From 6dc14db7b92a6a413a00337c489df0799172b96b Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Wed, 18 Sep 2024 13:55:14 -0700
Subject: [PATCH 01/14] Improve and make surface cleaning functions more robust

---
 examples/adcp_example.ipynb                   | 237 +++++++++---------
 .../dolfyn/test_data/AWAC_test01_clean.nc     | Bin 74049 -> 74049 bytes
 .../data/dolfyn/test_data/RDI_test01_clean.nc | Bin 0 -> 64249 bytes
 .../dolfyn/test_data/Sig1000_tidal_clean.nc   | Bin 224083 -> 228153 bytes
 .../dolfyn/test_data/Sig500_Echo_clean.nc     | Bin 256111 -> 262676 bytes
 mhkit/dolfyn/adp/clean.py                     | 235 ++++++++++++-----
 mhkit/dolfyn/adv/clean.py                     |   1 -
 mhkit/dolfyn/io/rdi.py                        |   6 -
 mhkit/dolfyn/velocity.py                      |  18 +-
 mhkit/tests/dolfyn/test_clean.py              |  21 +-
 10 files changed, 312 insertions(+), 206 deletions(-)
 create mode 100644 examples/data/dolfyn/test_data/RDI_test01_clean.nc

diff --git a/examples/adcp_example.ipynb b/examples/adcp_example.ipynb
index 717f48cb5..f83d007f8 100644
--- a/examples/adcp_example.ipynb
+++ b/examples/adcp_example.ipynb
@@ -82,7 +82,6 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Indexing data/dolfyn/Sig1000_tidal.ad2cp... Done.\n",
             "Reading file data/dolfyn/Sig1000_tidal.ad2cp ...\n"
           ]
         }
@@ -515,26 +514,26 @@
               "    coord_sys:             earth\n",
               "    fs:                    1\n",
               "    has_imu:               1\n",
-              "    beam_angle:            25</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-950f0867-1eec-4ff7-a86e-6337c6073946' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-950f0867-1eec-4ff7-a86e-6337c6073946' 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>: 55000</li><li><span class='xr-has-index'>time_b5</span>: 55000</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>range_b5</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6111bce4-0c5b-4fbc-8e90-d111ea73c832' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6111bce4-0c5b-4fbc-8e90-d111ea73c832' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2020-08-15T00:20:00.500999927 .....</div><input id='attrs-eabe2890-2c86-4622-991d-cb30e6751a54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eabe2890-2c86-4622-991d-cb30e6751a54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45175893-5a2b-439e-b700-c4417896b186' class='xr-var-data-in' type='checkbox'><label for='data-45175893-5a2b-439e-b700-c4417896b186' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</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;2020-08-15T00:20:00.500999927&#x27;, &#x27;2020-08-15T00:20:01.501100063&#x27;,\n",
+              "    beam_angle:            25</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-98124a36-96bc-49eb-b5f9-6eafb43abe7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-98124a36-96bc-49eb-b5f9-6eafb43abe7c' 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>: 55000</li><li><span class='xr-has-index'>time_b5</span>: 55000</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>range_b5</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-de6aea9e-79dd-486c-b771-84722fcd110b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de6aea9e-79dd-486c-b771-84722fcd110b' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2020-08-15T00:20:00.500999927 .....</div><input id='attrs-3742ab4e-df1d-4a84-a12d-79afb49b4db0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3742ab4e-df1d-4a84-a12d-79afb49b4db0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdded0e0-3e9f-48fe-88f6-1614cd5121de' class='xr-var-data-in' type='checkbox'><label for='data-bdded0e0-3e9f-48fe-88f6-1614cd5121de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</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;2020-08-15T00:20:00.500999927&#x27;, &#x27;2020-08-15T00:20:01.501100063&#x27;,\n",
               "       &#x27;2020-08-15T00:20:02.500999927&#x27;, ..., &#x27;2020-08-15T15:36:37.500999927&#x27;,\n",
               "       &#x27;2020-08-15T15:36:38.500999927&#x27;, &#x27;2020-08-15T15:36:39.500999927&#x27;],\n",
-              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.438499927 .....</div><input id='attrs-4bb32a18-37f7-458e-a07c-3fec6fc3b53d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bb32a18-37f7-458e-a07c-3fec6fc3b53d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-066d5294-35e7-43e1-a368-e08f570c63af' class='xr-var-data-in' type='checkbox'><label for='data-066d5294-35e7-43e1-a368-e08f570c63af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</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;2020-08-15T00:20:00.438499927&#x27;, &#x27;2020-08-15T00:20:01.438499927&#x27;,\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.438499927 .....</div><input id='attrs-86df1a4f-7b85-4d79-a471-bf8a985a6ca4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86df1a4f-7b85-4d79-a471-bf8a985a6ca4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba55dae5-9d35-4165-a756-455253700f33' class='xr-var-data-in' type='checkbox'><label for='data-ba55dae5-9d35-4165-a756-455253700f33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</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;2020-08-15T00:20:00.438499927&#x27;, &#x27;2020-08-15T00:20:01.438499927&#x27;,\n",
               "       &#x27;2020-08-15T00:20:02.438499927&#x27;, ..., &#x27;2020-08-15T15:36:37.438499927&#x27;,\n",
               "       &#x27;2020-08-15T15:36:38.438499927&#x27;, &#x27;2020-08-15T15:36:39.438499927&#x27;],\n",
-              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-a20cd54a-94b4-4def-884b-d745732017ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a20cd54a-94b4-4def-884b-d745732017ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-938391bf-2cf4-4b4b-9fd9-4da90b1575a9' class='xr-var-data-in' type='checkbox'><label for='data-938391bf-2cf4-4b4b-9fd9-4da90b1575a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-495f2410-37a2-4e3a-bf93-865045fbd173' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-495f2410-37a2-4e3a-bf93-865045fbd173' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aae4e1f6-e69e-4c68-9500-c51e2a8f7f80' class='xr-var-data-in' type='checkbox'><label for='data-aae4e1f6-e69e-4c68-9500-c51e2a8f7f80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
               "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
-              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-158bbb04-eed8-46fd-913c-e11fb6a6d9dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-158bbb04-eed8-46fd-913c-e11fb6a6d9dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-455b129b-3200-446c-b0cd-71bfb09e9a3b' class='xr-var-data-in' type='checkbox'><label for='data-455b129b-3200-446c-b0cd-71bfb09e9a3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-9948b1fb-e82c-401e-b753-ebc0a8d2ea7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9948b1fb-e82c-401e-b753-ebc0a8d2ea7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aef11db6-49e4-451d-995f-d506a9d0d77d' class='xr-var-data-in' type='checkbox'><label for='data-aef11db6-49e4-451d-995f-d506a9d0d77d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
               "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
-              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-0b020845-984d-41ae-8d8b-314fd0e6b119' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b020845-984d-41ae-8d8b-314fd0e6b119' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-262b26c3-ded2-48e3-b7d0-7eba55074fe4' class='xr-var-data-in' type='checkbox'><label for='data-262b26c3-ded2-48e3-b7d0-7eba55074fe4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;</div><input id='attrs-0ec34111-913f-4467-bedc-7117d2de980f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ec34111-913f-4467-bedc-7117d2de980f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea64f111-f9f4-4b18-ab3b-8902fe320935' class='xr-var-data-in' type='checkbox'><label for='data-ea64f111-f9f4-4b18-ab3b-8902fe320935' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-d801f97e-2133-451d-8155-d980236ff818' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d801f97e-2133-451d-8155-d980236ff818' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-649566e8-04d9-471f-8112-76b5123cada8' class='xr-var-data-in' type='checkbox'><label for='data-649566e8-04d9-471f-8112-76b5123cada8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-9b2804ba-2088-4377-b52c-88c49e64427b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b2804ba-2088-4377-b52c-88c49e64427b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aee6ee25-72c4-43be-993c-6e46d50b08ce' class='xr-var-data-in' type='checkbox'><label for='data-aee6ee25-72c4-43be-993c-6e46d50b08ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-fabbec87-b8b8-4785-ae06-174f17d7549d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fabbec87-b8b8-4785-ae06-174f17d7549d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e15197-2ce7-4677-8327-95f459f0f05d' class='xr-var-data-in' type='checkbox'><label for='data-b2e15197-2ce7-4677-8327-95f459f0f05d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-c2f83bb0-59fa-44a1-a871-cc5182db8bf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2f83bb0-59fa-44a1-a871-cc5182db8bf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6040e23-1018-4cb9-97fb-ad61b0a7de54' class='xr-var-data-in' type='checkbox'><label for='data-d6040e23-1018-4cb9-97fb-ad61b0a7de54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Quaternion Vector Components</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-f9579949-f631-4265-bd8e-054a54fd6397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9579949-f631-4265-bd8e-054a54fd6397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f2923d7-4826-4078-93be-7014bceaf36d' class='xr-var-data-in' type='checkbox'><label for='data-4f2923d7-4826-4078-93be-7014bceaf36d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-88ddb7a4-6c95-4493-a85f-b2231274e348' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88ddb7a4-6c95-4493-a85f-b2231274e348' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dbff821-d4d4-4b1b-84ea-eae6c418b393' class='xr-var-data-in' type='checkbox'><label for='data-4dbff821-d4d4-4b1b-84ea-eae6c418b393' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87833c7d-63c7-4cc4-8426-5e6ee843caa7' class='xr-section-summary-in' type='checkbox'  ><label for='section-87833c7d-63c7-4cc4-8426-5e6ee843caa7' class='xr-section-summary' >Data variables: <span>(38)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-e22bfdff-ac2b-4d84-8dde-25aa3de2ebd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e22bfdff-ac2b-4d84-8dde-25aa3de2ebd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c69756db-402b-45db-80cc-91baab835c09' class='xr-var-data-in' type='checkbox'><label for='data-c69756db-402b-45db-80cc-91baab835c09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.9, 1501.9, 1501.9, ..., 1498.4, 1498.3, 1498.3], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.55 14.55 14.55 ... 13.47 13.47</div><input id='attrs-8803c0eb-0d5a-4c3e-b961-198209cb2f48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8803c0eb-0d5a-4c3e-b961-198209cb2f48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4f3f772-59e2-4bf2-8cb8-e6c78d39ad31' class='xr-var-data-in' type='checkbox'><label for='data-f4f3f772-59e2-4bf2-8cb8-e6c78d39ad31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.55, 14.55, 14.55, ..., 13.47, 13.47, 13.47], dtype=float32)</pre></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'>float32</div><div class='xr-var-preview xr-preview'>9.713 9.718 9.718 ... 9.594 9.596</div><input id='attrs-1ee2bc51-7a10-4079-8f33-9ab4fa30c9e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ee2bc51-7a10-4079-8f33-9ab4fa30c9e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-742ca46c-455a-4282-ab71-9f4b73e9884d' class='xr-var-data-in' type='checkbox'><label for='data-742ca46c-455a-4282-ab71-9f4b73e9884d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.713, 9.718, 9.718, ..., 9.596, 9.594, 9.596], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.5 72.7 72.6 ... -197.2 -195.7</div><input id='attrs-6e7750f1-35b4-46d1-9547-162a38579e71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e7750f1-35b4-46d1-9547-162a38579e71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33d111a3-120b-4b7f-8467-d640e36bbc0c' class='xr-var-data-in' type='checkbox'><label for='data-33d111a3-120b-4b7f-8467-d640e36bbc0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.5,   72.7,   72.6, ...,   72.3,   72.8,   72.4],\n",
+              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-d4e49922-b4ee-4253-9a11-988b809916e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4e49922-b4ee-4253-9a11-988b809916e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32dc3787-5b6f-46f9-a052-28e997531bfd' class='xr-var-data-in' type='checkbox'><label for='data-32dc3787-5b6f-46f9-a052-28e997531bfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;</div><input id='attrs-d0b1a393-cea4-4da4-bc18-0934b4583c44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0b1a393-cea4-4da4-bc18-0934b4583c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dfd4045-9a39-4264-8fad-760841773634' class='xr-var-data-in' type='checkbox'><label for='data-7dfd4045-9a39-4264-8fad-760841773634' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-2e1e9dd1-99d7-4e79-b038-73162236ab86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e1e9dd1-99d7-4e79-b038-73162236ab86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-981a444f-3270-40b4-a7f6-4c2b8c43e979' class='xr-var-data-in' type='checkbox'><label for='data-981a444f-3270-40b4-a7f6-4c2b8c43e979' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-2e2f7940-a432-4382-b313-7bae9e851c67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e2f7940-a432-4382-b313-7bae9e851c67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b626ec3-7382-4e40-b6a1-1679d08ce051' class='xr-var-data-in' type='checkbox'><label for='data-4b626ec3-7382-4e40-b6a1-1679d08ce051' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-ffd700b9-c786-4b9b-9dbd-11d939d1f8d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffd700b9-c786-4b9b-9dbd-11d939d1f8d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fa9132a-fae0-48c8-b67f-fbb7ccb954c2' class='xr-var-data-in' type='checkbox'><label for='data-8fa9132a-fae0-48c8-b67f-fbb7ccb954c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-eae7666f-2493-43a2-a2fa-ed3121637043' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eae7666f-2493-43a2-a2fa-ed3121637043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-788be9be-fd0b-4d0d-9e4f-46eb70f8992b' class='xr-var-data-in' type='checkbox'><label for='data-788be9be-fd0b-4d0d-9e4f-46eb70f8992b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Quaternion Vector Components</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-44e6bf41-b2c7-4b87-8b92-f4970d2fc050' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44e6bf41-b2c7-4b87-8b92-f4970d2fc050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83fa7a02-f569-4ad1-861d-08c4c620ef76' class='xr-var-data-in' type='checkbox'><label for='data-83fa7a02-f569-4ad1-861d-08c4c620ef76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-9afbc5be-8650-4b4d-8c3b-b25619ba2c64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9afbc5be-8650-4b4d-8c3b-b25619ba2c64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b125bae-0d71-417d-a158-2c7b2de44f31' class='xr-var-data-in' type='checkbox'><label for='data-8b125bae-0d71-417d-a158-2c7b2de44f31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ef48832-a7b3-4149-ada9-67a760318115' class='xr-section-summary-in' type='checkbox'  ><label for='section-8ef48832-a7b3-4149-ada9-67a760318115' class='xr-section-summary' >Data variables: <span>(38)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-3a447ad8-2c4a-48e5-9f4f-2d0fd6d1a423' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a447ad8-2c4a-48e5-9f4f-2d0fd6d1a423' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d97a8d45-9d44-4719-8298-12415b532d0d' class='xr-var-data-in' type='checkbox'><label for='data-d97a8d45-9d44-4719-8298-12415b532d0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.9, 1501.9, 1501.9, ..., 1498.4, 1498.3, 1498.3], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.55 14.55 14.55 ... 13.47 13.47</div><input id='attrs-5f776757-1d3e-46bc-84f7-c2fde35622f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f776757-1d3e-46bc-84f7-c2fde35622f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b820e742-2deb-473f-b626-548a6c748fd2' class='xr-var-data-in' type='checkbox'><label for='data-b820e742-2deb-473f-b626-548a6c748fd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.55, 14.55, 14.55, ..., 13.47, 13.47, 13.47], dtype=float32)</pre></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'>float32</div><div class='xr-var-preview xr-preview'>9.713 9.718 9.718 ... 9.594 9.596</div><input id='attrs-5ef86819-35e0-424c-81da-2a96dffe7422' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ef86819-35e0-424c-81da-2a96dffe7422' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65af2c06-b899-48ff-ad68-bf47154c0ebb' class='xr-var-data-in' type='checkbox'><label for='data-65af2c06-b899-48ff-ad68-bf47154c0ebb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.713, 9.718, 9.718, ..., 9.596, 9.594, 9.596], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.5 72.7 72.6 ... -197.2 -195.7</div><input id='attrs-688d7e50-37ae-4bbf-9d57-8a731dc90429' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-688d7e50-37ae-4bbf-9d57-8a731dc90429' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cc0e053-b41e-4820-b61d-8e99cad9565a' class='xr-var-data-in' type='checkbox'><label for='data-3cc0e053-b41e-4820-b61d-8e99cad9565a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.5,   72.7,   72.6, ...,   72.3,   72.8,   72.4],\n",
               "       [   9.3,    9.4,    8.9, ...,    5.9,    6.3,    6.4],\n",
               "       [-198. , -196.3, -198.1, ..., -197.4, -197.2, -195.7]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.00479 -0.01437 ... 9.685 9.729</div><input id='attrs-9d845ca3-7530-41de-821c-82759ebc147e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d845ca3-7530-41de-821c-82759ebc147e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfda678b-8c55-484a-919d-0f6c6a9331f0' class='xr-var-data-in' type='checkbox'><label for='data-bfda678b-8c55-484a-919d-0f6c6a9331f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-4.7900393e-03, -1.4370117e-02, -2.3950195e-02, ...,\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.00479 -0.01437 ... 9.685 9.729</div><input id='attrs-c609f139-bba5-4107-afdc-04f5663cad7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c609f139-bba5-4107-afdc-04f5663cad7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22070743-46da-4e68-9348-fc63861ec623' class='xr-var-data-in' type='checkbox'><label for='data-22070743-46da-4e68-9348-fc63861ec623' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-4.7900393e-03, -1.4370117e-02, -2.3950195e-02, ...,\n",
               "        -1.4370117e-02, -4.7900393e-03, -2.3950195e-02],\n",
               "       [-1.2933105e+00, -1.2549902e+00, -1.2645704e+00, ...,\n",
               "        -1.1017090e+00, -1.0873389e+00, -1.0969189e+00],\n",
               "       [ 9.7285690e+00,  9.7141991e+00,  9.7237797e+00, ...,\n",
-              "         9.6710892e+00,  9.6854591e+00,  9.7285690e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.6 16.6 16.6 ... 16.4 16.4 15.2</div><input id='attrs-338d5a32-0798-4be9-baa9-36a80359ee1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-338d5a32-0798-4be9-baa9-36a80359ee1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6371f200-3168-426f-99a9-b40d63d1119f' class='xr-var-data-in' type='checkbox'><label for='data-6371f200-3168-426f-99a9-b40d63d1119f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.6, 16.6, 16.6, ..., 16.4, 16.4, 15.2], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-d71d2927-9a36-43b6-990a-caf8216fe72e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d71d2927-9a36-43b6-990a-caf8216fe72e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8005ecc-dff6-4f34-bb5a-1d0d29e33fcd' class='xr-var-data-in' type='checkbox'><label for='data-e8005ecc-dff6-4f34-bb5a-1d0d29e33fcd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20. , 20. , 20. , ..., 19.5, 19.5, 19.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-b5f6a925-b4f4-4869-a281-88eb4169138b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5f6a925-b4f4-4869-a281-88eb4169138b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19bf0736-2c9b-4f58-a032-f96f52dc807d' class='xr-var-data-in' type='checkbox'><label for='data-19bf0736-2c9b-4f58-a032-f96f52dc807d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-1b592ead-9844-4494-94a5-22947dfcb9c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b592ead-9844-4494-94a5-22947dfcb9c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74d36b3d-d30b-4710-9ef5-d5bdbf47775e' class='xr-var-data-in' type='checkbox'><label for='data-74d36b3d-d30b-4710-9ef5-d5bdbf47775e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
-              "       1053557762], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-fce467ea-a489-40b3-bce3-b596ee4015f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fce467ea-a489-40b3-bce3-b596ee4015f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e1dcdf-9896-42e7-b050-a3edf8e7262e' class='xr-var-data-in' type='checkbox'><label for='data-85e1dcdf-9896-42e7-b050-a3edf8e7262e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.015 -0.048 ... -0.015 -0.012</div><input id='attrs-21d15614-86cb-42eb-a4b4-2cd866d4472b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21d15614-86cb-42eb-a4b4-2cd866d4472b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7f085e6-20ee-48af-938c-88faf86bdf84' class='xr-var-data-in' type='checkbox'><label for='data-c7f085e6-20ee-48af-938c-88faf86bdf84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.015, -0.048,  0.008, ..., -0.176, -0.071, -0.027],\n",
+              "         9.6710892e+00,  9.6854591e+00,  9.7285690e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.6 16.6 16.6 ... 16.4 16.4 15.2</div><input id='attrs-23cd90d3-5c07-4e9d-bed3-65d380c13155' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23cd90d3-5c07-4e9d-bed3-65d380c13155' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7be5322-fbe0-4464-99e1-2e336f4d419e' class='xr-var-data-in' type='checkbox'><label for='data-a7be5322-fbe0-4464-99e1-2e336f4d419e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.6, 16.6, 16.6, ..., 16.4, 16.4, 15.2], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-b0c154a6-3e11-4c66-bf0e-4f519f1e8526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0c154a6-3e11-4c66-bf0e-4f519f1e8526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11ca357a-3b04-453a-b3de-3a35ff41cefc' class='xr-var-data-in' type='checkbox'><label for='data-11ca357a-3b04-453a-b3de-3a35ff41cefc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20. , 20. , 20. , ..., 19.5, 19.5, 19.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-d90a3447-3a27-45c5-9e4e-d5dc840476ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d90a3447-3a27-45c5-9e4e-d5dc840476ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec8696ce-f7ac-467a-b5bb-68ee49b5294c' class='xr-var-data-in' type='checkbox'><label for='data-ec8696ce-f7ac-467a-b5bb-68ee49b5294c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-99888522-e5ac-4f59-a224-5de6c1182612' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99888522-e5ac-4f59-a224-5de6c1182612' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a5eb8d-2f4b-4e4d-b258-b492076d4ee3' class='xr-var-data-in' type='checkbox'><label for='data-72a5eb8d-2f4b-4e4d-b258-b492076d4ee3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
+              "       1053557762], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-20941c67-f062-441b-87b6-4af785a8a441' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20941c67-f062-441b-87b6-4af785a8a441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-432e8d54-f9ec-4254-9e73-f36a2ec03a0f' class='xr-var-data-in' type='checkbox'><label for='data-432e8d54-f9ec-4254-9e73-f36a2ec03a0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.015 -0.048 ... -0.015 -0.012</div><input id='attrs-e3aae32a-8fc0-45f2-9287-8047a4605fc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3aae32a-8fc0-45f2-9287-8047a4605fc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e67527c-67a1-4eed-82f3-fce3f6f0b52b' class='xr-var-data-in' type='checkbox'><label for='data-4e67527c-67a1-4eed-82f3-fce3f6f0b52b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.015, -0.048,  0.008, ..., -0.176, -0.071, -0.027],\n",
               "        [-0.093, -0.124, -0.031, ..., -0.175, -0.16 , -0.03 ],\n",
               "        [-0.163, -0.135, -0.105, ..., -0.129, -0.089, -0.116],\n",
               "        ...,\n",
@@ -565,7 +564,7 @@
               "        [-0.044,  0.015, -0.04 , ..., -0.015,  0.006,  0.012],\n",
               "        [-0.013,  0.014, -0.032, ...,  0.01 ,  0.   , -0.015],\n",
               "        [ 0.007,  0.016, -0.027, ..., -0.002, -0.015, -0.012]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>81.0 80.0 80.5 ... 43.5 42.5 42.0</div><input id='attrs-2afbfb90-fce5-47b3-8f6b-7edcab8b46ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2afbfb90-fce5-47b3-8f6b-7edcab8b46ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63b4419e-cad3-4514-b67d-4219d522e7c2' class='xr-var-data-in' type='checkbox'><label for='data-63b4419e-cad3-4514-b67d-4219d522e7c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[81. , 80. , 80.5, ..., 81.5, 81.5, 81.5],\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>81.0 80.0 80.5 ... 43.5 42.5 42.0</div><input id='attrs-d3ca6285-a912-48ab-9b38-05dfd5e9bb71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3ca6285-a912-48ab-9b38-05dfd5e9bb71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da00b08e-f833-4de8-b2d4-a133fc5f9f12' class='xr-var-data-in' type='checkbox'><label for='data-da00b08e-f833-4de8-b2d4-a133fc5f9f12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[81. , 80. , 80.5, ..., 81.5, 81.5, 81.5],\n",
               "        [75. , 74.5, 75. , ..., 79. , 77. , 77.5],\n",
               "        [73.5, 72. , 72. , ..., 75. , 74.5, 75. ],\n",
               "        ...,\n",
@@ -595,7 +594,7 @@
               "        ...,\n",
               "        [51.5, 51. , 51. , ..., 46. , 46. , 45. ],\n",
               "        [51. , 50. , 51. , ..., 45. , 44. , 43.5],\n",
-              "        [50.5, 49.5, 50. , ..., 43.5, 42.5, 42. ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>100 91 91 97 99 ... 96 98 98 98 96</div><input id='attrs-5af33f7d-81a9-42b2-b6ae-80b63cdbc3b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5af33f7d-81a9-42b2-b6ae-80b63cdbc3b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5d96645-434f-49fa-a4d1-b2b4804501f2' class='xr-var-data-in' type='checkbox'><label for='data-e5d96645-434f-49fa-a4d1-b2b4804501f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[100,  91,  91, ...,  89,  90,  96],\n",
+              "        [50.5, 49.5, 50. , ..., 43.5, 42.5, 42. ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>100 91 91 97 99 ... 96 98 98 98 96</div><input id='attrs-7bebf0c8-8422-4e62-81e2-e5a1c52a1c0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bebf0c8-8422-4e62-81e2-e5a1c52a1c0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ccfbc2-0500-461a-ae25-f42a4181a274' class='xr-var-data-in' type='checkbox'><label for='data-92ccfbc2-0500-461a-ae25-f42a4181a274' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[100,  91,  91, ...,  89,  90,  96],\n",
               "        [ 74,  92,  96, ..., 100,  97, 100],\n",
               "        [100, 100,  99, ...,  99, 100, 100],\n",
               "        ...,\n",
@@ -625,7 +624,7 @@
               "        ...,\n",
               "        [100,  99,  96, ...,  98, 100, 100],\n",
               "        [100, 100, 100, ...,  94,  93,  95],\n",
-              "        [100,  98,  99, ...,  98,  98,  96]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2164 ... 0.9937 0.9937</div><input id='attrs-0df5195f-07fd-433d-b53e-1e7aba7c05c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0df5195f-07fd-433d-b53e-1e7aba7c05c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c32b7660-2536-4960-85f2-406742430ea2' class='xr-var-data-in' type='checkbox'><label for='data-c32b7660-2536-4960-85f2-406742430ea2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16688156e-01, -2.16397047e-01, -2.16593146e-01, ...,\n",
+              "        [100,  98,  99, ...,  98,  98,  96]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2164 ... 0.9937 0.9937</div><input id='attrs-1d3092f7-8c4b-49ca-a47b-f4fc5bbc03c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d3092f7-8c4b-49ca-a47b-f4fc5bbc03c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bfbe30c-ea74-4860-992c-8d05b8722d3a' class='xr-var-data-in' type='checkbox'><label for='data-6bfbe30c-ea74-4860-992c-8d05b8722d3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16688156e-01, -2.16397047e-01, -2.16593146e-01, ...,\n",
               "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
               "        [ 9.76196468e-01,  9.76219118e-01,  9.76202846e-01, ...,\n",
               "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
@@ -645,47 +644,47 @@
               "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02],\n",
               "        [ 9.91607308e-01,  9.91618454e-01,  9.91612315e-01, ...,\n",
               "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.00293</div><input id='attrs-08f788e2-1350-4119-9590-4d9147a4af82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f788e2-1350-4119-9590-4d9147a4af82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7f6d873-a97f-44a0-8932-101da3f16ae5' class='xr-var-data-in' type='checkbox'><label for='data-d7f6d873-a97f-44a0-8932-101da3f16ae5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00390625,\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.00293</div><input id='attrs-e2c45cf6-b614-498d-874d-62466fb2d72b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2c45cf6-b614-498d-874d-62466fb2d72b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3cbeb78-8eec-4f01-a0ae-3d388a5366be' class='xr-var-data-in' type='checkbox'><label for='data-d3cbeb78-8eec-4f01-a0ae-3d388a5366be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00390625,\n",
               "        -0.00292969, -0.00195312],\n",
               "       [ 0.00097656,  0.        ,  0.        , ...,  0.        ,\n",
               "         0.00097656,  0.00097656],\n",
               "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
-              "        -0.00195312, -0.00292969]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-cd459bd2-7a34-49be-8e2a-6c2e45e02d69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd459bd2-7a34-49be-8e2a-6c2e45e02d69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7cb0ea9-2e00-46e0-8a78-9ea895c188ba' class='xr-var-data-in' type='checkbox'><label for='data-a7cb0ea9-2e00-46e0-8a78-9ea895c188ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6244812 , ...,  0.625     ,\n",
+              "        -0.00195312, -0.00292969]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-9af830f4-6dab-4e91-88bf-8e0f80050b5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9af830f4-6dab-4e91-88bf-8e0f80050b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53f9e55d-55e3-4bbe-8e86-0a4732edbc86' class='xr-var-data-in' type='checkbox'><label for='data-53f9e55d-55e3-4bbe-8e86-0a4732edbc86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6244812 , ...,  0.625     ,\n",
               "         0.6247864 ,  0.62490845],\n",
               "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
               "        -0.03564453, -0.03561401],\n",
               "       [-0.05014038, -0.05010986, -0.05014038, ..., -0.04354858,\n",
               "        -0.04360962, -0.04360962],\n",
               "       [ 0.77835083,  0.7782593 ,  0.7783203 , ...,  0.77856445,\n",
-              "         0.77874756,  0.7786255 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-7f48e238-80f4-4d76-9f84-6fa2a5f6605e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f48e238-80f4-4d76-9f84-6fa2a5f6605e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-190b881d-9f50-4d13-b5da-d68f800ae600' class='xr-var-data-in' type='checkbox'><label for='data-190b881d-9f50-4d13-b5da-d68f800ae600' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.8, 12.8, 12.8, ..., 12. , 12. , 12. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>243 243 243 244 ... 246 246 246 246</div><input id='attrs-79e46abf-b287-4a1a-846e-993372294330' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79e46abf-b287-4a1a-846e-993372294330' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2bcf6bf-5e13-485e-bbaf-b77a9cac9a8e' class='xr-var-data-in' type='checkbox'><label for='data-d2bcf6bf-5e13-485e-bbaf-b77a9cac9a8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([243, 243, 243, ..., 246, 246, 246], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.9 72.8 72.4 ... -198.0 -195.7</div><input id='attrs-ffbe9243-e335-49d7-a652-fb30474a1bf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffbe9243-e335-49d7-a652-fb30474a1bf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a10ba1c0-e43d-4988-8c4c-0dc5958c25df' class='xr-var-data-in' type='checkbox'><label for='data-a10ba1c0-e43d-4988-8c4c-0dc5958c25df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.9,   72.8,   72.4, ...,   73. ,   73. ,   72.7],\n",
+              "         0.77874756,  0.7786255 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-78a79775-2c89-4e04-9134-784be901d9f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78a79775-2c89-4e04-9134-784be901d9f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39f93605-2afa-42d1-9bc5-390b36384524' class='xr-var-data-in' type='checkbox'><label for='data-39f93605-2afa-42d1-9bc5-390b36384524' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.8, 12.8, 12.8, ..., 12. , 12. , 12. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>243 243 243 244 ... 246 246 246 246</div><input id='attrs-7350c419-3426-49a9-b576-2c67bb5742fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7350c419-3426-49a9-b576-2c67bb5742fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07b23fcd-ed6a-41ab-82d7-eca594259ce9' class='xr-var-data-in' type='checkbox'><label for='data-07b23fcd-ed6a-41ab-82d7-eca594259ce9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([243, 243, 243, ..., 246, 246, 246], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.9 72.8 72.4 ... -198.0 -195.7</div><input id='attrs-682a5618-e5cb-447b-bd4b-73313a3cdc63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-682a5618-e5cb-447b-bd4b-73313a3cdc63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf4a4bf9-1734-48c7-b86e-69dce8a68a94' class='xr-var-data-in' type='checkbox'><label for='data-cf4a4bf9-1734-48c7-b86e-69dce8a68a94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.9,   72.8,   72.4, ...,   73. ,   73. ,   72.7],\n",
               "       [   9.5,    9.5,    9.8, ...,    6. ,    6.4,    5.9],\n",
               "       [-197.1, -197.2, -197.7, ..., -197.4, -198. , -195.7]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01437 0.0 ... 9.709 9.724</div><input id='attrs-8faa0886-eb08-4338-93ee-ad1c8fe83872' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8faa0886-eb08-4338-93ee-ad1c8fe83872' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c650ae17-1402-47ed-a8bf-f59bba2ff44e' class='xr-var-data-in' type='checkbox'><label for='data-c650ae17-1402-47ed-a8bf-f59bba2ff44e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.4370117e-02,  0.0000000e+00, -1.4370117e-02, ...,\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01437 0.0 ... 9.709 9.724</div><input id='attrs-1748d001-0e34-40ad-8462-d2dc941a2c93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1748d001-0e34-40ad-8462-d2dc941a2c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-745609ac-0e29-4f47-9aea-611e0e1d297e' class='xr-var-data-in' type='checkbox'><label for='data-745609ac-0e29-4f47-9aea-611e0e1d297e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.4370117e-02,  0.0000000e+00, -1.4370117e-02, ...,\n",
               "        -4.7900393e-03, -1.9160157e-02, -1.4370117e-02],\n",
               "       [-1.2693603e+00, -1.2885205e+00, -1.2837305e+00, ...,\n",
               "        -1.0825489e+00, -1.1017090e+00, -1.1064991e+00],\n",
               "       [ 9.7189894e+00,  9.7046194e+00,  9.7094088e+00, ...,\n",
-              "         9.7141991e+00,  9.7094088e+00,  9.7237797e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-7e9d67de-15b1-40d8-ac79-69b6dcf227f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e9d67de-15b1-40d8-ac79-69b6dcf227f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-187fb5ac-7da3-4d9f-b2c8-b5d54aa3b5af' class='xr-var-data-in' type='checkbox'><label for='data-187fb5ac-7da3-4d9f-b2c8-b5d54aa3b5af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-568b0a6f-023a-4231-875c-3f49afe58560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-568b0a6f-023a-4231-875c-3f49afe58560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b050248e-3799-4892-b611-51999e096d58' class='xr-var-data-in' type='checkbox'><label for='data-b050248e-3799-4892-b611-51999e096d58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
-              "       1053557762], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-01fa01bc-2d41-4ad9-bd9a-75bb9dbedff6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01fa01bc-2d41-4ad9-bd9a-75bb9dbedff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5524f8c-12a8-481f-a5dc-3310b81bb582' class='xr-var-data-in' type='checkbox'><label for='data-d5524f8c-12a8-481f-a5dc-3310b81bb582' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.003 0.021 0.018 ... -0.048 0.09</div><input id='attrs-d321bd40-9f39-4ebe-8f8d-6e138b7e6848' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d321bd40-9f39-4ebe-8f8d-6e138b7e6848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af63c5ec-aadb-45db-9cd4-63e759cf120b' class='xr-var-data-in' type='checkbox'><label for='data-af63c5ec-aadb-45db-9cd4-63e759cf120b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.003,  0.021,  0.018, ...,  0.   , -0.044,  0.009],\n",
+              "         9.7141991e+00,  9.7094088e+00,  9.7237797e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-a9624189-e8db-4899-a831-c2626ee1dece' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9624189-e8db-4899-a831-c2626ee1dece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e9252f-98ac-49f0-9993-c41e5679e209' class='xr-var-data-in' type='checkbox'><label for='data-99e9252f-98ac-49f0-9993-c41e5679e209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-56e84028-31b5-4b8d-828e-03542c01b6d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56e84028-31b5-4b8d-828e-03542c01b6d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f155c59-34ad-4a10-8f47-9fde95ab88c1' class='xr-var-data-in' type='checkbox'><label for='data-6f155c59-34ad-4a10-8f47-9fde95ab88c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
+              "       1053557762], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-20a7ed27-dd1c-4a2c-99b5-ddd4d3ff1faa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20a7ed27-dd1c-4a2c-99b5-ddd4d3ff1faa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e848770f-72e7-4ed8-ae45-8336994c6bc9' class='xr-var-data-in' type='checkbox'><label for='data-e848770f-72e7-4ed8-ae45-8336994c6bc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.003 0.021 0.018 ... -0.048 0.09</div><input id='attrs-887f00f4-e9e3-4eb5-86bd-21fc7bb8454d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-887f00f4-e9e3-4eb5-86bd-21fc7bb8454d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1661645e-905d-4714-8339-0cb23a0d253a' class='xr-var-data-in' type='checkbox'><label for='data-1661645e-905d-4714-8339-0cb23a0d253a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.003,  0.021,  0.018, ...,  0.   , -0.044,  0.009],\n",
               "       [ 0.057,  0.034,  0.017, ...,  0.01 ,  0.001, -0.002],\n",
               "       [ 0.039,  0.022,  0.02 , ...,  0.039, -0.008,  0.018],\n",
               "       ...,\n",
               "       [-0.036, -0.013,  0.058, ...,  0.058, -0.013,  0.028],\n",
               "       [-0.043, -0.009,  0.028, ...,  0.092, -0.021,  0.019],\n",
               "       [-0.073,  0.003,  0.   , ...,  0.049, -0.048,  0.09 ]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.5 80.5 80.0 ... 46.5 46.0 47.5</div><input id='attrs-640c516c-437f-420e-ac2e-b85955b836bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-640c516c-437f-420e-ac2e-b85955b836bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb5e69c0-8665-416c-9677-399679d2c63c' class='xr-var-data-in' type='checkbox'><label for='data-cb5e69c0-8665-416c-9677-399679d2c63c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[80.5, 80.5, 80. , ..., 82. , 81.5, 81. ],\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.5 80.5 80.0 ... 46.5 46.0 47.5</div><input id='attrs-919eed19-6960-4b0d-b18b-965fbfa3af0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-919eed19-6960-4b0d-b18b-965fbfa3af0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7703fa50-4ca7-40dc-a0be-3af7322d11c3' class='xr-var-data-in' type='checkbox'><label for='data-7703fa50-4ca7-40dc-a0be-3af7322d11c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[80.5, 80.5, 80. , ..., 82. , 81.5, 81. ],\n",
               "       [75.5, 76.5, 76. , ..., 78.5, 78. , 77.5],\n",
               "       [72.5, 73.5, 73.5, ..., 75. , 76.5, 75.5],\n",
               "       ...,\n",
               "       [57.5, 57.5, 57. , ..., 49. , 49. , 48.5],\n",
               "       [56.5, 57. , 56. , ..., 47.5, 48. , 48. ],\n",
-              "       [55.5, 55.5, 55. , ..., 46.5, 46. , 47.5]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>98 100 92 93 98 ... 100 99 94 100</div><input id='attrs-df29cfe6-fc5f-4ca5-bebc-13f8d4635da9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df29cfe6-fc5f-4ca5-bebc-13f8d4635da9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faef58da-8a72-4bdf-874f-21e85da2be59' class='xr-var-data-in' type='checkbox'><label for='data-faef58da-8a72-4bdf-874f-21e85da2be59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[ 98, 100,  92, ...,  88,  90,  91],\n",
+              "       [55.5, 55.5, 55. , ..., 46.5, 46. , 47.5]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>98 100 92 93 98 ... 100 99 94 100</div><input id='attrs-e2e77dea-5206-4488-90bd-ed575627b035' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2e77dea-5206-4488-90bd-ed575627b035' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1c8b8f3-3e55-449b-80be-4cedeb4fac39' class='xr-var-data-in' type='checkbox'><label for='data-a1c8b8f3-3e55-449b-80be-4cedeb4fac39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[ 98, 100,  92, ...,  88,  90,  91],\n",
               "       [ 97,  94,  98, ...,  97,  95,  97],\n",
               "       [100, 100, 100, ...,  93, 100,  96],\n",
               "       ...,\n",
               "       [100, 100, 100, ..., 100, 100,  98],\n",
               "       [100, 100, 100, ...,  99, 100,  98],\n",
-              "       [100, 100, 100, ...,  99,  94, 100]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2165 ... 0.9937 0.9937</div><input id='attrs-f06f5ab4-bc75-4552-b687-35d803252135' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f06f5ab4-bc75-4552-b687-35d803252135' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cd8ad12-bc6d-4d2d-9d63-c474203fec2f' class='xr-var-data-in' type='checkbox'><label for='data-4cd8ad12-bc6d-4d2d-9d63-c474203fec2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16694236e-01, -2.16498137e-01, -2.16593146e-01, ...,\n",
+              "       [100, 100, 100, ...,  99,  94, 100]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2165 ... 0.9937 0.9937</div><input id='attrs-f4ae3766-82c0-4173-980e-4a3c165caa49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4ae3766-82c0-4173-980e-4a3c165caa49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1740f1e-7555-4599-9a75-17247eb6fc15' class='xr-var-data-in' type='checkbox'><label for='data-b1740f1e-7555-4599-9a75-17247eb6fc15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16694236e-01, -2.16498137e-01, -2.16593146e-01, ...,\n",
               "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
               "        [-9.67967808e-01, -9.68039632e-01, -9.68030274e-01, ...,\n",
               "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
@@ -705,24 +704,24 @@
               "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01],\n",
               "        [ 9.91601229e-01,  9.91612315e-01,  9.91612315e-01, ...,\n",
               "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.001953</div><input id='attrs-d42551b7-1ac4-4ed9-b3d2-cf8248f58653' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d42551b7-1ac4-4ed9-b3d2-cf8248f58653' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32b86ab6-58dc-4450-bfb0-9dcae8af598d' class='xr-var-data-in' type='checkbox'><label for='data-32b86ab6-58dc-4450-bfb0-9dcae8af598d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00097656,\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.001953</div><input id='attrs-ef2586eb-d0af-45cd-bc4a-ae67663dea99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef2586eb-d0af-45cd-bc4a-ae67663dea99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4dbd6cf-56b8-40b1-8496-e29616db3be2' class='xr-var-data-in' type='checkbox'><label for='data-e4dbd6cf-56b8-40b1-8496-e29616db3be2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00097656,\n",
               "        -0.00195312, -0.00390625],\n",
               "       [ 0.        ,  0.        ,  0.00097656, ...,  0.00097656,\n",
               "         0.        ,  0.00097656],\n",
               "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
-              "        -0.00292969, -0.00195312]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-704d12dd-d24c-4f26-a021-f6bd0d2f2595' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-704d12dd-d24c-4f26-a021-f6bd0d2f2595' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cb0efbe-a098-4ff8-a13b-d66dfd2daf1f' class='xr-var-data-in' type='checkbox'><label for='data-6cb0efbe-a098-4ff8-a13b-d66dfd2daf1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6245117 , ...,  0.625     ,\n",
+              "        -0.00292969, -0.00195312]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-8faa1681-736e-4dfe-bf03-28ce586d6d0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8faa1681-736e-4dfe-bf03-28ce586d6d0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ecb0b20-9f03-480a-8871-97341c754bb8' class='xr-var-data-in' type='checkbox'><label for='data-8ecb0b20-9f03-480a-8871-97341c754bb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6245117 , ...,  0.625     ,\n",
               "         0.6247864 ,  0.62490845],\n",
               "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
               "        -0.03564453, -0.03561401],\n",
               "       [-0.0501709 , -0.05014038, -0.05014038, ..., -0.04354858,\n",
               "        -0.04360962, -0.04360962],\n",
               "       [ 0.77835083,  0.7782898 ,  0.7783203 , ...,  0.77856445,\n",
-              "         0.77874756,  0.7786255 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>65 65 65 65 65 ... 65 65 65 65 65</div><input id='attrs-16170dfa-9b79-4d3b-9417-d534b7058d7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16170dfa-9b79-4d3b-9417-d534b7058d7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc788a86-8712-41e4-a182-d3ab28e846fd' class='xr-var-data-in' type='checkbox'><label for='data-bc788a86-8712-41e4-a182-d3ab28e846fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65, 65, 65, ..., 65, 65, 65], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-e6512b44-aacc-463e-9f3b-0472549a2c0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6512b44-aacc-463e-9f3b-0472549a2c0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20146969-3d01-4b4c-9f01-735e4a9e9cb2' class='xr-var-data-in' type='checkbox'><label for='data-20146969-3d01-4b4c-9f01-735e4a9e9cb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-6026df2f-bf36-4b4c-8421-35f22a1822dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6026df2f-bf36-4b4c-8421-35f22a1822dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5e28ae6-d164-45d5-bb61-b63885606531' class='xr-var-data-in' type='checkbox'><label for='data-e5e28ae6-d164-45d5-bb61-b63885606531' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-79bd5e76-7bf8-4268-8bbc-f7ed09bf9426' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79bd5e76-7bf8-4268-8bbc-f7ed09bf9426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d466d4a4-31a5-4511-9854-9861ba245644' class='xr-var-data-in' type='checkbox'><label for='data-d466d4a4-31a5-4511-9854-9861ba245644' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1</div><input id='attrs-e536e545-bf36-40ab-95bf-59d066af6fcc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e536e545-bf36-40ab-95bf-59d066af6fcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c07d453-4785-4a81-9f65-b194bc1dca12' class='xr-var-data-in' type='checkbox'><label for='data-9c07d453-4785-4a81-9f65-b194bc1dca12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 1], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.52 -12.51 ... -12.52 -12.5</div><input id='attrs-842439f2-f01b-4748-bb1b-d22f43483e68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-842439f2-f01b-4748-bb1b-d22f43483e68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82ffbb6f-fa84-4c00-b5a1-53982a851e03' class='xr-var-data-in' type='checkbox'><label for='data-82ffbb6f-fa84-4c00-b5a1-53982a851e03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-12.52    , -12.505001, -12.51    , ..., -12.490002, -12.52    ,\n",
-              "       -12.500001], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.065 -0.06 -0.06 ... -0.05 -0.05</div><input id='attrs-824c57f1-8cb8-4467-b120-42abd784a3ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-824c57f1-8cb8-4467-b120-42abd784a3ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21eb3188-9d86-42e1-ad3e-709ee7384dcf' class='xr-var-data-in' type='checkbox'><label for='data-21eb3188-9d86-42e1-ad3e-709ee7384dcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.065, -0.06 , -0.06 , ..., -0.06 , -0.05 , -0.05 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.425 -7.42 -7.42 ... -6.45 -6.45</div><input id='attrs-21bce183-5d0c-4037-a1f2-fe46cc62738b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21bce183-5d0c-4037-a1f2-fe46cc62738b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50d03feb-5dfb-4c74-9d39-4c05fdc6c6d6' class='xr-var-data-in' type='checkbox'><label for='data-50d03feb-5dfb-4c74-9d39-4c05fdc6c6d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.4250007, -7.42     , -7.42     , ..., -6.45     , -6.45     ,\n",
-              "       -6.45     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.183 0.0 -1.183 ... 0.0 0.5518</div><input id='attrs-0aad57d9-1867-4f55-bd13-26e25c1bd930' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0aad57d9-1867-4f55-bd13-26e25c1bd930' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f4c6752-a20c-456f-97e7-30c8c99dee2c' class='xr-var-data-in' type='checkbox'><label for='data-6f4c6752-a20c-456f-97e7-30c8c99dee2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.1831,  0.    , -1.1831,  0.    ],\n",
+              "         0.77874756,  0.7786255 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>65 65 65 65 65 ... 65 65 65 65 65</div><input id='attrs-4a48fe97-fbbc-4fe5-9d5a-a5f01cf32ac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a48fe97-fbbc-4fe5-9d5a-a5f01cf32ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e662358-d488-45c4-8ca4-8f07a161a8e0' class='xr-var-data-in' type='checkbox'><label for='data-5e662358-d488-45c4-8ca4-8f07a161a8e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65, 65, 65, ..., 65, 65, 65], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-a219cfb7-9c63-4868-8ae3-58ab74036117' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a219cfb7-9c63-4868-8ae3-58ab74036117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e432d20-1e34-4f3f-9d14-e56bd16e7cd4' class='xr-var-data-in' type='checkbox'><label for='data-7e432d20-1e34-4f3f-9d14-e56bd16e7cd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-c025dd2a-d990-4384-a7e0-494aa0c4733c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c025dd2a-d990-4384-a7e0-494aa0c4733c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96a2da1e-6fca-4962-a587-ae91a21973da' class='xr-var-data-in' type='checkbox'><label for='data-96a2da1e-6fca-4962-a587-ae91a21973da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-d167b02b-873d-49df-979f-19efaa89ec4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d167b02b-873d-49df-979f-19efaa89ec4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-932400e3-16d4-4eba-9ea0-b3e0a333717d' class='xr-var-data-in' type='checkbox'><label for='data-932400e3-16d4-4eba-9ea0-b3e0a333717d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1</div><input id='attrs-8e7287ce-d793-467e-aaf4-b0ff192db21e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e7287ce-d793-467e-aaf4-b0ff192db21e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16e4bb0e-2b23-47ed-a6aa-a40d77d21afc' class='xr-var-data-in' type='checkbox'><label for='data-16e4bb0e-2b23-47ed-a6aa-a40d77d21afc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 1], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.52 -12.51 ... -12.52 -12.5</div><input id='attrs-782e7551-b59d-4161-af48-663e61a67864' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-782e7551-b59d-4161-af48-663e61a67864' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76ed6d42-361b-42c0-8abb-8faf3c1f6b8c' class='xr-var-data-in' type='checkbox'><label for='data-76ed6d42-361b-42c0-8abb-8faf3c1f6b8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-12.52    , -12.505001, -12.51    , ..., -12.490002, -12.52    ,\n",
+              "       -12.500001], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.065 -0.06 -0.06 ... -0.05 -0.05</div><input id='attrs-f09fac58-3ba1-4bc9-823a-cdf9a712a066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f09fac58-3ba1-4bc9-823a-cdf9a712a066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e870d55d-da25-4215-80fe-68abd2b3dbb4' class='xr-var-data-in' type='checkbox'><label for='data-e870d55d-da25-4215-80fe-68abd2b3dbb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.065, -0.06 , -0.06 , ..., -0.06 , -0.05 , -0.05 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.425 -7.42 -7.42 ... -6.45 -6.45</div><input id='attrs-0e952fbb-a22e-4afc-9048-e0ce1d9ae381' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e952fbb-a22e-4afc-9048-e0ce1d9ae381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73aee514-0620-4dd9-94cd-aea1240b5ccb' class='xr-var-data-in' type='checkbox'><label for='data-73aee514-0620-4dd9-94cd-aea1240b5ccb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.4250007, -7.42     , -7.42     , ..., -6.45     , -6.45     ,\n",
+              "       -6.45     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.183 0.0 -1.183 ... 0.0 0.5518</div><input id='attrs-bd9d01e9-2ab8-4054-9199-ad3a1f94860d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd9d01e9-2ab8-4054-9199-ad3a1f94860d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d82851d0-2b27-4c1c-802f-8ded8cb5bf34' class='xr-var-data-in' type='checkbox'><label for='data-d82851d0-2b27-4c1c-802f-8ded8cb5bf34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.1831,  0.    , -1.1831,  0.    ],\n",
               "       [ 0.    , -1.1831,  0.    ,  1.1831],\n",
               "       [ 0.5518,  0.    ,  0.5518,  0.    ],\n",
-              "       [ 0.    ,  0.5518,  0.    ,  0.5518]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a10ca90-d5a4-4d7e-b172-78579cb6bc9e' class='xr-section-summary-in' type='checkbox'  ><label for='section-8a10ca90-d5a4-4d7e-b172-78579cb6bc9e' class='xr-section-summary' >Indexes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6bf10a94-51db-4d5e-b210-d4c308cff5ee' class='xr-index-data-in' type='checkbox'/><label for='index-6bf10a94-51db-4d5e-b210-d4c308cff5ee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.500999927&#x27;,\n",
+              "       [ 0.    ,  0.5518,  0.    ,  0.5518]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6a922f3-03ee-4871-9470-c14496ee8155' class='xr-section-summary-in' type='checkbox'  ><label for='section-a6a922f3-03ee-4871-9470-c14496ee8155' class='xr-section-summary' >Indexes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd5e74da-de49-43c4-988e-48f7d4cf2e86' class='xr-index-data-in' type='checkbox'/><label for='index-bd5e74da-de49-43c4-988e-48f7d4cf2e86' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.500999927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:01.501100063&#x27;,\n",
               "               &#x27;2020-08-15 00:20:02.500999927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:03.501100063&#x27;,\n",
@@ -743,7 +742,7 @@
               "               &#x27;2020-08-15 15:36:37.500999927&#x27;,\n",
               "               &#x27;2020-08-15 15:36:38.500999927&#x27;,\n",
               "               &#x27;2020-08-15 15:36:39.500999927&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e4d1b2b4-c38f-47b8-b2ab-31d7088e7edb' class='xr-index-data-in' type='checkbox'/><label for='index-e4d1b2b4-c38f-47b8-b2ab-31d7088e7edb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.438499927&#x27;,\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a2b8a1d9-5018-46ab-a0c5-51edf40fb80c' class='xr-index-data-in' type='checkbox'/><label for='index-a2b8a1d9-5018-46ab-a0c5-51edf40fb80c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.438499927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:01.438499927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:02.438499927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:03.438499927&#x27;,\n",
@@ -764,13 +763,13 @@
               "               &#x27;2020-08-15 15:36:37.438499927&#x27;,\n",
               "               &#x27;2020-08-15 15:36:38.438499927&#x27;,\n",
               "               &#x27;2020-08-15 15:36:39.438499927&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d31ee5be-a3fc-4ede-a323-b7511b7c9d69' class='xr-index-data-in' type='checkbox'/><label for='index-d31ee5be-a3fc-4ede-a323-b7511b7c9d69' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6519ccac-ea74-4ca2-8a30-65b9044ed625' class='xr-index-data-in' type='checkbox'/><label for='index-6519ccac-ea74-4ca2-8a30-65b9044ed625' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
               "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
               "       12.6, 13.1, 13.6, 14.1],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65c31b59-38f0-42eb-b432-dfb5796746ec' class='xr-index-data-in' type='checkbox'/><label for='index-65c31b59-38f0-42eb-b432-dfb5796746ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bcddd0ef-e105-48db-887b-c827dbd31bfd' class='xr-index-data-in' type='checkbox'/><label for='index-bcddd0ef-e105-48db-887b-c827dbd31bfd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
               "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
               "       12.6, 13.1, 13.6, 14.1],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-19a4c600-05e4-4d71-a60d-fd480a398201' class='xr-index-data-in' type='checkbox'/><label for='index-19a4c600-05e4-4d71-a60d-fd480a398201' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-14834dff-b21b-4813-88ea-01693269d8df' class='xr-index-data-in' type='checkbox'/><label for='index-14834dff-b21b-4813-88ea-01693269d8df' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7bc3c2d4-a32b-4144-b901-25afcd54e74e' class='xr-index-data-in' type='checkbox'/><label for='index-7bc3c2d4-a32b-4144-b901-25afcd54e74e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cbd36be0-0303-44d8-853a-df149cbb46a1' class='xr-index-data-in' type='checkbox'/><label for='index-cbd36be0-0303-44d8-853a-df149cbb46a1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-acc71839-5610-4297-a208-7bc7a90addde' class='xr-index-data-in' type='checkbox'/><label for='index-acc71839-5610-4297-a208-7bc7a90addde' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f42ed765-1e50-408e-a06b-5aae30b63a09' class='xr-index-data-in' type='checkbox'/><label for='index-f42ed765-1e50-408e-a06b-5aae30b63a09' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a20eb60c-eddc-4a2d-b861-c6500fe62182' class='xr-index-data-in' type='checkbox'/><label for='index-a20eb60c-eddc-4a2d-b861-c6500fe62182' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3980cac-1d9a-4512-8443-1bee5148b8d9' class='xr-index-data-in' type='checkbox'/><label for='index-a3980cac-1d9a-4512-8443-1bee5148b8d9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db4e73b8-29e3-4b5e-bb59-b11ecf721cbd' class='xr-section-summary-in' type='checkbox'  ><label for='section-db4e73b8-29e3-4b5e-bb59-b11ecf721cbd' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>earth</dd><dt><span>fs :</span></dt><dd>1</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd></dl></div></li></ul></div></div>"
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-af85eef0-8306-4681-826e-f7f164a7fc23' class='xr-index-data-in' type='checkbox'/><label for='index-af85eef0-8306-4681-826e-f7f164a7fc23' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-60161a5a-845d-41cd-b889-ffb9a48cbbef' class='xr-index-data-in' type='checkbox'/><label for='index-60161a5a-845d-41cd-b889-ffb9a48cbbef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4e8ed12b-2907-4929-8cd8-5b9c7be25150' class='xr-index-data-in' type='checkbox'/><label for='index-4e8ed12b-2907-4929-8cd8-5b9c7be25150' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83bf96f1-f265-4cba-ab4c-2794512a16aa' class='xr-index-data-in' type='checkbox'/><label for='index-83bf96f1-f265-4cba-ab4c-2794512a16aa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9bc1d9b4-fe4e-453f-96bb-7ba90cbaa124' class='xr-index-data-in' type='checkbox'/><label for='index-9bc1d9b4-fe4e-453f-96bb-7ba90cbaa124' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d262abda-7774-4ebc-a43d-3f360af5cd7c' class='xr-index-data-in' type='checkbox'/><label for='index-d262abda-7774-4ebc-a43d-3f360af5cd7c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d298e25-23a7-4b96-b66a-d638fdfb1c6d' class='xr-index-data-in' type='checkbox'/><label for='index-3d298e25-23a7-4b96-b66a-d638fdfb1c6d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0bd70f8e-9dc1-4b58-a243-04f8ec9c4ab2' class='xr-index-data-in' type='checkbox'/><label for='index-0bd70f8e-9dc1-4b58-a243-04f8ec9c4ab2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-708b4009-6901-44a2-944d-2b4fa9f30ca1' class='xr-section-summary-in' type='checkbox'  ><label for='section-708b4009-6901-44a2-944d-2b4fa9f30ca1' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>earth</dd><dt><span>fs :</span></dt><dd>1</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd></dl></div></li></ul></div></div>"
