcr9u2bcPs2bPlWupOnDiBd955B7GxsbCxsQEA7NmzB5MnT0ZKSgoMDAzwxx9/ICAgAMnJyRD99y/WqlWrsHbtWsTFxanUWtfS5n8jpDLGGJLykmSSPEnSl/oqVeE2hiJDdLPrJr0Or5ttNxhrGzdw5KSpMjPjToNWRSBQnpRVTtrMzblpxghRVUs79qv9mrrg4GC0a9dOmtABwMCBA1FUVIQ7d+7A398fwcHB8PPzkyZ0kjIBAQGIjo6Gk5P8kAtFRUUoKiqSPs7Nza3fihDSyPF4PFjrW8Na3xp9nWWvv0vNT5UmeBGpEQhLDsPthNvILsrG6WencfrZaWlZDzMPaZLna+cLT3NPaPBpgk4iLyAAWLECyMwsX6epCXzwAddpwc0NMDDghiQhhLw+tSd1SUlJsLS0lFlnbGwMoVCIpKQkaRlHR0eZMpJtkpKSFCZ1K1euxPLly+snaEKaGXNdc5jrmuNNhzel60rFpXiQ/ADBceVj2z3NeIrHaY/xOO0xtoZtBQDoC/XR1barNMnrbtcdpjqm6qoKaUTmzQNmzQKOHAG2bwdOnOAG/d25E9i3Dxg2DJg0Cejfn0v2CCGvp1Zfo2XLllWbMIWEhKBz584q7U/R6VPGmMz6ymUkZ42VnXoNCAjA3LlzpY/j4+Ph6empUjyEkP+GQLH2hre1Nz7t8ikAIO1VGm7E3ZAmebfibyG3OBfnXpzDuRfnpNu6mrjC194X3W27w9feF+0s2kGTT0ftlkgkAkaM4JakJODvv4Ft24AHD4C9e7nF2hoYP55L8Nq2VXfEhDRdtfqVnTVrFsaMGVNlmcota8pYWVnh5s2bMusyMzNRUlIibY2zsrKSttpJpPw3w3PlVj4JkUgkc7o2JydHpXgIIcqZ6ZjhLZe30NO+JzILM5H+Kh3XYq/hxNMTuBR9CUVl3CUPURlRiMqIwo57OwAAugJdzO4+G9/2+Vad4RM1s7IC5s4F5swBwsK45O7vv7kx5H78kVsGDgQOHeJmfCCE1EytkjozMzOYmZnVSQC+vr5YsWIFEhMTYf3fJH+nT5+GSCSCj4+PtMzXX3+N4uJi6TAnp0+fho2NjcrJIyGEwxhDfkk+sgqzkFWYhcyCzPL7hUru/1cmszATOUU1/wcpvyQfJ5+epKSOAODmXn3+HHj1Sj55u3qVG+qEkjpCaq7ez4fExMQgIyMDMTExKCsrQ1hYGADAxcUFenp6GDBgADw9PTFhwgT8+OOPyMjIwPz58/Hhhx/CwMAAADB27FgsX74ckydPxtdff42oqCh89913WLJkCY1TR1qk4rLiqhMyyfoi2YRM8nypuPS1Y9AR6MBIywjGWsYw0jLi7msbw0hU4f5/6420jNDFpksd1Jw0RWVlwO3bwKlT3HLzJrdOQiQC3nyTa6UbPhwwpg7WhNRKvSd1S5Yswfbt26WPvf+bbO/ChQvo3bs3NDQ0cOzYMXz66afo2bOnzODDEoaGhjhz5gxmzpyJzp07w9jYGHPnzpW5Zo6QpkTMxMguzJZLyFRtNSsoLXjtGDT5mvIJWeUkTcHzkkWoIaz+RUiLlZBQnsSdOcNN2VWRhweXxA0cCPj50VAlhNSFBhunTt1a2lg1pOE9SnmEu4l3VTqNmVOUA4bX/+oZigxlki1VEjLJeh2BDrV0kzoVGspdI3fqFNcRoiJDQ6BvX2DQIGDAAMDBQT0xkpalpR37qTsaIXUgqzALXv/zqvFpTckpzJomZMbaxtAX6tP4cKTRyMoCundXPpWXjQ3XWnf6NHDnDjcwsamp4lsau46Q2qGkjpA6YCgyxDTvabj88jJic2KRV6za/EdCDSFMtE1gb2APOwM72BnYyd43tIeeUK+eoyfk9enrA9Oncx0d0tOBtDSgwvjviIjgFlVoanJztlaV+FW+NTIC+DSjHWnh6PQrIXWMMYacohzE5cQhNicWcTlx3P3sWMTllt/PLVZtlhNDkaE0wbPTL0/2KiaB+iL9eq4VITXDGNe7NS2tPMlLT5e9r+j21avavR6fzyWCFZO96hJBY2Ma9Li5a2nHfvo4E1LHeDweDLUMYahliLYWykdSlSZ+2RUSvxzZ+zlFOcguykZ2ajYepT5Sui8DkYF8K1+lFj8DkUF9VJcQhXg8QFeXW2py/VxBQXnyV10CKCmTkwOIxdy6tDTgyRPVX8/YuOrkT9E6gaDm7wchDYFa6ghpxHKKchCfE19li192UbZK+9IX6sPOwA5tLdpKZ3roZN0JWpo0IBhp2oqLuev1qkr8Kq+rOB9tTRkYcMmdvT2wdi3QoUPd1YXUrZZ27KeWOkIaMQORAQzMDdDGvI3SMrlFuYjPja+yxS+rMAu5xbmISItARFoEDoQfAAAI+AJ4W3tLkzxfO1+0MmxFvWJJkyIUcrNVWFlVXU4s5pK/5GQgPp67xi88HHj0iLtVNdHLyeGWFy+AK1coqSONB7XUEdIC5BXnIT4nHi+zXyI0MRTBccEIjgtGSn6KXFkrPSv42vmiu113+Nr5wsfGBzoCGkSMNE6S067JyfJLUpLs45QU2UGPVWFkBFhali9WVuX3HRyAPn0ADeqE3mi1tGM/JXWEtFCMMURnRSM4Lhg34m4gOC4YYUlhcsOyaPI10cm6E/4Y/Ac6WXdSU7SElFu1CtizpzxRE4trtr2JiWyiVjlZkywWFtxsF6TpamnHfjr9SkgLxePx4GTsBCdjJ4xtPxYAUFBSgDuJd6RJ3rWYa0jOT8at+Fs49fQUJXWkUfjzT+7UpyI+PoCbm/KEzdycO11LSHNESR0hREpboI03Wr2BN1q9ATETY8juITgWdQwuJi74tMun6g6PEADA+fPArl3cQMbBwUBphcbl0FAuaXN1Bd54A+jWjXqrkpaDTr8SQhRadXUVAs4FQKQhwo3pN+Bl5aXukAiRk5sLXLzIzS97+rT8cCb6+oC/Pzc1Wf/+XLJH/YBajpZ27KeWOkKInEvRl7Dw/EIAwNq31lJCRxotfX3g3Xe5BQBiYrgET7JkZACHD3MLALRqVZ7gDRtGp2JJ80KTqhBC5Mw/Mx9iJsaEDhMwvdN0dYdDiMpatQKmTQO2b+da8BYu5Do8SMTEAJs2AaNHA7NnqytKQuoHtdQRQuRIrsro69SXxqwjjZZYDMTGApGR3GnXyMjy5eXLqnvF2tgAvXo1XKyENARK6gghct5yeQt3Eu/gxNMTmOQ1Sd3hkBaMMW4GCEmyVjF5e/oUKCxUvq2BAeDuzvWGrbi4unKnbQlpbiipI4TIecv1LXx75VucenYKpeJSaPLpp4LUr/x8LklT1OpW1UwPAgHg4qI4ebOwoE4RpGWhX2pCWjgxEyPtVRric+IRnxuP+Jx4xGTHAACyCrNwI+4G3mj1hpqjJM3R9evA0qXA48dAXFzVZVu14hK1ysmbgwPN6ECIBCV1hDRjhaWFSMhNQFxOnEzSFp9bfj8hNwEl4hKl+1A0lRghdWHHDuDs2fLHpqayCZskgWvdGtChmeoIqRYldYQ0QYwxpBekyydqlRK29IJ0lfbHAw8WuhawNbCFrf5/i4EtPM09McxjWP1WhrRYixcDV68Cjx4BenrAgQNA797qjoqQpouSOkIameKyYiTkJsglanE5cTKta0VlRSrtT0tTS5qkVUzY7AzspPet9awh0KBh90nDsrUFrlwBhg7lbgcOBP76CxgxQt2REdI0UVJHSAOLTI/E88znSlvXUl+lqrwvcx1zuWSt8q2xljENS0IaLWNj4NQpYNw44OBBYNQo4JdfgE8/BTTpCEVIjdBXhpAGtPHORnx09KNabautqQ1XU1d4mHnA3dQdLiYuMNU2hZ5QD7pCXegJ9aSLrkCXWt5Ik6GtDezfD3z2GfDHH8AXXwBffgm0aQO0awe0b88t7doB9vbUo5UQZSipI6QBORs7w87ADmmv0lBYWsUAWwoUlBbgfvJ93E++r1J5oYZQLtGr8rGCxLDy87oCXWjwqashqXsaGsC6dYCjI/Dtt9ycrvfucUtFhobliV7FhM/YWC1hE9Ko8Jhk6PhmrqVN6ksav1JxKfKL85FXnIf8Eu5WskjWSx9Xfr7SY8k2ucW5KBWX1mvc2praqieCVSSOdgZ2MNE2qddYSdMkFnMzQjx4ADx8yN0+eMCNX1eq5ONtY1Oe4EkWLy9q1WvpWtqxn5I6QpqZ4rJi1RLDis+XyJevvI2YVTHnUi2INER4+OlDuJi41Ol+SfNVXMwldpIk79o1rvessqPYnDnATz81bIykcWlpx346/UpIMyPUEMJE2+S1W8EYY8gpykFSXhKS8pLwMvslnmU8w9PMp3iW8QzPMp8h7VVarffvZOwEA5HBa8VImr/SUuDFCy6Zq7wkJyvfTiTiZpogpCWp96RuxYoVOHbsGMLCwiAUCpGVlSXz/L1797Bq1SpcvXoVaWlpcHR0xIwZM/DFF19Iy0RHR8PJyUlu3ydOnMCgQYPquwqENBuMMeQV5yE5PxlJeUlIzvvvNr/S7X/rVR02RcJIywhWelaw1LWUvdWTfWyuaw6hhrCeakmaorQ0xYnbs2dAifKxsWFtzQ1SXHlxdKSZJkjLU+9JXXFxMUaOHAlfX19s3rxZ7vk7d+7A3Nwcu3btgr29Pa5fv46PPvoIGhoamDVrlkzZs2fPom3bttLHJiZ0PQ4hAPCq5JXiJC0vGUn5sutflbyq0b4NRAayyZmufJJmqWcJC10LaGlq1VMNSXNQVMQlaYqSt4wM5dtpa5fPMFFxcXMDDKixlxCpek/qli9fDgDYtm2bwuenTp0q89jZ2RnBwcEICgqSS+pMTU1hZWVVL3ES0hTkFOXg1xu/4l7yPZnkLa84r0b70RHowErPSmGrWuWWNW2Bdj3VhjRXjAExMcCtW8Dt21xnhydPuNOo4iouzWzVSnGrm50dwOc3XPyENFWN8pq67Oxsha1wQ4YMQWFhIVxdXTFnzhyMoGHHSQshZmJsC9uGgHMBSudi1dLUUunUp6WeJfSEeg1cA9KcpaYCISHccusWd5uqZAxtfX3FiZurK83vSsjranRJXXBwMPbt24djx45J1+np6eGnn35Cz549wefzcfjwYYwePRrbt2/H+PHjFe6nqKgIRUXl1wPl5ubWe+yE1Ifg2GB8fvJz3E64DQBwM3XDJ50/ga2+rUzSpi/Up5kjSL3LzQXu3i1P3kJCgOho+XKamkCHDkCXLtzQIh4eXPJmZUXDjBBSX2qV1C1btkx6WlWZkJAQdO7cuUb7ffToEYYOHYolS5agf//+0vVmZmaYM2eO9HHnzp2RmZmJH374QWlSt3LlympjJKQxS8hNwFdnv8Ku+7sAAPpCfSzxW4LPu31OnQxIgygqAu7fl22Fi4hQPISIuzvQtSuXxHXtCnTsCGjRJZaENKhaJXWzZs3CmDFjqizj6OhYo32Gh4ejT58++PDDD7Fo0aJqy3fv3h2bNm1S+nxAQADmzp0rfRwfHw9PT88axUSIOhSWFuLn4J+x4soK5JfkgwcepnhNwYq+K2ClR9eUkvrBGJewVTyFeu8eNzZcZfb2XPImSeB8fLiZHggh6lWrpM7MzAxmZmZ1FsSjR4/Qp08fTJo0CStWrFBpm9DQUFhbWyt9XiQSQSQSSR/n5OS8dpyENIT3972P41HHpY/fdX8X77i9Ix0AmM+jK8ZJ3RKLgffeA/79V/45E5Py5E2SyFF/NUIap3q/pi4mJgYZGRmIiYlBWVkZwsLCAAAuLi7Q09PDo0eP4O/vjwEDBmDu3LlISkoCAGhoaMDc3BwAsH37dggEAnh7e4PP5+PIkSP47bff8P3339d3+IQ0OFNtU5nHh58cxuEnhwEAugJdtLVoi/YW7dHBsgPaW7RHe8v2MNOpu3+ySMuzZg2X0AkEQPfusq1wTk50DRwhTUW9TxM2efJkbN++XW79hQsX0Lt3b6XX5zk4OCD6v6tvt2/fju+//x4vX76EhoYG3NzcMHv2bKXX0ynS0qYKIU0XYwzJ+cl4kPwA95Pv40HKAzxIeYBHKY+UDgZspWdVnuT9l+h5mnvSuHGkWqGhQLdu3AC/GzcC06erOyJC6k5LO/bT3K+ENBGl4lI8zXiKB8lckidJ+J5nPldYXoOnAVdTV2mi18GyA9pbtoejkSOdwiUAgFevgM6duWvphg0DgoKoVY40Ly3t2E9JHSFNXG5RLh6lPpIme5KEL6NA8RD9ekI9tDVviw6WHTDPdx7czdwbOGLSWMyaBaxbx3VyiIwELCzUHREhdaulHfsb3Th1hJCa0Rfpo7tdd3S36y5dxxhDYl6i9BTu/ZT7+Pfxv8gtzkVecR5uxt/EzfibyCrMwr6R+9QYPVGXsjJgyxbufnY2NySJnx/Quzfg7w+0b0+zOBDS1FBSR0gzxOPxYKVnhZjsGCTnJ+PKyyvILS4fgFtbUxuD3QbjG/9v1BglUScNDWDbNmDHDuDyZSAri+ssIekBa2LCJXn+/tzi6UlJHiGNHSV1hDQz95PvY2voVhyIOIC4nDjpel2BLt5xewcjPEfgLZe3oCvUVWOUpDEYNYpbSku5WSIuXgQuXACuXAEyMoCDB7kFAMzMuFY8b2/A2pob1sTKirtvbs4liYQQ9aJr6ghpBgpLC7H/0X5suLMB12OvS9frCfUwxH0IRrQZgYEuA6EjoMk1SfVKSoA7d7gE78IF4No1rlOFMnw+l9hJkjxJwqfosb4+dcYgDaelHfuppY6QJiwqPQr/u/M/bA3bKu0YocnXxFD3oZjYcSIGtB5Aw5qQGpOMV9e9OxAQwM0qERLCteQ9fQokJZUvKSnc4MXJydxy717V+9bRqTrpkzy2sODiIISojpI6QpqYkrISHH5yGBvubMDZ52el61sZtsJHnT7CVO+psNZXPtsKITUlFAI9e3JLZWVlQGpqeZKXmCib9FVcl5vLtfg9f84t1TEzq77lz9qa671LrX+EUFJHSJMRkx2DjXc2YlPoJiTlcTOv8MDDYLfBmOEzA4NcBkGDTxc2kYaloVGeYFUnP59rzauc+ClKBMvKgLQ0bnnwoOr9ikSqnfq1suISVEKaK0rqCGlk0l6l4XHaYzxOe4wnaU/wOJ27/yzjGRi4S2AtdS0xvdN0fNjpQzgYOag5YkJUo6sLODtzS1XEYiA9XT7pi4kBnjwBHj8G4sr7AKGoCHj5kluq83//B9AMk6S5oqSOEDUoFZfiReYLLnFLfyJN4h6nPUZ6QbrS7fo49cEMnxkY6jEUQg1qciBNR2kpNx5e5SUrq+rHFdcVFr5+HP9NL05Is0RJHSH1KLswG0/Sn3AtbmmPpa1uUelRKBGXKN3OwdABHmYe8DDzgLupOzzMPOBp7glLPcsGjJ4QjlgM5OSolngpe5yfX3fx6OkBRkbctXSSpfJjReuMjIAW0AGStGCU1BHymsRMjNjsWLkWt8dpj5GYl6h0O21Nbbibucskbh5mHnAzdaOhR0idYQzIy6t5q1jFJSen7uLR0ak+8aoqOTMwoDHxCFGGkjpCaig1PxV/3P4DEWkR0uveCkoLlJa31rOWa3XzMPOAvaE9+Dwaop/UzqtXwPbtQHx81QlbTg7X0lYXRCLVW8WUraNhSgipP5TUEVJDAecCsDl0s8w6AV8AV1NXmaRNksQZahmqKVLSnP35JzBnjurlNTVVP0WprIxIVD91IYTUDUrqCKkhT3NPAICptim2Dt0KDzMPOBk7QZNPXyfScD74gJuz9cQJ+Q4Ebm7A1KnAO+9wc7gaGgLa2jSWGyHNHZ37IaSGJnSYAE2+JtIL0uFo5AhXU1dK6EiDs7QEgoK4gX/37QPGjOE6EABAZCSwYAHg7w8sWQJcusRN/UUIad4oqSOkhsx1zTHEfQgAYEvoFjVHQ1o6PT1g5Ehg924uwTtyBJgyhWuhS00FNm0C3n6bm3Zr/Hjg4MGq53ElhDRdPMYYU3cQDaGlTepL6tfxqOMY/PdgaGlqwcfaB/aG9rA3+G8xtEcrw1awN7CHmY4ZeHTOi6hBSQl3ejYoiEvkEit0xNbVBQ4cAAYNUl98hDSElnbsp3NGhNTCwNYD4W7qjifpT3At9hoQq7iclqYW7AzspMlexcRPcmsoMqTEj9Q5gQDo2xcwN+eup1u3rvzau/x81WZfIIQ0LZTUEVILGnwN3P34Lu4n30dsdixic2LLb/+7n5SXhMLSQjzNeIqnGU+V7ktPqCdt2auc8Eluadw6UhMxMcDffwN//QU8fFi+3tCQO1U7fjzw5pvqi48QUj8oqSOklnQEOuhu1x3d7borfL64rBjxOfGyCV+lxC+9IB15xXkITw1HeGq40tcy0TYpP7Vr0Eou8bM1sKVpw1q4zEzulOquXdxpVwmhkOsFO24cd22dlpb6YiSE1C9K6gipJ0INIZyMneBk7KS0zKuSV4jLiUNMdozSxC+3OBcZBRnIKMjAveR7CvfDAw+WepZyp3n7OPWBt7V3fVWRqFlhIXD0KNcid/w4UFxc/lzv3lwi9/77gLGx2kIkhDQgSuoIUSMdgQ7cTN3gZuqmtEx2YbZMsheTHSPX+ldUVoSkvCQk5SUhJCFEui2fx8eKPivwfz3/j2avaCZKS4ELF7jTq0FBslN4dejAJXIffADY26svRkKIelBSR0gjZ6hlCEMtQ7SzaKfwecYY0l6lySV695Lv4dSzUwg4F4DguGBsH7YdRlpGDRs8qROMAbducYnc3r1AcnL5c/b2XBI3fjzQvr36YiSEqB8ldYQ0cTweD+a65jDXNUcn607S9YwxbA7djFnHZ+Hwk8Po/GdnHBh1AF5WXuoLltRIRASXyP39N/D8efl6U1Ng1Chg7FigRw+AT42whBBQUkdIs8Xj8TC903R4W3ljxP4ReJb5DL6bffHH4D8w2WuyusMjSsTGAnv2cIlcWFj5el1dYNgwLpHr358bsoQQQiqipI6QZs7Hxgd3PrqD8UHjceLpCUz5dwqeZTzDN32+UXdopJLx47lODxKamtwAwePGAe++yyV2hBCiTL032q9YsQI9evSAjo4OjIyMFJbh8Xhyy4YNG2TKPHjwAH5+ftDW1oatrS0CAwPRQibDIOS1mWib4OjYo5jvOx8AsD98v5ojIoqcPSv72MEB6NQJcHMDdGioQkJINeo9qSsuLsbIkSPxySefVFlu69atSExMlC6TJk2SPpeTk4P+/fvDxsYGISEhWLt2LVavXo2ffvqpvsMnpNng8/h4y/UtAIBAg87dNUZ37wJr1nADA/P5wLNnQGAg4OPDJXizZgFnzsgOXUIIIRL1fvp1+fLlAIBt27ZVWc7IyAhWVlYKn/vrr79QWFiIbdu2QSQSoV27doiMjMRPP/2EuXPn0hRLhKiouIzLBmig4sbJxgaYO5db0tKAY8eAf/8FTp3irrVbt45bDAy4gYSHDgXeeoubKYIQQhpNn6lZs2bBzMwMXbp0wYYNGyAWi6XPBQcHw8/PDyKRSLpu4MCBSEhIQHR0tBqiJaRpoqSu6TAzAyZN4saiS0sDjhwBpk8HLC25sen27OGGMjE3BwYM4JK9pCR1R00IUadGkdR988032L9/P86ePYsxY8Zg3rx5+O6776TPJyUlwdLSUmYbyeMkJb9iRUVFyMnJkS65ubn1VwFCmoCswixsvLsRAKCtqa3maEhNaGtzU31t3AgkJADXrwNffQV4eAAlJdwp2VmzADs7rkPFP/8ARUXqjpoQ0tBqldQtW7ZMYeeGisvt27dV3t+iRYvg6+sLLy8vzJs3D4GBgfjxxx9lylQ+xSrpJKHs1OvKlSthaGgoXTw9PWtYS0Kaj5D4EHT6XyccjTwKAV+AL7p9oe6QSC3x+YCvL7BqFTeO3ZMnwA8/AN26AWVl3LRhI0Zwp3I/+wy4c4cbvJgQ0vzVKqmbNWsWIiIiqlzatVM8+r0qunfvjpycHCT/N2y6lZWVXItcSkoKAMi14EkEBAQgOztbuoSHK58snZDmKiU/BWuur0HPLT3xIusFnIyccG3qNQz1GKru0EgdcXMDvvwSuHEDePwYCAgAbG2BjAzg99+Bzp25mSZWrwbohAUhzVutOkqYmZnBzMysrmORCg0NhZaWlnQIFF9fX3z99dcoLi6GUMhdC3T69GnY2NjA0dFR4T5EIpHMNXg5FSdIJKQZSnuVhjsJd3A74TZuJ97GnYQ7iM2JlT7/fpv3sWnIJpoqrBlzdwe++w745htueJR584BHj7jlyy+5261b1R0lIaS+1Hvv15iYGGRkZCAmJgZlZWUI+2+IdBcXF+jp6eHIkSNISkqCr68vtLW1ceHCBSxcuBAfffSRNCkbO3Ysli9fjsmTJ+Prr79GVFQUvvvuOyxZsoR6vpIWKaMgQy6Be5n9Uq4cDzy4m7nj866fY0bnGfR9aQGePOEGMP77b25IFAlzc+66PEJI81XvSd2SJUuwfft26WNvb28AwIULF9C7d28IBAKsX78ec+fOhVgshrOzMwIDAzFz5kzpNoaGhjhz5gxmzpyJzp07w9jYGHPnzsXcuXPrO3xC1C6zIBN3E+/idsJt3EnkErkXWS8UlnUzdUNnm87wsfZBZ5vO8Lbyhr5Iv4EjJg0tKYnrDfvXX0DFy5l1dIDhw7kZKfr1o6nFCGnueKyFTMsQFxcHe3t7xMbGws7OTt3hEKJQdmE27ibelSZvtxNu41nmM4VlXUxc5BI4Qy0asKylyM3lhjv56y/g3DlAMgqUhgYwcCCXyA0dSlOLkZatpR37ae5XQhqBbWHb8N2V7xCVEaXweUcjR3S17SpN4DpZd6Jr41qwr78GfvkFKCgoX2doyPV2/ewzwMJCbaERQtSIWuoIaQT6bO+DC9EXlD4v1BDCVt8WdgZ2sDWwhZ2+HewMZBcrPSto8DUaMGqiLubm3IDEimhpcVOKOTrKLpJ1lpbcsCiEtAQt7dhPLXWENAL7Ru7DyacnEZ8Tj7icOMTlxnG3OXFIzktGcVkxXmS9UHotHQBo8DRgpWcll+xVXGz0bWg2iWYgJITr3RodDbx8yd1GRwPx8UBhIddZ4skTxduKRFyCpyzxs7ampI+Qpopa6ghp5ErKSpCYlyhN8iov8bnxiM+JRxkrU2l/FroW5YlehRY/WwNb6X0dgU4914rUh+JiIC6uPMmrnPTFxZVfe6eMUAi0aiXbuldxsbbmrtsjpCloacd+SuoIaQbKxGVIyU9RmPBVfFxUptrcUcZaxjItfJJTv3YGdujZqif0hHr1XCNSH0pKuNa8iklfxcQvNpablaIqmprlSV/FxK9NG6BLl/qNn5CaamnHfkrqCGkhGGNIL0gvT/gUnOqNzY5Ffkl+lfvpad8TV6debaCoSUMqLeWSvoqtexWTvpgYrowy27cDEyc2TKyEqKKlHfvpmjpCWggejwczHTOY6ZjB09wTzzKe4XHaY+gIdCBmYuQU5VTb0cJU2xSDXAY1UMSkoWlqll9v9+ab3LqSEuDePeDaNeDKFeDECeDVK/ltnZ2B15gdkhBSByipI6QZyyzIxOO0x+VLOnf7LOOZ0mvw+Dw+nI2d4WHmAQ9TD+7WzAPuZu4w06m/6QFJ45CRwc0je+0acP06cOuWfBKnoQF4eQE9ewI9enCLvb1awiWEVEBJHSFNXJm4DDHZMQqTt5T8FKXb6Qn1pAlbxeTNxcQFIk2R0u1I88EYEBXFJW+SJC48XL6ckRHg61uexHXtSoMaE9IYUVJHSBNUXFaMBWcX4Ozzs4jKiEJhaaHSsnYGdnKJm4eZB2z0bWgu2BYmJQW4c4ebSuz2bS6JUzTenasrl7xJkrg2bWiYE0KaAkrqCGmCkvOS8cuNX8Ag28+pt2Nv9GrVC+6m7vAw84CbqRvN/dpCpafLJnB37nAdHSoTiYDOncuTOF9fmpGCkKaKkjpCmiB7Q3scG3sM60LW4eTTk9Lr467GXIWeUA/tLNrB09wT2gJtNUdKGkJmJnD3bnkCd/s211tVEXd3wMeHW3x9gU6duMSOENL00ZAmhDRxKfkp2PNwD3be34nbCbel6w1EBhjpORITOkxAL4de4PPo/FlzkJ3NJXAVW+GePVNc1sWFa4Xr3JlL4ry9uTliCWkpWtqxn5I6QpqRiNQI7Lq/C7se7EJMdvm5tlaGrTCu/ThM6jgJ7mbuaoyQ1ERZGdeBQXL69PZtIDJScVlnZy5xkyRxnTpxHRwIacla2rGfkjpCmiExE+PKyyvYeX8n9ofvR05RDgBAwBfg7sd30c6CBhRrCj7+GPjzT/n1Dg7lrW+SBM7UtOHjI6Sxa2nHfrqmjpBmiM/jw8/RDy4mLkgvSMehx4cAAKY6pjDWMlZvcERleXncraMjMH16+bVw5uZqDYsQ0kjRRTaENEMlZSVYfX01PNZ54NDjQ+Dz+Pis62eImBkBWwNbdYdHVDRiBHdbVgYEBACDBlFCRwhRjlrqCGlmLkZfxMzjMxGeyo0i62vni/WD18PLyku9gZEae+stQF8fiI3lZnno0UPdERFCGjNqqSOkGSgsLcTxqOMYfWA0/Lf7Izw1HGY6ZtgyZAuuTr1KCV0TpaUFDB3K3V+7FsjPV288hJDGjVrqCGmiMgsycTzqOA49OYSTT08ir5i7AIsHHmZ0noFv+3wLE20TNUdJXtf48cCuXcCePcDJk8BHHwGzZtFcq4QQeZTUEdKExGTH4N/H/+LfJ//i0stLKBWXSp+z0bfBUPehmN5pOjpZd1JjlKQuDRwIbNwIrFrFjUf3ww/AmjXc9XazZwPdu6s7QkJIY0FDmhDSyEWlR2Hvo70IighCaFKozHNtzdtimMcwDHUfCh8bHxpguBkrKwOOHQN++QW4cKF8fbduwB9/cAMLE0JktbRjP7XUEdIIxWbHYu+jvdjzcA/uJN6Rrufz+Ohp3xND3YdiqMdQuJi4qDFK0pA0NIAhQ7jl3j1gzBjg8WPg5k0u0du+Xd0REkLUjZI6QhqJ5LxkHAg/gD2P9uBqzFXpeg2eBvo598OotqPwrtu7MNelMS1asowM7lTs48fcYx8fYOlS9cZECGkcKKkjRI0yCzIRFBGEPY/24PyL8xAzsfS5Nx3exJi2Y/C+5/uw0LVQY5SksThxApg2DUhM5FruFi0CFi4EBAJ1R0YIaQwoqSOkgeUV5+Hwk8PY83APTj49iRJxifS5LjZdMKbdGIxqOwp2Bs3/+g+imrw8YN688inDPDyAHTuALl3UGxchpHGhpI6QBlRQUoD2f7RHdFa0dJ0GTwMf+XyEeb7z0NqktfqCI41SdDQ3k8STJ9zj2bOB774DtLXVGRUhpDGq965yK1asQI8ePaCjowMjIyO557dt2wYej6dwSUlJAQBER0crfP7kyZP1HT4hdUqDrwE9oZ7MujJWhj9u/4E3t72J9/a+h1VXV+HCiwvILcpVU5SksXj4EOjZk0vo7OyA8+eBn3+mhI4Qoli9t9QVFxdj5MiR8PX1xebNm+WeHz16NAYNGiSzbvLkySgsLISFhex1RGfPnkXbtm2lj01MaGBV0rQINYS4N+MeHqc9xs24m7gZzy0Pkh8gITcBBx8fxMHHBwFwPV09zT3RzbYbt9h1Q1vzttDga6i5FqQhXLsGvPMOkJUFtG0LnDoF2NK0vYSQKtR7Urd8+XIAXIucItra2tCu8G9namoqzp8/rzABNDU1hZWVVb3ESUhDkSRrnuaemOI9BQDwquQV7iTckSZ5N+NuIjYnFg9THuJhykNsDuW+D7oCXXS26SxN8rrZdoOtAR3pm5tjx4CRI4GCAsDXFzh6FKD/YQkh1Wl019Tt2LEDOjo6GDFihNxzQ4YMQWFhIVxdXTFnzhyFZQhpinQEOujl0Au9HHpJ1yXmJkoTvJvxNxGSEIK84jxcenkJl15ekpaz1bdFV9uu0g4WpGnbs4ebGqysDHj7bWD/fkBHR91REUKagkaX1G3ZsgVjx46Vab3T09PDTz/9hJ49e4LP5+Pw4cMYPXo0tm/fjvHjxyvcT1FREYqKiqSPc3Pp+iTStFjrW2OYxzAM8xgGACgTlyEiLQK34m9JE70HKQ8QnxuPg48P4t8n/6KvU1+Y6piqN3DyWpYu5RK6QYOAQ4douBJCiOpq1VFi2bJlSjs3SJbbt2/XeL/BwcEIDw/HtGnTZNabmZlhzpw56Nq1Kzp37ozAwEB8+umn+OGHH5Tua+XKlTA0NJQunp6eNY6HkMZEg6+BdhbtMNV7Kv737v8QNiMMOQtycGnyJTgYOkDMxLgZf1PdYZLXNOq/xtaICKCkpOqyhBBSUa2SulmzZiEiIqLKpV27djXe76ZNm+Dl5QUfH59qy3bv3h1RUVFKnw8ICEB2drZ0CQ8Pr3E8hDR2ukJdvOnwJno79gYA3Ii7od6AyGsLCABatQJevgS+/17d0RBCmpJanX41MzODmZlZnQaSl5eHffv2YeXKlSqVDw0NhbW1tdLnRSIRRCKR9HFOTs5rx0hIY9Xdrju239tOSV0zoKMDrFnDdZT4