docs/ADCP_Example.ipynb

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                            "# ADCP Example"
                     ]
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                            "The following example shows a typical workflow for analyzing ADCP data using DOLfYN's tools.\n",
                            "\n",
                            "A typical ADCP data workflow is broken down into\n",
                            "  1. Review the raw data\n",
                            "      - Check timestamps\n",
                            "      - Calculate/check that the depth bin locations are correct\n",
                            "      - Look at velocity, beam amplitude and/or beam correlation data quality\n",
                            "  2. Remove data located above the water surface or below the seafloor\n",
                            "  3. Check for spurious datapoints and remove if necessary\n",
                            "  4. If not already done within the instrument, average the data into bins of a set time length (normally 5 to 10 min)\n",
                            "  5. Conduct further analysis as required\n",
                            "\n",
                            "Start by importing the necessary DOLfYN tools through MHKiT:"
                     ]
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                            "# Import core DOLfYN functions\n",
                            "import dolfyn\n",
                            "# Import ADCP-specific API tools\n",
                            "from dolfyn.adp import api"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "## Read Raw Instrument Data"
                     ]
              },
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                            "The core benefit of DOLfYN is that it can read in raw data directly after transferring it off of the ADCP. The ADCP used here is a Nortek Signature 1000, with the file extension '.ad2cp'. This specific dataset contains several hours worth of velocity data collected at 1 Hz from the ADCP mounted on a bottom lander in a tidal inlet. \n",
                            "The instruments that DOLfYN supports are listed in the [docs](https://dolfyn.readthedocs.io/en/latest/about.html).\n",
                            "\n",
                            "Start by reading in the raw datafile downloaded from the instrument. The `read` function reads the raw file and dumps the information into an xarray Dataset, which contains a few groups of variables:\n",
                            "\n",
                            "1. Velocity in the instrument-saved coordinate system (beam, XYZ, ENU)\n",
                            "2. Beam amplitude and correlation data\n",
                            "3. Measurements of the instrument's bearing and environment\n",
                            "4. Orientation matrices DOLfYN uses for rotating through coordinate frames."
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                                   "text": [
                                          "Reading file ../dolfyn/example_data/Sig1000_tidal.ad2cp ...\n"
                                   ]
                            }
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                     "source": [
                            "ds = dolfyn.read('../dolfyn/example_data/Sig1000_tidal.ad2cp')"
                     ]
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                     "source": [
                            "There are two ways to see what's in a DOLfYN Dataset. The first is to simply type the dataset's name to see the standard xarray output. To access a particular variable in a dataset, use dict-style (`ds['vel']`) or attribute-style syntax (`ds.vel`). See the [xarray docs](http://xarray.pydata.org/en/stable/getting-started-guide/quick-overview.html) for more details on how to use the xarray format."
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                                                 "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
                                                 "Dimensions:              (time: 55000, dirIMU: 3, dir: 4, range: 28, beam: 4,\n",
                                                 "                          earth: 3, inst: 3, q: 4, time_b5: 55000,\n",
                                                 "                          range_b5: 28, x: 4, x*: 4)\n",
                                                 "Coordinates:\n",
                                                 "  * time                 (time) datetime64[ns] 2020-08-15T00:20:00.500999927 ...\n",
                                                 "  * dirIMU               (dirIMU) &lt;U1 &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
                                                 "  * dir                  (dir) &lt;U2 &#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;\n",
                                                 "  * range                (range) float64 0.6 1.1 1.6 2.1 ... 12.6 13.1 13.6 14.1\n",
                                                 "  * beam                 (beam) int32 1 2 3 4\n",
                                                 "  * earth                (earth) &lt;U1 &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
                                                 "  * inst                 (inst) &lt;U1 &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
                                                 "  * q                    (q) &lt;U1 &#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;\n",
                                                 "  * time_b5              (time_b5) datetime64[ns] 2020-08-15T00:20:00.4384999...\n",
                                                 "  * range_b5             (range_b5) float64 0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1\n",
                                                 "  * x                    (x) int32 1 2 3 4\n",
                                                 "  * x*                   (x*) int32 1 2 3 4\n",
                                                 "Data variables: (12/38)\n",
                                                 "    c_sound              (time) float32 1.502e+03 1.502e+03 ... 1.498e+03\n",
                                                 "    temp                 (time) float32 14.55 14.55 14.55 ... 13.47 13.47 13.47\n",
                                                 "    pressure             (time) float32 9.713 9.718 9.718 ... 9.596 9.594 9.596\n",
                                                 "    mag                  (dirIMU, time) float32 72.5 72.7 72.6 ... -197.2 -195.7\n",
                                                 "    accel                (dirIMU, time) float32 -0.00479 -0.01437 ... 9.729\n",
                                                 "    batt                 (time) float32 16.6 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
                                                 "    ...                   ...\n",
                                                 "    telemetry_data       (time) uint8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0\n",
                                                 "    boost_running        (time) uint8 0 0 0 0 0 0 0 0 1 0 ... 0 1 0 0 0 0 0 0 1\n",
                                                 "    heading              (time) float32 -12.52 -12.51 -12.51 ... -12.52 -12.5\n",
                                                 "    pitch                (time) float32 -0.065 -0.06 -0.06 ... -0.06 -0.05 -0.05\n",
                                                 "    roll                 (time) float32 -7.425 -7.42 -7.42 ... -6.45 -6.45 -6.45\n",
                                                 "    beam2inst_orientmat  (x, x*) float32 1.183 0.0 -1.183 ... 0.5518 0.0 0.5518\n",
                                                 "Attributes: (12/33)\n",
                                                 "    filehead_config:       {&#x27;CLOCKSTR&#x27;: {&#x27;TIME&#x27;: &#x27;&quot;2020-08-13 13:56:21&quot;&#x27;}, &#x27;I...\n",
                                                 "    inst_model:            Signature1000\n",
                                                 "    inst_make:             Nortek\n",
                                                 "    inst_type:             ADCP\n",
                                                 "    rotate_vars:           [&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, ...\n",
                                                 "    burst_config:          {&#x27;press_valid&#x27;: True, &#x27;temp_valid&#x27;: True, &#x27;compass...\n",
                                                 "    ...                    ...\n",
                                                 "    proc_idle_less_3pct:   0\n",
                                                 "    proc_idle_less_6pct:   0\n",
                                                 "    proc_idle_less_12pct:  0\n",
                                                 "    coord_sys:             earth\n",
                                                 "    has_imu:               1\n",