             ],
             "text/plain": [
               "<xarray.Dataset> Size: 83MB\n",
@@ -898,9 +897,9 @@
         "2. Blanking distance (the minimum distance from the ADCP to the first measurement point)\n",
         "3. Cell size (the vertical distance of each measurement bin in the water column)\n",
         "\n",
-        "To ensure accurate readings, it is critical to calibrate the 'range' coordinate to make '0' correspond to the seafloor. This calibration can be achieved using the `set_range_offset` function. This function is also useful when working with a down-facing instrument as it helps account for the depth below the water surface. \n",
+        "To ensure accurate readings, it is critical to calibrate the 'range' coordinate to make '0' correspond to either the seafloor or sea surface. This calibration can be achieved using the `set_range_offset` function. \n",
         "\n",
-        "For those using a Teledyne RDI ADCP, the TRDI deployment software will prompt you to specify the deployment height/depth during setup. If there's a need for calibration post-deployment, the `set_range_offset` function can be utilized in the same way as described above."
+        "For those using a Teledyne RDI ADCP, the TRDI deployment software will prompt you to specify the deployment height/depth during setup in order to automatically calculate water depth. If there's a need for calibration post-deployment, the `set_range_offset` function can be utilized in the same way as described above to override the initially specified value."
       ]
     },
     {
@@ -911,7 +910,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1f800403a50>"
+              "<matplotlib.collections.QuadMesh at 0x1c8b6554950>"
             ]
           },
           "execution_count": 5,
@@ -920,7 +919,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -1330,11 +1329,14 @@
               "Coordinates:\n",
               "  * range    (range) float64 224B 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\n",
               "Attributes:\n",
-              "    units:    m</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'>'range'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>range</span>: 28</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b7340fb8-02ae-4b3f-b02f-49273fca98e2' class='xr-array-in' type='checkbox' checked><label for='section-b7340fb8-02ae-4b3f-b02f-49273fca98e2' 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>1.2 1.7 2.2 2.7 3.2 3.7 4.2 4.7 ... 11.7 12.2 12.7 13.2 13.7 14.2 14.7</span></div><div class='xr-array-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+              "    coverage_content_type:  coordinate\n",
+              "    units:                  m\n",
+              "    long_name:              Profile Range\n",
+              "    description:            Distance to the center of each depth bin</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'>'range'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>range</span>: 28</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-eff961cb-d2af-4d36-8756-bef4c22b3dcf' class='xr-array-in' type='checkbox' checked><label for='section-eff961cb-d2af-4d36-8756-bef4c22b3dcf' 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>1.2 1.7 2.2 2.7 3.2 3.7 4.2 4.7 ... 11.7 12.2 12.7 13.2 13.7 14.2 14.7</span></div><div class='xr-array-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
               "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></div></li><li class='xr-section-item'><input id='section-c6fad552-e2a2-4cdf-ac53-7d6c8acde5fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6fad552-e2a2-4cdf-ac53-7d6c8acde5fb' 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'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-da994864-72da-4762-b0f0-18be2b44bbb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da994864-72da-4762-b0f0-18be2b44bbb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b70e673c-adc3-46a6-a21b-2dd9d3e9a65c' class='xr-var-data-in' type='checkbox'><label for='data-b70e673c-adc3-46a6-a21b-2dd9d3e9a65c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+              "       13.2, 13.7, 14.2, 14.7])</pre></div></div></li><li class='xr-section-item'><input id='section-164a5c6c-46f0-4dd6-9456-3ce42b7afbec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-164a5c6c-46f0-4dd6-9456-3ce42b7afbec' 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'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-7df415af-0f6f-4f1b-9636-8673695c0f0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7df415af-0f6f-4f1b-9636-8673695c0f0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3e12bdc-62d6-40fd-aad8-507e04b70d1b' class='xr-var-data-in' type='checkbox'><label for='data-d3e12bdc-62d6-40fd-aad8-507e04b70d1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
               "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30e7ae9a-11e7-4167-8936-d0a2706a5538' class='xr-section-summary-in' type='checkbox'  ><label for='section-30e7ae9a-11e7-4167-8936-d0a2706a5538' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-346a53a8-3af6-49c2-9d18-4011cb7a358c' class='xr-index-data-in' type='checkbox'/><label for='index-346a53a8-3af6-49c2-9d18-4011cb7a358c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
+              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bee506ed-ad82-49d3-83a3-fa7a84d3a41f' class='xr-section-summary-in' type='checkbox'  ><label for='section-bee506ed-ad82-49d3-83a3-fa7a84d3a41f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3d92044-d48c-4d4d-aeeb-b7b19517cc92' class='xr-index-data-in' type='checkbox'/><label for='index-a3d92044-d48c-4d4d-aeeb-b7b19517cc92' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
               "                      2.7,                3.2,                3.7,\n",
               "                      4.2,  4.699999999999999,  5.199999999999999,\n",
               "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
@@ -1344,7 +1346,7 @@
               "                     11.7,               12.2,               12.7,\n",
               "                     13.2,               13.7,               14.2,\n",
               "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6824ee54-f9b3-4dda-983c-2792a033d1a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6824ee54-f9b3-4dda-983c-2792a033d1a6' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div></li></ul></div></div>"
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3caee7ec-c705-4442-9438-ffe44198dda6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3caee7ec-c705-4442-9438-ffe44198dda6' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div></li></ul></div></div>"
             ],
             "text/plain": [
               "<xarray.DataArray 'range' (range: 28)> Size: 224B\n",
@@ -1354,7 +1356,10 @@
               "Coordinates:\n",
               "  * range    (range) float64 224B 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\n",
               "Attributes:\n",
-              "    units:    m"
+              "    coverage_content_type:  coordinate\n",
+              "    units:                  m\n",
+              "    long_name:              Profile Range\n",
+              "    description:            Distance to the center of each depth bin"
             ]
           },
           "execution_count": 7,
@@ -1371,13 +1376,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### 2.2. Discard Data Above Surface Level\n",
+        "### 2.2. Discard Data Above Surface Level and Surface Interference\n",
         "\n",
-        "To reduce computational load, we can exclude all data at or above the water surface level. Since the instrument was oriented upwards, we can utilize the pressure sensor data along with the function `find_surface_from_P`. However, this approach necessitates that the pressure sensor was calibrated or 'zeroed' prior to deployment. If the instrument is facing downwards or doesn't include pressure data, the function `find_surface` can be used to detect the seabed or water surface.\n",
+        "To reduce computational load, we can exclude all data at or above the water surface level, including that affected by surface interference. Since the instrument was oriented upwards, we can utilize the pressure sensor data along with the function `water_depth_from_pressure` to find the water depth. However, this approach necessitates that the pressure sensor was calibrated or 'zeroed' prior to deployment. If the instrument is facing downwards or doesn't include pressure data, the function `water_depth_from_amplitude` can be used to detect the seabed or water surface.\n",
         "\n",
-        "It's important to note that Acoustic Doppler Current Profilers (ADCPs) do not measure water salinity, so you'll need to supply this information to the function. The dataset returned by this function includes an additional variable, \"depth\". If `find_surface_from_P` is invoked after `set_range_offset`, \"depth\" represents the distance from the water surface to the seafloor. Otherwise, it indicates the distance to the ADCP pressure sensor.\n",
+        "It's important to note that Acoustic Doppler Current Profilers (ADCPs) do not measure water salinity, so you'll need to supply this information to the function. If `water_depth_from_pressure` is called after `set_range_offset`, \"depth\" represents the distance from the water surface to the seafloor. Otherwise, it indicates the distance to the ADCP pressure sensor.\n",
         "\n",
-        "After determining the \"depth\", you can use the nan_beyond_surface function to discard data in depth bins at or above the actual water surface. Be aware that this function will generate a new dataset."
+        "After determining the \"depth\", you can use the `remove_surface_interference` function to discard data in depth bins at or above the actual water surface. Be aware that this function will generate a new dataset."
       ]
     },
     {
@@ -1386,8 +1391,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "api.clean.find_surface_from_P(ds, salinity=31)\n",
-        "ds = api.clean.nan_beyond_surface(ds)"
+        "api.clean.water_depth_from_pressure(ds, salinity=31)\n",
+        "ds = api.clean.remove_surface_interference(ds)"
       ]
     },
     {
@@ -1398,7 +1403,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1f80028b8d0>"
+              "<matplotlib.collections.QuadMesh at 0x1c8b67bab90>"
             ]
           },
           "execution_count": 9,
@@ -1407,7 +1412,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -1442,7 +1447,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1f800335450>"
+              "<matplotlib.collections.QuadMesh at 0x1c8b6648ad0>"
             ]
           },
           "execution_count": 10,
@@ -1451,7 +1456,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -1492,7 +1497,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1f800cb7710>"
+              "<matplotlib.collections.QuadMesh at 0x1c8c1be4ed0>"
             ]
           },
           "execution_count": 12,
@@ -1501,7 +1506,7 @@
         },
         {
           "data": {
-            "image/png": 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/+MMfWtltIYQQ4lVnSw/g3Zw777yT/fbbj1QqxS677MINN9ww7v0cSy0dyVm4cCHGmM2+vqXXhBBCiFezZAKxNYr9o2Ftv+kBvKeeeirveMc7trr9008/zbHHHsvpp5/O9773PRYvXsw//dM/0dvby1FHHTXSbm9XE3pOjhBCCDFZjfapxcPdd0sP4H0l11xzDXPmzOHf/u3fANhjjz245557+MpXvvKqKXLkgp4QQgjxKjYwMDBkeekDcUfj3nvv5Ygjjhiy7qijjuLee+8dk/a3BylyhBBCiBbQWo96AZg5c+aQh+AuWrRoTPq3atUqpk6dOmTd1KlTGRgYoFqtjskxxptcrhJCCCFaYKwuV61YsWLIM+TkWXEvkiJHCCGEaIGxKnIKhcJWH5Q7Ej09PaxevXrIutWrV1MoFEin02N+vPEgl6uEEEII8TIHHnggixcvHrLu17/+NQceeGCLejR8UuQIIYQQLaCUbj4QcESLGt6v8FKpxNKlS1m6dCnw4gN4ly9fDiTZjyeeeGJz+9NPP52nnnqKz3zmMzz++OP8x3/8Bz/60Y8455xzxuwzGG9yuUoIIYRoge19C/mDDz7IYYcd1vz63HPPBeCkk07ihhtuYOXKlc2CB2DOnDn893//N+eccw5f/epXmTFjBt/+9rdfNbePgxQ5QgghxA5haw/gfaWnGS9cuJA//elP49ir8SVFjhBCCNEC23skZ0ckRY4QQgjRAtrS6FEUKqPZd0chn5AQQgghJiUZyRFCCCFaQGmF0qO4XKXVGPZmcpIiRwghhGgBmZMz/uQTEkIIIcSkJCM5QgghRAvISM74kyJHCCGEaIFNTzwezf5iy6TIEUIIIVpAWRbaska1v9gyKQOFEEIIMSnJSI4QQgjRAjInZ/xJkSOEEEK0gBQ5408+ISGEEEJMSjKSI4QQQrSA0qO8u2oU++4opMgRQgghWkAuV40/KXKEEEII0RIDAwPD3qdQKGzztlLkCCGEEC2gtBrdSM4kCOhsa2tDqW1/H0op/vrXvzJ37txt2l6KHCGEEKIFZE5O4ic/+QkdHR1b3c4Yw1ve8pZhtS1FjhBCCCFaYvbs2RxyyCF0dnZu0/Zz587FcZxtbl+KHCGEEKIFlLZQehSxDqPYd6J4+umnh7X9o48+OqztpcgRQgghWkFbyTKa/cUWTY4LekIIIcSrjdajX3YAq1ev5gtf+MKI9t0xPiEhhBBCvCqtWrWKSy65ZET7yuUqIYQQogWUZaGsUczJGcW+E8nDDz+8xdeXLVs24ralyBFCCCFaQebkALDvvvuilMIY87LXNq0fzrN0XkqKHCGEEEK0TEdHB1deeSWHH374K77+l7/8hbe+9a0jaluKHCGEEKIVtB7lSM7kmFa7//7788ILLzB79uxXfL2vr+8VR3m2hRQ5QgghRAvIE48Tp59+OuVyebOvz5o1i+uvv35EbUuRI4QQQoiW+cd//Mctvt7e3s5JJ500orYnRxkohBBCvNoo68XJxyNZ1OSYePxKfv/731Ov10fdjhQ5QgghRCuMpsAZ7Z1ZE9wxxxzD888/P+p2pMgRQgghxIQy0onGf0/m5AghhBAtIBOPx19LP6G77rqLt771rUybNg2lFLfddtuQ140xXHjhhfT29pJOpzniiCP429/+1prOCiGEEGNJLldt1rXXXsvUqVNH3U5Li5xyucxrXvMarr766ld8/corr+Tf//3fueaaa7j//vvJZrMcddRR1Gq17dxTIYQQYoxtek7OiJfJO5Lz/ve/nyiKuO2223jsscdG3E5LL1cdc8wxHHPMMa/4mjGGq666is9//vO8/e1vB+C73/0uU6dO5bbbbuO9733v9uyqEEIIIcbRu9/9bg455BDOPPNMqtUqr33ta3nmmWcwxvDDH/6Q448/fthtTtgy8Omnn2bVqlUcccQRzXXFYpHXv/713HvvvZvdr16vMzAwMGQRQgghJppNAZ2jWSaTu+66ize+8Y0A3HrrrRhj6Ovr49///d+57LLLRtTmhC1yVq1aBfCya3JTp05tvvZKFi1aRLFYbC4zZ84c134KIYQQI6L16JdJpL+/n46ODgBuv/12jj/+eDKZDMcee+yI5+NOrk8IOP/88+nv728uK1asaHWXhBBCiAnj6quvZqeddsLzPF7/+tfzwAMPbHbbG264AaXUkMXzvHHp18yZM7n33nspl8vcfvvtHHnkkQBs3LhxxMecsLeQ9/T0ALB69Wp6e3ub61evXs2+++672f1SqRSpVGq8uyeEEEKMzmjvkBrBvjfffDPnnnsu11xzDa9//eu56qqrOOqoo1i2bBnd3d2vuE+hUGDZsmXNr5VSI+7ylpx99tl84AMfIJfLMXv2bBYuXAgkl7Hmz58/ojYn7EjOnDlz6OnpYfHixc11AwMD3H///Rx44IEt7JkQQggxekpbo16G68tf/jKnnXYap5xyCnvuuSfXXHMNmUyG6667bvP9VIqenp7mMha3dr+Sj370o9x3331cd9113HPPPejG5bi5c+eOeE5OS0dySqUSTzzxRPPrp59+mqVLl9LR0cGsWbM4++yzueyyy9h1112ZM2cOF1xwAdOmTeO4445rXaeFEEKICeTvb7DZ3BUN3/d56KGHOP/885vrtNYcccQRW7yhp1QqMXv2bOI4Zr/99uPyyy9nr732Grs38BL7778/+++//5B1xx577Ijba+lIzoMPPsiCBQtYsGABAOeeey4LFizgwgsvBOAzn/kMH//4x/nwhz/M6173OkqlErfffvu4XQ8UQgghths1yknHKvkVPnPmzCE33CxatOgVD7du3TqiKBrWDT2777471113HT/72c/4z//8T+I45qCDDuK5554b289inLR0JGfhwoVbzKdQSvGFL3yBL3zhC9uxV0IIIcT4G+klp5fuD7BixQoKhUJz/VjOSz3wwAOHTBE56KCD2GOPPbj22mu59NJLx+w442XCTjwWQgghxNYVCoUhRc7mdHV1YVkWq1evHrJ+9erVzZt9tsZxHBYsWDBkqslENmEnHgshhBCT2naOdXBdl/3333/IDT1xHLN48eJtvqEniiIeeeSRIXc9T2QykiOEEEK0wmgf6DeCfc8991xOOukkXvva13LAAQdw1VVXUS6XOeWUUwA48cQTmT59enNezxe+8AX+3//7f+yyyy709fXxpS99iWeffZZ/+qd/Gnm/t6BWq/Hwww+zZs0a4jge8trb3va2YbcnRY4QQgjRAqONZhjJvu95z3tYu3YtF154IatWrWLffffl9ttvb05GXr58efPWbUgexHfaaaexatUq2tvb2X///fnDH/7AnnvuOeJ+b87tt9/OiSeeyLp16172mlKKKIqG3aYyW5r5OwkMDAxQLBbp7+/fpmuWQgghdlzb43fGpmNsuPdnFHLZkbdTKtNx4Nsnze+3XXfdlSOPPJILL7xwzJ7FIyM5QgghRCu04InHE9nq1as599xzx/RhgzLxWAghhGiFUU06HmWBNAG9853v5M477xzTNmUkRwghhBAt9/Wvf513vetd3H333cyfPx/HcYa8/olPfGLYbUqRI4QQQrSA0ho1irurRrPvRPSDH/yA//3f/8XzPO68884hQaBKKSlyhBBCiFcNNcpLTmpyXa763Oc+xyWXXMJ555035A6v0ZhcZaAQQgghXpV83+c973nPmBU4IEWOEEII0RpKJSGbI17U1o/xKnLSSSdx8803j2mbcrlKCCGEaAX1YpL4iPefRKIo4sorr+RXv/oV++yzz8smHn/5y18edptS5AghhBCi5R555BEWLFgAwKOPPjrkNTXCUattKnIefvjhYTe85557YttSQwkhhBCvxCiNGcVozGj2nYjuuOOOMW9zm6qQfffdF6UU25oAobXmr3/9K3Pnzh1V54QQQohJSy5XjbttHmq5//77mTJlyla3M8aw9957j6pTQgghxKSn1OgmD0+CicfveMc7uOGGG7Y5e+sDH/gAX/nKV+ju7t6m7bepyDn00EPZZZddaGtr26ZGDznkENLp9DZtK4QQQogd089+9jPWrl27TdsaY/iv//ovLr300rEtcoZ7neyXv/zlsLYXQgghdjhaJ8to9n+VM8aw2267jVv7MjNYCCGEaAGZeDyyycbTp0/f5m2HXeQYY/jJT37CHXfcwZo1a4jjeMjrP/3pT4fb5HYRP3EfcbENo23MwDqIY1TKQ7kecXkAwgCVzmKqZXTbFOJSH8qyiKtllO1iQh+dbydavxLtZcF2UKk0pjwAtgNxjL3g6Fa/TSGE2OH8cflGtFLsP7MNgL+sHMC2IIzAtRSWhiiGGMPu3QWeXV9iwI9I2xrPUlhaEcUG21Js3Fhq7ZvZwRx66KHj2v6wi5yzzz6ba6+9lsMOO4ypU6eO+N51IYQQYocmd1eNu2EXOTfddBM//elPectb3jIe/RFCCCF2DFLkjLthf0LFYlGefyOEEEKICW/YRc7FF1/MJZdcQrVaHY/+CCGEEDuGUYVzjnIUaAcx7MtV7373u/nBD35Ad3c3O+2008sCtJYsWTJmnRNCCCEmK6PUKO+umlxzYi+66CJOPfVUZs+ePWZtDrvIOemkk3jooYc44YQTZOKxEEIIIcbEz372M774xS9y6KGH8qEPfYjjjz+eVCo1qjaHXeT893//N7/61a94wxveMKoDCyGEEDs0mXg8xNKlS/nTn/7E9ddfz1lnncXHPvYx3vve93Lqqafyute9bkRtDvsTmjlz5jZnTAghhBBiMzZlV41mmWQWLFjAv//7v/PCCy/wne98h+eee46DDz6YffbZh69+9av09/cPq71hFzn/9m//xmc+8xmeeeaZ4e4qhBBCiE1k4vFmGWMIggDf9zHG0N7ezte//nVmzpzJzTffvM3tDPty1QknnEClUmHnnXcmk8m8bOLxhg0bhtukEEIIIQQPPfQQ119/PT/4wQ9IpVKceOKJXH311eyyyy4AfO1rX+MTn/gE73nPe7apvWEXOVddddVwdxFCCCHE35HsqqHmz5/P448/zpFHHsl3vvMd3vrWt2JZ1pBt3ve+93HWWWdtc5sjurtKCCGEEKOkRplCPsmKnHe/+92ceuqpWwzg7Orqellm5pZs0yc0MDCwzQ0CDA4ODmt7IYQQQuzYNs29+XvVapUvfOELI2pTGWPM1jayLIuVK1fS3d29TY0WCgWWLl06IeIfBgYGKBaLbFzyGwqFPMZOQXUAQh9sF+Wmida/gHI9VKGL6Lm/YnX2EJcH0NkCcXkAEwZoL4tyHEy9hkp5YLuYejVJL298TehjoghTqzSPHw9uxIQBJgywOnsw9RrxwHqs9m5MvUpcq0AcgbaIBzeiswWiwT606xFVK8RBSOm5tbTvvStBXx+xH2J5LoPLV6MsjYmSila7NoWdeln9wP/Rtc8uWJ6LiWOsbJ76mrUoS6Mdm3rfIEprOs/8Uqu+JUKIFjni6/dQrwbMnJpjfclnr+lFHl85QN6zyXkOUZz8Onhm9SBt+RR5zybt2pRqQbONNQN19ppebCZ3R7GhGkREsWF2ZwY/TH4mTSmkyLo2Gys+/ZWAWZ0Z/Cim6kd0ZV2C2KCVoj3tEEYxlSAiMtCddfFsTcaxiIwh79qsLvsUUzb5lEUQGVK2ph7GZF0LP4qThPHGr7LXzWrnkZX9RLHBtTSOTu5AStmKWR05nlo3iK0VlSBOEsqVQinwI4NnKUqDg+yx0zT6+/vH7U7iTb+X1j7zVwqF/CjaGWTKTruNa1+3p83VGuvXr6e7u5soiobd5jZdrjLG8O1vf5tcLrdNjQZBsPWNhBBCiB2ZPCdnCGPMKz5g+M9//jMdHR0janObipxZs2bxrW99a5sb7enpedldV0IIIYQQf6+9vR2lFEopdttttyGFThRFlEolTj/99BG1vU1FjjwTRwghhBhjMpIDJHdtG2M49dRTueSSSygWi83XXNdlp5124sADDxxR28O+u0oIIYQQoycBnYlNd23PmTOHgw46aEyvBEmRI4QQQoiWGBgYaE6aXrBgAdVqlWq1+orbjmRytRQ5QgghRCvI5Sra29ubd1S1tbW94sTjTROSx+3uqlaJooiLL76Y//zP/2TVqlVMmzaNk08+mc9//vOv+EEIIYQQrxqjDdmcBL8Hf/vb3zbvnPrtb3875r/bJ3SRc8UVV/CNb3yDG2+8kb322osHH3yQU045hWKxyCc+8YlWd08IIYQYORnJ4dBDD23+feHChWPe/og+obvvvpsTTjiBAw88kOeffx6Am266iXvuuWdMO/eHP/yBt7/97Rx77LHstNNOvPOd7+TII4/kgQceGNPjCCGEEDuKq6++mp122gnP83j961+/1d+pP/7xj5k3bx6e5zF//nx++ctfjku/rr/+en784x+/4vFvvPHGEbU57CLnlltu4aijjiKdTvOnP/2Jer0OQH9/P5dffvmIOrE5Bx10EIsXL+avf/0rkDwQ6J577uGYY47Z7D71ep2BgYEhixBCCDHRbAroHM0yXDfffDPnnnsuF110EUuWLOE1r3kNRx11FGvWrHnF7f/whz/wvve9jw996EP86U9/4rjjjuO4447j0UcfHe3bf5lFixbR1dX1svXd3d0jri+G/QlddtllXHPNNXzrW98acpvXwQcfzJIlS0bUic0577zzeO9738u8efNwHIcFCxZw9tln84EPfGCz+yxatIhisdhcZs6cOaZ9EkIIIcbEpstVo1mG6ctf/jKnnXYap5xyCnvuuSfXXHMNmUyG66677hW3/+pXv8rRRx/Npz/9afbYYw8uvfRS9ttvP77+9a+P9t2/zPLly5kzZ87L1s+ePZvly5ePqM1hf0LLli3jkEMOedn6YrFIX1/fiDqxOT/60Y/43ve+x/e//32WLFnCjTfeyL/+679ucdjq/PPPp7+/v7msWLFiTPskhBBCTCR/f/Vi0xWWv+f7Pg899BBHHHFEc53WmiOOOIJ77733Ffe59957h2wPcNRRR212+9Ho7u7m4Ycfftn6P//5z3R2do6ozWFPPO7p6eGJJ55gp512GrL+nnvuGfNAzk9/+tPN0RyA+fPn8+yzz7Jo0aLmw4P+XiqVIpVKvfwFpTDaBmOagZjKSbZT2kI5brLO9SCOULaLqdeSkM7BPnS+DVMro9LZZP84QlkWBkBbEEeYqLFOa+L+9eh8O2gLnc8SbVwLYYDxa8TlAaz2blQqDZVBlJcFQOfbUVonIZxZBzs9hcozzxAHIcQxSmsszwXAyXqENR+3kKG2fgDt2pg4Jjd9CiaOiWo+ytKEA/1oxyaq+QTlKraXIvKTbLEVn/8QytKkp7QxuGIN2Z4OtGNjuQ6VNRuxPZd1Dz9F+x6zqPeVcDIeqfYclZUbSLXnmPqZr7Fy0ceorFrPzl/9Ic9ddBr+QJlUW5563yDadbA9l3rfIKm2PCaKKa9az7xv38ZT57wfgLlf+f4YnjFCTGz7X3A72tZ4GYcwiPGrAV7WJQpjoigm5TkYY4hjg4kNU7qz9PXXeOHJDXTPakNrRUdHmqof0b++gpdxsB2LfWa3c/9fVlOr+Dgpm0zOJQxiOjrSbNhQZe6MAstXl2gvegT1kCg2pF2LtYM1qrWQvGfjh0nIpqUVJjaUaiGlWsiuPXksrbD0i/8n7sy5rOyvkU8lv0IiY3hsRR8z2tPNtv0wJu0YimmHNQN1SvWQgufghzFaKxwgZVvExqAbIZqerfHs5Di1MCbjWABYL7nhJohjoiAJ46wGMbFJAjuDCBxL8cjKfjZUAjoyDmlbEcZgaQhjw+OrB3AthaMVKUtRjwyWSl43BmxL4WzHubzJwwBHfjfRpn3//orFRRddxMUXX/yy7detW0cURUydOnXI+qlTp/L444+/4jFWrVr1ituvWrVqxP3enPe973184hOfIJ/PNwdTfve733HWWWc164DhGnaRc9ppp3HWWWdx3XXXoZTihRde4N577+VTn/oUF1xwwYg6sTmVSgWth55xlmURx/GYHkcIIYTY3oxJltHsD7BixYohD8p7xf/ovwpceumlPPPMMxx++OHYdlKexHHMiSeeOOI5OcMucs477zziOObwww+nUqlwyCGHkEql+NSnPsXHP/7xEXVic9761rfyxS9+kVmzZrHXXnvxpz/9iS9/+cuceuqpY3ocIYQQ4tWqUChs09OAu7q6sCyL1atXD1m/evVqenp6XnGfnp6eYW0/Gq7rcvPNN3PppZfy5z//mXQ6zfz585k9e/aI2xx2kaOU4nOf+xyf/vSneeKJJyiVSuy5557kcrkRd2Jzvva1r3HBBRfw0Y9+lDVr1jBt2jQ+8pGPcOGFF475sYQQQojtKTaGeBRDOcPd13Vd9t9/fxYvXsxxxx2XtBHHLF68mDPPPPMV9znwwANZvHgxZ599dnPdr3/96xEHZm6L3XbbjV133RVg1A8HHPHVR9d12XPPPTnggAPGpcAByOfzXHXVVTz77LNUq1WefPJJLrvsMlzXHZfjCSGEENuLGYNluM4991y+9a1vceONN/LYY49xxhlnUC6XOeWUUwA48cQTOf/885vbn3XWWdx+++3827/9G48//jgXX3wxDz744GaLotH67ne/y/z580mn06TTafbZZx9uuummEbc37JGcf/zHf3zFykophed57LLLLrz//e9n9913H3GnhBBCiMkuNskymv2H6z3veQ9r167lwgsvZNWqVey7777cfvvtzcnFy5cvHzIX9qCDDuL73/8+n//85/nnf/5ndt11V2677Tb23nvvkXd8M7785S9zwQUXcOaZZ3LwwQcDyU1Np59+OuvWreOcc84ZdpvDLnKKxSK33XYbbW1t7L///gAsWbKEvr4+jjzySG6++WauuOIKFi9e3OykEEIIISaGM888c7MjMXfeeefL1r3rXe/iXe961zj3Kpmi8o1vfIMTTzyxue5tb3sbe+21FxdffPH2KXJ6enp4//vfz9e//vVmtRfHMWeddRb5fJ4f/vCHnH766Xz2s58d85gHIYQQYrIwxmBGMSdnNPtORCtXruSggw562fqDDjqIlStXjqjNYc/J+c53vsPZZ589ZDhLa83HP/5xvvnNb6KU4swzzxyXRz4LIYQQk8Wmy1WjWSaTXXbZhR/96EcvW3/zzTc3JyIP17BHcsIw5PHHH2e33XYbsv7xxx8niiIAPM8b87h0IYQQQkxel1xyCe95z3u46667mtNdfv/737N48eJXLH62xbCLnA9+8IN86EMf4p//+Z953eteB8Af//hHLr/88uZ1tN/97nfstddeI+qQEEIIsaOYZIMxo3L88cdz//3385WvfIXbbrsNgD322IMHHniABQsWjKjNYRc5X/nKV5g6dSpXXnll8wFBU6dO5ZxzzuGzn/0sAEceeSRHH330iDokhBBC7AhacXfVRLf//vvzn//5n2PW3rCLHMuy+NznPsfnPvc5BgYGAF72pMVZs2aNTe+EEEIIMWltqiO2xbY81fnvDbvIGe0BW8VYLiiNcVIQ+Engpe1gwgC0hQl8dFBNtm3MLVLpLKZeRbleEqxZLaNcLwnhrCZhnUpbxIN9KC+TBG7GETqdJS4PElfLL3Yg9DGBD3GE31fC6iyj01lM4CcBnXHU7I9pZHOZahkTx3idhSQYtFQiDsIkRNNzMXFMqi1PUK6Rmz6FqObjdRab+/sDZex0Cn+gTL2vBEB6ShuZnXai75v/TFCu0rn3XMJqHdtzqa7pIzu9CxPF1PtK2D0dVNZVyJdrBANlLMfGRDH+YPK+1vzrWZgoxkQxz1/yEYJyjajmA2B7Keysx+Dy1aTakz46WY/qugp/O+OdKCuZuP6n444kqIW0z+lAuzZrHklm0HftPoXSygHyM9oIyjXqA3VShRSrlq5m6vxu4igmCmIsR5PpLrJqyXNM+39z6HtiFZ17zGDNn5fj5hwiP0JZmqCUBJamCi6V9VVKL5R408MPjPNZJyaCs259BD+Kyads+qoBVT+k4kdkXIu0a/PchgrdBY81AzX2nl7kuY0VihmXx5/rZ3Z3jmfXlDDGMHNKlqeeGyCODW7Kol4NCYOIXJtHUI/4/XlvYt9//iXZgkd5oAZAynOS4M20Q+hH6Eb4pNaKMIiIY4NSCidlYarJnTZKKdyUpjxQxw9jwiAmqFVIpbtYv7LEXnM7eOiRVWhbE0cGnVI8t7GC7VjN40Vh0s7GjTXi2DBYC3FSNoOVAG1p0q4NJJ+D1gpLK4ppl2oQsWaghpOyCeoh2tJEjf1729KUakHzc7WUophx6K8ElGoBO03NM1gL6cy5lGohOc+mvxoQxQZbK6p+RMFziGJDEBnKfsi0lN0M7owMaGMIYoNWCjBYOgnk1Eo1/rQJIkNHxqYWxDiWIjagVBLuGcQxjtJMybpExmBIwkOJkzmiSoEiCf8MY7D1i3NHbQ1hZPCj7Tc8IndXQVtb21bn8G76d7Fp3u9wDLvIWb16NZ/61KdYvHgxa9asedmHPJJOCCGEEDuauLGMZv9XuzvuuGNc2x92kXPyySezfPlyLrjgAnp7e+UuKiGEEEKMyKGHHjqu7Q+7yLnnnnu4++672XfffcehO0IIIcSOwZhkGc3+k83dd9/Ntddey1NPPcWPf/xjpk+fzk033cScOXN4wxveMOz2hv0wwJkzZ06K64BCCCFEK8nDAIe65ZZbOOqoo0in0yxZsoR6vQ5Af38/l19++YjaHHaRc9VVV3HeeefxzDPPjOiAQgghhHhx4vFolsnksssu45prruFb3/oWjuM01x988MEsWbJkRG0O+3LVe97zHiqVCjvvvDOZTGZIRwA2bNgwoo4IIYQQYse1bNkyDjnkkJetLxaL9PX1jajNYRc5V1111YgOJIQQQogXyd1VQ/X09PDEE0+w0047DVl/zz33MHfu3BG1Oewi56STThrRgYQQQgjxIsMoJx6PWU8mhtNOO42zzjqL6667DqUUL7zwAvfeey+f+tSnuOCCC0bU5qgeBlir1fB9f8i6V9MDAoUQQggxMZx33nnEcczhhx9OpVLhkEMOIZVK8alPfYqPf/zjI2pz2BOPy+UyZ555Jt3d3WSzWdrb24csQgghhNi62JhRL5PBa1/7Wq655hoGBwf53Oc+x4YNG3j00Ue57777WLt2LZdeeumI2x52kfOZz3yG3/72t3zjG98glUrx7W9/m0suuYRp06bx3e9+d8QdEUIIIXYkZgyWyeA1r3kNn/nMZ+jt7eXEE0/kD3/4A3vuuScHHHAAuVxuVG0Pu8j5r//6L/7jP/6D448/Htu2eeMb38jnP/95Lr/8cr73ve+NqjNCCCGE2LF85zvfYdWqVVx99dUsX76cww8/nF122YXLL7+c559/flRtD3tOzoYNG5qznAuFQvOW8Te84Q2cccYZo+rMuFIKFfmoOMQ4bhKWaTkobUEcYeo1jF8DrVGOm6yrltHZ/NCgTSCuJuGaysth/ComjtCOi7IdwAGlUc76ZGPtYeXbiDauBW2hvCypro4kxLNaRjkupjKQhIA2QkO1Y2OiiLBUws1niYMQQp+gXMVyHbRrYwUOtfVJeqvtuUlw5kCZ7KzpBI1b7eIgRBeyaMdOQj1dm6jmU38hOWnqfSXsjIeJ4iQ8szbAhr88TXHn6VTX9FHYqQe/HGCimF2/8ROWn38KpefXYXkpyqvWM2vR9az517PwB6rs8vUf8ed3H0N+ejtO1qPeN4id9XCyHk4m+RrAyTrY2TQA1TV9xJEhKAVo18bNZ8l0ZaisqwCQKqTY8MQ6ul8zk4HnlqMsjYkM9YE62rUory5juRbFOT0A5KZ3sf7/XsAfLBPWQrSliPyIVDGF5VrUBpIHS2U601TWVVl6/FGs/b91TH/dNJSlqKyrkulKM7iyxPQDdyWo1Ni4bCVeu4eJDMpSaMdm3f+t5eB77ubJs95LUK5hopj8rKn4A8l5Mvj8Rtp37mFg+Rrad5tBHISYKMbyXDLd7VTWbGyuA8j0duA0PhM745F93+fp++Y/E9bqpNry2G0dRIN9OFNnEtcqKK3x3nIGlVv+FavYSVwZxJ46KzkPXQ+0BVqjc22YegWy7RBHoDRYFsRxcn6nsqjIx2ibOJXHeHliO0U1iIkM+JHBj2IsrSi4GksrBuoxWsFj6yrMLHgM1COCOCaIDPmUxZMbKmQci43VgJStCWKDoxVBbNhYDfCjmDbPoRbGbKz4FNMOj68cZHZXhg0lH9fWpF0L19Y8vnKQeb15AFb11bC0oqfN48GnNzK7M8P6xvZ5z6an6LF2sE7Vj3h2fZnvn/g6zrzlYTKuBYBraSp+hKUVFT/J18t5DqVaEiBpaUW1FuLampznYCmFZWtW9lWJG98nP4yxHU0YxCitSOdc/FqIiQ1+PWTB5/4nCREMY9K5FJalKfVX8TJu0oe0TbXkY1maODbEjfDNMIiwbI1fT0I0lYZKyU8CQMOYykCddD4/5GdQFMW4aYd6NaDY5mFrhWUr6jWDZWnKAzUsWyehnWk7CeDMOJRqSZuWVqwbqJNyLUp9NaZ1JOdfFBsyrkWpEjB9SpaVG6pU/bD5Wtq1SbsWllb0V306cy5uI3TUDyMyrodra8LYUKqF+GHc/LynuBYDtQBLKywFaceiEkTkUjaxMeRdi0E/SvqQsnC0Ip9Kgjg925CyNZUgQitFFBsiY6iHcTPMU2uFozUxBj8wpJ3k/ItisO0Xny6slaIWxVhK4WhFOYgpuJpaZHC3c0zRaB/oN5keBpjJZDj55JM5+eSTefLJJ7n++uu59tprueiiizjyyCP50Ic+xDve8Y5htzvskZy5c+fy9NNPAzBv3jx+9KMfAckIT1tb27A7IIQQQuyQzIvF10iWSXO96u/svPPOXHbZZTzzzDP84Ac/4L777uNd73rXiNoadpFzyimn8Oc//xlIZkJfffXVeJ7HOeecw6c//ekRdUIIIYQQYpM777yzObITRRGnnXbaiNoZ9uWqc845p/n3I444gscff5yHHnqIXXbZhX322WdEnRBCCCF2NDGGeBTDMaPZdyJ67rnnuOGGG7jhhht46qmneOMb38h//Md/8K53vYt0Oj2iNodV5ARBwNFHH80111zDrrvuCsDs2bOZPXv2iA4uhBBC7KgkhTzxox/9iOuuu47FixfT3d3NSSedxKmnnsouu+wy6raHVeQ4jsPDDz886oMKIYQQOzqZeJw44YQTOPbYY7n11lt5y1vegtbDnkmzWcO+XHXCCSfwne98h3/5l38Zs04IIYQQYsf03HPP0d3dPS5tD7vICcOQ6667jt/85jfsv//+ZLPZIa9/+ctfHrPOCSGEEJOVXK5KjFeBAyMoch599FH2228/AP76178OeU1t52cMCCGEEK9WMvF4/A27yLnjjjvGox9CCCGEEGNq7Gb3CCGEEGKbjeZBgKO91DXRRFHEXXfdRV/jif1jRYocIYQQogUkhfxFlmVx5JFHsnHjxjFtV4ocIYQQQrTc3nvvzVNPPTWmbe4wRY6KAgCMtiFdQKXSmKCOCZP1aAtlOyi7EbSpLVQqCc2MK4OYwE/CPAGdLQAQu2niwY0QBsSDfZhaGRMGmKAO2kJnCsk+tovOtaHTyZ1oqhHOSRwlwZxRlHwd+km4IiQhikBQruLkM8TVMiaOifyA2A+p9w0SByH+YBll6SSM07EhjtGOTVCuYqIYE8Wk2vMoSxNWfaIgpLJmI2GtTuRHrF36NwAqa/qIanXiKMYfrJDp7UBbFtmpSZ//dsY7ieOYOf96E7bn4hYyPPiWwxl4ZiXatXjmsyeR7U5CBE0cY+KYsFxj7le+T1TzMXFMbX0/AE42ec/atXBzDtmpmWbfahtreIUU2rXxyz5aK+obB+l7th/Hs7HTNn3P9hOUfCzXIqwmn8HAc0kAaGFGkcHnNhL5EX3PJscLqyGRH+F4jTYtjYlilKUpbazhl30iP8JO29Q21hptVhhcvhY7beOXA6Igap5LQS3kobe+OQnmnDmV2sYaQblGHIQMPr8xCTwtZNCWRrs2UeP7FNV8/MHykO9bFIQE5Rr1jYP4A2X8vhKVW/6V8sr1BOUaytIQR1jZPHGpD0IfnW+ndvs3IQyS87haJh7cSNy/PjmftSbauJZowyqi/vWoKEjCaSO/Gc6J7WDsFMb2QNsoE4MxqDiiHhksBWFs8Bphi0EMJT/GGEPQWB80HtKRcazm/yiLnkPK1vTXQ3KuTcpKttNKUaqHzWBFz9b4YUx/I8jTD2PWDNaxtGoGPvYWPap+RKkWkvOSkMkoNhTTDjnPJjKGvGfTkXOpBkn45mAtJOc5nHXrI0TGMNg4ZsrW9Ff95PiNfltKkXbt5rq0ZzNYC0k7Fn4UY2JDGMYU8imUVoSxwXaSf59KKWqVgMpgHW0l/XVTyRRHy9bEYUwqbRPUIpRWjX8XoLRC25pU2sFJ2UmoZCppM6hHeFkH20lCM5MfA4bQj6iVK422FVU/xLI0Jjakcy6dOZdV6yuEQYzV6MumcM4oiqlXw+T9V5Iw0rgRSAokYahpG0trosb3cLAWMqMrQ08xTUchlbTX+Owtrci4FmsG6lhaNwNP065NxY8af7eoBhE5L1nnRzGZRqhnuhGYqrWimLKJY0PK0mQcC93oUz2KSTXeRy1Ifg5GxlDyIzKNz18rRcaxcKxknyim2X+NwrM1rqUIG/9sjQFLJ9tt+t57jX1zjXBOY5JzPtiOoyNRPPplMrnsssv41Kc+xS9+8QtWrlzJwMDAkGUkhj3xWAghhBCjN9pLTpPpchXAW97yFgDe9ra3Dblb2xiDUoooija362ZJkSOEEEKIlhuPu7elyBFCCCFaIDameZltpPtPJoceeuiYt7nDzMkRQgghJpIku2o0d1e1+h2MvbvvvpsTTjiBgw46iOeffx6Am266iXvuuWdE7UmRI4QQQrTARJ54vGHDBj7wgQ9QKBRoa2vjQx/6EKVSaYv7LFy4EKXUkOX000/f5mPecsstHHXUUaTTaZYsWUK9Xgegv7+fyy+/fETvQ4ocIYQQQgzxgQ98gL/85S/8+te/5he/+AV33XUXH/7wh7e632mnncbKlSuby5VXXrnNx7zsssu45ppr+Na3voXjOM31Bx98MEuWLBnR+5A5OUIIIUQLTNS7qx577DFuv/12/vjHP/La174WgK997Wu85S1v4V//9V+ZNm3aZvfNZDL09PSM6LjLli3jkEMOedn6YrE44ichT/iRnOeff54TTjiBzs5O0uk08+fP58EHH2x1t4QQQohRiRoTj0ezAC97nsymyzwjde+999LW1tYscACOOOIItNbcf//9W9z3e9/7Hl1dXey9996cf/75VCqVbT5uT08PTzzxxMvW33PPPcydO3fb38BLTOiRnI0bN3LwwQdz2GGH8T//8z9MmTKFv/3tb7S3t7e6a0IIIcSEMHPmzCFfX3TRRVx88cUjbm/VqlV0d3cPWWfbNh0dHaxatWqz+73//e9n9uzZTJs2jYcffpjPfvazLFu2jJ/+9KfbdNzTTjuNs846i+uuuw6lFC+88AL33nsvn/rUp7jgggtG9F4mdJFzxRVXMHPmTK6//vrmujlz5rSwR0IIIcTYiGFUd0htmne8YsUKCoVCc30qlXrF7c877zyuuOKKLbb52GOPjbg/L52zM3/+fHp7ezn88MN58skn2Xnnnbe6/3nnnUccxxx++OFUKhUOOeQQUqkUn/rUp/j4xz8+oj5N6CLn5z//OUcddRTvete7+N3vfsf06dP56Ec/ymmnnbbZfer1+pChupE+CloIIYQYTy+NGRnp/gCFQmFIkbM5n/zkJzn55JO3uM3cuXPp6elhzZo1Q9aHYciGDRuGNd/m9a9/PQBPPPHENhU5Sik+97nP8elPf5onnniCUqnEnnvuSS6X2+Zj/r0JPSfnqaee4hvf+Aa77rorv/rVrzjjjDP4xCc+wY033rjZfRYtWkSxWGwufz+MJ4QQQuyIpkyZwrx587a4uK7LgQceSF9fHw899FBz39/+9rfEcdwsXLbF0qVLAejt7d2m7U899VQGBwdxXZc999yTAw44gFwuR7lc5tRTTx3We91kQhc5cRyz3377cfnll7NgwQI+/OEPc9ppp3HNNddsdp/zzz+f/v7+5rJixYrt2GMhhBBi25hRPQjQYMbp7qo99tiDo48+mtNOO40HHniA3//+95x55pm8973vbd5Z9fzzzzNv3jweeOABAJ588kkuvfRSHnroIZ555hl+/vOfc+KJJ3LIIYewzz77bNNxb7zxRqrV6svWV6tVvvvd747ovUzoIqe3t5c999xzyLo99tiD5cuXb3afVCrVHLp76RCeCWoYNw0mThKX4whS2WSxXdAa42ZB6yQJPE6CwJT94r36KuVBI7Vcedkkvdl2Ua6Hsh1MECTtxRFWvq3ZBqH/4t83JY/7teTvXraxPsbUayjXQzs2JvSprt2Ik00TVpKE69gPcbJpopqP5blDksaVpUm15wlLJcJyjajmU1nbRxyE1Nb3U12zMUkr90NSbTkqKzdQWV9Fa01QrmJ7LmHVxy1kif2Q6pqN2FmPXG8Rf7CMdm2cjMcLl52R9C+K8do9wpqP7blordGOTVTzqa5PLhGWnl/H8vNPIazVif2Q0sp+wlpIeeV63EKWoJH+7WZdnKxHVKsTVkNqA3VMFDP4Qon8jCKllYNYjkUcxdieTVgLiWNDpitN+9w2Yj8EoLJyA+U1g6x+ZC3KUtQHfFLFFNq1KK2pEAUx9QGf9j1mkenKUNtYoz+IsVyLwZUltKWII0NlXZXyyg3EkcHJutQ21nDzHrWNNaJagOVotKWori8RVGqkuzJ4nY3zLIrZ8LeN1NYPUN1YQ1s6eV81n7iRLO8PlgGIgpD8rG6imk9t/QBxECap42FAVPOTbWo+9TVrm+n0KlMgLg8kifdxhAl8sB3QFsavYUIfU6sQD6wnLg9i5dswfhWUxtQbdzkoDVGECqpgYozlYBwvecnEWAoiAxlH40cGWyvqYUxsoBomw+uVIMbSkHY0sUlSvrVKkqWtxp/99TB5zdLUwxjX1qQdi5xrUwmiJD28kVqddi38MKbqR/hh8jl15FxKtZBSLcQPY4oZh6ofkfcaad8qSSwv1ULWDiSXqKuNtPj50wvJ65amLZP8G940tJ+yNbZWWFrhhzGdObeZkF2qhzy3sYKlFUorpnWk6cylsJ2kfxnPbpwnScq4ZWtc1yIOY5yUTSrtYDsapRVdhRRu2sbLOMSxIYpi4jBZojBGaUUcG/xqSK3soxp9iKM4OV41II5i3LSN7aYI/ZjQj6n4Ebk2DxMbTAx+mGzvpJLUcQDL0nhZh5Tn4KQs+ioBQT0kCmNyGYf+io/fSGjP55P5G6VagGvrRtq4prPx/fHDJEU87VhU/QjX1s20+E2J8Vbj89z0OedTNu1ph942j3zKTr7Hjc9w0/cu3UgUBwiimCAyWApybnLuRMaQdV/cppiySdkKx1IoBZUgwtEax1LNNPIYg22BUsmdS2lHkbIVMYYgNqRsRRAnx4FkmzA2+JEhJlnvvCQYcrxFZvTLePne977HvHnzOPzww3nLW97CG97wBr75zW82Xw+CgGXLljXvnnJdl9/85jcceeSRzJs3j09+8pMcf/zx/Nd//ddWjzUwMEB/fz/GGAYHB4fcKbZx40Z++ctfvmwi9Laa0HNyDj74YJYtWzZk3V//+ldmz57doh4JIYQQk19HRwff//73N/v6TjvtNGQkaebMmfzud78b0bHa2tqaT0jebbfdXva6UopLLrlkRG1P6CLnnHPO4aCDDuLyyy/n3e9+Nw888ADf/OY3h1STQgghxKvRRH0Y4PZ2xx13YIzhTW96E7fccgsdHR3N11zXbd6WPhITush53etex6233sr555/PF77wBebMmcNVV13FBz7wgVZ3TQghhBiVsbq76tVuU/r4008/zcyZM9F67GbSTOgiB+Af/uEf+Id/+IdWd0MIIYQYUzKSM9SmqSiVSoXly5fj+/6Q17d1AvNLTfgiRwghhBCT39q1aznllFP4n//5n1d8PYqiYbc5oe+uEkIIISariXx3VSucffbZ9PX1cf/995NOp7n99tu58cYb2XXXXfn5z38+ojZlJEcIIYRoAblcNdRvf/tbfvazn/Ha174WrTWzZ8/mzW9+M4VCgUWLFnHssccOu00ZyRFCCCFEy5XL5ebzcNrb21m7di2Q5GAtWbJkRG1KkSOEEEK0QBybUS+Tye677958Nt5rXvMarr32Wp5//nmuueaabY6G+HtyuUoIIYRogXiU82omWY3DWWedxcqVKwG46KKLOProo/ne976H67rccMMNI2pTihwhhBBCtMzTTz/NnDlzOOGEE5rr9t9/f5599lkef/xxZs2aRVdX14jaliJHCCGEaAGZeJzYeeedmT17NocddhhvetObWLhwITNmzCCTybDffvuNqu0dZk6Osl2M0igTY3RS26lGWKfSSSgilT4IA+LBPlQ6jwl8jF9DaQvluKhMEdMI6ETbqCh5UJHO5kFbqHQ2+dP1moGKOt+GCQJ0vq0Z9mlPmY5V7Ey2CX2IY1TKQ2ULmMoAyvUIK7Vm32M/TEIx45igXCUKAmwvRWFOL7aXwi0kIZ9RLQn11K7dDM6sru8n9sMk4DMISbXnsFyHsFYn8iOiIMQfqOAPlgnKdUwUUVm7kfpAnXrfIEprvM4isR8y/aJrmfb5b+AWMrj55Jgmion8sBHi6aDd5LN1MmnqA3W8zgJBuUZQrlNZVyXbnSeqBdT7BpPj+zH1gTq2lyKs+njtHrGfBI7aaZvdv/lTwlpI2+wCe3/vv6kP1Ml2Z4n8qBGgmaK8ZpBUwWXjk6uorKtSH/SJ/ZhUwSWsJuGd9YE6qYKLiWI2PrYcN+tQWVchbSksx2LguUGqG2tEQURlQxUTG+oDdZTWRH7ybAYn6+BkU2hLE9RClFas/OMz5KZPod5Xorq+hF8OiIII23Pxyz5BuYaTSRP7EZZjE0cxYdVHaY3tuViOQ+n5tWjXpr6xhD9QRmULydd9pWYQa9i3IQlw1Zq4PIC/fn3zHKmvWdc4z/xkG9tJQmBrZUwUNc87ZTuoKEjOW6WS0E5tJ+vqZVQc4isbWyvC2FAPY2ytGoGNCkuDHxnCGKblU/iRQasknLG/FlIPY7RSRMYQmeQHcNFzsLQi1wha9KOYahCRaYQ9Vv2IKYUUUWxwbc2UQqoZ1ulozU6dGXKNQE7X1qRdqxkECVBtfG9ynj0kJPKF/hqleogfxQzWwub2acci5znNIEo/jKj4EaVa0AijTNqLYkNvm0fOc0i7FuWBejOU0mqEUtqORVBvbB/F1Co+TsqiVg5QWtFXSX5W/L9duxphmgY37VDqqzGjK4Pt6OQ8qyXbaa34rw8f2Jxn4aaTYE/L1ngZF9X4aZ1qHP/+C9+MZSfvNwwi6tUQy9JoWxNFMVPb09QqPtOmZEm7FtrSpD27Gaw5sL5KvvF1X8XHD2Pa0g5tGae5zab3XEy7RMZQTDukXZuUrWlLO0mAaj2kM+s2P+Osm3wvcqnk+1bMOEzJp4iModAI6wyimCA2OJbGszU51ybjaDozLp6drAPQCoI4Ju/aZF2LKIasa2EplfypaQTEKoLIYEwS/hkZQ6meHMOPDFH84npjwEAjqFMRG3AthSYJ7xz+k1hGLvm3MrplMvjtb3/LSSedxFNPPcVpp53G7Nmz2XXXXfnIRz7CD3/4Q1avXj3itmUkRwghhBAts3DhQhYuXAhArVbjD3/4A3feeSd33nknN954I0EQMG/ePP7yl78Mu20pcoQQQogWGO0dUpPt7ioAz/N405vexBve8AYOO+ww/ud//odrr72Wxx9/fETtSZEjhBBCtEDE6O6u2p6X1sab7/vcd9993HHHHdx5553cf//9zJw5k0MOOYSvf/3rzRDP4ZIiRwghhGgBmXiceNOb3sT999/PnDlzOPTQQ/nIRz7C97///RE/G+elpMgRQgghRMvcfffd9Pb2Nu+sOvTQQ+ns7ByTtneYu6uEEEKIiUTurkr09fXxzW9+k0wmwxVXXMG0adOYP38+Z555Jj/5yU+a8Q4jISM5QgghRAvEsSGSicdks1mOPvpojj76aAAGBwe55557uOOOO7jyyiv5wAc+wK677sqjjz467LZlJEcIIYQQE0Y2m6Wjo4OOjg7a29uxbZvHHntsRG3JSI4QQgjRAtEoR3JGs+9EEscxDz74IHfeeSd33HEHv//97ymXy0yfPp3DDjuMq6++msMOO2xEbUuRI4QQQrSAFDmJtrY2yuUyPT09HHbYYXzlK19h4cKF7LzzzqNuW4ocIYQQQrTMl770JQ477DB22223MW9bihwhhBCiBaJ4dKMxUTyGnWmhj3zkI+PW9g4z8dhoOwnn3JRypy2MtjGWg0kX0MVOTOAnQZkpDxPUUY6brHM9iCMI65jQR6UyENYhilApDwDlOCgnhQrrNJP0INmWRkBoHKGzhcbxdTPIUznOi9tnCklIqB8S+UmwYBQERDUff6CCtixsL4V2bNJT2glrdeysh9Ka4ocuI/JDTBRjNYIyLcfG8lwsL4XXWcAfqFBd34+yLNLtHtU1ffiDFVJtecJaSFCuke3pJKwmwZ0AYc2nMKeXJ896L2v+9Sz8gQoDy9cA4GQ9+p7eSFjzcTIeTiaNk/EIa3WAZjjn4MpS8v4sjeU5VNYMYLkWqYKbhG3GMdrS7P9fv8ZO22jHITc1y2OnvI2wGtI2t5M/HXckma4MqUIKr5CitrFGZX0ZExncrEvsR0R+xJQ9kucrmMiQKqSSfno2YTWkfW4bXmee6sYa+d4ckQHLSz5/N+ugtaIUxjhZl6AUUFlTwk7bBOU6qUKadcvWkulKE/kRQTmgtrGGiWLCcjX5XvkR2e4s/mC5+dmlu9to32MWQblK319XJH2LY4JyDe3a1DZWSU9pa+4TD25MvmeOTX3jIF5nEX+gTFSvE61fhUqlUVpjohhTK2N5LvFgH8QxxI1zUlsQR5haJfmznnwvjZN8Hiqsg4nR1X6M5aDCGkQ+KX8QgJSlcK0k/HFT6KUmCTLsSFtoBcYkgZ2OVhQ9m0oQNR5uBt1Zl5xrE8UGrRTVIErCHi1NzrWIjKE97TClkGqGbLqWxrOT4M5cysaxFCnbophxKGacZqhnMePQnUvRlnHIeTbrSz5T80k7nVmX2Z0ZVvbXsLQin7LJuEkbg7Ww+Qsl7dr4UUwx42JplXwdxri2RaYRENpXCYhiQz5lY1ma7oJHxrWY1pnBr0cE9RAv4+D7EU7KRmlFteQTRTFuyqIt42C7Fk+tLRHUk0BXrRW2a7G+5JPJuuQ70lhWI4zSUhzx9XtIpR3iMKazN0db0cOyNLk2j2rJJ1tIUcy49K2t8NZv3kscG8LYoLSi0OaRa0u2d1JJSGYUxkxvzySBox1p0q5FFBtmtGdwUhaW1vQN1sk0AlSnd6RxbY0fxnRmXfKe3Qw4jWKDH8XNwNaOnEul8Zm7tibjWqRsjaWSMFWtFN35FB1Zl/5qgGdpMk7y2eZSNgO1gEIjxNPSinoYExtDzrXIOJpKkPwGd7RunEMxlgaNYs+e5OeopdSQwFZLKfxGUKdjvbgekrBPVytiknPUj00jpBNyrsa1FGEMQ/caX5suV41mEVu2wxQ5QgghhNixyOUqIYQQogVk4vH4kyJHCCGEaAF5GOD4kyJHCCGEaIHIjHIkZ5LEOownmZMjhBBCiElJRnKEEEKIFpA5OeNPihwhhBCiBaTIGX9yuUoIIYQQk5KM5AghhBAtEMYGaxSjMaGM5GyVFDlCCCFEC8jlqvEnl6uEEEIIMSnJSI4QQgjRAvIwwPG344zkKI1RGuOkUH4ZU6+CZWEsG6IIUyujLOvFME5ohB2mQWvi8mAj9DBO2opjqCXrTBC8eIygjrFcTL2WvFavoBwHXexK9tWNMD4vi0pnATBBgKnXUFqjUh5xGGB5Lk7Wo7a+H3+ggolj3EKGOIqo9w1SW99PMFihtn4AbTtENZ/nL/kIbiGDk880gzpNHFNbP4CJIlTj2MFAhXrfIPkZ7cnbDJJAUGVp7LRLUK7ilwPCcg1lWWitqW8ssfNXf4iJY5SlCcoBpvEPzPZs3HyGOAhxChmcrIdbaLy3OMZyLWzPxkQGO5vGyXpU1lWprKsSBTGpYoqoVqc+UOUnU/fC9myiRsDnHtf/HEiCLhfc9r+E1ZAoiPDLAamCS3l1GSfroCxFHBnstE3fU304uU3rYrSlSBVSREGc9COOm8cuOBba0liOReQnfd3gR3TMmw0kgZtAEsbZV6G6rkr77r107T6F0uoykR/T//QqahvLWI6F49lYroV2HDJdGSzHxkQxsR9SWdOPiQxxkAQeqkYwo4kM6SkdAGR6O4lqPm177IJ2kr76A2W0ZWFlc5g4wpQHmmGeJo5xpkxFOS4qnSWuDILtomwHEwYo18P4teSctpzkXA/qxF4eFfpgO2BMcs6m8qA0aScJK7S0Qqvkh0QtjIlJAjr9yKAUVILks9lQDbCUohYmgYpdGYd8yqLkh43AToPWCs9KghZTtpWELmpFdy7VHLLPuBbt6SQsNefaeJamO+vyt1Ulqn6EZ+khYYx+GGNpha1fDPJ0bY1ra3qLHpZO3oNra7oagZ7VRp8trYhiw65Tc42ATgtLK/oqPsW0Sy5lk/eS0M71ZZ/2do/eokcx7ZJ2bUxssB0LbWuKORetFLmi11xfyKfoqyT/RtYN1EnnUmit8DeFeoYxYRjjZRxsx0oW10oCNBtBlVbj/Wpbo22N7Vik8y7d+SRkNe/ZmLjxOViaeb0F5nbnkuBNS1NpnLvdhRR5z6YexqRsTd5LwjtzbR5VPyT0k9DNwVpIqRYyWAvJpWyCyLDr1DyZxmcDUKoF5FI2xYyDpRUpWzdDVtOujWtr1pX95OeKMcSxIWUn53l3LpUEeGqFoxWdGZeMY5FxLGphTCWIiAz010KCyNCRtin7MVnXIutqKkGERhHEMQ+t6Ev+XcYGSynqoaHoWcQkgaUpW2GppM9+FONaitiAIQn4jIzBbZzfhiRo1o8MkTFsz7IhMmbUi9iyHafIEUIIIcQ2+eIXv8hBBx1EJpOhra1tm/YxxnDhhRfS29tLOp3miCOO4G9/+9v4dnQrpMgRQgghWmDTKOZolvHi+z7vete7OOOMM7Z5nyuvvJJ///d/55prruH+++8nm81y1FFHUavVxq2fWyNzcoQQQogWmMh3V11yySUA3HDDDdu0vTGGq666is9//vO8/e1vB+C73/0uU6dO5bbbbuO9733veHV1i2QkRwghhGiBsRrJGRgYGLLU6/Xt/l6efvppVq1axRFHHNFcVywWef3rX8+999673fuziRQ5QgghxKvYzJkzKRaLzWXRokXbvQ+rVq0CYOrUqUPWT506tflaK8jlKiGEEKIFIhMTxfGo9gdYsWIFhUKhuT6VSr3i9ueddx5XXHHFFtt87LHHmDdv3oj7NNFIkSOEEEK0wFg9J6dQKAwpcjbnk5/8JCeffPIWt5k7d+6I+tLT0wPA6tWr6e3tba5fvXo1++6774jaHAuvqstV//Iv/4JSirPPPrvVXRFCCCFeVaZMmcK8efO2uLiuO6K258yZQ09PD4sXL26uGxgY4P777+fAAw8cq7cwbK+aIuePf/wj1157Lfvss0+ruyKEEEKM2kS+hXz58uUsXbqU5cuXE0URS5cuZenSpZRKpeY28+bN49ZbbwVoDkBcdtll/PznP+eRRx7hxBNPZNq0aRx33HHj1s+teVVcriqVSnzgAx/gW9/6FpdddlmruyOEEEKMWhiDGlUK+Rh25u9ceOGF3Hjjjc2vFyxYAMAdd9zBwoULAVi2bBn9/f3NbT7zmc9QLpf58Ic/TF9fH294wxu4/fbb8Txv/Dq6Fa+KkZyPfexjHHvssUNuTducer3+stvphBBCCLHtbrjhBowxL1s2FTiQPBvnpXN8lFJ84QtfYNWqVdRqNX7zm9+w2267bf/Ov8SEH8n54Q9/yJIlS/jjH/+4TdsvWrSo+RAjIYQQYqKKYoOeoA8DnCwm9EjOihUrOOuss/je9763zcNd559/Pv39/c1lxYoVABg3DZYLlotx0qhUGqMaAYlOCmwXEyWBdsrLNsMsN1G2A9pG59ow2k7CNLVOgj4hCUKsNa5V1gaTcE/HSf5M54ndNDqbR9luEsJZGcSUB1ApLwnqbBxP2Q4mSsYgTRRjeW4z0DHT04lbyFLfWCKs+UkAZxSDtlCWJtWeI/ZDauuT4cMoCAmrPtW1G9GuQxyExEFI6fl1OFkPpTWW51DfWMYpZEgVUlTWDDD3K9/HRIawVicsV6mu7yeoVHn8n45L3nMUUxuoE0eGyA/RliKq+fQ/vQoTxQTlGvW+QeLIMLh8Nbbnkiq42GmbYKBMUB76iG/bs/EHKgTlgLauDNpShDWfp+9czv1vPgxlKdY/vp7733wYlfUVAOIoCdt0sw5hLUwCRWsh6XYPZenmesu1iPyIKIiwHE26K492HKIgSgI8jWHDE+vJT8tRWZe03R8k7yGOYtY/sZF0u4fX7uGXAvrXlAkGytjZNCY2ZLrS1Ad8tKXxOvPEscH2bOp9g6QKKcKaj1vIJt+fQhplKbRjk57SjpPxsLzkc9HFzuSzdD3sQgGdayPTnQSoWq6DP1gmriX9M3FE7CfnhKlXIfSJ+tcn56SXJVq/EuPXGueshlxnEhjbuN1U2U4SzglYM+ejIj85h8I6xGHzB2cQG2IDkUkCLeuhoRrGWAqmZpL/H1WCCN0I5yx6NkEc42hNyrJwdLK+O+vSlXEJYkMtjPBsTcbROI1zvph2yKVe/P9WFBuKno3Wje9PuU76JeGdhZRNIWWTbgRa9rR5bKj6ZF2bjpzbbMMPY1xb01cJsBq5npuOU6oFlGoBhZRNFBtKtQBLK/orARvKdVxbE8WG9aU6UWyY0ZFphoj2NoI4uwopMjmX2Z1ZOruzzOjK0DYly5TuLKVKgGtrip0ZDti5E9vVTOnKkPIctK2TIE87CSnNtXkorUh5DpVaiGVr3JSN0ioJ07Q0YRDhZR1CPybn2USN6xTZfPJ+ywN18p5NbzH5Oal0EuCptWJDycfSmhntGYoZF9e26Mi55LMu60s+c6bncRvBmW4jwDPv2eRdiyg2pF2bud1ZSrWA3mKatYM1dp+SozPrknYtCo3PtDPrNtvZ9H0IYoNnJaGpOdciMtCRTs6FoHGeZRyLIIopeA4Zx6Lo2TiWwtEa11aEsSGKoS1tY2lw9Ishso5WNHJu0Qocrci5GkspYpLQTc/WzSDL2IClwVIKA9g6Ce7UgFLJeovtZyLPyZksJvRIzkMPPcSaNWvYb7/9muuiKOKuu+7i61//OvV6HcsaekqmUqnNPiNACCGEEDuOCV3kHH744TzyyCND1p1yyinMmzePz372sy8rcIQQQohXC7lcNf4mdJGTz+fZe++9h6zLZrN0dna+bL0QQgjxajJWDwMUmzehixwhhBBisopiM6pbyGUkZ+tedUXOnXfe2eouCCGEEOJV4FVX5AghhBCTgTEGM4rRGGNkJGdrpMgRQgghWiCOzajm1cicnK2b0M/JEUIIIYQYKRnJEUIIIVpgU1TCaPYXWyZFjhBCCNECJh7lnBy5XLVVcrlKCCGEEJOSjOQIIYQQLSATj8efFDlCCCFEC5i4mZs74v3Flu0wl6tU0EhhjpP0ZlJZUBoVhRjLRbmNZHK/hrE9sJwklbyR1gxgLAeVziapzZYDloNy3GQfJw1xhKmWkzTobAG0RVwtYywXXS+/2Jk4wurswersTdotD6CzBUwYQBxjZXPYhQLZ6VNwC1ksz22mokc1n3R3G07Ww23L4XUWUCkPJ5/ByaaTt9aexx+s4OYzpNpzWF4KN58hKNdQWlNaU06SuGs+cRCSas8SNl6LI8Njp7wNO20zsHw9djZNVAvwB5IE7KBcQ7s2jmeTKiRBqOmuDHY2jeUmWWK1Df1JwrmrUY2IYN1IBq/1VYhqAfWBOqmCi+VoIj8irIUoS1GYmU8Szv2IfE+WsJp8v7JTM4TVEL8UoLUiKAcU5/Q0EsdduvfsStK/B3wyXWncrIuTdXELGSzXwmv3sFyLoFzDRBGWY1Hvr+NmHNY9vp5MV5pMV4Z0VwZXKyqr1pMqpjCRITe9E9tz8cs+npf8v8BEEYUZ+UYCuk+mtwO3kPS3sq5CdV0FE8XEQUDkB+SmT0G7NrnpUzBxjJNN0qK17VDdWEPn20i15zGhj7Isov71eNOnUVs/QHV9P9qxCcs1wkoNq9iJ5bnJeWFZmHotOe+8LDqdxSp2omwHXeyEMEjO/5QHcZRELccRKqxjHI9gzTMQh6igmvzbUJpN/zeMYnA0RCZJH1fqxVPYj5KtKkGSKu7Zyfd5SsbFtqDkh0m6dGyoN5LIc66NpRW60U5XxqGQssm5NsVUkiAeRslP7ZxrkXEsUrZmwex2ZhTTOJamO5eiPe0QxIaOnItnabKujR/GZBwLt3G+pV0rSb5uJGpHBoppl7aMw9wpWabkPabkk+9BW8Yl5znkPRvX1ri2RX81SSWv1EKKaQdLqWZ7Oc8m1+ZhaUVbzmVud7aZ1m3bmhkdGRbM6aAt47BTd460a5FqJHW3tyfHLA/Uacs45DMOtq2Jopj2do+2XJLkrS3FjI4MxbSLl3UotKexLE0qbfPsugqWrSlmXHrb0kOeetuRc/FrIW7KZkrew20kt0dxTKkekk/ZFNNOM2V9dleWtGtjad1MYS+mXSytSDsWxYxD2rFo8xxynsOcKVlcO0kNb/ccSrWQXMqm4kcAzO7IAOBamjg2aKUY9CPSjkW9kZxe9GwqQYSjFZ6tSdmafMqmGkTkXQtHaypBjFaKKDa4liKI48afhkoQkXOT73OMoRrEzTRxSyXbWApcS+FaClurxrmcnHhW40SOTbJOK/Bjg9eIqg9kMu+kIiM5QgghRAvI3VXjT4ocIYQQogVkTs74kyJHCCGEaAG5hXz87TBzcoQQQgixY5GRHCGEEKIVRjmSg4zkbJUUOUIIIUQLxMagRjF5OJaJx1sll6uEEEIIMSnJSI4QQgjRAsaMcuKxjORslRQ5QgghRAvI3VXjTy5XCSGEEGJSkpEcIYQQogXiGNSoHgY4hp2ZpKTIEUIIIVpAYh3G3w5zucpYdjOAUDUCCVXogzGoOISwDl4e5Xpg4iRUM51NQg+zBVQ62wzgxMQYbWOqg8SDfck+SqO8LHFlAOPXmsGeyvWS9qMA7CTMU6Ubx2nQ7d1Y7d3oTJ54cGMS+OnXALBSKfKzpmJn0ljZHJEfvBh6mc7iZNNJn4CwWicKQkwUY6I4Ca/MZ3HzGeLG+qiW9MtybCzPZeNTfQAMLl+DtjROI4DS9mwi/8UgyfpAnaDs0//kCzhZjzg2yZ9BSFD2qa7pwy8H1NYn4ZxuIYvlWtiNIEkA7VqE1ZD6QB2v3cPNuoS1EBMbLMci3ZkFwPFsNj7Vh+VaVNZXcLNJiKWdtrHTNqliCq/do7jzdNrmdpJqz+KXfabsPYOwGjZDMtPtHkG5Rt8z/WQ60yhLYaKYjU+sBaBrXideu0emK00cGdrmdhKUfSwFYc3HzTpkutKUV24grPmUVpZId2WoD1TRrtP8/lmuRaoRlmqnbYJygHYtvM5iI6QzJDVzDv5ABWVpnKyHP1BBuzYmjsj35lC2S1SrEwxWwHaTYM04SgJRLY12bNSmP10PEze+l9p6MXxTW+hCBypTRKWzWO3dmDhCRT6mXktCZRs/FI22MdoGpUHbSXitSs4rBVg6CTeMTPJ1FEMUGyylqEUGWysspcg4VhK2aCkqQURfLSSKoT3tMOgn4Z1Fz+aZvirdWRdHJ2GeQWxI2UkoZMkP0ToJY2xPO0lQZC1kYzUJF92tK8esoseGakAtjCl6DnPa0knYp1asK9XpyXvExrCh7JNuBMXmUjZVP6Ij5xIbg2trihmHYsZh7+kFugupxjYhUWyYkveY3ZWlmHbYdWqOtGuTzzhEsSHvJSGgpVqIpRVT2zzSrk1vMU0+ZVOrBsydkqUt41D1I/wwpuJHzTDMtGuR92z26C3gZRxmzygwr7fAHr1JP1KeQ8ZNAklnd2XJtXl0ZF0iYyhmHLoKKeLYYNuavGeTb0/jhzFRHOOHMbk2j4ofsaqvhrYUM7uyPLW2RKHNI9fo+wFzOsh5Nr1tHp6lh4R7bvrM9uzJs6FcZ0ohRT2KqfoRnTmX9RUf19Lk3CTEdE3Zpx7FuLamEkRUg4icZ+NoRdpNAk4jA0EcN4NWN50PlSAJ8+zNp3AsRT2MqYUx7Y0wUcdSOFol3zNL08hsRaPwo+S8cRrnX86x0CoJ2dQKLJ1sp1XShjFJmGzUOO8tlXytVfL3WhhTC5PXYpL9I6kbJhUZyRFCCCFawMTJMpr9xZZJkSOEEEK0QBybUc7JkWGnrZEiRwghhGgBuYV8/O0wc3KEEEIIsWORkRwhhBCiBWQkZ/xJkSOEEEK0gAR0jj+5XCWEEEKISUlGcoQQQogWkMtV40+KHCGEEKIFJIV8/MnlKiGEEEIM8cUvfpGDDjqITCZDW1vbNu1z8skno5Qashx99NHj29GtkJEcIYQQogVMbEb1QL/xvFzl+z7vete7OPDAA/nOd76zzfsdffTRXH/99c2vU6nUeHRvm0mRI4QQQrTARA7ovOSSSwC44YYbhrVfKpWip6dnHHo0MjvO5SptoxohhLGXB50E0hnbSYI77RRU+pJtlQalMG4WY7kQRy8GamqNcdJQHWgGbaqUB6X1GCeNzrWh0nlMFBGXB1CpNNHa55LQzXoVZTsYN90I6kxCFHU6i863YcIgCVxsHEvn24nDACvfhokj4loFJ5vGyaZxC1lMvYZTyKDSWUycBEHW+0rNMEeA6tqNaNcmrPnUNvQTxzHtczoIa3VsL4VfDnDzWVLtWVJteSAJ4yzMKOLmksC8KIjI9RaT9jYmwaGRH6G0JqyFzW2AZnCkdmwsxyIoJ9un2rNoS1EbqFNaXcbNOnideXK9Rby2DJneDpysR743h18O8No9puzZhV8KyM8okunK4Jd8Mp1pnKxLqpDiqV/cj5vPop2kn3EcM7iyRKqQ/M8h05Vm41MbkwDNQhoTGcqry/jlJKS0trFG+9w2ps7vxnI02nGo99fZozNNfaCOiQyZzjTlNRXKq8t4bR5tswtU1lexPZfKuipdu3dSXp0EpK57+EmCUkC6K037zj1kezpw81kyPZ3EtQphLcTyXNx8lnV/WU5YruH3lXALGaL+9VheChPFEPrJ9zSKiGp1tGPjD1YwUYx27BfP3ShGFzpfPAcBU6s0wmPLyfpG4KxKecTp5Hto3Gxynpk4Obf9arLeagSpKoUO62gFrqUIY/DsJKwzbSchif31CM/R1MOYlJWEJCbBmzF+FDfDO2thjKWTv+dTFl0Zh4yT9D+IDJUgaoYpAqRsTWc26UctjCmmHIJGQmMxZdObTxFEMf31kK6Mi2clIZ+WAkcrihkHSyn6KwF5z2Za0aMnl8KPYkr1kBnFNHnXJo4Nlk6O21NMk3YsMq5FW9qhM+eSdq1mPyAJsOzIuQzWQ/wwZrAWsuvUHP1VH0drutrTtGdc/mGfacm/kyAi7zn0tnn0VwJStibnOYSxIQwiXFvTkXOJYsNe04u0t3vkPIfZnVm68yl2nl6kvxqw1/QCvcUkjHRGV4buQoopjfO7O5/8Obsri2sn/96j2OCmbHIpm4xrMaMjQ9q16Cmm8WzNlHwqCfY0sM/MIrM7M1T9kNmdGboLHoN+hKUVuzXCcqt+RDHjMCWbIjKG/nrAjPY07WmHlKWZ2ZFpHrcj66K1osNzyHk2XY1w01lFj3bPITYGRyeBnACWUs3vc0faJudaBLEhiAwZx6I7m/y7zqeS9xZj8ENDyk7CXJNz05C2NWlbE5ukTSv58U3QCJQ1BtK2Rinw4ySsM4qT8zwyybltNc5BWydhn682AwMDQ5Z6vd6yvtx55510d3ez++67c8YZZ7B+/fqW9QV2pCJHCCGEmEA23V01mgVg5syZFIvF5rJo0aKWvJ+jjz6a7373uyxevJgrrriC3/3udxxzzDFEUdSS/oBcrhJCCCFaIo4NjEFA54oVKygUCs31m5sHc95553HFFVdssc3HHnuMefPmjag/733ve5t/nz9/Pvvssw8777wzd955J4cffviI2hwtKXKEEEKIFjBxhIlHPsqxad9CoTCkyNmcT37yk5x88slb3Gbu3Lkj7s8rtdXV1cUTTzwhRY4QQgghxs+UKVOYMmXKdjvec889x/r16+nt7d1ux/x7MidHCCGEaIFNIzmjWcbL8uXLWbp0KcuXLyeKIpYuXcrSpUsplUrNbebNm8ett94KQKlU4tOf/jT33XcfzzzzDIsXL+btb387u+yyC0cdddS49XNrZCRHCCGEaAETx6O8XBWPYW+GuvDCC7nxxhubXy9YsACAO+64g4ULFwKwbNky+vv7AbAsi4cffpgbb7yRvr4+pk2bxpFHHsmll17a0mflSJEjhBBCiCFuuOGGrT4j56XP6Umn0/zqV78a514N34S+XLVo0SJe97rXkc/n6e7u5rjjjmPZsmWt7pYQQggxaiaKRr2ILZvQRc7vfvc7Pvaxj3Hffffx61//miAIOPLIIymXy63umhBCCDEqxoxyTo6RImdrJvTlqttvv33I1zfccAPd3d089NBDHHLIIS3qlRBCCCFeDSZ0kfP3Nk1w6ujo2Ow29Xp9yCOtBwYGxr1fQgghxHCN1XNyxOZN6MtVLxXHMWeffTYHH3wwe++992a3W7Ro0ZDHW8+cOXM79lIIIYTYNhP5FvLJ4lVT5HzsYx/j0Ucf5Yc//OEWtzv//PPp7+9vLitWrHjxxThGV/tRcYgJA4ybAaVRYYDRNsrLEVfLqMhHRQFYFsZJgeWAToIRVSqNCuvEg32YoJ4EbXo5sF1UvYRKpQGSoE5tQRyB1ijXQ+c7kjaCehKcmEpC8HQmj7IdnBk7N8MXdb4dZTtY2XwzsNNEMcrSmCjGaWsDbaG9DDqTR7seTjZNurNAHIRorcl0txOUa2hLYzk2SmvcfAavs4h2HfzBMqmCSxSEaMchCkLixjGSIEmPweWrCashdjZ5X5arqa7pQ1sa7ToorVBa4eY9vLY0JoqTQMogpDCnh8JOvaS728n0dJLpLhBWQ3JTs3jtScho224zUZamumYj6e520t3tFGbkSRVcsj2dZLrSRLUgef+xQVuaVFuewqwuTGRQlibb00F1Y42oVifTlSZVcAlqIU7WJTc1S32gjp1NE0eGwZUl0u0eqUISsJnrLRJHMdrShOUqlfVVpuzZlQSHtiefe3F2kTg25KZmm0GlSuskwLO3g8KMArX1A3idRZycQ89+s4iCkOz0KXidBUwcE25Yi+VYZLrbad9jNkHJp7ImCU+1vBT+889iOTZxFFF6enkSvGlZ2I1AVoCw9uIIpe2lsDwXnU1CVdEWyrIwfg2USgI+/VoS2JnKorwsxCHGTSfntWWj/EqSZEgS2qnCGsQhTmkNKgqaT5tXChRQCWKqYbLSaqQYZhwLx0oCDospB0drohhsC2YUUkzNulhKkXOtF7fXmu6sSyWIyLlJ6KZWiv2mF/FsTcaxWFP26c66BHGMY+nm+nbPSQJEVRKwmLI104vp5M+CRzHlkHNt0q6FpRU7tWeoRTG9+eR72ZVxGPSTUNm0mwSLdhdSzfdjacWM9jRrB+qkXYu9pxepBhEdOZcp2RS9RY+0a7FHb4G5nVnSrk1sDLM7s1gKevONbRyLtGsxuz1DxY+SIMlGMOnO04vMnZIjn0oG0otph7aMw+zOZFvX1uw6NdcIENVkGp/d3Ck5LK2Z3ZZmzzntdORc0q5Nb9GjLeNga0VHLgkV7S6kmNdbYF5vnp68R2+bh1aK/mpAR9bFsRSupfl/s9vpyKYoph3SjpV8hmkXrRTtnkMx49CZcenKOMydkqXqR0wtNP5deDbtaYfeXArXTr6nO7dnyKVsPEujlWJOewatFEXPpjeXoujZpB2LYspm0A+JYnAsRSODlSg2eLam4Fn4kSHdCOPMucnXWTf5lRXEBmOSczPGNMI4wY+S8E1LKTwrCeW0dLJeA1GchM5uCvF0tCK1aTsF4xjqLVrkVXG56swzz+QXv/gFd911FzNmzNjitqlUqqX35AshhBDbQi5Xjb8JXeQYY/j4xz/Orbfeyp133smcOXNa3SUhhBBiTEzkhwFOFhO6yPnYxz7G97//fX72s5+Rz+dZtWoVAMVikXQ63eLeCSGEECMXx1EypWE0+4stmtBzcr7xjW/Q39/PwoUL6e3tbS4333xzq7smhBBCiAluQo/kGJkFJoQQYpKSOTnjb0IXOUIIIcRkJUXO+JvQl6uEEEIIIUZKRnKEEEKIVogijB7FaIwEdG6VFDlCCCFECxgzururJKBz6+RylRBCCCEmJRnJEUIIIVrAxPHoRnLkYYBbJUWOEEII0QJmlA8DlLurtm7HKnKMARMTOxl0KkCFNYyVBNoZJwWDgyjHTdaZGBXUwWnkYMUh+FWU1hAFmNCHOErCM00MqSxGaVQcJttqG51vAzuVXBO0HIhDdDafbKc0KvKJAx96d8H0rcRsmkQWxyjbebH9OMIqduI/+yxuWw5lJVcZo2oFu1AkWr+KoFTG8lyq6wcAsDwXp1DAH3iKsOaTassBoCxNFIS07zaT+sZBymsG0VoTA7bnkiqkcQpZijv1EgUBz9/zf3TtPZMoSAIvwSc/ayruoyvITe8i9gP8wQp22iXT00m6s0i9bxClNdq1qa0fwPZc3EIWy7Gxlr6Q9ENrgnKNVFsuCagcqJLuLBLVfDLdRep9JaIgpGffqaS726mu2cicN+/B47csxUQRyrLI9RYwUUy6u50pe06l76n1dO3eiXYtqusqhNUAry1DpquK5di07zKF+kCdXG8RZWnCakhx5+nA8wTlGnY2TXF2kVQhRdc+u1Bd34/trW6ePumuDMWdpxPWks+ga8+1zVDQwk49hJUa+d4c2Z4O6n0lys+vxS1kiWo+dsbDyabwB8oU5+1D92ueprq+HxPF1Nb3U+8bJNWWIw6S8Eir2AlxhO25mCimbY9dqK9Zi7Jd0BaW52KlM0Qb16JcD6uzFxP6mCDA2C6mXkvOm/YpmDB4MYg25WKUxtgeKhxEhXWiXBfKxKjQxzhe43+XIV5K4UdJ8KGlFWlbE2MoOBbVMCbraKLYYCnVDEHcFLYYxeDZmnWNlE9LvRjoGWPIOBaVICY2ht5civ56SFfGYXXJJ2UlYZxFzybvJj+iKkHyb8OxFFak6K8ln1PRc8i5Bksr1lV8+usBO7dnsFSWlK3RKjl2R9qht+jhaE1vLsXGatAM5ezKpfDDmGltaZ5ZV2ZeV5Y4Nvx1dYmdu5OgzFItZOf2DE+sNcxuSxM13lcuZVNI2XQXUkQGNlSTMFlLKzozLmEU09vmsaq/SmfOZVZ7hkoQUQsjKkHEXtMKRI3ngXXkXFxbY2mFa2vmTklCP4EkRDPjkB606Mq4zOvN05Z2WJt26Mi5DNY8dpuaY6Ae8qa9pjKnI8Pais+UTPLzLe1adGddYmMIopiMYzWCU21md2XIOBa9bR7FlM38GUW6Mi7rKj5dWZeZRY/nB+rkUjadmaSNODZ0ZVz6ayEbqwFtaYf2tJN8T1I2T6wvk09ZFJWNZ2tqYRLi6jmavBviWJq8a1PwLAZqEXUTE5vk/Eg7qhkO62hFDPhhjKMVGUcn60wSGJtzNX2hoTOdhJhWw4icq0l+RCosBTUgMoYYKLjJ5xsbCGNDbJLwz2oYo5RCKUPGllkck8mOVeQIIYQQE0TyH4qRX3KSy1VbJ0WOEEII0QJyuWr8SZEjhBBCtIAUOeNPLj4KIYQQYlKSkRwhhBCiBeI4QslIzriSIkcIIYRoARPFoEZR5EQy8Xhr5HKVEEIIISYlGckRQgghWkCyq8afFDlCCCFEC5g4Gt3lKpmTs1VyuUoIIYQQk5KM5AghhBAtICM540+KHCGEEKIFpMgZf5O+yDGN8LuBUgnlW6jIJ4pddLUEtoOxXJRfxTgBqlROJoFFdhLQaeJkvV8FYzBRkAR0AmG5guWUMfUaWrkYbSchiHGYtKEtiHywAky9jPLjRkBohLGjZDtjiCtl8EqoUhkT+ETlClGlhtZV+LtJZdVqHde1MXGMjjVRtY5t1zD1GkG1jg4jBus+YS1ZrEqNwXqANga/5hPWfWxLU6/7WNU6fq1OKQix6z5xHGMrqPkBdt1HVevEYUApCHHqPlEQUvcDgiBgoJrsN1irM1gPCPwA27aIaj5BtY5f81FaoeOIWt3Hr9VxHBu/VqcchkRBSOwHOHUfU6tTqvvU/KTdct3Hrwf4fkBQ96n7AWHjdVP3KUcRg/UAZUVEjW10tc6gn/Q18gM0MdUgxAkg8G3KYZh8LvUg+bsfUAlCwpe8h9AP8Os+pSAk8DUD1Tq1mk/ZTwIXS2GI9gPcWp1So68lPwl5DPyAdLVOWPMpNbapv+R9K0tjQ7J9rY4qVxhsvKd0ox23VsdEcTN81SqVicpVBus+UbWOX6lRr9ZxUzWscgW/WscyGm0nobFap5oBndZgiahcxS6VUe6L55uxXIxvIPIxdoCulTBuROwm5zqAsYPkvNU29ZQiiAyDfoSlkrDOGINyLKqNW1dL9QhH62ZAZ2xMEtDpJOGY5ZJPiTplPyI2hnpocCObih9RqYbExlAyyeteZFMuB1SDiKofUVY+pbhO2Q+JTRL66VhQDmIq9ZAgNqQiJwkJ1YpKLaBarlNxIirVgMjWuKFDtVSjrAPq5RLlQY9KpU61GiQhmVZIteJTL9eoOiH1SoXyYIZqqUS9UqZaNtTLNVRgUSkp6uUSlZKiVq5QcSLqlRKVkkutXKZqHFKRQ71Sou5H1I1DpWSolWsE1RK1sk3ViaiGEfUwQmuF7dtExuBXS1TLLrVahKUhtjU1P6ZqHOqVMr4fUSvH+JUy5cFBauVBqo1j1ZyAeqVKtaSo+SE1P6biRtSqPpU4CeisVX3KnqFaqhNEMTqw8cOYsh0lbcUutXKd8qBDtVyiNKgoVwOqpSplJ6JaqlMNQirxiwGdpYyhUqpSDZLPoTzooVyLahBTKw9SHrTQCkJbUw9jtFKEjqZSqmNbGsu30IFFqRZRDWICYwi1ZlA5zYDOoBHQWYtijIHIfjGgsxrGGEdTqoa4QfKrrORHGEfjNoJijTHUGkGcoaXR8GJAp0nW24FNLYyxlKIaxviV0pDfHeMqChjVUaJgrHoyaSmzXb6TrfPcc88xc+bMVndDCCHEq8iKFSuYMWPGuLRdq9WYM2cOq1atGnVbPT09PP3003ieNwY9m3wmfZETxzEvvPAC+XwepVSru7NFAwMDzJw5kxUrVlAoFFrdnVGT9zOxyfuZ2OT9tIYxhsHBQaZNm4bW43dvTq1Ww/f9Ubfjuq4UOFsw6S9Xaa3HrRofL4VCYUL/EBgueT8Tm7yfiU3ez/ZXLBbH/Rie50lxsh3ILeRCCCGEmJSkyBFCCCHEpCRFzgSSSqW46KKLSKVSre7KmJD3M7HJ+5nY5P0IMXqTfuKxEEIIIXZMMpIjhBBCiElJihwhhBBCTEpS5AghhBBiUpIiRwghhBCTkhQ5Y+jqq69mp512wvM8Xv/61/PAAw8AsGHDBj7+8Y+z++67k06nmTVrFp/4xCfo7+/faps//vGPmTdvHp7nMX/+fH75y18Oed0Yw4UXXkhvby/pdJojjjiCv/3tb+P6fv7++McccwxKKW677bZX9fu59957edOb3kQ2m6VQKHDIIYdQrVa32Oadd97JfvvtRyqVYpddduGGG24Y9nHH4/2sWrWKD37wg/T09JDNZtlvv/245ZZbttpmq97PXXfdxVvf+lamTZv2iufSSM+Lifh+giDgs5/9LPPnzyebzTJt2jROPPFEXnjhhVfl+/l7p59+Okoprrrqqq2228p/P2IHYcSY+OEPf2hc1zXXXXed+ctf/mJOO+0009bWZlavXm0eeeQR8453vMP8/Oc/N0888YRZvHix2XXXXc3xxx+/xTZ///vfG8uyzJVXXmn+7//+z3z+8583juOYRx55pLnNv/zLv5hisWhuu+028+c//9m87W1vM3PmzDHVanXc3s9LffnLXzbHHHOMAcytt976qn0/f/jDH0yhUDCLFi0yjz76qHn88cfNzTffbGq12mbbfOqpp0wmkzHnnnuu+b//+z/zta99zViWZW6//fZtPu54vZ83v/nN5nWve525//77zZNPPmkuvfRSo7U2S5YsmZDv55e//KX53Oc+Z37605++4rk0kvNior6fvr4+c8QRR5ibb77ZPP744+bee+81BxxwgNl///232OZEfT8v9dOf/tS85jWvMdOmTTNf+cpXJuz7ETsOKXLGyAEHHGA+9rGPNb+OoshMmzbNLFq06BW3/9GPfmRc1zVBEGy2zXe/+93m2GOPHbLu9a9/vfnIRz5ijDEmjmPT09NjvvSlLzVf7+vrM6lUyvzgBz8YzdvZpvfzpz/9yUyf/v/bu/O4qMr9D+AflplhWIZRVhEBlQglXDDFsQwXAourolz1knGlKFzQUkvSnxqoqS2mkVLeSEFe6UW4bnlRuwhoaoCKQ24IiAulgNcQkQDZvr8/fHGuxxk2Q1n6vl+veek85znf83wPhzkPzznPnJ5UWFjYok5OR87H3d2dli1b1qqYoaGh5OLiIiqbNm0aeXt7t3i7j6u5uEZGRhQbGytap3v37hQVFdUh83nYo8fS4x4XHTUfbU6ePEkA6Pr1643W6ej5/Prrr9SzZ086f/482dvbN9vJ6Sj5sK6NL1e1gerqamRmZsLT01Mo09XVhaenJ9LS0rSuc/fuXSgUCujr/+/xYQ4ODggPDxfep6WliWICgLe3txDz6tWrKCoqEtUxNTWFu7t7o9ttq3wqKirw2muvITIyEtbW1lrjdJZ8bt26hYyMDFhaWmLEiBGwsrKCh4cHjh8/LoozatQoBAYGtjifxzku2iIfABgxYgR27tyJkpIS1NfXIy4uDlVVVRg1alSHy6c5LT0uOks+2ty9exc6OjpQKpVCWWfKp76+HgEBAVi0aBFcXFy01ulM+bCugzs5beD27duoq6uDlZWVqNzKygpFRUVa669atQrBwcGi8r59+8Lc3Fx4X1RU1GTMhn9but22zGfBggUYMWIEJk6c2GiczpLPlStXAADh4eF4++23cejQIbi5uWHs2LGi+z7s7OzQo0ePZvMpKytDZWVlq4+LtsoHAOLj41FTUwMzMzPIZDLMnDkTe/bsgaOjY4fLpzktPS46Sz6PqqqqwgcffAB/f3/Rgys7Uz6ffPIJ9PX18c477zRapzPlw7qOLv8U8o6mrKwMPj4+6N+/v2iUAwCSk5Pbp1Gt9P333yMlJQVqtbrJep0ln/r6egDAzJkz8cYbbwAABg8ejOTkZGzduhVr164FAMTGxrZbG1tr+fLlKC0txeHDh2Fubo69e/di6tSpOHbsGFxdXQF0rnxaojPmU1NTg6lTp4KI8PXXX4uWdZZ8MjMzERERgTNnzkBHR6fRep0lH9a18EhOGzA3N4eenh6Ki4tF5cXFxaJLOffu3cO4ceNgYmKCPXv2QCKRNBnX2tq6yZgN/za33bbOJyUlBfn5+VAqldDX1xcuufn5+Ykuh3SWfBr+uuzfv79oeb9+/VBQUNBo3MbyUSgUkMvlLT4u2jqf/Px8bNq0CVu3bsXYsWMxcOBAhIWF4fnnn0dkZGSHy6c5j3tcdNR8GjR0cK5fv46kpCTRKI42HTWfY8eO4datW7CzsxM+D65fv4733nsPDg4Oja7XUfNhXQt3ctqAVCrFkCFDRCMX9fX1SE5OhkqlAvBgBMfLywtSqRTff/89DAwMmo2rUqk0RkOSkpKEmL1794a1tbWoTllZGTIyMoQ6TyKfxYsX4+zZs8jKyhJeALBhwwZER0d3unwcHBxgY2ODnJwc0Xq5ubmwt7d/7Hxaclw8iXwqKioAPLh/4WF6enrCqFVHyqc5j3tcdNR8gP91cPLy8nD48GGYmZk1u05HzScgIEDj88DGxgaLFi3CDz/80Oh6HTUf1sW0953PXUVcXBzJZDKKiYmhixcvUnBwMCmVSioqKqK7d++Su7s7ubq60uXLl6mwsFB41dbWCjHGjBlDGzduFN6fOHGC9PX1ad26dZSdnU1hYWFap1wrlUrat28fnT17liZOnNhmU64by0cbaJlx0Zny2bBhAykUCkpISKC8vDxatmwZGRgY0OXLl4UYAQEBtHjxYuF9wxTYRYsWUXZ2NkVGRmqdAtua/dgW+VRXV5OjoyONHDmSMjIy6PLly7Ru3TrS0dGhxMTEDpnPvXv3SK1Wk1qtJgC0fv16UqvVwmyjlhwXnSWf6upqmjBhAtna2lJWVpbo8+D+/fudLh9ttM2u6kj5sD8P7uS0oY0bN5KdnR1JpVIaNmwYpaenExFRamoqAdD6unr1qrC+vb09hYWFiWLGx8eTk5MTSaVScnFxEZ2kiB5Mr12+fDlZWVmRTCajsWPHUk5OzhPNRxttnZzOls/atWvJ1taWDA0NSaVS0bFjx0TLPTw8aMaMGaKy1NRUGjRoEEmlUurTpw9FR0e3ertPIp/c3FyaPHkyWVpakqGhIQ0YMEBjSnlHyqex35GG9rXkuOgs+Vy9erXRz4PU1NROl4822jo5HSkf9uehQ0T05MeLGGOMMcaeLr4nhzHGGGNdEndyGGOMMdYlcSeHMcYYY10Sd3IYY4wx1iVxJ4cxxhhjXRJ3chhjjDHWJXEnhzHGGGNdEndy2BMVGBgIX1/fp77dmJgY6OjoQEdHB/Pnz3/q229LMTExUCqVTyS2g4MDvvjiiycSmz0Za9euxdChQ2FiYgJLS0v4+vpqPJKkqqoKISEhMDMzg7GxMfz8/ETPgPr555/h7++PXr16QS6Xo1+/foiIiNDY1pEjR+Dm5gaZTAZHR0fExMS0uJ0zZ86Enp4eEhISHjvXx7F69WqMGDEChoaGjf7eNHw2PPyKi4t7qu1kTwd3cthj0/ZB8fArPDwcERERrfpgbEsKhQKFhYVYtWpVu2y/Mzh16hSCg4PbtQ0//vgjxo8fDxsbG+jo6GDv3r0adYqLixEYGAgbGxsYGhpi3LhxyMvLE5Zfu3at0ePw4ZNsQUEBfHx8YGhoCEtLSyxatAi1tbXNtjEhIQHOzs4wMDCAq6srDhw4IFq+e/dueHl5wczMDDo6OsLz3JpTUlKC6dOnQ6FQQKlUIigoCOXl5cLyqqoqBAYGwtXVFfr6+vD19cXRo0cREhKC9PR0JCUloaamBl5eXvj999+F9RYsWID9+/cjISEBR48exc2bNzF58mRheWZmJiwtLfHdd9/hwoULWLp0KZYsWYJNmzYJda5evQofHx+MHj0aWVlZmD9/Pt56660mn0fVoKKiAnFxcQgNDcXWrVtbtC/aSnV1NaZMmYLZs2c3WS86OhqFhYXCqz3+GGNPQXt/5TLrvB5+5s4XX3xBCoVCVHbv3r12a1t0dDSZmpq22/bbUlfKRZsDBw7Q0qVLaffu3VofD1JfX0/Dhw+nkSNH0smTJ+nSpUsUHBxMdnZ2VF5eTkREtbW1omOvsLCQVqxYQcbGxsJxWFtbS8899xx5enqSWq2mAwcOkLm5OS1ZsqTJ9p04cYL09PTo008/pYsXL9KyZcs0nrkWGxtLK1asoKioKAJAarW6RbmPGzeOBg4cSOnp6XTs2DFydHQkf39/YXl5eTnNmjWLvvnmG/L29qaJEydqxLh16xYBoKNHjxIRUWlpKUkkEkpISBDqZGdnEwBKS0trtC1z5syh0aNHC+9DQ0PJxcVFVGfatGnk7e3dbF4xMTE0fPhwKi0tJUNDQyooKBAt9/DwoHfffVdUNnHiRNFjH27evEmvvvoqGRgYkIODA23fvl3r4yIa09TvjbbjjHVN3MlhbaKxD5QZM2aIPpg9PDxo7ty59O6775JSqSRLS0v65ptvqLy8nAIDA8nY2Jj69u1LBw4cEMU5d+4cjRs3joyMjMjS0pJef/11+u9//9vq9kRGRpKjoyPJZDKytLQkPz8/YVldXR2tWbOGHBwcyMDAgAYMGCA6URARnT9/nnx8fMjExISMjY3pxRdfFB7iWVdXRytWrKCePXuSVCqlgQMH0sGDB4V1G55ZtGvXLho1ahTJ5XIaMGAA/fTTTxpt79WrF8nlcvL19aV169aJcsnKyqJRo0aRsbExmZiYkJubG506dUrrfqivr6ewsDDq1asXSaVS6tGjB82bN09Y/uhJAwBFRUWRr68vyeVycnR0pH379rV4HxARRUVFkbOzM8lkMnr22WcpMjJSa9u00XbyycnJIQB0/vx5oayuro4sLCwoKiqq0ViDBg2iN998U3h/4MAB0tXVFT3c8euvvyaFQiF6MOajpk6dSj4+PqIyd3d3mjlzpkbdhp9xSzo5Fy9eJACin93BgwdJR0eHbty4oVH/0d+lBnl5eQRA6HQlJycTALpz546onp2dHa1fv77R9kyfPl30+zBy5EiNjsjWrVtJoVA0m9vIkSNp06ZNRETk5+dHK1euFC1vSSfH09OTBg0aROnp6ZSZmUkeHh4kl8vbrJNjY2NDZmZmNHToUNqyZQvV19e3KC7rXPhyFXvqtm3bBnNzc5w8eRLz5s3D7NmzMWXKFIwYMQJnzpyBl5cXAgICUFFRAQAoLS3FmDFjMHjwYJw+fRqHDh1CcXExpk6d2qrtnj59Gu+88w5WrlyJnJwcHDp0CC+99JKwfO3atYiNjcXmzZtx4cIFLFiwAK+//jqOHj0KALhx4wZeeuklyGQypKSkIDMzE2+++aZwuSMiIgKff/451q1bh7Nnz8Lb2xsTJkwQXVYBgKVLl+L9999HVlYWnJyc4O/vL8TIyMhAUFAQ5s6di6ysLIwePRofffSRaP3p06fD1tYWp06dQmZmJhYvXgyJRKI15127dmHDhg34xz/+gby8POzduxeurq5N7qcVK1Zg6tSpOHv2LF599VVMnz4dJSUlLdoH27dvx4cffojVq1cjOzsba9aswfLly7Ft27aW/pg03L9/HwBgYGAglOnq6kImk+H48eNa18nMzERWVhaCgoKEsrS0NLi6usLKykoo8/b2RllZGS5cuNDo9tPS0uDp6Skq8/b2Rlpa2mPl83BcpVKJ559/Xijz9PSErq4uMjIyWhSjvr4e8+fPxwsvvIDnnnsOAFBUVASpVKpxP4qVlRWKioq0xvnpp5+wc+dO0aXLoqIi0b5qiFFWVobKyspG25SXl4f09HRMmzYNAPD6668jOjoa1IrHJF66dAmHDx9GVFQU3N3d4ebmhm+//bbJ7bbGypUrER8fj6SkJPj5+WHOnDnYuHFjm8RmHUx797JY19CakZwXX3xReF9bW0tGRkYUEBAglBUWFoqG1letWkVeXl6iuL/88gsBaPQJ5dras2vXLlIoFFRWVqZRv6qqigwNDTVGVYKCgoTLB0uWLKHevXtTdXW11m3a2NjQ6tWrRWVDhw6lOXPmENH//sr/9ttvheUXLlwgAJSdnU1ERP7+/vTqq6+KYkybNk2Ui4mJCcXExGhtw6M+//xzcnJyarTN2kZyli1bJrwvLy8nAMKIVHP7oG/fvrRjxw5R2apVq0ilUrWovdAyklNdXU12dnY0ZcoUKikpofv379PHH39MADSOiwazZ8+mfv36icrefvttjfq///47AdAYOXyYRCLRyCkyMpIsLS016rZmJGf16tXk5OSkUW5hYUFfffWVRrm2kZxZs2aRvb09/fLLL0LZ9u3bSSqVaqw/dOhQCg0N1Sg/d+4cmZub06pVq0TlzzzzDK1Zs0ZUlpiYSACooqKCvvvuOzIyMhJeP/74IxERLV68mP7yl78I69y/f5+6d+9Ohw8fFsqaG8nZu3cv6evrU11dnahOt27dhON15syZou0/qjWXeZcvX062trYtqss6Fx7JYU/dgAEDhP/r6enBzMxMNLrQ8NfjrVu3ADyYCZKamgpjY2Ph5ezsDADIz89v8XZffvll2Nvbo0+fPggICMD27duF0aLLly+joqICL7/8smg7sbGxwjaysrIwcuRIraMmZWVluHnzJl544QVR+QsvvIDs7OxG8+/Ro4co1+zsbLi7u4vqq1Qq0fuFCxfirbfegqenJz7++OMm98GUKVNQWVmJPn364O2338aePXuavdH24fYZGRlBoVAI7WtqH/z+++/Iz89HUFCQaB9+9NFHrfo5PUoikWD37t3Izc1F9+7dYWhoiNTUVLzyyivQ1dX8CKusrMSOHTtEozgtUVBQIGr3mjVrHrvNj5o1a5YodluYO3cu/v3vfyM1NRW2trZCubW1Naqrq1FaWiqqX1xcDGtra1HZxYsXMXbsWAQHB2PZsmWiZdbW1qIZWQ0xFAoF5HI5JkyYgKysLOH1/PPPo66uDtu2bUNiYiL09fWhr68PQ0NDlJSUiG5A1tXV1RjZqampaVX+K1euFG3/j3B3d8evv/4qjBqyrkO/vRvA/nwePUHq6OiIynR0dAA8GIoHgPLycowfPx6ffPKJRqyGTkJLmJiY4MyZMzhy5Aj+85//4MMPP0R4eDhOnTolzGhJTExEz549RevJZDIAgFwub/G2mtJUri0RHh6O1157DYmJiTh48CDCwsIQFxeHSZMmadTt1asXcnJycPjwYSQlJWHOnDn47LPPcPTo0UYvcWn7+TS0r6l90LAPGy4xPExPT6/F+WkzZMgQZGVl4e7du6iuroaFhQXc3d1Fl3oa/Otf/0JFRQX+/ve/i8qtra1x8uRJUVnDSdza2ho2Njaik2X37t2FZdpO9o92GJqycuVKvP/++xrtaeg8NqitrUVJSUmTsYkI8+bNw549e3DkyBH07t1btHzIkCGQSCRITk6Gn58fACAnJwcFBQWiDvOFCxcwZswYzJgxA6tXr9bYjkql0phFlpSUJMQwMTGBiYmJaPn+/ftx7949qNVq0c/8/PnzeOONN1BaWgqlUgkLCwsUFhYKy+vq6nD+/HmMHj0aAPDss8+itrYWarUaQ4YMAfDgD5E7d+4I61haWsLS0rLR/dQaWVlZ6Natm/C7zroO7uSwDs/NzQ27du2Cg4MD9PX/2CGrr68PT09PeHp6IiwsDEqlEikpKXj55Zchk8lQUFAADw8PresOGDAA27ZtQ01NjUZHQKFQwMbGBidOnBCtf+LECQwbNqzF7evXr5/G/Rjp6eka9ZycnODk5IQFCxbA398f0dHRWjs5wIOOyfjx4zF+/HiEhITA2dkZ586dg5ubW4vb1aCpfWBlZQUbGxtcuXIF06dPb3XsljA1NQXw4L6P06dPa/16gC1btmDChAmwsLAQlatUKqxevRq3bt0STo5JSUlQKBTo378/9PX14ejoqBFPpVIhOTlZ9H1LD5/sW0LbCVmlUqG0tBSZmZnCiTwlJQX19fUancSHhYSEYMeOHdi3bx9MTEyE+2xMTU0hl8thamqKoKAgLFy4EN27d4dCocC8efOgUqkwfPhwAA86HWPGjIG3tzcWLlwoxNDT0xP226xZs7Bp0yaEhobizTffREpKCuLj45GYmNho27Zs2QIfHx8MHDhQVN6/f38sWLAA27dvR0hICMaMGYOFCxciMTERffv2xfr160UjT87OzvD09ERwcDC+/vprSCQSvPfee5DL5cIfBo0pKChASUkJCgoKUFdXJ3RcHR0dYWxsjP3796O4uBjDhw+HgYEBkpKSsGbNGo1OKOsi2vt6GesaWnNPzqPX4rVNC8VD92bcuHGDLCws6K9//SudPHmSLl++TIcOHaLAwECqra1tcXv2799PERERpFar6dq1a/TVV1+Rrq6uMGtn6dKlZGZmRjExMXT58mXKzMykL7/8Urj/5fbt22RmZkaTJ0+mU6dOUW5uLsXGxtKlS5eIiGjDhg2kUCgoLi6OLl26RB988AFJJBLKzc0lIu33a9y5c4cAUGpqKhERpaWlka6uLn322WeUm5tLGzduJKVSKeRSUVFBISEhlJqaSteuXaPjx49T3759td5r0bAfvv32Wzp37hzl5+fTsmXLSC6X0+3bt7Xue2i5J8bU1JSio6NbtA+ioqJILpdTREQE5eTk0NmzZ2nr1q30+eefa20fEdG9e/dIrVaTWq0mALR+/XpSq9V0/fp1oU58fDylpqZSfn4+7d27l+zt7Wny5MkasfLy8khHR0c0q61BwxRyLy8vysrKokOHDpGFhUWLppDr6+vTunXrKDs7m8LCwjSmkP/222+kVquFe1bi4uJIrVZTYWFhk7HHjRtHgwcPpoyMDDp+/Dg988wzoinkRA/u21Kr1TR+/HgaNWoUAdD6avgZERFVVlbSnDlzqFu3bmRoaEiTJk0StSUsLExrDHt7e9G2U1NTadCgQSSVSqlPnz6ibTyqqKiI9PX1KT4+Xuvy2bNn0+DBg4nowX1Ws2fPpu7du5OlpSWtXbtW6xTyV155hWQyGdnb29OOHTvI0tKSNm/e3OQ+nTFjhtbcGn7HDh48SIMGDSJjY2MyMjKigQMH0ubNmzXu/2FdA3dyWJt4kp0cIqLc3FyaNGkSKZVKksvl5OzsTPPnz2902qe29hw7dow8PDyoW7duwvTtnTt3Csvr6+vpiy++oGeffZYkEglZWFiQt7e38P0jREQ///wzeXl5kaGhIZmYmNDIkSMpPz+fiB5Maw4PD6eePXuSRCJpdAp5U50cIqItW7aQra0tyeVyGj9+vGgK+f379+lvf/ubMCXcxsaG5s6dS5WVlVr3w549e8jd3Z0UCgUZGRnR8OHDRTeAtraT09w+IHpw42vDibFbt2700ksv0e7du7W2j+jBiVTbSenhE15ERATZ2tqSRCIhOzs7WrZsmdZp30uWLKFevXo1esK6du0avfLKKySXy8nc3Jzee+89qqmpabRtDeLj48nJyYmkUim5uLhQYmKiaHl0dLTWHMLCwpqM+9tvv5G/vz8ZGxuTQqGgN954Q+P7pezt7bXG/rNpmGzw8PHLWHN0iFoxr4+xTiImJgbz58/XuPmSMdY5pKSkoLy8HK6urigsLERoaChu3LiB3NzcRu8nY+xRPLuKdVl3796FsbExPvjgg/ZuCmOslWpqavB///d/cHFxwaRJk2BhYYEjR45wB4e1Co/ksC7p3r17wowYpVIJc3Pzdm4RY4yxp407OYwxxhjrkvhyFWOMMca6JO7kMMYYY6xL4k4OY+ypcnBwgI6ODnR0dHj2G2PsieJODmPtLDIyEg4ODjAwMIC7u7vo0QNVVVUICQmBmZkZjI2N4efnp/GIAW0SEhLg7OwMAwMDuLq6anw9PxHhww8/RI8ePSCXy+Hp6anxtHRtjhw5Ajc3N8hkMjg6OiImJqZV+QDAqVOnsGvXrma3xRhjfxR3chhrRzt37sTChQsRFhaGM2fOYODAgfD29haeabRgwQLs378fCQkJOHr0KG7evInJkyc3GfOnn36Cv78/goKCoFar4evrC19fX5w/f16o8+mnn+LLL7/E5s2bkZGRASMjI3h7e6OqqqrRuFevXoWPjw9Gjx6NrKwszJ8/H2+99RZ++OGHFucDABYWFsJzoRhj7Ilqxy8iZOxPb9iwYRQSEiK8r6urIxsbG1q7di2VlpaSRCKhhIQEYXl2djYBoLS0tEZjTp06lXx8fERl7u7uNHPmTCJ68M3O1tbW9NlnnwnLS0tLSSaT0T//+c9G44aGhpKLi4uobNq0aeTt7d2ifB7W8C3Hd+7caXR7jDH2R/FIDmPtpLq6GpmZmfD09BTKdHV14enpibS0NGRmZqKmpka03NnZGXZ2dkhLSxPKHBwcEB4eLrxPS0sTrQMA3t7ewjpXr15FUVGRqI6pqSnc3d1FcUeNGoXAwMAWx20uH8YYe9q4k8NYO7l9+zbq6upgZWUlKreyskJRURGKiooglUqhVCq1Lm/Qt29f0ZcdFhUVNRqzYXlDWVNx7ezs0KNHj2bjlpWVobKystl8GGPsadNv7wYwxv6Y5OTkJxI3Njb2icRljLGnhUdyGGsn5ubm0NPT05gtVVxcDGtra1hbW6O6ulpjmnXD8sZYW1s3GrNheUNZW8RVKBSQy+XN5sMYY08bd3IYaydSqRRDhgwRjcTU19cjOTkZKpUKQ4YMgUQiES3PyclBQUEBVCpVo3FVKpXG6E5SUpKwTu/evWFtbS2qU1ZWhoyMjD8Ut7l8GGPsqWvvO58Z+zOLi4sjmUxGMTExdPHiRQoODialUklFRUVERDRr1iyys7OjlJQUOn36NKlUKlKpVKIYY8aMoY0bNwrvT5w4Qfr6+rRu3TrKzs6msLAwkkgkdO7cOaHOxx9/TEqlkvbt20dnz56liRMnUu/evamyslKoExAQQIsXLxbeX7lyhQwNDWnRokWUnZ1NkZGRpKenR4cOHWpxPg14dhVj7GngTg5j7Wzjxo1kZ2dHUqmUhg0bRunp6cKyyspKmjNnDnXr1o0MDQ1p0qRJVFhYKFrf3t6ewsLCRGXx8fHk5OREUqmUXFxcKDExUbS8vr6eli9fTlZWViSTyWjs2LGUk5MjquPh4UEzZswQlaWmptKgQYNIKpVSnz59KDo6ulX5PByHOzmMsSeNn0LOGHvqjhw5gtGjR+POnTsas8cYY6yt8OwqxthT5eLigitXrrR3MxhjfwI8ksMYe6quX7+OmpoaAECfPn2gq8vzHxhjTwZ3chhjjDHWJfGfUIwxxhjrkriTwxhjjLEuiTs5jDHGGOuSuJPDGGOMsS6JOzmMMcYY65K4k8MYY4yxLok7OYwxxhjrkriTwxhjjLEu6f8B68DaeKFOz/IAAAAASUVORK5CYII=",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -2002,10 +2007,10 @@
               "    ...                        ...\n",
               "    has_imu:                   1\n",
               "    beam_angle:                25\n",
-              "    h_deploy:                  0.6\n",
+              "    range_offset:              0.6\n",
               "    declination:               15.8\n",
               "    declination_in_orientmat:  1\n",
-              "    principal_heading:         11.1898</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-9c0d8d8f-edaa-4e84-967b-5cbe0b271e65' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c0d8d8f-edaa-4e84-967b-5cbe0b271e65' 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>: 183</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>time_b5</span>: 183</li><li><span class='xr-has-index'>range_b5</span>: 28</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a26ad22f-37f7-4cbd-a252-73a7e6ccae2c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a26ad22f-37f7-4cbd-a252-73a7e6ccae2c' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2020-08-15T00:22:30.001030683 .....</div><input id='attrs-0a64721b-8dcf-4a29-ad9d-8d70530ca488' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a64721b-8dcf-4a29-ad9d-8d70530ca488' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e1e03d-1400-46a3-9fa1-8cedb5c970b3' class='xr-var-data-in' type='checkbox'><label for='data-50e1e03d-1400-46a3-9fa1-8cedb5c970b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:30.001030683&#x27;, &#x27;2020-08-15T00:27:30.001019239&#x27;,\n",
+              "    principal_heading:         11.1898</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-76934faf-a19c-4510-bcf9-b5e1ce8a9353' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-76934faf-a19c-4510-bcf9-b5e1ce8a9353' 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>: 183</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>time_b5</span>: 183</li><li><span class='xr-has-index'>range_b5</span>: 28</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2f1b57a8-9e0b-4644-80d8-9f7611817e8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f1b57a8-9e0b-4644-80d8-9f7611817e8b' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2020-08-15T00:22:30.001030683 .....</div><input id='attrs-4086a38f-dbaf-4867-a078-3747a09e6c7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4086a38f-dbaf-4867-a078-3747a09e6c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a95f9125-ce3e-4d16-b4eb-9cf417d6a836' class='xr-var-data-in' type='checkbox'><label for='data-a95f9125-ce3e-4d16-b4eb-9cf417d6a836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:30.001030683&#x27;, &#x27;2020-08-15T00:27:30.001019239&#x27;,\n",
               "       &#x27;2020-08-15T00:32:30.001010656&#x27;, &#x27;2020-08-15T00:37:30.001015901&#x27;,\n",
               "       &#x27;2020-08-15T00:42:30.001019954&#x27;, &#x27;2020-08-15T00:47:30.001018524&#x27;,\n",
               "       &#x27;2020-08-15T00:52:30.001013994&#x27;, &#x27;2020-08-15T00:57:30.001026630&#x27;,\n",
@@ -2096,9 +2101,9 @@
               "       &#x27;2020-08-15T15:02:30.001021146&#x27;, &#x27;2020-08-15T15:07:30.001027345&#x27;,\n",
               "       &#x27;2020-08-15T15:12:30.001029014&#x27;, &#x27;2020-08-15T15:17:30.001026630&#x27;,\n",
               "       &#x27;2020-08-15T15:22:30.001026153&#x27;, &#x27;2020-08-15T15:27:30.001022577&#x27;,\n",
-              "       &#x27;2020-08-15T15:32:30.001030683&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27;</div><input id='attrs-646d4028-7d32-42bc-a2da-43e9294f5b1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-646d4028-7d32-42bc-a2da-43e9294f5b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e9a8531-0051-4969-90ca-12cb05dfe9b7' class='xr-var-data-in' type='checkbox'><label for='data-7e9a8531-0051-4969-90ca-12cb05dfe9b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-fbf44394-6d41-436d-a996-dfeef24a0d55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbf44394-6d41-436d-a996-dfeef24a0d55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bac069a1-9ea2-42b8-97df-191f75d53c0f' class='xr-var-data-in' type='checkbox'><label for='data-bac069a1-9ea2-42b8-97df-191f75d53c0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+              "       &#x27;2020-08-15T15:32:30.001030683&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27;</div><input id='attrs-991ec212-f5e6-4db6-87d4-b92333f5e4a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-991ec212-f5e6-4db6-87d4-b92333f5e4a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-296539d8-9fb1-4794-8075-a203a158dcbe' class='xr-var-data-in' type='checkbox'><label for='data-296539d8-9fb1-4794-8075-a203a158dcbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-acd69270-7935-45b9-b364-a0b2408e5e5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acd69270-7935-45b9-b364-a0b2408e5e5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9e58693-caa5-4c1b-82be-bb98b6f11889' class='xr-var-data-in' type='checkbox'><label for='data-d9e58693-caa5-4c1b-82be-bb98b6f11889' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