/ntg0iTA2VndURFCmoJ6v6YuJiYGGRkZiImJQVlZGcLCwgAALi4u0NMrH69r7969KC0txbhx4+T2sX37dggEAnh7e4PP5+PIkSP47bff8D39G0sIXmS+QGhiKADgZvxNMMbA4/HUHBV5He+/D3TuDNy+zV1jt3OnuiMihDQF9Z7ULVmyBNu3b5c+9vb2BgBcuHABvXv3lq7fvHkz3nvvPRgbGyvcz7fffouXL19CQ0MDbm5u2LJli9JOEoQ0d4WlhTgYcRCbQzfj3Itz0vVORk6U0DVxhYXAl19yCR0AaDa67myEkMaKxxhj6g6iIcTFxcHe3h6xsbGws6M5NUnTdC/pHjbd3YS/HvyFzMJM6fp+zv0wzXsahnkMg5amlhojJK8jKgoYPRoI5RpeMX8+sGIFIBSqNy5CmqqWduyn/wEJaeSyCrOw+8FubA7djDuJd6Tr7Q3sMcVrCqZ4T4GjkaP6AiR14u+/gY8/BvLyAFNTYMcObpw6QghRFSV1hDRSMdkxWHh+IQ6EH0BhaSEAQMAXYJjHMEzznoZ+zv1oyrBmoLQUmDEDkEyi8+abXIJHU4IRQmqqVkOaEELq38LzC7Hr/i5pQgcAHmYesNKzQmxOLG4n3EZ+cb4aIyR14cyZ8oQOAPT0uGnBYmPVFxMhpGmiljpCGqn5vvNRXFaM+8n3EZkeCTET40HKAzxIeSAtwwMPzsbO6GDZAe0t2qODZQd0sOwAZ2NnasVrInr1Aj77DPjnHyAhATh+nFsAoEMH7hTs4MFA9+7UaYIQUjXqKEFIE1BQUoCItAjcT76PB8kPcD+Fu03OT1ZYXltTG+0s2kkTvfaW3K2ZTt2OL0nqDmPAvXtcQnfsGHDjBiAWlz9vbAwMHMgleYMGAebm6ouVkKaipR37KakjpAlLyU/hkrzk+3iQwt0+Sn0kc8q2Ims9ay7BsyhP9NqYtYFIU6SwPFGf9HTg1CkuyTtxAsjIKH+OxwO6dStvxfPyAvh0MQ0hclrasZ+SOkKamTJxGZ5mPJUmeZLb55nPFZbX4Gng/9u796Ao6/0P4G+Ui8hxVxQV8QZeghBSwlHJmR/iIDBHs6E5EWEohY5NOWLaBaoZoDllTjpaJt7GKJtETo7OVJ4m8DoYmwgukxtxUITSAEHjYlqwLt/fH3t2jxvrZZd9nmWffb9mdph99rvL5z0L7Ifvc/nmz8/Hm//3psyV0oMyGICKCuMM3pEjwH+v4W42ZYrxOLywMKeURzRgudtnP4/QIFKYwYMGIzQgFKEBofhH+D/M229038CPbT+aZ/aONhxF7bVaGIQBujadEyum+xk8GIiJMd7++U/g11+Ns3f//rfxRIv6euOM3fffc7cskTvjTB2RG7pw/QJiP4lF8+/NiAqMwrFlx+Dva301FxrY2tqMJ1FcumRs+o4fB4bw+tNEANzvs59HYRC5mYb2BizYtwDNvzcjcnQkStJL2NC5sFGjjLtkhw8HNBogI8PyBAsich9s6ojchN6gR9nPZViwbwGudF3BwwEP4+iyozwjVgHCwoBDh4y7aYuLgbfecnZFROQMPKaOSKH0Bj0qmypxsvEkTv58Eqd/OY1b+lsAgGkjpuHYsmMY7TfayVWSPXp7jevEnjtnXCfW9NVgMD6+YYNx3dgRI5xbJxHJi00dkUL8tYn77pfvcFNvueLESN+RWDhlId5f+D7GDhvrpErJFj09QE2NZfNWXQ3ctLKYiKcnMH268VIn/tyjTuR22NQRuSi9QY+q5ipjE9donImz1sTND55vvoWPCscgDx51MVDdugX88IPlDJxOZ2zs/srXF5gxA4iKAh591Pg1IgLw4SUHidwWmzoiF/GgTVxscCzmTzI2cdNHT2cTN0C1txtn3EwNnFYL1NZaP8lBrf5f42b6+tBDXDaMiCzxTwKREx3+6TCe/NeTd31c5aOC/xDjfrRrt66xiXMRJSXGJb0c4dIlIDjYuIoEEdG9sKkjcqJv67+95+Nd3V3o6u4y3x/hO8K4K5VN3IB24oTjXiskxHGvRUTKxqaOyIl2Lt6J5LBk1LfXo1f0Yv/5/ejq7sLfp/0dwcODERUYBc9Bxl/Tv3n/DaEBoWziXMCGDUB8PPCf/1h//KefgMuXAZXKuPvV5MkngcDA/91PSZG2TiJSFq4oQURERIrkbp/9/JefiIiISAHY1BEREREpAJs6IiIiIgVgU0dERESkAGzqiIiIiBSATR0RERGRArCpIyIiIlIANnVERERECiBpU9fY2IjMzEyEhITA19cXU6ZMQW5uLnp6eizG/fLLL3j88cfh5+eHgIAArFmzps+Y8+fPIzY2Fr6+vhg3bhzefvttuMl1k4mIiIjuS9Jlwmpra9Hb24tdu3Zh6tSp0Ol0WLlyJW7evIlNmzYBAAwGAxYtWoRRo0bh9OnTuH79OpYvXw4hBLZt2wYA6OrqwsKFCxEXF4ezZ8+irq4OGRkZ8PPzw/r166WMQEREROQSZF8m7P3338eOHTtw6dIlAMA333yDxYsX4/LlywgKCgIAHDhwABkZGWhtbYVKpcKOHTuQk5ODq1evwsfHBwDw3nvvYdu2bbhy5Qo8PDzu+33dbakQIiIid+dun/2SztRZ09nZiREjRpjvazQaREREmBs6AEhMTER3dzeqqqoQFxcHjUaD2NhYc0NnGpOTk4PGxkaEhIT0+T7d3d3o7u62+L4A0NzcLEUsIiIiGmBMn/m9vb1OrkQesjZ19fX12LZtGzZv3mze1tLSgjFjxliM8/f3h7e3N1paWsxjgoODLcaYntPS0mK1qduwYQPy8/P7bJ89e3Z/YxAREZELuXz5MiZOnOjsMiRnV1OXl5dntWG609mzZzFr1izz/aamJiQlJeGpp57CihUrLMZa230qhLDY/tcxpr3Gd9v1mpOTg3Xr1pnv//bbbwgJCYFOp4Narb5n7Up048YNhIeHo6amBsOGDXN2ObJjfuZnfvfM787ZAebv7OxEREQEHn74YWeXIgu7mrrVq1cjNTX1nmPunFlrampCXFwcYmJisHv3botxgYGBOHPmjMW29vZ26PV682xcYGCgedbOpLW1FQD6zPKZ+Pj4WOyuNZkwYQJUKtU9a1eirq4uAMC4ceOYn/mdXI38mN9987tzdoD5TZk9PWU/2swp7EoZEBCAgICABxr766+/Ii4uDtHR0SgsLMSgQZZXUYmJicE777yD5uZmjB07FgBQUlICHx8fREdHm8e88cYb6Onpgbe3t3lMUFBQn92yRERERO5I0uvUNTU1Yf78+ZgwYQI2bdqEtrY2tLS0WMy6JSQkIDw8HOnp6dBqtTh27BheeeUVrFy50txhp6WlwcfHBxkZGdDpdDh8+DDeffddrFu37oHOfCUiIiJSOknnI0tKSnDx4kVcvHixz6nEpmPiBg8ejCNHjuDFF1/EvHnz4Ovri7S0NPN17ABArVajtLQUL730EmbNmgV/f3+sW7fO4pi5+/Hx8UFubq7VXbLugPmZn/mZ3x3zu3N2gPndLb/s16kjIiIiIsfj2q9ERERECsCmjoiIiEgB2NQRERERKQCbOiIiIiIFUHRT197ejvT0dKjVaqjVaqSnp6Ojo+OBn79q1Sp4eHhg69