                                                 "    fs:                    1</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-4b450fa2-1a4e-402e-b6da-fb1032c2b8e3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b450fa2-1a4e-402e-b6da-fb1032c2b8e3' 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'>dirIMU</span>: 3</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>range</span>: 28</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>: 55000</li><li><span class='xr-has-index'>range_b5</span>: 28</li><li><span class='xr-has-index'>x</span>: 4</li><li><span class='xr-has-index'>x*</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0561c2f0-face-47c4-93ee-3aa3feb8d70d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0561c2f0-face-47c4-93ee-3aa3feb8d70d' 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-5adb8876-cbc5-46a6-9108-80ff788abfec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5adb8876-cbc5-46a6-9108-80ff788abfec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9298ce94-31dd-4cb6-ae88-9d2e0ccabf2f' class='xr-var-data-in' type='checkbox'><label for='data-9298ce94-31dd-4cb6-ae88-9d2e0ccabf2f' title='Show/Hide data repr'><svg 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'>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-5dc370d2-a2be-41eb-8932-fe3ba39d482e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dc370d2-a2be-41eb-8932-fe3ba39d482e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3cdd640-2073-466a-b467-c06e30b67945' class='xr-var-data-in' type='checkbox'><label for='data-e3cdd640-2073-466a-b467-c06e30b67945' title='Show/Hide data repr'><svg 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'>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-7f369885-3107-424a-924b-326b5752d9db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f369885-3107-424a-924b-326b5752d9db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16806301-181f-4461-af8b-2761b8e49612' class='xr-var-data-in' type='checkbox'><label for='data-16806301-181f-4461-af8b-2761b8e49612' title='Show/Hide data repr'><svg 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'>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-b6998dd6-916a-4f60-bc96-b0abc06c9ac8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6998dd6-916a-4f60-bc96-b0abc06c9ac8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0304376-564b-4353-b3d8-b5a5b4e8a4e4' class='xr-var-data-in' type='checkbox'><label for='data-e0304376-564b-4353-b3d8-b5a5b4e8a4e4' title='Show/Hide data repr'><svg 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-779732a5-2707-41c2-9729-0af9a29dcdf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-779732a5-2707-41c2-9729-0af9a29dcdf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-931e6d10-2701-44c4-9ca5-cab949023d93' class='xr-var-data-in' type='checkbox'><label for='data-931e6d10-2701-44c4-9ca5-cab949023d93' title='Show/Hide data repr'><svg 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'>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-bcb47434-f3c6-48f5-958f-3b6c3637ee64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcb47434-f3c6-48f5-958f-3b6c3637ee64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b2e4b4b-d477-4f77-8fce-a4175a3426b6' class='xr-var-data-in' type='checkbox'><label for='data-8b2e4b4b-d477-4f77-8fce-a4175a3426b6' title='Show/Hide data repr'><svg 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-23935071-356d-4465-9b76-058915c2b3c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23935071-356d-4465-9b76-058915c2b3c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a63dd72-01b4-40d8-87c1-78c4a030834e' class='xr-var-data-in' type='checkbox'><label for='data-3a63dd72-01b4-40d8-87c1-78c4a030834e' title='Show/Hide data repr'><svg 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-d43befe1-22b3-44c5-a3f7-351334d5462f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d43befe1-22b3-44c5-a3f7-351334d5462f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9b076d-7057-4bda-b9e9-3b3b1471f7f8' class='xr-var-data-in' type='checkbox'><label for='data-3b9b076d-7057-4bda-b9e9-3b3b1471f7f8' title='Show/Hide data repr'><svg 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'>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-e577f960-7700-47fa-9c75-c61c47bda044' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e577f960-7700-47fa-9c75-c61c47bda044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b7ca898-54d8-4141-81b7-1e366baeda62' class='xr-var-data-in' type='checkbox'><label for='data-5b7ca898-54d8-4141-81b7-1e366baeda62' title='Show/Hide data repr'><svg 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_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-b7336eeb-1808-496b-a10a-34a22b789703' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7336eeb-1808-496b-a10a-34a22b789703' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fdf7085-fcba-4f06-852c-8c42ec02828d' class='xr-var-data-in' type='checkbox'><label for='data-0fdf7085-fcba-4f06-852c-8c42ec02828d' title='Show/Hide data repr'><svg 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-eb01dcc8-9113-4d84-bf74-a226a90e59f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb01dcc8-9113-4d84-bf74-a226a90e59f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80e9d135-5515-48e1-9156-ee3539fd1079' class='xr-var-data-in' type='checkbox'><label for='data-80e9d135-5515-48e1-9156-ee3539fd1079' title='Show/Hide data repr'><svg 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'>x*</span></div><div class='xr-var-dims'>(x*)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-1ce1ba35-e5b1-4db0-9dee-894b94d92de7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ce1ba35-e5b1-4db0-9dee-894b94d92de7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f542e14-823a-4a7d-9b35-299d22430a3f' class='xr-var-data-in' type='checkbox'><label for='data-3f542e14-823a-4a7d-9b35-299d22430a3f' title='Show/Hide data repr'><svg 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-79cb7304-3a80-47e9-aaaf-a3a825a9984e' class='xr-section-summary-in' type='checkbox'  ><label for='section-79cb7304-3a80-47e9-aaaf-a3a825a9984e' 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-68046b20-704b-4aba-8be1-c31cb2235d60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68046b20-704b-4aba-8be1-c31cb2235d60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a2e47d0-c919-4cd7-b32c-9d6e882c57d3' class='xr-var-data-in' type='checkbox'><label for='data-2a2e47d0-c919-4cd7-b32c-9d6e882c57d3' title='Show/Hide data repr'><svg 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-90f0b8c7-b3ce-47f7-9365-3fccbb02afec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90f0b8c7-b3ce-47f7-9365-3fccbb02afec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6899ac16-8900-41e1-ac48-27137914c502' class='xr-var-data-in' type='checkbox'><label for='data-6899ac16-8900-41e1-ac48-27137914c502' title='Show/Hide data repr'><svg 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-615635e9-7704-40c8-baaf-0a5f20ca4f73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-615635e9-7704-40c8-baaf-0a5f20ca4f73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3643eaf-f623-4293-8ac5-48d7c0bbbd7e' class='xr-var-data-in' type='checkbox'><label for='data-d3643eaf-f623-4293-8ac5-48d7c0bbbd7e' title='Show/Hide data repr'><svg 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-59659819-4344-4910-9314-920ad966ea48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59659819-4344-4910-9314-920ad966ea48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dc3e208-fa42-42eb-a458-d857a6a7d0ee' class='xr-var-data-in' type='checkbox'><label for='data-9dc3e208-fa42-42eb-a458-d857a6a7d0ee' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Compass</dd><dt><span>standard_name :</span></dt><dd>magnetic_field_vector</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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-5c5e448a-a1f3-4c5c-a79f-454f12a38157' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c5e448a-a1f3-4c5c-a79f-454f12a38157' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a10b87c-3cec-49f3-a997-970886c0c036' class='xr-var-data-in' type='checkbox'><label for='data-9a10b87c-3cec-49f3-a997-970886c0c036' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Acceleration</dd><dt><span>standard_name :</span></dt><dd>platform_acceleration</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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-55ea3247-eb98-4bbe-a839-5c54dd945149' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55ea3247-eb98-4bbe-a839-5c54dd945149' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-460d6890-4b75-4495-a8ab-d19c095ac006' class='xr-var-data-in' type='checkbox'><label for='data-460d6890-4b75-4495-a8ab-d19c095ac006' title='Show/Hide data repr'><svg 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-9787a411-6141-474e-9d09-8c20a76f510c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9787a411-6141-474e-9d09-8c20a76f510c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d7547e2-7a88-43c0-b31e-2f1f2b784761' class='xr-var-data-in' type='checkbox'><label for='data-2d7547e2-7a88-43c0-b31e-2f1f2b784761' title='Show/Hide data repr'><svg 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><dt><span>standard_name :</span></dt><dd></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-d9c34c0b-0bd5-4adf-a78f-98d0144b4ce2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9c34c0b-0bd5-4adf-a78f-98d0144b4ce2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e560343a-0cc0-4eb4-ba67-afa63a8210ee' class='xr-var-data-in' type='checkbox'><label for='data-e560343a-0cc0-4eb4-ba67-afa63a8210ee' title='Show/Hide data repr'><svg 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><dt><span>standard_name :</span></dt><dd></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-2ae8c3ae-d605-4016-8174-b9ed89caf482' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ae8c3ae-d605-4016-8174-b9ed89caf482' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fa43ce9-8932-476f-aee1-badd75afb50a' class='xr-var-data-in' type='checkbox'><label for='data-0fa43ce9-8932-476f-aee1-badd75afb50a' title='Show/Hide data repr'><svg 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><dt><span>standard_name :</span></dt><dd></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-eba233c7-1a02-458f-874b-f3686ac41ab3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eba233c7-1a02-458f-874b-f3686ac41ab3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-813ffe0a-ad9e-40e7-9941-b7b950818d6b' class='xr-var-data-in' type='checkbox'><label for='data-813ffe0a-ad9e-40e7-9941-b7b950818d6b' title='Show/Hide data repr'><svg 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-ca3917a6-b296-422a-b51f-4c9497e9d05f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca3917a6-b296-422a-b51f-4c9497e9d05f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9970b989-b307-4a99-ba86-948d7a97e7d0' class='xr-var-data-in' type='checkbox'><label for='data-9970b989-b307-4a99-ba86-948d7a97e7d0' title='Show/Hide data repr'><svg 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</dd><dt><span>standard_name :</span></dt><dd>velocity_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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",