               "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; ... &#x27;vert2&#x27;</div><input id='attrs-8b88deca-f29e-455c-82ed-02aca3151d2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b88deca-f29e-455c-82ed-02aca3151d2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcd73e00-fd14-491d-808a-c1b67e2f7077' class='xr-var-data-in' type='checkbox'><label for='data-bcd73e00-fd14-491d-808a-c1b67e2f7077' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-608327eb-6892-4837-aac0-47851f5b79f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-608327eb-6892-4837-aac0-47851f5b79f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23a728f7-3f7b-4e3f-a56e-4459ae03e67b' class='xr-var-data-in' type='checkbox'><label for='data-23a728f7-3f7b-4e3f-a56e-4459ae03e67b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-82d5cbf5-093b-4127-843c-009ab8ffadf3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82d5cbf5-093b-4127-843c-009ab8ffadf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eb9c8a5-6898-4892-a8a2-bd2d8387b08b' class='xr-var-data-in' type='checkbox'><label for='data-5eb9c8a5-6898-4892-a8a2-bd2d8387b08b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-e841acd2-8bdf-41cd-a36c-477201c197f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e841acd2-8bdf-41cd-a36c-477201c197f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e02942a5-3b08-4692-bd83-071ccce32e00' class='xr-var-data-in' type='checkbox'><label for='data-e02942a5-3b08-4692-bd83-071ccce32e00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-f0a426af-5ebc-4b57-befb-37c4051a97ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0a426af-5ebc-4b57-befb-37c4051a97ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8a51b8a-c4df-44c5-9dec-2a6578eca239' class='xr-var-data-in' type='checkbox'><label for='data-f8a51b8a-c4df-44c5-9dec-2a6578eca239' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:29.938495159 .....</div><input id='attrs-c2cdcc1e-d675-40c7-ac58-1339840cd6c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2cdcc1e-d675-40c7-ac58-1339840cd6c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b66c878-2b31-450b-ae7e-536be92e1957' class='xr-var-data-in' type='checkbox'><label for='data-8b66c878-2b31-450b-ae7e-536be92e1957' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:29.938495159&#x27;, &#x27;2020-08-15T00:27:29.938490867&#x27;,\n",
+              "       13.2, 13.7, 14.2, 14.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; ... &#x27;vert2&#x27;</div><input id='attrs-4bb38e42-213c-4ac9-90a8-cd1092e962b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bb38e42-213c-4ac9-90a8-cd1092e962b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea6576f-8880-461a-b253-db56b8ba9924' class='xr-var-data-in' type='checkbox'><label for='data-5ea6576f-8880-461a-b253-db56b8ba9924' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-946125ac-83cf-4627-b6c2-d35dd903c2a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-946125ac-83cf-4627-b6c2-d35dd903c2a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5eb9f70-959f-4101-a26a-7843f2455950' class='xr-var-data-in' type='checkbox'><label for='data-f5eb9f70-959f-4101-a26a-7843f2455950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-110198f1-fcb2-4b80-b51f-c7c44aa1e8f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-110198f1-fcb2-4b80-b51f-c7c44aa1e8f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ff5e431-351a-48fa-9c4f-b0fc39be916e' class='xr-var-data-in' type='checkbox'><label for='data-5ff5e431-351a-48fa-9c4f-b0fc39be916e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-a1c541d6-2c6e-4dca-bca3-ec27c7746caf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1c541d6-2c6e-4dca-bca3-ec27c7746caf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2490e8c2-a9c5-4561-b1f8-1ab670081160' class='xr-var-data-in' type='checkbox'><label for='data-2490e8c2-a9c5-4561-b1f8-1ab670081160' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-3a231bb8-f1f9-4cba-ac38-2fb1defd614f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a231bb8-f1f9-4cba-ac38-2fb1defd614f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2bf1dc3-cadc-431a-8106-d21d33b2b76a' class='xr-var-data-in' type='checkbox'><label for='data-a2bf1dc3-cadc-431a-8106-d21d33b2b76a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:29.938495159 .....</div><input id='attrs-76029295-006e-4132-a00f-72d760476a9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76029295-006e-4132-a00f-72d760476a9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1b49ff4-8259-4cf4-8cd4-93704914d844' class='xr-var-data-in' type='checkbox'><label for='data-f1b49ff4-8259-4cf4-8cd4-93704914d844' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:29.938495159&#x27;, &#x27;2020-08-15T00:27:29.938490867&#x27;,\n",
               "       &#x27;2020-08-15T00:32:29.938487529&#x27;, &#x27;2020-08-15T00:37:29.938496828&#x27;,\n",
               "       &#x27;2020-08-15T00:42:29.938495397&#x27;, &#x27;2020-08-15T00:47:29.938496589&#x27;,\n",
               "       &#x27;2020-08-15T00:52:29.938497781&#x27;, &#x27;2020-08-15T00:57:29.938495397&#x27;,\n",
@@ -2189,9 +2194,9 @@
               "       &#x27;2020-08-15T15:02:29.938493013&#x27;, &#x27;2020-08-15T15:07:29.938493967&#x27;,\n",
               "       &#x27;2020-08-15T15:12:29.938494920&#x27;, &#x27;2020-08-15T15:17:29.938493251&#x27;,\n",
               "       &#x27;2020-08-15T15:22:29.938486099&#x27;, &#x27;2020-08-15T15:27:29.938484191&#x27;,\n",
-              "       &#x27;2020-08-15T15:32:29.938490390&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-94bfcbee-b816-4835-a412-6d3eab0e6053' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94bfcbee-b816-4835-a412-6d3eab0e6053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3da466fc-3fd5-4a33-82de-73ae2173272e' class='xr-var-data-in' type='checkbox'><label for='data-3da466fc-3fd5-4a33-82de-73ae2173272e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+              "       &#x27;2020-08-15T15:32:29.938490390&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-50c387a1-c443-4298-a551-077e360722e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50c387a1-c443-4298-a551-077e360722e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-623fe903-5597-48e8-84d9-b98e66ad2acf' class='xr-var-data-in' type='checkbox'><label for='data-623fe903-5597-48e8-84d9-b98e66ad2acf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
               "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3e64723-fda9-4ae1-a854-d288f9941a9c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f3e64723-fda9-4ae1-a854-d288f9941a9c' class='xr-section-summary' >Data variables: <span>(38)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-a74d144c-d428-44e9-9e99-80167a987cc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a74d144c-d428-44e9-9e99-80167a987cc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5efa697-0057-404e-89f4-7e8cd846fb13' class='xr-var-data-in' type='checkbox'><label for='data-e5efa697-0057-404e-89f4-7e8cd846fb13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.6843, 1502.032 , 1501.8505, 1501.5714, 1501.8607, 1502.4133,\n",
+              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a44bbb16-b448-4b41-8935-9f77e2d80cce' class='xr-section-summary-in' type='checkbox'  ><label for='section-a44bbb16-b448-4b41-8935-9f77e2d80cce' class='xr-section-summary' >Data variables: <span>(38)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-b6d104f0-8675-45fc-9287-4a9869cdce93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6d104f0-8675-45fc-9287-4a9869cdce93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e37fb044-9056-4b0b-be54-0c20e958429e' class='xr-var-data-in' type='checkbox'><label for='data-e37fb044-9056-4b0b-be54-0c20e958429e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.6843, 1502.032 , 1501.8505, 1501.5714, 1501.8607, 1502.4133,\n",
               "       1502.8121, 1502.7344, 1502.8704, 1502.9467, 1502.883 , 1502.7606,\n",
               "       1502.6915, 1502.8214, 1502.8864, 1502.7937, 1502.6268, 1502.6473,\n",
               "       1502.4198, 1502.3577, 1501.9543, 1501.729 , 1501.32  , 1500.9684,\n",
@@ -2221,7 +2226,7 @@
               "       1499.5286, 1499.5846, 1499.6293, 1499.6262, 1499.6666, 1499.6971,\n",
               "       1499.6998, 1499.7269, 1499.7843, 1499.6895, 1499.7037, 1499.6376,\n",
               "       1499.4894, 1499.0636, 1499.0073, 1498.8967, 1498.8627, 1498.961 ,\n",
-              "       1498.8287, 1498.6285, 1498.472 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_std</span></div><div class='xr-var-dims'>(range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.04232 0.04293 0.04402 ... nan nan</div><input id='attrs-d83066c3-a7d9-4075-bcea-6f32f1d2707f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d83066c3-a7d9-4075-bcea-6f32f1d2707f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df7a9d70-9314-490c-a6c9-3646f054d07d' class='xr-var-data-in' type='checkbox'><label for='data-df7a9d70-9314-490c-a6c9-3646f054d07d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([[0.04231985, 0.04293143, 0.04401674, ..., 0.04380286, 0.04201568,\n",
+              "       1498.8287, 1498.6285, 1498.472 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_std</span></div><div class='xr-var-dims'>(range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.04232 0.04293 0.04402 ... nan nan</div><input id='attrs-cd23a532-102e-450b-85fd-85a97b83b56a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd23a532-102e-450b-85fd-85a97b83b56a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9467183-9cde-4308-b811-50f916a9ce2e' class='xr-var-data-in' type='checkbox'><label for='data-c9467183-9cde-4308-b811-50f916a9ce2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([[0.04231985, 0.04293143, 0.04401674, ..., 0.04380286, 0.04201568,\n",
               "        0.04634431],\n",
               "       [0.04002878, 0.04084466, 0.03949426, ..., 0.04000184, 0.04028782,\n",
               "        0.03873245],\n",
@@ -2233,7 +2238,7 @@
               "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
               "               nan],\n",
               "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-              "               nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.49 14.59 14.54 ... 13.56 13.5</div><input id='attrs-ecdfdbe7-d26e-49a9-a783-b8d139a9a2a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ecdfdbe7-d26e-49a9-a783-b8d139a9a2a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50de93b6-9a55-44eb-a0d1-5ca30a8ad90f' class='xr-var-data-in' type='checkbox'><label for='data-50de93b6-9a55-44eb-a0d1-5ca30a8ad90f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.4871   , 14.592732 , 14.5369   , 14.450967 , 14.540566 ,\n",
+              "               nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.49 14.59 14.54 ... 13.56 13.5</div><input id='attrs-89b799c9-be53-4f7a-afe9-e6247765b059' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89b799c9-be53-4f7a-afe9-e6247765b059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-594a4b5b-a7ec-439d-9067-064ee9678900' class='xr-var-data-in' type='checkbox'><label for='data-594a4b5b-a7ec-439d-9067-064ee9678900' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.4871   , 14.592732 , 14.5369   , 14.450967 , 14.540566 ,\n",
               "       14.712167 , 14.838034 , 14.812099 , 14.855833 , 14.879432 ,\n",
               "       14.860366 , 14.821934 , 14.799266 , 14.840433 , 14.8621   ,\n",
               "       14.833766 , 14.782434 , 14.788966 , 14.716966 , 14.695799 ,\n",
@@ -2269,7 +2274,7 @@
               "       13.860667 , 13.870465 , 13.876833 , 13.878232 , 13.884268 ,\n",
               "       13.907367 , 13.875599 , 13.875534 , 13.861733 , 13.8139   ,\n",
               "       13.689401 , 13.665034 , 13.632333 , 13.6227665, 13.651935 ,\n",
-              "       13.6164665, 13.5551   , 13.503766 ], dtype=float32)</pre></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'>float32</div><div class='xr-var-preview xr-preview'>9.712 9.699 9.685 ... 9.584 9.591</div><input id='attrs-0a82ddb2-8119-48d9-92b6-fc0b2f2ea42c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a82ddb2-8119-48d9-92b6-fc0b2f2ea42c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fccabb8-7792-4aa0-b380-7d3273d9d9a1' class='xr-var-data-in' type='checkbox'><label for='data-0fccabb8-7792-4aa0-b380-7d3273d9d9a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.712007 , 9.699074 , 9.685317 , 9.670219 , 9.65324  , 9.640334 ,\n",
+              "       13.6164665, 13.5551   , 13.503766 ], dtype=float32)</pre></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'>float32</div><div class='xr-var-preview xr-preview'>9.712 9.699 9.685 ... 9.584 9.591</div><input id='attrs-1c947889-6802-48d6-8b8a-124794d8cbcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c947889-6802-48d6-8b8a-124794d8cbcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-693a9a8f-88ea-4e64-abad-3e4f79a52135' class='xr-var-data-in' type='checkbox'><label for='data-693a9a8f-88ea-4e64-abad-3e4f79a52135' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.712007 , 9.699074 , 9.685317 , 9.670219 , 9.65324  , 9.640334 ,\n",
               "       9.62429  , 9.608537 , 9.594397 , 9.576526 , 9.56048  , 9.541069 ,\n",
               "       9.524289 , 9.505187 , 9.486143 , 9.464264 , 9.44223  , 9.416344 ,\n",
               "       9.393453 , 9.367396 , 9.339387 , 9.305754 , 9.278394 , 9.245257 ,\n",
@@ -2299,7 +2304,7 @@
               "       9.35049  , 9.37106  , 9.393917 , 9.40948  , 9.428807 , 9.441616 ,\n",
               "       9.459889 , 9.474596 , 9.488944 , 9.495657 , 9.510249 , 9.520061 ,\n",
               "       9.530423 , 9.537613 , 9.546494 , 9.554764 , 9.561024 , 9.57158  ,\n",
-              "       9.579874 , 9.583782 , 9.591404 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.534 3.565 3.547 ... -197.1 -197.1</div><input id='attrs-e8d63021-2f0a-4c47-b7aa-c91a6565212c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8d63021-2f0a-4c47-b7aa-c91a6565212c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88a72ec8-ebd2-4d59-a167-87d18dd49ba9' class='xr-var-data-in' type='checkbox'><label for='data-88a72ec8-ebd2-4d59-a167-87d18dd49ba9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.53383756e+00,  3.56493616e+00,  3.54652500e+00,\n",
+              "       9.579874 , 9.583782 , 9.591404 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.534 3.565 3.547 ... -197.1 -197.1</div><input id='attrs-fd590823-10f2-4c33-b708-ece4d51f2fab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd590823-10f2-4c33-b708-ece4d51f2fab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fe2b112-2736-4c6b-86cb-17644db61fc8' class='xr-var-data-in' type='checkbox'><label for='data-2fe2b112-2736-4c6b-86cb-17644db61fc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.53383756e+00,  3.56493616e+00,  3.54652500e+00,\n",
               "         3.60746217e+00,  3.56795740e+00,  3.58543777e+00,\n",
               "         3.56357908e+00,  3.57667398e+00,  3.60632110e+00,\n",
               "         3.56949067e+00,  3.57479167e+00,  3.59876704e+00,\n",
@@ -2339,7 +2344,7 @@
               "        -1.97044174e+02, -1.97063873e+02, -1.97090515e+02,\n",
               "        -1.97129501e+02, -1.97007523e+02, -1.97017838e+02,\n",
               "        -1.97019165e+02, -1.97129166e+02, -1.97051849e+02,\n",
-              "        -1.97113190e+02, -1.97104507e+02, -1.97053848e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.261 -1.263 -1.26 ... 9.714 9.712</div><input id='attrs-7cbb1de9-c5f9-43f1-bde7-f55710c23c78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cbb1de9-c5f9-43f1-bde7-f55710c23c78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f377219-021a-480c-ac6d-99c6794e2efe' class='xr-var-data-in' type='checkbox'><label for='data-8f377219-021a-480c-ac6d-99c6794e2efe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2608225 , -1.2632061 , -1.2602278 , -1.2620431 , -1.2610018 ,\n",
+              "        -1.97113190e+02, -1.97104507e+02, -1.97053848e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.261 -1.263 -1.26 ... 9.714 9.712</div><input id='attrs-645a3f3f-e131-4435-b62e-75ff0f1ce4ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-645a3f3f-e131-4435-b62e-75ff0f1ce4ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faeedbbc-3bf4-4333-a184-2b1c56f01c02' class='xr-var-data-in' type='checkbox'><label for='data-faeedbbc-3bf4-4333-a184-2b1c56f01c02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2608225 , -1.2632061 , -1.2602278 , -1.2620431 , -1.2610018 ,\n",
               "        -1.2613868 , -1.2610568 , -1.2621608 , -1.2631991 , -1.2639122 ,\n",
               "        -1.2618904 , -1.258823  , -1.2596216 , -1.258702  , -1.2583325 ,\n",
               "        -1.2578007 , -1.2583394 , -1.2576339 , -1.2579147 , -1.2534361 ,\n",
@@ -2379,7 +2384,7 @@
               "         9.714537  ,  9.714841  ,  9.714376  ,  9.71848   ,  9.7165    ,\n",
               "         9.715717  ,  9.711741  ,  9.710465  ,  9.712621  ,  9.71337   ,\n",
               "         9.715622  ,  9.715989  ,  9.711598  ,  9.71219   ,  9.71021   ,\n",
-              "         9.712366  ,  9.7141695 ,  9.711616  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.48 16.48 16.48 ... 16.26 16.26</div><input id='attrs-118aaf88-98be-467f-88d4-2d212336dc40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-118aaf88-98be-467f-88d4-2d212336dc40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f77cfc8-4045-4c97-b656-272c87344e56' class='xr-var-data-in' type='checkbox'><label for='data-8f77cfc8-4045-4c97-b656-272c87344e56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.481167, 16.478   , 16.478   , 16.477167, 16.481   , 16.484167,\n",
+              "         9.712366  ,  9.7141695 ,  9.711616  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.48 16.48 16.48 ... 16.26 16.26</div><input id='attrs-515dc633-b2f5-47b7-a6c1-397ab3130ce4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-515dc633-b2f5-47b7-a6c1-397ab3130ce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05d4d539-58cf-4146-a3fe-69228727033c' class='xr-var-data-in' type='checkbox'><label for='data-05d4d539-58cf-4146-a3fe-69228727033c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.481167, 16.478   , 16.478   , 16.477167, 16.481   , 16.484167,\n",
               "       16.477167, 16.479834, 16.479166, 16.482666, 16.478167, 16.481668,\n",
               "       16.479668, 16.475334, 16.479668, 16.479   , 16.474   , 16.475334,\n",
               "       16.477667, 16.473167, 16.472168, 16.468334, 16.472334, 16.471169,\n",
@@ -2409,7 +2414,7 @@
               "       16.258333, 16.262335, 16.262169, 16.2575  , 16.261   , 16.260332,\n",
               "       16.25633 , 16.257998, 16.258333, 16.253334, 16.257998, 16.257332,\n",
               "       16.253166, 16.256998, 16.256998, 16.252666, 16.256998, 16.256998,\n",
-              "       16.252666, 16.256998, 16.256998], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-a8d84a45-d9e6-4585-b101-f355e5016ae0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8d84a45-d9e6-4585-b101-f355e5016ae0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a8063eb-dd84-46dd-b03a-0ed980a639fa' class='xr-var-data-in' type='checkbox'><label for='data-0a8063eb-dd84-46dd-b03a-0ed980a639fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20.      , 20.      , 20.      , 20.043333, 20.25    , 20.25    ,\n",
+              "       16.252666, 16.256998, 16.256998], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-beafc30b-b7fa-4682-bddb-f6abc027a023' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beafc30b-b7fa-4682-bddb-f6abc027a023' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f49b50ad-427e-4a60-9f8c-526f64876ba5' class='xr-var-data-in' type='checkbox'><label for='data-f49b50ad-427e-4a60-9f8c-526f64876ba5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20.      , 20.      , 20.      , 20.043333, 20.25    , 20.25    ,\n",
               "       20.25    , 20.25    , 20.263334, 20.446667, 20.5     , 20.5     ,\n",
               "       20.5     , 20.5     , 20.5     , 20.5     , 20.5     , 20.5     ,\n",
               "       20.5     , 20.5     , 20.5     , 20.5     , 20.5     , 20.31    ,\n",
@@ -2439,7 +2444,7 @@
               "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.53    ,\n",
               "       19.696667, 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
               "       19.75    , 19.75    , 19.71    , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-217d93c2-38ac-48ab-b11a-4fdaf1b24460' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-217d93c2-38ac-48ab-b11a-4fdaf1b24460' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34345e89-d26c-411f-875d-af2071918a72' class='xr-var-data-in' type='checkbox'><label for='data-34345e89-d26c-411f-875d-af2071918a72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "       19.5     , 19.5     , 19.5     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-136cd155-908f-4417-a00a-5f726d101d9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-136cd155-908f-4417-a00a-5f726d101d9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e1d23be-b1aa-41a2-bea9-566ef876a6d7' class='xr-var-data-in' type='checkbox'><label for='data-5e1d23be-b1aa-41a2-bea9-566ef876a6d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -2449,7 +2454,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-39c5e313-522b-42c3-9a9a-e9414a87da42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39c5e313-522b-42c3-9a9a-e9414a87da42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd4bb60e-aefc-4dda-91bb-a35b0648cd2c' class='xr-var-data-in' type='checkbox'><label for='data-cd4bb60e-aefc-4dda-91bb-a35b0648cd2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-ef94cf8f-9370-4f1b-a8ae-d726be31b4c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef94cf8f-9370-4f1b-a8ae-d726be31b4c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1017b663-7d54-4771-a202-0218d1b545ca' class='xr-var-data-in' type='checkbox'><label for='data-1017b663-7d54-4771-a202-0218d1b545ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
@@ -2489,7 +2494,7 @@
               "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
               "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
               "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2358 0.266 0.3007 ... nan nan nan</div><input id='attrs-36640c02-267c-40bf-a158-325a7864e5e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36640c02-267c-40bf-a158-325a7864e5e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e81b5ef6-e0fa-4043-a01d-9b09863ceed9' class='xr-var-data-in' type='checkbox'><label for='data-e81b5ef6-e0fa-4043-a01d-9b09863ceed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.23582192,  0.2660148 ,  0.30069652, ..., -0.179669  ,\n",
+              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2358 0.266 0.3007 ... nan nan nan</div><input id='attrs-e3b4292a-425b-4950-b2fc-a8d9efbfb23c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3b4292a-425b-4950-b2fc-a8d9efbfb23c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48dc5038-7f2a-40d1-a642-b55dbece095e' class='xr-var-data-in' type='checkbox'><label for='data-48dc5038-7f2a-40d1-a642-b55dbece095e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.23582192,  0.2660148 ,  0.30069652, ..., -0.179669  ,\n",
               "         -0.18383324, -0.17606331],\n",
               "        [ 0.25633767,  0.28601688,  0.3117814 , ..., -0.19580415,\n",
               "         -0.19696543, -0.20011853],\n",
@@ -2529,7 +2534,7 @@
               "        [        nan,         nan,         nan, ...,         nan,\n",
               "                 nan,         nan],\n",
               "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>79.95 80.28 80.69 ... nan nan nan</div><input id='attrs-1dd6ff0c-a597-405c-98c5-e295dc18faf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dd6ff0c-a597-405c-98c5-e295dc18faf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11d54632-4aca-446e-9021-64ce79784842' class='xr-var-data-in' type='checkbox'><label for='data-11d54632-4aca-446e-9021-64ce79784842' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[79.94666 , 80.28167 , 80.68667 , ..., 81.721664, 81.80334 ,\n",
+              "                 nan,         nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>79.95 80.28 80.69 ... nan nan nan</div><input id='attrs-09e41d60-52b6-42c5-b3fc-3426bf48169b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09e41d60-52b6-42c5-b3fc-3426bf48169b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce44551e-20df-42c2-9f7b-8b534197f409' class='xr-var-data-in' type='checkbox'><label for='data-ce44551e-20df-42c2-9f7b-8b534197f409' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[79.94666 , 80.28167 , 80.68667 , ..., 81.721664, 81.80334 ,\n",
               "         81.68667 ],\n",
               "        [75.535   , 76.043335, 76.66167 , ..., 77.62666 , 77.73    ,\n",
               "         77.528336],\n",
@@ -2569,7 +2574,7 @@
               "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
               "               nan],\n",
               "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>92.59 92.61 92.47 ... 0.0 0.0 0.0</div><input id='attrs-e5981662-7a32-4277-b12e-faaa2ce65b36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5981662-7a32-4277-b12e-faaa2ce65b36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6326ffdb-f837-4f73-ac6f-481ffe57ec80' class='xr-var-data-in' type='checkbox'><label for='data-6326ffdb-f837-4f73-ac6f-481ffe57ec80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[92.58667 , 92.613335, 92.47    , ..., 91.933334, 91.96667 ,\n",
+              "               nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>92.59 92.61 92.47 ... 0.0 0.0 0.0</div><input id='attrs-e7ca759a-3efb-4800-b7f8-5157a85f734a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7ca759a-3efb-4800-b7f8-5157a85f734a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa206765-4a5c-493d-b3a2-5e9c8b3e4445' class='xr-var-data-in' type='checkbox'><label for='data-fa206765-4a5c-493d-b3a2-5e9c8b3e4445' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[92.58667 , 92.613335, 92.47    , ..., 91.933334, 91.96667 ,\n",
               "         92.06333 ],\n",
               "        [96.236664, 96.526665, 96.206665, ..., 96.00667 , 96.3     ,\n",
               "         96.03333 ],\n",
@@ -2609,7 +2614,7 @@
               "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
               "          0.      ],\n",
               "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05743 0.05697 ... 0.9937 0.9937</div><input id='attrs-9a3aa348-7cfd-4dea-89b1-048a41275324' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a3aa348-7cfd-4dea-89b1-048a41275324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c53498c-cc64-4896-b067-1a48e61a6abc' class='xr-var-data-in' type='checkbox'><label for='data-4c53498c-cc64-4896-b067-1a48e61a6abc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[ 5.74317947e-02,  5.69723397e-02,  5.82992062e-02, ...,\n",
+              "          0.      ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05743 0.05697 ... 0.9937 0.9937</div><input id='attrs-47bfe4a9-8140-46d9-9d38-82f9abaf17d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47bfe4a9-8140-46d9-9d38-82f9abaf17d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a9f918-28da-40c9-9a9e-8e4094c38401' class='xr-var-data-in' type='checkbox'><label for='data-00a9f918-28da-40c9-9a9e-8e4094c38401' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[ 5.74317947e-02,  5.69723397e-02,  5.82992062e-02, ...,\n",
               "          5.81927113e-02,  5.85613623e-02,  5.85512854e-02],\n",
               "        [ 9.98300374e-01,  9.98323679e-01,  9.98248577e-01, ...,\n",
               "          9.98254061e-01,  9.98234987e-01,  9.98234689e-01],\n",
@@ -2629,7 +2634,7 @@
               "          7.35414820e-03,  7.39390310e-03,  7.38611678e-03],\n",
               "        [ 9.91627276e-01,  9.91604209e-01,  9.91649806e-01, ...,\n",
               "          9.93667483e-01,  9.93692815e-01,  9.93687749e-01]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.000807 0.0008203 ... -0.002598</div><input id='attrs-b63e7e1b-9ac9-443d-855c-72e2cf8931e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b63e7e1b-9ac9-443d-855c-72e2cf8931e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8809526-0415-4b29-9128-4f035a5219b3' class='xr-var-data-in' type='checkbox'><label for='data-f8809526-0415-4b29-9128-4f035a5219b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00080699,  0.00082028,  0.00085015,  0.0008093 ,  0.00077764,\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.000807 0.0008203 ... -0.002598</div><input id='attrs-ac2fb82a-7974-4514-8ffe-2e2a48dfca60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac2fb82a-7974-4514-8ffe-2e2a48dfca60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7ab1296-2028-44e7-8867-e2508f5a87c8' class='xr-var-data-in' type='checkbox'><label for='data-b7ab1296-2028-44e7-8867-e2508f5a87c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00080699,  0.00082028,  0.00085015,  0.0008093 ,  0.00077764,\n",
               "         0.00076247,  0.00084005,  0.00080013,  0.00073725,  0.00069978,\n",
               "         0.00068869,  0.000708  ,  0.00070397,  0.00064101,  0.0007507 ,\n",
               "         0.00071757,  0.00060209,  0.00062077,  0.00062501,  0.0005675 ,\n",
@@ -2669,7 +2674,7 @@
               "        -0.00263345, -0.00258136, -0.00250975, -0.00261717, -0.00264321,\n",
               "        -0.00260741, -0.00261066, -0.00250649, -0.00248371, -0.0026074 ,\n",
               "        -0.00260415, -0.00260415, -0.00250324, -0.00259764, -0.002513  ,\n",
-              "        -0.00256509, -0.00253904, -0.00259764]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-0476d2f7-5868-4387-acd8-52308452ee40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0476d2f7-5868-4387-acd8-52308452ee40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-849ace6c-c4e4-43a4-ac42-d112deb17ca2' class='xr-var-data-in' type='checkbox'><label for='data-849ace6c-c4e4-43a4-ac42-d112deb17ca2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245074 ,  0.624316  ,  0.6248463 ,  0.6245108 ,  0.6247046 ,\n",
+              "        -0.00256509, -0.00253904, -0.00259764]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-26506b36-f604-4cff-9dbd-9526c8fef1d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26506b36-f604-4cff-9dbd-9526c8fef1d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53c4959c-000e-4511-96f6-ce1c57443f03' class='xr-var-data-in' type='checkbox'><label for='data-53c4959c-000e-4511-96f6-ce1c57443f03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245074 ,  0.624316  ,  0.6248463 ,  0.6245108 ,  0.6247046 ,\n",
               "         0.62467265,  0.62460417,  0.62476116,  0.6245991 ,  0.6246464 ,\n",
               "         0.624686  ,  0.6249721 ,  0.6249648 ,  0.6250649 ,  0.62494904,\n",
               "         0.62500936,  0.6250884 ,  0.6250855 ,  0.6253171 ,  0.6254035 ,\n",
@@ -2709,7 +2714,7 @@
               "         0.7785521 ,  0.77869254,  0.77849793,  0.7787016 ,  0.77872854,\n",
               "         0.77860415,  0.7784391 ,  0.7783881 ,  0.7785563 ,  0.77852774,\n",
               "         0.77856874,  0.77847373,  0.7784385 ,  0.7782882 ,  0.778279  ,\n",
-              "         0.778462  ,  0.77835196,  0.77835387]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-08b18ee0-4c29-4dce-8543-f489cac54beb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08b18ee0-4c29-4dce-8543-f489cac54beb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62c3355e-f7fb-490e-962d-d99b84f4f562' class='xr-var-data-in' type='checkbox'><label for='data-62c3355e-f7fb-490e-962d-d99b84f4f562' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.800002 , 12.800002 , 12.800002 , 12.800002 , 12.800002 ,\n",
+              "         0.778462  ,  0.77835196,  0.77835387]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-8308da19-cd0d-4b50-9436-88a6bd1ea6ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8308da19-cd0d-4b50-9436-88a6bd1ea6ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7bae97a-4fb6-4670-a9c3-0cd1153185a0' class='xr-var-data-in' type='checkbox'><label for='data-b7bae97a-4fb6-4670-a9c3-0cd1153185a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.800002 , 12.800002 , 12.800002 , 12.800002 , 12.800002 ,\n",
               "       12.966666 , 13.       , 13.050666 , 13.199998 , 13.199998 ,\n",
               "       13.199998 , 13.199998 , 13.199998 , 13.199998 , 13.199998 ,\n",
               "       13.199998 , 13.199998 , 13.199998 , 13.176665 , 13.       ,\n",
@@ -2745,7 +2750,7 @@
               "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
               "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
               "       12.1119995, 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.       ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>244.0 244.0 244.0 ... 246.0 246.0</div><input id='attrs-2f321638-a996-4f18-bd39-b120309dce51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f321638-a996-4f18-bd39-b120309dce51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a23a7882-6c51-4191-bde6-cac5645392ce' class='xr-var-data-in' type='checkbox'><label for='data-a23a7882-6c51-4191-bde6-cac5645392ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([244.04333, 243.96   , 244.04666, 244.12334, 244.30667, 244.32   ,\n",
+              "       12.       , 12.       , 12.       ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>244.0 244.0 244.0 ... 246.0 246.0</div><input id='attrs-ec8aa61b-ef20-4f4a-84d3-ae6ca5a7f53f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec8aa61b-ef20-4f4a-84d3-ae6ca5a7f53f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26f81164-f4ae-4064-aebe-b077d17f6f23' class='xr-var-data-in' type='checkbox'><label for='data-26f81164-f4ae-4064-aebe-b077d17f6f23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([244.04333, 243.96   , 244.04666, 244.12334, 244.30667, 244.32   ,\n",
               "       244.04   , 243.95667, 243.72   , 243.45334, 243.46   , 243.49333,\n",
               "       243.49667, 243.69667, 243.80333, 243.78334, 243.81667, 244.     ,\n",
               "       244.27333, 244.39667, 244.58333, 244.73334, 244.95334, 245.     ,\n",
@@ -2775,7 +2780,7 @@
               "       245.95667, 245.94   , 245.93   , 245.95667, 245.95   , 245.95334,\n",
               "       245.94667, 245.96   , 245.96333, 245.97333, 245.96666, 245.96   ,\n",
               "       245.97667, 245.99   , 245.98   , 245.98666, 245.98666, 245.98334,\n",
-              "       245.99   , 245.98334, 245.99   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.589 3.576 3.611 ... -197.1 -197.1</div><input id='attrs-8436aa9a-d6c2-445d-aab5-fb075916b481' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8436aa9a-d6c2-445d-aab5-fb075916b481' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60c73c88-42f4-42dd-9159-e42019b0dfa0' class='xr-var-data-in' type='checkbox'><label for='data-60c73c88-42f4-42dd-9159-e42019b0dfa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.5890043e+00,  3.5764265e+00,  3.6108000e+00,  3.5779481e+00,\n",
+              "       245.99   , 245.98334, 245.99   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.589 3.576 3.611 ... -197.1 -197.1</div><input id='attrs-47d8bc58-c734-4d61-a704-91fb0af64746' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47d8bc58-c734-4d61-a704-91fb0af64746' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85c7a851-a71e-4311-a82b-71e45b8238a4' class='xr-var-data-in' type='checkbox'><label for='data-85c7a851-a71e-4311-a82b-71e45b8238a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.5890043e+00,  3.5764265e+00,  3.6108000e+00,  3.5779481e+00,\n",
               "         3.6116719e+00,  3.5757926e+00,  3.5947008e+00,  3.6243417e+00,\n",
               "         3.6057498e+00,  3.5963907e+00,  3.5682683e+00,  3.5830538e+00,\n",
               "         3.6165168e+00,  3.5789921e+00,  3.5747981e+00,  3.5690453e+00,\n",
@@ -2815,7 +2820,7 @@
               "        -1.9710718e+02, -1.9718983e+02, -1.9715218e+02, -1.9712483e+02,\n",
               "        -1.9706255e+02, -1.9714519e+02, -1.9712418e+02, -1.9705786e+02,\n",
               "        -1.9708685e+02, -1.9711749e+02, -1.9707716e+02, -1.9707919e+02,\n",
-              "        -1.9719186e+02, -1.9713850e+02, -1.9714883e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.262 -1.263 -1.26 ... 9.714 9.713</div><input id='attrs-1472fffb-c679-43b8-ad12-945e48bcc4f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1472fffb-c679-43b8-ad12-945e48bcc4f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8ea7b57-476f-4d8f-9b70-d3d7a207ff1e' class='xr-var-data-in' type='checkbox'><label for='data-d8ea7b57-476f-4d8f-9b70-d3d7a207ff1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2623771 , -1.2625197 , -1.2600151 , -1.2631888 , -1.2615377 ,\n",
+              "        -1.9719186e+02, -1.9713850e+02, -1.9714883e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.262 -1.263 -1.26 ... 9.714 9.713</div><input id='attrs-c1d6d653-5b93-469f-a4c4-dd67053289bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1d6d653-5b93-469f-a4c4-dd67053289bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2f95eee-74c9-4a93-ae4e-d670fd81dd64' class='xr-var-data-in' type='checkbox'><label for='data-b2f95eee-74c9-4a93-ae4e-d670fd81dd64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2623771 , -1.2625197 , -1.2600151 , -1.2631888 , -1.2615377 ,\n",
               "        -1.2616537 , -1.262574  , -1.2627327 , -1.2632962 , -1.2634871 ,\n",
               "        -1.2617764 , -1.2599101 , -1.2592376 , -1.2577692 , -1.259011  ,\n",
               "        -1.2584039 , -1.2585176 , -1.2577502 , -1.2569212 , -1.2552022 ,\n",
@@ -2855,7 +2860,7 @@
               "         9.714553  ,  9.715957  ,  9.7148075 ,  9.717874  ,  9.716309  ,\n",
               "         9.715032  ,  9.7090435 ,  9.711168  ,  9.7130995 ,  9.713355  ,\n",
               "         9.715256  ,  9.714936  ,  9.7148075 ,  9.711726  ,  9.711886  ,\n",
-              "         9.713675  ,  9.714042  ,  9.712781  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-724b082d-0ffb-449f-8046-e180816cb1b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-724b082d-0ffb-449f-8046-e180816cb1b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac76ee7d-c2c6-4e33-9eaf-10ab7c7ebc7f' class='xr-var-data-in' type='checkbox'><label for='data-ac76ee7d-c2c6-4e33-9eaf-10ab7c7ebc7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "         9.713675  ,  9.714042  ,  9.712781  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-2c6d8191-7791-49a1-a910-c8e51da81970' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c6d8191-7791-49a1-a910-c8e51da81970' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05ff37e1-6593-4dc1-a257-fb203a586dd5' class='xr-var-data-in' type='checkbox'><label for='data-05ff37e1-6593-4dc1-a257-fb203a586dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -2865,7 +2870,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-0bc0a0d7-4597-41a5-aa5e-5cda68aefc40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bc0a0d7-4597-41a5-aa5e-5cda68aefc40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cd35b4b-45ab-477b-a38f-cfe6eb9c21c6' class='xr-var-data-in' type='checkbox'><label for='data-2cd35b4b-45ab-477b-a38f-cfe6eb9c21c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-5f588f8e-fb6d-4463-a95a-1283770f8dda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f588f8e-fb6d-4463-a95a-1283770f8dda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1351ed27-e678-435e-b2fc-4d153781ad3f' class='xr-var-data-in' type='checkbox'><label for='data-1351ed27-e678-435e-b2fc-4d153781ad3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
@@ -2905,7 +2910,7 @@
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0006333 -0.004143 ... nan nan</div><input id='attrs-530ae23f-d4f6-4119-89e5-b5847e4056a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-530ae23f-d4f6-4119-89e5-b5847e4056a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd05afb3-0162-442f-9133-b50a5dd937ee' class='xr-var-data-in' type='checkbox'><label for='data-bd05afb3-0162-442f-9133-b50a5dd937ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00063333, -0.00414333, -0.00776667, ..., -0.01952   ,\n",
+              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0006333 -0.004143 ... nan nan</div><input id='attrs-9c1e5567-1f67-4cad-a3c1-e8e1df96eef4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c1e5567-1f67-4cad-a3c1-e8e1df96eef4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6d5596c-4a46-4d42-9e65-dbf21650e6d8' class='xr-var-data-in' type='checkbox'><label for='data-c6d5596c-4a46-4d42-9e65-dbf21650e6d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00063333, -0.00414333, -0.00776667, ..., -0.01952   ,\n",
               "        -0.02035667, -0.01573333],\n",
               "       [ 0.02268667,  0.01564333,  0.01504   , ..., -0.00309667,\n",
               "        -0.00658667, -0.00238   ],\n",
@@ -2917,7 +2922,7 @@
               "       [        nan,         nan,         nan, ...,         nan,\n",
               "                nan,         nan],\n",
               "       [        nan,         nan,         nan, ...,         nan,\n",
-              "                nan,         nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.08 80.44 80.75 ... nan nan nan</div><input id='attrs-b5a78d15-3a5d-44d7-89e4-eadb998ad9aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5a78d15-3a5d-44d7-89e4-eadb998ad9aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-631fec96-b84c-4cb3-a997-0431f580f516' class='xr-var-data-in' type='checkbox'><label for='data-631fec96-b84c-4cb3-a997-0431f580f516' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[80.085   , 80.43833 , 80.75166 , ..., 81.761665, 81.86    ,\n",
+              "                nan,         nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.08 80.44 80.75 ... nan nan nan</div><input id='attrs-e31b9542-cb75-4807-8fb2-3cb8dcf2df32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e31b9542-cb75-4807-8fb2-3cb8dcf2df32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c145cf74-6074-4243-85c2-7a0413485b51' class='xr-var-data-in' type='checkbox'><label for='data-c145cf74-6074-4243-85c2-7a0413485b51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[80.085   , 80.43833 , 80.75166 , ..., 81.761665, 81.86    ,\n",
               "        81.745   ],\n",
               "       [76.05    , 76.60333 , 77.066666, ..., 78.23    , 78.21667 ,\n",
               "        78.045   ],\n",
@@ -2929,7 +2934,7 @@
               "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
               "              nan],\n",
               "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "              nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>93.52 94.19 93.97 ... 0.0 0.0 0.0</div><input id='attrs-76b3f1dd-5bab-4021-81bf-23bf324a6ed5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76b3f1dd-5bab-4021-81bf-23bf324a6ed5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22c8ba42-14f2-4b44-8780-0e0ffc9dc17b' class='xr-var-data-in' type='checkbox'><label for='data-22c8ba42-14f2-4b44-8780-0e0ffc9dc17b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[93.51667 , 94.18667 , 93.97    , ..., 92.276665, 92.09    ,\n",
+              "              nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>93.52 94.19 93.97 ... 0.0 0.0 0.0</div><input id='attrs-eee961d6-8d00-481a-80af-520b5b5d08a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eee961d6-8d00-481a-80af-520b5b5d08a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1425e06-5587-41d3-8c6f-8b939eec3ba9' class='xr-var-data-in' type='checkbox'><label for='data-a1425e06-5587-41d3-8c6f-8b939eec3ba9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[93.51667 , 94.18667 , 93.97    , ..., 92.276665, 92.09    ,\n",
               "        92.61667 ],\n",
               "       [96.75    , 96.403336, 96.32    , ..., 96.433334, 96.89    ,\n",
               "        96.57667 ],\n",
@@ -2941,7 +2946,7 @@
               "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
               "         0.      ],\n",
               "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "         0.      ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2166 -0.217 ... 0.9937 0.9937</div><input id='attrs-b9e79966-18d2-4495-8fde-34445d034255' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9e79966-18d2-4495-8fde-34445d034255' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a65ac90-4765-4c84-9270-611a307ffa24' class='xr-var-data-in' type='checkbox'><label for='data-7a65ac90-4765-4c84-9270-611a307ffa24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16565624e-01, -2.17020363e-01, -2.15714216e-01, ...,\n",
+              "         0.      ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2166 -0.217 ... 0.9937 0.9937</div><input id='attrs-27e0e37b-b4e2-442e-b0f2-e2dba8d4f75d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27e0e37b-b4e2-442e-b0f2-e2dba8d4f75d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e9a21ba-68cb-44a3-9810-700cda0b98ed' class='xr-var-data-in' type='checkbox'><label for='data-0e9a21ba-68cb-44a3-9810-700cda0b98ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16565624e-01, -2.17020363e-01, -2.15714216e-01, ...,\n",
               "         -2.15817824e-01, -2.15457186e-01, -2.15469748e-01],\n",
               "        [-9.68014121e-01, -9.67880666e-01, -9.68221545e-01, ...,\n",
               "         -9.70180333e-01, -9.70283389e-01, -9.70276892e-01],\n",
@@ -2961,7 +2966,7 @@
               "         -1.12357967e-01, -1.12130813e-01, -1.12177916e-01],\n",
               "        [ 9.91627216e-01,  9.91603792e-01,  9.91649806e-01, ...,\n",
               "          9.93667185e-01,  9.93692756e-01,  9.93687451e-01]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0008123 0.0008472 ... -0.002637</div><input id='attrs-51683e0a-a07d-4a63-a867-92210098f8a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51683e0a-a07d-4a63-a867-92210098f8a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07c0e8d2-afd1-4b32-9dcb-7912d4d19be0' class='xr-var-data-in' type='checkbox'><label for='data-07c0e8d2-afd1-4b32-9dcb-7912d4d19be0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00081228,  0.00084717,  0.00085335,  0.00075436,  0.00084513,\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0008123 0.0008472 ... -0.002637</div><input id='attrs-de1cf8b3-ccc0-405c-a9a2-285ff8c771c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de1cf8b3-ccc0-405c-a9a2-285ff8c771c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daaafa6b-1361-4cbe-b5bd-941f17673f7f' class='xr-var-data-in' type='checkbox'><label for='data-daaafa6b-1361-4cbe-b5bd-941f17673f7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00081228,  0.00084717,  0.00085335,  0.00075436,  0.00084513,\n",
               "         0.00085451,  0.00073719,  0.00067843,  0.00075248,  0.00074199,\n",
               "         0.00070569,  0.00065896,  0.0006914 ,  0.00069842,  0.00074138,\n",
               "         0.0006676 ,  0.00060215,  0.00059847,  0.0006675 ,  0.0006136 ,\n",
@@ -3001,7 +3006,7 @@
               "        -0.00265949, -0.00270831, -0.00258787, -0.0025423 , -0.00262368,\n",
               "        -0.00264647, -0.00268879, -0.00251626, -0.00256183, -0.00255207,\n",
               "        -0.00251626, -0.00251626, -0.00249998, -0.00248371, -0.0024479 ,\n",
-              "        -0.00254881, -0.00254881, -0.0026367 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-5ae9f8cb-a28c-4f25-8ed0-08079ef606cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ae9f8cb-a28c-4f25-8ed0-08079ef606cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b170530a-4842-4469-aa8e-b5a718938f62' class='xr-var-data-in' type='checkbox'><label for='data-b170530a-4842-4469-aa8e-b5a718938f62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245025 ,  0.62430865,  0.62484366,  0.62450564,  0.624701  ,\n",
+              "        -0.00254881, -0.00254881, -0.0026367 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-dd6027fd-956e-45c7-9a97-3702dd1a52fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd6027fd-956e-45c7-9a97-3702dd1a52fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4cadd9b-bbfe-4b06-92a4-42395cd50159' class='xr-var-data-in' type='checkbox'><label for='data-d4cadd9b-bbfe-4b06-92a4-42395cd50159' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245025 ,  0.62430865,  0.62484366,  0.62450564,  0.624701  ,\n",
               "         0.62466866,  0.62460196,  0.6247584 ,  0.62459546,  0.62464297,\n",
               "         0.6246826 ,  0.62496835,  0.6249581 ,  0.6250585 ,  0.62494546,\n",
               "         0.6250052 ,  0.62508506,  0.62507963,  0.6253132 ,  0.6254015 ,\n",
@@ -3041,7 +3046,7 @@
               "         0.77855694,  0.778695  ,  0.77849764,  0.7787041 ,  0.77873075,\n",
               "         0.7786052 ,  0.7784392 ,  0.7783923 ,  0.77855927,  0.77852917,\n",
               "         0.7785722 ,  0.77847767,  0.77844226,  0.77829295,  0.7782816 ,\n",
-              "         0.77846414,  0.778354  ,  0.77835685]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>65.0 65.0 65.0 ... 65.0 65.0 65.0</div><input id='attrs-e2862aa8-0c3a-48f1-b199-cd39ab623cd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2862aa8-0c3a-48f1-b199-cd39ab623cd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47029c90-be52-4ea2-add1-3f594c0afe11' class='xr-var-data-in' type='checkbox'><label for='data-47029c90-be52-4ea2-add1-3f594c0afe11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+              "         0.77846414,  0.778354  ,  0.77835685]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>65.0 65.0 65.0 ... 65.0 65.0 65.0</div><input id='attrs-57ba0c05-0b71-4dba-ae61-9bf5d5743f31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57ba0c05-0b71-4dba-ae61-9bf5d5743f31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eec2bdb9-0e7e-4f8b-9cb9-70faf8c5fc3a' class='xr-var-data-in' type='checkbox'><label for='data-eec2bdb9-0e7e-4f8b-9cb9-70faf8c5fc3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
@@ -3055,7 +3060,7 @@
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-6169d7ec-2ce2-427f-8d11-54ad9941b223' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6169d7ec-2ce2-427f-8d11-54ad9941b223' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a681cc0d-12b0-470b-bb23-9317994ad6c3' class='xr-var-data-in' type='checkbox'><label for='data-a681cc0d-12b0-470b-bb23-9317994ad6c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "       65.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-0eda79e2-a2db-4fc7-a06b-6105d09e81d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0eda79e2-a2db-4fc7-a06b-6105d09e81d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5180daea-6030-4163-b44d-247bbbe17b36' class='xr-var-data-in' type='checkbox'><label for='data-5180daea-6030-4163-b44d-247bbbe17b36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -3065,7 +3070,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-18f0380f-5adf-44aa-90aa-90b4796f1bce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18f0380f-5adf-44aa-90aa-90b4796f1bce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2b00296-a077-4150-98db-42f59e432416' class='xr-var-data-in' type='checkbox'><label for='data-e2b00296-a077-4150-98db-42f59e432416' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-4c28ce24-b344-4bbd-8cbe-a64c644928cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c28ce24-b344-4bbd-8cbe-a64c644928cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45439abd-0d0b-40ad-ac5c-76786b9e0fbe' class='xr-var-data-in' type='checkbox'><label for='data-45439abd-0d0b-40ad-ac5c-76786b9e0fbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -3075,7 +3080,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-4d7a4c86-bf63-46d4-a782-e950a8f09391' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d7a4c86-bf63-46d4-a782-e950a8f09391' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9dac025-d69c-4578-b70b-bfcc82c4a0f6' class='xr-var-data-in' type='checkbox'><label for='data-a9dac025-d69c-4578-b70b-bfcc82c4a0f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-3a8f49c3-438d-4f76-bad6-fffb9de626e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a8f49c3-438d-4f76-bad6-fffb9de626e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c1bf1ee-e389-48f3-9009-53f673d17d12' class='xr-var-data-in' type='checkbox'><label for='data-4c1bf1ee-e389-48f3-9009-53f673d17d12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -3085,7 +3090,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1267 0.1333 0.13 ... 0.22 0.22</div><input id='attrs-387f38c2-509e-48a9-9763-82ff14f865f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-387f38c2-509e-48a9-9763-82ff14f865f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa1da171-267f-4cb9-b029-34887f051e22' class='xr-var-data-in' type='checkbox'><label for='data-aa1da171-267f-4cb9-b029-34887f051e22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.12666667, 0.13333334, 0.13      , 0.13333334, 0.13      ,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1267 0.1333 0.13 ... 0.22 0.22</div><input id='attrs-e65b9c28-3705-4b21-9b10-e6c5e04d835c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e65b9c28-3705-4b21-9b10-e6c5e04d835c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d8157f-8cb7-41a4-9f33-a1eea3d9cd81' class='xr-var-data-in' type='checkbox'><label for='data-42d8157f-8cb7-41a4-9f33-a1eea3d9cd81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.12666667, 0.13333334, 0.13      , 0.13333334, 0.13      ,\n",
               "       0.13      , 0.13      , 0.11666667, 0.12333333, 0.11666667,\n",
               "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11666667,\n",
               "       0.11      , 0.11333334, 0.11333334, 0.11333334, 0.12333333,\n",
@@ -3121,7 +3126,7 @@
               "       0.22666667, 0.22      , 0.22      , 0.22666667, 0.22      ,\n",
               "       0.22      , 0.22666667, 0.22      , 0.22      , 0.22666667,\n",
               "       0.22      , 0.22      , 0.22666667, 0.22      , 0.22      ,\n",
-              "       0.22666667, 0.22      , 0.22      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.287 3.261 3.337 ... 3.352 3.352</div><input id='attrs-4d8bca77-cf53-4e33-8ad2-41a767d67a9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d8bca77-cf53-4e33-8ad2-41a767d67a9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f435cf-1592-4c8e-a24d-b6334347db2d' class='xr-var-data-in' type='checkbox'><label for='data-30f435cf-1592-4c8e-a24d-b6334347db2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([3.287083 , 3.2605155, 3.3369133, 3.2892823, 3.3165808, 3.3119812,\n",
+              "       0.22666667, 0.22      , 0.22      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.287 3.261 3.337 ... 3.352 3.352</div><input id='attrs-3bed8e10-6fb4-4199-a70e-8730edcba36e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bed8e10-6fb4-4199-a70e-8730edcba36e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c62cf3ec-2e64-48bc-9255-5218bf5e7841' class='xr-var-data-in' type='checkbox'><label for='data-c62cf3ec-2e64-48bc-9255-5218bf5e7841' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([3.287083 , 3.2605155, 3.3369133, 3.2892823, 3.3165808, 3.3119812,\n",
               "       3.3027983, 3.3258302, 3.3029814, 3.3100154, 3.3152144, 3.3549793,\n",
               "       3.3538957, 3.3682969, 3.3522127, 3.3599124, 3.3716784, 3.370829 ,\n",
               "       3.404896 , 3.4170458, 3.4101129, 3.4259632, 3.3813803, 3.352863 ,\n",
@@ -3151,7 +3156,7 @@
               "       3.2660322, 3.2617817, 3.3116815, 3.314548 , 3.289132 , 3.3249977,\n",
               "       3.287216 , 3.282166 , 3.306248 , 3.3363798, 3.3452635, 3.314365 ,\n",
               "       3.3198478, 3.3122149, 3.3298814, 3.3361466, 3.3632295, 3.3652124,\n",
-              "       3.331114 , 3.352297 , 3.351696 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.05523 -0.07217 ... -0.0429</div><input id='attrs-e3fbd03a-3f96-4ef5-8c9d-457e6a60f31d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3fbd03a-3f96-4ef5-8c9d-457e6a60f31d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f480db05-4be4-47d9-8edd-856888fc1642' class='xr-var-data-in' type='checkbox'><label for='data-f480db05-4be4-47d9-8edd-856888fc1642' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.05523333, -0.07216667, -0.05975001, -0.0719    , -0.06111666,\n",
+              "       3.331114 , 3.352297 , 3.351696 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.05523 -0.07217 ... -0.0429</div><input id='attrs-6ce6cfa4-4d2f-4e6e-b3e4-0e71f7de28e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ce6cfa4-4d2f-4e6e-b3e4-0e71f7de28e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fce35df6-b5db-48b6-8219-4946768943a0' class='xr-var-data-in' type='checkbox'><label for='data-fce35df6-b5db-48b6-8219-4946768943a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.05523333, -0.07216667, -0.05975001, -0.0719    , -0.06111666,\n",
               "       -0.05995   , -0.07213333, -0.07148333, -0.07696667, -0.08181667,\n",
               "       -0.0731    , -0.0579    , -0.05488333, -0.05605   , -0.05533333,\n",
               "       -0.05278333, -0.047     , -0.0521    , -0.05088333, -0.04305   ,\n",
@@ -3187,7 +3192,7 @@
               "       -0.04428333, -0.05206667, -0.04676667, -0.05188334, -0.0574    ,\n",
               "       -0.06316667, -0.0484    , -0.04308333, -0.04601667, -0.0499    ,\n",
               "       -0.05471666, -0.05536667, -0.04616667, -0.02825   , -0.03388334,\n",
-              "       -0.0423    , -0.04288333, -0.0429    ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.414 -7.424 ... -6.433 -6.436</div><input id='attrs-b0e39fe5-b2db-4394-901d-eba4ae1db4c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0e39fe5-b2db-4394-901d-eba4ae1db4c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c8af690-be9a-448c-83d0-292870151110' class='xr-var-data-in' type='checkbox'><label for='data-1c8af690-be9a-448c-83d0-292870151110' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.414066 , -7.424116 , -7.4041324, -7.422417 , -7.4120665,\n",
+              "       -0.0423    , -0.04288333, -0.0429    ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.414 -7.424 ... -6.433 -6.436</div><input id='attrs-8db76249-ff61-4aa8-909b-9af199cafcf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8db76249-ff61-4aa8-909b-9af199cafcf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7efb92ed-0095-4d84-b650-4be2eebe0461' class='xr-var-data-in' type='checkbox'><label for='data-7efb92ed-0095-4d84-b650-4be2eebe0461' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.414066 , -7.424116 , -7.4041324, -7.422417 , -7.4120665,\n",
               "       -7.4167333, -7.4193   , -7.4148664, -7.4301662, -7.419133 ,\n",
               "       -7.4179006, -7.402567 , -7.4040666, -7.394583 , -7.403933 ,\n",
               "       -7.394567 , -7.39615  , -7.3910007, -7.386317 , -7.3802505,\n",
@@ -3223,7 +3228,7 @@
               "       -6.4438   , -6.450166 , -6.4428334, -6.45625  , -6.455867 ,\n",
               "       -6.44465  , -6.436783 , -6.430967 , -6.4454665, -6.441799 ,\n",
               "       -6.4480996, -6.4399   , -6.4372997, -6.4309335, -6.4305167,\n",
-              "       -6.4460497, -6.4332495, -6.4359174], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_density</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.023e+03 1.023e+03 ... 1.023e+03</div><input id='attrs-d8a0f4a6-abfc-4f4b-9fb0-a5fae1acd397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8a0f4a6-abfc-4f4b-9fb0-a5fae1acd397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6d0ee4d-65b7-4dc0-b765-b66cd80efb09' class='xr-var-data-in' type='checkbox'><label for='data-c6d0ee4d-65b7-4dc0-b765-b66cd80efb09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Water Density</dd><dt><span>standard_name :</span></dt><dd>sea_water_density</dd><dt><span>description :</span></dt><dd>Water density from linear approximation of sea water equation of state</dd></dl></div><div class='xr-var-data'><pre>array([1023.2506 , 1023.2348 , 1023.2431 , 1023.2559 , 1023.2423 ,\n",
+              "       -6.4460497, -6.4332495, -6.4359174], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_density</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.023e+03 1.023e+03 ... 1.023e+03</div><input id='attrs-08c16091-8a9b-498b-b54d-d6c1bc07b97d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08c16091-8a9b-498b-b54d-d6c1bc07b97d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05a32057-2580-42ce-bb33-fe8146682073' class='xr-var-data-in' type='checkbox'><label for='data-05a32057-2580-42ce-bb33-fe8146682073' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Water Density</dd><dt><span>standard_name :</span></dt><dd>sea_water_density</dd><dt><span>description :</span></dt><dd>Water density from linear approximation of sea water equation of state</dd></dl></div><div class='xr-var-data'><pre>array([1023.2506 , 1023.2348 , 1023.2431 , 1023.2559 , 1023.2423 ,\n",
               "       1023.21655, 1023.1977 , 1023.2015 , 1023.19476, 1023.19116,\n",
               "       1023.194  , 1023.1996 , 1023.20294, 1023.19666, 1023.19336,\n",
               "       1023.19763, 1023.205  , 1023.20404, 1023.2148 , 1023.2179 ,\n",
@@ -3259,7 +3264,7 @@
               "       1023.3432 , 1023.34186, 1023.34094, 1023.3408 , 1023.34   ,\n",
               "       1023.33655, 1023.34143, 1023.34155, 1023.34357, 1023.3508 ,\n",
               "       1023.3694 , 1023.37335, 1023.3781 , 1023.3796 , 1023.3752 ,\n",
-              "       1023.3806 , 1023.3899 , 1023.3975 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.28 10.26 10.25 ... 10.15 10.15</div><input id='attrs-5bcf8a7a-7783-4c39-b8db-ef0dded343bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bcf8a7a-7783-4c39-b8db-ef0dded343bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed08c902-6e99-4566-bd91-01fc50e4d819' class='xr-var-data-in' type='checkbox'><label for='data-ed08c902-6e99-4566-bd91-01fc50e4d819' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Depth to Seafloor</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([10.275155 , 10.262421 , 10.248638 , 10.233478 , 10.216689 ,\n",
+              "       1023.3806 , 1023.3899 , 1023.3975 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.28 10.26 10.25 ... 10.15 10.15</div><input id='attrs-e0ce5d26-b410-4f9b-a569-5da23b0269af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0ce5d26-b410-4f9b-a569-5da23b0269af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8eb08c7-e3d7-452d-b09a-4e193313b655' class='xr-var-data-in' type='checkbox'><label for='data-b8eb08c7-e3d7-452d-b09a-4e193313b655' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Water Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([10.275155 , 10.262421 , 10.248638 , 10.233478 , 10.216689 ,\n",
               "       10.204074 , 10.188269 , 10.172538 , 10.158512 , 10.140744 ,\n",