atktUoJXvy5+XlISwsDH5+fvD390d8fHyfi0O7Clvz6/V6vP7664iMjISfnx+CgoKwbNkyNDU1yVe0g9jz3h86dAiJiYkICAiAh4cHqqurZanVEQoKChASEoIhQ4YgOjoaZWVl9xx/6tQpREdHY8iQIZg8eTJ27twpU6XSsCV/c3Mz0tLSEBoaikGDBmHt2rXyFSoRW/IfOnQICxcuxKhRo6BSqRATE4Nvv/1Wxmodz5b8p0+fxrx58zBy5Ej4+voiLCwMW7ZskbFax7P199/ku+++g6enJ2bOnCltgXISCpaUlCQiIiJEeXm5KC8vFxEREWLx4sUP9NzDhw+LGTNmiKCgILFlyxZpC5WIPfk///xzUVpaKurr64VOpxOZmZlCpVKJ1tZWmap2HFvzd3R0iPj4eFFcXCxqa2uFRqMRc+bMEdHR0TJW7Rj2vPf79u0T+fn5Ys+ePQKA0Gq18hTbTwcOHBBeXl5iz549oqamRmRlZQk/Pz/x888/Wx1/6dIlMXToUJGVlSVqamrEnj17hJeXlzh48KDMlTuGrfkbGhrEmjVrxKeffipmzpwpsrKy5C3YwWzNn5WVJTZu3CgqKipEXV2dyMnJEV5eXuLcuXMyV+4YtuY/d+6c2L9/v9DpdKKhoUF89tlnYujQoWLXrl0yV+4YtuY36ejoEJMnTxYJCQlixowZ8hQrA8U2dTU1NQKA+P77783bNBqNACBqa2vv+dwrV66IcePGCZ1OJyZNmuSSTV1/8t+ps7NTABBHjx6VokzJOCp/RUWFAHDfPxADSX+zNzQ0uFRTN3v2bPHCCy9YbAsLCxPZ2dlWx7/22msiLCzMYtuqVavE3LlzJatRSrbmv1NsbKzLN3X9yW8SHh4u8vPzHV2aLByRPzk5WTz77LOOLk0W9uZ/+umnxVtvvSVyc3MV1dQpdverRqOBWq3GnDlzzNvmzp0LtVqN8vLyuz6vt7cX6enpePXVVzF9+nQ5SpWEvfnv1NPTg927d0OtVmPGjBlSlSoJR+QHjItBe3h4YPjw4RJUKQ1HZXcFPT09qKqqQkJCgsX2hISEu2bVaDR9xicmJqKyshJ6vV6yWqVgT34lcUT+3t5e3LhxAyNGjJCiREk5Ir9Wq0V5eTliY2OlKFFS9uYvLCxEfX09cnNzpS5Rdopd4balpQWjR4/us3306NEWy5T91caNG+Hp6Yk1a9ZIWZ7k7M0PAF9//TVSU1Nx69YtjB07FqWlpQ+81u9A0Z/8Jn/++Seys7ORlpbmUgthOyK7q7h27RoMBgPGjBljsX3MmDF3zdrS0mJ1/O3bt3Ht2jXzGtSuwJ78SuKI/Js3b8bNmzeRkpIiRYmS6k/+8ePHo62tDbdv30ZeXh5WrFghZamSsCf/hQsXkJ2djbKyMnh6Kq8FcrmZury8PHh4eNzzVllZCQBW14UVQtx1vdiqqip88MEH+OSTTwbsmrJS5jeJi4tDdXU1ysvLkZSUhJSUFLS2tkqSx1Zy5AeMJ02kpqait7cXBQUFDs9hD7myu6K/5rpfVmvjrW13FbbmVxp78xcVFSEvLw/FxcVW/xFyFfbkLysrQ2VlJXbu3ImtW7eiqKhIyhIl9aD5DQYD0tLSkJ+fj4ceekiu8mTlcm3q6tWrkZqaes8xwcHB+OGHH3D16tU+j7W1tfXp6k3KysrQ2tqKiRMnmrcZDAasX78eW7duRWNjY79qdwQp85v4+flh6tSpmDp1KubOnYtp06Zh7969yMnJ6VftjiBHfr1ej5SUFDQ0NOD48eMDZpZOjuyuJiAgAIMHD+7zX3lra+tdswYGBlod7+npiZEjR0pWqxTsya8k/clfXFyMzMxMfPHFF4iPj5eyTMn0J39ISAgAIDIyElevXkVeXh6eeeYZyWqVgq35b9y4gcrKSmi1WqxevRqAcfe7EAKenp4oKSnBggULZKldKi7X1AUEBDzQrsCYmBh0dnaioqICs2fPBgCcOXMGnZ2deOyxx6w+Jz09vc8vd2JiItLT0/Hcc8/1v3gHkDL/3Qgh0N3dbVe9jiZ1flNDd+HCBZw4cWJAfcg7470f6Ly9vREdHY3S0lIkJyebt5eWluKJJ56w+pyYmBh89dVXFttKSkowa9YseHl5SVqvo9mTX0nszV9UVITnn38eRUVFWLRokRylSsJR7/9A+htvC1vzq1QqnD9/3mJbQUEBjh8/joMHD5obXZfmlNMzZJKUlCQeeeQRodFohEajEZGRkX0u6xAaGioOHTp019dw1bNfhbA9/++//y5ycnKERqMRjY2NoqqqSmRmZgofHx+h0+mcEaFfbM2v1+vFkiVLxPjx40V1dbVobm4237q7u50RwW72/Oxfv35daLVaceTIEQFAHDhwQGi1WtHc3Cx3+TYxXdJg7969oqamRqxdu1b4+fmJxsZGIYQQ2dnZIj093TzedEmTl19+WdTU1Ii9e/cq4pImD5pfCCG0Wq3QarUiOjpapKWlCa1WK3788UdnlN9vtubfv3+/8PT0FNu3b7f4He/o6HBWhH6xNf9HH30kvvzyS1FXVyfq6urExx9/LFQqlXjzzTedFaFf7Pn5v5PSzn5VdFN3/fp1sXTpUjFs2DAxbNgwsXTpUtHe3m4xBoAoLCy862u4clNna/4//vhDJCcni6CgIOHt7S3Gjh0rlixZIioqKuQv3gFszW+6lIe124kTJ2Svvz/s+dkvLCy0mj03N1fW2u2xfft2MWnSJOHt7S0effRRcerUKfNjy5cvF7GxsRbjT548KaKiooS3t7cIDg4WO3bskLlix7I1v7X3edKkSfIW7UC25I+NjbWaf/ny5fIX7iC25P/www/F9OnTxdChQ4VKpRJRUVGioKBAGAwGJ1TuGLb+/N9JaU2dhxD/PUKYiIiIiFyWy539SkRERER9sakjIiIiUgA2dUREREQKwKaOiIiISAHY1BEREREpAJs6IiIiIgVgU0dERESkAGzqiIiIiBSATR0RERGRArCpIyIiIlIANnVERERECsCmjoiIiEgB/h/91iICZO/jKAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 600x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "how_many_current_charts = 1\n",
     "first_current_chart = 200\n",
@@ -507,41 +474,49 @@
    "id": "3f63d009-d81b-4976-8407-1d7301502ee9",
    "metadata": {},
    "source": [
-    "## From individual profiles to bundle charts\n",
+    "## Another visualization: bundle charts\n",
     "\n",
     "\n",
-    "The charts above demonstrate the majority of available sensors in terms of 'single profile' depth charts.\n",