                                                 "        [-0.169, -0.013, -0.124, ..., -0.062,  0.091, -0.009],\n",
                                                 "        [-0.16 , -0.043, -0.111, ...,  0.012,  0.089,  0.021],\n",
                                                 "        [-0.158, -0.057, -0.13 , ..., -0.055,  0.104,  0.013]],\n",
                                                 "\n",
                                                 "       [[ 0.243,  0.204,  0.234, ..., -0.163, -0.135, -0.18 ],\n",
                                                 "        [ 0.272,  0.248,  0.236, ..., -0.254, -0.169, -0.204],\n",
                                                 "        [ 0.257,  0.294,  0.333, ..., -0.299, -0.226, -0.273],\n",
                                                 "        ...,\n",
                                                 "        [ 0.305,  0.377,  0.283, ..., -0.198, -0.229, -0.121],\n",
                                                 "        [ 0.328,  0.33 ,  0.294, ..., -0.161, -0.292, -0.103],\n",
                                                 "        [ 0.31 ,  0.314,  0.226, ..., -0.219, -0.292, -0.049]],\n",
                                                 "\n",
                                                 "       [[ 0.016,  0.012,  0.01 , ...,  0.009,  0.006, -0.018],\n",
                                                 "        [ 0.01 ,  0.011, -0.013, ...,  0.002, -0.027,  0.009],\n",
                                                 "        [ 0.01 ,  0.009,  0.023, ..., -0.018, -0.002, -0.024],\n",
                                                 "        ...,\n",
                                                 "        [ 0.034, -0.061, -0.024, ..., -0.014,  0.086,  0.01 ],\n",
                                                 "        [ 0.032, -0.04 , -0.027, ..., -0.014,  0.063, -0.01 ],\n",
                                                 "        [ 0.004, -0.037, -0.018, ..., -0.001,  0.059,  0.021]],\n",
                                                 "\n",
                                                 "       [[ 0.003, -0.007,  0.046, ..., -0.032, -0.008, -0.029],\n",
                                                 "        [ 0.017,  0.033,  0.035, ..., -0.016, -0.022, -0.034],\n",
                                                 "        [ 0.016, -0.012,  0.005, ...,  0.002, -0.045, -0.026],\n",
                                                 "        ...,\n",
                                                 "        [-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-6169f49b-c390-4cc5-b0b4-c15d0883812b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6169f49b-c390-4cc5-b0b4-c15d0883812b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d416904-3816-48e0-88ce-83f10bb08fd9' class='xr-var-data-in' type='checkbox'><label for='data-2d416904-3816-48e0-88ce-83f10bb08fd9' title='Show/Hide data repr'><svg 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>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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",
                                                 "        [54.5, 55.5, 54.5, ..., 50. , 50. , 49.5],\n",
                                                 "        [53.5, 54.5, 54.5, ..., 46.5, 47. , 46.5],\n",
                                                 "        [53. , 54. , 53. , ..., 45. , 46. , 44.5]],\n",
                                                 "\n",
                                                 "       [[81.5, 80. , 80.5, ..., 82. , 81.5, 82. ],\n",
                                                 "        [76. , 77. , 76.5, ..., 79. , 77.5, 77.5],\n",
                                                 "        [73.5, 74. , 73. , ..., 75.5, 74.5, 74.5],\n",
                                                 "        ...,\n",
                                                 "        [57. , 58.5, 58. , ..., 53.5, 52. , 52.5],\n",
                                                 "        [55.5, 56.5, 55.5, ..., 50. , 49. , 49. ],\n",
                                                 "        [52.5, 54. , 54. , ..., 47.5, 47.5, 47. ]],\n",
                                                 "\n",
                                                 "       [[80.5, 79.5, 80. , ..., 81.5, 82.5, 81.5],\n",
                                                 "        [76. , 75.5, 75. , ..., 77. , 78.5, 77. ],\n",
                                                 "        [72. , 73.5, 72. , ..., 74. , 75. , 73.5],\n",
                                                 "        ...,\n",
                                                 "        [56.5, 56.5, 57. , ..., 47.5, 47. , 48. ],\n",
                                                 "        [56. , 55.5, 56.5, ..., 46.5, 47. , 47. ],\n",
                                                 "        [54.5, 54. , 55.5, ..., 45. , 45.5, 45.5]],\n",
                                                 "\n",
                                                 "       [[80. , 81. , 80. , ..., 81. , 81. , 81.5],\n",
                                                 "        [75.5, 75.5, 74.5, ..., 76.5, 76.5, 77.5],\n",
                                                 "        [73. , 72.5, 73. , ..., 75.5, 74.5, 74. ],\n",
                                                 "        ...,\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-fa23e905-15e4-4f08-a985-73c3674be7aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa23e905-15e4-4f08-a985-73c3674be7aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-218ab6a8-cfce-4ceb-8b39-e11bbcb2b2d3' class='xr-var-data-in' type='checkbox'><label for='data-218ab6a8-cfce-4ceb-8b39-e11bbcb2b2d3' title='Show/Hide data repr'><svg 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>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>coverage_content_type :</span></dt><dd>physicalMeasurement</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",
                                                 "        [ 99, 100, 100, ...,  96,  96,  99],\n",
                                                 "        [ 99,  97, 100, ...,  94,  97,  96],\n",
                                                 "        [ 98, 100, 100, ..., 100, 100,  98]],\n",
                                                 "\n",
                                                 "       [[ 94,  90,  89, ...,  91,  92,  90],\n",
                                                 "        [ 94, 100, 100, ...,  96, 100,  93],\n",
                                                 "        [ 99,  95,  97, ...,  98,  98, 100],\n",
                                                 "        ...,\n",
                                                 "        [100, 100, 100, ...,  98,  99, 100],\n",
                                                 "        [ 99, 100,  99, ...,  97,  94,  94],\n",
                                                 "        [ 96,  99,  96, ...,  97, 100,  97]],\n",
                                                 "\n",
                                                 "       [[ 92,  93,  93, ...,  93,  97,  89],\n",
                                                 "        [ 99, 100,  99, ...,  96,  88,  98],\n",
                                                 "        [ 97, 100,  98, ..., 100,  99,  97],\n",
                                                 "        ...,\n",
                                                 "        [100, 100,  99, ...,  97,  94,  95],\n",
                                                 "        [100, 100, 100, ...,  96, 100, 100],\n",
                                                 "        [100, 100, 100, ...,  96,  95,  94]],\n",
                                                 "\n",
                                                 "       [[ 87,  97,  87, ...,  91,  87,  90],\n",
                                                 "        [100,  93,  93, ...,  96,  96,  97],\n",
                                                 "        [100, 100, 100, ..., 100, 100,  97],\n",
                                                 "        ...,\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-0b481ced-110f-4661-9601-9e3c2d0ca04a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b481ced-110f-4661-9601-9e3c2d0ca04a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4efe3963-286d-4ca7-abe6-803b6418d175' class='xr-var-data-in' type='checkbox'><label for='data-4efe3963-286d-4ca7-abe6-803b6418d175' title='Show/Hide data repr'><svg 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>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",
                                                 "        [-1.22870505e-03, -1.20262802e-03, -1.17563456e-03, ...,\n",
                                                 "         -1.16243958e-03, -1.02279335e-03, -9.55920666e-04]],\n",
                                                 "\n",
                                                 "       [[-9.67970312e-01, -9.68004048e-01, -9.67982769e-01, ...,\n",
                                                 "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
                                                 "        [-2.15024590e-01, -2.14734554e-01, -2.14924574e-01, ...,\n",
                                                 "         -2.14874983e-01, -2.15436578e-01, -2.15052128e-01],\n",
                                                 "        [-1.29278094e-01, -1.29190803e-01, -1.29239410e-01, ...,\n",
                                                 "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01]],\n",
                                                 "\n",