               "       10.124731 , 10.10534  , 10.088593 , 10.069619 , 10.050677 ,\n",
               "       10.028841 , 10.00682  ,  9.981041 ,  9.958139 ,  9.932152 ,\n",
@@ -3295,7 +3300,7 @@
               "        9.972928 ,  9.992192 , 10.00496  , 10.023165 , 10.037822 ,\n",
               "       10.052144 , 10.058786 , 10.073322 , 10.083076 , 10.093331 ,\n",
               "       10.100321 , 10.109131 , 10.117323 , 10.123545 , 10.1341   ,\n",
-              "       10.142311 , 10.14612  , 10.153638 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a103d7e7-83e1-415b-8de8-03c7fb10bbc4' class='xr-section-summary-in' type='checkbox'  ><label for='section-a103d7e7-83e1-415b-8de8-03c7fb10bbc4' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b04ad8c4-c3c0-4bde-ba1b-a4ac47ff38b6' class='xr-index-data-in' type='checkbox'/><label for='index-b04ad8c4-c3c0-4bde-ba1b-a4ac47ff38b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:30.001030683&#x27;,\n",
+              "       10.142311 , 10.14612  , 10.153638 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8dc246c4-9ab6-44bd-a7c2-ee8705784f0f' class='xr-section-summary-in' type='checkbox'  ><label for='section-8dc246c4-9ab6-44bd-a7c2-ee8705784f0f' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-100a5734-9280-4bc1-a604-7e228c512459' class='xr-index-data-in' type='checkbox'/><label for='index-100a5734-9280-4bc1-a604-7e228c512459' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:30.001030683&#x27;,\n",
               "               &#x27;2020-08-15 00:27:30.001019239&#x27;,\n",
               "               &#x27;2020-08-15 00:32:30.001010656&#x27;,\n",
               "               &#x27;2020-08-15 00:37:30.001015901&#x27;,\n",
@@ -3316,7 +3321,7 @@
               "               &#x27;2020-08-15 15:22:30.001026153&#x27;,\n",
               "               &#x27;2020-08-15 15:27:30.001022577&#x27;,\n",
               "               &#x27;2020-08-15 15:32:30.001030683&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d76ce326-ad96-4baa-ad8e-d4cd3fb3489d' class='xr-index-data-in' type='checkbox'/><label for='index-d76ce326-ad96-4baa-ad8e-d4cd3fb3489d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-04e01752-1f88-472c-b550-5438bea78684' class='xr-index-data-in' type='checkbox'/><label for='index-04e01752-1f88-472c-b550-5438bea78684' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3bc8ab76-75a3-4430-a447-2f16fe9419c7' class='xr-index-data-in' type='checkbox'/><label for='index-3bc8ab76-75a3-4430-a447-2f16fe9419c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-56a81a3c-206c-43c8-8faa-01b5acba7255' class='xr-index-data-in' type='checkbox'/><label for='index-56a81a3c-206c-43c8-8faa-01b5acba7255' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
               "                      2.7,                3.2,                3.7,\n",
               "                      4.2,  4.699999999999999,  5.199999999999999,\n",
               "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
@@ -3326,7 +3331,7 @@
               "                     11.7,               12.2,               12.7,\n",
               "                     13.2,               13.7,               14.2,\n",
               "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-05407fc6-c904-4afb-9020-6beb903b465a' class='xr-index-data-in' type='checkbox'/><label for='index-05407fc6-c904-4afb-9020-6beb903b465a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-00d88c37-2f7c-4aba-b1a4-68a296e567ae' class='xr-index-data-in' type='checkbox'/><label for='index-00d88c37-2f7c-4aba-b1a4-68a296e567ae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-462b1a6b-9515-42db-9b7f-788caba3cfc4' class='xr-index-data-in' type='checkbox'/><label for='index-462b1a6b-9515-42db-9b7f-788caba3cfc4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2e9b0c14-4668-4955-a205-ff4a0031b7ba' class='xr-index-data-in' type='checkbox'/><label for='index-2e9b0c14-4668-4955-a205-ff4a0031b7ba' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1b9cb3c5-a2e7-4481-9d98-fd4730eae6f0' class='xr-index-data-in' type='checkbox'/><label for='index-1b9cb3c5-a2e7-4481-9d98-fd4730eae6f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76848ca2-bd18-4e8d-b4c6-55c75fa7ccb3' class='xr-index-data-in' type='checkbox'/><label for='index-76848ca2-bd18-4e8d-b4c6-55c75fa7ccb3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:29.938495159&#x27;,\n",
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-09c4822e-635a-4d43-867e-d55e2ea1d80f' class='xr-index-data-in' type='checkbox'/><label for='index-09c4822e-635a-4d43-867e-d55e2ea1d80f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a05d4196-a586-4fab-a7c7-3695847efce5' class='xr-index-data-in' type='checkbox'/><label for='index-a05d4196-a586-4fab-a7c7-3695847efce5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1960540d-de8b-4b04-a8fc-7f9a690e9700' class='xr-index-data-in' type='checkbox'/><label for='index-1960540d-de8b-4b04-a8fc-7f9a690e9700' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-383bc76d-0cda-4895-b5de-080c9f67c1fb' class='xr-index-data-in' type='checkbox'/><label for='index-383bc76d-0cda-4895-b5de-080c9f67c1fb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c5995d6-7c6f-474e-b9e0-97a579d5ed66' class='xr-index-data-in' type='checkbox'/><label for='index-9c5995d6-7c6f-474e-b9e0-97a579d5ed66' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f4cee843-5a81-4daf-98d4-f456387cf394' class='xr-index-data-in' type='checkbox'/><label for='index-f4cee843-5a81-4daf-98d4-f456387cf394' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:29.938495159&#x27;,\n",
               "               &#x27;2020-08-15 00:27:29.938490867&#x27;,\n",
               "               &#x27;2020-08-15 00:32:29.938487529&#x27;,\n",
               "               &#x27;2020-08-15 00:37:29.938496828&#x27;,\n",
@@ -3347,7 +3352,7 @@
               "               &#x27;2020-08-15 15:22:29.938486099&#x27;,\n",
               "               &#x27;2020-08-15 15:27:29.938484191&#x27;,\n",
               "               &#x27;2020-08-15 15:32:29.938490390&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dac26890-7cb1-4d6b-a542-9b3d1cd0262a' class='xr-index-data-in' type='checkbox'/><label for='index-dac26890-7cb1-4d6b-a542-9b3d1cd0262a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a6fc0f7c-1e76-4ec2-bc41-01ab387e3ba4' class='xr-index-data-in' type='checkbox'/><label for='index-a6fc0f7c-1e76-4ec2-bc41-01ab387e3ba4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
               "                      2.7,                3.2,                3.7,\n",
               "                      4.2,  4.699999999999999,  5.199999999999999,\n",
               "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
@@ -3357,7 +3362,7 @@
               "                     11.7,               12.2,               12.7,\n",
               "                     13.2,               13.7,               14.2,\n",
               "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62cfc183-7ea6-4d25-b845-bc4039a7fbc0' class='xr-section-summary-in' type='checkbox'  ><label for='section-62cfc183-7ea6-4d25-b845-bc4039a7fbc0' class='xr-section-summary' >Attributes: <span>(41)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>fs :</span></dt><dd>1</dd><dt><span>n_bin :</span></dt><dd>300</dd><dt><span>n_fft :</span></dt><dd>300</dd><dt><span>description :</span></dt><dd>Binned averages calculated from ensembles of size &quot;n_bin&quot;</dd><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>principal</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd><dt><span>h_deploy :</span></dt><dd>0.6</dd><dt><span>declination :</span></dt><dd>15.8</dd><dt><span>declination_in_orientmat :</span></dt><dd>1</dd><dt><span>principal_heading :</span></dt><dd>11.1898</dd></dl></div></li></ul></div></div>"
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56680903-578e-4a86-98df-4d65112bfa9c' class='xr-section-summary-in' type='checkbox'  ><label for='section-56680903-578e-4a86-98df-4d65112bfa9c' class='xr-section-summary' >Attributes: <span>(41)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>fs :</span></dt><dd>1</dd><dt><span>n_bin :</span></dt><dd>300</dd><dt><span>n_fft :</span></dt><dd>300</dd><dt><span>description :</span></dt><dd>Binned averages calculated from ensembles of size &quot;n_bin&quot;</dd><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>principal</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd><dt><span>range_offset :</span></dt><dd>0.6</dd><dt><span>declination :</span></dt><dd>15.8</dd><dt><span>declination_in_orientmat :</span></dt><dd>1</dd><dt><span>principal_heading :</span></dt><dd>11.1898</dd></dl></div></li></ul></div></div>"
             ],
             "text/plain": [
               "<xarray.Dataset> Size: 379kB\n",
@@ -3398,7 +3403,7 @@
               "    ...                        ...\n",
               "    has_imu:                   1\n",
               "    beam_angle:                25\n",
-              "    h_deploy:                  0.6\n",
+              "    range_offset:              0.6\n",
               "    declination:               15.8\n",
               "    declination_in_orientmat:  1\n",
               "    principal_heading:         11.1898"
@@ -3461,7 +3466,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.colorbar.Colorbar at 0x1f800c89650>"
+              "<matplotlib.colorbar.Colorbar at 0x1c8b919ac90>"
             ]
           },
           "execution_count": 18,
@@ -3470,7 +3475,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 1000x600 with 2 Axes>"
             ]
@@ -3509,7 +3514,7 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 1000x600 with 2 Axes>"
             ]
@@ -3582,7 +3587,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1f802e62190>"
+              "<matplotlib.collections.QuadMesh at 0x1c8b91f69d0>"
             ]
           },
           "execution_count": 21,
@@ -3591,7 +3596,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -3667,7 +3672,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 500x500 with 2 Axes>"
             ]
@@ -3883,7 +3888,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1f804f86290>"
+              "<matplotlib.collections.QuadMesh at 0x1c8c1dc3910>"
             ]
           },
           "execution_count": 28,
@@ -3892,7 +3897,7 @@
         },
         {
           "data": {
-            "image/png": 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",
+            "image/png": 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NKCMsYmNjCQwMJCYmxmogSCGEEOJJWfE3w1zH79W8yO2R8QDvgUGhykG9/H17CvLly8edO3cyNMdtWsgVPCGEEMJNaTLYEzZ5fpEzSYAnhBBCuCmNVoPGiVu0EuDlXBLgCSGEEG4qo2PZJc8vcib5aoUQQgghchi5gieEEEK4qafVi1Y4r3bt2sTGxmZa+RLgCSGEEG5KnsFzX//3f/+XqeVLgCeEEEII8ZRcunSJAwcOUKRIEWrVquWyciXAE0IIIdyUdLJwD0OHDrW89vf3Z8KECQB88cUXvPfeexgMBgBatmzJ2rVrbWbwyAj5aoUQQgg3Zb5F68wiMt+MGTNYtmwZVapUsUzZtm/fPgYPHkxiYiKvvvoqn3/+OdHR0UybNs0ldcoVPCGEEEKITPb9999Tp04dy/shQ4YA0K1bN7777jvL6wYNGjBixAin65MATwghhHBTWo2TvWjlAl6WyJs3r1Vwt3HjRg4cOIC/vz/Tp0+3rA8KCuLOnTsuqVMCPCGEEMJNabTO9YSVXrRZI1++fCQmJuLp6YnRaGTEiBFoNBoGDx5Mvnz5LOn++ecfrl275pI65asVQgghhMhEzZo1o1evXqxfv562bdty7NgxChcuzPvvv29Jo9frGTBggMvqlABPCCGEcFNancbpRWS+iRMnEhcXx0svvcTPP/9MgQIFWLlyJblz5wZg3rx5VKtWjQ0bNrisTrlFK4QQQrgpGejYPfj5+bF27VquXLnCzZs3KVu2LD4+PpbtVapU4dtvv3VpnRLguVCdiqMA2HN8cprz1KsaCYDh4b8A7Ds90/UNE0IIkWGNW/0EwNb1rZ9yS2xpdBo0TlyF05hc2BiRqpCQEEJCQmzWV61a1eV1SewuhBBCCJHDyBU8IYQQwl05O1ixXObJsSTAE0IIIdyUsx0ltHKLNseS2F0IIYQQIoeRK3hCCCGEm5JOFsIRCfCEEEIIN6XR4NwwKRrFha0R2YncohVCCCGEeIoePHjAo0ePXFqmBHhCCCGEmzLfonVmEZnn4sWLHDx4kBs3bqSYTq/X06JFC3r27MmmTZtcUrcEeEIIIYSbkgAve/r777+pW7cuJUuWpGbNmhQuXJjatWuzdu1au+mDg4P54osvWLJkCa1atXJJGyTAE0IIIYRwkevXr1OvXj327duHoigoikK+fPn4/fffad++PS1atOD69es2+SpUqICvry+K4prnIiXAE0IIIdyURqd1ehGuNWHCBK5fv45Op2Ps2LHcu3eP69ev8/DhQ7Zs2UKuXLmoVKkS+/bts8nr6+vrsnbINyuEEEK4KblFm/388ssvaDQaIiMjmTBhAgEBAQDodDoaNmzIDz/8wIoVK3jjjTf48ccfrfJqNK77PmSYFCGEEMJNOT0OngR4Lnfz5k00Gg0DBgxwmKZBgwbs3r2b1157jTt37tC7d2+Xt0Ou4AkhhBBCuEiRIkXQarV4eXmlmC4oKIgff/yR/fv3M336dJe3QwI8IYQQwk1ptE7eonVikGRhX/v27TEajezduzfVtFqtlrlz53Lt2jUmTZrk0nZIgCeEEEK4Kelkkf2MHz+eF154gSFDhnD//v005Zk6dSoxMTHcuXPHZe2QZ/BcoEZoLwD2n18IQO1ywwDYe3IqAOGFXrSkPfTvOgBqhvUDQOf7DADGR7fSXF+jJisA2Lalc8YbnQbm/dL55gWS9kcIIeypVWYQAPtOz3SYpn519Tyi0am3r3bsG2Q3nfn8A0nnVrPqxbtYvfev0AOArb8kjR822lP98zYp0QBAw4bfARAV1SPFfbDHI7xxmtM2arICgyEu3XWInMPPz49du3bx4Ycf0qJFC4YPH07r1q1TzffJJ5/w8OFD5syZ45J2SIAnhBBCuCnpZJE9+fn58fHHH2MymeyOeefI7Nmz8ff3d0kbJMATQggh3JVOoy7O5BeZRqvVUrhw4XTluXv3rmvqdkkpQgghhBAiw4xGI1FRUXz77bcuKU+u4AkhhBBuSm7RugeTycTHH3/Md999x6VLl3j06FGm1ykBnhBCCOGmnO0Jq9G5sDHCoffee4+ZM2emaZ5ZV81mIbdohRBCCCEy0aJFiwD44IMPuHHjBkajEZPJZLNERUWh1bomNJMreEIIIYS70jrZyUIGOs4SXl5e5M2bl3HjxqWYLiIigqZNm7qkzhx/BW/37t20atWKggULUrRoUUqXLs348eOJj49/2k0TQgghnOLULBZOPr8n0q579+4YDIY03aJdvny5S+rM0QHe6tWriYiIoEaNGly+fJnLly+zYsUKFixYQOPGjdHr9U+7iUIIIUTG6TSg0zqxSICXFT766CPKlSvHsmXLUk37/PPPu6TOHHuLNiEhgQEDBtCkSRPGjx9vWV+5cmUmTZpEz549mT9/PgMGDHiKrRRCCCFETufr68umTZvo06cPAQEBBAYG2qQxz19748YNl9SZYwO848ePEx0dTaVKlWy2ValSBYATJ05kcauEEEII15FhUtzDzZs36d27Nxs2bGDFihVZUmeODfD8/PwA+O2332y2mSf/tRf8CSGEEG5DZrJwC2+++Sbr168HoGTJkhQuXNimt6zJZOLUqVNER0e7pM4cG+A9//zzhIWFsXPnTubPn0+fPn0s29auXUuFChV47bXXnmILhRBCCPFfsG3bNjQaDatXr6Zt27YO0924cYNnn33WJXXm2E4WGo2Gr7/+Gi8vLwYMGMDSpUsB2LNnD4cPH2br1q14e3vbzZuQkEBsbKzVIoQQQmQ35oGOnVlE5itevDiFChVKMbgDKFCgAKNGjXJJnTn6m23QoAHff/89Hh4e9OjRg3fffZe1a9eyceNG8uXL5zBfZGQkgYGBlqVo0aJZ2GohhBAijczj4GV0kXHwssSHH37I3bt3iYuLSzVt7dq1XVJnjg7wAF566SU++eQTBg0axMyZM/nyyy9ZvXp1inlGjhxJTEyMZbl8+XIWtVYIIYRwD3q9nilTplC6dGlKlixJREQEO3fuTHc5169fp1+/foSGhlKiRAk6derEpUuXUsyzZs0aqlWrRmhoKBUrVuSbb75xeR3JXblyhaCgIHr16pXmPMm1bduW6dOnM3Xq1FTTdu7cOUN1PCnHPoNn9sknnxAWFsbAgQOpXbs23bt3p2vXrty+fZuBAwfazePt7e3w9q09PgXUXrnhhV4E4NC/66y2P/keIOHOSQCORO8GoE5F60uy1Yt3sbz2DCwBgEartmn30fFWaRu3+gmAretbO2yjuXz9ndMAHLzyg8O0ZvvPL0w1jRDi6TKfKw78kzQ4qvn37p2/olXabVus/3A0+9SY9OaPUwAYrv5hlbZ5b3V94qVjNmU063EUgE3fVQLAt3gEADXD+gEQUO1NdfuycEuenQeG2d2P2uXU9Sa92gnO3vmnVplBgPW+AtStNOHxq1aWdZMSDQBE1JoJwI59g+zWm1yDiPkAbN/Rx2r9xg/9Us3b+CX1AfptWzoTGxtLYGCfVHK4yFPqZJGQkEDLli25ceMGmzdvplixYqxevZomTZqwdOlSOnTokKZyLly4QL169ahduzYnT57Ey8uLYcOGER4ezq5duyhdurRNnlGjRjFr1ix++eUXIiIiOH36NPXr1+f48ePMnDnTJXUkpygKvXv35t69e2naJ3s+/PBDALZu3cr9+/fx9/e3SWMwGDh06BB37tzJcD3J5egAb+rUqfzwww+WnrQdO3bEy8uL9u3bM2jQICpXrkydOnWeciuFEEKIDHpKAd7w4cOJiopi//79FCtWDIAOHTqwdu1aevXqRXh4OCVKlEixDKPRSIcOHdDr9SxYsABfX19A/dv9/fff07FjRw4dOoSnp6clz48//khkZCQff/wxERHqfyjKlCnDxIkT6devH3Xr1qVjx45O1fGkL7/8kn379mXoczKLioqyXN3ctWuX3TQajQZFUdBoXHPbPMfeov3nn38YM2YML774otX6V155hSlTpqAoChMmTHCQWwghhBD2XLx4kdmzZ1O2bFmqV69uta1Hjx7ExcUxcuTIVMtZvnw5hw8fpkOHDpahzQB0Oh1dunTh+PHjzJ8/37LeZDLx/vvvo9FobG6Vdu3aFZ1Ox9ChQzEak65Mp7eOJ509e5ZPPvmE0aNHp7o/Kfnwww9RFIX27dszYsQIxo8fb7OMHDnSErS6Qo69grdu3ToSEhLInz+/zbZ3332XTz75hAMHDjyFlgkhhBAuonWyo0QG8q5cuRKDwWC3M0CNGjUAdTiy6OhonnnmGYflmEe3sFdOzZo1AZg3bx79+/cH4ODBg5w9e5ZSpUrZ/G3PnTs35cqV4/jx46xfv56XX345Q3UkZzQaee2115g+fbpl/NyMqlevHt26deO7775LMZ3JZLIbt2REjr2CZ55n9sqVKzbbPDw8ePbZZ/Hy8srqZgkhhBCu49Q8tI+XdFq3Tn2uPDQ01GZbcHAwRYoUQa/Xs2fPHodlxMXFsX37doflVKhQAYAjR45Ynn1Lqd7keaKiojJcR3Iff/wxpUqVol27dg73Iz0mTpxIYmJiimm0Wi07duxwSX05NsB76aWX0Ol0rFmzBoPBYLUtJiaG06dPu+xLE0IIIZ4KZ4ZISfb83pNjvyYkJDis8siRIwCEhITY3Z4nTx4Ajh496rCMU6dOER8f77AccxmKonD8+PEM1ZuROsyOHTvGggULmDVrlsN9cGTZsmV21xcvXjzFZ/3MypUrl65yHcmxAV6pUqX49NNPOX36NK+//joxMTGA2lW6c+fOFC9enMmTJz/lVgohhBBPX9GiRa3Gf42MjLSbLj4+ngcPHgBJAdKTAgMDAbh9+7bD+m7dumV5ba8ccxnJyzHnSWu9GakD1DuAr732Gl9//bVVmrT64IMP0p0nM8rNsc/gAQwZMoTSpUszbdo0SpQogb+/Pz4+PrRv355Vq1bZ7aYshBBCuA2tk71oHz+Dd/nyZQICAiyrHQ0Vlnye1Fy5ctkv8vEcq+arZxkpJ/k8reZyzHnSWm9G6gAYN24cjRo1omHDhg7bnxJFUTKUz9Xl5ugAD6BVq1a0atUq9YRCCCGEu3HRMCkBAQFWAZ4jyZ9ddxRwmJ+BDw4OznA55jKSl2POk9Z6M1LH3r17Wb9+vVOdMF01zImz5eb4AE8IIYQQrhEcHIyXlxd6vZ6HDx/aTWPusJA3b16H5RQsWNDy+uHDhza3QpN3ejCXU7BgQf78888015veOh4+fMgbb7zBkiVL8PHxcdj21Fy8eNFhRxBnXL16NV3pJcATQggh3FUWD5Oi0+koW7YsR48e5dq1a3bT3LhxA4BKlSo5LKd8+fKWgX2vXbtmE3yZy/Dy8uL5558HoGLFimzbti3N9aa3jh9++IFTp05RtWpVh+1etGgRixYtonjx4ly8eNFuGoPB4HCbM9J7BS/HdrIQQgghcjpFp3F6Sa/mzZsDcPLkSZttt2/fJiYmBj8/P+rVq+ewjKCgIMsgyfbKOXfuHAD169e3DFCcUr3J87Rs2TJDdeTOnZvSpUvbXcxXAwMCAixz7zpiMpkyZUk+gHNaSIAnhBBCiDTr06cPWq3WMvVWcuYpvdq1a5fqnO59+/YFSLGcrl27WtY1adKEEiVKcOrUKasesqDebj116hQlSpSwDGCc3jpeffVVTp8+bXcx9yo2p9m6dWuK+5YdSIAnhBBCuCvzLVpnlnQKCwujb9++nDhxwmasu0WLFuHr68v48eMt66KioqhRowYzZ860StujRw8qVKjAqlWrrHqx6vV6VqxYQfny5enevbtlvYeHB5GRkZhMJssMFWZLlizBZDIxadIkdDpdhuvISSTAE0IIIdzU07hFCzB16lSqVq1K//79uXPnDoqiMGvWLH7++WcWL15s1clg2rRpHDhwwGY+V09PT5YtW4bBYGDo0KEYDAbi4uLo3bs3JpOJNWvW2AwM3KlTJ/r168fEiRMtgxPv2rWL0aNHM2TIELp06eJ0HTmFdLIQQgghRLr4+fkRFRXF2LFjCQ8PR6vVUr58eQ4ePEjFihWt0nbp0oWdO3fSs2dPm3LKly/Pvn37GDFiBGFhYXh6etKsWTOOHTvmcE7Wr776ivLly9O5c2cSEhIoUKAAixcvpk2bNnbTZ6SOnECjZNaIfDlIbGwsgYGBVAlpx+HLaxymqxbSFoCDV35wmKZmWD+r97+dnWs3XYOI+ZbX+tunANB6qgMz7z6qXvquW2mC1Xtn1a8+FYCdB4YBUKfiKAD2HJcZP4RIjfm3bf5NN6g7B4Dtu9VJzJsPTJoXe+MX9qdaSknjVj8BkHj7LyDpdxpe6EUA8vZKmsbo4aYvAcgV3kmtb656NcV8Xtm+ow8AzV77w5Jn06LyADRsqE6G/ujKbqv9MWva6TfL680ra1pta9T8ewBMD9Xno+Iuq3NqHvhneVKaJisAiP/3EAB+ZV9R9+tftS36u+qD754BxSx5vIqpD8rH/PY5AN551QDCIyDEYVsLnd8FwL+hjh/0h6TzNqR87nakWVd1PzYtC7esM//NiImJSdPYchlhruPOyqoE5NKlnsFROXFGgjsdztS2iqdDruAJIYQQbkrRkeHbrOb8ImeSAE8IIYRwVxonx8HLpFkXxNMnnSyEEEIIIXIYCfCEEEIIN6VoNU4vIms8evSICRMmUKFCBZ599lnL+u3bt9OzZ09+/fVXl9Ynt2iFEEIIN6Vo1cWZ/CLz3b17lwYNGvDHH3+gKIrVPL0NGjSgXLlyNGrUiF9//dVmvMCMkq9WCCGEECITjRkzhhMnTlC2bFk6d+6Mj4+P1fZ8+fIxefJkZs+ezRdffOGSOiXAE0IIIdyU+QqeM4vIfGvXrmXUqFGcOHGCZcuWkTt3bps0lStXRlEUvvrqK5fUKbdohRBCCDflzGwU5vwi85lMJiZMmJBiGvP8un///bdL6pTYXQghhBAiE4WEhKBJZUiar7/+GoACBQq4pE4J8IQQQgg3Jbdo3UObNm2YOHGiw+2zZ89m7ty5aDQa2rVr55I65RatEEII4aakF617GDZsGPXq1WP//v20b9+euLg4Nm3axJkzZ1i1ahV79+4FICwsjHHjxrmkTgnwhBBCCCEyka+vL1u3bmXQoEH07dsXo9FIy5YtAVAUBYDWrVvz9ddfkydPHpfUKQGeEEII4abkCp77CAwMZNGiRXz88cfs3LmTS5cuYTAYKFSoEPXq1SM0NNSl9UmAJ4QQQrgpkwZMTgRpJulEmyV+/vlnXn75ZQAKFixIx44d7aZbunQp3bp1c0mdErsLIYQQbko6WbiH9957L03pwsPDZaBjIYQQQoicpECBAsyZM8clZckt2nTwzBNmd33NsH5pLsP4SB3IUKPzBqBGaC8A9p9fCEC1kLYABFTsZcljunYAgL0np1qV5ZW3tNX7xi+tt7yOO7fBaptG52W3jOR2Hhhm9X7P8ckO0/5XhRz70/L6ygtlAWiXOBqA7z0n2c1T5OQxy+ur5V6wm6bpDPUhW/bsAWDz6rqWbY2afw+AZ8M26ooLlwBQShUHYNP/dABE1Eqav3DHvkEANGz4HQBRUT3s1lun4ijLa/P33bz3KQAe/r4KgNDDdwFYrJtht4yUNHvtD7WsI8ss67wLVQVg20b7QwFUzd8EgMM3t1jWmX9jWi9/AHL1+xQAZZM6OfeDP9W2egUl/SYSYy8C8NvZuelut1l4oRfVcoOfB5J+P83fPGtJs3Geel7Q+T4DJH3mCbeTjhWAjV+EOKzH/N1pvPwA2L6jj00ajafv43L/sFrvU6A6ABsiA5NWRo4EoEFd8x+K/lblNut6SF2tS/oTYD4X5SreGIDEGHWwVfO+mvdTW7qCJU/14l0A8AoqA4B34cpqPbv7P67fdl+3bekMQP3qV4CkY71G6DcAeOQuDIDx0V1Lns0rawJQq4x6HO06PNKqzVqvAJt6/g2tZ1u5HQev/GCzzt4xCFCvaqRV/QAP/1DzN6irfqbmfc8qJq2Tt2jlMk+m2LVrF5MmTUKv1wNw9epVGjVqlGKe+Ph4Tp06haenp0vaIAGeEEII4aakk0X2VK9ePebPn88bb7zBxo0b0Wg0bN++PdV8np6ecgVPCCGEECK7KlKkCOvWraNv375s3LiRJUuWOEyr0Wjw9fXlueeeIzAw0GG69JAATwghhHBTipO3aOUKXubSarXMnTuXzp07ExERkbV1Z2ltQgghhHAZRas4vYjMpdPpWL16darp1q5dy57Hz2G7ggR4QgghhBBPWevWrenQoQPr1q1zSXlyi1YIIYRwU9KL1n3Mnz+f9evXExMTg8lkstpmMpm4ffs2169f5+233+bFF190uj4J8IQQQgg3JQGee5g5cybvvvtumtIWKVLEJXVKgCeEEEK4KZNWweTEc3TO5BVp9+2331KtWjXefvttihQpwqRJk3jvvffInTs3AIqi8NFHH/Hmm286nMYsvSTAE0IIIYTIRP/88w/nz58nKCgIgOjoaG7cuGF1KzYwMJD27dvTrFkzgoODna5TLs4KIYQQbsqkSbpNm6FF87T34L+haNGiluAOoG3btqxatcoqTeXKldHr9QwcONAldUqAJ4QQQrgp8y1aZxaR+QIDA/n+++8t7z08PGjUqBGffvqpZd3ly5e5desWv/zyi0vqlFu0QgghhBCZ6N1336VDhw5oNBoqVarE4cOHeeedd6hYsSIHDx6kWLFirFixgsTEREJCHM9bnR4S4AkhhBBuSnrRuod27doxadIkPvnkEzw81NDL19eXVatW0bx5c27fvg2ogyJPmDDBJXVKgCeEEEK4KZPGuefo5Bm8rDNy5EhGjhxpta5y5cqcOHGCNWvWYDKZaNy4MWXLlnVJfRLgCSGEEEI8JQUKFODtt9+2vG/cuDFbt251ulwJ8IQQQgg3pd6idWYcPBc2Rjjt1KlTbN++3SVlSYCXDpv2WF9arVVmEADGR7cA8MitPhhZvXgXAA78s9ymDL9nmwGQcPtPAPadnglAo+bfW5WxdX3rZLnU103abAZgy/81BcCU+BCAiFpqGTv2DUqWpxUAtcsNA2Dvyal298mc1za/Y41b/WR5bd3Op6fh10mvo/pmXj1XXrC9dP695yS7aZu23Q7A1R8aWNY1nq3++88LVwE4V1cdsVx3IxaADavrqnk7/WbJ49HkFQA2/k8HQKWfCwBw9GWdVX0+ldvYtMEjbwkAGtSdo6ap00mt52O1u/6e45MtaZsPvKLW8+3zADRqUhqAxbrOdvcvJfWrq8fbzgPDHq9JqqdBxPwU8x6+uQVI+n0B/HZ2rt20zQ4XAyB3WXVg0LhzGxzmqRHaC4D95xdatWP7jj6WNObfi0l/HwDfwur3seuw9W/fePNisndhQNJnWafiqMdlxNptsz0anZdNW55kehj9OK23VT3e+SsC0LDhd5a0Wr9n1LSeflZlmNOYt3s8W9Gyzfy51AzrB0BQreEAGP49+ziFup8bP0xW5ofqOc58TtDlCwWSzlWGBzesygTwKVBZ3fbwutU2c/3m733f6aRjxkzno7bb/D3lLtMWyNh5KKXztPkYfJL5ODCfr8H6NwTq/hhM+nS3J6MUJ5/BUyTAy1Y++ugjl5UlX60QQgghRCa4cuUKR48e5dGjR6mmHTp0KCtWrHBZ3RLgCSGEEG7KpFGcXoTr/f3330RERFC8eHGqVq1KUFAQo0ePxmg02qS9fv06TZo04fPPPwdcNxetBHhCCCGEmzJqnV+Ea929e5fGjRuze/duFEVBURT0ej1Tpkxh0CDrR6HWrVtHxYoViYqKQlEUOnfuzPHjx13SDvlqhRBCCDfl1DRlTj6/J+ybPXs2ly5dwsfHh/bt2zN8+HDatm2LTqdj7ty5XLhwgcTERAYPHkzr1q25ffs2gYGBLF26lGXLlpEnTx6XtEM6WQghhBBCuMivv/5K8eLF2bRpE2FhYZb1+/bto3nz5kRGRnLw4EGOHz+Ooig0atSIhQsXumwGCzMJ8IQQQgg35exzdPIMnuudP3+eOXPmWAV3ALVq1WLMmDGMGDECAB8fHyZPnsy7776bKe2Qi7NCCCGEm5Jn8LKfmJgYGjZsaHdbly7q8DyVKlXi0KFDdoO7lStXuqQdLrmCt2PHDtasWUN0dDTLli0DICoqigMHDtCzZ08KFSrkimqcYjAYWLlyJb/88gseHh6EhITQt29fSpQo8bSbJoQQQogcwmAwEBAQYHdb0aJFKViwIAcOHLDMSfukoUOH0qlTJ6fb4VSApygKb7zxBgsXLgTgmWeesWxr2LAhGo2GOnXq8Mknn9C+fXunGuqM33//ne7du1OmTBk+++wzihcv/tTaIoQQQriKCSfnonVZS4SZwWDg4sWLFCtWzGaboijkyZPH0rM2ubi4ONasWcP169dd0g6nArxp06axYMECtFot+fPnR1Gs7+U3aNCAESNG0LlzZzZs2ECTJk2camxG/N///R+dO3dmxIgRjB8/PsvrF0IIITKLycnbrNKLNnOULFkyxe0+Pj6Z3ganvtp58+bRrFkzrl27xrVr1+x27W3atCkmk4kJEyY4U1WG7Nixg06dOtGnTx8J7oQQQgiRJczj32VkcRWnruBdv36d/fv3WwI7jcb2OrF51Obff//dmarS7caNG7z66qsUKlSIqVPtz8MqhBBCuDOTxslbtE7kFfb5+/vz6aefUrJkSYfP2dkTFxfHypUr+e6771JPnAZOBXjPPvtsqgPyrV27FgBfX19nqkq3ESNGcPfuXSZPnpwll0KFEEKIrGbUqIsz+YVr9enTh759+2Yob/PmzS1xk7OcukVbo0YNfvnlF4fbDx06xEcffYRGo6Fx48bOVJUuV65cYdGiRXh7exMWFka/fv2IiIigaNGiNGnShG3btqWYPyEhgdjYWKtFCCGEECI1zvQ30Gq1DBgwwCXtcCrAGzduHAMHDmTq1KlcuHABAL1ez4kTJxgzZgwRERE8ePAAPz8/PvzwQ5c0OC3WrFmDoih4eXnx22+/MWnSJHbs2MHXX3/NoUOHaNq0qWU4F3siIyMJDAy0LEWLFs2ytgshhBBp9TSnKjPPr1q6dGlKlixJREQEO3fuTHc5169fp1+/foSGhlKiRAk6derEpUuXUsyzZs0aqlWrRmhoKBUrVuSbb75xaR2nT5/m1VdfpUCBAuTOnZuGDRuyb9++NO1Pq1at0pTOkSlTpjiV38ypAC8kJIS1a9fy9ddfU6pUKU6fPo2vry+VKlUiMjKSR48eUahQIdavX0/p0qVd0uC02LFjBwC9e/dm9OjR5M2bF4CWLVsydepUTCYTffv25datW3bzjxw5kpiYGMty+fLlLGu7EEIIkVbmW7TOLBmRkJBAixYt+O6779i8eTN///03AwcOpEmTJqxevTrN5Vy4cIHw8HDu3r3LyZMnOXfuHIULFyY8PJwzZ87YzTNq1Chef/11pk6dyvnz51m1ahWjRo1i0KBBLqnjjz/+oHr16pZY4uHDh2zfvp1GjRpx9OjRNO/b0+b0QMeVK1fmzz//ZO3atURFRXHp0iUMBgOFChWifv36dO7cOcufv7ty5QoARYoUsdnWrVs3hg0bRkxMDKtWreLtt9+2SePt7Y23t7fN+kYV+3PoYtKVv32nZwJQvbg6MrXhwRW77albKakH8e6j463WVc2vXsr1Ci4LgE+hag73a8v/NbWqT6NT27j//EIAmnU9ZEm7aVk4AHtPptzBZMc++z+IlGxd3zrdeVzl2QPnAbhYPdRqfVTGHnfIFM3HPQRAm6eA+n6KwbJt6wj1J9ds2uPBv+uq/2yIDLQqw3jvquW1123zf46CAMj3dy77FSfqbVbpr6idmzyCngUg5v/U46+FcZxa79S8lrSaPNZteHh+w+NXnQFo2FB98FcxJdWzfUcfqzzNe58CYOeBYfbbCOj8rQc+r11OTfvkseoVVMomb7WQtgAEVlVvYWh0nlbbtV65La9rhvUDwDdE/ZA9A9RBzc2/PfNv0VwmwMErP1jl3XV4JJD0mzvwz3Ig6beYXJ2KowDYc3wykPR5mTVqssLyOuHmcattfpW7Wr2vX139LOx9jrlCmwGwbWM7q/XJzzMeieox6PGM9VANHvnVqZM2r6wJQOOX1lu21Qz7CACTXn0kRRuQDwDDzb/VtK1+Utf7JA3gmhitbjMfBy0G/wuA8Zb6OzV/xvWqRlrybN/dH4AmbTar5fkFWbXRN6S2zT6bv6NcRRuo9caqd4yePBeZvzeA387OtSkHkr4Xr6AyNttqlVHPh+Zzu1mN0F5A0rk27vymZFutv4ffzs4lNjaWwMCFdut3tafVyWL48OFERUWxf/9+y3hvHTp0YO3atfTq1Yvw8PBUJxIwGo106NABvV7PggULLLHC1KlT+f777+nYsSOHDh3C0zPpd/7jjz8SGRnJxx9/TEREBABlypRh4sSJ9OvXj7p169KxY8cM12EymRgwYAALFiygbdu2aDQadu3aRbt27bh16xYffvghP/zwQ8Y+tCzmkhFwPDw86NChA19++SW//PILGzZsYMGCBbz++utZHtwBlmfm7I0k7evra5lC5NSpU1naLiGEEMLdXbx4kdmzZ1O2bFmqV69uta1Hjx7ExcUxcuTIVMtZvnw5hw8fpkOHDvj5+VnW63Q6unTpwvHjx5k/f75lvclk4v3330ej0dCrVy+rsrp27YpOp2Po0KGW0TsyUseBAweYOHEi7dq1s4wMUq9ePcttU0dXFbMjpwK81q3TfiXn559/plOnTrRt25Z58+ZZfQGuli+f+j9PR50jQkJCAPVgEUIIIdyVUaNxekmvlStXYjAYqF3b9mprjRo1AHUEjejo6BTLWbp0KYDdcmrWVK8yz5s3z7Lu4MGDnD17lpIlS5I/f36r9Llz56ZcuXJcvXqV9euTrkynt46aNWtargzaS1upUqUU9yk7cSrAO336NEajkX379rFmzRp+/PFHS2eL5JYuXcorr7xC/vz5mThxIv/88w/16tXj3r17zlTvUHi4envy5MmTdrebh0157rnnMqV+IYQQIiuYb9E6s6TXunXrAAgNDbXZFhwcTJEiRdDr9ezZs8dhGXFxcWzfvt1hORUqVADgyJEjllghpXqT54mKispwHY5cv34db29vRowYkWK67MSpAO/q1asULlyYunXr0qlTJ9q1a0epUqVo3Lgx586ds6SbPn06oN6zL1u2LBMnTqR+/fq8+uqrzrXegc6d1WeGNm/ejMFgsNl+4cIFtFotr7zySqbUL4QQQriTJ4cGS0hIcJj2yJEjQNLdsCeZx8dNqUPCqVOniI+Pd1iOuQxFUTh+/HiG6s1IHY58+eWXzJ8/3xIUZiZXzfzlVID36NEjbt26haIoBAUFUalSJV544QUOHTpE7dq1LV2QzfesCxVKerh6yJAh7Nixg5UrVzrTBLtq1qzJq6++yrVr11iyZInVtps3b7Jhwwb69u3Ls88+6/K6hRBCiKxicvL2rOnxLdqiRYtaDQ8WGRlpt774+HgePHgA4HCig8BAtcPW7du3HbY7+SgW9soxl5G8HHOetNabkTqedPfuXV5//XX27NmTJZMmnDt3zmXDpLikF+3UqVMtHRdAvSw6dOhQJkyYwPz583n06BGgPtRoVqBAAQoWLMiiRYvo1KmTs82wsWDBAs6ePcugQYMoWbIk9erV486dO/Ts2ZPKlSvz2WefubxOIYQQIiu5aiaLy5cvW3VMtDeSBGD1XF2uXPZ79Gu16rUj89WzjJRjLiN5OeY8aa03I3UkN2PGDGbNmsX582qv8Pbt2zN48GBmzJhhf6dSsWTJEpYtW8alS5d49OiRzbyziYmJXL9+3WX9A5wK8HQ6HRs3brSMM2eWK1cuZsyYQeXKlS2T59qbj83f3z/T5qgNDAxk165djB49mo4dO+Lt7U2ePHno2LEjw4YNw8vLK1PqFUIIIdxNQECA3ZEnnpT8b+eTAYqZXq8OpRQcHJzhcsxlJC/HnCet9WakjuTeffddBg8ezO7duxk+fDj79u3j888/58UXX6RpU9uhklIyfvx4Jk6c6LDtyWky0PHFHqcCvIIFC9oEd2YPHjwgMTHREonaGy4lNjaWu3fvOtOEFOXJk4fZs2cze/bsTKtDCCGEeFrUjhIZDwjS28kiODgYLy8v9Ho9Dx8+tJvG3GHBUXwAavxg9vDhQ6vbpcnLSF5OwYIF+fPPP9Ncb0bqeJJGo6FevXrs2LGD+vXr89tvv7F8+fJ0B3hffvklAK+99hp9+vShUKFCdi98HTp0yNKPwFlOPYNXvHhxu5cqz507R4cOHXjhhRdITEwEbO9/3759mxs3bth84EIIIYRIm6weJkWn01G2rDow/7Vr1+ymuXHjBpDykCLly5e3XKmyV465DC8vL55//nkAKlasmK56M1KHI56enowbNy7F+lPi4eFB3rx5WbBgAXXr1qVkyZIUL17cZmnXrp3LOnI4FeANHTqUoUOH8vzzz9O+fXvat29PtWrVeP7558mdOzfbt2+3elAzJibG8nrx4sUAVKvmeOYGIYQQQmQvzZs3B+wPRXb79m1iYmLw8/OjXr16DssICgqyDJJsrxzzSBz169e3DFCcUr3J87Rs2TLDdaSkbl11VpzkHUbTql+/fiQmJqbpFq15mBdnORXgtW3blokTJ3Lu3Dl++OEHfvjhBw4fPkzbtm1Zu3YtM2fO5OOPPwbg9ddfp0OHDkyZMoWpU6cyZswYNBoNvXv3dsmOCCGEEP81RjROL+nVp08ftFotO3futNm2b98+ANq1a+ewo4ZZ377qHJMpldO1a9I0fk2aNKFEiRKcOnXKZi75e/fucerUKUqUKGEZlDgjdaTEfJGqTZs2aUqf3NixY6lVq5bNyB72lCtXLt3l2+P0VGWjRo3i9OnTzJw5k6lTp7Jnzx5WrlyJh4cH3bp14+zZs+zatYsPPviAtWvXcv78eYYPH058fDzt27enXbt2qVcihBBCCBumx0OdOLOkV1hYGH379uXEiRM2Y90tWrQIX19fxo8fb1kXFRVFjRo1mDnTeo7fHj16UKFCBVatWmXVi1Wv17NixQrKly9P9+7dLes9PDyIjIzEZDJZZqgwW7JkCSaTiUmTJlmN2JHeOlKyePFiGjRokKEAb8+ePQwZMoQFCxawdu1adu7cabNs27aNjz76iH///Tfd5dvj9DApACVLlmTgwIF2txUtWpSiRYsC4Ofnx9dff83QoUO5cOECzZo1c0X1QgghxH+SES1GJ67VZDTv1KlTOXjwIP3792f9+vUEBQXxxRdf8PPPP7N06VKrmSOmTZvGgQMH+PPPPxk0aJBlvaenJ8uWLaNBgwYMHTqUmTNnotfr6du3LyaTiTVr1uDp6WlVb6dOnYiKimLixIk0atSIihUrWkbMGDJkCF26dLFKn946zHcZX375ZYYPH06ZMmUwGAzMmzePHTt28MMPP2Sol2vv3r0tM33t2LEj3fkzwiUBXlq88cYbfPPNNwCUKVOGMmXKZFXVQgghhHAhPz8/oqKiGDt2LOHh4Wi1WsqXL8/BgwctnSHMunTpws6dO+nZs6dNOeXLl2ffvn2MGDGCsLAwPD09adasGceOHbOZb9bsq6++onz58nTu3JmEhAQKFCjA4sWLHV5ZS08dNWrUoFChQixfvpxly5bx/PPPU6ZMGV599VU2bNiQ4SFMRo0axRtvvEFISAhFixbF09PTpqz4+HhOnz5NbGxshup4UqYHeEajkZ07d7J48WJLgCeEEEII55ky0BP2yfwZ5e/vz4wZM1Id+Ldbt25069bN4fawsDC+//77NNer0WgYOHCgwzuHztTRsGFDTpw4keZy0+q1115j3rx5luf+HLlz5w7FihVzSZ1OBXhGo5HIyEirkZmFEEIIkTVMaDE5cYvWmbwi7XQ6HR9++CEGg8Hu+HdmwcHBfP755y6p06kAb+DAgXz99ddZOjLz06QY9Vbva4T2AkDnmw+Afadn2t0eUKmXTVmeQerzCYmx6j15w4Mr6vu7z9ikrRnWD4Dfzs4F4MA/ywGoVWaQVbpNy8Jt8tYuNwyAvSen2tkjaNRkheX1ti3Wgys2iJgPwPYdfQBo/uZZADbOC7NbVno1n6D+h2DjeNtBsB25WD009USZoMjVXwG4WqRlqmljyqmjp//2oTquUvU1SZN211yp/lZ+e8/TNmMyhs5tLa+199V/W4xRX8SVMU+5o7PKk/x7aTFcHUDc+Ej9V+OlDgHw5DGa3IaJ/lbv/cJetnofFdUjxTYDbPzW/lhSjVv9ZHn96B9zb7ZWgONjc8e+QXbXA2h81LbGn1OHE8hVS22bcmGbJY3592L+nZj3vX516/oOXvnBpnwPv8JW782/OXvMv0+fwtWt1pvi1c++Qd05AGzf3d+yrUHde1brmn+kHiPm39zOA+rvttlrf1jyGB/etFt/varqUFS7j463ux1sPwNzPd7P1bek0ZxTezzmLtNefe8fBICHsTgAcX+uA0DrlTSchNYrN5B0nvErr+bdur612v6uh9QyApImeq8Woh7bQY0/BGDTovIANG27HQDdM88CULdS0oTrvoVrA2B4eN3u/rUYrD6Ubv7OAaoXV5/FMn93SedrdUDbhFtHbcpx9PtQjAlW75PXY25nSp+/EIBlcOTExET279/PpUuXCAgIoHz58jz77LOWdH369HFJfU6F7suXqz+ccePGcenSJcvMFU8uhw4dsnlQUgghhBDOyeqBjoVzZsyYQUhICBEREfTo0YM2bdpQsmRJatasybZt21IvIB2cuoKXJ08ecuXKxQcffJBiuipVqlgGKBRCCCGEazytXrQi/V577TWWLFliuevp5+dHnjx5MBgMHDp0iKZNmzJhwgTGjBnjkvqc+mbfe+89Hjx4YDVhryPJZ7QQQgghhPivWLx4Md999x2lSpXiyy+/5J9//uH+/ftcvnyZf//9l4cPH7J27Vq++eYbNm/e7JI6nQrw3nnnHbp06WKZRDcljRs3dqYqIYQQQjzBfAXPmUVkvjlz5tCxY0f++OMP+vfvbxkf2Mzb25vWrVvz448/Mn36dJfU6dQt2sWLF1OnTh3mzJmDyWQib968NmkMBgN79+7l5k37DwgLIYQQImMMGh0GjS71hA7zp95JUjjv3LlzbNq0KdX+CJUqVeLKlSsuqdOpAO+zzz7j+PHjAOzfv99hOkVRckQvWiGEEEKI9CpUqBC5c+dONd3t27e5evWqS+p0KsAbO3Ys7du3p1atWjz77LMOR2b+/fffOXfunFMNFUIIIYQ1ExqnbrOakIsvWaFw4cL8/vvvVKlSxWGa+/fv0717d8LCXDMUmVMBXtu2bWnatCkbN25MMV18fDwFChRwpiohhBBCPMGAFoMTAZ4zeUXaDR48mJdffpnIyEheeeUVAgICAEhISODMmTMsX76cRYsWcePGDb799luX1On0VGWTJ09OcWTmo0ePUrZsWVatWuVsVUIIIYRIxogOIxl/Bs+IyYWtEY60aNGCfv360atXL7RaLYGBgWg0Gu7evWtJoygKvXr14rXXXnNJnU6H7lWrVk1x2g1/f38aNGhA5cqVna1KCCGEEMItjRs3jg0bNlClShXu3r3LnTt3UBQFRVEoXLgwc+bMcdnVO3DBFbw7d+6wZ88eYmJiMJms/ydgMpm4ffs2x44do0ePHqneyhVCCCFE2hnQYXDiCp5BruBlqWbNmtGsWTPOnz/PyZMniYuLo2TJklSpUgWt1rW3y50K8A4ePEjz5s2JiYlJMZ2iKPz+++/OVCWEEEKIJ8gzeO4pNDSU0FD7c6vfvHmT/PnzO12HUwHehAkTePDgAREREYSEhLBt2zbLZLpmv/76K+3ataNHj9QnKhdCCCGE+C+rWLEi169fd7ocpwK8AwcOsGnTJho0aADApEmTiIiIoG7dupY0CxYsYMeOHdSsWdOphgohhBDCmsnJThYmuUXrUlu2bOGnn36iT58+vPDCC5b1ixcvTjVvYmIi27dv59atWy5pi1MBXu7cuS3BHUDv3r0ZOXKkVYDXo0cPhgwZwqxZs3jnnXecqU4IIYQQyRgUHTrFiWfwFAnwXKljx47ExMRw+PBh9uzZY1k/adKkNI0H7MqJIZwK8AIDA7l9+7ZlirJChQqhKApHjhyx9JrVarV4eHgwdepUtw/wNDovq/eKMQEA4yPraLvyM2qA65NPHdBw8w8NLNtqhPYCYP/5hY/fbwXAO68a6Rvjo23q1Xr52y3/SPTuVNu89+RUABo1/x6Ah2d/tqp/25bODvMaH1hPL7dxnu3gi81e+wOATYvKp9qWJ20c75vuPI5UX5NgeX2gvbfLyjW7WqSlw22FTh8E4N8y1QC4U9D8HRZ02J4KG9TnVis3HQ/AYt0Mq+1n6xywvA68WgKA0w3VZzLqL1CnFmr+5lk1gdEAgOl+0rGjfV79/XkXVv/dur41AI2arADAs14bADR37lny6E/uAJKOiW0b2wFQM6wfALlCm1mtB6hXNRKAXYdH2uxjcvdPLre89gttlWJae2qXGwbAwSs/ANCg7hx1P/I+B0DCCbUDl9Yz6bdiPub3nZ5plce3xQAAmnb6DYDNK23vLniHqN9leKEXATj07zqHbfMNUX+PugD1+zZ/JjrfILWN+ctYrQcwPPzXqoyNYx8fI2P7WK3X5itoea35Wz3/bN/dH4DGL60Hkj578/4B6PIUU/M3UY9bv9IvA9Csx1G1jB1qPS2GJv3GPQPUPJ41Wqj1xcQCYEqMt9qfHfsG2XwGDRt+p6Z9eMd6g6cPAFFRyR/RUV8363oIgKZttwOQePO0Wq+nHwBeBZLOKbo8RQB4dEb9ns3nL7MNnxcCoHrxLpZ1B/5Rjznz5671UscdMx8jh29usdmPqvmb2N1mLsvMfB631xbx3/buu++yatUq+vfvb7W+X79+/O9//6NRo0YUKFDA7pRler2e/fv3c+HCBZe0xakAr2XLltSoUYP69etTpUoV3nnnHUaOHMmLL77IrFmzKFasGNOmTePOnTs2PWyFEEII4RwDWnRO9aI1urA1Yty4cYwbN85m/euvv87evXtZs2ZNivnj4uIoXLiwS9riVIA3YsQI1q1bx6JFi1ixYgVvvfUWZcqU4a233uKll16yuszYtm1bpxsrhBBCiCQGdE4GeBnPK9IuKCiIIUOGpJouV65cLF261CV1OhXgPXr0iH79+pE7d24qVKiATqceKO+99x558uRhzpw5mEwmmjdvzpgxY1zSYCGEEEIIdzJnzhyb27b2zJo1i7ffftsldTo1AE6zZs1455132LJli81MFX369OHgwYMcPnyYyZMnkytXLqcaKoQQQghrxscDHWd0caYHrki76dOnpyldp06dmDRpkkvqdCrAM3flbd26tUsaI4QQQoi0MygeTi8i+7h//77Lpitz6pudMWMGb7/9Nm3atEk1bVhYGGfPnnWmOiGEEEIkY0CHVp7By3Z27drFsGHDLBfCrl696nDmCrP4+Hhu3ryZPTpZdOzYkUKFCvH222/z3nvvUbp0aZs08fHxfPvtt5w/f96ZqoQQQggh3EK9evWIiopi8ODBzJ8/H41Gw8WLF1PNlz9/fubMmZNqurRwehy8Bw8eADB//nyXNEgIIYQQaWNQdGidGuhYruBllly5cjFv3jyKFCnC4sWLiYqKcphWo9Hg6+tLvnz5XFa/UwHem2++meYHB101MrMQQgghVAa0Tt6idepRfJEGH3zwAXfv3qV48eJZWq9T3+y7775LcHAwf/31F4mJiZhMJpvFaDSyefNmCfCEEEII8Z/0+eefp5pm9erVrFixwmV1OnUFLyQkhLfffptSpUo5TKPRaGjcuLEMdCyEEEK4mNHJnrBGRWayyC5effVVChcuTFxcHL1793a6PKf7R0+YMCFN6VatWuVsVUIIIYRIxqDo0MgzeNmewWBgwoQJrF+/npiYGJvpW00mE/fu3SM2NpaRI0dmjwBPCCGEEEI4NnHixDQPYNygQQOX1ClPVwohhBBuyoiH04vIfCtWrOCll15i+/btnD17ljZt2nD48GEuXLjAhQsXOH/+PO3bt+fnn39m5cqVLqlTvlkhhBDCTcktWvdw8+ZNjh49io+PDwB9+/Zl586dDB482JJm8uTJ1KlThwMHDrikx61cwRNCCCGEyETFihWzBHcALVq0YN26dSQmJlrWlSpVCh8fH9566y2X1ClX8NJh2/E51AzrZ3l/4J/ldtMdid5t9b5aSFIPYv8yHa22eeRWpyTZfXQ8ALXLDbMpz6S/b/Xet2BNABo2/A6A+H8PAqDReVnS7D051SpP4u2/ADA+umW3zfbsOjzSqh5Tt+4AeD1ISrN5UXkAWoxR27hhor9VGeb1ybc1n/AIgOgK6tA5h9qqB33TGYpa5rvpH1LnTsHbyd4VSXf+ChtiADjRIjDdef8tUw2ACbRXy6ptntHF9nmLknsvA5CnznoAFutmWG1vMVJtR54mJS3rTjXOC0Ab/TgAfDarcz/HtHkBAO8ZXwHgVaiiJc/GD/0AaNo2AIAmbTYDsG1LZwCaTXv8gO8NvSWPZ5m6Vm1p3OonAH47OxeAOhVHPd7SzpLGfIw8KaLWTAB27BsE2P+tmMt/eHadVT3m30DyY/jJ41mXp5j6b0B+ADYtU9vUvF/SjDn3d39p1V7jo7tA0mcDNe22HWDrL60AqBbyDQB1K6mdycy/0+S0vuoxo/ULAsCrQDn1vb/aNiVe/Q34ln/Zkke5rx6vLQb/C0Di30cA8CxT3apsfcm8ltfe95tZbTM9jAagURN1WIXtu/vbtK1Zj6MAbP6/pgBU+TEOgOaT1T8qG6fnT0rbtYFaZ4j6+XhduaTW00A9Lry2GoCkYwlgy+NyE++qn7vnE/Vvenx+SK5pB/X8aLh9Qa0v+hQAWk/1/OAVoJ4TjQ+TzlXbVpqPOevvzPy9aL3UNts7zgwP1c943+mZNtuedPjmFgBqlRmUYp795xfarDP/PvYcn5xqPa5kULROXsGT6zxZIX/+/EybNo327dtTqFAhvLy8aN++Pe+//z6fffYZAMeOHePatWtER0e7pE4J8IQQQgg3ZcQDjRN/yuUZvKwxcuRImjRpwvvvv0+hQoW4dOkSffr0oUaNGlSrVo3ixYuzceNGTCYTzz77rEvqlNBdCCGEcFNGRef0IjJfw4YN+e6776hYsSK1a9dGq9Wi0+lYvXo1MTEx/PDDDzx8+JDcuXMzY8YMl9QpobsQQgghRCbr2rUrXbt2tVpXokQJTpw4wZYtWzCZTNSqVYu8efM6KCF9JMATQggh3JRB0YH0onVr3t7evPjiiy4vVwI8IYQQwk0ZFQ80Tk1VJmFAVjpw4ACLFi3i8OHD3Lt3D39/f55//nlefvll2rVrh1bruifn5JsVQgghhMhEJpOJt99+m6+//hoARVEs2w4fPszSpUspV64cK1asoGzZsi6pUwI8IYQQwk3JLVr3MGnSJObOVYeCKlSoEN26dSM8PJzg4GBu3brF2bNnmT9/PhERERw8eNAlPWn/UwHeL7/8wssvv8yCBQvo1avX026OEEII4RSTonWqJ6xJxsHLEt988w0ajYaXXnqJ5cuXkytXLps0I0eOpF+/fowZM4YlS5Y4Xed/JsCLjo7mzTfffNrNEEIIIcR/zJ07d/Dw8GDJkiV2gzsAT09PvvjiC8LCwlxS538mwOvfvz8PHjxIPaEQQgjhJoyKB0gni2yvYcOGHD16FH9//xTT5cqVCy8vL7vbDAYDHh5p/77+E9dmly5dyo0bN3j11VefdlOEEEIIl5GBjt3DzJkz0Wg07NixI8V0f/zxB/nz57e7Lb3P5eX40P3q1auMGjWK7du3M2HChKfdHCGEEEL8x2i1WlavXs1HH30EqAMcJ6fX6zl16hTDhg1j3LhxXLp0yWrbpk2b+Pfff9NVZ44P8Pr06cP48eNtPkwhhBDC3Rmd7EUrV/CyRu3atS0B2vr161NM27NnT5fUmaNv0X711Vf4+PjQu3fvdOVLSEggNjbWahFCCCGyG5Oic3rJKL1ez5QpUyhdujQlS5YkIiKCnTt3pruc69ev069fP0JDQylRogSdOnWyuoJlz5o1a6hWrRqhoaFUrFiRb775xqV1REVF0bhxY/z9/fHz86N27dqsWLEi3ftmNnjwYBRFwcfHh5CQEIoVK5bmpVChQhkaADnHXsH7+++/+fTTT/ntt9/SnTcyMlJu5wohhMj2jIrWySt4GbvOk5CQQMuWLblx4wabN2+mWLFirF69miZNmrB06VI6dOiQpnIuXLhAvXr1qF27NidPnsTLy4thw4YRHh7Orl27KF26tE2eUaNGMWvWLH755RciIiI4ffo09evX5/jx48ycOdPpOpYsWcJrr72GyWTCw8MDg8HAvn372LdvHwcPHmTatGnp/rz69+/Pl19+yfHjx1PtaGHPmTNnqFChQrry5MgreCaTiZ49e/LZZ585fFgxJSNHjiQmJsayXL58ORNaKYQQQrin4cOHExUVxYIFCyhWrBgAHTp0oH379vTq1YsLFy6kWobRaKRDhw7o9XoWLFiAr68vOp2OqVOn4uPjQ8eOHUlMTLTK8+OPPxIZGcnYsWOJiIgAoEyZMkycOJFZs2axatUqp+q4desWAwcOZPTo0fz7778kJiZy+PBhwsPDAZg+fTqbNm1K9+fl7+/PuHHjMhTcAZQuXZqmTZumK49GST5fRg4xZcoUy6jQyfXq1YtFixale6Dj2NhYAgMDiYmJISAgIM35aob1AyBPm0mWdRum5k0xT0Qt9X8fO/YNSrX82uWGAWDS3wfAO285yzb93XNWaX2Lqz+EuPPqgfnbWXVE7bqVkq5U7j463ipPrTJqG7wLvADA9h19bNrQ+CX1WYKtv7Sy28bmA69YXm/8IiTF/Wkx/C4AGz4OSlo3MgaAi80SADjdMPWAvULMAgBOBL6eYrqFpgaW172026227Y6tBsAdH/X/QK299jss5/mttwE41Vj9bjfEVwdg3C+7ANAak/4f9VsnTwBqrky0ev+k36OrWV6P/HI3AH++eBEAY/5/ALgW0sxhm8wa1J0DgMbTDwDvik0AUILzALBxvK9Nnua9T6nbvn0+1fLTqkmbzZbXxtjrAERF9Ugxj/l4AEg8oX6WCdeOAEnHatMO6mej9X8GAMPNpD8qSrz6aMW2LZ2t2qDxUU+wpvs3Adi6vrUlj/nz8iqmnswfHFNvyew9ORVIOt6N95Ju7egCCqv1vayWs22Aur5pJ/XuweaVNQFo9tofljyGq+rruDfbAZDnRLy64b46lNOGzwvZ/Uwg6XNR7qv7pwnMA0D83jWWNMYH6r7tOjwSgFK7r6r/rlaPRSVRr7ajXHFLnvi5owDIXVc9b0U3KQDAM7+on6n5eGjc6idLHo+i5dVtc0Ottpk/0xaD1WeOlLzBSTvwr/r9b/wyqW57zOc3SPr8n9ym9VS/S4/calu37+7vsLz61dUydh4YZrW+av4mltc+BdXfbsKtYwAc+nddim10JKN/MzJSR+iF+ej87Y+rlhbG+3GcL9EnXW29ePEiYWFhPPfcc5w8edJq26+//kqrVq3o1KlTqrc0lyxZQo8ePXjrrbeYPXu21bbhw4fzySef8NVXX9G/v/q9mkwmypQpw7lz57h+/brVBZwHDx6QJ08eChYsyD///INOp8tQHZ9//jkeHh68/fbbVmlv3bpFmTJluHPnDj169GDx4sVp+qwyYtSoUUyePNnpcnLcFbzjx4+zcOFCZsyY8bSbIoQQQmQqk5NDpGTkGbyVK1diMBioXbu2zbYaNWoAsHbtWqKjo1MsZ+nSpQB2y6lZU/2P0bx58yzrDh48yNmzZylZsqTN3bncuXNTrlw5rl69atWJIb11eHl5MWDAAJu0+fLl47XXXgPUYC8ld+7cSXF7Sv766y8+++yzDOdPLscFeJ9//jlnzpwhICAAjUZjtSxatAiA119/HY1GI9OVCSGEEOm0bp16ZTM0NNRmW3BwMEWKFEGv17Nnzx6HZcTFxbF9+3aH5ZifNzty5Aj37t1Ltd7keaKiojJcx4ABAxx2aDDPMFG8uOOrzy1atCBfvnwMHTrUan2ZMmUIDQ1NcSlatCjlypVDr9c7LD89clwni/z589t9KBPg33//JTY2loIFCxIYGEihQo5vgwghhBDZnUnRghPzyZrnon1ytAhvb2+8vb3t5jlyRH1UIiTE/mM3efLk4erVqxw9epTWrVvbTXPq1Cni4+MdlpMnTx4AFEXh+PHj1K9fP031Ahw9ejTDdaTk9m31cZw2bdo4THPmzBkUReHMmTNW6ytXrszKlStTLN9Mo9GkKV1qclyAFxkZSWRkpN1t5mfwIiMj5eqdEEIIt6coOhQnetGa8xYtWtRq/fjx4/nggw9s0sfHx1um/TQHSE8KDAwEkgIie5Lf5rRXjrmM5OWY86S13ozUkZItW7ZQoUIFmjdv7jDN3r172bNnD82aWT8j/dZbb/H777/z7bffUqBAATw9bZ+/1uv1REVF8dZbb6XalrTIcQGeEEIIIdLn8uXLVp0sHF29S/5cXa5c9jt3mG9xmq+eZaSc5LdJzeWY86S13ozU4cixY8fYvXs327dvT3FMukKFCtG+fXub9fXq1aN169bUqVMnxXrCwsKcGm8vOQnwhBBCCDdlMunAlPEreKbHeQMCAtLUi9bLy8vy2tEgHOZnyIKDg+1uT0s5yZ9DM5djzpPWejNShyNDhgxh+PDh1KtXL8V0Kfn000/TlG7btm0ZriO5HNfJQgghhPivUBSt00t6BAcHWwKnhw8f2k1j7rCQN6/jYcEKFixoeW2vHHMZycsx50lrvRmpw57PP/+c3LlzW+aRdbVLly6xZs0a9u3b59Jy/1NX8BYuXMjChQufdjOEEEIIt6TT6ShbtixHjx7l2rVrdtPcuHEDgEqVKjksp3z58mg0GhRF4dq1a1bPwyUvw8vLi+efV8dgrFixItu2bUtzvRmp40k7duxg7dq1rF+/3jK2XkYk71Xr7+9vmS1r9uzZDB06FIPBAEDLli1Zu3at3Wf00kuu4AkhhBBuymTSOr2kl7mTwZODHIPaWSEmJgY/P78Ub2cGBQVRvXp1h+WcO6cO1l+/fn38/PxSrTd5npYtW2a4juROnjzJ2LFjWbt2rcPn/tJqxowZLFu2jCpVqjB69GgA9u3bx6BBg0hMTOTVV1/l888/Jzo6OkNTodkjAZ4QQgjhptTbrDonlvSHAX369EGr1bJz506bbebbjO3atXPYUcOsb9++ACmW07VrV8u6Jk2aUKJECU6dOmUz2PC9e/c4deoUJUqUsAxgnJE6zP766y8GDBjAqlWrCAoKstmelqnYnvT999/TvXt3yy3uIUOGANCtWzfWrFnDwIEDWb9+PcuXL0932fZIgCeEEEK4K/M4eM4s6RQWFkbfvn05ceKEZcw5s0WLFuHr68v48UlTX0ZFRVGjRg1mzpxplbZHjx5UqFCBVatWWfVi1ev1rFixgvLly9O9e3fLeg8PDyIjIzGZTJYZKsyWLFmCyWRi0qRJVrdS01sHqMFd7969WbJkidVzfACPHj1i+vTpfPfdd2n8tFR58+a16kG7ceNGDhw4QO7cuZk+fbplfVBQkFMzYSQnAZ4QQggh0mXq1KlUrVqV/v37c+fOHRRFYdasWfz8888sXrzYauaIadOmceDAAcutSTNPT0+WLVuGwWCwPIcWFxdH7969MZlMrFmzxuZZtE6dOtGvXz8mTpzI8ePHAdi1axejR49myJAhdOnSxak6zAMeHz16lCpVqpA3b17LEhQURO7cuXnvvffsXvVLSb58+UhMVOcgNxqNjBgxAo1Gw+DBg8mXL58l3T///OPwGcP0+k91shBCCCFyEsWkRcnAc3TJ82eEn58fUVFRjB07lvDwcLRaLeXLl+fgwYNUrFjRKm2XLl3YuXMnPXv2tCmnfPny7Nu3jxEjRhAWFoanpyfNmjXj2LFjNvPNmn311VeUL1+ezp07k5CQQIECBVi8eLHDGSbSWse1a9do0KABd+/eBRz31q1evTqlSpVK0+dk1qxZM3r16kW3bt2YO3cux44do0iRIrz//vuWNHq93u48uBklAZ4QQgjhrpwcB8+ZvP7+/syYMYMZM2akmK5bt25069bN4fawsDC+//77NNer0WgYOHAgAwcOTHOetNRRuHBhl90efdLEiRPp3r07L730EgAFChRg5cqV5M6dG4B58+bxxRdfcOLECZmqTAghhBDCHfj5+bF27VquXLnCzZs3KVu2LD4+PpbtVapU4dtvv3VpnRLgCSGEEO4qgx0lrPKLLBMSEkJISIjN+qpVq7q8Lo3iaM4PYREbG0tgYCDhpfrh5Zs0ncme45Ot0lUvrj7cqfNVH5jcd1rtMdS833lLmo1z1QdPa5UZBIBiTADgt7NzAaiavwkAh29usWlHzbB+aU5rViO0FwAandpd3TPgWQB2HR4JQN1KEyxpdx8db1WPOa1vWXVMoYRzajfzHfsGWfKY9828XxG11H32rdUZANOtpIdFtQXUg3rDVHXE8BbDblu9b9JmMwD6V5pY8ux8Xb1U/dyO6wAUPF8AAO8Ydfsfr0YBUHFhLUse5Yw63pE2tLRa/kR/AFYZagPQ0WMvjkQ9rAZAQ7+Ddrc3eJQ01cyFk68A8E94mFUb9we2A6CSYaHV9uS6GoYBsMxjqtX6xrPVf7e+bVt3i+HqcyEbPrbust98ijpAJleuW9ZpHv/PUHnca0xjHuPp8UO+F19W3xZfFmPJs3GebTuTMx8r5uMkOfNxFljnXQC0hYtbtbVxq58sabeub52mcutUHGV5bf6t1asaCYBn3ucAMNxVhyrYeUD9POtXT/o8PQLU403jo06/pPVTf7ubVyYNoQDQoO4cy2uNp/o5KSZ1CiPt4/fbtnS2ytNkZtJp89E36oPjJr06AbvWS73lkju8BwDG6CsAbPm/ppY8Db9W/41SR3DghXX3ASiw4m+1HbkeTxlVoIAlz5W6apuKbjICcLWJ+mD4iRbq4K3NxyU9L7TxQ7Xd5t+YX+RnAHzvOUlN++ZZAC5097fkefZbNa3mcQ/ER/XKAOB7IU5N4Pn4eoAuKSDYOMr6AfhGzdVbYJ4Rj7/jCxcBUJ5LeuA+tpgJgN86qXlbDP5XTVNAPQ8oZ86o7fBJGnfMdO8mAIlXjwKwfXd/7EnpODN78jxqT+1y6vGk83kGSDpfmtfvPTnVfsbHzH8zYmJi0jT9V0aY68j/+ya0/rZjuKWV6f5DblZplqltFSlbtmwZe/fuJSQkhDfffJNnnnnGJeXKFTwhhBBCiExUpUoVQB3qpVGjRkyZMgVQO6CsWrXKMlfuV199xcGDBx12MEkPuTYrhBBCuCtFl9TRIiOL4kQHDZFmR48exdfXl5UrV1qCu8WLF7Ny5Uo8PT2ZOXMmx48f58UXX2TMmDEuqVOu4AkhhBDuyqQBJ4ZJweSaHpsiZRqNhhUrVlC0aFEAEhMTGTt2LBqNhg8++MDSI3jmzJm88MILLqlTruAJIYQQQmSiIkWKWII7gPnz53P58mWKFSvGe++9Z1nv4eHB9evX7RWRbnIFTwghhHBTGpMOjRNj2TmTV6RdUFAQN2/eJH/+/Ny8eZMPPvgAjUbD+PHjrWbS2LNnD/fu3XNJnRLgCSGEEG5Ko+jQOPEcnTN5Rdr179+fRo0a0apVK9asWcPNmzepVasWvXr1sqQ5f/48vXv3dlmdEuAJIYQQ7sqkdfIZPHlSKysMGDAAvV7PrFmzuH37Ni+//DJz5yYN1dOvXz9+/PFH4uLiyJUrVwolpZ18s0IIIYQQmWzw4MGcO3eO2NhY/u///o+CBQtats2dO5cbN25w//597t+/75L65AqeEEII4abkGbycx/ysnrMkwBNCCCHclMaoQ2N0IsBzIq/IHBUrVnRJT1oJ8IQQQgghXGDLli389NNP9OnTx2o8u8WLF6eaNzExke3bt3Pr1i2XtEUCPCGEEMJNaU1atNLJItvo2LEjMTExHD58mD179ljWT5o0iXPnzqWaX1EUNBrXDD4tAZ4QQgjhpjQmrZPP4EmA50rvvvsuq1aton///lbr+/Xrx//+9z8aNWpEgQIFrMa+M9Pr9ezfv58LFy64pC0S4AkhhBBCuMC4ceMYN26czfrXX3+dvXv3smbNmhTzx8XFUbhwYZe0RQI8IYQQwk1pFK1TV+E0ilzBywpBQUEMGTIk1XS5cuVi6dKlLqlTAjwhhBDCTWlNOrTODHUiw6RkmTp16qQp3YsvvuiS+iTAS4fEu6dRHvg53H7gn+UA1AjtZbV+49xQm7SKMQGA387OtVqfq2hjh+Wb85gF1x1j9b568S6W18ZHai8cjc4bAL9nmwPw6No+qzy7j463vA4vpB5UvoXrAuAREAJA/Jkt6vugEgA0bvWTJY8uj3opuVHzI4/r81Lbmj8YgE3THY/lo8THA9C0029qWeH1Adj5etIDpi1GxgBQpIQ6IKTX4fMAGCqqn+kL059XE+bSJxVcXu25dK+k+rbxbPXfrW/vBaBM1E21vsSkZyAeBN8GQB88HYB/H39lhc6pef4tVRuA7b7/S6onXP2nzhIjALlzBwDQocbLALTxngLAczsmWbJ4JqjfR6H6eQFoNE9dr92wGYC/5qj70TFxpyXPKs+PATBd/tu6YrOERAA2fhFiWdV8sroupmQgAHl+f6Bu8FbrP91QXd901p/JCgoDoHa5YQD4Fle/j63rW6tt9HJ87O8/v9DqfdMOux+/Uo+luPNbkm1t7bCc5LSe/jbrdh0eafW+SZvNVu93HhhmeV0zrB8A/pV6AJBwwdymmgA0m2YCYPtu62dlAFoMU4+HDVPV78l8HCaeUB+a3vpLK0va5n+9BcCDnV+q9TV5FwDj5b/U/QjIZ1N+VF/r98deVPe1xc7iAJjyq9+PJi7Rkiau8gb1xaYIAGLz3ny8RU3LlUuWtE3aXFHb8GJTtfxr6nmlsFb9Hq7NawJAuXtJPfv0f8cB4FVMPb7O1VB/08VyVQDgt07q76XChpikfe99DYDEaurv0KtSI7X9Z84AcLVbEQCKbo215Cny7lQAGjYso+Z5Tv2jp4lRj9GEi/sB8G7UNekDuqXumy5PMQAaRMxX8xZRf+txf64DwCN3AUuWelUjgaRjpvFL69W26ZPaAtCg7hzLa/OxsPfkVOxxtF6IlOj1ejZs2MCOHTs4f/48cXFxBAUF8dxzz9G4cWMiIiIypV4J8IQQQgg3pXaycOIWrXSyyFTz5s1j/Pjx3Lhxw+72SZMmUa5cOWbMmEGjRo1cWrd8s0IIIYSb0hh1aJ1YZKDjzGEymejevTv9+/fn+vXrKIricPnjjz9o1qwZ06dPd2kbJMATQggh3JR5HDxnFuF6Y8aMYdmyZSiKwrPPPsuUKVPYv38/t27dQq/XEx0dze+//86MGTMoW7YsJpOJ//3vf6n2sk0P+WaFEEIIIVzk0qVLTJs2Da1Wy+jRozl79izvv/8+1apV45lnnsHDw4OgoCAqVarEoEGD+OOPP5g2bRoeHh4MHToUvV6feiVpIAGeEEII4abMz+A5swjXWrFiBYmJiURGRvLRRx+h06V+G3zIkCEsWLCAK1eusHLlSpe0Q75ZIYQQwk1pjVqnnsHTGiUMcLXdu3dTp04d/ve//6WeOJmuXbvSuXNnNm3a5JJ2yDcrhBBCCOEiZ8+eZfDgwRnK++6773Lo0CGXtEOGSRFCCCHclLMdJRS5RetyN27coEGDBhnKW716dR4+fOiSdkiAJ4QQQrgprUnr1G1WCfBcLyEhgbx582Y4v6enZ+qJ0kC+WSGEEEIIF4mPjycxMTH1hA7cv3/fJe2QAE8IIYRwU9KLNvtRFIW//vorQ3nv3r1LdHS0S9oht2iFEEIIN6U1atAaNakndEBxIq9wrFOnTgQHB6c7X1xcnMvaIAGeEEIIIYQL/fnnnxnOq9G4JuiWAE8IIYRwU9KLNvvRaDR06NCBUqVKpavDhMFg4Ny5c6xatcol7ZAATwghhHBTWpMGrcmJW7RO5BX2de7cmaVLl2Y4v6+vr0vaIQFeOuw8/xNNKvSzWV+30gQAFFMCAPvPLwSgTsVRAOw5PtmStkmbzQAYH90CoPFL6wFIvKlezlWMtnPQmcsxl9u833kANv/QAIBmXdVBEXW++Sx5Dvyz3MFeDAKgevEuAARWf9uy5dC/6wBo1GQFAJ4lqwOwcW6og7Kgadvtarv16nMDHs+UBEBzV+0F1CDih6TERrVXkU/VNgCc66oA8HftmgC0GBmj/js86RkEJXcuAKL6Pq5vRgkAbpZU0xa5om5XPG0P5YBL6v9MEwPV9w2/Vv893Tc/AG+Zkr7L3376TK276ngAntsRBkDRW1UBCHmofj/GPNcteUpubwDAntfUaWjKbVK//wu/dwOgXa3eAJw4U9CSx7wfzSe/B8DJ9vsB+HdAUwCKXf8ZgFWeH9vsj6ZiJQCe33obgJjnjgJQwVttR+NWPyUl1qqfx2+jWgFQxdsbgHxb7gBQc6X6ufnevWzJ0rz3KQD8a/QBIOGvHVb1J9z+w6ZNtcsNA2DvyakANGz4nbqfUT2s0u07PTOpnsfHr/m40vkGAVCrjHpsevgVAmDX4ZE29TXroe6zMfqS+m/sNbXMyQ0AiCmZlPa3s3PVPI9/H77hHQBoMUz9/DZNtR3GoNmnRgCU8+Z9VcvdEPn4IEL9PCv9nDROVUEvL6sylPhHAJga1AXA81/bHnHmNvzbUP1eDJ4GAIpc/huAG/VLA1Bg6RlLHr3+8bHu7wfAP+Fq+82fyabvKiWr4XkAmk9Q21K90HYA/s/rQ6t25Pmlm+W1Ls9GtZxl4QAU+ks9Nu83V88lxQ/VUkveEmTJY3yo/g49b6vHvnLvrvrvfXX/TjYrr7bxmNGS58TedwAIraj+/jd8rn7f5t+n9/0m6osrVyx5zOc68/nTu5yaZuOXxQGIqLUXAK1PHkuexFj1GDEfV+ZjsEHdS1afwfbd/S2vnzxnP5m38jPqd5qnYtK5I+6frUDS+TmraYzODZNikpksXK5u3bpPNb+ZfLNCCCGEEC4yYMAAp/L37t3bJe2QK3hCCCGEm9IYNWic6AnrTF6RvUmAJ4QQQrgpreLcM3haRQK8nEpu0QohhBBC5DByBU8IIYRwU3KLVjgiAZ4QQgjhppwdJsWZvCJ7k1u0QgghhBA5TI4O8BRFYe7cubzwwgv4+PgQHBxMmzZtOHTo0NNumhBCCOE0jdH5RTxdp0+f5p133uGXX34hMTHRZeXm6ACvX79+9O/fn+PHj2MwGLh79y4//fQTtWvX5ocffki9ACGEECIbM9+idWYRrvXBBx/w3nvv0a9fvzTFGmXKlGH8+PEsXryYfPny0aNHj1TzpEWODfB+/fVX1q5dy6JFi4iNjSU+Pp4ff/yRfPnykZiYyOuvv87t27efdjOFEEIIkYN8+OGH/PXXX4wZM4a2bdsCcOnSJbuLWd68eVm1ahVdunRh2bJlLmlHjg3wFi5cyObNm+nZsyf+/v54eHjQpk0bli9Xp92JjY3lp59+SqUUIYQQIvuSW7TZ07x58yhatKjl/erVq2nVqhUlSpSgUqVKTJs2jQ0bNtjki4yMRKNxzVXVHNuLtl69elSqVMlmfePGjalcuTJHjhzh1q1bWd8wIYQQwkU0JnVxJr9wrVy5clGwYEGrde+99x4dOnTg2Wef5aeffnI432yePHkoUKCAS9qRY6/gDRw40OG2sDB1IvnixYtnVXOEEEIIl9OYFDRGJxaTkuG69Xo9U6ZMoXTp0pQsWZKIiAh27tyZ7nKuX79Ov379CA0NpUSJEnTq1Mnq9qU9a9asoVq1aoSGhlKxYkW++eYbl9cBcPz4cbp06UKfPn3SvD9eXl521xcrVox8+fI5DO7M/Pz80lxXSnJsgJeS27dv4+3tTYsWLZ52U4QQQgi3k5CQQIsWLfjuu+/YvHkzf//9NwMHDqRJkyasXr06zeVcuHCB8PBw7t69y8mTJzl37hyFCxcmPDycM2fO2M0zatQoXn/9daZOncr58+dZtWoVo0aNYtCgQS6r48CBA3To0IFKlSqxYsUKjEbX3MsODg52STlp8Z8L8OLi4ti3bx9vvPEGefLksZsmISGB2NhYq0UIIYTIbsy3aJ1ZMmL48OFERUWxYMECihUrBkCHDh1o3749vXr14sKFC6mWYTQa6dChA3q9ngULFuDr64tOp2Pq1Kn4+PjQsWNHm2FDfvzxRyIjIxk7diwRERGA2gt14sSJzJo1i1WrVjldB0C+fPlYuXIlPXv2zNgH5ICrnq9Li/9cgPfNN9/g7+/Phx9+6DBNZGQkgYGBliX5g5JCCCFEduHU7dnHS3pdvHiR2bNnU7ZsWapXr261rUePHsTFxTFy5MhUy1m+fDmHDx+mQ4cOVrcldTodXbp04fjx48yfP9+y3mQy8f7776PRaOjVq5dVWV27dkWn0zF06FCrq23prcOsRIkSaLVaatSokep+uJper3dJOTm2k4U90dHRTJo0iUWLFqV4mXTkyJEMHTrU8j42NtYS5B34Z7lN+t1Hx1u9r1c1EgDDg2s2abf8X1MAapfbCMCjC9sA0Ois79mby0heTu1ywwDwyvs8AI2aHADAo0h5AHQ+z9jUVzV/EwAO39wCQM2wflb70bTtdkvaZl3VAaBN8fcAeLBPfaahUfOqAGzb2A6AupUmWPJ4FVDrfnh+PQC+hWup78/8rLbNr5Alba5a6tg+Gz5X17UYGQNA8+3q/54SD/6qfhavdrbk8Th15fGrEAD+qnsOAH1utY2FTlQBQHvngSWPMTiXuh+e6v+Ubj57F4ACfwep9Y17qJYRMteSJ/yNtwCYrFG7py/zPa+W1fz/ACg3oxcA7w3rZckz0bQfgCInjwEwotlwAD75/XMAvty4BoDbjfZa8tRcWU1dV/8mAH431f3iOfWfSwVfBqDChhhLnhMtAtV9b6B+FkHX86tpCm5WywpUv2OvsrUteRR/9URW6+FMAG5UU587feai+lhCnr3X1f3LWyIpT7z6uWxaFg5AkzZXSM4vVM1bIzTpM9h/fqFVmqgo9Ttu9ql6gt30Px1P2jg3FIDGrdRe7Dv2Wd9WaRChnmybdthtWWd6pF5FT7z5p7qvhV4AwLNIJbXMUZ4AtBiWNPRR40Xq5+5RUN13Yxn1KoP5ikWLwf+qL3x8LHkMz6rHiOGK+lto/pb6+Tw6pN5y2nlA/Q3mX37Kkuf0u+q55OL0yVb70bTTb2rbw6vZfAYbpuYFkj4nzaAp6n40G6227fFvY8Pj78LK49PNQlMDAP6YPAaAF9bdt0laKM4AwP95qf+hDTmmfn4lD6rnEKN/0uWbZ9buUOsepv7BPvLGEAB6TVS/J2WS+ns5Y+hmyVPygPrHL6aAWs9vY0Mfb1H/NX+HhVasseSJXfMpAHGV1D8/5u/Bs9jjB8sf/3G+0iGvJU+zHkcB0P+r/tZ2Pj6PmnmXUI99/dVjlnV7jqvfh/l8Zf4+FKP6x7NWGfW4885b1pLHpFfPI+bzZK7QZgA0bPid+plEJx2TSazHLGvW4ygG/QM76XKOlStXYjAYqF27ts02c0C0du1aoqOjeeYZ279LZkuXLgWwW07NmjUBtTdq//79ATh48CBnz56lVKlS5M+f3yp97ty5KVeuHMePH2f9+vW8/PLLGarjST7Jzg9plZCQwIIFC1AU2+A5JibG4TaTycS5c+e4fPlyuuu05z8V4L355pv873//S/XZO29vb7y9vbOoVUIIIUQGmRR1cSZ/Oq1btw6A0NBQm23BwcEUKVKEq1evsmfPHlq3bm23jLi4OLZv3+6wnAoVKgBw5MgR7t27R548eVKs15zn+PHjREVF8fLLL2eojidl5Jbqo0ePeOONNxxuT2mbK/1nArzJkydTrFgxhg0b9rSbIoQQQrhERm+zJs8P2DxrntKFjiNHjgAQEhJid3uePHm4evUqR48edRjgnTp1ivj4eIflmIMtRVE4fvw49evXT1O9AEePHs1wHa7i6elJ/vz50els72I4YjAYiI6OJiEhwSVt+E8EeEuWLOHMmTMsXLjwaTdFCCGEyHaefNZ8/PjxfPDBBzbp4uPjefBAvQXtqKNiYKD6WElKs0UlH4fWXjnmMpKXY86T1nozUocr1KhRg+3bt2foTmBcXBzNmjVzSTtyfID3ww8/8OOPP7JixQqbS61Go5Fr165JJwohhBDuyUW3aC9fvkxAQIBltaPgJDo62vI6V65cdtNotWr/TfPVs4yUYy4jeTnmPGmtNyN1uEKLFi0y/JhXrly56Nq1q0vakaN70f74448sWrSIpUuX4uFhHctev36dXr16cf78+afUOiGEEMJJRpPzCxAQEGC1OApQkg/ia6+jACT1Ak2pM2Nq5STvSWoux5wnrfVmpA5XcPYZu+SBqTNybIC3dOlSOnTowM6dOylSpAh58+a1LAEBARQqVIgdO3a49J67EEIIkZMFBwdbAqeHDx/aTXPv3j0A8ubNa3c7YDWVl71yzGUkL8ecJ631ZqQOVyhSpEiG8+r1eiZPnpx6wjTIkQHeunXr6NGjBwaDgXv37hEdHW213L+vDifQpUuXLB10UAghhHApk8n5JR10Oh1ly6pDy1y7ZjsUGMCNGzcA7M4Hb1a+fHnL31975ZjL8PLy4vnn1WF9KlasmK56M1KHK2zZsiXDeb///nuXjYOXIwO8F198EZPJhKIoKS4ff/zx026qEEIIkXEmJ2/PpjPAA2jevDkAJ0+etNl2+/ZtYmJi8PPzo169eg7LCAoKsgySbK+cc+fUMU/r169vGaA4pXqT52nZsmWG63CFNm3aYDAY0p0vJiaG999/32XtyJEBnhBCCPFfoDGZnF7Sq0+fPmi1Wnbu3Gmzbd++fQC0a9cu1Y4Gffv2BUixnOQdDpo0aUKJEiU4deqUVQ9ZUG+3njp1ihIlSlgGMM5IHa7w6NEjvvvuu3Tl0ev1tG3blqtXr7qsHRLgCSGEECLNwsLC6Nu3LydOnLCMOWe2aNEifH19GT8+aYanqKgoatSowcyZM63S9ujRgwoVKrBq1SqrXqx6vZ4VK1ZQvnx5unfvblnv4eFBZGQkJpPJMkOF2ZIlSzCZTEyaNMlq7Ln01vEk85W45NOfpcXw4cPTPCOF0Wika9eulkGZXUUCPCGEEMJduagXbXpNnTqVqlWr0r9/f+7cuYOiKMyaNYuff/6ZxYsXW80cMW3aNA4cOMDo0aOtyvD09GTZsmUYDAaGDh2KwWAgLi6O3r17YzKZWLNmDZ6enlZ5OnXqRL9+/Zg4cSLHjx8HYNeuXYwePZohQ4bQpUsXp+tIbv9+dUrKY8eOpWsoldu3b9OyZUvu3r2bYjqTyUTPnj1Zu3atw97BGSUBnhBCCOGusriThZmfnx9RUVHUrFmT8PBwwsLC2Lp1KwcPHqR9+/ZWabt06YK/vz+vvfaaTTnly5dn37593Lhxg7CwMCpVqkSePHk4duwYpUuXtlv3V199xQcffEDnzp0pWbIkw4cPZ/HixUyfPt1u+ozU8fnnn/PMM8/wzTfqnOwnTpwgb968dvfhSZ6enqxYsYKwsDAaN25s1Vs3OUVR6N27N8uXq3PDjxkzhqNHj1oN7+KMHD/QsRBCCCFcz9/fnxkzZjBjxowU03Xr1o1u3bo53B4WFsb333+f5no1Gg0DBw5k4MCBac6T3joGDx7M4MGD05w+uVdeeYWOHTvSsWNHBg8eTKNGjdi2bZvNbBr9+vVj8eLFaDQaxowZw4QJEwD1NrcryBU8IYQQwl0ZTWA0OrFk7AqecGzlypWW159//jm1a9emSZMmxMTEWNYPHDiQb775Bo1Gw7vvvmsJ7gA6duzoknZIgCeEEEK4q6d0i1ak3RdffEGZMmVo0aIF9+/fZ8iQIXz55ZdoNBrefPNNpk2blin1SoAnhBBCCOEiQ4cOtVm3cOFCAgICKFOmjKU3cefOnZkzZ06a8meERnF1t40cKDY2lsDAQGJiYqwmY24QMR+A7Tv62M3XqMkKABRjomWdxkud8HjbxnZprr9e1UgAdh0eaXd7jdBeAHjkLmxZt+e4/alOqhdXexgd+Ed9qLPxS+st27S+6r4l/vuH2lZPdeDHqKgeVmU0f/Os5fXGeWHW2976R13/ZXFHu2PJb7qnjiKurVxDzTNK7cnUfFzSlDKmU0fUNFXVcY2Otz8AQPHD1QAI+EUdvPKvd5K+lzLfBwHwKCwPAB4J6kjmHjfUcjUJ6ijhxwcetuS5VrQJAM0+VbvCX30hFoCTzYKs2l7o9EHLa41J7YpfbnwcAGsD2wLQrujjz2C8r5rn/C5LnopL1UE3uaGO4XTpVR8A7hX9G4B/n1M/i+prEnhS8FG13UputVyuXLcq41TjpKl26lefCoB3ibpqW73UNMboSwDo8qjHiulhUg8vY6xanndldTDRDdPzW9Vfp+IowPrYenKd+bt98rhIifl3ZIi5AoBvaAQAj87vsKTxDWus7odPbjXtTfXzSrz9F5D022gx5r4lz6OtCwDYsW+QVX0thqv7fL6V+hmHbvZzmKfOEvV42NNd/a6b9z6ltsM/jyXPH73V8kJ/V0fC9358F2bzu+pxV2uZOszC5XJ/WfJceUGdCaD5hEdA0rFS6Wf1GD36sp/V++TrnsxjVmp30vhZ8f5qIwqfLanWXUX93QT+oz5Qfrqh9XcLUCbqptW2Chsel7FWnRdz49xQmzzmz3LDx+rvpNBfao9D83Fslvx4vpdPzRMyeQ8AnnVfUssfq46ZVnKvOrTE37WLWvI076fOGa7Jl0+tb6K/uv7xZ6G5cw8Aw8U/LHm0AWraTd9VAqBRc/X5qyfPvc26HrK83rQs3Gqb+fy49ZdWT+56ihz9zXAlcx2N+53Bw9s/w+UYEu6zdW7pTG3rf01QUBDR0dFotdbX0B4+fEhERAS///47r7zyCmvWrLFJYzAYyJcvX6q9b9NCOlkIIYQQbkoxGVHSOUbbk/mFa8XExNC7d286duyIj4+P1bahQ4fy0Ucf8eabb9qMe/fo0SPWrl1LbGysS9ohAZ4QQgghhAt99913Kc5m8dJLL2V6GyTAE0IIIdyVyaguzuQXLufM028ajcYlbZBOFkIIIYS7cmqIlMeLcLmFCxcSFxeHyWRK8xIXF8fixYtd1gYJ8IQQQgh3ZTIlXcXL0CLDpLhawYIF6dmzp83zd6nx8fGhe/fu5M9v2wkqIyTAE0IIIYRwEXtDn6TH3LlzXdIOeQZPCCGEcFdGg7o4k1+41OXLl5kyZQr3798nLCyMXr16pSt/69atXdIOCfCEEEIIN6UYnRwmRZ7Bc7l33nmH8PBwFi1axPPPP//U2iEBnhBCCCGEi2g0GlavXk3x4o4H/M8KEuAJIYQQ7spkUBdn8guXKlSokFPB3c2bN13S0UI6WQghhBDuymRIeg4vI4sEeC6XK1cup/LXqFEj9URpIAGeEEIIIUQ2cObMGa5cueKSsuQWrRBCCOGmFKMRRZvxq3DSycL1rly5Qv369dOdLy4ujlOnTmFy0diEEuAJIYQQ7kqewct24uPj2b17d4bzu2qqMgnwhBBCCCFcJCgoiEGDBqU734MHD9i0aRN//PGHS9ohAZ4QQgjhrowGcOIWrQx07HrBwcGMHz8+Q3nHjBlDwYIFXdIOjaIoiktKysFiY2MJDAwkJiaGgICAp90cIYQQ2VhW/M0w1xHRci0enn4ZLseQ+JAdv74qf99cKCwsjLNnz2Y4f7Nmzdi0aZPT7ZAreEIIIYSbUowGFG2iU/mFa927d8+p/K4I7kCGSRFCCCGEcJno6Gi2bt36tJshAZ4QQgjhtsy9aJ1ZhMu98sorjBo1iv379z+1NsgtWiGEEMJNKaZEFKMTt2hNGc8r7Lt37x4PHjzg/v37aLVP7zqaBHhCCCGEEC4SEBCQLTqsSIAnhBBCuCtjImicuArnxNU/kb1JgCeEEEK4KcVoQHEiwJNetDmXdLIQQgghhMhh5AqeEEII4aYUkx7FpHMqv8iZJMATQggh3JUxETRO/CmXZ/ByLLlFK4QQQgiRw8gVPCGEEMJNKcZEFI0Tt2jlCl6OJQGeEEII4aYUkx7FmPGbcfIMXs4lAZ4QQgjhphRjIgpyBU/YkmfwhBBCCCFyGLmCJ4QQQrgpxahHceJajWKUW7Q5lQR4QgghhJtSTHoUjcap/CJnklu0QgghhBA5jFzBE0IIIdyUeovWiSt4cos2x5IATwghhHBTai9aZwI86UWbU8ktWiGEEEKIHEau4AkhhBBuSjEloKA4kV9u0eZUEuAJIYQQ7sqYgKJkPMBDArwcS27RCiGEEELkMDk+wNPr9UyZMoXSpUtTsmRJIiIi2Llz59NulhBCCOE0xahHMSY4scgVvJwqR9+iTUhIoGXLlty4cYPNmzdTrFgxVq9eTZMmTVi6dCkdOnR42k0UQgghMkwxJqAopoznN0kv2pwqRwd4w4cPJyoqiv3791OsWDEAOnTowNq1a+nVqxfh4eGUKFHiKbdSCCGEyBgJ8IQjOfYW7cWLF5k9ezZly5alevXqVtt69OhBXFwcI0eOfEqtE0IIIYTIPDn2Ct7KlSsxGAzUrl3bZluNGjUAWLt2LdHR0TzzzDNZ3TwhhBDCaYpR7+QVPIMLWyOykxx7BW/dunUAhIaG2mwLDg6mSJEi6PV69uzZk9VNE0IIIVzCZIrHZHRiMcU/7V0QmSTHXsE7cuQIACEhIXa358mTh6tXr3L06FFat25ttS0hIYGEhATL+5iYGABiY2MzqbVCCCFyCvPfCqfGp0sjo2J8qvlF9pUjA7z4+HgePHgAqIGcPYGBgQDcvn3bZltkZCQTJkywWV+0aFHXNVIIIUSOdv/+fcvfGlfz8vKiYMGC/Hn9kNNlFSxYEC8vLxe0SmQnOTLAi46OtrzOlSuX3TRarXp3Oj7e9vL0yJEjGTp0qOW9yWTizp07PPPMM2g0GZ/UOavExsZStGhRLl++TEBAwNNujtNkf7I32Z/sTfYn6ymKwv379ylcuHCm1eHj48OFCxfQ650fx87LywsfHx8XtEpkJzkywEv+PxFHl8jNP4rg4GCbbd7e3nh7e1utc3QlMDsLCAjItifAjJD9yd5kf7I32Z+slVlX7pLz8fGRwEw4lCM7WQQHB1uCvIcPH9pNc+/ePQDy5s2bVc0SQgghhMgSOTLA0+l0lC1bFoBr167ZTXPjxg0AKlWqlFXNEkIIIYTIEjkywANo3rw5ACdPnrTZdvv2bWJiYvDz86NevXpZ3bRM5+3tzfjx421uM7sr2Z/sTfYne5P9EeK/SaNkRT/up+Ds2bOUKVOGcuXKcfz4cattP//8M61bt6Znz54sWrToKbVQCCGEECJz5NgreGFhYfTt25cTJ05w9OhRq22LFi3C19eX8ePHP53GCSGEEEJkohx7BQ/UDhYRERF4eHiwfv16goKC+OKLLxg2bBhLly6lffv2T7uJQgghhBAulyOHSTHz8/MjKiqKsWPHEh4ejlarpXz58hw8eJCKFSs+7eYJIYQQQmSKHH0FTwghhBDivyjHPoOXnen1eqZMmULp0qUpWbIkERER7Ny5027av//+m27dupEvXz68vb15/vnnmTRpktVcuZlVt9nvv//Oiy++SIkSJShVqhTDhw/n0aNHTpVpZjQaqVmzJs8++2yO2J+zZ88ycuRIWrduzeDBg/n222/dan/i4uIYO3YspUuXJiQkhIIFC/Liiy+yd+/edO1HZu6P2bp166hduzYLFy50SXnusD/ucD5Iz/48KTueD4RwW4rIUvHx8UrDhg2VsmXLKv/884+iKIqyatUqxdPTU1m1apVV2j/++EMJCgpSAMXDw0MBLEvdunWVuLi4TKvb7KefflK8vb2VadOmKYqiKPfu3VPq1Kmj1KpVS3nw4EGGykzuo48+UgClePHi6dqX7LY/er1eGTFihJI/f35l8eLFitFodLv9efTokVK9enWldOnSyvHjxxVFUZSEhARl6NChik6nU3788cenvj+KoigrV65UqlevbvktLFiwwGEb0lKeu+yPO5wP0rM/9mS384EQ7kwCvCw2ePBgBVD2799vtb5Lly5Krly5lPPnz1vWVa9eXWnfvr1y8uRJxWQyKVeuXFF69OhhOXGOHDky0+pWFEW5dOmS4u/vr7Rs2dJq/enTpxWNRqMMGDAg3WUmd+TIESU4ODjDJ/Tssj8PHz5UGjdurJQoUSLF/c3u+zN16lQFUPbu3WuV1mg0KqVKlVIKFSqUrsA1M/ZHURTl77//VuLj45WwsLAUA4i0lucu++MO54P07M+TsuP5QAh3JgFeFrpw4YLi4eGhlC1b1mbb+vXrFUDp1KmToiiKcvToUaVjx46KyWSySmcymZT69esrgFK0aNFMqdusT58+CmD3f7/m/6Gnt0yz+Ph4pUKFCsrXX3+doRN6dtkfg8GgvPTSS0pAQIBy7ty5dO1DdtufVq1aKYDdK0Ht27dXAOXGjRtPbX80Go3y559/WtZ17NgxxQAiveVl5/1xh/NBer+f5LLj+SA9x4cQ2ZE8g5eFVq5cicFgoHbt2jbbatSoAcDatWuJjo7mypUrfPzxx2g0Gqt0Go2G9957D4Bbt25lSt0AiYmJrF69GsBunpo1awKkq8zkxo4dS0REBE2bNk3zPmTH/Rk7diy//PILH3/8MSVLlszQvmSX/fHz8wPgt99+s0l///59ChcuTL58+Z7a/iiKwjfffGNZl9Ik6xkpLzvvjzucD9KzP0/KjueD9BwfQmRHEuBloXXr1gEQGhpqsy04OJgiRYqg1+vZs2cPL774osMHjcPCwgAoXry4zbapU6fi7+/Pp59+muG6AXbt2kVsbCze3t4UKVLEJk+FChUsr9Naptnu3btZt24dn3zyid39c5f9OXv2LNOmTSMkJIQ+ffqkui/ZfX9at24NwIABA3j48KElbXR0NLt27eLTTz+1CTCyen+ioqIs655sS3IZKS8774+7nA/Suj/JZefzwZPHhxDuRAK8LHTkyBEAQkJC7G7PkycPgM3MG0+6ffs2AG3atLHZtmjRIh48eMDixYudqtuc3t7JL3l6gMKFC6epTFAHn37jjTdYuHAhvr6+dvMll53355NPPkGv1/PKK6/wzTff0KZNG8LCwihTpgzDhw/nwYMHbrU/Xbt2pUWLFpw5c4YWLVpw9+5dTCYTb731FrNnz6Zr165PfX9OnDiB0Wi0m8Ze/ektL7vuT0qy0/kgvfuT3c8Hrvh+hHhaJMDLIvHx8ZY/+Mn/+CYXGBgIJJ2wHdmyZQs+Pj688847NttGjx5N1apVGT16tFN1m2/3OEqf/GTs6emZpjIBhg4dSpcuXahWrZrdPE/KrvtjMBhYu3YtAMeOHaN8+fL88MMP7N+/n8qVK/PJJ5/QoEED4uLi3GJ/ALRaLd9//z3Nmzdn9+7d1KlTh379+jFmzBh69eplt4ys2h9zeoPBQExMjN00yWW0vOy6PynJDueDjO5Pdj0fuPL7EeJpydEzWWQnyZ/bypUrl900Wq0ab8fHxzssJyEhgXnz5jFmzBi7/1vt3LkznTt3drpucx5H6ZNfndLpdGkqc8OGDRw9epTZs2fbTW9Pdt2fkydPWsr86aefLH8ogoODWbhwIQcOHODw4cN88MEHVreesuv+JK9/+fLljB49ml27dvHNN99w4cIFVq9eTVBQkE0ZWbU/5vRPtteRjJaXXffHkexyPsjI/mTn84Grvh8hnia5gpdFvLy8LK8VB5OH6PV6QA0SHJkyZQqlSpVixIgRmVq3OY+j9MnX+/v7p1rm3bt3eeedd1i8eDEeHs79vyI77M+VK1cA9Q/Ek1cBvL29Lc/kpWWQ1+ywP2b//PMPgwYN4vPPP2f37t3Ur1+frVu3UrduXW7evJnqvmTW/pjTP9ne1NrgivKyw/44kl3OB+ndn+x+PnDV9yPE0yQBXhYJDg62nFSSP8Ce3L179wDImzev3e0HDx5kxYoVrFmzxuFVGVfVXbBgwRTTJ38uJTExMdUyBwwYwODBgyldunSa2+1Idtif2NhYAAICAuymffHFFwH1VlBqt9yzw/6AevuqQYMGdOvWDU9PTwIDA/n111+pV68ef/75Jz169EhxPzJzf8zp/fz80tQ705XlZYf9sSc7nQ/Suz/Z/Xzgiu9HiKdNArwsotPpKFu2LADXrl2zm+bGjRsAVKpUyWbb9evXGTBgAP/3f/9H/vz5M73uihUrppg++ZAMqZVZsGBBVq5cyTvvvINGo7FaSpQoAahXjszrLl68mK33p1KlSpbhQsyB3pOS3y4zmUx205hlh/0BGDNmDNeuXaNJkyaWNLly5eLHH3+kaNGibNq0KU1TlmXG/qT027DHleVlh/15UnY7H6Rnf65cuZLtzwfOfj9CZAcS4GWh5s2bA3Dy5Embbbdv3yYmJgY/Pz/q1atnte3+/ft07tyZr7/+mueeey5L6m7YsCFeXl7cvHnT7hWoc+fOWV6npczSpUvbXczDGnh4eFjWOeoUkJ32p0qVKuh0OuLi4uz+ATL/rz8oKChNY8c97f0B+OGHHwgODra5ZRYcHGwZa+3AgQOp7ktm7k/Lli3TVL+ry3va+5Ncdj4fpGV/NBqN25wPMvL9CJFtZN2YyuKvv/5StFqtUqFCBZttP/30kwIoPXv2tFr/4MEDpXnz5sqOHTvslpnWqbEyUrd5GqTvv//eJk+VKlUUjUaT7jKfdOHChQyNXJ8d9qd58+YKoMyePdsm/YkTJxRAef31191mfwIDAxWtVqskJCTYpF+3bp3Dfc2q/dFqtcpff/1lWffaa6+lOFNCesvL7vujKNn7fJCR/XlSdjofpOf4ECI7kgAvi/Xv318BlCNHjlitb9euneLr66v8/ff/t3fnUTXn/x/An233ppRWpE6E0Mk6NCNhmMGUhnDEscYIYzL2ZaYZmsmR7DtDuQZJGMs42TKDsZZ9RhJlKxmqq4WSbt3X749+9/O9171Rt+3WvB7nfM7R+/N+v97vd457X97vz/JAKHv9+jV5enrSyZMn1eLI5XI6evQoDR48WKU8KiqKunTpovH1O+Xpm4goOTmZTE1NycfHR6VckbxMmjSp3DHf9aEPdF2ez/Xr18nQ0JBatWpFBQUFKvVnz56t8f2XujwfxRfyrl271Ma2YMECEolEwkvca2o+ykaNGkUAKDw8XK1vbeLp+nxqw+dBeeajia59HjBWm3GCV81ev35NnTt3pk8++YSkUinJ5XJat24diUQi2r9/v1AvKyuL3N3dSSQSkbW1tcphaWlJRkZGBIDCwsJU4ru6uhIAatu2rdZ9K4uIiCBDQ0PhS//JkyfUoUMH8vDwoLy8PK1iKvvQB7quzyc8PJwA0MiRIykvL4+IiA4cOED169engwcP1qr5pKenk7OzM9nY2NDJkydJLpeTXC6nqKgoMjExoV9++aXG56OQn59P7dq1IwDk7++vMVZ54un6fGrL50FZ51MaXfs8YKw24wSvBuTm5tL06dPJycmJWrRoQT4+PvT333+r1FG8LP59h1gspqysLJV2ISEhZGpqSqGhoVr3/a6YmBhyd3cnJycncnV1pRUrVqhs42kTU+FDH+i1YT7R0dHUtWtXsrCwIBcXFxo4cCBdvXq1Vs5HKpXSrFmzyMnJiWxtbcne3p48PT3p9OnTOjOf4cOHk4mJicq/BSsrK9q8ebNW8WrDfGrT50F5/36U6eLnAWO1lR5RKQ8CYowxxhhjtRLfRcsYY4wxVsdwgscYY4wxVsdwgscYY4wxVsdwgscYY4wxVsdwgscYY4wxVsdwgscYY4wxVsdwgscYY4wxVsdwgscYY4wxVsdwgscYY4wxVsdwgscYY4wxVsdwgscYY5WAiLBlyxZ06NABxsbGsLKygo+PD65du1Zqmxs3bsDb2xtOTk5o2bIl5s+fjzdv3lRa/PL2oa24uDiMHTu2UmNWpxcvXmD+/Plo3759meofOnQIenp6asfw4cOreKSMlUPNvgqXMcbqhokTJxIAAkAGBgbCn42MjOjAgQNq9Y8cOUJisZhWrlxJRETZ2dnk4eFB7u7u9Pr16wrH16YPbQ0fPpzEYjG9ePGi0mJWh9TUVJo5cybVq1ePAFDTpk3L1M7NzU34/SsfsbGxVTtgxsqBEzzGajGJREJmZmYkkUhqeihakclktG/fPurZsyeNGzeupoejtWPHjpGNjQ3t2LGDcnNzSSaT0eHDh8nW1pYAkLm5OWVkZAj1U1JSyMzMjLy8vFTiJCYmkp6eHk2ZMqVC8bXpQ1tPnz4lQ0NDAkCLFi2qlJjVJS0tjd68eUMSiaTMCd6pU6eoe/fudPfuXZXj3r17VT9gxsqBEzzGarH+/fsTAPL29q7poZTb8+fPafz48WRpaUkAyM/Pr9wx/v33XxKLxWorKfb29lRYWFj5gy7FsGHD6ObNm2rlf/zxhzCmbdu2CeUTJkwgALRv3z61Nh9//DHp6elRQkKC1vG16UNb3333HZmZmQm/d5lMVuGY1S0hIaHMCd5nn31Gx48fr/pBMVZBfA0eY7XEuXPn1MqmT58ONzc3TJs2rQZGVDGNGjWCRCLBnDlztI6xatUqvH37Vq182rRpMDIyqsjwyqVHjx7o2LGjWvnnn3+OTp06AQAyMjIAADKZDPv37wcAdOvWTa1N165dQUQIDw/XKr62fWjjzZs3CA8Px2+//QYjIyOkpaXh4MGDFYpZE4yNjctU78qVK7h06RKePHmCxMTEKh4VYxXDCR5jtYBcLkdAQIBaeb9+/XDlyhX069evBkZVORo2bKhVu6ysLERGRuLWrVu4e/euylHdCe/UqVNLPefs7AwAaNq0KQDg/PnzyM3NhVgshr29vVr9du3aAQDOnDmjVXxt+9DGrl270LlzZ/Tr1w9Dhw4FAKxfv77U+jNmzIBYLBZuSjh79qxwrm3btkJ5s2bNNLYPCwtDp06d4ODgAH19faF+gwYN4OzsjMmTJ2s1Dz09vTLVCwkJQUFBAb7++mu4uLjAzc0NJ0+e1KpPxqoaJ3iM1QJLlixBfHx8TQ+jShgaGmrVbsOGDRg9ejQ6dOiANm3aqBxlXZGpDpmZmRCLxfD09AQA3Lx5EwA0Jl4AYGFhAQC4ffs2iouLyx2/Kvoozbp16zB79mwAwMyZMwEAFy5cwK1btzTWX7NmDR49egQbGxu1c/Hx8di1a1epfU2aNAmTJk1CYGAgnj59iqSkJDg6OgIAmjVrhqSkJGzZskXruXyIVCpFZmYmWrduDQMDAwDAtWvX4OnpiZkzZ4KIqqxvxrTBCR5jOk4ikWDBggU1PQydkp+fj3Xr1kEul+PixYuQy+U1PSSN8vPzcfnyZfj7+wtJlWIrVfHzuxo0aAAAKCoqQk5OTrnjV3YfpTl16hT09fXRt29fAICbm5uwHfy+VbwmTZrAxcVF4zl3d3eN5WfOnEFYWBj69OkDX19fAECLFi0QFBQEAPjnn3+Qlpam1TzKytraGhcuXEBiYiIyMzMhkUhgZ2cHoCRxVYyFMV3BCR5jOmzZsmVYsmSJsDrQsmVLtGzZEoGBgcjLy0NYWBg++ugj/PTTTyrtcnNzERwcLHzBv3z5EuPHj4e5uTmaNm2KX3/9Vah79uxZeHh4wMTEBJ06dcLly5c1jiUhIQEjRoxA27ZtUb9+fbRv3x4SiaRS51tcXIxFixbB3t4e9evXh4+PD5KTk9Xqbd26FZmZmVi+fDm6d++OZs2aYePGjRVajaoK4eHhMDMzQ3BwsFAmlUoBACYmJhrb6Ov/72O5oKCg3PEru4/SrF27FrNmzVIpmzFjBgAgMjJSGMOH+lemWBl7V3R0NID/bS0rKJI9AFWe4CmzsLDA+PHjkZiYCA8PDwBAaGgoHj16VG1jYOxDOMFjTIfNmzcPSUlJws/JyclITk7GmDFjMHHiREybNk3YjlMICwtDx44dERQUhJycHLx8+RI9evRATEwMZDIZUlJSMGHCBMTGxuLw4cPw9PREamoqCgsLcevWLQwYMEBtVefkyZPw9vZGQEAA4uPjkZiYCAMDA0yYMAE///xzpcyViDBq1CisXLkSRIS8vDwcOXIE7u7uaknetWvX0LZtWyGBSU1NxdSpU9GnTx9kZWVVyngqSiqVYvHixdixYwesrKyEcpFIBAClbukVFhYKf1ZuV9b4ldlHaZKSknDz5k2MHDlSpXzIkCFwdHREQUFBhW/gUKY8XmVmZmawtLQEoP1Wf0WYm5vj2LFjaNq0KWQyGQ4cOFDtY2CsNJzgMVYLubi4IDIyEqNGjVI7N2bMGGHFAwAWLFiArVu3Ii0tDRkZGXB3d4dcLscPP/yAPXv24P79+0hJScHjx4/h4OAAqVSK48ePC+2lUilGjhyJ0NBQdO/eHQDg4OCArVu3AgCCg4M1rrKVV3R0NDw8PJCZmYlnz57h999/h4mJCTIzM9VuMImIiMDt27eRlZWFmJgYdOnSBUDJauSQIUN0Yst24sSJmDt3rsq1cQDQuHFjAEBeXp7GdtnZ2QAAU1PT915LWFp8bfuwsbHReFy8eFGt/dq1a1FYWIj27durXPvo6uoqvCVj06ZNlbaiqti6ffLkido5IoJIJEKbNm2EMh8fH41zWbZsWaWMR5m5uTl+/PFHAMCDBw8qPT5j2uIEj7FarFGjRmplxsbGaNGihfBzaGiosI1Uv359fPPNNwBKvuSjoqKEC9UdHBwwYMAAAEBKSorQXiKR4NWrV/Dx8VHpR/FaJ7lcjkOHDlV4Lv3798e3334rrMQMHDhQ2HqOiYnRuAUnEonQt29fxMXFYfr06QBKkrzIyMgKj6ciQkJC4OjoqPERMIrf27NnzzS2ffHiBQBofCxKWeJr24dUKtV4yGQylbY5OTmIiopCQkICEhMT1Y74+HiIRCKkpKTgyJEjpc6hPHx9fdGtWzfExMQIySlQsnKbnZ2NESNGqGxH5+TkaJxLfn5+pYznXX369AFQ8u+LMV3BCR5jtVhpz3oTi8XCn83MzFTONWnSRCh/9/EQirsbld9Vevr0aRAROnbsqLJa06FDB1hbW8Pa2hrp6ekVnoum668CAgKE7cbS7swESq7pWrNmDQYPHgwAiIqKqvB4tBUREYF79+5h9erVGs/37t0bIpEI6enpyMzMVDuvWA318vLSKr62fVDJg+/Vjl69eqm0DQ8Ph5eXF2xtbTX23bBhQwwaNAhAyV22mpT1sSQKRkZGiImJQatWrTB58mS8evUKOTk5CAgIQPPmzbF8+XKV+mfPntU4l3evVa0sipstunbtWiXxGdMGJ3iM/ceUdoG78jnla7dSUlJgZWWlcbUmMzNTuNmhKpiYmAgrjMorN6UJDQ2Fnp5ejW2VHTx4EIcPH8a2bdvUkpji4mKkpqbC3NxceCm9podXX758Gfr6+hg2bJhW8QFUqI/3KS4uxoYNGzBlypT31ps0aRKAkkTr9u3baufr1asHoOTmH2WKaz81XXO3fv16pKWlIT8/H+3bt0fXrl3h6OiIy5cvl5psVpf4+Hg4OTnhyy+/rNFxMKaMEzzG2HsVFRUhIyOjTAlWVVA8CLm0R34oa9WqFRwdHWtkq+zw4cPYsWMHdu/erXbB//PnzzFu3Dg8fPgQABAUFARTU1Ps3LlTpV58fDxu3LgBf39/4QHG2sTXto8PiYyMBBFpfDuGst69ews3byxZskTtvOKhzNu3bxfu4j1x4oRwV67iPw6K/2jExsYiMDAQwcHB2L9/P5KSknDnzh1s2LBB6wdlKysqKgKA914zKJfLS72BJzQ0FBKJRGXlnLGaxgkeY+y97OzsQETYt2+fxvNEpPJGgsqm+FJ93zVpyuzs7Kp9q2z37t3w9fXFuXPnYG9vr3Jhv7m5Oezs7PDXX3+hZ8+eAEqe4bZlyxYcPXoUERERAEpWSkePHg0PDw+17dfyxtemjw+5d+8e5s6dCyJ67yNQgJKVOMWW+549e1QeywNAeOtFdHQ0GjZsiMaNG2P+/PnCSrBMJkOXLl2Ex/AkJyeDiDB58mTUq1cPRkZGMDAwgL6+PszMzODh4YETJ06Uaz7KYmNjAQDp6ekqSbKyQYMGwdbWFjNmzBBWHjMyMjBr1iz4+/urbWUzVuOq55W3jLGKwP+/UP5dQUFBBICCgoLK3ObMmTMEgD799NMyxVuwYAEBIBsbG3rw4IFam+3bt9Pu3bvLNZ932wMgPz8/tXM5OTlkaGhIPXv2LFMsmUxGtra2dOfOHa3HU17R0dGkp6cn/L5LO+bNm6fWNiYmhtzd3cnJyYlcXV1pxYoV9Pbt20qLX9Y+PmTx4sUqYxCJRBQQEKCx7urVq8nY2FhtfJaWlir9bty4kezs7Mjc3Jz8/Pzo5cuX9OjRI7KxsaFFixZRdna2UFcul9O4cePIycmJ7OzsyMTEhPT19VXiGxgY0PXr18s1r9jYWLKzs1OJY2hoSM7Ozmp1z5w5Q25ubmRqakoWFhY0YMAACg0NJalUWq4+GasunOAxVgvUq1ePANCbN29Uyr///nsCQIGBgSrlhYWFwheWTCZTOffnn38SAOrevbtaPwsXLlSLl5qaSqampgSAGjZsSJs3b6aHDx/SvXv3aOnSpdSqVSt69eqV1nNTJHhjx45VO7d8+XISi8V09epVlfKMjAyNsVatWkWLFy/WeixMNz179oz69etHBQUFaucKCgooKSmJhgwZQnPmzKmB0TGmm3iLlrFawNXVFQBw8eJF5OfnY/HixZDL5Th//jyAkpfLK18/pCgHgKtXr6rEUmynJicnq9xhWVRUhEuXLgEouQhfEc/BwQESiQSGhoZIT0/HlClT0Lx5c7Ru3RoLFy5EeHh4ha55s7e3h0gkwt69e7F582bhsRwHDhxASEgIoqKihOfcAcCqVatga2sLLy8vJCYmAgDevn2LdevWoaioCIGBgVqPhemmsWPHws/PT+M1bmKxGC1btsRXX32l9kgXxv7TajrDZIx9WFxcHDk7O5OtrS3NnDmTzp8/T1ZWVmpbYNeuXaPx48eTgYGBytaVp6cn5ebmUqNGjVTaiMViWrhwIZ06dYosLCxUzllYWFBCQoIwhkuXLtEXX3xBZmZmZGpqSn379qW4uLhKmV9SUhJNnTqVWrRoQZaWltSxY0fy8/Oj+/fvq9VNSUkhb29vsrCwIGNjY+rRowfNnz+f7t69WyljYbrl+PHjBID27t373nojR46kEydOVNOoGNN9ekSlvMuGMcYYq2GbNm1CQEAArK2tsXTpUgwdOhQNGjQQzj9+/BjBwcHIzs7GwYMHa3CkjOkWTvAYY4zprNevX8PLywsXLlwAUPKQZDs7O5iYmCAnJwcZGRnw9fXFzp073/tqN8b+azjBY4wxptOKi4uxfft2RERE4NatW8jPz4e1tTXc3d0xYcIEeHt71/QQGdM5nOAxxhhjjNUxfBctY4wxxlgdY/jhKowx9mG9evXC06dPy9VG8dJ7xhhjlYu3aBljjDHG6hjeomWMMcYYq2M4wWOMMcYYq2M4wWOMMcYYq2M4wWOMMcYYq2M4wWOMMcYYq2M4wWOMMcYYq2M4wWOMMcYYq2P+D/v1abNDSalyAAAAAElFTkSuQmCC",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -3933,7 +3938,7 @@
         },
         {
           "data": {
-            "image/png": 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",
+            "image/png": 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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -3987,9 +3992,9 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "c:\\users\\sterl\\codes\\mhkit-python\\mhkit\\dolfyn\\adp\\turbulence.py:407: UserWarning:     The beam-variance algorithms assume the instrument's (XYZ) coordinate system is aligned with the principal flow directions.\n",
+            "c:\\users\\mcve343\\mhkit-python\\mhkit\\dolfyn\\adp\\turbulence.py:407: UserWarning:     The beam-variance algorithms assume the instrument's (XYZ) coordinate system is aligned with the principal flow directions.\n",
             "  warnings.warn(\n",
-            "c:\\users\\sterl\\codes\\mhkit-python\\mhkit\\dolfyn\\adp\\turbulence.py:417: UserWarning:     100.0 % of measurements have a tilt greater than 5 degrees.\n",
+            "c:\\users\\mcve343\\mhkit-python\\mhkit\\dolfyn\\adp\\turbulence.py:417: UserWarning:     100.0 % of measurements have a tilt greater than 5 degrees.\n",
             "  warnings.warn(\n"
           ]
         }
@@ -4036,7 +4041,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "c:\\users\\sterl\\codes\\mhkit-python\\mhkit\\dolfyn\\rotate\\api.py:72: UserWarning: You are attempting to rotate into the 'principal' coordinate system, but the dataset is in the inst coordinate system. Be sure that 'principal_heading' is defined based on the earth coordinate system.\n",
+            "c:\\users\\mcve343\\mhkit-python\\mhkit\\dolfyn\\rotate\\api.py:72: UserWarning: You are attempting to rotate into the 'principal' coordinate system, but the dataset is in the inst coordinate system. Be sure that 'principal_heading' is defined based on the earth coordinate system.\n",
             "  warnings.warn(\n"
           ]
         }
@@ -4083,7 +4088,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -4093,7 +4098,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -4144,7 +4149,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.7"
+      "version": "3.11.10"
     }
   },
   "nbformat": 4,
diff --git a/examples/data/dolfyn/test_data/AWAC_test01_clean.nc b/examples/data/dolfyn/test_data/AWAC_test01_clean.nc
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diff --git a/examples/data/dolfyn/test_data/RDI_test01_clean.nc b/examples/data/dolfyn/test_data/RDI_test01_clean.nc
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diff --git a/examples/data/dolfyn/test_data/Sig1000_tidal_clean.nc b/examples/data/dolfyn/test_data/Sig1000_tidal_clean.nc
index 8cb9e667851071815ab154e4bca8e68e9441412e..b78e028c22b76e6da236aad4a8c05ecd21230afa 100644
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diff --git a/examples/data/dolfyn/test_data/Sig500_Echo_clean.nc b/examples/data/dolfyn/test_data/Sig500_Echo_clean.nc
index 099334d74b45801d165a23d7958e1fc4419e62b6..d6f187116f54d5782ca28b2ecd1760509add0c3b 100644
GIT binary patch
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diff --git a/mhkit/dolfyn/adp/clean.py b/mhkit/dolfyn/adp/clean.py
index 520df023a..a919e40f9 100644
--- a/mhkit/dolfyn/adp/clean.py
+++ b/mhkit/dolfyn/adp/clean.py
@@ -1,62 +1,110 @@
 """Module containing functions to clean data
 """
 