-    "An evident next step would be to bundle together a series of consecutive profiles to get a sense of\n",
-    "variability.\n"
+    "The charts above show single profiles. Next we look at some 'bundle charts' showing multiple profiles for a given sensor.\n",
+    "This introduces the element of variability across a stretch of time, again recalling that nine profiles reflect a\n",
+    "single day. \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "663a8767-a802-4ec4-8b98-43de19ff1bf5",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'temperature'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ShowStaticBundles(d, profiles)\n",
+      "File \u001b[0;32m~/oceanography/book/chapters/charts.py:270\u001b[0m, in \u001b[0;36mShowStaticBundles\u001b[0;34m(d, profiles)\u001b[0m\n\u001b[1;32m    268\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''creates bundle charts for Jan 2022, Oregon Slope Base'''\u001b[39;00m\n\u001b[1;32m    269\u001b[0m BundleChart(profiles, dt64(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m), dt64(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-02-01\u001b[39m\u001b[38;5;124m'\u001b[39m), td64(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m'\u001b[39m), td64(\u001b[38;5;241m24\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m'\u001b[39m), \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m6\u001b[39m, d[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdo\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDissolved Oxygen\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 270\u001b[0m BundleChart(profiles, dt64(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m), dt64(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-02-01\u001b[39m\u001b[38;5;124m'\u001b[39m), td64(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m'\u001b[39m), td64(\u001b[38;5;241m24\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m'\u001b[39m), \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m6\u001b[39m, d[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtemperature\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTemperature\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    271\u001b[0m BundleChart(profiles, dt64(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m), dt64(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-02-01\u001b[39m\u001b[38;5;124m'\u001b[39m), td64(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m'\u001b[39m), td64(\u001b[38;5;241m24\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m'\u001b[39m), \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m6\u001b[39m, d[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdensity\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDensity\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    272\u001b[0m BundleChart(profiles, dt64(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m), dt64(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-02-01\u001b[39m\u001b[38;5;124m'\u001b[39m), td64(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m'\u001b[39m), td64(\u001b[38;5;241m24\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m'\u001b[39m), \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m6\u001b[39m, d[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msalinity\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSalinity\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'temperature'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ShowStaticBundles(d, profiles)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4a46ac1-3a93-4142-9ac9-07e526ef117d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# This code is busted: I tried to import all the deets to chart.py but there is a problem with passing the data dictionary d\n",