                                                 "       [[-1.26469478e-01, -1.26385525e-01, -1.26422524e-01, ...,\n",
                                                 "         -1.10033900e-01, -1.10009573e-01, -1.09963998e-01],\n",
                                                 "        [-2.68291458e-02, -2.68030688e-02, -2.68616974e-02, ...,\n",
                                                 "         -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-05c64ba6-22f5-4af3-bc5e-5fbfe6e5a0b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05c64ba6-22f5-4af3-bc5e-5fbfe6e5a0b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-716954d7-54d8-40b4-8f7d-af69947c8781' class='xr-var-data-in' type='checkbox'><label for='data-716954d7-54d8-40b4-8f7d-af69947c8781' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Angular Velocity</dd><dt><span>standard_name :</span></dt><dd>platform_angular_velocity</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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-255755ff-3179-4086-8c92-1c19dc6a3623' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-255755ff-3179-4086-8c92-1c19dc6a3623' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f05e7bb-8c50-4cac-8dbb-2f6380d9765b' class='xr-var-data-in' type='checkbox'><label for='data-4f05e7bb-8c50-4cac-8dbb-2f6380d9765b' title='Show/Hide data repr'><svg 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>Quaternions</dd><dt><span>standard_name :</span></dt><dd>platform_quaternion_vector</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-2fb0fe68-75e3-4cbd-9b76-4c8d3d372fd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fb0fe68-75e3-4cbd-9b76-4c8d3d372fd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62a63a07-adce-4bc5-9f13-9691f3635002' class='xr-var-data-in' type='checkbox'><label for='data-62a63a07-adce-4bc5-9f13-9691f3635002' title='Show/Hide data repr'><svg 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><dt><span>standard_name :</span></dt><dd></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-c073dfa2-d9c4-4354-8124-3e2c589765bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c073dfa2-d9c4-4354-8124-3e2c589765bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92c19697-2b8d-4127-a699-2d67a27f6f8f' class='xr-var-data-in' type='checkbox'><label for='data-92c19697-2b8d-4127-a699-2d67a27f6f8f' title='Show/Hide data repr'><svg 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>Transmit Energy</dd><dt><span>standard_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-67142611-2232-46df-9222-36a1b94b3337' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67142611-2232-46df-9222-36a1b94b3337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73e693ac-9b15-440e-9aff-dc602d0e8a46' class='xr-var-data-in' type='checkbox'><label for='data-73e693ac-9b15-440e-9aff-dc602d0e8a46' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Compass</dd><dt><span>standard_name :</span></dt><dd>magnetic_field_vector</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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-dcacf9eb-3257-49f7-bab0-56d1e379bd46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcacf9eb-3257-49f7-bab0-56d1e379bd46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51063f4e-e9c5-4260-87bd-ec09fa27eb62' class='xr-var-data-in' type='checkbox'><label for='data-51063f4e-e9c5-4260-87bd-ec09fa27eb62' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Acceleration</dd><dt><span>standard_name :</span></dt><dd>platform_acceleration</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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-1b955734-ea1c-4082-89cb-fb8711d5e45f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b955734-ea1c-4082-89cb-fb8711d5e45f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be34e559-26d9-41dd-a49a-7c274f245534' class='xr-var-data-in' type='checkbox'><label for='data-be34e559-26d9-41dd-a49a-7c274f245534' title='Show/Hide data repr'><svg 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><dt><span>standard_name :</span></dt><dd></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-514c3ba0-b92f-4f40-878e-ed5674453d9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-514c3ba0-b92f-4f40-878e-ed5674453d9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60f120a6-3ec2-4fa0-bced-a1bd460f7c0c' class='xr-var-data-in' type='checkbox'><label for='data-60f120a6-3ec2-4fa0-bced-a1bd460f7c0c' title='Show/Hide data repr'><svg 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><dt><span>standard_name :</span></dt><dd></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-7f90f4cb-951b-4a19-a7cf-5b1424a0f902' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f90f4cb-951b-4a19-a7cf-5b1424a0f902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c192057-019d-4f11-b23e-c712ee41b68b' class='xr-var-data-in' type='checkbox'><label for='data-6c192057-019d-4f11-b23e-c712ee41b68b' title='Show/Hide data repr'><svg 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-a3555980-2978-4de4-b8ff-2e4dc3007403' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3555980-2978-4de4-b8ff-2e4dc3007403' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-364416bb-e328-4c14-a33a-8193746d0731' class='xr-var-data-in' type='checkbox'><label for='data-364416bb-e328-4c14-a33a-8193746d0731' title='Show/Hide data repr'><svg 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</dd><dt><span>standard_name :</span></dt><dd>velocity_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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-8785e358-a686-47db-a675-c397603152a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8785e358-a686-47db-a675-c397603152a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b54daba-a460-41e5-8b2d-78a4d888fbda' class='xr-var-data-in' type='checkbox'><label for='data-2b54daba-a460-41e5-8b2d-78a4d888fbda' title='Show/Hide data repr'><svg 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>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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-b425bcff-9fdd-4e12-8dda-5614cf150d70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b425bcff-9fdd-4e12-8dda-5614cf150d70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57605f9f-9053-4f5a-afd2-9c0213175f2a' class='xr-var-data-in' type='checkbox'><label for='data-57605f9f-9053-4f5a-afd2-9c0213175f2a' title='Show/Hide data repr'><svg 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>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>coverage_content_type :</span></dt><dd>physicalMeasurement</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-ae06e61e-86fa-4597-9e7c-eaa3c6fa03a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae06e61e-86fa-4597-9e7c-eaa3c6fa03a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-225cecf0-0478-4382-8271-858ca4c2864d' class='xr-var-data-in' type='checkbox'><label for='data-225cecf0-0478-4382-8271-858ca4c2864d' title='Show/Hide data repr'><svg 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>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",
                                                 "        [-1.26507595e-01, -1.26426160e-01, -1.26425594e-01, ...,\n",
                                                 "         -1.10033900e-01, -1.10009573e-01, -1.09963998e-01]],\n",
                                                 "\n",
                                                 "       [[ 9.76198971e-01,  9.76259708e-01,  9.76250350e-01, ...,\n",
                                                 "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
                                                 "        [-2.15024590e-01, -2.14829564e-01, -2.14924574e-01, ...,\n",
                                                 "         -2.14874983e-01, -2.15436578e-01, -2.15052128e-01],\n",
                                                 "        [-2.68766508e-02, -2.68536285e-02, -2.68591940e-02, ...,\n",
                                                 "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02]],\n",
                                                 "\n",
                                                 "       [[-1.19058788e-03, -1.16700679e-03, -1.17257237e-03, ...,\n",
                                                 "         -1.16243958e-03, -1.02279335e-03, -9.55920666e-04],\n",
                                                 "        [-1.29325598e-01, -1.29241347e-01, -1.29241914e-01, ...,\n",
                                                 "         -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-386727e3-c93e-477e-bf0e-aa4bf6a35a7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-386727e3-c93e-477e-bf0e-aa4bf6a35a7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f862021-215d-4721-8268-98271d3529a1' class='xr-var-data-in' type='checkbox'><label for='data-3f862021-215d-4721-8268-98271d3529a1' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Angular Velocity</dd><dt><span>standard_name :</span></dt><dd>platform_angular_velocity</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</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-dfab305e-47b1-435c-b073-518a5ca1452e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfab305e-47b1-435c-b073-518a5ca1452e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3acbb36-05fc-4264-b98a-d79d1707b9c6' class='xr-var-data-in' type='checkbox'><label for='data-e3acbb36-05fc-4264-b98a-d79d1707b9c6' title='Show/Hide data repr'><svg 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>Quaternions</dd><dt><span>standard_name :</span></dt><dd>platform_quaternion_vector</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-b64e6426-56ce-4e5d-84e5-6957136552f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b64e6426-56ce-4e5d-84e5-6957136552f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a33298d-140d-497b-91aa-6d3ea16a91ff' class='xr-var-data-in' type='checkbox'><label for='data-7a33298d-140d-497b-91aa-6d3ea16a91ff' title='Show/Hide data repr'><svg 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>Transmit