+import warnings
 import numpy as np
 import xarray as xr
 from scipy.signal import medfilt
 from ..tools.misc import medfiltnan
 from ..rotate.api import rotate2
-from ..rotate.base import _make_model, quaternion2orient
+from ..rotate.base import quaternion2orient
+
+
+def __check_for_range_offset(ds):
+    if "bin1_dist_m" in ds.attrs:
+        return ds.attrs["bin1_dist_m"] - ds.attrs["blank_dist"] - ds.attrs["cell_size"]
+    elif "range_offset" in ds.attrs:
+        return ds.attrs["range_offset"]
+    elif "h_deploy" in ds.attrs:
+        warnings.warn(
+            "The attribute 'h_deploy' will be dropped in a future release."
+            "It will now be renamed to 'range_offset'.",
+            DeprecationWarning,
+        )
+        ds.attrs["range_offset"] = ds.attrs.pop("h_deploy")
+        return ds.attrs["range_offset"]
+    else:
+        return 0
 
 
-def set_range_offset(ds, h_deploy):
+def set_range_offset(ds, range_offset):
     """
     Adds an instrument's height above seafloor (for an up-facing instrument)
     or depth below water surface (for a down-facing instrument) to the range
     coordinate. Also adds an attribute to the Dataset with the current
-    "h_deploy" distance.
+    "range_offset" distance.
 