-    "# BundleInteractor(d, profiles, False)\n",
-    "\n",
-    "# The following 3 cells are from the in-notebook sequence that formerly worked"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/book/chapters/shallowprofiler.py b/book/chapters/shallowprofiler.py
index 7c286c8..8e71108 100644
--- a/book/chapters/shallowprofiler.py
+++ b/book/chapters/shallowprofiler.py
@@ -64,7 +64,7 @@ def GenerateTimeWindowIndices(profiles, date0, date1, time0, time1):
 
 
 sensors = [
-['conductivity', 'ctd'], ['density', 'ctd'], ['pressure', 'ctd'], ['salinity', 'ctd'], ['temperature', 'ctd'],
+['conductivity', 'ctd'], ['density', 'ctd'], ['pressure', 'ctd'], ['salinity', 'ctd'], ['temp', 'ctd'],
 ['chlora', 'fluor'], ['backscatter', 'fluor'], ['fdom', 'fluor'],
 ['spkir412nm', 'spkir'], ['spkir443nm', 'spkir'], ['spkir490nm', 'spkir'], ['spkir510nm', 'spkir'], ['spkir555nm', 'spkir'], ['spkir620nm', 'spkir'], ['spkir683nm', 'spkir'],
 ['nitrate', 'nitrate'],
@@ -75,7 +75,7 @@ def GenerateTimeWindowIndices(profiles, date0, date1, time0, time1):
 ['up', 'vel'], ['east', 'vel'], ['north', 'vel']]
 
 ranges = {
-'conductivity':(32.0,34.5),'density':(1024, 1028),'pressure':(0.,200.),'salinity':(32, 34),'temperature':(7, 11),
+'conductivity':(3.2,3.7),'density':(1024, 1028),'pressure':(0.,200.),'salinity':(32, 34),'temp':(7, 11),
 'chlora':(0.,1.5),'backscatter':(0.00,0.006),'fdom':(0.5,4.5),
 'spkir412nm':(0.0, 15.0), 'spkir443nm':(0.0, 15.0), 'spkir490nm':(0.0, 15.0), 'spkir510nm':(0.0, 15.0), 'spkir555nm':(0.0, 15.0), 'spkir620nm':(0.0, 15.0), 'spkir683nm':(0.0, 15.0),
 'nitrate':(0., 35.),
@@ -87,7 +87,7 @@ def GenerateTimeWindowIndices(profiles, date0, date1, time0, time1):
 }
 
 standard_deviations = {
-'conductivity':(0.1, 0.6),'density':(0., .3),'pressure':(0.,10.),'salinity':(.0, .4),'temperature':(.0, .7),
+'conductivity':(0.1, 0.6),'density':(0., .3),'pressure':(0.,10.),'salinity':(.0, .4),'temp':(.0, .7),
 'chlora':(0.0, 0.5),'backscatter':(0.0,0.003),'fdom':(0.0,0.7),
 'spkir412nm':(0.0, .5), 'spkir443nm':(0.0, .5), 'spkir490nm':(0.0, .5), 'spkir510nm':(0.0, .5), 'spkir555nm':(0.0, .5), 'spkir620nm':(0.0, .5), 'spkir683nm':(0.0, .5),
 'nitrate':(0., 4.),
@@ -99,7 +99,7 @@ def GenerateTimeWindowIndices(profiles, date0, date1, time0, time1):
 }
 
 colors = {
-'conductivity':'xkcd:maroon','density':'xkcd:brick red','pressure':'xkcd:eggplant','salinity':'cyan','temperature':'red',
+'conductivity':'xkcd:maroon','density':'xkcd:brick red','pressure':'xkcd:eggplant','salinity':'cyan','temp':'red',
 'chlora':'green','backscatter':'xkcd:blood orange','fdom':'xkcd:olive drab',
 'spkir412nm':'black', 'spkir443nm':'black', 'spkir490nm':'black', 'spkir510nm':'black', 
 'spkir555nm':'black', 'spkir620nm':'black', 'spkir683nm':'black',
@@ -113,7 +113,7 @@ def GenerateTimeWindowIndices(profiles, date0, date1, time0, time1):
 
 sensor_names = {
 'conductivity':'Conductivity','density':'Density (kg m-3)','pressure':'Pressure',
-'salinity':'Salinity','temperature':'Temperature (deg C)', 'temp':'Temperature (deg C)',
+'salinity':'Salinity','temp':'Temperature (deg C)',
 'chlora':'Chlorophyll-A','backscatter':'Particulate Backscatter','fdom':'Fluorescent DOM',
 'spkir412nm':'Spectral Irradiance 412nm',
 'spkir443nm':'Spectral Irradiance 443nm',
diff --git a/environment.yml b/environment.yml
index de8a0fa..a245f1e 100644
--- a/environment.yml
+++ b/environment.yml
@@ -12,6 +12,7 @@ dependencies:
   - xarray
   - ipywidgets
   - netcdf4
+  - s3fs