Energy</dd><dt><span>standard_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-645cd932-4ff6-471b-ba62-086ab9966c65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-645cd932-4ff6-471b-ba62-086ab9966c65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e0b3fe5-9cd0-4364-ba50-07ade2d66ecf' class='xr-var-data-in' type='checkbox'><label for='data-2e0b3fe5-9cd0-4364-ba50-07ade2d66ecf' title='Show/Hide data 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-dd298db9-9b69-4c53-943d-b6f014ca7169' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd298db9-9b69-4c53-943d-b6f014ca7169' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74162bef-8071-4d35-ae6a-59274fb6f735' class='xr-var-data-in' type='checkbox'><label for='data-74162bef-8071-4d35-ae6a-59274fb6f735' title='Show/Hide data 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-f1c4f432-d85f-40a0-8311-f4109f18a5aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1c4f432-d85f-40a0-8311-f4109f18a5aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3da8cbc-b112-438d-99f5-c41ad210f6c7' class='xr-var-data-in' type='checkbox'><label for='data-a3da8cbc-b112-438d-99f5-c41ad210f6c7' title='Show/Hide data 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-24243bf1-d2f3-40ed-ada3-29d6f3560dd2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24243bf1-d2f3-40ed-ada3-29d6f3560dd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5055babd-9f71-4316-ad75-4140be809165' class='xr-var-data-in' type='checkbox'><label for='data-5055babd-9f71-4316-ad75-4140be809165' title='Show/Hide data 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-d7b8fe5a-4b86-4417-bac0-a15f18b77e4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7b8fe5a-4b86-4417-bac0-a15f18b77e4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7353b40-9c38-42b9-9112-b54f0fe36b69' class='xr-var-data-in' type='checkbox'><label for='data-c7353b40-9c38-42b9-9112-b54f0fe36b69' title='Show/Hide data repr'><svg 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-9cfd3632-921b-4fdf-b4d4-53409572769d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cfd3632-921b-4fdf-b4d4-53409572769d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81132372-83f7-41eb-a0c8-ad5bb78a614a' class='xr-var-data-in' type='checkbox'><label for='data-81132372-83f7-41eb-a0c8-ad5bb78a614a' title='Show/Hide data repr'><svg 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-b8875b39-24b0-4d88-a49b-b6ab583350fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8875b39-24b0-4d88-a49b-b6ab583350fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29a79d42-e5ec-4bc1-96ec-13ecb0022d15' class='xr-var-data-in' type='checkbox'><label for='data-29a79d42-e5ec-4bc1-96ec-13ecb0022d15' title='Show/Hide data repr'><svg 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'>(x, x*)</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-ae1f9632-9e98-460c-be81-0816ae45c80e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae1f9632-9e98-460c-be81-0816ae45c80e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62655d3f-7d91-4b51-acd9-7443ba8c3af7' class='xr-var-data-in' type='checkbox'><label for='data-62655d3f-7d91-4b51-acd9-7443ba8c3af7' title='Show/Hide data repr'><svg 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>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-5bc91d2b-6d97-4f23-881b-3cb61088a6fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-5bc91d2b-6d97-4f23-881b-3cb61088a6fc' class='xr-section-summary' >Attributes: <span>(33)</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>{&#x27;CLOCKSTR&#x27;: {&#x27;TIME&#x27;: &#x27;&quot;2020-08-13 13:56:21&quot;&#x27;}, &#x27;ID&#x27;: &#x27;STR=&quot;Signature1000&quot;,SN=101663&#x27;, &#x27;HW&#x27;: {&#x27;FW&#x27;: 2212, &#x27;FPGA&#x27;: 169, &#x27;DIGITAL&#x27;: &#x27;&quot;I-3&quot;&#x27;, &#x27;INTERFACE&#x27;: &#x27;&quot;H-2&quot;&#x27;, &#x27;ANALOG&#x27;: &#x27;&quot;G-1&quot;&#x27;, &#x27;SENSOR&#x27;: &#x27;&quot;D-1(AHRS)&quot;&#x27;, &#x27;BOOT&#x27;: 21, &#x27;FWMINOR&#x27;: 11}, &#x27;BOARDSENSGET&#x27;: &#x27;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&#x27;, &#x27;PWR&#x27;: {&#x27;PLAN&#x27;: 1940.43, &#x27;BURST&#x27;: 1937.69, &#x27;AVG&#x27;: 0.0, &#x27;PLAN1&#x27;: 0.0, &#x27;BURST1&#x27;: 0.0, &#x27;AVG1&#x27;: 0.0, &#x27;TOTAL&#x27;: 1940.43}, &#x27;MEM&#x27;: {&#x27;PLAN&#x27;: 2.733, &#x27;BURST&#x27;: 2.733, &#x27;AVG&#x27;: 0.0, &#x27;PLAN1&#x27;: 0.0, &#x27;BURST1&#x27;: 0.0, &#x27;AVG1&#x27;: 0.0, &#x27;TOTAL&#x27;: 2.733}, &#x27;PRECISION&#x27;: {&#x27;AVGHORZ&#x27;: -9.99, &#x27;BURSTHORZ&#x27;: 2.6, &#x27;BEAM5&#x27;: 1.62, &#x27;AVGBEAM&#x27;: -9.99, &#x27;BURSTBEAM&#x27;: 1.55}, &#x27;PLAN&#x27;: {&#x27;MIAVG&#x27;: 600, &#x27;AVG&#x27;: 0, &#x27;DIAVG&#x27;: 0, &#x27;VD&#x27;: 0, &#x27;MV&#x27;: 10, &#x27;SA&#x27;: 32.0, &#x27;BURST&#x27;: 1, &#x27;MIBURST&#x27;: 1, &#x27;DIBURST&#x27;: 0, &#x27;SV&#x27;: 0.0, &#x27;FN&#x27;: &#x27;&quot;S101663A007_sea_spider.ad2cp&quot;&#x27;, &#x27;SO&#x27;: 0, &#x27;FREQ&#x27;: 1000, &#x27;NSTT&#x27;: 0}, &#x27;BURST&#x27;: {&#x27;NC&#x27;: 28, &#x27;NB&#x27;: 5, &#x27;CS&#x27;: 0.5, &#x27;BD&#x27;: 0.1, &#x27;CY&#x27;: &#x27;&quot;ENU&quot;&#x27;, &#x27;PL&#x27;: 0.0, &#x27;NS&#x27;: 1, &#x27;VR&#x27;: 2.5, &#x27;VP&#x27;: 0.0, &#x27;DF&#x27;: 3, &#x27;NPING&#x27;: 8, &#x27;CH&#x27;: 0, &#x27;ALTI&#x27;: 0, &#x27;VR5&#x27;: 2.5, &#x27;BT&#x27;: 0, &#x27;DISV&#x27;: 0, &#x27;ECHO&#x27;: 0, &#x27;RAWALTI&#x27;: 60, &#x27;ALTISTART&#x27;: 0.1, &#x27;ALTIEND&#x27;: 30.0, &#x27;HR&#x27;: 0, &#x27;HR5&#x27;: 0}, &#x27;USER&#x27;: {&#x27;POFF&#x27;: 10.2, &#x27;DECL&#x27;: 0.0, &#x27;HX&#x27;: -48, &#x27;HY&#x27;: 48, &#x27;HZ&#x27;: 0}, &#x27;INST&#x27;: {&#x27;BR&#x27;: 9600, &#x27;RS&#x27;: 232, &#x27;LED&#x27;: &#x27;&quot;OFF&quot;&#x27;, &#x27;ORIENT&#x27;: &#x27;&quot;AHRS3D&quot;&#x27;, &#x27;CMTOUT&#x27;: 300, &#x27;DMTOUT&#x27;: 60, &#x27;CFMTOUT&#x27;: 60}, &#x27;COMPASSCAL&#x27;: {&#x27;DX&#x27;: 155, &#x27;DY&#x27;: 778, &#x27;DZ&#x27;: -486, &#x27;M11&#x27;: 31481, &#x27;M12&#x27;: 1726, &#x27;M13&#x27;: -1109, &#x27;M21&#x27;: -1955, &#x27;M22&#x27;: 32767, &#x27;M23&#x27;: 379, &#x27;M31&#x27;: 750, &#x27;M32&#x27;: -1851, &#x27;M33&#x27;: 31699}, &#x27;READAHRS&#x27;: &#x27;STR=&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM&quot;&#x27;, &#x27;RECSTAT&#x27;: &#x27;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&#x27;, &#x27;BEAMCFGLIST&#x27;: [&#x27;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&#x27;, &#x27;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&#x27;, &#x27;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&#x27;, &#x27;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&#x27;, &#x27;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&#x27;], &#x27;BEAMIMPLIST&#x27;: [&#x27;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&#x27;, &#x27;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&#x27;, &#x27;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&#x27;, &#x27;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&#x27;, &#x27;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&#x27;], &#x27;LISTLICENSE&#x27;: [&#x27;KEY=&quot;C390Y1LU32C1B&quot;,DESC=&quot;Averaging Mode&quot;,TYPE=1&#x27;, &#x27;KEY=&quot;6F2UB3HTH2C1B&quot;,DESC=&quot;Burst Five Beams&quot;,TYPE=17&#x27;, &#x27;KEY=&quot;R0X9JSZPH2C1B&quot;,DESC=&quot;128GB Recorder&quot;,TYPE=14&#x27;], &#x27;CALCOMPGET&#x27;: &#x27;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&#x27;, &#x27;CALTEMPGET&#x27;: &#x27;SC=0.99906&#x27;, &#x27;CALTILTGET&#x27;: &#x27;PO=0.00,RO=0.00,MAGG=1,HO=0.00&#x27;, &#x27;CALACCLGET&#x27;: [&#x27;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&#x27;, &#x27;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&#x27;, &#x27;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&#x27;], &#x27;CALGYROGET&#x27;: [&#x27;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&#x27;, &#x27;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&#x27;, &#x27;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&#x27;], &#x27;CALPRESSGET&#x27;: &#x27;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;&#x27;, &#x27;CALPRESSCOEFFGET&#x27;: [&#x27;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&#x27;, &#x27;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&#x27;], &#x27;CALROTACCLGET&#x27;: &#x27;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&#x27;, &#x27;CALROTGYROGET&#x27;: &#x27;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&#x27;, &#x27;CALECHOGET&#x27;: &#x27;CHA0=0.00,CHB0=-17.69,CHC0=0.00&#x27;}</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>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>burst_config :</span></dt><dd>{&#x27;press_valid&#x27;: True, &#x27;temp_valid&#x27;: True, &#x27;compass_valid&#x27;: True, &#x27;tilt_valid&#x27;: True, &#x27;vel&#x27;: True, &#x27;amp&#x27;: True, &#x27;corr&#x27;: True, &#x27;alt&#x27;: False, &#x27;alt_raw&#x27;: False, &#x27;ast&#x27;: False, &#x27;echo&#x27;: False, &#x27;ahrs&#x27;: True, &#x27;p_gd&#x27;: False, &#x27;std&#x27;: 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>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&#x27;press_valid&#x27;: True, &#x27;temp_valid&#x27;: True, &#x27;compass_valid&#x27;: True, &#x27;tilt_valid&#x27;: True, &#x27;vel&#x27;: True, &#x27;amp&#x27;: True, &#x27;corr&#x27;: True, &#x27;alt&#x27;: False, &#x27;alt_raw&#x27;: False, &#x27;ast&#x27;: False, &#x27;echo&#x27;: False, &#x27;ahrs&#x27;: True, &#x27;p_gd&#x27;: False, &#x27;std&#x27;: 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>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</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>coord_sys :</span></dt><dd>earth</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>fs :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
                                          ],
                                          "text/plain": [
                                                 "<xarray.Dataset>\n",
                                                 "Dimensions:              (time: 55000, dirIMU: 3, dir: 4, range: 28, beam: 4,\n",