     Parameters
     ----------
     ds : xarray.Dataset
-      The adcp dataset to ajust 'range' on
-    h_deploy : numeric
+      The ADCP dataset to adjust 'range' on
+    range_offset : numeric
       Deployment location in the water column, in [m]
 
     Returns
     -------
-    None, operates "in place"
+    ds : xarray.Dataset
+      The ADCP dataset with applied range_offset
 
     Notes
     -----
-    `Center of bin 1 = h_deploy + blank_dist + cell_size`
+    `Center of bin 1 = range_offset + blank_dist + cell_size`
 
-    Nortek doesn't take `h_deploy` into account, so the range that DOLfYN
-    calculates distance is from the ADCP transducers. TRDI asks for `h_deploy`
+    Nortek doesn't take `range_offset` into account, so the range that DOLfYN
+    calculates distance is from the ADCP transducers. TRDI asks for `range_offset`
     input in their deployment software and is thereby known by DOLfYN.
 
-    If the ADCP is mounted on a tripod on the seafloor, `h_deploy` will be
+    If the ADCP is mounted on a tripod on the seafloor, `range_offset` will be
     the height of the tripod +/- any extra distance to the transducer faces.
-    If the instrument is vessel-mounted, `h_deploy` is the distance between
+    If the instrument is vessel-mounted, `range_offset` is the distance between
     the surface and downward-facing ADCP's transducers.
     """
 
+    current_offset = __check_for_range_offset(ds)
+
+    if current_offset:
+        warnings.warn(
+            "The 'range_offset' is either already known or can be calculated "
+            f"from 'bin1_dist_m': {current_offset} m. If you would like to override "
+            f"this value with {range_offset} m, ignore this warning. "
+            "If this behavior is not desired, do not use this function."
+        )
+        # Remove offset from depth variable if exists
+        if "depth" in ds.data_vars:
+            ds["depth"].values -= current_offset
+
+    # Add offset to each range coordinate
     r = [s for s in ds.dims if "range" in s]
-    for val in r:
-        ds[val] = ds[val].values + h_deploy
-        ds[val].attrs["units"] = "m"
+    for coord in r:
+        coord_attrs = ds[coord].attrs
+        ds[coord] = ds[coord].values + range_offset
+        ds[coord].attrs = coord_attrs
 
-    if hasattr(ds, "h_deploy"):
-        ds.attrs["h_deploy"] += h_deploy
-    else:
-        ds.attrs["h_deploy"] = h_deploy
+    # Add to depth variable if exists
+    if "depth" in ds.data_vars:
+        ds["depth"].values += range_offset
+
+    # Add back to dataset
+    ds.attrs["range_offset"] = range_offset
 
 
-def find_surface(ds, thresh=10, nfilt=None):
+def find_surface(*args, **kwargs):
+    warnings.warn(
+        "The 'find_surface_from_P' function was renamed to 'find_surface_from_pressure"
+        "and will be dropped in a future release.",
+        DeprecationWarning,
+    )
+    return water_depth_from_amplitude(*args, **kwargs)
+
+
+def water_depth_from_amplitude(ds, thresh=10, nfilt=None):
     """
     Find the surface (water level or seafloor) from amplitude data and
     adds the variable "depth" to the input Dataset.
 
+    Depth is either water depth or the distance from the ADCP to
+    surface/seafloor, depending on if "range_offset" has been set.
+
     Parameters
     ----------
     ds : xarray.Dataset
@@ -74,56 +122,78 @@ def find_surface(ds, thresh=10, nfilt=None):
     None, operates "in place"
     """
 
+    if "depth" in ds.data_vars:
+        raise Exception(
+            "The variable 'depth' already exists. "
+            "Please manually remove 'depth' if it needs to be recalculated."
+        )
+
     # This finds the maximum of the echo profile:
-    inds = np.argmax(ds.amp.values, axis=1)
+    inds = np.argmax(ds["amp"].values, axis=1)
     # This finds the first point that increases (away from the profiler) in
     # the echo profile
-    edf = np.diff(ds.amp.values.astype(np.int16), axis=1)
+    edf = np.diff(ds["amp"].values.astype(np.int16), axis=1)
     inds2 = (
         np.max(
-            (edf < 0) * np.arange(ds.vel.shape[1] - 1, dtype=np.uint8)[None, :, None],
+            (edf < 0)
+            * np.arange(ds["vel"].shape[1] - 1, dtype=np.uint8)[None, :, None],
             axis=1,
         )
         + 1
     )
 
     # Calculate the depth of these quantities
-    d1 = ds.range.values[inds]
-    d2 = ds.range.values[inds2]
+    d1 = ds["range"].values[inds]
+    d2 = ds["range"].values[inds2]
     # Combine them:
     D = np.vstack((d1, d2))
     # Take the median value as the estimate of the surface:
     d = np.median(D, axis=0)
 
     # Throw out values that do not increase near the surface by *thresh*
-    for ip in range(ds.vel.shape[1]):
+    for ip in range(ds["vel"].shape[1]):
         itmp = np.min(inds[:, ip])
         if (edf[itmp:, :, ip] < thresh).all():
-            d[ip] = np.nan
+            d[ip] = np.NaN
 
     if nfilt:
         dfilt = medfiltnan(d, nfilt, thresh=4)
-        dfilt[dfilt == 0] = np.nan
+        dfilt[dfilt == 0] = np.NaN
         d = dfilt
 
+    range_offset = __check_for_range_offset(ds)
+    if range_offset:
+        d += range_offset
+        long_name = "Water Depth"
+    else:
+        long_name = "Instrument Depth"
+
     ds["depth"] = xr.DataArray(
         d.astype("float32"),
         dims=["time"],
-        attrs={
-            "units": "m",
-            "long_name": "Depth",
-            "standard_name": "depth",
-            "positive": "down",
-        },
+        attrs={"units": "m", "long_name": long_name, "standard_name": "depth"},
     )
 
 
-def find_surface_from_P(ds, salinity=35):
+def find_surface_from_P(*args, **kwargs):
+    warnings.warn(
+        "The 'find_surface_from_P' function was renamed to 'find_surface_from_pressure"
+        "and will be dropped in a future release. This function now returns a single"
+        "DataArray, 'depth'",
+        DeprecationWarning,
+    )
+    return water_depth_from_pressure(*args, **kwargs)
+
+
+def water_depth_from_pressure(ds, salinity=35):
     """
     Calculates the distance to the water surface. Temperature and salinity
     are used to calculate seawater density, which is in turn used with the
     pressure data to calculate depth.
 
+    Depth is either water depth or the distance from the ADCP to
+    surface/seafloor, depending on if "range_offset" has been set.
+
     Parameters
     ----------
     ds : xarray.Dataset
@@ -153,9 +223,21 @@ def find_surface_from_P(ds, salinity=35):
     compressibility.
     """
 
+    if "depth" in ds.data_vars:
+        raise Exception(
+            "The variable 'depth' already exists. "
+            "Please manually remove 'depth' if it needs to be recalculated."
+        )
+    if "pressure" not in ds.data_vars:
+        raise NameError("The variable 'pressure' does not exist.")
+    elif not ds["pressure"].sum():
+        raise ValueError("Pressure data not recorded.")
+    if "temp" not in ds.data_vars:
+        raise NameError("The variable 'temp' does not exist.")
+
     # Density calcation
-    P = ds.pressure.values
-    T = ds.temp.values  # temperature, degC
+    P = ds["pressure"].values
+    T = ds["temp"].values  # temperature, degC
     S = salinity  # practical salinity
     rho0 = 1027  # kg/m^3
     T0 = 10  # degC
@@ -166,13 +248,15 @@ def find_surface_from_P(ds, salinity=35):
     rho = rho0 - a * (T - T0) + b * (S - S0) + k * P
 
     # Depth = pressure (conversion from dbar to MPa) / water weight
-    d = (ds.pressure * 10000) / (9.81 * rho)
+    d = (P * 10000) / (9.81 * rho)
 
-    if hasattr(ds, "h_deploy"):
-        d += ds.h_deploy
-        description = "Depth to Seafloor"
+    # Apply range_offset if available
+    range_offset = __check_for_range_offset(ds)
+    if range_offset:
+        d += range_offset
+        long_name = "Water Depth"
     else:
-        description = "Depth to Instrument"
+        long_name = "Instrument Depth"
 
     ds["water_density"] = xr.DataArray(
         rho.astype("float32"),
@@ -187,16 +271,20 @@ def find_surface_from_P(ds, salinity=35):
     ds["depth"] = xr.DataArray(
         d.astype("float32"),
         dims=["time"],
-        attrs={
-            "units": "m",
-            "long_name": description,
-            "standard_name": "depth",
-            "positive": "down",
-        },
+        attrs={"units": "m", "long_name": long_name, "standard_name": "depth"},
     )
 
 
-def nan_beyond_surface(ds, val=np.nan, beam_angle=None, inplace=False):
+def nan_beyond_surface(*args, **kwargs):
+    warnings.warn(
+        "The 'nan_beyond_surface' function was renamed to 'remove_surface_interference"
+        "and will be dropped in a future release.",
+        DeprecationWarning,
+    )
+    return remove_surface_interference(*args, **kwargs)
+
+
+def remove_surface_interference(ds, val=np.nan, beam_angle=None, inplace=False):
     """
     Mask the values of 3D data (vel, amp, corr, echo) that are beyond the surface.
 
@@ -207,7 +295,7 @@ def nan_beyond_surface(ds, val=np.nan, beam_angle=None, inplace=False):
     val : nan or numeric
       Specifies the value to set the bad values to. Default is `numpy.nan`
     beam_angle : int
-      ADCP beam inclination angle. Default = dataset.attrs['beam_angle']
+      ADCP beam inclination angle in degrees. Default = dataset.attrs['beam_angle']
     inplace : bool
       When True the existing data object is modified. When False
       a copy is returned. Default = False
@@ -224,45 +312,56 @@ def nan_beyond_surface(ds, val=np.nan, beam_angle=None, inplace=False):
     `distance > range * cos(beam angle) - cell size`
     """
 
-    if not inplace:
-        ds = ds.copy(deep=True)
-
-    # Get all variables with 'range' coordinate
-    var = [h for h in ds.keys() if any(s for s in ds[h].dims if "range" in s)]
+    if "depth" not in ds.data_vars:
+        raise KeyError(
+            "Depth variable 'depth' does not exist in input dataset."
+            "Please calculate 'depth' using the function 'water_depth_from_pressure'"
+            "or 'water_depth_from_amplitude."
+        )
 
     if beam_angle is None:
         if hasattr(ds, "beam_angle"):
-            beam_angle = ds.beam_angle * (np.pi / 180)
+            beam_angle = np.deg2rad(ds.attrs["beam_angle"])
         else:
             raise Exception(
                 "'beam_angle` not found in dataset attributes. "
                 "Please supply the ADCP's beam angle."
             )
+    else:
+        beam_angle = np.deg2rad(beam_angle)
+
+    if not inplace:
+        ds = ds.copy(deep=True)
+
+    # Get all variables with 'range' coordinate
+    profile_vars = [h for h in ds.keys() if any(s for s in ds[h].dims if "range" in s)]
 
-    # Surface interference distance calculated from distance of transducers to surface
-    if hasattr(ds, "h_deploy"):
+    # Surface interference distance
+    # Apply range_offset if available
+    range_offset = __check_for_range_offset(ds)
+    if range_offset:
         range_limit = (
-            (ds.depth - ds.h_deploy) * np.cos(beam_angle) - ds.cell_size
-        ) + ds.h_deploy
+            (ds["depth"] - range_offset) * np.cos(beam_angle) - ds.attrs["cell_size"]
+        ) + range_offset
     else:
-        range_limit = ds.depth * np.cos(beam_angle) - ds.cell_size
+        range_limit = ds["depth"] * np.cos(beam_angle) - ds.attrs["cell_size"]
 
-    bds = ds.range > range_limit
+    bds = ds["range"] > range_limit
 
     # Echosounder data needs only be trimmed at water surface
-    if "echo" in var:
-        bds_echo = ds.range_echo > ds.depth
-        ds["echo"].values[..., bds_echo] = val
-        var.remove("echo")
+    if "echo" in profile_vars:
+        mask_echo = ds["range_echo"] > ds["depth"]
+        ds["echo"].values[..., mask_echo] = val
+        profile_vars.remove("echo")
 
     # Correct rest of "range" data for surface interference
-    for nm in var:
-        a = ds[nm].values
+    for var in profile_vars:
+        a = ds[var].values
         try:  # float dtype
             a[..., bds] = val
         except:  # int dtype
             a[..., bds] = 0
-        ds[nm].values = a
+        ds[var].values = a
 
     if not inplace:
         return ds
diff --git a/mhkit/dolfyn/adv/clean.py b/mhkit/dolfyn/adv/clean.py
index bf91efacf..69a03587b 100644
--- a/mhkit/dolfyn/adv/clean.py
+++ b/mhkit/dolfyn/adv/clean.py
@@ -6,7 +6,6 @@
 from ..velocity import VelBinner
 from ..tools.misc import group, slice1d_along_axis
 
-warnings.simplefilter("ignore", np.exceptions.RankWarning)
 
 sin = np.sin
 cos = np.cos
diff --git a/mhkit/dolfyn/io/rdi.py b/mhkit/dolfyn/io/rdi.py
index 640adc7ce..0b61e3561 100644
--- a/mhkit/dolfyn/io/rdi.py
+++ b/mhkit/dolfyn/io/rdi.py
@@ -115,12 +115,6 @@ def read_rdi(
         # Check magnetic declination if provided via software and/or userdata
         _set_rdi_declination(ds, filename, inplace=True)
 
-        # VMDAS applies gps correction on velocity in .ENX files only
-        if filename.rsplit(".")[-1] == "ENX":
-            ds.attrs["vel_gps_corrected"] = 1
-        else:  # (not ENR or ENS) or WinRiver files
-            ds.attrs["vel_gps_corrected"] = 0
-
         dss += [ds]
 
     if len(dss) == 2:
diff --git a/mhkit/dolfyn/velocity.py b/mhkit/dolfyn/velocity.py
index 24b14d375..c1923800e 100644
--- a/mhkit/dolfyn/velocity.py
+++ b/mhkit/dolfyn/velocity.py
@@ -307,7 +307,7 @@ def u(
         - earth:     east
         - principal: streamwise
         """
-        return self.ds["vel"][0].drop("dir")
+        return self.ds["vel"][0].drop_vars("dir")
 
     @property
     def v(
@@ -325,7 +325,7 @@ def v(
         - earth:     north
         - principal: cross-stream
         """
-        return self.ds["vel"][1].drop("dir")
+        return self.ds["vel"][1].drop_vars("dir")
 
     @property
     def w(
@@ -343,7 +343,7 @@ def w(
         - earth:     up
         - principal: up
         """
-        return self.ds["vel"][2].drop("dir")
+        return self.ds["vel"][2].drop_vars("dir")
 
     @property
     def U(
@@ -483,7 +483,7 @@ def upvp_(
     ):
         """u'v'bar Reynolds stress"""
 
-        return self.ds["stress_vec"].sel(tau="upvp_").drop("tau")
+        return self.ds["stress_vec"].sel(tau="upvp_").drop_vars("tau")
 
     @property
     def upwp_(
@@ -491,7 +491,7 @@ def upwp_(
     ):
         """u'w'bar Reynolds stress"""
 
-        return self.ds["stress_vec"].sel(tau="upwp_").drop("tau")
+        return self.ds["stress_vec"].sel(tau="upwp_").drop_vars("tau")
 
     @property
     def vpwp_(
@@ -499,7 +499,7 @@ def vpwp_(
     ):
         """v'w'bar Reynolds stress"""
 
-        return self.ds["stress_vec"].sel(tau="vpwp_").drop("tau")
+        return self.ds["stress_vec"].sel(tau="vpwp_").drop_vars("tau")
 
     @property
     def upup_(
@@ -507,7 +507,7 @@ def upup_(
     ):
         """u'u'bar component of the tke"""
 
-        return self.ds["tke_vec"].sel(tke="upup_").drop("tke")
+        return self.ds["tke_vec"].sel(tke="upup_").drop_vars("tke")
 
     @property
     def vpvp_(
@@ -515,7 +515,7 @@ def vpvp_(
     ):
         """v'v'bar component of the tke"""
 
-        return self.ds["tke_vec"].sel(tke="vpvp_").drop("tke")
+        return self.ds["tke_vec"].sel(tke="vpvp_").drop_vars("tke")
 
     @property
     def wpwp_(
@@ -523,7 +523,7 @@ def wpwp_(
     ):
         """w'w'bar component of the tke"""
 
-        return self.ds["tke_vec"].sel(tke="wpwp_").drop("tke")
+        return self.ds["tke_vec"].sel(tke="wpwp_").drop_vars("tke")
 
 
 class VelBinner(TimeBinner):
diff --git a/mhkit/tests/dolfyn/test_clean.py b/mhkit/tests/dolfyn/test_clean.py
index e50054eee..a441a1b2c 100644
--- a/mhkit/tests/dolfyn/test_clean.py
+++ b/mhkit/tests/dolfyn/test_clean.py
@@ -61,22 +61,31 @@ def test_range_limit(self):
     def test_clean_upADCP(self):
         td_awac = tp.dat_awac.copy(deep=True)
         td_sig = tp.dat_sig_tide.copy(deep=True)
+        td_rdi = tp.dat_rdi.copy(deep=True)
 
-        apm.clean.find_surface_from_P(td_awac, salinity=30)
-        td_awac = apm.clean.nan_beyond_surface(td_awac, beam_angle=20)
+        apm.clean.water_depth_from_pressure(td_awac, salinity=30)
+        apm.clean.remove_surface_interference(td_awac, beam_angle=20, inplace=True)
 
         apm.clean.set_range_offset(td_sig, 0.6)
-        apm.clean.find_surface_from_P(td_sig, salinity=31)
-        td_sig = apm.clean.nan_beyond_surface(td_sig)
+        apm.clean.water_depth_from_pressure(td_sig, salinity=31)
+        apm.clean.remove_surface_interference(td_sig, inplace=True)
         td_sig = apm.clean.correlation_filter(td_sig, thresh=50)
 
+        # Depth should already be found for this RDI file, but it's bad
+        td_rdi["pressure"] /= 10  # set to something reasonable
+        td_rdi = td_rdi.drop_vars("depth")
+        apm.clean.water_depth_from_pressure(td_rdi, salinity=35)
+        apm.clean.remove_surface_interference(td_rdi, inplace=True)
+
         if make_data:
             save(td_awac, "AWAC_test01_clean.nc")
             save(td_sig, "Sig1000_tidal_clean.nc")
+            save(td_rdi, "RDI_test01_clean.nc")
             return
 
         assert_allclose(td_awac, load("AWAC_test01_clean.nc"), atol=1e-6)
         assert_allclose(td_sig, load("Sig1000_tidal_clean.nc"), atol=1e-6)
+        assert_allclose(td_rdi, load("RDI_test01_clean.nc"), atol=1e-6)
 
     def test_clean_downADCP(self):
         td = tp.dat_sig_ie.copy(deep=True)
@@ -90,8 +99,8 @@ def test_clean_downADCP(self):
 
         # Then clean below seabed
         apm.clean.set_range_offset(td, 0.5)
-        apm.clean.find_surface(td, thresh=10, nfilt=3)
-        td = apm.clean.nan_beyond_surface(td)
+        apm.clean.water_depth_from_amplitude(td, thresh=10, nfilt=3)
+        td = apm.clean.remove_surface_interference(td)
 
         if make_data:
             save(td, "Sig500_Echo_clean.nc")

From 525160625cb44ce63517e8e39aee701c6ab933ca Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Wed, 18 Sep 2024 14:03:07 -0700
Subject: [PATCH 02/14] Cleanup

---
 mhkit/dolfyn/adp/clean.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/mhkit/dolfyn/adp/clean.py b/mhkit/dolfyn/adp/clean.py
index a919e40f9..ca7776624 100644
--- a/mhkit/dolfyn/adp/clean.py
+++ b/mhkit/dolfyn/adp/clean.py
@@ -11,6 +11,9 @@
 
 
 def __check_for_range_offset(ds):
+    """
+    Fetches or calculates range offset from dataset attributes.
+    """
     if "bin1_dist_m" in ds.attrs:
         return ds.attrs["bin1_dist_m"] - ds.attrs["blank_dist"] - ds.attrs["cell_size"]
     elif "range_offset" in ds.attrs:
@@ -84,7 +87,7 @@ def set_range_offset(ds, range_offset):
     if "depth" in ds.data_vars:
         ds["depth"].values += range_offset
 
-    # Add back to dataset
+    # Add to dataset
     ds.attrs["range_offset"] = range_offset
 
 
@@ -112,14 +115,15 @@ def water_depth_from_amplitude(ds, thresh=10, nfilt=None):
     thresh : int
       Specifies the threshold used in detecting the surface. Default = 10
       (The amount that amplitude must increase by near the surface for it to
-      be considered a surface hit)
+      be considered a surface hit.)
     nfilt : int
       Specifies the width of the median filter applied, must be odd.
       Default is None
 
     Returns
     -------
-    None, operates "in place"
+    None, operates "in place" and adds the variable "depth" to the
+    input dataset
     """
 
     if "depth" in ds.data_vars:
@@ -178,8 +182,7 @@ def water_depth_from_amplitude(ds, thresh=10, nfilt=None):
 def find_surface_from_P(*args, **kwargs):
     warnings.warn(
         "The 'find_surface_from_P' function was renamed to 'find_surface_from_pressure"
-        "and will be dropped in a future release. This function now returns a single"
-        "DataArray, 'depth'",
+        "and will be dropped in a future release.",
         DeprecationWarning,
     )
     return water_depth_from_pressure(*args, **kwargs)

From 5d7d520282c07311b4f3fdbe0b779ae204f5c8e2 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Wed, 18 Sep 2024 15:45:08 -0700
Subject: [PATCH 03/14] Get deprecation names right

---
 mhkit/dolfyn/adp/clean.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/mhkit/dolfyn/adp/clean.py b/mhkit/dolfyn/adp/clean.py
index ca7776624..1943d094f 100644
--- a/mhkit/dolfyn/adp/clean.py
+++ b/mhkit/dolfyn/adp/clean.py
@@ -20,7 +20,7 @@ def __check_for_range_offset(ds):
         return ds.attrs["range_offset"]
     elif "h_deploy" in ds.attrs:
         warnings.warn(
-            "The attribute 'h_deploy' will be dropped in a future release."
+            "The attribute 'h_deploy' is no longer in use."
             "It will now be renamed to 'range_offset'.",
             DeprecationWarning,
         )
@@ -68,9 +68,10 @@ def set_range_offset(ds, range_offset):
     if current_offset:
         warnings.warn(
             "The 'range_offset' is either already known or can be calculated "
-            f"from 'bin1_dist_m': {current_offset} m. If you would like to override "
-            f"this value with {range_offset} m, ignore this warning. "
-            "If this behavior is not desired, do not use this function."
+            f"from 'bin1_dist_m': {current_offset} m. If you would like to "
+            f"override this value with {range_offset} m, ignore this warning. "
+            "If you do not want to override this value, you do not need to use "
+            "this function."
         )
         # Remove offset from depth variable if exists
         if "depth" in ds.data_vars:
@@ -93,7 +94,7 @@ def set_range_offset(ds, range_offset):
 
 def find_surface(*args, **kwargs):
     warnings.warn(
-        "The 'find_surface_from_P' function was renamed to 'find_surface_from_pressure"
+        "The 'find_surface' function was renamed to 'water_depth_from_amplitude"
         "and will be dropped in a future release.",
         DeprecationWarning,
     )
@@ -181,7 +182,7 @@ def water_depth_from_amplitude(ds, thresh=10, nfilt=None):
 
 def find_surface_from_P(*args, **kwargs):
     warnings.warn(
-        "The 'find_surface_from_P' function was renamed to 'find_surface_from_pressure"
+        "The 'find_surface_from_P' function was renamed to 'water_depth_from_pressure"
         "and will be dropped in a future release.",
         DeprecationWarning,
     )

From 33635f8d3893dcb6bd8e515320ecf6d4de11549b Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Thu, 19 Sep 2024 14:59:14 -0700
Subject: [PATCH 04/14] Consistently use 'degree' singular for units

---
 mhkit/dolfyn/io/rdi_defs.py   |  4 ++--
 mhkit/dolfyn/rotate/base.py   | 18 +++++++++++++-----
 mhkit/dolfyn/rotate/vector.py |  8 ++++++--
 mhkit/dolfyn/velocity.py      |  4 ++--
 4 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/mhkit/dolfyn/io/rdi_defs.py b/mhkit/dolfyn/io/rdi_defs.py
index 3ba022822..04a6f1fbe 100644
--- a/mhkit/dolfyn/io/rdi_defs.py
+++ b/mhkit/dolfyn/io/rdi_defs.py
@@ -195,7 +195,7 @@
         [],
         "data_vars",
         "float32",
-        "degrees_north",
+        "degree_north",
         "Latitude",
         "latitude",
     ),
@@ -203,7 +203,7 @@
         [],
         "data_vars",
         "float32",
-        "degrees_east",
+        "degree_east",
         "Longitude",
         "longitude",
     ),
diff --git a/mhkit/dolfyn/rotate/base.py b/mhkit/dolfyn/rotate/base.py
index d7cdef541..9ffa4f282 100644
--- a/mhkit/dolfyn/rotate/base.py
+++ b/mhkit/dolfyn/rotate/base.py
@@ -163,7 +163,7 @@ def _beam2inst(dat, reverse=False, force=False):
     return dat
 
 
-def euler2orient(heading, pitch, roll, units="degrees"):
+def euler2orient(heading, pitch, roll, units="degree"):
     """
     Calculate the orientation matrix from DOLfYN-defined euler angles.
 
@@ -210,14 +210,14 @@ def euler2orient(heading, pitch, roll, units="degrees"):
          instrument's x-axis
     """
 
-    if units.lower() == "degrees":
+    if "deg" in units.lower():
         pitch = np.deg2rad(pitch)
         roll = np.deg2rad(roll)
         heading = np.deg2rad(heading)
-    elif units.lower() == "radians":
+    elif "rad" in units.lower():
         pass
     else:
-        raise Exception("Invalid units")
+        raise ValueError("Invalid units")
 
     # Converts the DOLfYN-defined heading to one that follows the right-hand-rule
     # reports heading as rotation of the y-axis positive counterclockwise from North
@@ -361,7 +361,7 @@ def quaternion2orient(quaternions):
     return omat.assign_coords({"earth": earth, "inst": inst, "time": quaternions.time})
 
 
-def calc_tilt(pitch, roll):
+def calc_tilt(pitch, roll, units="degree"):
     """
     Calculate "tilt", the vertical inclination, from pitch and roll.
 
@@ -378,6 +378,14 @@ def calc_tilt(pitch, roll):
       Vertical inclination of the instrument in degrees
     """
 
+    if "deg" in units.lower():
+        pitch = np.deg2rad(pitch)
+        roll = np.deg2rad(roll)
+    elif "rad" in units.lower():
+        pass
+    else:
+        raise ValueError("Invalid units")
+
     if "xarray" in type(pitch).__module__:
         pitch = pitch.values
     if "xarray" in type(roll).__module__:
diff --git a/mhkit/dolfyn/rotate/vector.py b/mhkit/dolfyn/rotate/vector.py
index 3fcd856a3..e390322f8 100644
--- a/mhkit/dolfyn/rotate/vector.py
+++ b/mhkit/dolfyn/rotate/vector.py
@@ -279,7 +279,7 @@ def _calc_omat(time, hh, pp, rr, orientation_down=None):
     return _euler2orient(time, hh, pp, rr)
 
 
-def _euler2orient(time, heading, pitch, roll, units="degrees"):
+def _euler2orient(time, heading, pitch, roll, units="degree"):
     # For Nortek data only.
     # The heading, pitch, roll used here are from the Nortek binary files.
 
@@ -287,10 +287,14 @@ def _euler2orient(time, heading, pitch, roll, units="degrees"):
     # Returns a rotation matrix that rotates earth (ENU) -> inst.
     # This is based on the Nortek `Transforms.m` file, available in
     # the refs folder.
-    if units.lower() == "degrees":
+    if "deg" in units.lower():
         pitch = np.deg2rad(pitch)
         roll = np.deg2rad(roll)
         heading = np.deg2rad(heading)
+    elif "rad" in units.lower():
+        pass
+    else:
+        raise ValueError("Invalid units")
 
     # The definition of heading below is consistent with the right-hand-rule;
     # heading is the angle positive counterclockwise from North of the y-axis.
diff --git a/mhkit/dolfyn/velocity.py b/mhkit/dolfyn/velocity.py
index c1923800e..414ea103e 100644
--- a/mhkit/dolfyn/velocity.py
+++ b/mhkit/dolfyn/velocity.py
@@ -402,8 +402,8 @@ def convert_to_CW(angle):
             dims=self.U.dims,
             coords=self.U.coords,
             attrs={
-                "units": "degrees_CW_from_" + str(rel),
-                "long_name": "Water Direction",
+                "units": "degree",
+                "long_name": "Water Direction, CW from " + str(rel),
                 "standard_name": "sea_water_to_direction",
             },
         )

From 35c5ff23a1586d52414d7bf676cda13a0b5a9b36 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Tue, 15 Oct 2024 15:35:34 -0700
Subject: [PATCH 05/14] Fix nan's that were somehow reverted

---
 mhkit/dolfyn/adp/clean.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mhkit/dolfyn/adp/clean.py b/mhkit/dolfyn/adp/clean.py
index 1943d094f..5e8f29c06 100644
--- a/mhkit/dolfyn/adp/clean.py
+++ b/mhkit/dolfyn/adp/clean.py
@@ -159,11 +159,11 @@ def water_depth_from_amplitude(ds, thresh=10, nfilt=None):
     for ip in range(ds["vel"].shape[1]):
         itmp = np.min(inds[:, ip])
         if (edf[itmp:, :, ip] < thresh).all():
-            d[ip] = np.NaN
+            d[ip] = np.nan
 
     if nfilt:
         dfilt = medfiltnan(d, nfilt, thresh=4)
-        dfilt[dfilt == 0] = np.NaN
+        dfilt[dfilt == 0] = np.nan
         d = dfilt
 
     range_offset = __check_for_range_offset(ds)

From 0eabef891c738e9a995a67d17e1a6a4637662a98 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Tue, 22 Oct 2024 11:06:56 -0700
Subject: [PATCH 06/14] RDI beam angle and frequency attribute fixes

---
 examples/data/dolfyn/test_data/vmdas02_os.nc  | Bin 279764 -> 279777 bytes
 .../dolfyn/test_data/winriver02_transect.nc   | Bin 208601 -> 207943 bytes
 mhkit/dolfyn/io/rdi.py                        |  28 +++++++++++++-----
 mhkit/dolfyn/io/rdi_defs.py                   |   2 ++
 4 files changed, 22 insertions(+), 8 deletions(-)

diff --git a/examples/data/dolfyn/test_data/vmdas02_os.nc b/examples/data/dolfyn/test_data/vmdas02_os.nc
index f891b342be10feb727021d773e9d9844f09c3fd1..e39001daa46e2176aa510144607b79bc74b852dc 100644
GIT binary patch
delta 6089
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diff --git a/examples/data/dolfyn/test_data/winriver02_transect.nc b/examples/data/dolfyn/test_data/winriver02_transect.nc
index d48140c416ff4aee85597ef987db0bbde9235ff7..f6ea605903fe6091f1fef9383927b9372bdf45e0 100644
GIT binary patch
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E-$+=EP5=M^

diff --git a/mhkit/dolfyn/io/rdi.py b/mhkit/dolfyn/io/rdi.py
index 0b61e3561..0719676a4 100644
--- a/mhkit/dolfyn/io/rdi.py
+++ b/mhkit/dolfyn/io/rdi.py
@@ -754,11 +754,23 @@ def read_cfgseg(self, bb=False):
         tmp = fd.read_ui8(5)
         prog_ver0 = tmp[0]
         cfg["prog_ver"] = float(tmp[0] + tmp[1] * 0.01)
-        cfg["inst_model"] = defs.adcp_type.get(tmp[0], "unrecognized firmware version")
+        cfg["inst_model"] = defs.adcp_type.get(tmp[0], "unrecognized instrument")
         config = tmp[2:4]
-        cfg["beam_angle"] = [15, 20, 30][(config[1] & 3)]
+        cfg["beam_angle"] = [15, 20, 30, [0, 25][int(tmp[0] in [11, 47, 66])]][
+            (config[1] & 3)
+        ]
         beam5 = [0, 1][int((config[1] & 16) == 16)]
-        cfg["freq"] = [75, 150, 300, 600, 1200, 2400, 38][(config[0] & 7)]
+        # Carrier frequency
+        if tmp[0] in [47, 66]:  # new freqs for Sentinel Vs
+            cfg["freq"] = [38.4, 76.8, 153.6, 307.2, 491.52, 983.04, 2457.6][
+                (config[0] & 7)
+            ]
+        elif tmp[0] == 31:
+            cfg["freq"] = 2000
+        elif tmp[0] == 61:
+            cfg["freq"] = 44
+        else:
+            cfg["freq"] = [75, 150, 300, 600, 1200, 2400, 38][(config[0] & 7)]
         cfg["beam_pattern"] = ["concave", "convex"][int((config[0] & 8) == 8)]
         cfg["orientation"] = ["down", "up"][int((config[0] & 128) == 128)]
         simflag = ["real", "simulated"][tmp[4]]
@@ -810,15 +822,15 @@ def read_cfgseg(self, bb=False):
         if cfg["prog_ver"] >= 8.24:
             cfg["bandwidth"] = fd.read_ui16(1)
             self._nbyte += 2
-        if cfg["prog_ver"] >= 16.05:
+        if cfg["prog_ver"] >= 9.68:
             cfg["power_level"] = fd.read_ui8(1)
-            self._nbyte += 1
-        if cfg["prog_ver"] >= 16.27:
             # cfg['navigator_basefreqindex'] = fd.read_ui8(1)
             fd.seek(1, 1)
             cfg["serialnum"] = fd.read_ui32(1)
-            cfg["beam_angle"] = fd.read_ui8(1)
-            self._nbyte += 6
+            ba = fd.read_ui8(1)
+            if not cfg["beam_angle"]:
+                cfg["beam_angle"] = ba
+            self._nbyte += 7
 
         self.configsize = self.f.tell() - cfgstart
         if self._debug_level > -1:
diff --git a/mhkit/dolfyn/io/rdi_defs.py b/mhkit/dolfyn/io/rdi_defs.py
index 04a6f1fbe..ae358632d 100644
--- a/mhkit/dolfyn/io/rdi_defs.py
+++ b/mhkit/dolfyn/io/rdi_defs.py
@@ -5,6 +5,8 @@
     4: "Broadband",
     5: "Broadband",
     6: "Navigator",
+    8: "Workhorse",
+    9: "Navigator",
     10: "Rio Grande",
     11: "H-ADCP",
     14: "Ocean Surveyor",

From 6f5c559787c6737203247255a4979d7cde3bfffb Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Tue, 22 Oct 2024 11:07:21 -0700
Subject: [PATCH 07/14] Convert to int instead of using bool type as an index

---
 mhkit/dolfyn/io/nortek.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mhkit/dolfyn/io/nortek.py b/mhkit/dolfyn/io/nortek.py
index 0a0656fa7..20605a7c0 100644
--- a/mhkit/dolfyn/io/nortek.py
+++ b/mhkit/dolfyn/io/nortek.py
@@ -518,13 +518,13 @@ def read_user_cfg(self):
         TimCtrlReg = _int2binarray(tmp[8], 16)
         # From the nortek system integrator manual
         # (note: bit numbering is zero-based)
-        cfg_u["usr"]["profile_mode"] = ["single", "continuous"][TimCtrlReg[1]]
+        cfg_u["usr"]["profile_mode"] = ["single", "continuous"][int(TimCtrlReg[1])]
         cfg_u["usr"]["burst_mode"] = str(bool(~TimCtrlReg[2]))
         cfg_u["usr"]["power_level"] = int(TimCtrlReg[5] + 2 * TimCtrlReg[6] + 1)
         cfg_u["usr"]["sync_out_pos"] = [
             "middle",
             "end",
-        ][TimCtrlReg[7]]
+        ][int(TimCtrlReg[7])]
         cfg_u["usr"]["sample_on_sync"] = str(bool(TimCtrlReg[8]))
         cfg_u["usr"]["start_on_sync"] = str(bool(TimCtrlReg[9]))
         cfg_u["PwrCtrlReg"] = _int2binarray(tmp[9], 16)

From effc64ecbaf33698be2d7c7e2d305fd6090dbeca Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Fri, 25 Oct 2024 13:46:46 -0700
Subject: [PATCH 08/14] Do some reshuffling of RDI reader to make it easier to
 read

---
 mhkit/dolfyn/io/base.py     |  28 ++
 mhkit/dolfyn/io/rdi.py      | 788 ++++--------------------------------
 mhkit/dolfyn/io/rdi_defs.py | 688 +++++++++++++++++++++++++++----
 mhkit/dolfyn/io/rdi_lib.py  |  91 +++++
 4 files changed, 818 insertions(+), 777 deletions(-)

diff --git a/mhkit/dolfyn/io/base.py b/mhkit/dolfyn/io/base.py
index 545035cdb..a1ae8b801 100644
--- a/mhkit/dolfyn/io/base.py
+++ b/mhkit/dolfyn/io/base.py
@@ -112,6 +112,34 @@ def _handle_nan(data):
     return data
 
 
+def _remove_gps_duplicates(dat):
+    """
+    Removes duplicate and nan timestamp values in 'time_gps' coordinate,
+    and add hardware (ADCP DAQ) timestamp corresponding to GPS acquisition
+    (in addition to the GPS unit's timestamp).
+    """
+
+    dat["data_vars"]["hdwtime_gps"] = dat["coords"]["time"]
+
+    # Remove duplicate timestamp values, if applicable
+    dat["coords"]["time_gps"], idx = np.unique(
+        dat["coords"]["time_gps"], return_index=True
+    )
+    # Remove nan values, if applicable
+    nan = np.zeros(dat["coords"]["time"].shape, dtype=bool)
+    if any(np.isnan(dat["coords"]["time_gps"])):
+        nan = np.isnan(dat["coords"]["time_gps"])
+        dat["coords"]["time_gps"] = dat["coords"]["time_gps"][~nan]
+
+    for key in dat["data_vars"]:
+        if ("gps" in key) or ("nmea" in key):
+            dat["data_vars"][key] = dat["data_vars"][key][idx]
+            if sum(nan) > 0:
+                dat["data_vars"][key] = dat["data_vars"][key][~nan]
+
+    return dat
+
+
 def _create_dataset(data):
     """
     Creates an xarray dataset from dictionary created from binary
diff --git a/mhkit/dolfyn/io/rdi.py b/mhkit/dolfyn/io/rdi.py
index 0719676a4..8ff1c9489 100644
--- a/mhkit/dolfyn/io/rdi.py
+++ b/mhkit/dolfyn/io/rdi.py
@@ -5,10 +5,10 @@
 from pathlib import Path
 import logging
 
-from .rdi_lib import bin_reader
-from . import rdi_defs as defs
-from .base import _find_userdata, _create_dataset, _abspath
+from . import base
 from .. import time as tmlib
+from . import rdi_lib as lib
+from . import rdi_defs as defs
 from ..rotate.rdi import _calc_beam_orientmat, _calc_orientmat
 from ..rotate.base import _set_coords
 from ..rotate.api import set_declination
@@ -72,7 +72,7 @@ def read_rdi(
     dats = [dat for dat in [datNB, datBB] if dat is not None]
 
     # Read in userdata
-    userdata = _find_userdata(filename, userdata)
+    userdata = base._find_userdata(filename, userdata)
     dss = []
     for dat in dats:
         for nm in userdata:
@@ -84,7 +84,7 @@ def read_rdi(
 
         # GPS data not necessarily sampling at the same rate as ADCP DAQ.
         if "time_gps" in dat["coords"]:
-            dat = _remove_gps_duplicates(dat)
+            dat = base._remove_gps_duplicates(dat)
 
         # Convert time coords to dt64
         t_coords = [t for t in dat["coords"] if "time" in t]
@@ -97,7 +97,7 @@ def read_rdi(
             dat["data_vars"][ky] = tmlib.epoch2dt64(dat["data_vars"][ky])
 
         # Create xarray dataset from upper level dictionary
-        ds = _create_dataset(dat)
+        ds = base._create_dataset(dat)
         ds = _set_coords(ds, ref_frame=ds.coord_sys)
 
         # Create orientation matrices
@@ -131,34 +131,6 @@ def read_rdi(
     return dss[0]
 
 
-def _remove_gps_duplicates(dat):
-    """
-    Removes duplicate and nan timestamp values in 'time_gps' coordinate,
-    and add hardware (ADCP DAQ) timestamp corresponding to GPS acquisition
-    (in addition to the GPS unit's timestamp).
-    """
-
-    dat["data_vars"]["hdwtime_gps"] = dat["coords"]["time"]
-
-    # Remove duplicate timestamp values, if applicable
-    dat["coords"]["time_gps"], idx = np.unique(
-        dat["coords"]["time_gps"], return_index=True
-    )
-    # Remove nan values, if applicable
-    nan = np.zeros(dat["coords"]["time"].shape, dtype=bool)
-    if any(np.isnan(dat["coords"]["time_gps"])):
-        nan = np.isnan(dat["coords"]["time_gps"])
-        dat["coords"]["time_gps"] = dat["coords"]["time_gps"][~nan]
-
-    for key in dat["data_vars"]:
-        if ("gps" in key) or ("nmea" in key):
-            dat["data_vars"][key] = dat["data_vars"][key][idx]
-            if sum(nan) > 0:
-                dat["data_vars"][key] = dat["data_vars"][key][~nan]
-
-    return dat
-
-
 def _set_rdi_declination(dat, fname, inplace):
     """
     If magnetic_var_deg is set, this means that the declination is already
@@ -190,7 +162,7 @@ class _RDIReader:
     def __init__(
         self, fname, navg=1, debug_level=-1, vmdas_search=False, winriver=False
     ):
-        self.fname = _abspath(fname)
+        self.fname = base._abspath(fname)
         print("\nReading file {} ...".format(fname))
         self._debug_level = debug_level
         self._vmdas_search = vmdas_search
@@ -209,7 +181,7 @@ def __init__(
         self.cfg = {}
         self.cfgbb = {}
         self.hdr = {}
-        self.f = bin_reader(self.fname)
+        self.f = lib.bin_reader(self.fname)
 
         # Check header, double buffer, and get filesize
         self._filesize = getsize(self.fname)
@@ -222,13 +194,13 @@ def __init__(
         self.f.seek(self._pos, 0)
         self.n_avg = navg
 
-        self.ensemble = defs._ensemble(self.n_avg, self.cfg["n_cells"])
+        self.ensemble = lib._ensemble(self.n_avg, self.cfg["n_cells"])
         if self._bb:
-            self.ensembleBB = defs._ensemble(self.n_avg, self.cfgbb["n_cells"])
+            self.ensembleBB = lib._ensemble(self.n_avg, self.cfgbb["n_cells"])
 
-        self.vars_read = defs._variable_setlist(["time"])
+        self.vars_read = lib._variable_setlist(["time"])
         if self._bb:
-            self.vars_readBB = defs._variable_setlist(["time"])
+            self.vars_readBB = lib._variable_setlist(["time"])
 
     def code_spacing(self, iternum=50):
         """
@@ -258,9 +230,7 @@ def code_spacing(self, iternum=50):
         return size
 
     def read_hdr(self):
-        """
-        Scan file until 7f7f is found
-        """
+        """Scan file until 7f7f is found"""
         if not self.search_buffer():
             return False
         self._pos = self.f.tell() - 2
@@ -294,17 +264,18 @@ def check_for_double_buffer(self):
             if self._debug_level > -1:
                 logging.info("id {} offset {}".format(id, offset))
             if id == 1:
-                self.read_fixed(bb=True)
+                defs.read_fixed(self, bb=True)
                 found = True
             elif id == 0:
-                self.read_fixed(bb=False)
+                defs.read_fixed(self, bb=False)
             elif id == 16:
-                self.read_fixed_sl()  # bb=True
+                defs.read_fixed_sl(self)  # bb=True
             elif id == 8192:
                 self._vmdas_search = True
         return found
 
     def load_data(self, nens=None):
+        """Main function run after reader class is initiated."""
         if nens is None:
             # Attempt to overshoot WinRiver2 or *Pro filesize
             if (self.cfg["coord_sys"] == "ship") or (
@@ -381,6 +352,7 @@ def load_data(self, nens=None):
         return self.outd, datbb
 
     def init_data(self):
+        """Initiate data structure"""
         outd = {
             "data_vars": {},
             "coords": {},
@@ -416,15 +388,15 @@ def init_data(self):
 
         # Preallocate variables and data sizes
         for nm in defs.data_defs:
-            outd = defs._idata(
-                outd, nm, sz=defs._get_size(nm, self._nens, self.cfg["n_cells"])
+            outd = lib._idata(
+                outd, nm, sz=lib._get_size(nm, self._nens, self.cfg["n_cells"])
             )
         self.outd = outd
 
         if self._bb:
             for nm in defs.data_defs:
-                outdbb = defs._idata(
-                    outdbb, nm, sz=defs._get_size(nm, self._nens, self.cfgbb["n_cells"])
+                outdbb = lib._idata(
+                    outdbb, nm, sz=lib._get_size(nm, self._nens, self.cfgbb["n_cells"])
                 )
             self.outdBB = outdbb
             if self._debug_level > 1:
@@ -436,6 +408,7 @@ def init_data(self):
                 logging.info("{} ncells, BB".format(self.cfgbb["n_cells"]))
 
     def read_buffer(self):
+        """Read through the file"""
         fd = self.f
         self.ensemble.k = -1  # so that k+=1 gives 0 on the first loop.
         if self._bb:
@@ -500,16 +473,6 @@ def read_buffer(self):
 
         return True
 
-    def print_progress(self):
-        self.progress = self.f.tell()
-        if self._debug_level > 1:
-            logging.debug(
-                "  pos %0.0fmb/%0.0fmb\n"
-                % (self.f.tell() / 1048576, self._filesize / 1048576)
-            )
-        if (self.f.tell() - self.progress) < 1048576:
-            return
-
     def search_buffer(self):
         """
         Check to see if the next bytes indicate the beginning of a
@@ -560,9 +523,7 @@ def search_buffer(self):
         return True
 
     def check_eof(self):
-        """
-        Returns True if next header is bad or at end of file.
-        """
+        """Returns True if next header is bad or at end of file."""
         fd = self.f
         out = True
         numbytes = fd.read_i16(1)
@@ -583,6 +544,17 @@ def check_eof(self):
             fd.seek(-2, 1)
         return out
 
+    def print_progress(self):
+        """Print the buffer progress, used for debugging."""
+        self.progress = self.f.tell()
+        if self._debug_level > 1:
+            logging.debug(
+                "  pos %0.0fmb/%0.0fmb\n"
+                % (self.f.tell() / 1048576, self._filesize / 1048576)
+            )
+        if (self.f.tell() - self.progress) < 1048576:
+            return
+
     def print_pos(self, byte_offset=-1):
         """Print the position in the file, used for debugging."""
         if self._debug_level > 1:
@@ -595,60 +567,61 @@ def print_pos(self, byte_offset=-1):
             )
 
     def read_dat(self, id):
+        """Main function map used to read or skip stored IDs"""
         function_map = {
             # 0000 1st profile fixed leader
-            0: (self.read_fixed, []),
+            0: (defs.read_fixed, [self, False]),
             # 0001 2nd profile fixed leader
-            1: (self.read_fixed, [True]),
+            1: (defs.read_fixed, [self, True]),
             # 0010 Surface layer fixed leader (RiverPro & StreamPro)
-            16: (self.read_fixed_sl, []),
+            16: (defs.read_fixed_sl, [self]),
             # 0080 1st profile variable leader
-            128: (self.read_var, [0]),
+            128: (defs.read_var, [self, False]),
             # 0081 2nd profile variable leader
-            129: (self.read_var, [1]),
+            129: (defs.read_var, [self, True]),
             # 0100 1st profile velocity
-            256: (self.read_vel, [0]),
+            256: (defs.read_vel, [self, 0]),
             # 0101 2nd profile velocity
-            257: (self.read_vel, [1]),
+            257: (defs.read_vel, [self, 1]),
             # 0103 Waves first leader
             259: (self.skip_Nbyte, [74]),
             # 0110 Surface layer velocity (RiverPro & StreamPro)
-            272: (self.read_vel, [2]),
+            272: (defs.read_vel, [self, 2]),
             # 0200 1st profile correlation
-            512: (self.read_corr, [0]),
+            512: (defs.read_corr, [self, 0]),
             # 0201 2nd profile correlation
-            513: (self.read_corr, [1]),
+            513: (defs.read_corr, [self, 1]),
             # 0203 Waves data
             515: (self.skip_Nbyte, [186]),
             # 020C Ambient sound profile
             524: (self.skip_Nbyte, [4]),
             # 0210 Surface layer correlation (RiverPro & StreamPro)
-            528: (self.read_corr, [2]),
+            528: (defs.read_corr, [self, 2]),
             # 0300 1st profile amplitude
-            768: (self.read_amp, [0]),
+            768: (defs.read_amp, [self, 0]),
             # 0301 2nd profile amplitude
-            769: (self.read_amp, [1]),
+            769: (defs.read_amp, [self, 1]),
             # 0302 Beam 5 Sum of squared velocities
             770: (self.skip_Ncol, []),
             # 0303 Waves last leader
             771: (self.skip_Ncol, [18]),
             # 0310 Surface layer amplitude (RiverPro & StreamPro)
-            784: (self.read_amp, [2]),
+            784: (defs.read_amp, [self, 2]),
             # 0400 1st profile % good
-            1024: (self.read_prcnt_gd, [0]),
+            1024: (defs.read_prcnt_gd, [self, 0]),
             # 0401 2nd profile pct good
-            1025: (self.read_prcnt_gd, [1]),
+            1025: (defs.read_prcnt_gd, [self, 1]),
             # 0403 Waves HPR data
             1027: (self.skip_Nbyte, [6]),
             # 0410 Surface layer pct good (RiverPro & StreamPro)
-            1040: (self.read_prcnt_gd, [2]),
+            1040: (defs.read_prcnt_gd, [self, 2]),
             # 0500 1st profile status
-            1280: (self.read_status, [0]),
+            1280: (defs.read_status, [self, 0]),
             # 0501 2nd profile status
-            1281: (self.read_status, [1]),
+            1281: (defs.read_status, [self, 1]),
             # 0510 Surface layer status (RiverPro & StreamPro)
-            1296: (self.read_status, [2]),
-            1536: (self.read_bottom, []),  # 0600 bottom tracking
+            1296: (defs.read_status, [self, 2]),
+            1536: (defs.read_bottom, [self]),  # 0600 bottom tracking
             1793: (self.skip_Ncol, [4]),  # 0701 number of pings
             1794: (self.skip_Ncol, [4]),  # 0702 sum of squared vel
             1795: (self.skip_Ncol, [4]),  # 0703 sum of velocities
@@ -659,15 +632,15 @@ def read_dat(self, id):
             # Fixed attitude data format for Ocean Surveyor ADCPs
             3000: (self.skip_Nbyte, [32]),
             3841: (self.skip_Nbyte, [38]),  # 0F01 Beam 5 leader
-            8192: (self.read_vmdas, []),  # 2000
+            8192: (defs.read_vmdas, [self]),  # 2000
             # 2013 Navigation parameter data
             8211: (self.skip_Nbyte, [83]),
-            8226: (self.read_winriver2, []),  # 2022
-            8448: (self.read_winriver, [38]),  # 2100
-            8449: (self.read_winriver, [97]),  # 2101
-            8450: (self.read_winriver, [45]),  # 2102
-            8451: (self.read_winriver, [60]),  # 2103
-            8452: (self.read_winriver, [38]),  # 2104
+            8226: (defs.read_winriver2, [self]),  # 2022
+            8448: (defs.read_winriver, [self]),  # 2100
+            8449: (defs.read_winriver, [self]),  # 2101
+            8450: (defs.read_winriver, [self]),  # 2102
+            8451: (defs.read_winriver, [self]),  # 2103
+            8452: (defs.read_winriver, [self]),  # 2104
             # 3200 Transformation matrix
             12800: (self.skip_Nbyte, [32]),
             # 3000 Fixed attitude data format for Ocean Surveyor ADCPs
@@ -675,7 +648,7 @@ def read_dat(self, id):
             12496: (self.skip_Nbyte, [24]),  # 30D0
             12504: (self.skip_Nbyte, [48]),  # 30D8
             # 4100 beam 5 range
-            16640: (self.read_alt, []),
+            16640: (defs.read_alt, [self]),
             # 4400 Firmware status data (RiverPro & StreamPro)
             17408: (self.skip_Nbyte, [28]),
             # 4401 Auto mode setup (RiverPro & StreamPro)
@@ -705,611 +678,18 @@ def read_dat(self, id):
         else:
             self.read_nocode(id)
 
