From 763684d1fddc0e2bc25295c053349d555362a6fe Mon Sep 17 00:00:00 2001
From: robfatland <rob5@uw.edu>
Date: Sun, 30 Jun 2024 16:38:04 -0700
Subject: [PATCH] sigh netcdf4

---
 book/chapters/data2.ipynb | 3600 +------------------------------------
 1 file changed, 68 insertions(+), 3532 deletions(-)

diff --git a/book/chapters/data2.ipynb b/book/chapters/data2.ipynb
index c478625..20f5df7 100755
--- a/book/chapters/data2.ipynb
+++ b/book/chapters/data2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "id": "fc03490e-6875-4e62-af5c-e5978d95dc4e",
    "metadata": {
     "tags": []
@@ -127,18 +127,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n",
-      "ooi-data/RS01SBPS-SF01A-2D-PHSENA101-streamed-phsen_data_record\n",
-      "ooi-data/RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record\n",
-      "ooi-data/RS01SBPS-SF01A-3B-OPTAAD101-streamed-optaa_sample\n",
-      "ooi-data/RS01SBPS-SF01A-3C-PARADA101-streamed-parad_sa_sample\n",
-      "ooi-data/RS01SBPS-SF01A-3D-SPKIRA101-streamed-spkir_data_record\n",
-      "ooi-data/RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_dark_sample\n",
-      "ooi-data/RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample\n",
-      "ooi-data/RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data\n",
-      "ooi-data/RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record\n",
-      "\n",
-      "ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n",
+      "OSB CTD stream: ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n",
       "\n"
      ]
     }
@@ -146,10 +135,9 @@
    "source": [
     "osb_profiler_streams = [sname for sname in streamlist if 'SF01A' in sname]\n",
     "for s in osb_profiler_streams: \n",
-    "    print(s)\n",
     "    if 'ctdpf' in s: osb_sp_ctd = s\n",
-    "print()\n",
-    "print(osb_sp_ctd)\n",
+    "\n",
+    "print('OSB CTD stream:', osb_sp_ctd)\n",
     "print()"
    ]
   },
@@ -162,3435 +150,62 @@
    },
    "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:                                            (time: 220634822)\n",
-       "Coordinates:\n",
-       "  * time                                               (time) datetime64[ns] ...\n",
-       "Data variables: (12/39)\n",
-       "    conductivity                                       (time) float64 dask.array&lt;chunksize=(12000000,), meta=np.ndarray&gt;\n",
-       "    corrected_dissolved_oxygen                         (time) float64 dask.array&lt;chunksize=(12000000,), meta=np.ndarray&gt;\n",
-       "    corrected_dissolved_oxygen_qartod_executed         (time) &lt;U2 dask.array&lt;chunksize=(12000000,), meta=np.ndarray&gt;\n",
-       "    corrected_dissolved_oxygen_qartod_results          (time) uint8 dask.array&lt;chunksize=(100000000,), meta=np.ndarray&gt;\n",
-       "    corrected_dissolved_oxygen_qc_executed             (time) uint8 dask.array&lt;chunksize=(100000000,), meta=np.ndarray&gt;\n",
-       "    corrected_dissolved_oxygen_qc_results              (time) uint8 dask.array&lt;chunksize=(100000000,), meta=np.ndarray&gt;\n",
-       "    ...                                                 ...\n",
-       "    sea_water_temperature                              (time) float64 dask.array&lt;chunksize=(12000000,), meta=np.ndarray&gt;\n",
-       "    sea_water_temperature_qartod_executed              (time) &lt;U2 dask.array&lt;chunksize=(12000000,), meta=np.ndarray&gt;\n",
-       "    sea_water_temperature_qartod_results               (time) uint8 dask.array&lt;chunksize=(100000000,), meta=np.ndarray&gt;\n",
-       "    sea_water_temperature_qc_executed                  (time) uint8 dask.array&lt;chunksize=(100000000,), meta=np.ndarray&gt;\n",
-       "    sea_water_temperature_qc_results                   (time) uint8 dask.array&lt;chunksize=(100000000,), meta=np.ndarray&gt;\n",
-       "    temperature                                        (time) float64 dask.array&lt;chunksize=(12000000,), meta=np.ndarray&gt;\n",
-       "Attributes: (12/62)\n",
-       "    AssetManagementRecordLastModified:  2024-06-27T13:16:21.544000\n",
-       "    AssetUniqueID:                      ATAPL-66662-00008\n",
-       "    Conventions:                        CF-1.6\n",
-       "    Description:                        CTD Profiler: CTDPF Series A\n",
-       "    FirmwareVersion:                    Not specified.\n",
-       "    Manufacturer:                       Sea-Bird Electronics\n",
-       "    ...                                 ...\n",
-       "    stream:                             ctdpf_sbe43_sample\n",
-       "    subsite:                            RS01SBPS\n",
-       "    summary:                            Dataset Generated by Stream Engine fr...\n",
-       "    time_coverage_end:                  2024-06-29T11:13:01.519435776\n",
-       "    time_coverage_start:                2014-10-06T22:05:23.269171200\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-d5a1f721-db74-41de-8d1b-70cbf07edccb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5a1f721-db74-41de-8d1b-70cbf07edccb' 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>: 220634822</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9842e148-7969-4fe7-8bcf-a2d50b601798' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9842e148-7969-4fe7-8bcf-a2d50b601798' 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'>2014-10-06T22:05:23.269171200 .....</div><input id='attrs-2b62e2e5-6a40-40af-bb08-90591a1a292f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b62e2e5-6a40-40af-bb08-90591a1a292f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9e73749-e602-4e56-b81f-5237adf9edf4' class='xr-var-data-in' type='checkbox'><label for='data-c9e73749-e602-4e56-b81f-5237adf9edf4' title='Show/Hide data 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;2014-10-06T22:05:23.269171200&#x27;, &#x27;2014-10-06T22:05:24.268649984&#x27;,\n",
-       "       &#x27;2014-10-06T22:05:25.268961792&#x27;, ..., &#x27;2024-06-29T11:12:59.519420928&#x27;,\n",
-       "       &#x27;2024-06-29T11:13:00.519323648&#x27;, &#x27;2024-06-29T11:13:01.519435776&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a785d8a-695d-4c32-97f9-267f058aecbb' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a785d8a-695d-4c32-97f9-267f058aecbb' class='xr-section-summary' >Data variables: <span>(39)</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>conductivity</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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-f7b0b826-b160-4979-b5fc-8e07532d25ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7b0b826-b160-4979-b5fc-8e07532d25ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c14633b5-e371-4a0e-b32a-d6f481772902' class='xr-var-data-in' type='checkbox'><label for='data-c14633b5-e371-4a0e-b32a-d6f481772902' title='Show/Hide data 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>Unprocessed conductivity data that are output directly from the sensor. Seawater conductivity refers to the ability of seawater to conduct electricity. The presence of ions in the seawater, such as salt, increases the electrical conducting ability of seawater. As such, conductivity can be used as a proxy for determining the quantity of salt in a sample of seawater.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CONDWAT_L0</dd><dt><span>long_name :</span></dt><dd>Unprocessed (L0) Seawater Conductivity</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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-       "                        <td colspan=\"2\"> 19 chunks in 2 graph layers </td>\n",
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-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_dissolved_oxygen</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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-e6d5bf92-26c1-4a61-b96d-8bfbc227b7d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6d5bf92-26c1-4a61-b96d-8bfbc227b7d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b963e30c-4b7e-433f-9dc7-2f3bec9ab4d2' class='xr-var-data-in' type='checkbox'><label for='data-b963e30c-4b7e-433f-9dc7-2f3bec9ab4d2' title='Show/Hide data 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>corrected_dissolved_oxygen_qartod_results corrected_dissolved_oxygen_qartod_executed corrected_dissolved_oxygen</dd><dt><span>comment :</span></dt><dd>Dissolved Oxygen (DO) Concentration from the Fast Response (Fastrep) DO Instrument is a measure of the concentration of gaseous oxygen mixed in seawater. This Instrument measures dissolved oxygen concentrations on shallow coastal profilers through rapid oxygen gradients. This data product is corrected for salinity, temperature, and depth from a collocated CTD.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Dissolved Oxygen - Pressure Temp Sal Corrected (CTD)</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>umol kg-1</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
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-       "                        <td> (12000000,) </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_dissolved_oxygen_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-d71cf25c-2dab-4f03-aa8d-3b4e30a4fa83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d71cf25c-2dab-4f03-aa8d-3b4e30a4fa83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff430439-7b9e-4c2f-92e4-066f9201aa59' class='xr-var-data-in' type='checkbox'><label for='data-ff430439-7b9e-4c2f-92e4-066f9201aa59' title='Show/Hide data 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 Oxygen - Pressure Temp Sal Corrected (CTD) 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>corrected_dissolved_oxygen status_flag</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
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-       "                        <td> (100000000,) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 graph layers </td>\n",
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-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_dissolved_oxygen_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-5136b208-7616-451a-90a1-86c39d5b395d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5136b208-7616-451a-90a1-86c39d5b395d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29e23789-faa5-44ee-9093-cd3a735010b5' class='xr-var-data-in' type='checkbox'><label for='data-29e23789-faa5-44ee-9093-cd3a735010b5' title='Show/Hide data 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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-       "                        <td> (100000000,) </td>\n",
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-       "                        <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>corrected_dissolved_oxygen_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-0de5351f-eee6-4e37-a2a8-8deb162dda3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0de5351f-eee6-4e37-a2a8-8deb162dda3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdbedeec-7ec2-4885-beda-791ea7abbdff' class='xr-var-data-in' type='checkbox'><label for='data-bdbedeec-7ec2-4885-beda-791ea7abbdff' title='Show/Hide data 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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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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-       "    </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=(25000000,), meta=np.ndarray&gt;</div><input id='attrs-8fb2c5e1-d065-4eba-a21a-09fc975ca3d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fb2c5e1-d065-4eba-a21a-09fc975ca3d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d104158-6e06-4c05-889a-095064309f10' class='xr-var-data-in' type='checkbox'><label for='data-0d104158-6e06-4c05-889a-095064309f10' title='Show/Hide data 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",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 841.66 MiB </td>\n",
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-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (25000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 9 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>do_fast_sample-corrected_dissolved_oxygen</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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-c02ffe28-c3f3-428e-8bc0-5394655d9f56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c02ffe28-c3f3-428e-8bc0-5394655d9f56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c8bd85c-db73-447d-a671-c968d09a1383' class='xr-var-data-in' type='checkbox'><label for='data-1c8bd85c-db73-447d-a671-c968d09a1383' title='Show/Hide data 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 Oxygen (DO) Concentration from the Fast Response (Fastrep) DO Instrument is a measure of the concentration of gaseous oxygen mixed in seawater. This Instrument measures dissolved oxygen concentrations on shallow coastal profilers through rapid oxygen gradients. This data product is corrected for salinity, temperature, and depth from a collocated CTD.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>DOCONCF_L2</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-DOFSTA102</dd><dt><span>long_name :</span></dt><dd>Dissolved Oxygen - Pressure Temp Sal Corrected (CTD)</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>moles_of_oxygen_per_unit_mass_in_sea_water</dd><dt><span>stream :</span></dt><dd>do_fast_sample</dd><dt><span>units :</span></dt><dd>umol kg-1</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <td> 1.64 GiB </td>\n",
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-       "                        <td> (12000000,) </td>\n",
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-       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
-       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
-       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
-       "  <line x1=\"117\" y1=\"0\" x2=\"117\" y2=\"25\" />\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>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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-58978332-4cda-48bf-b98b-e63c3e0c4ef0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58978332-4cda-48bf-b98b-e63c3e0c4ef0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25155459-bf69-43c9-9214-547f6f6edcaa' class='xr-var-data-in' type='checkbox'><label for='data-25155459-bf69-43c9-9214-547f6f6edcaa' title='Show/Hide data 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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 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=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
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-       "  <line x1=\"91\" y1=\"0\" x2=\"91\" y2=\"25\" />\n",
-       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
-       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
-       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
-       "  <line x1=\"117\" y1=\"0\" x2=\"117\" y2=\"25\" />\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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-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >220634822</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>ext_volt0</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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-baa0fe01-670b-46bc-a378-d3456ef00c54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-baa0fe01-670b-46bc-a378-d3456ef00c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77daa24a-3308-44cd-a1e7-fadf9f3b5999' class='xr-var-data-in' type='checkbox'><label for='data-77daa24a-3308-44cd-a1e7-fadf9f3b5999' title='Show/Hide data 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>SBE43 voltage reading</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>External Voltage Reading From Oxygen Sensor</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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 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=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n",
-       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
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-       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
-       "  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"25\" />\n",
-       "  <line x1=\"91\" y1=\"0\" x2=\"91\" y2=\"25\" />\n",
-       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
-       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
-       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
-       "  <line x1=\"117\" y1=\"0\" x2=\"117\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >220634822</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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-992e0111-102d-4599-abac-45f211298fa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-992e0111-102d-4599-abac-45f211298fa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70f8db81-f9da-46bf-a289-a05312dbb023' class='xr-var-data-in' type='checkbox'><label for='data-70f8db81-f9da-46bf-a289-a05312dbb023' title='Show/Hide data 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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 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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-       "  <line x1=\"91\" y1=\"0\" x2=\"91\" y2=\"25\" />\n",
-       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
-       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
-       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
-       "  <line x1=\"117\" y1=\"0\" x2=\"117\" y2=\"25\" />\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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-       "  <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>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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-cc7e8d9c-c9a1-4504-ba93-8f1673aca68e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc7e8d9c-c9a1-4504-ba93-8f1673aca68e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80728ba5-15ec-491d-b529-23f3c1488070' class='xr-var-data-in' type='checkbox'><label for='data-80728ba5-15ec-491d-b529-23f3c1488070' title='Show/Hide data 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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 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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-       "        </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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-cc051c4e-dac3-4d10-a3f7-1b208617916c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc051c4e-dac3-4d10-a3f7-1b208617916c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aee1f541-cb01-4853-b798-482278e535b4' class='xr-var-data-in' type='checkbox'><label for='data-aee1f541-cb01-4853-b798-482278e535b4' title='Show/Hide data 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",
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-       "                        <td> </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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 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",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-b4b421c7-f55c-47fa-935f-89c2e943fb7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4b421c7-f55c-47fa-935f-89c2e943fb7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-718f0762-821c-4c18-a63d-4f0db924f7b0' class='xr-var-data-in' type='checkbox'><label for='data-718f0762-821c-4c18-a63d-4f0db924f7b0' title='Show/Hide data 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>Unprocessed pressure data that are output directly from the sensor. 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_L0</dd><dt><span>long_name :</span></dt><dd>Unprocessed (L0) Seawater 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",
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-       "                        <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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 graph layers </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",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_temp</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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-98d3770a-f81e-419b-b94a-b5c922849ec6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98d3770a-f81e-419b-b94a-b5c922849ec6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c6504cb-ddb6-4836-8e17-b35ada83db9b' class='xr-var-data-in' type='checkbox'><label for='data-9c6504cb-ddb6-4836-8e17-b35ada83db9b' title='Show/Hide data 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>Unprocessed temperature from the pressure sensor (inside the housing, but isolated from housing and the electronics) used to calculate CTD output parameters.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Pressure Sensor 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",
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-       "                        <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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
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-       "                        <th> Dask graph </th>\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>sea_water_density</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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-698aae85-de48-49ad-af69-5ed17dcdc4b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-698aae85-de48-49ad-af69-5ed17dcdc4b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a84928f5-153f-4cf9-8271-0814e91e0371' class='xr-var-data-in' type='checkbox'><label for='data-a84928f5-153f-4cf9-8271-0814e91e0371' title='Show/Hide data 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>density</dd><dt><span>ancillary_variables :</span></dt><dd>sea_water_pressure sea_water_practical_salinity sea_water_temperature</dd><dt><span>comment :</span></dt><dd>Seawater Density is defined as mass per unit volume and is calculated from the conductivity, temperature and depth of a seawater sample using the TEOS-10 equation.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>DENSITY_L2</dd><dt><span>long_name :</span></dt><dd>Seawater Density</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>sea_water_density</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 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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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_density_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-71a22aeb-4294-4fac-85d9-c52121b91067' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71a22aeb-4294-4fac-85d9-c52121b91067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e2206e-665d-4398-a6f6-e9f2d15221c2' class='xr-var-data-in' type='checkbox'><label for='data-99e2206e-665d-4398-a6f6-e9f2d15221c2' title='Show/Hide data 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>density_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",
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-       "                        <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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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_density_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-6accbf9c-191f-43ea-a3d2-726c2feb7ffe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6accbf9c-191f-43ea-a3d2-726c2feb7ffe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-223e3c29-e67f-4aff-8ee8-366b7268bda3' class='xr-var-data-in' type='checkbox'><label for='data-223e3c29-e67f-4aff-8ee8-366b7268bda3' title='Show/Hide data 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>density_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",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
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-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 graph layers </td>\n",
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-       "                        <th> Data type </th>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_electrical_conductivity</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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-20a67895-aeaf-48b6-97ee-0079a84b7567' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20a67895-aeaf-48b6-97ee-0079a84b7567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbb9d7e3-64a5-464b-b1fc-826696ddebe9' class='xr-var-data-in' type='checkbox'><label for='data-fbb9d7e3-64a5-464b-b1fc-826696ddebe9' title='Show/Hide data 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>seawater_conductivity</dd><dt><span>ancillary_variables :</span></dt><dd>seawater_conductivity_qartod_results seawater_conductivity_qartod_executed sea_water_pressure sea_water_temperature conductivity</dd><dt><span>comment :</span></dt><dd>Seawater conductivity refers to the ability of seawater to conduct electricity. The presence of ions in the seawater, such as salt, increases the electrical conducting ability of seawater. As such, conductivity can be used as a proxy for determining the quantity of salt in a sample of seawater.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CONDWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Conductivity</dd><dt><span>precision :</span></dt><dd>6</dd><dt><span>standard_name :</span></dt><dd>sea_water_electrical_conductivity</dd><dt><span>units :</span></dt><dd>S m-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
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-       "                        <td> (12000000,) </td>\n",
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-       "                        <th> Data type </th>\n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 841.66 MiB </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_electrical_conductivity_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-1af63314-95e0-4bd3-81ae-3e9a251cba2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1af63314-95e0-4bd3-81ae-3e9a251cba2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45bb3f1b-ead7-4d33-935e-88897002516a' class='xr-var-data-in' type='checkbox'><label for='data-45bb3f1b-ead7-4d33-935e-88897002516a' title='Show/Hide data 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>seawater_conductivity_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",
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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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-a30bcead-166e-475b-983d-ceb18947e58b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a30bcead-166e-475b-983d-ceb18947e58b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-635424c4-4d96-42ba-a0de-7d1776b2f678' class='xr-var-data-in' type='checkbox'><label for='data-635424c4-4d96-42ba-a0de-7d1776b2f678' title='Show/Hide data 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>practical_salinity</dd><dt><span>ancillary_variables :</span></dt><dd>practical_salinity_qartod_results practical_salinity_qartod_executed sea_water_pressure sea_water_electrical_conductivity sea_water_temperature</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>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>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_practical_salinity_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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-5c9bb440-0e2b-4526-9f3b-99de9c94268a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c9bb440-0e2b-4526-9f3b-99de9c94268a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb051ead-bb40-4486-aa95-f8b6843d624e' class='xr-var-data-in' type='checkbox'><label for='data-bb051ead-bb40-4486-aa95-f8b6843d624e' title='Show/Hide data 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>practical_salinity_qartod_executed</dd><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>Practical Salinity 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>sea_water_practical_salinity 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",
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-       "                        <td> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
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-       "                        <td> (12000000,) </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_practical_salinity_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-5556077b-c11c-42a5-997b-98e0a2914de5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5556077b-c11c-42a5-997b-98e0a2914de5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa4f94eb-aaca-4c74-b173-5e53768140ca' class='xr-var-data-in' type='checkbox'><label for='data-fa4f94eb-aaca-4c74-b173-5e53768140ca' title='Show/Hide data 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>practical_salinity_qartod_results</dd><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>Practical Salinity 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>sea_water_practical_salinity status_flag</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
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-       "                    <tr>\n",
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-       "                        <th> Data type </th>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_practical_salinity_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-5d96475d-ea7e-45b1-9d6e-a1709fd58df7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d96475d-ea7e-45b1-9d6e-a1709fd58df7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e80969a-928f-48d8-84dd-39b2ade0e284' class='xr-var-data-in' type='checkbox'><label for='data-9e80969a-928f-48d8-84dd-39b2ade0e284' title='Show/Hide data 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>practical_salinity_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",
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-       "                <tbody>\n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (100000000,) </td>\n",
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-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_practical_salinity_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-c6762178-1dbc-4c1f-8c4a-43e6b5d2a546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6762178-1dbc-4c1f-8c4a-43e6b5d2a546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ab096fb-50fd-4a31-a82c-e82146865aef' class='xr-var-data-in' type='checkbox'><label for='data-0ab096fb-50fd-4a31-a82c-e82146865aef' title='Show/Hide data 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>practical_salinity_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",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (100000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 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_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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-7bda4058-03eb-4015-87ac-d3c55364bb6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bda4058-03eb-4015-87ac-d3c55364bb6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98eab673-d44a-4bf5-88de-9c2e3d236002' class='xr-var-data-in' type='checkbox'><label for='data-98eab673-d44a-4bf5-88de-9c2e3d236002' title='Show/Hide data 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>seawater_pressure</dd><dt><span>ancillary_variables :</span></dt><dd>seawater_pressure_qartod_results seawater_pressure_qartod_executed pressure pressure_temp</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>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",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 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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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_pressure_qartod_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25000000,), meta=np.ndarray&gt;</div><input id='attrs-be5010be-6f33-443a-931e-f2a7fa27c193' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be5010be-6f33-443a-931e-f2a7fa27c193' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c107382-3f30-4710-80a2-1d61234b722d' class='xr-var-data-in' type='checkbox'><label for='data-4c107382-3f30-4710-80a2-1d61234b722d' title='Show/Hide data 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>seawater_pressure_qartod_executed</dd><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>Seawater Pressure 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>sea_water_pressure status_flag</dd><dt><span>tests_executed :</span></dt><dd>gross_range_test</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",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 841.66 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
-       "                    </tr>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (25000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 9 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
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-       "            </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_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-a694e913-2ac3-4dd5-af42-7680ca45f7f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a694e913-2ac3-4dd5-af42-7680ca45f7f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99781fd9-00e3-4822-8a6f-120dfa8ff0af' class='xr-var-data-in' type='checkbox'><label for='data-99781fd9-00e3-4822-8a6f-120dfa8ff0af' title='Show/Hide data 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>seawater_pressure_qartod_results</dd><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>Seawater Pressure 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>sea_water_pressure status_flag</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (100000000,) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 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>\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_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-54e0db24-5553-40f4-9656-9c3861f297f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54e0db24-5553-40f4-9656-9c3861f297f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b23d858c-82a4-4c86-ae5b-ad497778742a' class='xr-var-data-in' type='checkbox'><label for='data-b23d858c-82a4-4c86-ae5b-ad497778742a' title='Show/Hide data 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>seawater_pressure_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",
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-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (100000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 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",
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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_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-92d1fd13-df3c-47e2-8ae9-6083875cf8c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92d1fd13-df3c-47e2-8ae9-6083875cf8c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-774b20b0-a473-4541-9ffb-ce34e980a7ca' class='xr-var-data-in' type='checkbox'><label for='data-774b20b0-a473-4541-9ffb-ce34e980a7ca' title='Show/Hide data 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>seawater_pressure_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",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (100000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 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>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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-31c119e7-eeb6-431e-a85a-045994005184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31c119e7-eeb6-431e-a85a-045994005184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a55f9d5a-7b72-4d1c-8ca0-8d3655f5e4d1' class='xr-var-data-in' type='checkbox'><label for='data-a55f9d5a-7b72-4d1c-8ca0-8d3655f5e4d1' title='Show/Hide data 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>seawater_temperature</dd><dt><span>ancillary_variables :</span></dt><dd>seawater_temperature_qartod_results seawater_temperature_qartod_executed 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>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>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",
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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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (12000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 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",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_temperature_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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-93707d80-c3e3-414d-9804-91b9dcaa80ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93707d80-c3e3-414d-9804-91b9dcaa80ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f33d081-5735-4bf8-99d2-b8d0383c7b62' class='xr-var-data-in' type='checkbox'><label for='data-9f33d081-5735-4bf8-99d2-b8d0383c7b62' title='Show/Hide data 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>seawater_temperature_qartod_executed</dd><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>Seawater Temperature 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>sea_water_temperature 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",
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-       "                    <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> 1.64 GiB </td>\n",
-       "                        <td> 91.55 MiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                        <td> (12000000,) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 19 chunks in 2 graph layers </td>\n",
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-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U2 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>sea_water_temperature_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-9e3a23ba-b527-407d-bafa-3d41cc6018e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e3a23ba-b527-407d-bafa-3d41cc6018e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8884e40-810d-4645-93ab-388b7ee98930' class='xr-var-data-in' type='checkbox'><label for='data-e8884e40-810d-4645-93ab-388b7ee98930' title='Show/Hide data 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>seawater_temperature_qartod_results</dd><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>Seawater Temperature 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>sea_water_temperature status_flag</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (100000000,) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 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",
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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_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-2e1ee77e-72ed-4efd-8f8f-0d1a8e28915d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e1ee77e-72ed-4efd-8f8f-0d1a8e28915d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b45bc35-d52c-412d-a8ce-cd65922ba5c6' class='xr-var-data-in' type='checkbox'><label for='data-9b45bc35-d52c-412d-a8ce-cd65922ba5c6' title='Show/Hide data 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>seawater_temperature_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",
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-       "                    <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> 210.41 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (220634822,) </td>\n",
-       "                        <td> (100000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 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>sea_water_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=(100000000,), meta=np.ndarray&gt;</div><input id='attrs-6da5d213-1909-4057-8767-2bae1d82309b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6da5d213-1909-4057-8767-2bae1d82309b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cb41485-c4fe-476d-b609-cabd9934c644' class='xr-var-data-in' type='checkbox'><label for='data-8cb41485-c4fe-476d-b609-cabd9934c644' title='Show/Hide data 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>seawater_temperature_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",
-       "        <td>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.41 MiB </td>\n",
-       "                        <td> 95.37 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (100000000,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3 chunks in 2 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>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=(12000000,), meta=np.ndarray&gt;</div><input id='attrs-2887784d-7d08-41cf-82e4-93713941e65c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2887784d-7d08-41cf-82e4-93713941e65c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b485761-756f-4d44-baba-956c61103065' class='xr-var-data-in' type='checkbox'><label for='data-6b485761-756f-4d44-baba-956c61103065' title='Show/Hide data 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>Unprocessed seawater temperature measurement in counts.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>TEMPWAT_L0</dd><dt><span>long_name :</span></dt><dd>Unprocessed (L0) Seawater 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",
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-       "               ...\n",
-       "               &#x27;2024-06-29 11:12:52.519265280&#x27;,\n",
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-       "               &#x27;2024-06-29 11:13:00.519323648&#x27;,\n",
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-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=220634822, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b764b483-a529-4fcf-b424-e88e5ef7584f' class='xr-section-summary-in' type='checkbox'  ><label for='section-b764b483-a529-4fcf-b424-e88e5ef7584f' 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>2024-06-27T13:16:21.544000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATAPL-66662-00008</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>CTD Profiler: CTDPF Series A</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Sea-Bird Electronics</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>SBE 16plus V2</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>16-50115</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>2024-06-29T11:13:07.703774</dd><dt><span>date_downloaded :</span></dt><dd>2024-06-29T11:13:03.794113</dd><dt><span>date_modified :</span></dt><dd>2024-06-29T11:13:07.703776</dd><dt><span>date_processed :</span></dt><dd>2024-06-29T11:18:11.043579</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>2024-06-29T11:13:07.703746 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_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>2A-CTDPFA102</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_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>ctdpf_sbe43_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>2024-06-29T11:13:01.519435776</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-06T22:05:23.269171200</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample</dd></dl></div></li></ul></div></div>"
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-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:                                            (time: 220634822)\n",
-       "Coordinates:\n",
-       "  * time                                               (time) datetime64[ns] ...\n",
-       "Data variables: (12/39)\n",
-       "    conductivity                                       (time) float64 dask.array<chunksize=(12000000,), meta=np.ndarray>\n",
-       "    corrected_dissolved_oxygen                         (time) float64 dask.array<chunksize=(12000000,), meta=np.ndarray>\n",
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-       "    corrected_dissolved_oxygen_qartod_results          (time) uint8 dask.array<chunksize=(100000000,), meta=np.ndarray>\n",
-       "    corrected_dissolved_oxygen_qc_executed             (time) uint8 dask.array<chunksize=(100000000,), meta=np.ndarray>\n",
-       "    corrected_dissolved_oxygen_qc_results              (time) uint8 dask.array<chunksize=(100000000,), meta=np.ndarray>\n",
-       "    ...                                                 ...\n",
-       "    sea_water_temperature                              (time) float64 dask.array<chunksize=(12000000,), meta=np.ndarray>\n",
-       "    sea_water_temperature_qartod_executed              (time) <U2 dask.array<chunksize=(12000000,), meta=np.ndarray>\n",
-       "    sea_water_temperature_qartod_results               (time) uint8 dask.array<chunksize=(100000000,), meta=np.ndarray>\n",
-       "    sea_water_temperature_qc_executed                  (time) uint8 dask.array<chunksize=(100000000,), meta=np.ndarray>\n",
-       "    sea_water_temperature_qc_results                   (time) uint8 dask.array<chunksize=(100000000,), meta=np.ndarray>\n",
-       "    temperature                                        (time) float64 dask.array<chunksize=(12000000,), meta=np.ndarray>\n",
-       "Attributes: (12/62)\n",
-       "    AssetManagementRecordLastModified:  2024-06-27T13:16:21.544000\n",
-       "    AssetUniqueID:                      ATAPL-66662-00008\n",
-       "    Conventions:                        CF-1.6\n",
-       "    Description:                        CTD Profiler: CTDPF Series A\n",
-       "    FirmwareVersion:                    Not specified.\n",
-       "    Manufacturer:                       Sea-Bird Electronics\n",
-       "    ...                                 ...\n",
-       "    stream:                             ctdpf_sbe43_sample\n",
-       "    subsite:                            RS01SBPS\n",
-       "    summary:                            Dataset Generated by Stream Engine fr...\n",
-       "    time_coverage_end:                  2024-06-29T11:13:01.519435776\n",
-       "    time_coverage_start:                2014-10-06T22:05:23.269171200\n",
-       "    title:                              Data produced by Stream Engine versio..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ValueError",
+     "evalue": "unrecognized engine zarr must be one of: ['netcdf4', 'scipy', 'store']",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m     ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_zarr(zarr_store, consolidated\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n\u001b[0;32m----> 7\u001b[0m siteData \u001b[38;5;241m=\u001b[39m loadData(osb_sp_ctd)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# siteData\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msite data is now lazy-loaded\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[3], line 4\u001b[0m, in \u001b[0;36mloadData\u001b[0;34m(stream_name)\u001b[0m\n\u001b[1;32m      2\u001b[0m fs \u001b[38;5;241m=\u001b[39m s3fs\u001b[38;5;241m.\u001b[39mS3FileSystem(anon\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m zarr_store \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_mapper(stream_name)\n\u001b[0;32m----> 4\u001b[0m ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_zarr(zarr_store, consolidated\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/zarr.py:867\u001b[0m, in \u001b[0;36mopen_zarr\u001b[0;34m(store, group, synchronizer, chunks, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, consolidated, overwrite_encoded_chunks, chunk_store, storage_options, decode_timedelta, use_cftime, zarr_version, chunked_array_type, from_array_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    853\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    854\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopen_zarr() got unexpected keyword arguments \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(kwargs\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m    855\u001b[0m     )\n\u001b[1;32m    857\u001b[0m backend_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    858\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msynchronizer\u001b[39m\u001b[38;5;124m\"\u001b[39m: synchronizer,\n\u001b[1;32m    859\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconsolidated\u001b[39m\u001b[38;5;124m\"\u001b[39m: consolidated,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    864\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzarr_version\u001b[39m\u001b[38;5;124m\"\u001b[39m: zarr_version,\n\u001b[1;32m    865\u001b[0m }\n\u001b[0;32m--> 867\u001b[0m ds \u001b[38;5;241m=\u001b[39m open_dataset(\n\u001b[1;32m    868\u001b[0m     filename_or_obj\u001b[38;5;241m=\u001b[39mstore,\n\u001b[1;32m    869\u001b[0m     group\u001b[38;5;241m=\u001b[39mgroup,\n\u001b[1;32m    870\u001b[0m     decode_cf\u001b[38;5;241m=\u001b[39mdecode_cf,\n\u001b[1;32m    871\u001b[0m     mask_and_scale\u001b[38;5;241m=\u001b[39mmask_and_scale,\n\u001b[1;32m    872\u001b[0m     decode_times\u001b[38;5;241m=\u001b[39mdecode_times,\n\u001b[1;32m    873\u001b[0m     concat_characters\u001b[38;5;241m=\u001b[39mconcat_characters,\n\u001b[1;32m    874\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    875\u001b[0m     engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzarr\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    876\u001b[0m     chunks\u001b[38;5;241m=\u001b[39mchunks,\n\u001b[1;32m    877\u001b[0m     drop_variables\u001b[38;5;241m=\u001b[39mdrop_variables,\n\u001b[1;32m    878\u001b[0m     chunked_array_type\u001b[38;5;241m=\u001b[39mchunked_array_type,\n\u001b[1;32m    879\u001b[0m     from_array_kwargs\u001b[38;5;241m=\u001b[39mfrom_array_kwargs,\n\u001b[1;32m    880\u001b[0m     backend_kwargs\u001b[38;5;241m=\u001b[39mbackend_kwargs,\n\u001b[1;32m    881\u001b[0m     decode_timedelta\u001b[38;5;241m=\u001b[39mdecode_timedelta,\n\u001b[1;32m    882\u001b[0m     use_cftime\u001b[38;5;241m=\u001b[39muse_cftime,\n\u001b[1;32m    883\u001b[0m     zarr_version\u001b[38;5;241m=\u001b[39mzarr_version,\n\u001b[1;32m    884\u001b[0m )\n\u001b[1;32m    885\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/api.py:552\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m from_array_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    550\u001b[0m     from_array_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 552\u001b[0m backend \u001b[38;5;241m=\u001b[39m plugins\u001b[38;5;241m.\u001b[39mget_backend(engine)\n\u001b[1;32m    554\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    555\u001b[0m     decode_cf,\n\u001b[1;32m    556\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    562\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    563\u001b[0m )\n\u001b[1;32m    565\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/plugins.py:205\u001b[0m, in \u001b[0;36mget_backend\u001b[0;34m(engine)\u001b[0m\n\u001b[1;32m    203\u001b[0m     engines \u001b[38;5;241m=\u001b[39m list_engines()\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m engine \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m engines:\n\u001b[0;32m--> 205\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    206\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munrecognized engine \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mengine\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be one of: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(engines)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    207\u001b[0m         )\n\u001b[1;32m    208\u001b[0m     backend \u001b[38;5;241m=\u001b[39m engines[engine]\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(engine, \u001b[38;5;28mtype\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(engine, BackendEntrypoint):\n",
+      "\u001b[0;31mValueError\u001b[0m: unrecognized engine zarr must be one of: ['netcdf4', 'scipy', 'store']"
+     ]
     }
    ],
    "source": [
-    "def loadData(zarrDir):\n",
+    "def loadData(stream_name):\n",
     "    fs = s3fs.S3FileSystem(anon=True)\n",
-    "    zarr_store = fs.get_mapper(zarrDir)\n",
+    "    zarr_store = fs.get_mapper(stream_name)\n",
     "    ds = xr.open_zarr(zarr_store, consolidated=True)\n",
     "    return ds\n",
     "\n",
     "siteData = loadData(osb_sp_ctd)\n",
-    "siteData"
+    "# siteData\n",
+    "print('site data is now lazy-loaded')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "6d82a0a7-c1a0-48dc-ae14-f32a41356e99",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
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-       "\n",
-       ".xr-var-list,\n",
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-       "\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.DataArray &#x27;time&#x27; ()&gt;\n",
-       "array(&#x27;2024-06-29T11:13:01.519435776&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
-       "Coordinates:\n",
-       "    time     datetime64[ns] 2024-06-29T11:13:01.519435776\n",
-       "Attributes:\n",
-       "    axis:           T\n",
-       "    long_name:      time\n",
-       "    standard_name:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-63b10a51-8717-4735-8485-130b7b87586f' class='xr-array-in' type='checkbox' checked><label for='section-63b10a51-8717-4735-8485-130b7b87586f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2024-06-29T11:13:01.519435776</span></div><div class='xr-array-data'><pre>array(&#x27;2024-06-29T11:13:01.519435776&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-35afb1c5-a003-4ac1-8f01-52f21c26f4c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35afb1c5-a003-4ac1-8f01-52f21c26f4c2' 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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-29T11:13:01.519435776</div><input id='attrs-bdc0562b-d892-4624-961b-e02994a731da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdc0562b-d892-4624-961b-e02994a731da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7075cf08-1581-42fe-a707-0f3fff3b4f07' class='xr-var-data-in' type='checkbox'><label for='data-7075cf08-1581-42fe-a707-0f3fff3b4f07' title='Show/Hide data 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;2024-06-29T11:13:01.519435776&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-12175e1f-6204-4e7d-9080-87773570da43' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-12175e1f-6204-4e7d-9080-87773570da43' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0ec47ced-92ba-41bd-b835-0a832d1fbb6d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ec47ced-92ba-41bd-b835-0a832d1fbb6d' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'time' ()>\n",
-       "array('2024-06-29T11:13:01.519435776', dtype='datetime64[ns]')\n",
-       "Coordinates:\n",
-       "    time     datetime64[ns] 2024-06-29T11:13:01.519435776\n",
-       "Attributes:\n",
-       "    axis:           T\n",
-       "    long_name:      time\n",
-       "    standard_name:  time"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "siteData.time[-1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "54497e55-6c75-468b-b96f-d87641df4d4f",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Jupyter Notebook running Python 3\n"
+     ]
+    }
+   ],
    "source": [
     "from shallowprofiler import *\n",
     "from charts import *\n",
@@ -3602,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "72fef540-903a-4b8a-ad7d-8536a422dcbe",
    "metadata": {
     "tags": []
@@ -3642,12 +257,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "f36780d5-3c04-442d-ac24-3b474266843b",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "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"
+    }
+   ],
    "source": [
     "# temperature and salinity\n",
     "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['salinity']], profile_list, \n",
@@ -3706,38 +340,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6eb876a5-4a96-4dcc-82cb-a78d2094a2fd",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<xarray.DataArray 'time' ()>\n",
-       " array('2022-01-01T00:00:00.097717760', dtype='datetime64[ns]')\n",
-       " Coordinates:\n",
-       "     time     datetime64[ns] 2022-01-01T00:00:00.097717760\n",
-       " Attributes:\n",
-       "     axis:           T\n",
-       "     long_name:      time\n",
-       "     standard_name:  time,\n",
-       " <xarray.DataArray 'time' ()>\n",
-       " array('2022-12-31T23:59:59.150689280', dtype='datetime64[ns]')\n",
-       " Coordinates:\n",
-       "     time     datetime64[ns] 2022-12-31T23:59:59.150689280\n",
-       " Attributes:\n",
-       "     axis:           T\n",
-       "     long_name:      time\n",
-       "     standard_name:  time)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "t0, t1 = '2022-01-01T00', '2022-12-31T23'\n",
     "ds = siteData.sel(time=slice(t0, t1))\n",
@@ -3746,27 +354,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "af8d207e-0f4c-4e14-8a2a-e85be9150ccc",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "looks ok\n",
-      "<xarray.Dataset>\n",
-      "Dimensions:      (time: 2678084)\n",
-      "Coordinates:\n",
-      "  * time         (time) datetime64[ns] 2022-01-01T00:00:00.097717760 ... 2022...\n",
-      "Data variables:\n",
-      "    depth        (time) float64 dask.array<chunksize=(2678084,), meta=np.ndarray>\n",
-      "    temperature  (time) float64 dask.array<chunksize=(2678084,), meta=np.ndarray>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def SPDataReduce(ds, t0, t1, keepers, rename):\n",
     "    \"\"\"\n",
@@ -3805,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "cf6b3ace-e719-40a7-8336-5180ee389197",
    "metadata": {
     "tags": []
@@ -3817,31 +410,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "879592d0-2abb-4e28-b5d5-c77e83ec0b9a",
    "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": [
     "# temperature: ascent versus descent\n",
     "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0, 1, 2], \n",
@@ -3853,31 +427,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "abc613ab-176c-4534-a395-f6e2dc42c565",
    "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": [
     "# temperature: ascent versus descent\n",
     "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0, 1, 2], \n",
@@ -3900,31 +455,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "99991068-c65d-476a-8eff-034a5f5b7462",
    "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": [
     "ds_read = xr.open_dataset('./data/osb_temp_jan_2022.nc')\n",
     "\n",
@@ -3953,7 +489,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,