                                                 "                          earth: 3, inst: 3, q: 4, time_b5: 55000,\n",
                                                 "                          range_b5: 28, x: 4, x*: 4)\n",
                                                 "Coordinates:\n",
                                                 "  * time                 (time) datetime64[ns] 2020-08-15T00:20:00.500999927 ...\n",
                                                 "  * dirIMU               (dirIMU) <U1 'E' 'N' 'U'\n",
                                                 "  * dir                  (dir) <U2 'E' 'N' 'U1' 'U2'\n",
                                                 "  * range                (range) float64 0.6 1.1 1.6 2.1 ... 12.6 13.1 13.6 14.1\n",
                                                 "  * beam                 (beam) int32 1 2 3 4\n",
                                                 "  * earth                (earth) <U1 'E' 'N' 'U'\n",
                                                 "  * inst                 (inst) <U1 'X' 'Y' 'Z'\n",
                                                 "  * q                    (q) <U1 'w' 'x' 'y' 'z'\n",
                                                 "  * time_b5              (time_b5) datetime64[ns] 2020-08-15T00:20:00.4384999...\n",
                                                 "  * range_b5             (range_b5) float64 0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1\n",
                                                 "  * x                    (x) int32 1 2 3 4\n",
                                                 "  * x*                   (x*) int32 1 2 3 4\n",
                                                 "Data variables: (12/38)\n",
                                                 "    c_sound              (time) float32 1.502e+03 1.502e+03 ... 1.498e+03\n",
                                                 "    temp                 (time) float32 14.55 14.55 14.55 ... 13.47 13.47 13.47\n",
                                                 "    pressure             (time) float32 9.713 9.718 9.718 ... 9.596 9.594 9.596\n",
                                                 "    mag                  (dirIMU, time) float32 72.5 72.7 72.6 ... -197.2 -195.7\n",
                                                 "    accel                (dirIMU, time) float32 -0.00479 -0.01437 ... 9.729\n",
                                                 "    batt                 (time) float32 16.6 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
                                                 "    ...                   ...\n",
                                                 "    telemetry_data       (time) uint8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0\n",
                                                 "    boost_running        (time) uint8 0 0 0 0 0 0 0 0 1 0 ... 0 1 0 0 0 0 0 0 1\n",
                                                 "    heading              (time) float32 -12.52 -12.51 -12.51 ... -12.52 -12.5\n",
                                                 "    pitch                (time) float32 -0.065 -0.06 -0.06 ... -0.06 -0.05 -0.05\n",
                                                 "    roll                 (time) float32 -7.425 -7.42 -7.42 ... -6.45 -6.45 -6.45\n",
                                                 "    beam2inst_orientmat  (x, x*) float32 1.183 0.0 -1.183 ... 0.5518 0.0 0.5518\n",
                                                 "Attributes: (12/33)\n",
                                                 "    filehead_config:       {'CLOCKSTR': {'TIME': '\"2020-08-13 13:56:21\"'}, 'I...\n",
                                                 "    inst_model:            Signature1000\n",
                                                 "    inst_make:             Nortek\n",
                                                 "    inst_type:             ADCP\n",
                                                 "    rotate_vars:           ['vel', 'accel', 'accel_b5', 'angrt', 'angrt_b5', ...\n",
                                                 "    burst_config:          {'press_valid': True, 'temp_valid': True, 'compass...\n",
                                                 "    ...                    ...\n",
                                                 "    proc_idle_less_3pct:   0\n",
                                                 "    proc_idle_less_6pct:   0\n",
                                                 "    proc_idle_less_12pct:  0\n",
                                                 "    coord_sys:             earth\n",
                                                 "    has_imu:               1\n",
                                                 "    fs:                    1"
                                          ]
                                   },
                                   "execution_count": 3,
                                   "metadata": {},
                                   "output_type": "execute_result"
                            }
                     ],
                     "source": [
                            "# print the dataset\n",
                            "ds"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "A second way provided to look at data is through the *DOLfYN view*. This view has several convenience methods, shortcuts, and functions built-in. It includes an alternate – and somewhat more informative/compact – description of the data object when in interactive mode. This can be accessed using"
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 4,
                     "metadata": {},
                     "outputs": [
                            {
                                   "data": {
                                          "text/plain": [
                                                 "<ADCP data object>:  Nortek Signature1000\n",
                                                 "  . 15.28 hours (started: Aug 15, 2020 00:20)\n",
                                                 "  . earth-frame\n",
                                                 "  . (55000 pings @ 1Hz)\n",
                                                 "  Variables:\n",
                                                 "  - time ('time',)\n",
                                                 "  - time_b5 ('time_b5',)\n",
                                                 "  - vel ('dir', 'range', 'time')\n",
                                                 "  - vel_b5 ('range_b5', 'time_b5')\n",
                                                 "  - range ('range',)\n",
                                                 "  - orientmat ('earth', 'inst', 'time')\n",
                                                 "  - heading ('time',)\n",
                                                 "  - pitch ('time',)\n",
                                                 "  - roll ('time',)\n",
                                                 "  - temp ('time',)\n",
                                                 "  - pressure ('time',)\n",
                                                 "  - amp ('beam', 'range', 'time')\n",
                                                 "  - amp_b5 ('range_b5', 'time_b5')\n",
                                                 "  - corr ('beam', 'range', 'time')\n",
                                                 "  - corr_b5 ('range_b5', 'time_b5')\n",
                                                 "  - accel ('dirIMU', 'time')\n",
                                                 "  - angrt ('dirIMU', 'time')\n",
                                                 "  - mag ('dirIMU', 'time')\n",
                                                 "  ... and others (see `<obj>.variables`)"
                                          ]
                                   },
                                   "execution_count": 4,
                                   "metadata": {},
                                   "output_type": "execute_result"
                            }
                     ],
                     "source": [
                            "ds_dolfyn = ds.velds\n",
                            "ds_dolfyn"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "## First Steps and QC'ing Data"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "### 1.) Set deployment height"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "Because this is a Nortek instrument, the deployment software doesn't take into account the deployment height, aka where in the water column the ADCP is. The center of the first depth bin is located at a distance = deployment height + blanking distance + cell size, so the `range` coordinate needs to be corrected so that '0' corresponds to the seafloor. This can be done in DOLfYN using the `set_range_offset` function. This same function can be used to account for the depth of a down-facing instrument below the water surface.\n",
                            "\n",
                            "Note, if using a Teledyne RDI ADCP, TRDI's deployment software asks the user to enter the deployment height/depth during configuration. If needed, this can be adjusted after-the-fact using `set_range_offset` as well."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 5,
                     "metadata": {},
                     "outputs": [],
                     "source": [
                            "# The ADCP transducers were measured to be 0.6 m from the feet of the lander\n",
                            "api.clean.set_range_offset(ds, 0.6)"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "So, the center of bin 1 is located at 1.2 m:"
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 6,
                     "metadata": {},
                     "outputs": [
                            {
                                   "data": {
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                                                 "\n",
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                                                 "  grid-column: 1;\n",
                                                 "}\n",
                                                 "\n",
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                                                 "  padding-right: 10px;\n",
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                                                 "\n",
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                                                 "\n",
                                                 ".xr-icon-database,\n",
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                                                 "  stroke: currentColor;\n",
                                                 "  fill: currentColor;\n",
                                                 "}\n",
                                                 "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;range&#x27; (range: 28)&gt;\n",
                                                 "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])\n",
                                                 "Coordinates:\n",
                                                 "  * range    (range) float64 1.2 1.7 2.2 2.7 3.2 ... 12.7 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-6feff3cc-c4a2-419f-8dea-4fe6927db4c5' class='xr-array-in' type='checkbox' checked><label for='section-6feff3cc-c4a2-419f-8dea-4fe6927db4c5' 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-5865fbbc-95d0-45c5-83ca-7f4be2f65519' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5865fbbc-95d0-45c5-83ca-7f4be2f65519' 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-1cd6a19f-a77e-46e8-b028-68458f754d12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cd6a19f-a77e-46e8-b028-68458f754d12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-701c5d68-951a-4f5d-84f2-492d481f1a3c' class='xr-var-data-in' type='checkbox'><label for='data-701c5d68-951a-4f5d-84f2-492d481f1a3c' title='Show/Hide data repr'><svg 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",
                                                 "        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-73ffd0a1-8298-438f-b3f8-10ee46cc602f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73ffd0a1-8298-438f-b3f8-10ee46cc602f' 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>"
                                          ],
                                          "text/plain": [
                                                 "<xarray.DataArray 'range' (range: 28)>\n",
                                                 "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])\n",
                                                 "Coordinates:\n",
                                                 "  * range    (range) float64 1.2 1.7 2.2 2.7 3.2 ... 12.7 13.2 13.7 14.2 14.7\n",
                                                 "Attributes:\n",
                                                 "    units:    m"
                                          ]
                                   },
                                   "execution_count": 6,
                                   "metadata": {},
                                   "output_type": "execute_result"
                            }
                     ],
                     "source": [
                            "ds.range"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "### 2.) Remove data beyond surface level"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "To reduce the amount of data the code must run through, we can remove all data at and above the water surface. Because the instrument was looking up, we can use the pressure sensor data and the function `find_surface_from_P`. This does require that the pressure sensor was 'zeroed' prior to deployment. If the instrument is looking down or lacks pressure data, use the function `find_surface` to detect the seabed or water surface.\n",
                            "\n",
                            "ADCPs don't measure water salinity, so it will need to be given to the function. The returned dataset contains the an additional variable \"depth\". If `find_surface_from_P` is run after `set_range_offset`, depth is the distance of the water surface away from the seafloor; otherwise it is the distance to the ADCP pressure sensor.\n",
                            "\n",
                            "After calculating depth, data in depth bins at and above the physical water surface can be removed using `nan_beyond_surface`. Note that this function returns a new dataset."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 7,
                     "metadata": {},
                     "outputs": [],
                     "source": [
                            "api.clean.find_surface_from_P(ds, salinity=31)\n",
                            "ds = api.clean.nan_beyond_surface(ds)"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "### 3.) Correlation filter"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "Once beyond-surface bins have been removed, ADCP data is typically filtered by acoustic signal correlation to clear out spurious velocity datapoints (caused by bubbles, kelp, fish, etc moving through one or multiple beams).\n",
                            "\n",
                            "We can take a quick look at the data to see about where this value should be using xarray's built-in plotting.\n",
                            "In the following line of code, we use xarray's slicing capabilities to show data from beam 1 between a range of 0 to 10 m from the ADCP.\n",
                            "\n",
                            "Not all ADCPs return acoustic signal correlation, which in essence is a quantitative measure of signal quality. ADCPs with older hardware do not provide a correlation measurement, so this step will be skipped with these instruments."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 8,
                     "metadata": {},
                     "outputs": [
                            {
                                   "data": {
                                          "text/plain": [
                                                 "<matplotlib.collections.QuadMesh at 0x22bc6980cd0>"
                                          ]
                                   },
                                   "execution_count": 8,
                                   "metadata": {},
                                   "output_type": "execute_result"
                            },
                            {
                                   "data": {
                                          "image/png": 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",
                                          "text/plain": [
                                                 "<Figure size 640x480 with 2 Axes>"
                                          ]
                                   },
                                   "metadata": {},
                                   "output_type": "display_data"
                            }
                     ],
                     "source": [
                            "%matplotlib inline\n",
                            "ds['corr'].sel(beam=1, range=slice(0,10)).plot()"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "It's a good idea to check the other beams as well. Much of this data is high quality, and to not lose data will low correlation caused by natural variation, we'll use the `correlation_filter` to set velocity values corresponding to correlations below 50% to NaN.\n",
                            "\n",
                            "Note that this threshold is dependent on the deployment environment and instrument, and it isn't uncommon to use a value as low as 30%, or to pass on this function completely."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 9,
                     "metadata": {},
                     "outputs": [],
                     "source": [
                            "ds = api.clean.correlation_filter(ds, thresh=50)"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "## Review the Data"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "Now that the data has been cleaned, the next step is to rotate the velocity data into true East, North, Up coordinates.\n",
                            "\n",
                            "ADCPs use an internal compass or magnetometer to determine magnetic ENU directions. The `set_declination` function takes the user supplied magnetic declination (which can be looked up online for specific coordinates) and adjusts the velocity data accordingly.\n",
                            "\n",
                            "Instruments save vector data in the coordinate system specified in the deployment configuration file. To make the data useful, it must be rotated through coordinate systems (\"beam\"<->\"inst\"<->\"earth\"<->\"principal\"), done through the `rotate2` function. If the \"earth\" (ENU) coordinate system is specified, DOLfYN will automatically rotate the dataset through the necessary coordinate systems to get there. The `inplace` set as true will alter the input dataset \"in place\", a.k.a. it not create a new dataset.\n",
                            "\n",
                            "Because this ADCP data was already in the \"earth\" coordinate system, `rotate2` will return the input dataset. `set_declination` will run correctly no matter the coordinate system."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 10,
                     "metadata": {},
                     "outputs": [
                            {
                                   "name": "stdout",
                                   "output_type": "stream",
                                   "text": [
                                          "Data is already in the earth coordinate system\n"
                                   ]
                            }
                     ],
                     "source": [
                            "dolfyn.set_declination(ds, declin=15.8, inplace=True) # 15.8 deg East\n",
                            "dolfyn.rotate2(ds, 'earth', inplace=True)"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "To rotate into the principal frame of reference (streamwise, cross-stream, vertical), if desired, we must first calculate the depth-averaged principal flow heading and add it to the dataset attributes. Then the dataset can be rotated using the same `rotate2` function. We use `inplace=False` because we do not want to alter the input dataset."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 11,
                     "metadata": {},
                     "outputs": [],
                     "source": [
                            "ds.attrs['principal_heading'] = dolfyn.calc_principal_heading(ds['vel'].mean('range'))\n",
                            "ds_streamwise = dolfyn.rotate2(ds, 'principal', inplace=False)"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "Because this deployment was set up in \"burst mode\", the next standard step in this analysis is to average the velocity data into time bins. \n",
                            "\n",
                            "If an instrument was set up to record velocity data in an \"averaging mode\" (a specific profile and/or average interval, e.g. take 5 minutes of data every 30 minutes), this step was completed within the ADCP during deployment and can be skipped.\n",
                            "\n",
                            "To average the data into time bins (aka ensembles), start by initiating the binning tool `VelBinner`. \"n_bin\" is the number of data points in each ensemble, in this case 300 seconds worth of data, and \"fs\" is the sampling frequency, which is 1 Hz for this deployment. Once initiated, average the data into ensembles using the binning tool's `do_avg` function."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 12,
                     "metadata": {
                            "scrolled": true
                     },
                     "outputs": [],
                     "source": [
                            "avg_tool = api.VelBinner(n_bin=ds.fs*300, fs=ds.fs)\n",
                            "ds_avg = avg_tool.do_avg(ds)"
                     ]
              },
              {
                     "attachments": {},
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "Two more variables not automatically provided that may be of interest are the horizontal velocity magnitude (speed) and its direction, respectively `U_mag` and `U_dir`. There are included as \"shortcut\" functions, and are accessed through the keyword `velds`, as shown in the code block below. The full list of \"shorcuts\" are listed [here](https://dolfyn.readthedocs.io/en/latest/apidoc/dolfyn.shortcuts.html).\n"
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 13,
                     "metadata": {
                            "scrolled": true
                     },
                     "outputs": [],
                     "source": [
                            "ds_avg['U_mag'] = ds_avg.velds.U_mag\n",
                            "ds_avg['U_dir'] = ds_avg.velds.U_dir"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "Plotting can be accomplished through the user's preferred package. Matplotlib is shown here for simplicity, and flow speed and direction are plotted below with a blue line delineating the water surface level."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 14,
                     "metadata": {},
                     "outputs": [
                            {
                                   "data": {
                                          "text/plain": [
                                                 "<matplotlib.colorbar.Colorbar at 0x22bc076b3a0>"
                                          ]
                                   },
                                   "execution_count": 14,
                                   "metadata": {},
                                   "output_type": "execute_result"
                            },
                            {
                                   "data": {
                                          "image/png": 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",
                                          "text/plain": [
                                                 "<Figure size 1200x800 with 2 Axes>"
                                          ]
                                   },
                                   "metadata": {},
                                   "output_type": "display_data"
                            }
                     ],
                     "source": [
                            "%matplotlib inline\n",
                            "from matplotlib import pyplot as plt\n",
                            "import matplotlib.dates as dt\n",
                            "\n",
                            "ax = plt.figure(figsize=(12,8)).add_axes([.14, .14, .8, .74])\n",
                            "# Plot flow speed\n",
                            "t = dolfyn.time.dt642date(ds_avg.time)\n",
                            "plt.pcolormesh(t, ds_avg['range'], ds_avg['U_mag'], cmap='Blues', shading='nearest')\n",
                            "# Plot the water surface\n",
                            "ax.plot(t, ds_avg['depth'])\n",
                            "\n",
                            "# Set up time on x-axis\n",
                            "ax.set_xlabel('Time')\n",
                            "ax.xaxis.set_major_formatter(dt.DateFormatter('%H:%M'))\n",
                            "\n",
                            "ax.set_ylabel('Altitude [m]')\n",
                            "ax.set_ylim([0, 12])\n",
                            "plt.colorbar(label='Horizontal Vel [m/s]')"
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 15,
                     "metadata": {
                            "scrolled": false
                     },
                     "outputs": [
                            {
                                   "data": {
                                          "text/plain": [
                                                 "<matplotlib.colorbar.Colorbar at 0x22bc6204190>"
                                          ]
                                   },
                                   "execution_count": 15,
                                   "metadata": {},
                                   "output_type": "execute_result"
                            },
                            {
                                   "data": {
                                          "image/png": 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",
                                          "text/plain": [
                                                 "<Figure size 1200x800 with 2 Axes>"
                                          ]
                                   },
                                   "metadata": {},
                                   "output_type": "display_data"
                            }
                     ],
                     "source": [
                            "ax = plt.figure(figsize=(12,8)).add_axes([.14, .14, .8, .74])\n",
                            "# Plot flow direction\n",
                            "plt.pcolormesh(t, ds_avg['range'], ds_avg['U_dir'], cmap='twilight', shading='nearest')\n",
                            "# Plot the water surface\n",
                            "ax.plot(t, ds_avg['depth'])\n",
                            "\n",
                            "# set up time on x-axis\n",
                            "ax.set_xlabel('Time')\n",
                            "ax.xaxis.set_major_formatter(dt.DateFormatter('%H:%M'))\n",
                            "\n",
                            "ax.set_ylabel('Altitude [m]')\n",
                            "ax.set_ylim([0, 12])\n",
                            "plt.colorbar(label='Horizontal Vel Dir [deg CW from true N]')"
                     ]
              },
              {
                     "cell_type": "markdown",
                     "metadata": {},
                     "source": [
                            "## Saving and Loading DOLfYN datasets\n",
                            "Datasets can be saved and reloaded using the `save` and `load` functions. Xarray is saved natively in netCDF format, hence the \".nc\" extension.\n",
                            "\n",
                            "Note: DOLfYN datasets cannot be saved using xarray's native `ds.to_netcdf`; however, DOLfYN datasets can be opened using `xarray.open_dataset`."
                     ]
              },
              {
                     "cell_type": "code",
                     "execution_count": 16,
                     "metadata": {},
                     "outputs": [],
                     "source": [
                            "# Uncomment these lines to save and load to your current working directory\n",
                            "#dolfyn.save(ds, 'your_data.nc')\n",
                            "#ds_saved = dolfyn.load('your_data.nc')"
                     ]
              }
       ],
       "metadata": {
              "interpreter": {
                     "hash": "357206ab7e4935423e95e994af80e27e7e6c0672abcebb9d86ab743298213348"
              },
              "kernelspec": {
                     "display_name": "Python 3.9.7 ('base')",
                     "language": "python",
                     "name": "python3"
              },
              "language_info": {
                     "codemirror_mode": {
                            "name": "ipython",
                            "version": 3
                     },
                     "file_extension": ".py",
                     "mimetype": "text/x-python",
                     "name": "python",
                     "nbconvert_exporter": "python",
                     "pygments_lexer": "ipython3",
                     "version": "3.9.15"
              }
       },
       "nbformat": 4,
       "nbformat_minor": 4
}