-    def read_fixed(self, bb=False):
-        self.read_cfgseg(bb=bb)
-        self._nbyte += 2
-        if self._debug_level > -1:
-            logging.info("Read Fixed")
-
-        # Check if n_cells has increased (for winriver transect files)
-        if hasattr(self, "ensemble"):
-            self.n_cells_diff = self.cfg["n_cells"] - self.ensemble["n_cells"]
-            # Increase n_cells if greater than 0
-            if self.n_cells_diff > 0:
-                self.ensemble = defs._ensemble(self.n_avg, self.cfg["n_cells"])
-                if self._debug_level > 0:
-                    logging.warning(
-                        f"Maximum number of cells increased to {self.cfg['n_cells']}"
-                    )
-
-    def read_fixed_sl(self):
-        # Surface layer profile
-        cfg = self.cfg
-        cfg["surface_layer"] = 1
-        n_cells = self.f.read_ui8(1)
-        # Check if n_cells is greater than what was used in prior profiles
-        if n_cells > self.n_cells_sl:
-            self.n_cells_sl = n_cells
-            if self._debug_level > 0:
-                logging.warning(
-                    f"Maximum number of surface layer cells increased to {n_cells}"
-                )
-        cfg["n_cells_sl"] = n_cells
-        # Assuming surface layer profile cell size never changes
-        cfg["cell_size_sl"] = float(self.f.read_ui16(1) * 0.01)
-        cfg["bin1_dist_m_sl"] = round(float(self.f.read_ui16(1) * 0.01), 4)
-
-        if self._debug_level > -1:
-            logging.info("Read Surface Layer Config")
-        self._nbyte = 2 + 5
-
-    def read_cfgseg(self, bb=False):
-        cfgstart = self.f.tell()
-
-        if bb:
-            cfg = self.cfgbb
-        else:
-            cfg = self.cfg
-        fd = self.f
-        tmp = fd.read_ui8(5)
-        prog_ver0 = tmp[0]
-        cfg["prog_ver"] = float(tmp[0] + tmp[1] * 0.01)
-        cfg["inst_model"] = defs.adcp_type.get(tmp[0], "unrecognized instrument")
-        config = tmp[2:4]
-        cfg["beam_angle"] = [15, 20, 30, [0, 25][int(tmp[0] in [11, 47, 66])]][
-            (config[1] & 3)
-        ]
-        beam5 = [0, 1][int((config[1] & 16) == 16)]
-        # Carrier frequency
-        if tmp[0] in [47, 66]:  # new freqs for Sentinel Vs
-            cfg["freq"] = [38.4, 76.8, 153.6, 307.2, 491.52, 983.04, 2457.6][
-                (config[0] & 7)
-            ]
-        elif tmp[0] == 31:
-            cfg["freq"] = 2000
-        elif tmp[0] == 61:
-            cfg["freq"] = 44
-        else:
-            cfg["freq"] = [75, 150, 300, 600, 1200, 2400, 38][(config[0] & 7)]
-        cfg["beam_pattern"] = ["concave", "convex"][int((config[0] & 8) == 8)]
-        cfg["orientation"] = ["down", "up"][int((config[0] & 128) == 128)]
-        simflag = ["real", "simulated"][tmp[4]]
-        fd.seek(1, 1)
-        cfg["n_beams"] = fd.read_ui8(1) + beam5
-        # Check if number of cells has changed
-        n_cells = fd.read_ui8(1)
-        if ("n_cells" not in cfg) or (n_cells != cfg["n_cells"]):
-            cfg["n_cells"] = n_cells
-            if self._debug_level > 0:
-                logging.info(f"Number of cells set to {cfg['n_cells']}")
-        cfg["pings_per_ensemble"] = fd.read_ui16(1)
-        # Check if cell size has changed
-        cs = float(fd.read_ui16(1) * 0.01)
-        if ("cell_size" not in cfg) or (cs != cfg["cell_size"]):
-            self.cs_diff = cs if "cell_size" not in cfg else (cs - cfg["cell_size"])
-            cfg["cell_size"] = cs
-            if self._debug_level > 0:
-                logging.info(f"Cell size set to {cfg['cell_size']}")
-        cfg["blank_dist"] = round(float(fd.read_ui16(1) * 0.01), 2)
-        cfg["profiling_mode"] = fd.read_ui8(1)
-        cfg["min_corr_threshold"] = fd.read_ui8(1)
-        cfg["n_code_reps"] = fd.read_ui8(1)
-        cfg["min_prcnt_gd"] = fd.read_ui8(1)
-        cfg["max_error_vel"] = float(fd.read_ui16(1) * 0.001)
-        cfg["sec_between_ping_groups"] = round(
-            float(np.sum(np.array(fd.read_ui8(3)) * [60.0, 1.0, 0.01])), 3
-        )
-        coord_sys = fd.read_ui8(1)
-        cfg["coord_sys"] = ["beam", "inst", "ship", "earth"][((coord_sys >> 3) & 3)]
-        cfg["use_pitchroll"] = ["no", "yes"][(coord_sys & 4) == 4]
-        cfg["use_3beam"] = ["no", "yes"][(coord_sys & 2) == 2]
-        cfg["bin_mapping"] = ["no", "yes"][(coord_sys & 1) == 1]
-        cfg["heading_misalign_deg"] = float(fd.read_i16(1) * 0.01)
-        cfg["magnetic_var_deg"] = float(fd.read_i16(1) * 0.01)
-        cfg["sensors_src"] = np.binary_repr(fd.read_ui8(1), 8)
-        cfg["sensors_avail"] = np.binary_repr(fd.read_ui8(1), 8)
-        cfg["bin1_dist_m"] = round(float(fd.read_ui16(1) * 0.01), 4)
-        cfg["transmit_pulse_m"] = round(float(fd.read_ui16(1) * 0.01), 2)
-        cfg["water_ref_cells"] = list(fd.read_ui8(2).astype(list))  # list for attrs
-        cfg["false_target_threshold"] = fd.read_ui8(1)
-        fd.seek(1, 1)
-        cfg["transmit_lag_m"] = float(fd.read_ui16(1) * 0.01)
-        self._nbyte = 40
-
-        if cfg["prog_ver"] >= 8.14:
-            cpu_serialnum = fd.read_ui8(8)
-            self._nbyte += 8
-        if cfg["prog_ver"] >= 8.24:
-            cfg["bandwidth"] = fd.read_ui16(1)
-            self._nbyte += 2
-        if cfg["prog_ver"] >= 9.68:
-            cfg["power_level"] = fd.read_ui8(1)
-            # cfg['navigator_basefreqindex'] = fd.read_ui8(1)
-            fd.seek(1, 1)
-            cfg["serialnum"] = fd.read_ui32(1)
-            ba = fd.read_ui8(1)
-            if not cfg["beam_angle"]:
-                cfg["beam_angle"] = ba
-            self._nbyte += 7
-
-        self.configsize = self.f.tell() - cfgstart
-        if self._debug_level > -1:
-            logging.info("Read Config")
-
-    def read_var(self, bb=False):
-        """Read variable leader"""
-        fd = self.f
-        if bb:
-            ens = self.ensembleBB
-        else:
-            ens = self.ensemble
-        ens.k += 1
-        ens = self.ensemble
-        k = ens.k
-        self.vars_read += [
-            "number",
-            "rtc",
-            "number",
-            "builtin_test_fail",
-            "c_sound",
-            "depth",
-            "heading",
-            "pitch",
-            "roll",
-            "salinity",
-            "temp",
-            "min_preping_wait",
-            "heading_std",
-            "pitch_std",
-            "roll_std",
-            "adc",
-        ]
-        ens.number[k] = fd.read_ui16(1)
-        ens.rtc[:, k] = fd.read_ui8(7)
-        ens.number[k] += 65535 * fd.read_ui8(1)
-        ens.builtin_test_fail[k] = fd.read_ui16(1)
-        ens.c_sound[k] = fd.read_ui16(1)
-        ens.depth[k] = fd.read_ui16(1) * 0.1
-        ens.heading[k] = fd.read_ui16(1) * 0.01
-        ens.pitch[k] = fd.read_i16(1) * 0.01
-        ens.roll[k] = fd.read_i16(1) * 0.01
-        ens.salinity[k] = fd.read_i16(1)
-        ens.temp[k] = fd.read_i16(1) * 0.01
-        ens.min_preping_wait[k] = (fd.read_ui8(3) * np.array([60, 1, 0.01])).sum()
-        ens.heading_std[k] = fd.read_ui8(1)
-        ens.pitch_std[k] = fd.read_ui8(1) * 0.1
-        ens.roll_std[k] = fd.read_ui8(1) * 0.1
-        ens.adc[:, k] = fd.read_i8(8)
-        self._nbyte = 2 + 40
-
-        cfg = self.cfg
-        if cfg["inst_model"].lower() == "broadband":
-            if cfg["prog_ver"] >= 5.55:
-                fd.seek(15, 1)
-                cent = fd.read_ui8(1)
-                ens.rtc[:, k] = fd.read_ui8(7)
-                ens.rtc[0, k] = ens.rtc[0, k] + cent * 100
-                self._nbyte += 23
-        elif cfg["inst_model"].lower() == "ocean surveyor":
-            fd.seek(16, 1)  # 30 bytes all set to zero, 14 read above
-            self._nbyte += 16
-            if cfg["prog_ver"] > 23:
-                fd.seek(2, 1)
-                self._nbyte += 2
-        else:
-            ens.error_status[k] = np.binary_repr(fd.read_ui32(1), 32)
-            self.vars_read += ["pressure", "pressure_std"]
-            self._nbyte += 4
-            if cfg["prog_ver"] >= 8.13:
-                # Added pressure sensor stuff in 8.13
-                fd.seek(2, 1)
-                ens.pressure[k] = fd.read_ui32(1) * 0.001  # dPa to dbar
-                ens.pressure_std[k] = fd.read_ui32(1) * 0.001
-                self._nbyte += 10
-            if cfg["prog_ver"] >= 8.24:
-                # Spare byte added 8.24
-                fd.seek(1, 1)
-                self._nbyte += 1
-            if cfg["prog_ver"] >= 16.05:
-                # Added more fields with century in clock
-                cent = fd.read_ui8(1)
-                ens.rtc[:, k] = fd.read_ui8(7)
-                ens.rtc[0, k] = ens.rtc[0, k] + cent * 100
-                self._nbyte += 8
-            if cfg["prog_ver"] >= 56:
-                fd.seek(1)  # lag near bottom flag
-                self._nbyte += 1
-
-        if self._debug_level > -1:
-            logging.info("Read Var")
-
-    def switch_profile(self, bb):
-        if bb == 1:
-            ens = self.ensembleBB
-            cfg = self.cfgbb
-            # Placeholder for dual profile mode
-            # Solution for vmdas profile in bb spot (vs nb)
-            tag = ""
-        elif bb == 2:
-            ens = self.ensemble
-            cfg = self.cfg
-            tag = "_sl"
-        else:
-            ens = self.ensemble
-            cfg = self.cfg
-            tag = ""
-
-        return ens, cfg, tag
-
-    def read_vel(self, bb=0):
-        ens, cfg, tg = self.switch_profile(bb)
-        self.vars_read += ["vel" + tg]
-        n_cells = cfg["n_cells" + tg]
-
-        k = ens.k
-        vel = np.array(self.f.read_i16(4 * n_cells)).reshape((n_cells, 4)) * 0.001
-        ens["vel" + tg][:n_cells, :, k] = vel
-        self._nbyte = 2 + 4 * n_cells * 2
-        if self._debug_level > -1:
-            logging.info("Read Vel")
-
-    def read_corr(self, bb=0):
-        ens, cfg, tg = self.switch_profile(bb)
-        self.vars_read += ["corr" + tg]
-        n_cells = cfg["n_cells" + tg]
-
-        k = ens.k
-        ens["corr" + tg][:n_cells, :, k] = np.array(
-            self.f.read_ui8(4 * n_cells)
-        ).reshape((n_cells, 4))
-        self._nbyte = 2 + 4 * n_cells
-        if self._debug_level > -1:
-            logging.info("Read Corr")
-
-    def read_amp(self, bb=0):
-        ens, cfg, tg = self.switch_profile(bb)
-        self.vars_read += ["amp" + tg]
-        n_cells = cfg["n_cells" + tg]
-
-        k = ens.k
-        ens["amp" + tg][:n_cells, :, k] = np.array(
-            self.f.read_ui8(4 * n_cells)
-        ).reshape((n_cells, 4))
-        self._nbyte = 2 + 4 * n_cells
-        if self._debug_level > -1:
-            logging.info("Read Amp")
-
-    def read_prcnt_gd(self, bb=0):
-        ens, cfg, tg = self.switch_profile(bb)
-        self.vars_read += ["prcnt_gd" + tg]
-        n_cells = cfg["n_cells" + tg]
-
-        ens["prcnt_gd" + tg][:n_cells, :, ens.k] = np.array(
-            self.f.read_ui8(4 * n_cells)
-        ).reshape((n_cells, 4))
-        self._nbyte = 2 + 4 * n_cells
-        if self._debug_level > -1:
-            logging.info("Read PG")
-
-    def read_status(self, bb=0):
-        ens, cfg, tg = self.switch_profile(bb)
-        self.vars_read += ["status" + tg]
-        n_cells = cfg["n_cells" + tg]
-
-        ens["status" + tg][:n_cells, :, ens.k] = np.array(
-            self.f.read_ui8(4 * n_cells)
-        ).reshape((n_cells, 4))
-        self._nbyte = 2 + 4 * n_cells
-        if self._debug_level > -1:
-            logging.info("Read Status")
-
-    def read_bottom(self):
-        self.vars_read += ["dist_bt", "vel_bt", "corr_bt", "amp_bt", "prcnt_gd_bt"]
-        fd = self.f
-        ens = self.ensemble
-        k = ens.k
-        cfg = self.cfg
-        if self._vm_source == 2:
-            self.vars_read += ["latitude_gps", "longitude_gps"]
-            fd.seek(2, 1)
-            long1 = fd.read_ui16(1)
-            fd.seek(6, 1)
-            ens.latitude_gps[k] = fd.read_i32(1) * self._cfac32
-            if ens.latitude_gps[k] == 0:
-                ens.latitude_gps[k] = np.nan
-        else:
-            fd.seek(14, 1)
-        ens.dist_bt[:, k] = fd.read_ui16(4) * 0.01
-        ens.vel_bt[:, k] = fd.read_i16(4) * 0.001
-        ens.corr_bt[:, k] = fd.read_ui8(4)
-        ens.amp_bt[:, k] = fd.read_ui8(4)
-        ens.prcnt_gd_bt[:, k] = fd.read_ui8(4)
-        if self._vm_source == 2:
-            fd.seek(2, 1)
-            ens.longitude_gps[k] = (long1 + 65536 * fd.read_ui16(1)) * self._cfac32
-            if ens.longitude_gps[k] > 180:
-                ens.longitude_gps[k] = ens.longitude_gps[k] - 360
-            if ens.longitude_gps[k] == 0:
-                ens.longitude_gps[k] = np.nan
-            fd.seek(16, 1)
-            qual = fd.read_ui8(1)
-            if qual == 0:
-                if self._debug_level > 0:
-                    logging.info(
-                        "  qual==%d,%f %f"
-                        % (qual, ens.latitude_gps[k], ens.longitude_gps[k])
-                    )
-                ens.latitude_gps[k] = np.nan
-                ens.longitude_gps[k] = np.nan
-            fd.seek(71 - 45 - 16 - 17, 1)
-            self._nbyte = 2 + 68
-        else:
-            # Skip reference layer data
-            fd.seek(26, 1)
-            self._nbyte = 2 + 68
-        if cfg["prog_ver"] >= 5.3:
-            fd.seek(7, 1)  # skip to rangeMsb bytes
-            ens.dist_bt[:, k] = ens.dist_bt[:, k] + fd.read_ui8(4) * 655.36
-            self._nbyte += 11
-        if cfg["prog_ver"] >= 16.2 and (cfg.get("sourceprog") != "WINRIVER"):
-            fd.seek(4, 1)  # not documented
-            self._nbyte += 4
-        if cfg["prog_ver"] >= 56.1:
-            fd.seek(4, 1)  # not documented
-            self._nbyte += 4
-
-        if self._debug_level > -1:
-            logging.info("Read Bottom Track")
-
-    def read_alt(self):
-        """Read altimeter (vertical beam range)"""
-        fd = self.f
-        ens = self.ensemble
-        k = ens.k
-        self.vars_read += ["alt_dist", "alt_rssi", "alt_eval", "alt_status"]
-        ens.alt_eval[k] = fd.read_ui8(1)  # evaluation amplitude
-        ens.alt_rssi[k] = fd.read_ui8(1)  # RSSI amplitude
-        ens.alt_dist[k] = fd.read_ui32(1) * 0.001  # range to surface/seafloor
-        ens.alt_status[k] = fd.read_ui8(1)  # status bit flags
-        self._nbyte = 7 + 2
-        if self._debug_level > -1:
-            logging.info("Read Altimeter")
-
-    def read_vmdas(self):
-        """Read VMDAS Navigation block"""
-        fd = self.f
-        self.cfg["sourceprog"] = "VMDAS"
-        ens = self.ensemble
-        k = ens.k
-        if self._vm_source != 1 and self._debug_level > -1:
-            logging.info("  \n***** Apparently a VMDAS file \n\n")
-        self._vm_source = 1
-        self.vars_read += [
-            "time_gps",
-            "clock_offset_UTC_gps",
-            "latitude_gps",
-            "longitude_gps",
-            "avg_speed_gps",
-            "avg_dir_gps",
-            "speed_made_good_gps",
-            "dir_made_good_gps",
-            "flags_gps",
-            "pitch_gps",
-            "roll_gps",
-            "heading_gps",
-        ]
-        # UTC date time
-        utim = fd.read_ui8(4)
-        date_utc = tmlib.datetime(utim[2] + utim[3] * 256, utim[1], utim[0])
-
-        # 1st lat/lon position after previous ADCP ping
-        # This byte is in hundredths of seconds (10s of milliseconds):
-        utc_time_first_fix = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) * 0.1)))
-        ens.clock_offset_UTC_gps[k] = (
-            fd.read_i32(1) * 0.001
-        )  # "PC clock offset from UTC" in ms
-        latitude_first_gps = fd.read_i32(1) * self._cfac32
-        longitude_first_gps = fd.read_i32(1) * self._cfac32
-
-        # Last lat/lon position prior to current ADCP ping
-        utc_time_fix = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) * 0.1)))
-        ens.time_gps[k] = tmlib.date2epoch(date_utc + utc_time_fix)[0]
-        ens.latitude_gps[k] = fd.read_i32(1) * self._cfac32
-        ens.longitude_gps[k] = fd.read_i32(1) * self._cfac32
-
-        ens.avg_speed_gps[k] = fd.read_ui16(1) * 0.001
-        ens.avg_dir_gps[k] = fd.read_ui16(1) * self._cfac16  # avg true track
-        fd.seek(2, 1)  # avg magnetic track
-        ens.speed_made_good_gps[k] = fd.read_ui16(1) * 0.001
-        ens.dir_made_good_gps[k] = fd.read_ui16(1) * self._cfac16
-        fd.seek(2, 1)  # reserved
-        ens.flags_gps[k] = int(np.binary_repr(fd.read_ui16(1)))
-        fd.seek(6, 1)  # reserved, ADCP ensemble #
-
-        # ADCP date time
-        utim = fd.read_ui8(4)
-        date_adcp = tmlib.datetime(utim[0] + utim[1] * 256, utim[3], utim[2])
-        time_adcp = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) * 0.1)))
-
-        ens.pitch_gps[k] = fd.read_ui16(1) * self._cfac16
-        ens.roll_gps[k] = fd.read_ui16(1) * self._cfac16
-        ens.heading_gps[k] = fd.read_ui16(1) * self._cfac16
-
-        fd.seek(10, 1)
-        self._nbyte = 2 + 76
-
-        if self._debug_level > -1:
-            logging.info("Read VMDAS")
-        self._read_vmdas = True
-
-    def read_winriver2(self):
-        startpos = self.f.tell()
-        self._winrivprob = True
-        self.cfg["sourceprog"] = "WinRiver2"
-        ens = self.ensemble
-        k = ens.k
-        if self._debug_level > -1:
-            logging.info("Read WinRiver2")
-        self._vm_source = 3
-
-        spid = self.f.read_ui16(1)  # NMEA specific IDs
-        if spid in [4, 104]:  # GGA
-            sz = self.f.read_ui16(1)
-            dtime = self.f.read_f64(1)
-            if sz <= 43:  # If no sentence, data is still stored in nmea format
-                empty_gps = self.f.reads(sz - 2)
-                self.f.seek(2, 1)
-            else:  # TRDI rewrites the nmea string into their format if one is found
-                start_string = self.f.reads(6)
-                if not isinstance(start_string, str):
-                    if self._debug_level > 0:
-                        logging.warning(
-                            f"Invalid GGA string found in ensemble {k}," " skipping..."
-                        )
-                    return "FAIL"
-                self.f.seek(1, 1)
-                gga_time = self.f.reads(9)
-                time = tmlib.timedelta(
-                    hours=int(gga_time[0:2]),
-                    minutes=int(gga_time[2:4]),
-                    seconds=int(gga_time[4:6]),
-                    milliseconds=int(float(gga_time[6:]) * float(1000)),
-                )
-                clock = self.ensemble.rtc[:, :]
-                if clock[0, 0] < 100:
-                    clock[0, :] += defs.century
-                date = tmlib.datetime(*clock[:3, 0]) + time
-                ens.time_gps[k] = tmlib.date2epoch(date)[0]
-                self.f.seek(1, 1)
-                ens.latitude_gps[k] = self.f.read_f64(1)
-                tcNS = self.f.reads(1)  # 'N' or 'S'
-                if tcNS == "S":
-                    ens.latitude_gps[k] *= -1
-                ens.longitude_gps[k] = self.f.read_f64(1)
-                tcEW = self.f.reads(1)  # 'E' or 'W'
-                if tcEW == "W":
-                    ens.longitude_gps[k] *= -1
-                ens.fix_gps[k] = self.f.read_ui8(1)  # gps fix type/quality
-                ens.n_sat_gps[k] = self.f.read_ui8(1)  # of satellites
-                # horizontal dilution of precision
-                ens.hdop_gps[k] = self.f.read_f32(1)
-                ens.elevation_gps[k] = self.f.read_f32(1)  # altitude
-                m = self.f.reads(1)  # altitude unit, 'm'
-                h_geoid = self.f.read_f32(1)  # height of geoid
-                m2 = self.f.reads(1)  # geoid unit, 'm'
-                ens.rtk_age_gps[k] = self.f.read_f32(1)
-                station_id = self.f.read_ui16(1)
-            self.vars_read += [
-                "time_gps",
-                "longitude_gps",
-                "latitude_gps",
-                "fix_gps",
-                "n_sat_gps",
-                "hdop_gps",
-                "elevation_gps",
-                "rtk_age_gps",
-            ]
-            self._nbyte = self.f.tell() - startpos + 2
-
-        elif spid in [5, 105]:  # VTG
-            sz = self.f.read_ui16(1)
-            dtime = self.f.read_f64(1)
-            if sz <= 22:  # if no data
-                empty_gps = self.f.reads(sz - 2)
-                self.f.seek(2, 1)
-            else:
-                start_string = self.f.reads(6)
-                if not isinstance(start_string, str):
-                    if self._debug_level > 0:
-                        logging.warning(
-                            f"Invalid VTG string found in ensemble {k}," " skipping..."
-                        )
-                    return "FAIL"
-                self.f.seek(1, 1)
-                true_track = self.f.read_f32(1)
-                t = self.f.reads(1)  # 'T'
-                magn_track = self.f.read_f32(1)
-                m = self.f.reads(1)  # 'M'
-                speed_knot = self.f.read_f32(1)
-                kts = self.f.reads(1)  # 'N'
-                speed_kph = self.f.read_f32(1)
-                kph = self.f.reads(1)  # 'K'
-                mode = self.f.reads(1)
-                # knots -> m/s
-                ens.speed_over_grnd_gps[k] = speed_knot / 1.944
-                ens.dir_over_grnd_gps[k] = true_track
-            self.vars_read += ["speed_over_grnd_gps", "dir_over_grnd_gps"]
-            self._nbyte = self.f.tell() - startpos + 2
-
-        elif spid in [6, 106]:  # 'DBT' depth sounder
-            sz = self.f.read_ui16(1)
-            dtime = self.f.read_f64(1)
-            if sz <= 20:
-                empty_gps = self.f.reads(sz - 2)
-                self.f.seek(2, 1)
-            else:
-                start_string = self.f.reads(6)
-                if not isinstance(start_string, str):
-                    if self._debug_level > 0:
-                        logging.warning(
-                            f"Invalid DBT string found in ensemble {k}," " skipping..."
-                        )
-                    return "FAIL"
-                self.f.seek(1, 1)
-                depth_ft = self.f.read_f32(1)
-                ft = self.f.reads(1)  # 'f'
-                depth_m = self.f.read_f32(1)
-                m = self.f.reads(1)  # 'm'
-                depth_fathom = self.f.read_f32(1)
-                f = self.f.reads(1)  # 'F'
-                ens.dist_nmea[k] = depth_m
-            self.vars_read += ["dist_nmea"]
-            self._nbyte = self.f.tell() - startpos + 2
-
-        elif spid in [7, 107]:  # 'HDT'
-            sz = self.f.read_ui16(1)
-            dtime = self.f.read_f64(1)
-            if sz <= 14:
-                empty_gps = self.f.reads(sz - 2)
-                self.f.seek(2, 1)
-            else:
-                start_string = self.f.reads(6)
-                if not isinstance(start_string, str):
-                    if self._debug_level > 0:
-                        logging.warning(
-                            f"Invalid HDT string found in ensemble {k}," " skipping..."
-                        )
-                    return "FAIL"
-                self.f.seek(1, 1)
-                ens.heading_gps[k] = self.f.read_f64(1)
-                tt = self.f.reads(1)
-            self.vars_read += ["heading_gps"]
-            self._nbyte = self.f.tell() - startpos + 2
-
-    def read_winriver(self, nbt):
-        self._winrivprob = True
-        self.cfg["sourceprog"] = "WINRIVER"
-        if self._vm_source not in [2, 3]:
-            if self._debug_level > -1:
-                logging.warning(
-                    "\n***** Apparently a WINRIVER file - "
-                    "Raw NMEA data handler not yet implemented\n"
-                )
-            self._vm_source = 2
-        startpos = self.f.tell()
-        sz = self.f.read_ui16(1)
-        tmp = self.f.reads(sz - 2)
-        self._nbyte = self.f.tell() - startpos + 2
-
     def skip_Ncol(self, n_skip=1):
+        """Skip specified number of columns. For profile measurements."""
         self.f.seek(n_skip * self.cfg["n_cells"], 1)
         self._nbyte = 2 + n_skip * self.cfg["n_cells"]
 
     def skip_Nbyte(self, n_skip):
+        """Skip specified number of bytes. For non-profile measurements."""
         self.f.seek(n_skip, 1)
         self._nbyte = 2 + n_skip
 
     def read_nocode(self, id):
+        """Identify filler or unknown bytes and bypass them"""
         # Skipping bytes from codes 0340-30FC, commented if needed
         hxid = hex(id)
         if hxid[2:4] == "30":
@@ -1331,7 +711,7 @@ def read_nocode(self, id):
             self.skip_nocode(id)
 
     def skip_nocode(self, id):
-        # Skipping bytes if ID isn't known
+        """Skip bytes if ID is not in function map"""
         offsets = list(self.id_positions.values())
         idx = np.where(offsets == self.id_positions[id])[0][0]
         byte_len = offsets[idx + 1] - offsets[idx] - 2
@@ -1341,6 +721,10 @@ def skip_nocode(self, id):
             logging.debug(f"Skipping ID code {id}\n")
 
     def check_offset(self, offset, readbytes):
+        """
+        Check distance to nearest function ID and adjust file
+        position as necessary
+        """
         fd = self.f
         if offset != 4 and self._fixoffset == 0:
             if self._debug_level > 0:
@@ -1358,14 +742,16 @@ def check_offset(self, offset, readbytes):
         fd.seek(4 + self._fixoffset, 1)
 
     def remove_end(self, iens):
+        """Remove uncompleted measurements"""
         dat = self.outd
         if self._debug_level > 0:
             logging.info("  Encountered end of file.  Cleaning up data.")
         for nm in self.vars_read:
-            defs._setd(dat, nm, defs._get(dat, nm)[..., :iens])
+            lib._setd(dat, nm, lib._get(dat, nm)[..., :iens])
 
     def save_profiles(self, dat, nm, en, iens):
-        ds = defs._get(dat, nm)
+        """Reformat and save profile measurements"""
+        ds = lib._get(dat, nm)
         if self.n_avg == 1:
             bn = en[nm][..., 0]
         else:
@@ -1386,7 +772,7 @@ def save_profiles(self, dat, nm, en, iens):
                 if self.n_cells_diff > 0:
                     a = np.empty((self.n_cells_diff, ds.shape[1], ds.shape[2])) * np.nan
                     ds = np.append(ds, a.astype(ds.dtype), axis=0)
-                    defs._setd(dat, nm, ds)
+                    lib._setd(dat, nm, ds)
             # If the number of cells decreases, set extra cells to nan instead of
             # whatever is stuck in memory
             if ds.shape[0] != bn.shape[0]:
@@ -1401,11 +787,13 @@ def save_profiles(self, dat, nm, en, iens):
 
         # Then copy the ensemble to the dataset.
         ds[..., iens] = bn
-        defs._setd(dat, nm, ds)
+        lib._setd(dat, nm, ds)
 
         return dat
 
     def cleanup(self, dat, cfg):
+        """Clean up recorded data, particularly variablem cell sizes
+        and profile ranges."""
         # Clean up changing cell size, if necessary
         cs = np.array(self.cs, dtype=np.float32)
         cell_sizes = cs[:, 1]
@@ -1456,9 +844,7 @@ def cleanup(self, dat, cfg):
         return dat, cfg
 
     def reshape(self, arr, n_bin=None):
-        """
-        Reshape the array `arr` to shape (...,n,n_bin).
-        """
+        """Reshape the array `arr` to shape (...,n,n_bin)."""
 
         out = np.zeros(
             list(arr.shape[:-1]) + [int(arr.shape[-1] // n_bin), int(n_bin)],
@@ -1484,12 +870,10 @@ def reshape(self, arr, n_bin=None):
         return out
 
     def finalize(self, dat):
-        """
-        Remove the attributes from the data that were never loaded.
-        """
+        """Remove the attributes from the data that were never loaded."""
 
         for nm in set(defs.data_defs.keys()) - self.vars_read:
-            defs._pop(dat, nm)
+            lib._pop(dat, nm)
         for nm in self.cfg:
             dat["attrs"][nm] = self.cfg[nm]
 
@@ -1504,7 +888,7 @@ def finalize(self, dat):
 
         for nm in defs.data_defs:
             shp = defs.data_defs[nm][0]
-            if len(shp) and shp[0] == "nc" and defs._in_group(dat, nm):
-                defs._setd(dat, nm, np.swapaxes(defs._get(dat, nm), 0, 1))
+            if len(shp) and shp[0] == "nc" and lib._in_group(dat, nm):
+                lib._setd(dat, nm, np.swapaxes(lib._get(dat, nm), 0, 1))
 
         return dat
diff --git a/mhkit/dolfyn/io/rdi_defs.py b/mhkit/dolfyn/io/rdi_defs.py
index ae358632d..adb5991ac 100644
--- a/mhkit/dolfyn/io/rdi_defs.py
+++ b/mhkit/dolfyn/io/rdi_defs.py
@@ -1,4 +1,9 @@
 import numpy as np
+import logging
+
+from . import rdi_lib as lib
+from .. import time as tmlib
+
 
 century = np.uint16(2000)
 adcp_type = {
@@ -324,90 +329,623 @@
 }
 
 
-def _get(dat, nm):
-    grp = data_defs[nm][1]
-    if grp is None:
-        return dat[nm]
+def switch_profile(rdr, bb):
+    """Switch between bb, nb and sl profiles"""
+    if bb == 1:
+        ens = rdr.ensembleBB
+        cfg = rdr.cfgbb
+        # Placeholder for dual profile mode
+        # Solution for vmdas profile in bb spot (vs nb)
+        tag = ""
+    elif bb == 2:
+        ens = rdr.ensemble
+        cfg = rdr.cfg
+        tag = "_sl"
     else:
-        return dat[grp][nm]
+        ens = rdr.ensemble
+        cfg = rdr.cfg
+        tag = ""
+
+    return ens, cfg, tag
+
 
+def read_cfgseg(rdr, bb=False):
+    """Read ensemble configuration header"""
+    cfgstart = rdr.f.tell()
 
-def _in_group(dat, nm):
-    grp = data_defs[nm][1]
-    if grp is None:
-        return nm in dat
+    if bb:
+        cfg = rdr.cfgbb
     else:
-        return nm in dat[grp]
+        cfg = rdr.cfg
+    fd = rdr.f
+    tmp = fd.read_ui8(5)
+    prog_ver0 = tmp[0]
+    cfg["prog_ver"] = float(tmp[0] + tmp[1] * 0.01)
+    cfg["inst_model"] = adcp_type.get(tmp[0], "unrecognized instrument")
+    config = tmp[2:4]
+    cfg["beam_angle"] = [15, 20, 30, [0, 25][int(tmp[0] in [11, 47, 66])]][
+        (config[1] & 3)
+    ]
+    beam5 = [0, 1][int((config[1] & 16) == 16)]
+    # Carrier frequency
+    if tmp[0] in [47, 66]:  # new freqs for Sentinel Vs
+        cfg["freq"] = [38.4, 76.8, 153.6, 307.2, 491.52, 983.04, 2457.6][
+            (config[0] & 7)
+        ]
+    elif tmp[0] == 31:
+        cfg["freq"] = 2000
+    elif tmp[0] == 61:
+        cfg["freq"] = 44
+    else:
+        cfg["freq"] = [75, 150, 300, 600, 1200, 2400, 38][(config[0] & 7)]
+    cfg["beam_pattern"] = ["concave", "convex"][int((config[0] & 8) == 8)]
+    cfg["orientation"] = ["down", "up"][int((config[0] & 128) == 128)]
+    simflag = ["real", "simulated"][tmp[4]]
+    fd.seek(1, 1)
+    cfg["n_beams"] = fd.read_ui8(1) + beam5
+    # Check if number of cells has changed
+    n_cells = fd.read_ui8(1)
+    if ("n_cells" not in cfg) or (n_cells != cfg["n_cells"]):
+        cfg["n_cells"] = n_cells
+        if rdr._debug_level > 0:
+            logging.info(f"Number of cells set to {cfg['n_cells']}")
+    cfg["pings_per_ensemble"] = fd.read_ui16(1)
+    # Check if cell size has changed
+    cs = float(fd.read_ui16(1) * 0.01)
+    if ("cell_size" not in cfg) or (cs != cfg["cell_size"]):
+        rdr.cs_diff = cs if "cell_size" not in cfg else (cs - cfg["cell_size"])
+        cfg["cell_size"] = cs
+        if rdr._debug_level > 0:
+            logging.info(f"Cell size set to {cfg['cell_size']}")
+    cfg["blank_dist"] = round(float(fd.read_ui16(1) * 0.01), 2)
+    cfg["profiling_mode"] = fd.read_ui8(1)
+    cfg["min_corr_threshold"] = fd.read_ui8(1)
+    cfg["n_code_reps"] = fd.read_ui8(1)
+    cfg["min_prcnt_gd"] = fd.read_ui8(1)
+    cfg["max_error_vel"] = float(fd.read_ui16(1) * 0.001)
+    cfg["sec_between_ping_groups"] = round(
+        float(np.sum(np.array(fd.read_ui8(3)) * [60.0, 1.0, 0.01])), 3
+    )
+    coord_sys = fd.read_ui8(1)
+    cfg["coord_sys"] = ["beam", "inst", "ship", "earth"][((coord_sys >> 3) & 3)]
+    cfg["use_pitchroll"] = ["no", "yes"][(coord_sys & 4) == 4]
+    cfg["use_3beam"] = ["no", "yes"][(coord_sys & 2) == 2]
+    cfg["bin_mapping"] = ["no", "yes"][(coord_sys & 1) == 1]
+    cfg["heading_misalign_deg"] = float(fd.read_i16(1) * 0.01)
+    cfg["magnetic_var_deg"] = float(fd.read_i16(1) * 0.01)
+    cfg["sensors_src"] = np.binary_repr(fd.read_ui8(1), 8)
+    cfg["sensors_avail"] = np.binary_repr(fd.read_ui8(1), 8)
+    cfg["bin1_dist_m"] = round(float(fd.read_ui16(1) * 0.01), 4)
+    cfg["transmit_pulse_m"] = round(float(fd.read_ui16(1) * 0.01), 2)
+    cfg["water_ref_cells"] = list(fd.read_ui8(2).astype(list))  # list for attrs
+    cfg["false_target_threshold"] = fd.read_ui8(1)
+    fd.seek(1, 1)
+    cfg["transmit_lag_m"] = float(fd.read_ui16(1) * 0.01)
+    rdr._nbyte = 40
+
+    if cfg["prog_ver"] >= 8.14:
+        cpu_serialnum = fd.read_ui8(8)
+        rdr._nbyte += 8
+    if cfg["prog_ver"] >= 8.24:
+        cfg["bandwidth"] = fd.read_ui16(1)
+        rdr._nbyte += 2
+    if cfg["prog_ver"] >= 9.68:
+        cfg["power_level"] = fd.read_ui8(1)
+        # cfg['navigator_basefreqindex'] = fd.read_ui8(1)
+        fd.seek(1, 1)
+        cfg["serialnum"] = fd.read_ui32(1)
+        ba = fd.read_ui8(1)
+        if not cfg["beam_angle"]:
+            cfg["beam_angle"] = ba
+        rdr._nbyte += 7
+
+    rdr.configsize = rdr.f.tell() - cfgstart
+    if rdr._debug_level > -1:
+        logging.info("Read Config")
+
+
+def read_fixed(rdr, bb=False):
+    """Read fixed header"""
+    read_cfgseg(rdr, bb=bb)
+    rdr._nbyte += 2
+    if rdr._debug_level > -1:
+        logging.info("Read Fixed")
+
+    # Check if n_cells has increased (for winriver transect files)
+    if hasattr(rdr, "ensemble"):
+        rdr.n_cells_diff = rdr.cfg["n_cells"] - rdr.ensemble["n_cells"]
+        # Increase n_cells if greater than 0
+        if rdr.n_cells_diff > 0:
+            rdr.ensemble = lib._ensemble(rdr.n_avg, rdr.cfg["n_cells"])
+            if rdr._debug_level > 0:
+                logging.warning(
+                    f"Maximum number of cells increased to {rdr.cfg['n_cells']}"
+                )
+
+
+def read_fixed_sl(rdr):
+    """Read surface layer fixed header"""
+    cfg = rdr.cfg
+    cfg["surface_layer"] = 1
+    n_cells = rdr.f.read_ui8(1)
+    # Check if n_cells is greater than what was used in prior profiles
+    if n_cells > rdr.n_cells_sl:
+        rdr.n_cells_sl = n_cells
+        if rdr._debug_level > 0:
+            logging.warning(
+                f"Maximum number of surface layer cells increased to {n_cells}"
+            )
+    cfg["n_cells_sl"] = n_cells
+    # Assuming surface layer profile cell size never changes
+    cfg["cell_size_sl"] = float(rdr.f.read_ui16(1) * 0.01)
+    cfg["bin1_dist_m_sl"] = round(float(rdr.f.read_ui16(1) * 0.01), 4)
+
+    if rdr._debug_level > -1:
+        logging.info("Read Surface Layer Config")
+    rdr._nbyte = 2 + 5
 
 
-def _pop(dat, nm):
-    grp = data_defs[nm][1]
-    if grp is None:
-        dat.pop(nm)
+def read_var(rdr, bb=False):
+    """Read variable header"""
+    fd = rdr.f
+    if bb:
+        ens = rdr.ensembleBB
     else:
-        dat[grp].pop(nm)
+        ens = rdr.ensemble
+    ens.k += 1
+    ens = rdr.ensemble
+    k = ens.k
+    rdr.vars_read += [
+        "number",
+        "rtc",
+        "number",
+        "builtin_test_fail",
+        "c_sound",
+        "depth",
+        "heading",
+        "pitch",
+        "roll",
+        "salinity",
+        "temp",
+        "min_preping_wait",
+        "heading_std",
+        "pitch_std",
+        "roll_std",
+        "adc",
+    ]
+    ens.number[k] = fd.read_ui16(1)
+    ens.rtc[:, k] = fd.read_ui8(7)
+    ens.number[k] += 65535 * fd.read_ui8(1)
+    ens.builtin_test_fail[k] = fd.read_ui16(1)
+    ens.c_sound[k] = fd.read_ui16(1)
+    ens.depth[k] = fd.read_ui16(1) * 0.1
+    ens.heading[k] = fd.read_ui16(1) * 0.01
+    ens.pitch[k] = fd.read_i16(1) * 0.01
+    ens.roll[k] = fd.read_i16(1) * 0.01
+    ens.salinity[k] = fd.read_i16(1)
+    ens.temp[k] = fd.read_i16(1) * 0.01
+    ens.min_preping_wait[k] = (fd.read_ui8(3) * np.array([60, 1, 0.01])).sum()
+    ens.heading_std[k] = fd.read_ui8(1)
+    ens.pitch_std[k] = fd.read_ui8(1) * 0.1
+    ens.roll_std[k] = fd.read_ui8(1) * 0.1
+    ens.adc[:, k] = fd.read_i8(8)
+    rdr._nbyte = 2 + 40
+
+    cfg = rdr.cfg
+    if cfg["inst_model"].lower() == "broadband":
+        if cfg["prog_ver"] >= 5.55:
+            fd.seek(15, 1)
+            cent = fd.read_ui8(1)
+            ens.rtc[:, k] = fd.read_ui8(7)
+            ens.rtc[0, k] = ens.rtc[0, k] + cent * 100
+            rdr._nbyte += 23
+    elif cfg["inst_model"].lower() == "ocean surveyor":
+        fd.seek(16, 1)  # 30 bytes all set to zero, 14 read above
+        rdr._nbyte += 16
+        if cfg["prog_ver"] > 23:
+            fd.seek(2, 1)
+            rdr._nbyte += 2
+    else:
+        ens.error_status[k] = np.binary_repr(fd.read_ui32(1), 32)
+        rdr.vars_read += ["pressure", "pressure_std"]
+        rdr._nbyte += 4
+        if cfg["prog_ver"] >= 8.13:
+            # Added pressure sensor stuff in 8.13
+            fd.seek(2, 1)
+            ens.pressure[k] = fd.read_ui32(1) * 0.001  # dPa to dbar
+            ens.pressure_std[k] = fd.read_ui32(1) * 0.001
+            rdr._nbyte += 10
+        if cfg["prog_ver"] >= 8.24:
+            # Spare byte added 8.24
+            fd.seek(1, 1)
+            rdr._nbyte += 1
+        if cfg["prog_ver"] >= 16.05:
+            # Added more fields with century in clock
+            cent = fd.read_ui8(1)
+            ens.rtc[:, k] = fd.read_ui8(7)
+            ens.rtc[0, k] = ens.rtc[0, k] + cent * 100
+            rdr._nbyte += 8
+        if cfg["prog_ver"] >= 56:
+            fd.seek(1)  # lag near bottom flag
+            rdr._nbyte += 1
+
+    if rdr._debug_level > -1:
+        logging.info("Read Var")
+
+
+def read_vel(rdr, bb=0):
+    """Read water velocity block"""
+    ens, cfg, tg = switch_profile(rdr, bb)
+    rdr.vars_read += ["vel" + tg]
+    n_cells = cfg["n_cells" + tg]
+
+    k = ens.k
+    vel = np.array(rdr.f.read_i16(4 * n_cells)).reshape((n_cells, 4)) * 0.001
+    ens["vel" + tg][:n_cells, :, k] = vel
+    rdr._nbyte = 2 + 4 * n_cells * 2
+    if rdr._debug_level > -1:
+        logging.info("Read Vel")
+
+
+def read_corr(rdr, bb=0):
+    """Read acoustic signal correlation block"""
+    ens, cfg, tg = switch_profile(rdr, bb)
+    rdr.vars_read += ["corr" + tg]
+    n_cells = cfg["n_cells" + tg]
+
+    k = ens.k
+    ens["corr" + tg][:n_cells, :, k] = np.array(rdr.f.read_ui8(4 * n_cells)).reshape(
+        (n_cells, 4)
+    )
+    rdr._nbyte = 2 + 4 * n_cells
+    if rdr._debug_level > -1:
+        logging.info("Read Corr")
+
+
+def read_amp(rdr, bb=0):
+    """Read acoustic signal amplitude block"""
+    ens, cfg, tg = switch_profile(rdr, bb)
+    rdr.vars_read += ["amp" + tg]
+    n_cells = cfg["n_cells" + tg]
+
+    k = ens.k
+    ens["amp" + tg][:n_cells, :, k] = np.array(rdr.f.read_ui8(4 * n_cells)).reshape(
+        (n_cells, 4)
+    )
+    rdr._nbyte = 2 + 4 * n_cells
+    if rdr._debug_level > -1:
+        logging.info("Read Amp")
+
 
+def read_prcnt_gd(rdr, bb=0):
+    """Read acoustic signal 'percent good' block"""
+    ens, cfg, tg = switch_profile(rdr, bb)
+    rdr.vars_read += ["prcnt_gd" + tg]
+    n_cells = cfg["n_cells" + tg]
 
-def _setd(dat, nm, val):
-    grp = data_defs[nm][1]
-    if grp is None:
-        dat[nm] = val
+    ens["prcnt_gd" + tg][:n_cells, :, ens.k] = np.array(
+        rdr.f.read_ui8(4 * n_cells)
+    ).reshape((n_cells, 4))
+    rdr._nbyte = 2 + 4 * n_cells
+    if rdr._debug_level > -1:
+        logging.info("Read PG")
+
+
+def read_status(rdr, bb=0):
+    """Read ADCP status block"""
+    ens, cfg, tg = switch_profile(rdr, bb)
+    rdr.vars_read += ["status" + tg]
+    n_cells = cfg["n_cells" + tg]
+
+    ens["status" + tg][:n_cells, :, ens.k] = np.array(
+        rdr.f.read_ui8(4 * n_cells)
+    ).reshape((n_cells, 4))
+    rdr._nbyte = 2 + 4 * n_cells
+    if rdr._debug_level > -1:
+        logging.info("Read Status")
+
+
+def read_bottom(rdr):
+    """Read bottom track block"""
+    rdr.vars_read += ["dist_bt", "vel_bt", "corr_bt", "amp_bt", "prcnt_gd_bt"]
+    fd = rdr.f
+    ens = rdr.ensemble
+    k = ens.k
+    cfg = rdr.cfg
+    if rdr._vm_source == 2:
+        rdr.vars_read += ["latitude_gps", "longitude_gps"]
+        fd.seek(2, 1)
+        long1 = fd.read_ui16(1)
+        fd.seek(6, 1)
+        ens.latitude_gps[k] = fd.read_i32(1) * rdr._cfac32
+        if ens.latitude_gps[k] == 0:
+            ens.latitude_gps[k] = np.nan
+    else:
+        fd.seek(14, 1)
+    ens.dist_bt[:, k] = fd.read_ui16(4) * 0.01
+    ens.vel_bt[:, k] = fd.read_i16(4) * 0.001
+    ens.corr_bt[:, k] = fd.read_ui8(4)
+    ens.amp_bt[:, k] = fd.read_ui8(4)
+    ens.prcnt_gd_bt[:, k] = fd.read_ui8(4)
+    if rdr._vm_source == 2:
+        fd.seek(2, 1)
+        ens.longitude_gps[k] = (long1 + 65536 * fd.read_ui16(1)) * rdr._cfac32
+        if ens.longitude_gps[k] > 180:
+            ens.longitude_gps[k] = ens.longitude_gps[k] - 360
+        if ens.longitude_gps[k] == 0:
+            ens.longitude_gps[k] = np.nan
+        fd.seek(16, 1)
+        qual = fd.read_ui8(1)
+        if qual == 0:
+            if rdr._debug_level > 0:
+                logging.info(
+                    "  qual==%d,%f %f"
+                    % (qual, ens.latitude_gps[k], ens.longitude_gps[k])
+                )
+            ens.latitude_gps[k] = np.nan
+            ens.longitude_gps[k] = np.nan
+        fd.seek(71 - 45 - 16 - 17, 1)
+        rdr._nbyte = 2 + 68
     else:
-        dat[grp][nm] = val
-
-
-def _idata(dat, nm, sz):
-    group = data_defs[nm][1]
-    dtype = data_defs[nm][2]
-    units = data_defs[nm][3]
-    long_name = data_defs[nm][4]
-    standard_name = data_defs[nm][5]
-    arr = np.empty(sz, dtype=dtype)
-    if dtype.startswith("float"):
-        arr[:] = np.nan
-    dat[group][nm] = arr
-    dat["units"][nm] = units
-    dat["long_name"][nm] = long_name
-    if standard_name:
-        dat["standard_name"][nm] = standard_name
-    return dat
-
-
-def _get_size(name, n=None, ncell=0):
-    sz = list(data_defs[name][0])  # create a copy!
-    if "nc" in sz:
-        sz.insert(sz.index("nc"), ncell)
-        sz.remove("nc")
-    if n is None:
-        return tuple(sz)
-    return tuple(sz + [n])
-
-
-class _variable_setlist(set):
-    def __iadd__(self, vals):
-        if vals[0] not in self:
-            self |= set(vals)
-        return self
-
-
-class _ensemble:
-    n_avg = 1
-    k = -1  # This is the counter for filling the ensemble object
-
-    def __getitem__(self, nm):
-        return getattr(self, nm)
-
-    def __init__(self, navg, n_cells):
-        if navg is None or navg == 0:
-            navg = 1
-        self.n_avg = navg
-        self.n_cells = n_cells
-        for nm in data_defs:
-            setattr(
-                self,
-                nm,
-                np.zeros(_get_size(nm, n=navg, ncell=n_cells), dtype=data_defs[nm][2]),
+        # Skip reference layer data
+        fd.seek(26, 1)
+        rdr._nbyte = 2 + 68
+    if cfg["prog_ver"] >= 5.3:
+        fd.seek(7, 1)  # skip to rangeMsb bytes
+        ens.dist_bt[:, k] = ens.dist_bt[:, k] + fd.read_ui8(4) * 655.36
+        rdr._nbyte += 11
+    if cfg["prog_ver"] >= 16.2 and (cfg.get("sourceprog", "").lower() != "winriver"):
+        fd.seek(4, 1)  # not documented
+        rdr._nbyte += 4
+    if cfg["prog_ver"] >= 56.1:
+        fd.seek(4, 1)  # not documented
+        rdr._nbyte += 4
+
+    if rdr._debug_level > -1:
+        logging.info("Read Bottom Track")
+
+
+def read_alt(rdr):
+    """Read altimeter (range of vertical beam) block"""
+    fd = rdr.f
+    ens = rdr.ensemble
+    k = ens.k
+    rdr.vars_read += ["alt_dist", "alt_rssi", "alt_eval", "alt_status"]
+    ens.alt_eval[k] = fd.read_ui8(1)  # evaluation amplitude
+    ens.alt_rssi[k] = fd.read_ui8(1)  # RSSI amplitude
+    ens.alt_dist[k] = fd.read_ui32(1) * 0.001  # range to surface/seafloor
+    ens.alt_status[k] = fd.read_ui8(1)  # status bit flags
+    rdr._nbyte = 7 + 2
+    if rdr._debug_level > -1:
+        logging.info("Read Altimeter")
+
+
+def read_winriver(rdr):
+    """Skip WinRiver1 Navigation block (outdated)"""
+    rdr._winrivprob = True
+    rdr.cfg["sourceprog"] = "WINRIVER"
+    if rdr._vm_source not in [2, 3]:
+        if rdr._debug_level > -1:
+            logging.warning(
+                "\n***** Apparently a WinRiver1 file - "
+                "NMEA data handler for WinRiver1 not implemented\n"
+            )
+        rdr._vm_source = 2
+    startpos = rdr.f.tell()
+    sz = rdr.f.read_ui16(1)
+    tmp = rdr.f.reads(sz - 2)
+    rdr._nbyte = rdr.f.tell() - startpos + 2
+
+
+def read_winriver2(rdr):
+    """Read WinRiver2 Navigation block"""
+    startpos = rdr.f.tell()
+    rdr._winrivprob = True
+    rdr.cfg["sourceprog"] = "WinRiver2"
+    ens = rdr.ensemble
+    k = ens.k
+    if rdr._debug_level > -1:
+        logging.info("Read WinRiver2")
+    rdr._vm_source = 3
+
+    spid = rdr.f.read_ui16(1)  # NMEA specific IDs
+    if spid in [4, 104]:  # GGA
+        sz = rdr.f.read_ui16(1)
+        dtime = rdr.f.read_f64(1)
+        if sz <= 43:  # If no sentence, data is still stored in nmea format
+            empty_gps = rdr.f.reads(sz - 2)
+            rdr.f.seek(2, 1)
+        else:  # TRDI rewrites the nmea string into their format if one is found
+            start_string = rdr.f.reads(6)
+            if not isinstance(start_string, str):
+                if rdr._debug_level > 0:
+                    logging.warning(
+                        f"Invalid GGA string found in ensemble {k}," " skipping..."
+                    )
+                return "FAIL"
+            rdr.f.seek(1, 1)
+            gga_time = rdr.f.reads(9)
+            time = tmlib.timedelta(
+                hours=int(gga_time[0:2]),
+                minutes=int(gga_time[2:4]),
+                seconds=int(gga_time[4:6]),
+                milliseconds=int(float(gga_time[6:]) * float(1000)),
             )
+            clock = rdr.ensemble.rtc[:, :]
+            if clock[0, 0] < 100:
+                clock[0, :] += century
+            date = tmlib.datetime(*clock[:3, 0]) + time
+            ens.time_gps[k] = tmlib.date2epoch(date)[0]
+            rdr.f.seek(1, 1)
+            ens.latitude_gps[k] = rdr.f.read_f64(1)
+            tcNS = rdr.f.reads(1)  # 'N' or 'S'
+            if tcNS == "S":
+                ens.latitude_gps[k] *= -1
+            ens.longitude_gps[k] = rdr.f.read_f64(1)
+            tcEW = rdr.f.reads(1)  # 'E' or 'W'
+            if tcEW == "W":
+                ens.longitude_gps[k] *= -1
+            ens.fix_gps[k] = rdr.f.read_ui8(1)  # gps fix type/quality
+            ens.n_sat_gps[k] = rdr.f.read_ui8(1)  # of satellites
+            # horizontal dilution of precision
+            ens.hdop_gps[k] = rdr.f.read_f32(1)
+            ens.elevation_gps[k] = rdr.f.read_f32(1)  # altitude
+            m = rdr.f.reads(1)  # altitude unit, 'm'
+            h_geoid = rdr.f.read_f32(1)  # height of geoid
+            m2 = rdr.f.reads(1)  # geoid unit, 'm'
+            ens.rtk_age_gps[k] = rdr.f.read_f32(1)
+            station_id = rdr.f.read_ui16(1)
+        rdr.vars_read += [
+            "time_gps",
+            "longitude_gps",
+            "latitude_gps",
+            "fix_gps",
+            "n_sat_gps",
+            "hdop_gps",
+            "elevation_gps",
+            "rtk_age_gps",
+        ]
+        rdr._nbyte = rdr.f.tell() - startpos + 2
+
+    elif spid in [5, 105]:  # VTG
+        sz = rdr.f.read_ui16(1)
+        dtime = rdr.f.read_f64(1)
+        if sz <= 22:  # if no data
+            empty_gps = rdr.f.reads(sz - 2)
+            rdr.f.seek(2, 1)
+        else:
+            start_string = rdr.f.reads(6)
+            if not isinstance(start_string, str):
+                if rdr._debug_level > 0:
+                    logging.warning(
+                        f"Invalid VTG string found in ensemble {k}," " skipping..."
+                    )
+                return "FAIL"
+            rdr.f.seek(1, 1)
+            true_track = rdr.f.read_f32(1)
+            t = rdr.f.reads(1)  # 'T'
+            magn_track = rdr.f.read_f32(1)
+            m = rdr.f.reads(1)  # 'M'
+            speed_knot = rdr.f.read_f32(1)
+            kts = rdr.f.reads(1)  # 'N'
+            speed_kph = rdr.f.read_f32(1)
+            kph = rdr.f.reads(1)  # 'K'
+            mode = rdr.f.reads(1)
+            # knots -> m/s
+            ens.speed_over_grnd_gps[k] = speed_knot / 1.944
+            ens.dir_over_grnd_gps[k] = true_track
+        rdr.vars_read += ["speed_over_grnd_gps", "dir_over_grnd_gps"]
+        rdr._nbyte = rdr.f.tell() - startpos + 2
+
+    elif spid in [6, 106]:  # 'DBT' depth sounder
+        sz = rdr.f.read_ui16(1)
+        dtime = rdr.f.read_f64(1)
+        if sz <= 20:
+            empty_gps = rdr.f.reads(sz - 2)
+            rdr.f.seek(2, 1)
+        else:
+            start_string = rdr.f.reads(6)
+            if not isinstance(start_string, str):
+                if rdr._debug_level > 0:
+                    logging.warning(
+                        f"Invalid DBT string found in ensemble {k}," " skipping..."
+                    )
+                return "FAIL"
+            rdr.f.seek(1, 1)
+            depth_ft = rdr.f.read_f32(1)
+            ft = rdr.f.reads(1)  # 'f'
+            depth_m = rdr.f.read_f32(1)
+            m = rdr.f.reads(1)  # 'm'
+            depth_fathom = rdr.f.read_f32(1)
+            f = rdr.f.reads(1)  # 'F'
+            ens.dist_nmea[k] = depth_m
+        rdr.vars_read += ["dist_nmea"]
+        rdr._nbyte = rdr.f.tell() - startpos + 2
+
+    elif spid in [7, 107]:  # 'HDT'
+        sz = rdr.f.read_ui16(1)
+        dtime = rdr.f.read_f64(1)
+        if sz <= 14:
+            empty_gps = rdr.f.reads(sz - 2)
+            rdr.f.seek(2, 1)
+        else:
+            start_string = rdr.f.reads(6)
+            if not isinstance(start_string, str):
+                if rdr._debug_level > 0:
+                    logging.warning(
+                        f"Invalid HDT string found in ensemble {k}," " skipping..."
+                    )
+                return "FAIL"
+            rdr.f.seek(1, 1)
+            ens.heading_gps[k] = rdr.f.read_f64(1)
+            tt = rdr.f.reads(1)
+        rdr.vars_read += ["heading_gps"]
+        rdr._nbyte = rdr.f.tell() - startpos + 2
+
+
+def read_vmdas(rdr):
+    """Read VMDAS Navigation block"""
+    fd = rdr.f
+    rdr.cfg["sourceprog"] = "VMDAS"
+    ens = rdr.ensemble
+    k = ens.k
+    if rdr._vm_source != 1 and rdr._debug_level > -1:
+        logging.info("  \n***** Apparently a VMDAS file \n\n")
+    rdr._vm_source = 1
+    rdr.vars_read += [
+        "time_gps",
+        "clock_offset_UTC_gps",
+        "latitude_gps",
+        "longitude_gps",
+        "avg_speed_gps",
+        "avg_dir_gps",
+        "speed_made_good_gps",
+        "dir_made_good_gps",
+        "flags_gps",
+        "pitch_gps",
+        "roll_gps",
+        "heading_gps",
+    ]
+    # UTC date time
+    utim = fd.read_ui8(4)
+    date_utc = tmlib.datetime(utim[2] + utim[3] * 256, utim[1], utim[0])
+
+    # 1st lat/lon position after previous ADCP ping
+    # This byte is in hundredths of seconds (10s of milliseconds):
+    utc_time_first_fix = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) * 0.1)))
+    ens.clock_offset_UTC_gps[k] = (
+        fd.read_i32(1) * 0.001
+    )  # "PC clock offset from UTC" in ms
+    latitude_first_gps = fd.read_i32(1) * rdr._cfac32
+    longitude_first_gps = fd.read_i32(1) * rdr._cfac32
+
+    # Last lat/lon position prior to current ADCP ping
+    utc_time_fix = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) * 0.1)))
+    ens.time_gps[k] = tmlib.date2epoch(date_utc + utc_time_fix)[0]
+    ens.latitude_gps[k] = fd.read_i32(1) * rdr._cfac32
+    ens.longitude_gps[k] = fd.read_i32(1) * rdr._cfac32
+
+    ens.avg_speed_gps[k] = fd.read_ui16(1) * 0.001
+    ens.avg_dir_gps[k] = fd.read_ui16(1) * rdr._cfac16  # avg true track
+    fd.seek(2, 1)  # avg magnetic track
+    ens.speed_made_good_gps[k] = fd.read_ui16(1) * 0.001
+    ens.dir_made_good_gps[k] = fd.read_ui16(1) * rdr._cfac16
+    fd.seek(2, 1)  # reserved
+    ens.flags_gps[k] = int(np.binary_repr(fd.read_ui16(1)))
+    fd.seek(6, 1)  # reserved, ADCP ensemble #
+
+    # ADCP date time
+    utim = fd.read_ui8(4)
+    date_adcp = tmlib.datetime(utim[0] + utim[1] * 256, utim[3], utim[2])
+    time_adcp = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) * 0.1)))
+
+    ens.pitch_gps[k] = fd.read_ui16(1) * rdr._cfac16
+    ens.roll_gps[k] = fd.read_ui16(1) * rdr._cfac16
+    ens.heading_gps[k] = fd.read_ui16(1) * rdr._cfac16
+
+    fd.seek(10, 1)
+    rdr._nbyte = 2 + 76
 
-    def clean_data(self):
-        self["vel"][self["vel"] == -32.768] = np.nan
+    if rdr._debug_level > -1:
+        logging.info("Read VMDAS")
+    rdr._read_vmdas = True
diff --git a/mhkit/dolfyn/io/rdi_lib.py b/mhkit/dolfyn/io/rdi_lib.py
index df0851a0f..03e8e2c60 100644
--- a/mhkit/dolfyn/io/rdi_lib.py
+++ b/mhkit/dolfyn/io/rdi_lib.py
@@ -2,6 +2,8 @@
 from struct import unpack
 from os.path import expanduser
 
+from .rdi_defs import data_defs
+
 
 class bin_reader:
     """
@@ -110,3 +112,92 @@ def read_ui32(self, n):
 
     def read_i32(self, n):
         return self.read(n, "l")
+
+
+class _variable_setlist(set):
+    def __iadd__(self, vals):
+        if vals[0] not in self:
+            self |= set(vals)
+        return self
+
+
+class _ensemble:
+    n_avg = 1
+    k = -1  # This is the counter for filling the ensemble object
+
+    def __getitem__(self, nm):
+        return getattr(self, nm)
+
+    def __init__(self, navg, n_cells):
+        if navg is None or navg == 0:
+            navg = 1
+        self.n_avg = navg
+        self.n_cells = n_cells
+        for nm in data_defs:
+            setattr(
+                self,
+                nm,
+                np.zeros(_get_size(nm, n=navg, ncell=n_cells), dtype=data_defs[nm][2]),
+            )
+
+    def clean_data(self):
+        self["vel"][self["vel"] == -32.768] = np.nan
+
+
+def _get(dat, nm):
+    grp = data_defs[nm][1]
+    if grp is None:
+        return dat[nm]
+    else:
+        return dat[grp][nm]
+
+
+def _in_group(dat, nm):
+    grp = data_defs[nm][1]
+    if grp is None:
+        return nm in dat
+    else:
+        return nm in dat[grp]
+
+
+def _pop(dat, nm):
+    grp = data_defs[nm][1]
+    if grp is None:
+        dat.pop(nm)
+    else:
+        dat[grp].pop(nm)
+
+
+def _setd(dat, nm, val):
+    grp = data_defs[nm][1]
+    if grp is None:
+        dat[nm] = val
+    else:
+        dat[grp][nm] = val
+
+
+def _idata(dat, nm, sz):
+    group = data_defs[nm][1]
+    dtype = data_defs[nm][2]
+    units = data_defs[nm][3]
+    long_name = data_defs[nm][4]
+    standard_name = data_defs[nm][5]
+    arr = np.empty(sz, dtype=dtype)
+    if dtype.startswith("float"):
+        arr[:] = np.nan
+    dat[group][nm] = arr
+    dat["units"][nm] = units
+    dat["long_name"][nm] = long_name
+    if standard_name:
+        dat["standard_name"][nm] = standard_name
+    return dat
+
+
+def _get_size(name, n=None, ncell=0):
+    sz = list(data_defs[name][0])  # create a copy!
+    if "nc" in sz:
+        sz.insert(sz.index("nc"), ncell)
+        sz.remove("nc")
+    if n is None:
+        return tuple(sz)
+    return tuple(sz + [n])

From 9cbb933cee500de68bf2be74380d7d55e71ceb48 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Fri, 25 Oct 2024 14:38:41 -0700
Subject: [PATCH 09/14] Organize nortek classic reader and rename some
 functions

---
 .../dolfyn/test_data/AWAC_test01.dolfyn.log   |   2 +-
 .../test_data/vector_data_imu01.dolfyn.log    |   2 +-
 mhkit/dolfyn/io/nortek.py                     | 754 ++++++++----------
 mhkit/dolfyn/io/nortek_defs.py                |   2 +-
 mhkit/dolfyn/io/nortek_lib.py                 |  60 ++
 5 files changed, 417 insertions(+), 403 deletions(-)
 create mode 100644 mhkit/dolfyn/io/nortek_lib.py

diff --git a/examples/data/dolfyn/test_data/AWAC_test01.dolfyn.log b/examples/data/dolfyn/test_data/AWAC_test01.dolfyn.log
index 289b73f01..b881b58da 100644
--- a/examples/data/dolfyn/test_data/AWAC_test01.dolfyn.log
+++ b/examples/data/dolfyn/test_data/AWAC_test01.dolfyn.log
@@ -1,7 +1,7 @@
 root - INFO - Position: 2, codes: (165, 5)
 root - INFO - Reading hardware configuration (0x05) ping #0 @ 2...
 root - INFO - Position: 50, codes: (165, 4)
-root - INFO - Reading head configuration (0x04) ping #0 @ 50...
+root - INFO - Reading header configuration (0x04) ping #0 @ 50...
 root - INFO - Position: 274, codes: (165, 0)
 root - INFO - Reading user configuration (0x00) ping #0 @ 274...
 root - INFO - Position: 786, codes: (165, 32)
diff --git a/examples/data/dolfyn/test_data/vector_data_imu01.dolfyn.log b/examples/data/dolfyn/test_data/vector_data_imu01.dolfyn.log
index 1b62584eb..649692018 100644
--- a/examples/data/dolfyn/test_data/vector_data_imu01.dolfyn.log
+++ b/examples/data/dolfyn/test_data/vector_data_imu01.dolfyn.log
@@ -1,7 +1,7 @@
 root - INFO - Position: 2, codes: (165, 5)
 root - INFO - Reading hardware configuration (0x05) ping #0 @ 2...
 root - INFO - Position: 50, codes: (165, 4)
-root - INFO - Reading head configuration (0x04) ping #0 @ 50...
+root - INFO - Reading header configuration (0x04) ping #0 @ 50...
 root - INFO - Position: 274, codes: (165, 0)
 root - INFO - Reading user configuration (0x00) ping #0 @ 274...
 root - INFO - Position: 786, codes: (165, 18)
diff --git a/mhkit/dolfyn/io/nortek.py b/mhkit/dolfyn/io/nortek.py
index 20605a7c0..0e81a874d 100644
--- a/mhkit/dolfyn/io/nortek.py
+++ b/mhkit/dolfyn/io/nortek.py
@@ -3,11 +3,12 @@
 import numpy as np
 from struct import unpack
 from pathlib import Path
-from datetime import datetime
 
-from . import nortek_defs
+
 from .. import time
-from .base import _find_userdata, _create_dataset, _handle_nan, _abspath
+from . import base
+from . import nortek_defs as defs
+from . import nortek_lib as lib
 from ..tools import misc as tbx
 from ..rotate.vector import _calc_omat
 from ..rotate.base import _set_coords
@@ -54,15 +55,15 @@ def read_nortek(
             format="%(name)s - %(levelname)s - %(message)s",
         )
 
-    userdata = _find_userdata(filename, userdata)
+    userdata = base._find_userdata(filename, userdata)
 
     rdr = _NortekReader(filename, debug=debug, do_checksum=do_checksum, nens=nens)
     rdr.readfile()
-    rdr.dat2sci()
+    rdr.cleanup()
     dat = rdr.data
 
     # Remove trailing nan's in time and orientation data
-    dat = _handle_nan(dat)
+    dat = base._handle_nan(dat)
 
     # Search for missing timestamps and interpolate them
     coords = dat["coords"]
@@ -92,7 +93,7 @@ def read_nortek(
             dat["attrs"][nm] = userdata[nm]
 
     # Create xarray dataset from upper level dictionary
-    ds = _create_dataset(dat)
+    ds = base._create_dataset(dat)
     ds = _set_coords(ds, ref_frame=ds.coord_sys)
 
     if "orientmat" not in ds:
@@ -118,27 +119,6 @@ def read_nortek(
     return ds
 
 
-def _bcd2char(cBCD):
-    """Taken from the Nortek System Integrator Manual
-    "Example Program" Chapter.
-    """
-    cBCD = min(cBCD, 153)
-    c = cBCD & 15
-    c += 10 * (cBCD >> 4)
-    return c
-
-
-def _bitshift8(val):
-    return val >> 8
-
-
-def _int2binarray(val, n):
-    out = np.zeros(n, dtype="bool")
-    for idx, n in enumerate(range(n)):
-        out[idx] = val & (2**n)
-    return out
-
-
 class _NortekReader:
     """
     A class for reading reading nortek binary files.
@@ -175,7 +155,7 @@ class _NortekReader:
         "0x30": "read_awac_waves",
         "0x31": "read_awac_waves_hdr",
         "0x36": "read_awac_waves",  # "SUV"
-        "0x71": "read_microstrain",
+        "0x71": "read_imu",
     }
 
     def __init__(
@@ -189,7 +169,7 @@ def __init__(
     ):
         self.fname = fname
         self._bufsize = bufsize
-        self.f = open(_abspath(fname), "rb", 1000)
+        self.f = open(base._abspath(fname), "rb", 1000)
         self.do_checksum = do_checksum
         self.filesize  # initialize the filesize.
         self.debug = debug
@@ -256,7 +236,7 @@ def __init__(
         getattr(self, "init_" + self._inst)()
         self.f.close()  # This has a small buffer, so close it.
         # This has a large buffer...
-        self.f = open(_abspath(fname), "rb", bufsize)
+        self.f = open(base._abspath(fname), "rb", bufsize)
         self.close = self.f.close
         if self._npings is not None:
             self.n_samp_guess = self._npings
@@ -359,6 +339,34 @@ def init_AWAC(self):
         else:
             self.n_samp_guess = int(self.filesize / space + 1)
 
+    def _init_data(self, vardict):
+        """Initialize the data object according to vardict.
+
+        Parameters
+        ----------
+        vardict : (dict of :class:`<VarAttrs>`)
+          The variable definitions in the :class:`<VarAttrs>` specify
+          how to initialize each data variable.
+
+        """
+        shape_args = {"n": self.n_samp_guess}
+        try:
+            shape_args["nbins"] = self.config["usr"]["n_bins"]
+        except KeyError:
+            pass
+        for nm, va in list(vardict.items()):
+            if va.group is None:
+                # These have to stay separated.
+                if nm not in self.data:
+                    self.data[nm] = va._empty_array(**shape_args)
+            else:
+                if nm not in self.data[va.group]:
+                    self.data[va.group][nm] = va._empty_array(**shape_args)
+                    self.data["units"][nm] = va.units
+                    self.data["long_name"][nm] = va.long_name
+                    if va.standard_name:
+                        self.data["standard_name"][nm] = va.standard_name
+
     def read(self, nbyte):
         byts = self.f.read(nbyte)
         if not (len(byts) == nbyte):
@@ -371,11 +379,11 @@ def findnext(self, do_cs=True):
         """
         sum = np.uint16(int("0xb58c", 0))  # Initialize the sum
         cs = 0
-        func = _bitshift8
+        func = lib._bitshift8
         func2 = np.uint8
         if self.endian == "<":
             func = np.uint8
-            func2 = _bitshift8
+            func2 = lib._bitshift8
         while True:
             val = unpack(self.endian + "H", self.read(2))[0]
             if np.array(val).astype(func) == 165 and (not do_cs or cs == sum):
@@ -428,7 +436,7 @@ def readfile(self, nlines=None):
                     self.findnext()
                     retval = None
                 if self._npings is not None and self.c >= self._npings:
-                    if "microstrain" in self._dtypes:
+                    if "imu" in self._dtypes:
                         try:
                             self.readnext()
                         except:
@@ -441,7 +449,7 @@ def readfile(self, nlines=None):
             if self.debug:
                 logging.info(" stopped at {} bytes.".format(self.pos))
         self.c -= 1
-        _crop_data(self.data, slice(0, self.c), self.n_samp_guess)
+        lib._crop_data(self.data, slice(0, self.c), self.n_samp_guess)
 
     def findnextid(self, id):
         if id.__class__ is str:
@@ -492,7 +500,7 @@ def checksum(self, byts):
             self.f.seek(2, 1)
 
     def read_user_cfg(self):
-        # ID: '0x00 = 00
+        """Read User configuration data block (0x00)"""
         if self.debug:
             logging.info(
                 "Reading user configuration (0x00) ping #{} @ {}...".format(
@@ -515,7 +523,7 @@ def read_user_cfg(self):
         cfg_u["adv"]["n_pings_per_burst"] = tmp[5]
         cfg_u["awac"]["avg_interval"] = tmp[6]
         cfg_u["usr"]["n_beams"] = int(tmp[7])
-        TimCtrlReg = _int2binarray(tmp[8], 16)
+        TimCtrlReg = lib._int2binarray(tmp[8], 16)
         # From the nortek system integrator manual
         # (note: bit numbering is zero-based)
         cfg_u["usr"]["profile_mode"] = ["single", "continuous"][int(TimCtrlReg[1])]
@@ -527,7 +535,7 @@ def read_user_cfg(self):
         ][int(TimCtrlReg[7])]
         cfg_u["usr"]["sample_on_sync"] = str(bool(TimCtrlReg[8]))
         cfg_u["usr"]["start_on_sync"] = str(bool(TimCtrlReg[9]))
-        cfg_u["PwrCtrlReg"] = _int2binarray(tmp[9], 16)
+        cfg_u["PwrCtrlReg"] = lib._int2binarray(tmp[9], 16)
         cfg_u["A1"] = tmp[10]
         cfg_u["B0"] = tmp[11]
         cfg_u["B1"] = tmp[12]
@@ -540,12 +548,12 @@ def read_user_cfg(self):
         cfg_u["usr"]["wrap_mode"] = str(bool(tmp[19]))
         cfg_u["deployment_time"] = np.array(tmp[20:23])
         cfg_u["diagnotics_interval"] = tmp[23]
-        Mode0 = _int2binarray(tmp[24], 16)
+        Mode0 = lib._int2binarray(tmp[24], 16)
         cfg_u["user_soundspeed_adj_factor"] = tmp[25]
         cfg_u["n_samples_diag"] = tmp[26]
         cfg_u["n_beams_cells_diag"] = tmp[27]
         cfg_u["n_pings_diag_wave"] = tmp[28]
-        ModeTest = _int2binarray(tmp[29], 16)
+        ModeTest = lib._int2binarray(tmp[29], 16)
         cfg_u["usr"]["analog_in"] = tmp[30]
         sfw_ver = str(tmp[31])
         cfg_u["usr"]["software_version"] = (
@@ -561,7 +569,7 @@ def read_user_cfg(self):
             "MLMST",
             "None",
         ][tmp[124]]
-        Mode1 = _int2binarray(tmp[125], 16)
+        Mode1 = lib._int2binarray(tmp[125], 16)
         cfg_u["awac"]["prc_dyn_wave_cell_pos"] = int(tmp[126] / 32767 * 100)
         cfg_u["wave_transmit_pulse"] = tmp[127]
         cfg_u["wave_blank_dist"] = tmp[128]
@@ -594,10 +602,11 @@ def read_user_cfg(self):
         ]  # noqa
 
     def read_head_cfg(self):
+        """Read header configuration block (0x04)"""
         # ID: '0x04 = 04
         if self.debug:
             logging.info(
-                "Reading head configuration (0x04) ping #{} @ {}...".format(
+                "Reading header configuration (0x04) ping #{} @ {}...".format(
                     self.c, self.pos
                 )
             )
@@ -605,7 +614,7 @@ def read_head_cfg(self):
         cfg["head"] = {}
         byts = self.read(220)
         tmp = unpack(self.endian + "2x3H12s176s22sH", byts)
-        head_config = _int2binarray(tmp[0], 16).astype(int)
+        head_config = lib._int2binarray(tmp[0], 16).astype(int)
         cfg["head"]["pressure_sensor"] = ["no", "yes"][head_config[0]]
         cfg["head"]["compass"] = ["no", "yes"][head_config[1]]
         cfg["head"]["tilt_sensor"] = ["no", "yes"][head_config[2]]
@@ -616,6 +625,7 @@ def read_head_cfg(self):
         self.checksum(byts)
 
     def read_hw_cfg(self):
+        """Read hardware configuration block (0x05)"""
         # ID 0x05 = 05
         if self.debug:
             logging.info(
@@ -635,66 +645,55 @@ def read_hw_cfg(self):
         cfg_hw["hdw"]["PIC_version"] = tmp[3]
         cfg_hw["hdw"]["hardware_rev"] = tmp[4]
         cfg_hw["hdw"]["recorder_size_bytes"] = tmp[5] * 65536
-        status = _int2binarray(tmp[6], 16).astype(int)
+        status = lib._int2binarray(tmp[6], 16).astype(int)
         cfg_hw["hdw"]["vel_range"] = ["normal", "high"][status[0]]
         cfg_hw["hdw"]["firmware_version"] = tmp[7].decode("utf-8")
         self.checksum(byts)
 
-    def rd_time(self, strng):
-        """Read the time from the first 6bytes of the input string."""
-        min, sec, day, hour, year, month = unpack("BBBBBB", strng[:6])
-        return time.date2epoch(
-            datetime(
-                time._fullyear(_bcd2char(year)),
-                _bcd2char(month),
-                _bcd2char(day),
-                _bcd2char(hour),
-                _bcd2char(min),
-                _bcd2char(sec),
+    def read_vec_checkdata(self):
+        """Read Vector check data block (0x07)"""
+        # ID: 0x07 = 07
+        if self.debug:
+            logging.info(
+                "Reading Vector check data (0x07) ping #{} @ {}...".format(
+                    self.c, self.pos
+                )
             )
-        )[0]
-
-    def _init_data(self, vardict):
-        """Initialize the data object according to vardict.
-
-        Parameters
-        ----------
-        vardict : (dict of :class:`<VarAttrs>`)
-          The variable definitions in the :class:`<VarAttrs>` specify
-          how to initialize each data variable.
-
-        """
-        shape_args = {"n": self.n_samp_guess}
-        try:
-            shape_args["nbins"] = self.config["usr"]["n_bins"]
-        except KeyError:
-            pass
-        for nm, va in list(vardict.items()):
-            if va.group is None:
-                # These have to stay separated.
-                if nm not in self.data:
-                    self.data[nm] = va._empty_array(**shape_args)
-            else:
-                if nm not in self.data[va.group]:
-                    self.data[va.group][nm] = va._empty_array(**shape_args)
-                    self.data["units"][nm] = va.units
-                    self.data["long_name"][nm] = va.long_name
-                    if va.standard_name:
-                        self.data["standard_name"][nm] = va.standard_name
+        byts0 = self.read(6)
+        checknow = {}
+        tmp = unpack(self.endian + "2x2H", byts0)  # The first two are size.
+        checknow["Samples"] = tmp[0]
+        n = checknow["Samples"]
+        checknow["First_samp"] = tmp[1]
+        checknow["Amp1"] = tbx._nans(n, dtype=np.uint8) + 8
+        checknow["Amp2"] = tbx._nans(n, dtype=np.uint8) + 8
+        checknow["Amp3"] = tbx._nans(n, dtype=np.uint8) + 8
+        byts1 = self.read(3 * n)
+        tmp = unpack(self.endian + (3 * n * "B"), byts1)
+        for idx, nm in enumerate(["Amp1", "Amp2", "Amp3"]):
+            checknow[nm] = np.array(tmp[idx * n : (idx + 1) * n], dtype=np.uint8)
+        self.checksum(byts0 + byts1)
+        if "checkdata" not in self.config:
+            self.config["checkdata"] = checknow
+        else:
+            if not isinstance(self.config["checkdata"], list):
+                self.config["checkdata"] = [self.config["checkdata"]]
+            self.config["checkdata"] += [checknow]
 
     def read_vec_data(self):
+        """Read Vector measurement data block (0x10)"""
         # ID: 0x10 = 16
         c = self.c
         dat = self.data
         if self.debug:
             logging.info(
-                "Reading vector velocity data (0x10) ping #{} @ {}...".format(
+                "Reading Vector measurements (0x10) ping #{} @ {}...".format(
                     self.c, self.pos
                 )
             )
 
         if "vel" not in dat["data_vars"]:
-            self._init_data(nortek_defs.vec_data)
+            self._init_data(defs.vec_data)
             self._dtypes += ["vec_data"]
 
         byts = self.read(20)
@@ -721,80 +720,49 @@ def read_vec_data(self):
         self.checksum(byts)
         self.c += 1
 
-    def read_vec_checkdata(self):
-        # ID: 0x07 = 07
+    def read_vec_sysdata(self):
+        """Read Vector system data block (0x11)"""
+        # ID: 0x11 = 17
+        c = self.c
         if self.debug:
             logging.info(
-                "Reading vector check data (0x07) ping #{} @ {}...".format(
+                "Reading Vector system data (0x11) ping #{} @ {}...".format(
                     self.c, self.pos
                 )
             )
-        byts0 = self.read(6)
-        checknow = {}
-        tmp = unpack(self.endian + "2x2H", byts0)  # The first two are size.
-        checknow["Samples"] = tmp[0]
-        n = checknow["Samples"]
-        checknow["First_samp"] = tmp[1]
-        checknow["Amp1"] = tbx._nans(n, dtype=np.uint8) + 8
-        checknow["Amp2"] = tbx._nans(n, dtype=np.uint8) + 8
-        checknow["Amp3"] = tbx._nans(n, dtype=np.uint8) + 8
-        byts1 = self.read(3 * n)
-        tmp = unpack(self.endian + (3 * n * "B"), byts1)
-        for idx, nm in enumerate(["Amp1", "Amp2", "Amp3"]):
-            checknow[nm] = np.array(tmp[idx * n : (idx + 1) * n], dtype=np.uint8)
-        self.checksum(byts0 + byts1)
-        if "checkdata" not in self.config:
-            self.config["checkdata"] = checknow
-        else:
-            if not isinstance(self.config["checkdata"], list):
-                self.config["checkdata"] = [self.config["checkdata"]]
-            self.config["checkdata"] += [checknow]
-
-    def _sci_data(self, vardict):
-        """
-        Convert the data to scientific units accordint to vardict.
-
-        Parameters
-        ----------
-        vardict : (dict of :class:`<VarAttrs>`)
-          The variable definitions in the :class:`<VarAttrs>` specify
-          how to scale each data variable.
-        """
-
-        for nm, vd in list(vardict.items()):
-            if vd.group is None:
-                dat = self.data
-            else:
-                dat = self.data[vd.group]
-            retval = vd.sci_func(dat[nm])
-            # This checks whether a new data object was created:
-            # sci_func returns None if it modifies the existing data.
-            if retval is not None:
-                dat[nm] = retval
-
-    def sci_vec_data(self):
-        self._sci_data(nortek_defs.vec_data)
         dat = self.data
-
-        dat["data_vars"]["pressure"] = (
-            dat["data_vars"]["PressureMSB"].astype("float32") * 65536
-            + dat["data_vars"]["PressureLSW"].astype("float32")
-        ) / 1000.0
-        dat["units"]["pressure"] = "dbar"
-        dat["long_name"]["pressure"] = "Pressure"
-        dat["standard_name"]["pressure"] = "sea_water_pressure"
-
-        dat["data_vars"].pop("PressureMSB")
-        dat["data_vars"].pop("PressureLSW")
-
-        # Apply velocity scaling (1 or 0.1)
-        dat["data_vars"]["vel"] *= self.config["vel_scale_mm"]
+        if self._lastread[:2] == [
+            "vec_checkdata",
+            "vec_hdr",
+        ]:
+            self.burst_start[c] = True
+        if "time" not in dat["coords"]:
+            self._init_data(defs.vec_sysdata)
+            self._dtypes += ["vec_sysdata"]
+        byts = self.read(24)
+        # The first two are size (skip them).
+        dat["coords"]["time"][c] = lib.rd_time(byts[2:8])
+        ds = dat["sys"]
+        dv = dat["data_vars"]
+        (
+            dv["batt"][c],
+            dv["c_sound"][c],
+            dv["heading"][c],
+            dv["pitch"][c],
+            dv["roll"][c],
+            dv["temp"][c],
+            dv["error"][c],
+            dv["status"][c],
+            ds["AnaIn"][c],
+        ) = unpack(self.endian + "3H3h2BH", byts[8:])
+        self.checksum(byts)
 
     def read_vec_hdr(self):
+        """Read Vector header block (0x12)"""
         # ID: '0x12 = 18
         if self.debug:
             logging.info(
-                "Reading vector header data (0x12) ping #{} @ {}...".format(
+                "Reading Vector header data (0x12) ping #{} @ {}...".format(
                     self.c, self.pos
                 )
             )
@@ -802,7 +770,7 @@ def read_vec_hdr(self):
         # The first two are size, the next 6 are time.
         tmp = unpack(self.endian + "8xH7B21x", byts)
         hdrnow = {}
-        hdrnow["time"] = self.rd_time(byts[2:8])
+        hdrnow["time"] = lib.rd_time(byts[2:8])
         hdrnow["NRecords"] = tmp[0]
         hdrnow["Noise1"] = tmp[1]
         hdrnow["Noise2"] = tmp[2]
@@ -820,95 +788,140 @@ def read_vec_hdr(self):
                 self.config["data_header"] = [self.config["data_header"]]
             self.config["data_header"] += [hdrnow]
 
-    def read_vec_sysdata(self):
-        # ID: 0x11 = 17
-        c = self.c
+    def read_awac_profile(self):
+        """Read AWAC profile measurements block (0x20)"""
+        # ID: '0x20' = 32
+        dat = self.data
         if self.debug:
             logging.info(
-                "Reading vector system data (0x11) ping #{} @ {}...".format(
+                "Reading AWAC velocity data (0x20) ping #{} @ {}...".format(
                     self.c, self.pos
                 )
             )
-        dat = self.data
-        if self._lastread[:2] == [
-            "vec_checkdata",
-            "vec_hdr",
-        ]:
-            self.burst_start[c] = True
-        if "time" not in dat["coords"]:
-            self._init_data(nortek_defs.vec_sysdata)
-            self._dtypes += ["vec_sysdata"]
-        byts = self.read(24)
-        # The first two are size (skip them).
-        dat["coords"]["time"][c] = self.rd_time(byts[2:8])
+        nbins = self.config["usr"]["n_bins"]
+        if "temp" not in dat["data_vars"]:
+            self._init_data(defs.awac_profile)
+            self._dtypes += ["awac_profile"]
+
+        # Note: docs state there is 'fill' byte at the end, if nbins is odd,
+        # but doesn't appear to be the case
+        n = self.config["usr"]["n_beams"]
+        byts = self.read(116 + n * 3 * nbins)
+        c = self.c
+        dat["coords"]["time"][c] = lib.rd_time(byts[2:8])
         ds = dat["sys"]
         dv = dat["data_vars"]
         (
+            dv["error"][c],
+            ds["AnaIn1"][c],
             dv["batt"][c],
             dv["c_sound"][c],
             dv["heading"][c],
             dv["pitch"][c],
             dv["roll"][c],
-            dv["temp"][c],
-            dv["error"][c],
+            p_msb,
             dv["status"][c],
-            ds["AnaIn"][c],
-        ) = unpack(self.endian + "3H3h2BH", byts[8:])
+            p_lsw,
+            dv["temp"][c],
+        ) = unpack(self.endian + "5H2hBBHh", byts[8:28])
+        dv["pressure"][c] = 65536 * p_msb + p_lsw
+        # The nortek system integrator manual specifies an 88byte 'spare'
+        # field, therefore we start at 116.
+        tmp = unpack(
+            self.endian + str(n * nbins) + "h" + str(n * nbins) + "B",
+            byts[116 : 116 + n * 3 * nbins],
+        )
+        for idx in range(n):
+            dv["vel"][idx, :, c] = tmp[idx * nbins : (idx + 1) * nbins]
+            dv["amp"][idx, :, c] = tmp[(idx + n) * nbins : (idx + n + 1) * nbins]
         self.checksum(byts)
+        self.c += 1
 
-    def sci_vec_sysdata(self):
-        """Translate the data in the vec_sysdata structure into
-        scientific units.
-        """
+    def read_awac_waves(self):
+        """Read AWAC wave (0x30) and SUV (0x36) data blocks"""
+        # IDs: 0x30 & 0x36
+        c = self.c
         dat = self.data
-        fs = dat["attrs"]["fs"]
-        self._sci_data(nortek_defs.vec_sysdata)
-        t = dat["coords"]["time"]
+        if self.debug:
+            print(
+                "Reading AWAC wave data (0x30) ping #{} @ {}...".format(
+                    self.c, self.pos
+                )
+            )
+        if "dist1_alt" not in dat["data_vars"]:
+            self._init_data(defs.wave_data)
+            self._dtypes += ["wave_data"]
+        # The first two are size
+        byts = self.read(20)
+        ds = dat["sys"]
         dv = dat["data_vars"]
-        dat["sys"]["_sysi"] = ~np.isnan(t)
-        # These are the indices in the sysdata variables
-        # that are not interpolated.
-        nburst = self.config["n_burst"]
-        dv["orientation_down"] = tbx._nans(len(t), dtype="bool")
-        if nburst == 0:
-            num_bursts = 1
-            nburst = len(t)
-        else:
-            num_bursts = int(len(t) // nburst + 1)
-        for nb in range(num_bursts):
-            iburst = slice(nb * nburst, (nb + 1) * nburst)
-            sysi = dat["sys"]["_sysi"][iburst]
-            if len(sysi) == 0:
-                break
-            # Skip the first entry for the interpolation process
-            inds = np.nonzero(sysi)[0][1:]
-            arng = np.arange(len(t[iburst]), dtype=np.float64)
-            if len(inds) >= 2:
-                p = np.poly1d(np.polyfit(inds, t[iburst][inds], 1))
-                t[iburst] = p(arng)
-            elif len(inds) == 1:
-                t[iburst] = (arng - inds[0]) / (fs * 3600 * 24) + t[iburst][inds[0]]
-            else:
-                t[iburst] = t[iburst][0] + arng / (fs * 24 * 3600)
+        (
+            dv["pressure"][c],  # (0.001 dbar)
+            dv["dist1_alt"][c],  # distance 1 to surface, vertical beam (mm)
+            ds["AnaIn_alt"][c],  # analog input 1
+            dv["vel_alt"][0, c],  # velocity beam 1 (mm/s) East for SUV
+            dv["vel_alt"][1, c],  # North for SUV
+            dv["vel_alt"][2, c],  # Up for SUV
+            dv["dist2_alt"][
+                c
+            ],  # distance 2 to surface, vertical beam (mm) or vel 4 for non-AST
+            dv["amp_alt"][0, c],  # amplitude beam 1 (counts)
+            dv["amp_alt"][1, c],  # amplitude beam 2 (counts)
+            dv["amp_alt"][2, c],  # amplitude beam 3 (counts)
+            # AST quality (counts) or amplitude beam 4 for non-AST
+            dv["quality_alt"][c],
+        ) = unpack(self.endian + "3H4h4B", byts)
+        self.checksum(byts)
+        self.c += 1
 
-            tmpd = tbx._nans_like(dv["heading"][iburst])
-            # The first status bit should be the orientation.
-            tmpd[sysi] = dv["status"][iburst][sysi] & 1
-            tbx.fillgaps(tmpd, extrapFlg=True)
-            tmpd = np.nan_to_num(tmpd, nan=0)  # nans in pitch roll heading
-            slope = np.diff(tmpd)
-            tmpd[1:][slope < 0] = 1
-            tmpd[:-1][slope > 0] = 0
-            dv["orientation_down"][iburst] = tmpd.astype("bool")
-        tbx.interpgaps(dv["batt"], t)
-        tbx.interpgaps(dv["c_sound"], t)
-        tbx.interpgaps(dv["heading"], t)
-        tbx.interpgaps(dv["pitch"], t)
-        tbx.interpgaps(dv["roll"], t)
-        tbx.interpgaps(dv["temp"], t)
+    def read_awac_waves_hdr(self):
+        """Read AWAC header bock for wave data (0x31)"""
+        # ID: '0x31'
+        c = self.c
+        if self.debug:
+            print(
+                "Reading AWAC header data (0x31) ping #{} @ {}...".format(
+                    self.c, self.pos
+                )
+            )
+        hdrnow = {}
+        dat = self.data
+        ds = dat["sys"]
+        dv = dat["data_vars"]
+        if "time" not in dat["coords"]:
+            self._init_data(defs.waves_hdrdata)
+        byts = self.read(56)
+        # The first two are size, the next 6 are time.
+        tmp = unpack(self.endian + "8x4H3h2HhH4B6H5h", byts)
+        dat["coords"]["time"][c] = lib.rd_time(byts[2:8])
+        hdrnow["n_records_alt"] = tmp[0]
+        hdrnow["blank_dist_alt"] = tmp[1]  # counts
+        ds["batt_alt"][c] = tmp[2]  # voltage (0.1 V)
+        dv["c_sound_alt"][c] = tmp[3]  # c (0.1 m/s)
+        dv["heading_alt"][c] = tmp[4]  # (0.1 deg)
+        dv["pitch_alt"][c] = tmp[5]  # (0.1 deg)
+        dv["roll_alt"][c] = tmp[6]  # (0.1 deg)
+        dv["pressure1_alt"][c] = tmp[7]  # min pressure previous profile (0.001 dbar)
+        dv["pressure2_alt"][c] = tmp[8]  # max pressure previous profile (0.001 dbar)
+        dv["temp_alt"][c] = tmp[9]  # (0.01 deg C)
+        hdrnow["cell_size_alt"][c] = tmp[10]  # (counts of T3)
+        hdrnow["noise_alt"][c] = tmp[11:15]  # noise amplitude beam 1-4 (counts)
+        hdrnow["proc_magn_alt"][c] = tmp[15:19]  # processing magnitude beam 1-4
+        hdrnow["n_past_window_alt"] = tmp[
+            19
+        ]  # number of samples of AST window past boundary
+        hdrnow["n_window_alt"] = tmp[20]  # AST window size (# samples)
+        hdrnow["Spare1"] = tmp[21:]
+        self.checksum(byts)
+        if "data_header" not in self.config:
+            self.config["data_header"] = hdrnow
+        else:
+            if not isinstance(self.config["data_header"], list):
+                self.config["data_header"] = [self.config["data_header"]]
+            self.config["data_header"] += [hdrnow]
 
-    def read_microstrain(self):
-        """Read ADV microstrain sensor (IMU) data"""
+    def read_imu(self):
+        """Read ADV inertial measurement unit (IMU) data block (0x71)"""
 
         def update_defs(dat, mag=False, orientmat=False):
             imu_data = {
@@ -930,14 +943,13 @@ def update_defs(dat, mag=False, orientmat=False):
         # 0x71 = 113
         if self.c == 0:
             logging.warning(
-                'First "microstrain data" block '
-                'is before first "vector system data" block.'
+                'First "IMU data" block ' 'is before first "vector system data" block.'
             )
         else:
             self.c -= 1
         if self.debug:
             logging.info(
-                "Reading vector microstrain data (0x71) ping #{} @ {}...".format(
+                "Reading Vector IMU data (0x71) ping #{} @ {}...".format(
                     self.c, self.pos
                 )
             )
@@ -956,7 +968,7 @@ def update_defs(dat, mag=False, orientmat=False):
         da = dat["attrs"]
         da["has_imu"] = 1  # logical
         if "accel" not in dv:
-            self._dtypes += ["microstrain"]
+            self._dtypes += ["imu"]
             if ahrsid == 195:
                 self._orient_dnames = ["accel", "angrt", "orientmat"]
                 dv["accel"] = tbx._nans((3, self.n_samp_guess), dtype=np.float32)
@@ -1026,81 +1038,107 @@ def update_defs(dat, mag=False, orientmat=False):
         self.checksum(byts0 + byts)
         self.c += 1  # reset the increment
 
-    def sci_microstrain(self):
-        """Rotate orientation data into ADV coordinate system."""
-        # MS = MicroStrain
-        dv = self.data["data_vars"]
-        for nm in self._orient_dnames:
-            # Rotate the MS orientation data (in MS coordinate system)
-            # to be consistent with the ADV coordinate system.
-            # (x,y,-z)_ms = (z,y,x)_adv
-            (dv[nm][2], dv[nm][0]) = (dv[nm][0], -dv[nm][2].copy())
-        if "orientmat" in self._orient_dnames:
-            # MS coordinate system is in North-East-Down (NED),
-            # we want East-North-Up (ENU)
-            dv["orientmat"][:, 2] *= -1
-            (dv["orientmat"][:, 0], dv["orientmat"][:, 1]) = (
-                dv["orientmat"][:, 1],
-                dv["orientmat"][:, 0].copy(),
-            )
-        if "accel" in dv:
-            # This value comes from the MS 3DM-GX3 MIP manual
-            dv["accel"] *= 9.80665
-        if self._ahrsid in [195, 211]:
-            # These are DAng and DVel, so we convert them to angrt, accel here
-            dv["angrt"] *= self.config["fs"]
-            dv["accel"] *= self.config["fs"]
+    def cleanup(self):
+        """Convert and scale raw measurements to physical quantities."""
+        for nm in self._dtypes:
+            getattr(self, "convert_" + nm)()
+        for nm in ["data_header", "checkdata"]:
+            if nm in self.config and isinstance(self.config[nm], list):
+                self.config[nm] = lib._recatenate(self.config[nm])
 
-    def read_awac_profile(self):
-        # ID: '0x20' = 32
-        dat = self.data
-        if self.debug:
-            logging.info(
-                "Reading AWAC velocity data (0x20) ping #{} @ {}...".format(
-                    self.c, self.pos
-                )
-            )
-        nbins = self.config["usr"]["n_bins"]
-        if "temp" not in dat["data_vars"]:
-            self._init_data(nortek_defs.awac_profile)
-            self._dtypes += ["awac_profile"]
+    def _convert_data(self, vardict):
+        """
+        Convert the data to scientific units according to 'vardict'.
 
-        # Note: docs state there is 'fill' byte at the end, if nbins is odd,
-        # but doesn't appear to be the case
-        n = self.config["usr"]["n_beams"]
-        byts = self.read(116 + n * 3 * nbins)
-        c = self.c
-        dat["coords"]["time"][c] = self.rd_time(byts[2:8])
-        ds = dat["sys"]
+        Parameters
+        ----------
+        vardict : (dict of :class:`<VarAttrs>`)
+            The variable definitions in the :class:`<VarAttrs>` specify
+            how to scale each data variable.
+        """
+
+        for nm, vd in list(vardict.items()):
+            if vd.group is None:
+                dat = self.data
+            else:
+                dat = self.data[vd.group]
+            retval = vd.scale(dat[nm])
+            # This checks whether a new data object was created:
+            # 'scale' returns None if it modifies the existing data.
+            if retval is not None:
+                dat[nm] = retval
+
+    def convert_vec_sysdata(self):
+        """Convert raw Vector system data into physical quantities."""
+        dat = self.data
+        fs = dat["attrs"]["fs"]
+        self._convert_data(defs.vec_sysdata)
+        t = dat["coords"]["time"]
         dv = dat["data_vars"]
-        (
-            dv["error"][c],
-            ds["AnaIn1"][c],
-            dv["batt"][c],
-            dv["c_sound"][c],
-            dv["heading"][c],
-            dv["pitch"][c],
-            dv["roll"][c],
-            p_msb,
-            dv["status"][c],
-            p_lsw,
-            dv["temp"][c],
-        ) = unpack(self.endian + "5H2hBBHh", byts[8:28])
-        dv["pressure"][c] = 65536 * p_msb + p_lsw
-        # The nortek system integrator manual specifies an 88byte 'spare'
-        # field, therefore we start at 116.
-        tmp = unpack(
-            self.endian + str(n * nbins) + "h" + str(n * nbins) + "B",
-            byts[116 : 116 + n * 3 * nbins],
-        )
-        for idx in range(n):
-            dv["vel"][idx, :, c] = tmp[idx * nbins : (idx + 1) * nbins]
-            dv["amp"][idx, :, c] = tmp[(idx + n) * nbins : (idx + n + 1) * nbins]
-        self.checksum(byts)
-        self.c += 1
+        dat["sys"]["_sysi"] = ~np.isnan(t)
+        # These are the indices in the sysdata variables
+        # that are not interpolated.
+        nburst = self.config["n_burst"]
+        dv["orientation_down"] = tbx._nans(len(t), dtype="bool")
+        if nburst == 0:
+            num_bursts = 1
+            nburst = len(t)
+        else:
+            num_bursts = int(len(t) // nburst + 1)
+        for nb in range(num_bursts):
+            iburst = slice(nb * nburst, (nb + 1) * nburst)
+            sysi = dat["sys"]["_sysi"][iburst]
+            if len(sysi) == 0:
+                break
+            # Skip the first entry for the interpolation process
+            inds = np.nonzero(sysi)[0][1:]
+            arng = np.arange(len(t[iburst]), dtype=np.float64)
+            if len(inds) >= 2:
+                p = np.poly1d(np.polyfit(inds, t[iburst][inds], 1))
+                t[iburst] = p(arng)
+            elif len(inds) == 1:
+                t[iburst] = (arng - inds[0]) / (fs * 3600 * 24) + t[iburst][inds[0]]
+            else:
+                t[iburst] = t[iburst][0] + arng / (fs * 24 * 3600)
+
+            tmpd = tbx._nans_like(dv["heading"][iburst])
+            # The first status bit should be the orientation.
+            tmpd[sysi] = dv["status"][iburst][sysi] & 1
+            tbx.fillgaps(tmpd, extrapFlg=True)
+            tmpd = np.nan_to_num(tmpd, nan=0)  # nans in pitch roll heading
+            slope = np.diff(tmpd)
+            tmpd[1:][slope < 0] = 1
+            tmpd[:-1][slope > 0] = 0
+            dv["orientation_down"][iburst] = tmpd.astype("bool")
+        tbx.interpgaps(dv["batt"], t)
+        tbx.interpgaps(dv["c_sound"], t)
+        tbx.interpgaps(dv["heading"], t)
+        tbx.interpgaps(dv["pitch"], t)
+        tbx.interpgaps(dv["roll"], t)
+        tbx.interpgaps(dv["temp"], t)
+
+    def convert_vec_data(self):
+        """Convert raw Vector measurements to physical quantities."""
+        self._convert_data(defs.vec_data)
+        dat = self.data
+
+        dat["data_vars"]["pressure"] = (
+            dat["data_vars"]["PressureMSB"].astype("float32") * 65536
+            + dat["data_vars"]["PressureLSW"].astype("float32")
+        ) / 1000.0
+        dat["units"]["pressure"] = "dbar"
+        dat["long_name"]["pressure"] = "Pressure"
+        dat["standard_name"]["pressure"] = "sea_water_pressure"
+
+        dat["data_vars"].pop("PressureMSB")
+        dat["data_vars"].pop("PressureLSW")
+
+        # Apply velocity scaling (1 or 0.1)
+        dat["data_vars"]["vel"] *= self.config["vel_scale_mm"]
 
-    def sci_awac_profile(self):
-        self._sci_data(nortek_defs.awac_profile)
+    def convert_awac_profile(self):
+        """Convert raw AWAC profile measurements to physical quantities."""
+        self._convert_data(defs.awac_profile)
         # Calculate the ranges.
         cs_coefs = {2000: 0.0239, 1000: 0.0478, 600: 0.0797, 400: 0.1195}
         h_ang = 25 * (np.pi / 180)  # Head angle is 25 degrees for all awacs.
@@ -1120,110 +1158,26 @@ def sci_awac_profile(self):
         self.data["attrs"]["cell_size"] = float(cs)
         self.data["attrs"]["blank_dist"] = float(bd)
 
-    def read_awac_waves_hdr(self):
-        # ID: '0x31'
-        c = self.c
-        if self.debug:
-            print(
-                "Reading vector header data (0x31) ping #{} @ {}...".format(
-                    self.c, self.pos
-                )
-            )
-        hdrnow = {}
-        dat = self.data
-        ds = dat["sys"]
-        dv = dat["data_vars"]
-        if "time" not in dat["coords"]:
-            self._init_data(nortek_defs.waves_hdrdata)
-        byts = self.read(56)
-        # The first two are size, the next 6 are time.
-        tmp = unpack(self.endian + "8x4H3h2HhH4B6H5h", byts)
-        dat["coords"]["time"][c] = self.rd_time(byts[2:8])
-        hdrnow["n_records_alt"] = tmp[0]
-        hdrnow["blank_dist_alt"] = tmp[1]  # counts
-        ds["batt_alt"][c] = tmp[2]  # voltage (0.1 V)
-        dv["c_sound_alt"][c] = tmp[3]  # c (0.1 m/s)
-        dv["heading_alt"][c] = tmp[4]  # (0.1 deg)
-        dv["pitch_alt"][c] = tmp[5]  # (0.1 deg)
-        dv["roll_alt"][c] = tmp[6]  # (0.1 deg)
-        dv["pressure1_alt"][c] = tmp[7]  # min pressure previous profile (0.001 dbar)
-        dv["pressure2_alt"][c] = tmp[8]  # max pressure previous profile (0.001 dbar)
-        dv["temp_alt"][c] = tmp[9]  # (0.01 deg C)
-        hdrnow["cell_size_alt"][c] = tmp[10]  # (counts of T3)
-        hdrnow["noise_alt"][c] = tmp[11:15]  # noise amplitude beam 1-4 (counts)
-        hdrnow["proc_magn_alt"][c] = tmp[15:19]  # processing magnitude beam 1-4
-        hdrnow["n_past_window_alt"] = tmp[
-            19
-        ]  # number of samples of AST window past boundary
-        hdrnow["n_window_alt"] = tmp[20]  # AST window size (# samples)
-        hdrnow["Spare1"] = tmp[21:]
-        self.checksum(byts)
-        if "data_header" not in self.config:
-            self.config["data_header"] = hdrnow
-        else:
-            if not isinstance(self.config["data_header"], list):
-                self.config["data_header"] = [self.config["data_header"]]
-            self.config["data_header"] += [hdrnow]
-
-    def read_awac_waves(self):
-        """Read awac wave and suv data"""
-        # IDs: 0x30 & 0x36
-        c = self.c
-        dat = self.data
-        if self.debug:
-            print(
-                "Reading awac wave data (0x30) ping #{} @ {}...".format(
-                    self.c, self.pos
-                )
+    def convert_imu(self):
+        """Rotate IMU data into ADV coordinate system."""
+        dv = self.data["data_vars"]
+        for nm in self._orient_dnames:
+            # Rotate the MS orientation data (in MS coordinate system)
+            # to be consistent with the ADV coordinate system.
+            # (x,y,-z)_ms = (z,y,x)_adv
+            (dv[nm][2], dv[nm][0]) = (dv[nm][0], -dv[nm][2].copy())
+        if "orientmat" in self._orient_dnames:
+            # MS coordinate system is in North-East-Down (NED),
+            # we want East-North-Up (ENU)
+            dv["orientmat"][:, 2] *= -1
+            (dv["orientmat"][:, 0], dv["orientmat"][:, 1]) = (
+                dv["orientmat"][:, 1],
+                dv["orientmat"][:, 0].copy(),
             )
-        if "dist1_alt" not in dat["data_vars"]:
-            self._init_data(nortek_defs.wave_data)
-            self._dtypes += ["wave_data"]
-        # The first two are size
-        byts = self.read(20)
-        ds = dat["sys"]
-        dv = dat["data_vars"]
-        (
-            dv["pressure"][c],  # (0.001 dbar)
-            dv["dist1_alt"][c],  # distance 1 to surface, vertical beam (mm)
-            ds["AnaIn_alt"][c],  # analog input 1
-            dv["vel_alt"][0, c],  # velocity beam 1 (mm/s) East for SUV
-            dv["vel_alt"][1, c],  # North for SUV
-            dv["vel_alt"][2, c],  # Up for SUV
-            dv["dist2_alt"][
-                c
-            ],  # distance 2 to surface, vertical beam (mm) or vel 4 for non-AST
-            dv["amp_alt"][0, c],  # amplitude beam 1 (counts)
-            dv["amp_alt"][1, c],  # amplitude beam 2 (counts)
-            dv["amp_alt"][2, c],  # amplitude beam 3 (counts)
-            # AST quality (counts) or amplitude beam 4 for non-AST
-            dv["quality_alt"][c],
-        ) = unpack(self.endian + "3H4h4B", byts)
-        self.checksum(byts)
-        self.c += 1
-
-    def dat2sci(self):
-        for nm in self._dtypes:
-            getattr(self, "sci_" + nm)()
-        for nm in ["data_header", "checkdata"]:
-            if nm in self.config and isinstance(self.config[nm], list):
-                self.config[nm] = _recatenate(self.config[nm])
-
-
-def _crop_data(obj, range, n_lastdim):
-    for nm, dat in obj.items():
-        if isinstance(dat, np.ndarray) and (dat.shape[-1] == n_lastdim):
-            obj[nm] = dat[..., range]
-
-
-def _recatenate(obj):
-    out = type(obj[0])()
-    for ky in list(obj[0].keys()):
-        if ky in ["__data_groups__", "_type"]:
-            continue
-        val0 = obj[0][ky]
-        if isinstance(val0, np.ndarray) and val0.size > 1:
-            out[ky] = np.concatenate([val[ky][..., None] for val in obj], axis=-1)
-        else:
-            out[ky] = np.array([val[ky] for val in obj])
-    return out
+        if "accel" in dv:
+            # This value comes from the MS 3DM-GX3 MIP manual
+            dv["accel"] *= 9.80665
+        if self._ahrsid in [195, 211]:
+            # These are DAng and DVel, so we convert them to angrt, accel here
+            dv["angrt"] *= self.config["fs"]
+            dv["accel"] *= self.config["fs"]
diff --git a/mhkit/dolfyn/io/nortek_defs.py b/mhkit/dolfyn/io/nortek_defs.py
index da672b5bc..c1c3fe935 100644
--- a/mhkit/dolfyn/io/nortek_defs.py
+++ b/mhkit/dolfyn/io/nortek_defs.py
@@ -89,7 +89,7 @@ def _empty_array(self, **kwargs):
             out = out.view(self.view_type)
         return out
 
-    def sci_func(self, data):
+    def scale(self, data):
         """
         Scale the data to scientific units.
 
diff --git a/mhkit/dolfyn/io/nortek_lib.py b/mhkit/dolfyn/io/nortek_lib.py
new file mode 100644
index 000000000..c8caaf6d9
--- /dev/null
+++ b/mhkit/dolfyn/io/nortek_lib.py
@@ -0,0 +1,60 @@
+from struct import unpack
+import numpy as np
+from datetime import datetime
+
+from .. import time
+
+
+def _bcd2char(cBCD):
+    """Taken from the Nortek System Integrator Manual
+    "Example Program" Chapter.
+    """
+    cBCD = min(cBCD, 153)
+    c = cBCD & 15
+    c += 10 * (cBCD >> 4)
+    return c
+
+
+def _bitshift8(val):
+    return val >> 8
+
+
+def _int2binarray(val, n):
+    out = np.zeros(n, dtype="bool")
+    for idx, n in enumerate(range(n)):
+        out[idx] = val & (2**n)
+    return out
+
+
+def _crop_data(obj, range, n_lastdim):
+    for nm, dat in obj.items():
+        if isinstance(dat, np.ndarray) and (dat.shape[-1] == n_lastdim):
+            obj[nm] = dat[..., range]
+
+
+def _recatenate(obj):
+    out = type(obj[0])()
+    for ky in list(obj[0].keys()):
+        if ky in ["__data_groups__", "_type"]:
+            continue
+        val0 = obj[0][ky]
+        if isinstance(val0, np.ndarray) and val0.size > 1:
+            out[ky] = np.concatenate([val[ky][..., None] for val in obj], axis=-1)
+        else:
+            out[ky] = np.array([val[ky] for val in obj])
+    return out
+
+
+def rd_time(strng):
+    """Read the time from the first 6bytes of the input string."""
+    min, sec, day, hour, year, month = unpack("BBBBBB", strng[:6])
+    return time.date2epoch(
+        datetime(
+            time._fullyear(_bcd2char(year)),
+            _bcd2char(month),
+            _bcd2char(day),
+            _bcd2char(hour),
+            _bcd2char(min),
+            _bcd2char(sec),
+        )
+    )[0]

From 48977bed6904e9c47ff4cd270c1ef6058ee90b14 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Mon, 28 Oct 2024 10:37:54 -0700
Subject: [PATCH 10/14] Set proper turbulence intensity units

---
 mhkit/dolfyn/velocity.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mhkit/dolfyn/velocity.py b/mhkit/dolfyn/velocity.py
index 414ea103e..9b77f3c27 100644
--- a/mhkit/dolfyn/velocity.py
+++ b/mhkit/dolfyn/velocity.py
@@ -851,7 +851,7 @@ def turbulence_intensity(self, U_mag, noise=0, thresh=0, detrend=False):
             coords=coords,
             dims=dims,
             attrs={
-                "units": "% [0,1]",
+                "units": "%",
                 "long_name": "Turbulence Intensity",
                 "comment": f"TI was corrected from a noise level of {noise} m/s",
             },

From 6a69d6cd38e8e1601831ca6fad2046e37040e276 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Mon, 28 Oct 2024 10:42:17 -0700
Subject: [PATCH 11/14] Correct turbulence intensity units

---
 mhkit/dolfyn/velocity.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mhkit/dolfyn/velocity.py b/mhkit/dolfyn/velocity.py
index 9b77f3c27..adfa942f3 100644
--- a/mhkit/dolfyn/velocity.py
+++ b/mhkit/dolfyn/velocity.py
@@ -446,7 +446,7 @@ def I_tke(self, thresh=0):
             I_tke.data.astype("float32"),
             coords=self.U_mag.coords,
             dims=self.U_mag.dims,
-            attrs={"units": "% [0,1]", "long_name": "TKE Intensity"},
+            attrs={"units": "1", "long_name": "TKE Intensity"},
         )
 
     @property
@@ -462,7 +462,7 @@ def I(self, thresh=0):
             I.data.astype("float32"),
             coords=self.U_mag.coords,
             dims=self.U_mag.dims,
-            attrs={"units": "% [0,1]", "long_name": "Turbulence Intensity"},
+            attrs={"units": "1", "long_name": "Turbulence Intensity"},
         )
 
     @property
@@ -851,7 +851,7 @@ def turbulence_intensity(self, U_mag, noise=0, thresh=0, detrend=False):
             coords=coords,
             dims=dims,
             attrs={
-                "units": "%",
+                "units": "1",
                 "long_name": "Turbulence Intensity",
                 "comment": f"TI was corrected from a noise level of {noise} m/s",
             },

From f511dd97a2fcf1a6478321ff6f5f222108764144 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Tue, 29 Oct 2024 11:39:17 -0700
Subject: [PATCH 12/14] Add another encoding parameter to skip

---
 mhkit/dolfyn/io/api.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/mhkit/dolfyn/io/api.py b/mhkit/dolfyn/io/api.py
index 02ef1674a..1b62bd60d 100644
--- a/mhkit/dolfyn/io/api.py
+++ b/mhkit/dolfyn/io/api.py
@@ -198,7 +198,15 @@ def save(ds, filename, format="NETCDF4", engine="netcdf4", compression=False, **
         enc[ky] = ds[ky].encoding
         # Remove unexpected netCDF4 encoding parameters
         # https://github.com/pydata/xarray/discussions/5709
-        params = ["szip", "zstd", "bzip2", "blosc", "contiguous", "chunksizes"]
+        params = [
+            "szip",
+            "zstd",
+            "bzip2",
+            "blosc",
+            "contiguous",
+            "chunksizes",
+            "preferred_chunks",
+        ]
         [enc[ky].pop(p) for p in params if p in enc[ky]]
 
         if compression:

From 1059bd3be6140b2c4e61c00272eea2a697d1e6f7 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Tue, 29 Oct 2024 14:21:49 -0700
Subject: [PATCH 13/14] Better dry code for rdi reader changes

---
 mhkit/dolfyn/io/rdi.py      | 128 +++++++++++++++++-------------------
 mhkit/dolfyn/io/rdi_defs.py |  12 ++++
 2 files changed, 71 insertions(+), 69 deletions(-)

diff --git a/mhkit/dolfyn/io/rdi.py b/mhkit/dolfyn/io/rdi.py
index 8ff1c9489..5968613e6 100644
--- a/mhkit/dolfyn/io/rdi.py
+++ b/mhkit/dolfyn/io/rdi.py
@@ -570,99 +570,99 @@ def read_dat(self, id):
         """Main function map used to read or skip stored IDs"""
         function_map = {
             # 0000 1st profile fixed leader
-            0: (defs.read_fixed, [self, False]),
+            0: (defs.read_fixed, [False]),
             # 0001 2nd profile fixed leader
-            1: (defs.read_fixed, [self, True]),
+            1: (defs.read_fixed, [True]),
             # 0010 Surface layer fixed leader (RiverPro & StreamPro)
-            16: (defs.read_fixed_sl, [self]),
+            16: (defs.read_fixed_sl, []),
             # 0080 1st profile variable leader
-            128: (defs.read_var, [self, False]),
+            128: (defs.read_var, [False]),
             # 0081 2nd profile variable leader
-            129: (defs.read_var, [self, True]),
+            129: (defs.read_var, [True]),
             # 0100 1st profile velocity
-            256: (defs.read_vel, [self, 0]),
+            256: (defs.read_vel, [0]),
             # 0101 2nd profile velocity
-            257: (defs.read_vel, [self, 1]),
+            257: (defs.read_vel, [1]),
             # 0103 Waves first leader
-            259: (self.skip_Nbyte, [74]),
+            259: (defs.skip_Nbyte, [74]),
             # 0110 Surface layer velocity (RiverPro & StreamPro)
-            272: (defs.read_vel, [self, 2]),
+            272: (defs.read_vel, [2]),
             # 0200 1st profile correlation
-            512: (defs.read_corr, [self, 0]),
+            512: (defs.read_corr, [0]),
             # 0201 2nd profile correlation
-            513: (defs.read_corr, [self, 1]),
+            513: (defs.read_corr, [1]),
             # 0203 Waves data
-            515: (self.skip_Nbyte, [186]),
+            515: (defs.skip_Nbyte, [186]),
             # 020C Ambient sound profile
-            524: (self.skip_Nbyte, [4]),
+            524: (defs.skip_Nbyte, [4]),
             # 0210 Surface layer correlation (RiverPro & StreamPro)
-            528: (defs.read_corr, [self, 2]),
+            528: (defs.read_corr, [2]),
             # 0300 1st profile amplitude
-            768: (defs.read_amp, [self, 0]),
+            768: (defs.read_amp, [0]),
             # 0301 2nd profile amplitude
-            769: (defs.read_amp, [self, 1]),
+            769: (defs.read_amp, [1]),
             # 0302 Beam 5 Sum of squared velocities
-            770: (self.skip_Ncol, []),
+            770: (defs.skip_Ncol, []),
             # 0303 Waves last leader
-            771: (self.skip_Ncol, [18]),
+            771: (defs.skip_Ncol, [18]),
             # 0310 Surface layer amplitude (RiverPro & StreamPro)
-            784: (defs.read_amp, [self, 2]),
+            784: (defs.read_amp, [2]),
             # 0400 1st profile % good
-            1024: (defs.read_prcnt_gd, [self, 0]),
+            1024: (defs.read_prcnt_gd, [0]),
             # 0401 2nd profile pct good
-            1025: (defs.read_prcnt_gd, [self, 1]),
+            1025: (defs.read_prcnt_gd, [1]),
             # 0403 Waves HPR data
-            1027: (self.skip_Nbyte, [6]),
+            1027: (defs.skip_Nbyte, [6]),
             # 0410 Surface layer pct good (RiverPro & StreamPro)
-            1040: (defs.read_prcnt_gd, [self, 2]),
+            1040: (defs.read_prcnt_gd, [2]),
             # 0500 1st profile status
-            1280: (defs.read_status, [self, 0]),
+            1280: (defs.read_status, [0]),
             # 0501 2nd profile status
-            1281: (defs.read_status, [self, 1]),
+            1281: (defs.read_status, [1]),
             # 0510 Surface layer status (RiverPro & StreamPro)
-            1296: (defs.read_status, [self, 2]),
-            1536: (defs.read_bottom, [self]),  # 0600 bottom tracking
-            1793: (self.skip_Ncol, [4]),  # 0701 number of pings
-            1794: (self.skip_Ncol, [4]),  # 0702 sum of squared vel
-            1795: (self.skip_Ncol, [4]),  # 0703 sum of velocities
-            2560: (self.skip_Ncol, []),  # 0A00 Beam 5 velocity
-            2816: (self.skip_Ncol, []),  # 0B00 Beam 5 correlation
-            3072: (self.skip_Ncol, []),  # 0C00 Beam 5 amplitude
-            3328: (self.skip_Ncol, []),  # 0D00 Beam 5 pct_good
+            1296: (defs.read_status, [2]),
+            1536: (defs.read_bottom, []),  # 0600 bottom tracking
+            1793: (defs.skip_Ncol, [4]),  # 0701 number of pings
+            1794: (defs.skip_Ncol, [4]),  # 0702 sum of squared vel
+            1795: (defs.skip_Ncol, [4]),  # 0703 sum of velocities
+            2560: (defs.skip_Ncol, []),  # 0A00 Beam 5 velocity
+            2816: (defs.skip_Ncol, []),  # 0B00 Beam 5 correlation
+            3072: (defs.skip_Ncol, []),  # 0C00 Beam 5 amplitude
+            3328: (defs.skip_Ncol, []),  # 0D00 Beam 5 pct_good
             # Fixed attitude data format for Ocean Surveyor ADCPs
-            3000: (self.skip_Nbyte, [32]),
-            3841: (self.skip_Nbyte, [38]),  # 0F01 Beam 5 leader
-            8192: (defs.read_vmdas, [self]),  # 2000
+            3000: (defs.skip_Nbyte, [32]),
+            3841: (defs.skip_Nbyte, [38]),  # 0F01 Beam 5 leader
+            8192: (defs.read_vmdas, []),  # 2000
             # 2013 Navigation parameter data
-            8211: (self.skip_Nbyte, [83]),
-            8226: (defs.read_winriver2, [self]),  # 2022
-            8448: (defs.read_winriver, [self]),  # 2100
-            8449: (defs.read_winriver, [self]),  # 2101
-            8450: (defs.read_winriver, [self]),  # 2102
-            8451: (defs.read_winriver, [self]),  # 2103
-            8452: (defs.read_winriver, [self]),  # 2104
+            8211: (defs.skip_Nbyte, [83]),
+            8226: (defs.read_winriver2, []),  # 2022
+            8448: (defs.read_winriver, []),  # 2100
+            8449: (defs.read_winriver, []),  # 2101
+            8450: (defs.read_winriver, []),  # 2102
+            8451: (defs.read_winriver, []),  # 2103
+            8452: (defs.read_winriver, []),  # 2104
             # 3200 Transformation matrix
-            12800: (self.skip_Nbyte, [32]),
+            12800: (defs.skip_Nbyte, [32]),
             # 3000 Fixed attitude data format for Ocean Surveyor ADCPs
-            12288: (self.skip_Nbyte, [32]),
-            12496: (self.skip_Nbyte, [24]),  # 30D0
-            12504: (self.skip_Nbyte, [48]),  # 30D8
+            12288: (defs.skip_Nbyte, [32]),
+            12496: (defs.skip_Nbyte, [24]),  # 30D0
+            12504: (defs.skip_Nbyte, [48]),  # 30D8
             # 4100 beam 5 range
-            16640: (defs.read_alt, [self]),
+            16640: (defs.read_alt, []),
             # 4400 Firmware status data (RiverPro & StreamPro)
-            17408: (self.skip_Nbyte, [28]),
+            17408: (defs.skip_Nbyte, [28]),
             # 4401 Auto mode setup (RiverPro & StreamPro)
-            17409: (self.skip_Nbyte, [82]),
+            17409: (defs.skip_Nbyte, [82]),
             # 5803 High resolution bottom track velocity
-            22531: (self.skip_Nbyte, [68]),
+            22531: (defs.skip_Nbyte, [68]),
             # 5804 Bottom track range
-            22532: (self.skip_Nbyte, [21]),
+            22532: (defs.skip_Nbyte, [21]),
             # 5901 ISM (IMU) data
-            22785: (self.skip_Nbyte, [65]),
+            22785: (defs.skip_Nbyte, [65]),
             # 5902 Ping attitude
-            22786: (self.skip_Nbyte, [105]),
+            22786: (defs.skip_Nbyte, [105]),
             # 7001 ADC data
-            28673: (self.skip_Nbyte, [14]),
+            28673: (defs.skip_Nbyte, [14]),
         }
         # Call the correct function:
         if self._debug_level > 1:
@@ -670,7 +670,7 @@ def read_dat(self, id):
         if id in function_map:
             if self._debug_level > 1:
                 logging.info("  Reading code {}...".format(hex(id)))
-            retval = function_map.get(id)[0](*function_map[id][1])
+            retval = function_map.get(id)[0](self, *function_map[id][1])
             if retval:
                 return retval
             if self._debug_level > 1:
@@ -678,16 +678,6 @@ def read_dat(self, id):
         else:
             self.read_nocode(id)
 
-    def skip_Ncol(self, n_skip=1):
-        """Skip specified number of columns. For profile measurements."""
-        self.f.seek(n_skip * self.cfg["n_cells"], 1)
-        self._nbyte = 2 + n_skip * self.cfg["n_cells"]
-
-    def skip_Nbyte(self, n_skip):
-        """Skip specified number of bytes. For non-profile measurements."""
-        self.f.seek(n_skip, 1)
-        self._nbyte = 2 + n_skip
-
     def read_nocode(self, id):
         """Identify filler or unknown bytes and bypass them"""
         # Skipping bytes from codes 0340-30FC, commented if needed
@@ -704,7 +694,7 @@ def read_nocode(self, id):
             # 2 bits of byte 3
             # 3 is a 0b00000011 mask:
             dfac = bin(int(hxid[3], 0) & 3).count("1")
-            self.skip_Nbyte(12 * nflds * dfac)
+            defs.skip_Nbyte(self, 12 * nflds * dfac)
         else:
             if self._debug_level > -1:
                 logging.warning("  Unrecognized ID code: %0.4X" % id)
@@ -716,7 +706,7 @@ def skip_nocode(self, id):
         idx = np.where(offsets == self.id_positions[id])[0][0]
         byte_len = offsets[idx + 1] - offsets[idx] - 2
 
-        self.skip_Nbyte(byte_len)
+        defs.skip_Nbyte(self, byte_len)
         if self._debug_level > -1:
             logging.debug(f"Skipping ID code {id}\n")
 
diff --git a/mhkit/dolfyn/io/rdi_defs.py b/mhkit/dolfyn/io/rdi_defs.py
index adb5991ac..addbb3ea2 100644
--- a/mhkit/dolfyn/io/rdi_defs.py
+++ b/mhkit/dolfyn/io/rdi_defs.py
@@ -329,6 +329,18 @@
 }
 
 
+def skip_Ncol(rdr, n_skip=1):
+    """Skip specified number of columns. For profile measurements."""
+    rdr.f.seek(n_skip * rdr.cfg["n_cells"], 1)
+    rdr._nbyte = 2 + n_skip * rdr.cfg["n_cells"]
+
+
+def skip_Nbyte(rdr, n_skip):
+    """Skip specified number of bytes. For non-profile measurements."""
+    rdr.f.seek(n_skip, 1)
+    rdr._nbyte = 2 + n_skip
+
+
 def switch_profile(rdr, bb):
     """Switch between bb, nb and sl profiles"""
     if bb == 1:

From b65e0d8a6976ca14c352f598fd9e404514f09d31 Mon Sep 17 00:00:00 2001
From: jmcvey3 <53623232+jmcvey3@users.noreply.github.com>
Date: Thu, 14 Nov 2024 15:22:39 -0800
Subject: [PATCH 14/14] Requested changes

---
 mhkit/dolfyn/adp/clean.py |  64 ++++++++++++++++++------
 mhkit/dolfyn/io/rdi.py    | 101 +++++++++++++++++++++++++++++++++++---
 2 files changed, 141 insertions(+), 24 deletions(-)

diff --git a/mhkit/dolfyn/adp/clean.py b/mhkit/dolfyn/adp/clean.py
index 5e8f29c06..b05d5577a 100644
--- a/mhkit/dolfyn/adp/clean.py
+++ b/mhkit/dolfyn/adp/clean.py
@@ -1,7 +1,7 @@
-"""Module containing functions to clean data
-"""
+"""Module containing functions to clean data."""
 
 import warnings
+from typer import Optional
 import numpy as np
 import xarray as xr
 from scipy.signal import medfilt
@@ -10,10 +10,31 @@
 from ..rotate.base import quaternion2orient
 
 
-def __check_for_range_offset(ds):
+def __check_for_range_offset(ds) -> float:
     """
-    Fetches or calculates range offset from dataset attributes.
+    Determines the range offset based on a variety of possible dataset attributes.
+    The function first checks if specific attributes are present in the dataset (`ds`)
+    and calculates the range offset accordingly. If the attribute `h_deploy`
+    is found, it is renamed to `range_offset` with a deprecation warning.
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        Dataset containing attributes used to calculate the range offset.
+
+    Returns
+    -------
+    float
+        The calculated or retrieved range offset. Returns 0 if no
+        relevant attributes are present.
+
+    Raises
+    ------
+    DeprecationWarning
+        Warns that the attribute `h_deploy` is deprecated and has been
+        renamed to `range_offset`.
     """
+
     if "bin1_dist_m" in ds.attrs:
         return ds.attrs["bin1_dist_m"] - ds.attrs["blank_dist"] - ds.attrs["cell_size"]
     elif "range_offset" in ds.attrs:
@@ -27,10 +48,10 @@ def __check_for_range_offset(ds):
         ds.attrs["range_offset"] = ds.attrs.pop("h_deploy")
         return ds.attrs["range_offset"]
     else:
-        return 0
+        return 0.0
 
 
-def set_range_offset(ds, range_offset):
+def set_range_offset(ds, range_offset) -> None:
     """
     Adds an instrument's height above seafloor (for an up-facing instrument)
     or depth below water surface (for a down-facing instrument) to the range
@@ -93,6 +114,9 @@ def set_range_offset(ds, range_offset):
 
 
 def find_surface(*args, **kwargs):
+    """
+    Deprecated function. Use `water_depth_from_amplitude` instead.
+    """
     warnings.warn(
         "The 'find_surface' function was renamed to 'water_depth_from_amplitude"
         "and will be dropped in a future release.",
@@ -101,7 +125,7 @@ def find_surface(*args, **kwargs):
     return water_depth_from_amplitude(*args, **kwargs)
 
 
-def water_depth_from_amplitude(ds, thresh=10, nfilt=None):
+def water_depth_from_amplitude(ds, thresh=10, nfilt=None) -> None:
     """
     Find the surface (water level or seafloor) from amplitude data and
     adds the variable "depth" to the input Dataset.
@@ -181,6 +205,9 @@ def water_depth_from_amplitude(ds, thresh=10, nfilt=None):
 
 
 def find_surface_from_P(*args, **kwargs):
+    """
+    Deprecated function. Use `water_depth_from_pressure` instead.
+    """
     warnings.warn(
         "The 'find_surface_from_P' function was renamed to 'water_depth_from_pressure"
         "and will be dropped in a future release.",
@@ -189,7 +216,7 @@ def find_surface_from_P(*args, **kwargs):
     return water_depth_from_pressure(*args, **kwargs)
 
 
-def water_depth_from_pressure(ds, salinity=35):
+def water_depth_from_pressure(ds, salinity=35) -> None:
     """
     Calculates the distance to the water surface. Temperature and salinity
     are used to calculate seawater density, which is in turn used with the
@@ -280,6 +307,9 @@ def water_depth_from_pressure(ds, salinity=35):
 
 
 def nan_beyond_surface(*args, **kwargs):
+    """
+    Deprecated function. Use `remove_surface_interference` instead.
+    """
     warnings.warn(
         "The 'nan_beyond_surface' function was renamed to 'remove_surface_interference"
         "and will be dropped in a future release.",
@@ -288,7 +318,9 @@ def nan_beyond_surface(*args, **kwargs):
     return remove_surface_interference(*args, **kwargs)
 
 
-def remove_surface_interference(ds, val=np.nan, beam_angle=None, inplace=False):
+def remove_surface_interference(
+    ds, val=np.nan, beam_angle=None, inplace=False
+) -> Optional[xr.Dataset]:
     """
     Mask the values of 3D data (vel, amp, corr, echo) that are beyond the surface.
 
@@ -371,7 +403,7 @@ def remove_surface_interference(ds, val=np.nan, beam_angle=None, inplace=False):
         return ds
 
 
-def correlation_filter(ds, thresh=50, inplace=False):
+def correlation_filter(ds, thresh=50, inplace=False) -> Optional[xr.Dataset]:
     """
     Filters out data where correlation is below a threshold in the
     along-beam correlation data.
@@ -433,7 +465,7 @@ def correlation_filter(ds, thresh=50, inplace=False):
         return ds
 
 
-def medfilt_orient(ds, nfilt=7):
+def medfilt_orient(ds, nfilt=7) -> xr.Dataset:
     """
     Median filters the orientation data (heading-pitch-roll or quaternions)
 
@@ -475,7 +507,7 @@ def medfilt_orient(ds, nfilt=7):
         return ds.drop_vars("orientmat")
 
 
-def val_exceeds_thresh(var, thresh=5, val=np.nan):
+def val_exceeds_thresh(var, thresh=5, val=np.nan) -> xr.DataArray:
     """
     Find values of a variable that exceed a threshold value,
     and assign "val" to the velocity data where the threshold is
@@ -492,8 +524,8 @@ def val_exceeds_thresh(var, thresh=5, val=np.nan):
 
     Returns
     -------
-    ds : xarray.Dataset
-      The adcp dataset with datapoints beyond thresh are set to `val`
+    ds : xarray.DataArray
+      The adcp dataarray with datapoints beyond thresh are set to `val`
     """
 
     var = var.copy(deep=True)
@@ -506,7 +538,7 @@ def val_exceeds_thresh(var, thresh=5, val=np.nan):
     return var
 
 
-def fillgaps_time(var, method="cubic", maxgap=None):
+def fillgaps_time(var, method="cubic", maxgap=None) -> xr.DataArray:
     """
     Fill gaps (nan values) in var across time using the specified method
 
@@ -536,7 +568,7 @@ def fillgaps_time(var, method="cubic", maxgap=None):
     )
 
 
-def fillgaps_depth(var, method="cubic", maxgap=None):
+def fillgaps_depth(var, method="cubic", maxgap=None) -> xr.DataArray:
     """
     Fill gaps (nan values) in var along the depth profile using the specified method
 
diff --git a/mhkit/dolfyn/io/rdi.py b/mhkit/dolfyn/io/rdi.py
index 5968613e6..20cc41672 100644
--- a/mhkit/dolfyn/io/rdi.py
+++ b/mhkit/dolfyn/io/rdi.py
@@ -22,7 +22,7 @@ def read_rdi(
     vmdas_search=False,
     winriver=False,
     **kwargs,
-):
+) -> xr.Dataset:
     """
     Read a TRDI binary data file.
 
@@ -732,7 +732,18 @@ def check_offset(self, offset, readbytes):
         fd.seek(4 + self._fixoffset, 1)
 
     def remove_end(self, iens):
-        """Remove uncompleted measurements"""
+        """
+        Removes incomplete measurements from the dataset.
+
+        This method cleans up any partially read data by truncating measurements
+        to the specified ensemble index (`iens`). This is typically called upon
+        reaching the end of the file to ensure only complete data is retained.
+
+        Parameters
+        ----------
+        iens : int
+            The index up to which data is considered complete and should be retained.
+        """
         dat = self.outd
         if self._debug_level > 0:
             logging.info("  Encountered end of file.  Cleaning up data.")
@@ -740,7 +751,29 @@ def remove_end(self, iens):
             lib._setd(dat, nm, lib._get(dat, nm)[..., :iens])
 
     def save_profiles(self, dat, nm, en, iens):
-        """Reformat and save profile measurements"""
+        """
+        Reformats profile measurements in the retrieved measurements.
+
+        This method processes profile measurements from individual pings,
+        adapting to changing cell counts and cell sizes as needed (from the WinRiver2
+        program with one of the ***Pro ADCPs).
+
+        Parameters
+        ----------
+        dat : dict
+            Raw data dictionary
+        nm : str
+            The name of the profile variable
+        en : dict
+            The dictionary containing ensemble profiles
+        iens : int
+            The index of the current ensemble
+
+        Returns
+        -------
+        dict
+            The updated dataset dictionary with the reformatted profile measurements.
+        """
         ds = lib._get(dat, nm)
         if self.n_avg == 1:
             bn = en[nm][..., 0]
@@ -782,8 +815,27 @@ def save_profiles(self, dat, nm, en, iens):
         return dat
 
     def cleanup(self, dat, cfg):
-        """Clean up recorded data, particularly variablem cell sizes
-        and profile ranges."""
+        """
+        Cleans up recorded data by adjusting variable cell sizes and profile ranges.
+
+        This method handles adjustments when cell sizes change during data collection,
+        performing depth-bin averaging for smaller cells if needed. It also updates
+        the configuration data, range coordinates, and manages any surface layer profiles.
+
+        Parameters
+        ----------
+        dat : dict
+            The dataset dictionary containing data variables and coordinates to be cleaned up.
+        cfg : dict
+            Configuration dictionary, which is updated with cell size, range, and additional
+            attributes after cleanup.
+
+        Returns
+        -------
+        tuple
+            - dict : The updated dataset dictionary with cleaned data.
+            - dict : The updated configuration dictionary with new attributes.
+        """
         # Clean up changing cell size, if necessary
         cs = np.array(self.cs, dtype=np.float32)
         cell_sizes = cs[:, 1]
@@ -834,7 +886,24 @@ def cleanup(self, dat, cfg):
         return dat, cfg
 
     def reshape(self, arr, n_bin=None):
-        """Reshape the array `arr` to shape (...,n,n_bin)."""
+        """
+        Reshapes the input array `arr` to a shape of (..., n, n_bin).
+
+        Parameters
+        ----------
+        arr : np.ndarray
+            The array to reshape. The last dimension of `arr` will be divided into
+            `(n, n_bin)` based on the value of `n_bin`.
+        n_bin : int or float, optional
+            The bin size for reshaping. If `n_bin` is an integer, it divides the
+            last dimension directly. If not, indices are adjusted, and excess
+            bins may be removed. Default is None.
+
+        Returns
+        -------
+        np.ndarray
+            The reshaped array with shape (..., n, n_bin).
+        """
 
         out = np.zeros(
             list(arr.shape[:-1]) + [int(arr.shape[-1] // n_bin), int(n_bin)],
@@ -860,8 +929,24 @@ def reshape(self, arr, n_bin=None):
         return out
 
     def finalize(self, dat):
-        """Remove the attributes from the data that were never loaded."""
-
+        """
+        This method cleans up the dataset by removing any attributes that were
+        defined but not loaded, updates configuration attributes, and sets the
+        sampling frequency (fs) based on the data source program. Additionally,
+        it adjusts the axes of certain variables as defined in data_defs.
+
+        Parameters
+        ----------
+        dat : dict
+            The dataset dictionary to be finalized. This dictionary is modified
+            in place by removing unused attributes, setting configuration values
+            as attributes, and calculating `fs`.
+
+        Returns
+        -------
+        dict
+            The finalized dataset dictionary with cleaned attributes and added metadata.
+        """
         for nm in set(defs.data_defs.keys()) - self.vars_read:
             lib._pop(dat, nm)
         for nm in self.cfg: