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    "## Analyzing Wave Statistics from ADCP Altimeter Data\n",
    "\n",
    "The following notebook illustrates how to process and compute wave statistics from acoustic Doppler current profilers (ADCPs) with the ability to measure waves (Nortek AWAC, Signature and RDI SentinelV). This example assumes the user is familiar with the basic workflow for processing ADCP data.\n",
    "\n",
    "Firstly, the ADCP must have been configured in wave measurement mode for deployment. This mode is only available for ADCPs with a vertically-oriented center transducer, which is used to located the water surface. The other four beams are utilized to measure water velocity at and around the water surface in order to track wave orbitals. The following basic workflow runs through the following:\n",
    "\n",
    "1. Import Data\n",
    "2. Review and QC altimeter measurements\n",
    "3. Create energy spectra from surface height\n",
    "4. Compute wave height and period\n",
    "5. Review and QC surface velocity measurements\n",
    "6. Use \"SUV\" method to compute wave direction and spread\n",
    "\n",
    "We'll begin analysis by importing the DOLfYN and wave modules from MHKiT. "
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    "from mhkit import dolfyn\n",
    "from mhkit import wave"
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    "### 1. Import Raw Datafile\n",
    "\n",
    "We'll start by using DOLfYN to read the binary file into python. This particular file is a clip from a Nortek Signature250 deployed at PacWave South, Oregon. In this case, the instrument was configured on a duty cycle to measure both waves and water velocity. Because two different profiling methods were used, DOLfYN will output two individual datasets.\n",
    "\n",
    "In this notebook, we'll focus on the waves dataset. A workflow for the water velocity dataset, which is the traditional ADCP measurement scheme, can be found in the ADCP example notebook."
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     "text": [
      "C:\\Users\\mcve343\\MHKiT-Python\\mhkit\\dolfyn\\io\\nortek2_lib.py:304: UserWarning: Dual Profile detected... Two datasets will be returned.\n",
      "  warnings.warn(msg)\n",
      "Dual Profile detected... Two datasets will be returned.\n"
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      "Reading file data/dolfyn/Sig250_PacWave.ad2cp ...\n"
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    "ds_waves, ds_avg = dolfyn.read(\"data/dolfyn/Sig250_PacWave.ad2cp\")"
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    "If we print the dataset, we can see all of the data variables that the ADCP collects for wave measurements. ADCPs measure basic wave information by using an altimeter pulse to detect where the surface is. \n",
    "\n",
    "The ADCP first (and periodically thereafter) sends out a series of sample pulses to calibrate the altimeter. These are saved in the dataset with the tag \"_altraw\". The ADCP then sends out altimeter pings at the user-defined sampling rate, which are saved in the dataset with the \"_alt\" tag.\n",
    "\n",
    "The altimeter pings are interpreted by the instrument using two different algorithms, called \"acoustic surface tracking\" (AST) and the \"leading edge\" (LE) method. These variables have either an \"ast_\" or \"le_\" prefix to their names. AST is typically used for measuring the air-water interface, while LE is better for detecting boundaries between water and other dense mediums, such as sea ice or seafloor."
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       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11MB\n",
       "Dimensions:                 (time: 18073, time_altraw: 35, range: 13, beam: 4,\n",
       "                             dir: 4, dirIMU: 3, n_altraw: 2583, x1: 4, x2: 4,\n",
       "                             earth: 3, inst: 3)\n",
       "Coordinates:\n",
       "  * time                    (time) datetime64[ns] 145kB 2024-10-22T19:29:36.3...\n",
       "  * time_altraw             (time_altraw) datetime64[ns] 280B 2024-10-22T19:2...\n",
       "  * range                   (range) float64 104B 38.7 42.2 45.7 ... 77.2 80.7\n",
       "  * beam                    (beam) int32 16B 1 2 3 4\n",
       "  * dir                     (dir) int32 16B 1 2 3 4\n",
       "  * dirIMU                  (dirIMU) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
       "  * x1                      (x1) int32 16B 1 2 3 4\n",
       "  * x2                      (x2) int32 16B 1 2 3 4\n",
       "  * earth                   (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
       "  * inst                    (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
       "Dimensions without coordinates: n_altraw\n",
       "Data variables: (12/39)\n",
       "    c_sound                 (time) float32 72kB 1.503e+03 ... 1.485e+03\n",
       "    temp                    (time) float32 72kB 13.93 13.92 13.92 ... 8.23 8.23\n",
       "    pressure                (time) float32 72kB 0.768 0.765 ... 74.71 74.71\n",
       "    heading                 (time) float32 72kB 216.1 215.8 ... 212.7 212.9\n",
       "    pitch                   (time) float32 72kB -27.59 -28.03 ... 2.21 2.22\n",
       "    roll                    (time) float32 72kB 73.04 71.68 67.74 ... -0.36 -0.4\n",
       "    ...                      ...\n",
       "    low_volt_skip           (time) uint8 18kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0\n",
       "    active_config           (time) uint8 18kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0\n",
       "    telemetry_data          (time) uint8 18kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0\n",
       "    boost_running           (time) uint8 18kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0\n",
       "    beam2inst_orientmat     (x1, x2) float32 64B 1.462 0.0 -1.462 ... 0.0 0.532\n",
       "    orientmat               (earth, inst, time) float32 651kB -0.5221 ... 0.9992\n",
       "Attributes: (12/39)\n",
       "    filehead_config:       {&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2024-10-22 19:29:32\\&quot;&quot;}, ...\n",
       "    inst_model:            Signature250\n",
       "    inst_make:             Nortek\n",
       "    inst_type:             ADCP\n",
       "    burst_config:          {&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass...\n",
       "    n_cells:               13\n",
       "    ...                    ...\n",
       "    rotate_vars:           [&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;mag&#x27;]\n",
       "    coord_sys:             beam\n",
       "    fs:                    1\n",
       "    bandwidth:             &quot;BROAD&quot;\n",
       "    has_imu:               0\n",
       "    beam_angle:            20</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-5d999aca-1678-4838-a66b-eb7dc2983e73' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d999aca-1678-4838-a66b-eb7dc2983e73' 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>: 18073</li><li><span class='xr-has-index'>time_altraw</span>: 35</li><li><span class='xr-has-index'>range</span>: 13</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span>n_altraw</span>: 2583</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c7caa3aa-86df-4606-aeaa-a0c72cf41d90' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7caa3aa-86df-4606-aeaa-a0c72cf41d90' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-10-22T19:29:36.334399938 .....</div><input id='attrs-a0951f09-1486-4d1d-baad-53c7a01eb31c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0951f09-1486-4d1d-baad-53c7a01eb31c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f81df920-491e-447b-949f-dd117b36fd88' class='xr-var-data-in' type='checkbox'><label for='data-f81df920-491e-447b-949f-dd117b36fd88' title='Show/Hide data repr'><svg 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;2024-10-22T19:29:36.334399938&#x27;, &#x27;2024-10-22T19:29:37.334399938&#x27;,\n",
       "       &#x27;2024-10-22T19:29:38.334399938&#x27;, ..., &#x27;2024-10-23T04:10:38.334399938&#x27;,\n",
       "       &#x27;2024-10-23T04:10:39.334199905&#x27;, &#x27;2024-10-23T04:10:40.334399938&#x27;],\n",
       "      shape=(18073,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-10-22T19:29:36.000999927 .....</div><input id='attrs-62e065c8-7144-459e-a541-387bcd6cc0d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62e065c8-7144-459e-a541-387bcd6cc0d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6737f532-67fb-445d-972f-4eb75b517b0f' class='xr-var-data-in' type='checkbox'><label for='data-6737f532-67fb-445d-972f-4eb75b517b0f' title='Show/Hide data repr'><svg 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 altraw</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-10-22T19:29:36.000999927&#x27;, &#x27;2024-10-22T19:46:39.000999927&#x27;,\n",
       "       &#x27;2024-10-22T19:59:36.000999927&#x27;, &#x27;2024-10-22T20:16:39.000999927&#x27;,\n",
       "       &#x27;2024-10-22T20:29:36.000999927&#x27;, &#x27;2024-10-22T20:46:39.000999927&#x27;,\n",
       "       &#x27;2024-10-22T20:59:36.000999927&#x27;, &#x27;2024-10-22T21:16:39.000999927&#x27;,\n",
       "       &#x27;2024-10-22T21:29:36.000999927&#x27;, &#x27;2024-10-22T21:46:39.001100063&#x27;,\n",
       "       &#x27;2024-10-22T21:59:36.000999927&#x27;, &#x27;2024-10-22T22:16:39.001100063&#x27;,\n",
       "       &#x27;2024-10-22T22:29:36.000999927&#x27;, &#x27;2024-10-22T22:46:39.001100063&#x27;,\n",
       "       &#x27;2024-10-22T22:59:36.000999927&#x27;, &#x27;2024-10-22T23:16:39.001100063&#x27;,\n",
       "       &#x27;2024-10-22T23:29:36.000900030&#x27;, &#x27;2024-10-22T23:46:39.000999927&#x27;,\n",
       "       &#x27;2024-10-22T23:59:36.000999927&#x27;, &#x27;2024-10-23T00:16:39.000999927&#x27;,\n",
       "       &#x27;2024-10-23T00:29:36.000999927&#x27;, &#x27;2024-10-23T00:46:39.000999927&#x27;,\n",
       "       &#x27;2024-10-23T00:59:36.000999927&#x27;, &#x27;2024-10-23T01:16:39.000999927&#x27;,\n",
       "       &#x27;2024-10-23T01:29:36.000999927&#x27;, &#x27;2024-10-23T01:46:39.000999927&#x27;,\n",
       "       &#x27;2024-10-23T01:59:36.000999927&#x27;, &#x27;2024-10-23T02:16:39.000999927&#x27;,\n",
       "       &#x27;2024-10-23T02:29:36.000999927&#x27;, &#x27;2024-10-23T02:46:39.000999927&#x27;,\n",
       "       &#x27;2024-10-23T02:59:36.000999927&#x27;, &#x27;2024-10-23T03:16:39.000999927&#x27;,\n",
       "       &#x27;2024-10-23T03:29:36.000999927&#x27;, &#x27;2024-10-23T03:46:39.000999927&#x27;,\n",
       "       &#x27;2024-10-23T03:59:36.000999927&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.7 42.2 45.7 ... 73.7 77.2 80.7</div><input id='attrs-b480889a-2752-4d80-a556-14d2d3fa49e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b480889a-2752-4d80-a556-14d2d3fa49e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8f43935-6b41-4b38-8d68-1a480818f72e' class='xr-var-data-in' type='checkbox'><label for='data-f8f43935-6b41-4b38-8d68-1a480818f72e' title='Show/Hide data repr'><svg 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([38.7, 42.2, 45.7, 49.2, 52.7, 56.2, 59.7, 63.2, 66.7, 70.2, 73.7, 77.2,\n",
       "       80.7])</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-7a128d3d-f06e-4611-8b9d-eea8bd218e6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a128d3d-f06e-4611-8b9d-eea8bd218e6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb17efd8-df6d-42d7-9487-c4d7a003c709' class='xr-var-data-in' type='checkbox'><label for='data-bb17efd8-df6d-42d7-9487-c4d7a003c709' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-9d8e48d2-05e1-4aa8-b3d2-69da9063835c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d8e48d2-05e1-4aa8-b3d2-69da9063835c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31cb655a-ab4b-450d-be6b-54a17704ea5c' class='xr-var-data-in' type='checkbox'><label for='data-31cb655a-ab4b-450d-be6b-54a17704ea5c' title='Show/Hide data repr'><svg 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>beam</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-e906626f-cef0-4bd6-a4fd-4c57ce51247c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e906626f-cef0-4bd6-a4fd-4c57ce51247c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a598964-fc16-44a5-bb04-641cf8c57cda' class='xr-var-data-in' type='checkbox'><label for='data-1a598964-fc16-44a5-bb04-641cf8c57cda' title='Show/Hide data repr'><svg 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>beam</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'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-9f561613-9800-4d14-907a-19e2ee16d103' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f561613-9800-4d14-907a-19e2ee16d103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f88ae1a6-4daa-4e41-b553-61ac143ffc68' class='xr-var-data-in' type='checkbox'><label for='data-f88ae1a6-4daa-4e41-b553-61ac143ffc68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-1b6bcf3d-bfce-4941-ad0d-8cd07a48962e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b6bcf3d-bfce-4941-ad0d-8cd07a48962e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54dbc7f9-5458-4da4-bba5-1f3c23680b94' class='xr-var-data-in' type='checkbox'><label for='data-54dbc7f9-5458-4da4-bba5-1f3c23680b94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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-e32a7cd2-e4e7-4733-8a25-a915f4f25ed4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e32a7cd2-e4e7-4733-8a25-a915f4f25ed4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-848f9801-4af8-4273-b8d8-d6af17cf7125' class='xr-var-data-in' type='checkbox'><label for='data-848f9801-4af8-4273-b8d8-d6af17cf7125' title='Show/Hide data repr'><svg 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-482c3815-d1d8-4a0c-9906-a4c105ae5108' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-482c3815-d1d8-4a0c-9906-a4c105ae5108' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92047868-1584-4ae7-ae74-ae8f784bf576' class='xr-var-data-in' type='checkbox'><label for='data-92047868-1584-4ae7-ae74-ae8f784bf576' title='Show/Hide data repr'><svg 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></ul></div></li><li class='xr-section-item'><input id='section-a573ed7c-ccb0-4f73-b9cf-d31743ebb182' class='xr-section-summary-in' type='checkbox'  ><label for='section-a573ed7c-ccb0-4f73-b9cf-d31743ebb182' class='xr-section-summary' >Data variables: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>1.503e+03 1.503e+03 ... 1.485e+03</div><input id='attrs-a54c0da6-de6a-4ecb-85dc-6b01a15ed416' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a54c0da6-de6a-4ecb-85dc-6b01a15ed416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7749a1bb-8b02-45ff-b205-cd1128bb7752' class='xr-var-data-in' type='checkbox'><label for='data-7749a1bb-8b02-45ff-b205-cd1128bb7752' title='Show/Hide data repr'><svg 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([1503.2, 1503.2, 1503.2, ..., 1484.6, 1484.5, 1484.6],\n",
       "      shape=(18073,), 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'>13.93 13.92 13.92 ... 8.23 8.23</div><input id='attrs-025bbdc9-a713-4ee2-bd2a-2457abc667d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-025bbdc9-a713-4ee2-bd2a-2457abc667d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d199a491-6d9a-4cee-a1fb-306d816d9105' class='xr-var-data-in' type='checkbox'><label for='data-d199a491-6d9a-4cee-a1fb-306d816d9105' title='Show/Hide data repr'><svg 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([13.93, 13.92, 13.92, ...,  8.23,  8.23,  8.23],\n",
       "      shape=(18073,), 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'>0.768 0.765 0.762 ... 74.71 74.71</div><input id='attrs-2f678b9a-815f-4b48-8c7d-fea6de6d3863' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f678b9a-815f-4b48-8c7d-fea6de6d3863' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7af5288-d2a7-431c-89b2-3ac45cfff57d' class='xr-var-data-in' type='checkbox'><label for='data-e7af5288-d2a7-431c-89b2-3ac45cfff57d' title='Show/Hide data repr'><svg 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([ 0.768,  0.765,  0.762, ..., 74.694, 74.706, 74.711],\n",
       "      shape=(18073,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>216.1 215.8 216.7 ... 212.7 212.9</div><input id='attrs-99367d76-603e-4887-8f01-19db9dcee92e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99367d76-603e-4887-8f01-19db9dcee92e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-191b6e6b-d697-428b-9708-f408b829b1eb' class='xr-var-data-in' type='checkbox'><label for='data-191b6e6b-d697-428b-9708-f408b829b1eb' title='Show/Hide data repr'><svg 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([216.09, 215.8 , 216.7 , ..., 212.88, 212.72, 212.95],\n",
       "      shape=(18073,), 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'>-27.59 -28.03 -29.35 ... 2.21 2.22</div><input id='attrs-0ed157b7-9e3c-441c-bb17-0a45517876d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ed157b7-9e3c-441c-bb17-0a45517876d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1bf99de-922b-44f3-824d-87336cccd4a7' class='xr-var-data-in' type='checkbox'><label for='data-d1bf99de-922b-44f3-824d-87336cccd4a7' title='Show/Hide data repr'><svg 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([-27.59, -28.03, -29.35, ...,   2.19,   2.21,   2.22],\n",
       "      shape=(18073,), 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'>73.04 71.68 67.74 ... -0.36 -0.4</div><input id='attrs-70225c8c-c8bd-412a-bdd5-b6b0cca70cd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70225c8c-c8bd-412a-bdd5-b6b0cca70cd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbaea8c3-cda8-41d4-8543-4d6ac43e3ad6' class='xr-var-data-in' type='checkbox'><label for='data-dbaea8c3-cda8-41d4-8543-4d6ac43e3ad6' title='Show/Hide data repr'><svg 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([73.04, 71.68, 67.74, ..., -0.38, -0.36, -0.4 ],\n",
       "      shape=(18073,), 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'>5.9 8.8 8.4 ... -60.9 -60.8 -60.9</div><input id='attrs-9c02d73c-1ab9-4824-bb2c-00ed13e32809' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c02d73c-1ab9-4824-bb2c-00ed13e32809' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ab23978-23b5-4891-87d7-326a67f846ab' class='xr-var-data-in' type='checkbox'><label for='data-2ab23978-23b5-4891-87d7-326a67f846ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  5.9,   8.8,   8.4, ..., -21.7, -21.8, -21.7],\n",
       "       [-57.6, -56.9, -56.9, ..., -12.1, -12.1, -12.1],\n",
       "       [ -1.9,  -4.9,  -7.3, ..., -60.9, -60.8, -60.9]],\n",
       "      shape=(3, 18073), 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'>-4.489 -4.6 -4.715 ... 9.773 9.777</div><input id='attrs-cf0d45e1-8fbc-4d91-9fb6-156ed9f78c79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf0d45e1-8fbc-4d91-9fb6-156ed9f78c79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f66ea4-1ac5-4209-b1f4-4f665a3e368c' class='xr-var-data-in' type='checkbox'><label for='data-99f66ea4-1ac5-4209-b1f4-4f665a3e368c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-4.4888654 , -4.6002336 , -4.714596  , ...,  0.37541932,\n",
       "         0.37781432,  0.38020936],\n",
       "       [ 8.21312   ,  8.201145  ,  7.7568693 , ..., -0.06586304,\n",
       "        -0.06346802, -0.06825805],\n",
       "       [ 2.503993  ,  2.7147546 ,  3.1745985 , ...,  9.772877  ,\n",
       "         9.772877  ,  9.777068  ]], shape=(3, 18073), 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'>23.4 23.4 23.4 ... 18.5 18.5 18.5</div><input id='attrs-06300640-9ae8-4611-a8f4-d8ae7c8d246d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06300640-9ae8-4611-a8f4-d8ae7c8d246d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-847024c3-c198-471c-b272-d7e703c855e5' class='xr-var-data-in' type='checkbox'><label for='data-847024c3-c198-471c-b272-d7e703c855e5' title='Show/Hide data repr'><svg 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([23.4, 23.4, 23.4, ..., 18.5, 18.5, 18.5],\n",
       "      shape=(18073,), 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'>19.75 19.75 19.75 ... 9.75 9.75</div><input id='attrs-8f8395e7-da3e-471c-a49e-f77a80a2687b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f8395e7-da3e-471c-a49e-f77a80a2687b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3315053-15f2-41fc-8411-27ba60694b30' class='xr-var-data-in' type='checkbox'><label for='data-d3315053-15f2-41fc-8411-27ba60694b30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([19.75, 19.75, 19.75, ...,  9.75,  9.75,  9.75],\n",
       "      shape=(18073,), 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-be55a666-829b-4bae-b954-7668554cada0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be55a666-829b-4bae-b954-7668554cada0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7ca32ff-a69e-4c53-bfa4-e057464df7dd' class='xr-var-data-in' type='checkbox'><label for='data-a7ca32ff-a69e-4c53-bfa4-e057464df7dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(18073,), 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'>675807234 675807234 ... 944504834</div><input id='attrs-e67c54cc-26aa-4da6-a682-7f0fa6dcd01a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e67c54cc-26aa-4da6-a682-7f0fa6dcd01a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a09841e0-3bc1-44e5-8392-ee0107aece0c' class='xr-var-data-in' type='checkbox'><label for='data-a09841e0-3bc1-44e5-8392-ee0107aece0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([675807234, 675807234, 675807234, ..., 944504834, 944504834,\n",
       "       944504834], shape=(18073,), 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 ... 24285 24287 24289 24291</div><input id='attrs-4a5bdc63-be24-46b4-81a6-3a7f0f84b72f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a5bdc63-be24-46b4-81a6-3a7f0f84b72f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b4b1f06-ac65-400b-bf6f-ba2f22bf52a6' class='xr-var-data-in' type='checkbox'><label for='data-5b4b1f06-ac65-400b-bf6f-ba2f22bf52a6' title='Show/Hide data repr'><svg 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, ..., 24287, 24289, 24291],\n",
       "      shape=(18073,), 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'>1.231 -2.796 0.219 ... -0.264 0.694</div><input id='attrs-f87b142e-8c08-46fa-84fd-11eb44d61d45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f87b142e-8c08-46fa-84fd-11eb44d61d45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c7f9ab0-6a25-461b-9dad-14c7e1425bed' class='xr-var-data-in' type='checkbox'><label for='data-6c7f9ab0-6a25-461b-9dad-14c7e1425bed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[ 1.231e+00, -2.796e+00,  2.190e-01, ...,  1.280e-01,\n",
       "          6.200e-02,  6.400e-02],\n",
       "        [-4.220e+00, -1.316e+00,  1.423e+00, ...,  8.900e-02,\n",
       "          3.900e-02,  8.000e-03],\n",
       "        [ 3.722e+00,  2.967e+00, -3.881e+00, ...,  1.280e-01,\n",
       "          8.000e-02,  4.100e-02],\n",
       "        ...,\n",
       "        [ 3.608e+00, -4.118e+00,  9.000e-02, ...,  5.770e-01,\n",
       "          5.840e-01,  1.810e-01],\n",
       "        [-2.383e+00,  3.820e+00, -1.982e+00, ...,  9.310e-01,\n",
       "          7.980e-01, -6.210e-01],\n",
       "        [-1.288e+00, -5.273e+00,  4.996e+00, ...,  8.020e-01,\n",
       "          6.680e-01,  1.020e-01]],\n",
       "\n",
       "       [[ 1.096e+00, -2.937e+00,  3.400e-01, ...,  7.100e-02,\n",
       "         -8.000e-03,  1.000e-02],\n",
       "        [-4.220e+00, -1.478e+00,  1.309e+00, ...,  1.150e-01,\n",
       "         -5.000e-03, -9.600e-02],\n",
       "        [ 3.097e+00,  2.886e+00, -4.042e+00, ...,  4.800e-02,\n",
       "         -4.800e-02, -9.000e-02],\n",
       "...\n",
       "        [ 3.524e+00, -3.629e+00,  1.000e-02, ..., -2.840e-01,\n",
       "         -2.460e-01,  7.240e-01],\n",
       "        [-2.416e+00,  3.327e+00, -2.317e+00, ..., -2.630e-01,\n",
       "         -2.010e-01,  6.630e-01],\n",
       "        [-2.929e+00,  4.419e+00,  4.493e+00, ..., -1.730e-01,\n",
       "         -4.800e-02, -4.700e-02]],\n",
       "\n",
       "       [[ 1.081e+00, -2.931e+00,  6.330e-01, ..., -3.400e-02,\n",
       "         -1.800e-02,  6.400e-02],\n",
       "        [-3.349e+00, -1.862e+00,  1.275e+00, ...,  9.000e-03,\n",
       "          1.100e-02,  1.700e-02],\n",
       "        [ 3.056e+00,  2.772e+00, -3.975e+00, ...,  7.100e-02,\n",
       "          2.800e-02,  6.500e-02],\n",
       "        ...,\n",
       "        [ 3.464e+00, -3.135e+00, -4.500e-02, ..., -3.730e-01,\n",
       "         -1.370e-01,  6.630e-01],\n",
       "        [-1.834e+00,  3.931e+00, -2.047e+00, ..., -2.470e-01,\n",
       "         -5.820e-01,  3.050e-01],\n",
       "        [-2.065e+00,  5.129e+00,  4.794e+00, ..., -6.620e-01,\n",
       "         -2.640e-01,  6.940e-01]]], shape=(4, 13, 18073), 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'>38.5 39.5 36.5 ... 48.0 43.0 46.5</div><input id='attrs-7914c53a-36d6-4692-bd9a-b287aab32c81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7914c53a-36d6-4692-bd9a-b287aab32c81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ff945e8-d2bb-4e29-b462-28236f17a268' class='xr-var-data-in' type='checkbox'><label for='data-5ff945e8-d2bb-4e29-b462-28236f17a268' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[ 38.5,  39.5,  36.5, ...,  45. ,  43. ,  42. ],\n",
       "        [ 34.5,  38.5,  37. , ...,  42.5,  41. ,  42. ],\n",
       "        [ 38.5,  36. ,  38.5, ...,  42.5,  41. ,  42. ],\n",
       "        ...,\n",
       "        [ 36.5,  37.5,  38. , ...,  95. ,  96.5, 100. ],\n",
       "        [ 35. ,  37.5,  37. , ...,  60.5,  73. ,  75.5],\n",
       "        [ 35.5,  33.5,  36.5, ...,  43.5,  41.5,  43.5]],\n",
       "\n",
       "       [[ 41.5,  41.5,  38.5, ...,  43.5,  44.5,  45.5],\n",
       "        [ 37. ,  40. ,  38.5, ...,  43. ,  41.5,  42.5],\n",
       "        [ 41.5,  37.5,  40.5, ...,  41. ,  41. ,  41.5],\n",
       "        ...,\n",
       "        [ 38.5,  40. ,  41.5, ...,  89. ,  96. ,  96. ],\n",
       "        [ 36.5,  41. ,  40.5, ...,  86. ,  87.5,  87. ],\n",
       "        [ 38. ,  37. ,  40.5, ...,  49. ,  46.5,  46. ]],\n",
       "\n",
       "       [[ 32.5,  31.5,  30. , ...,  43. ,  45.5,  44.5],\n",
       "        [ 29.5,  31. ,  29.5, ...,  43.5,  43.5,  43.5],\n",
       "        [ 33. ,  29.5,  31. , ...,  43.5,  42. ,  42.5],\n",
       "        ...,\n",
       "        [ 29.5,  31. ,  33. , ...,  99.5,  95. ,  86. ],\n",
       "        [ 29. ,  33.5,  33.5, ...,  94.5,  88.5,  87.5],\n",
       "        [ 29.5,  31. ,  35. , ...,  41.5,  44. ,  50. ]],\n",
       "\n",
       "       [[ 33. ,  31.5,  30.5, ...,  43. ,  44. ,  42.5],\n",
       "        [ 29.5,  30.5,  30. , ...,  43.5,  44.5,  43.5],\n",
       "        [ 33. ,  29. ,  31. , ...,  41.5,  42. ,  41.5],\n",
       "        ...,\n",
       "        [ 29.5,  31.5,  33.5, ...,  91. ,  89.5,  96. ],\n",
       "        [ 28.5,  33.5,  33.5, ...,  81. ,  71.5,  80. ],\n",
       "        [ 30. ,  31. ,  34.5, ...,  48. ,  43. ,  46.5]]],\n",
       "      shape=(4, 13, 18073), 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'>26 65 32 45 32 1 ... 91 87 86 84 88</div><input id='attrs-3cebf583-cc25-47c1-8c77-685315a2f4b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cebf583-cc25-47c1-8c77-685315a2f4b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30ad181a-5bbe-4ae8-9577-cd864ad2a5ae' class='xr-var-data-in' type='checkbox'><label for='data-30ad181a-5bbe-4ae8-9577-cd864ad2a5ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[ 26,  65,  32, ..., 100,  99,  95],\n",
       "        [ 21,  58,  34, ...,  98,  96,  98],\n",
       "        [ 20,  61,  50, ...,  98,  97,  97],\n",
       "        ...,\n",
       "        [ 46,  22,  64, ..., 100, 100, 100],\n",
       "        [  7,   8,  29, ...,  57,  31,  36],\n",
       "        [  9,  19,  35, ...,  96,  87,  89]],\n",
       "\n",
       "       [[ 31,  62,  32, ...,  94, 100, 100],\n",
       "        [ 12,  56,  32, ...,  99,  98,  96],\n",
       "        [ 23,  53,  46, ...,  95,  98,  97],\n",
       "        ...,\n",
       "        [ 42,  20,  70, ...,  99, 100, 100],\n",
       "        [  7,   7,  30, ...,  97,  83,  85],\n",
       "        [ 11,  21,  36, ...,  80,  72,  93]],\n",
       "\n",
       "       [[ 33,  45,  25, ...,  94, 100,  98],\n",
       "        [  8,  38,  24, ...,  99, 100,  99],\n",
       "        [ 11,  26,  36, ...,  97,  94,  97],\n",
       "        ...,\n",
       "        [ 24,  14,  64, ..., 100, 100,  98],\n",
       "        [  3,   7,  23, ...,  94,  90,  97],\n",
       "        [  8,  18,  28, ...,  80,  73,  69]],\n",
       "\n",
       "       [[ 42,  46,  22, ...,  96,  99,  97],\n",
       "        [  5,  48,  22, ..., 100, 100, 100],\n",
       "        [ 19,  32,  33, ...,  97,  96,  95],\n",
       "        ...,\n",
       "        [ 27,  13,  64, ..., 100, 100, 100],\n",
       "        [  7,   5,  24, ...,  76,  50,  62],\n",
       "        [ 13,  20,  31, ...,  86,  84,  88]]],\n",
       "      shape=(4, 13, 18073), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_alt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.766 0.76 0.766 ... 74.7 74.71</div><input id='attrs-14af6ea0-5df1-4a99-a429-a9d84c2851e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14af6ea0-5df1-4a99-a429-a9d84c2851e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3b44c25-93d1-4f9a-b524-11853f0f053f' class='xr-var-data-in' type='checkbox'><label for='data-b3b44c25-93d1-4f9a-b524-11853f0f053f' title='Show/Hide data repr'><svg 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 measured during AST ping</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([ 0.766,  0.76 ,  0.766, ..., 74.672, 74.703, 74.707],\n",
       "      shape=(18073,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>le_dist_alt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>42.22 42.22 42.22 ... 73.36 73.73</div><input id='attrs-116ab0e0-f0df-4a83-8c01-04a7346bfbd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-116ab0e0-f0df-4a83-8c01-04a7346bfbd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e98b614-b92c-4c5b-8ffb-de6e4a793845' class='xr-var-data-in' type='checkbox'><label for='data-3e98b614-b92c-4c5b-8ffb-de6e4a793845' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Altimeter Range Leading Edge Algorithm</dd><dt><span>standard_name :</span></dt><dd>altimeter_range</dd></dl></div><div class='xr-var-data'><pre>array([42.22273 , 42.222515, 42.222153, ..., 73.09078 , 73.35817 ,\n",
       "       73.73138 ], shape=(18073,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>le_quality_alt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 168.8 150.8 171.1</div><input id='attrs-32540a4d-e75a-4c59-be6d-b7954b1adfc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32540a4d-e75a-4c59-be6d-b7954b1adfc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2655d95c-6e0e-409f-8a75-04a25453f425' class='xr-var-data-in' type='checkbox'><label for='data-2655d95c-6e0e-409f-8a75-04a25453f425' title='Show/Hide data repr'><svg 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>Altimeter Quality Indicator Leading Edge Algorithm</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   0.  ,   0.  , ..., 168.79, 150.79, 171.05],\n",
       "      shape=(18073,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ast_dist_alt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 73.21 73.48 73.87</div><input id='attrs-5d315a11-4474-4eb0-9d53-569528f5ab74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d315a11-4474-4eb0-9d53-569528f5ab74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fd8a2b9-9289-4f72-b16a-e1ed15057cce' class='xr-var-data-in' type='checkbox'><label for='data-7fd8a2b9-9289-4f72-b16a-e1ed15057cce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Altimeter Range Acoustic Surface Tracking</dd><dt><span>standard_name :</span></dt><dd>altimeter_range</dd></dl></div><div class='xr-var-data'><pre>array([ 0.      ,  0.      ,  0.      , ..., 73.210526, 73.476036,\n",
       "       73.86819 ], shape=(18073,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ast_quality_alt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 58.44 58.52 61.61</div><input id='attrs-9c26543e-0b77-4d42-b7ee-3bd46d080300' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c26543e-0b77-4d42-b7ee-3bd46d080300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7420cdce-cf4e-4b41-8d7c-0830186c452f' class='xr-var-data-in' type='checkbox'><label for='data-7420cdce-cf4e-4b41-8d7c-0830186c452f' title='Show/Hide data repr'><svg 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>Altimeter Quality Indicator Acoustic Surface Tracking</dd></dl></div><div class='xr-var-data'><pre>array([ 0.  ,  0.  ,  0.  , ..., 58.44, 58.52, 61.61],\n",
       "      shape=(18073,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ast_offset_time_alt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.3334 -0.3333 ... -0.3331 -0.3333</div><input id='attrs-9892fa89-2708-44a5-bf59-1e82bdf02307' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9892fa89-2708-44a5-bf59-1e82bdf02307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-504f2a41-c288-42ec-83ed-3f8cf30bd9c2' class='xr-var-data-in' type='checkbox'><label for='data-504f2a41-c288-42ec-83ed-3f8cf30bd9c2' title='Show/Hide data repr'><svg 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>s</dd><dt><span>long_name :</span></dt><dd>Acoustic Surface Tracking Time Offset to Velocity Ping</dd></dl></div><div class='xr-var-data'><pre>array([-0.3334, -0.3333, -0.3332, ..., -0.3333, -0.3331, -0.3333],\n",
       "      shape=(18073,), 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'>12.4 12.4 12.4 12.4 ... 7.0 7.0 7.0</div><input id='attrs-166cc5c9-d569-49e2-bd68-3f82e9409ce5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-166cc5c9-d569-49e2-bd68-3f82e9409ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f75fb38-03f4-470d-8e83-22daa5ae9aa0' class='xr-var-data-in' type='checkbox'><label for='data-3f75fb38-03f4-470d-8e83-22daa5ae9aa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.4, 12.4, 12.4, ...,  7. ,  7. ,  7. ],\n",
       "      shape=(18073,), 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'>170 169 169 169 ... 194 194 194 194</div><input id='attrs-8fcf50a9-75fa-45b5-bf06-1e7dbe791a1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fcf50a9-75fa-45b5-bf06-1e7dbe791a1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26150dc9-8646-4c53-ac67-cf6251cad50d' class='xr-var-data-in' type='checkbox'><label for='data-26150dc9-8646-4c53-ac67-cf6251cad50d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([170, 169, 169, ..., 194, 194, 194], shape=(18073,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.766 0.773 0.769 ... 74.83 74.74</div><input id='attrs-c41fb70d-685f-43e9-a527-afd8d1df5883' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c41fb70d-685f-43e9-a527-afd8d1df5883' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-642821e5-6375-4dab-9952-f674145f73b8' class='xr-var-data-in' type='checkbox'><label for='data-642821e5-6375-4dab-9952-f674145f73b8' title='Show/Hide data repr'><svg 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([ 0.766,  0.773,  0.769, 25.605, 61.209,  0.777, 34.85 , 62.615,\n",
       "       76.575, 76.654, 76.721, 76.75 , 76.721, 76.793, 76.756, 76.727,\n",
       "       76.724, 76.687, 76.631, 76.559, 76.467, 76.287, 76.343, 76.16 ,\n",
       "       76.039, 75.903, 75.822, 75.618, 75.482, 75.335, 75.275, 75.104,\n",
       "       74.998, 74.829, 74.743], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>le_dist_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>42.22 42.22 42.25 ... 72.77 73.4</div><input id='attrs-7ac413f2-a4e0-4e7b-95f4-6ec807c44aae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ac413f2-a4e0-4e7b-95f4-6ec807c44aae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29a7e235-3421-4a2c-9efa-43ba426e632b' class='xr-var-data-in' type='checkbox'><label for='data-29a7e235-3421-4a2c-9efa-43ba426e632b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Altimeter Range Leading Edge Algorithm</dd><dt><span>standard_name :</span></dt><dd>altimeter_range</dd></dl></div><div class='xr-var-data'><pre>array([42.22273 , 42.22245 , 42.248127, 53.838898, 60.334473, 42.095585,\n",
       "       41.693676, 60.747128, 74.52789 , 75.47511 , 75.798546, 75.46673 ,\n",
       "       75.08749 , 75.53308 , 75.00033 , 75.2663  , 75.62667 , 75.66341 ,\n",
       "       75.13237 , 75.59258 , 75.48645 , 74.45196 , 75.09751 , 74.65656 ,\n",
       "       74.63918 , 74.48755 , 74.70199 , 74.48076 , 74.06819 , 73.60065 ,\n",
       "       73.40783 , 73.81639 , 74.335014, 72.76904 , 73.397766],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>le_quality_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 67.05 206.4 146.7</div><input id='attrs-1f7c9ad8-c93d-43fb-b66a-f511e6a53947' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f7c9ad8-c93d-43fb-b66a-f511e6a53947' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0e4cbb1-18fb-4331-86f9-d2d566e8f8c0' class='xr-var-data-in' type='checkbox'><label for='data-a0e4cbb1-18fb-4331-86f9-d2d566e8f8c0' title='Show/Hide data repr'><svg 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>Altimeter Quality Indicator Leading Edge Algorithm</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   0.  ,   0.  , 108.67, 135.71,   0.  ,   0.  , 189.97,\n",
       "       119.84, 127.84, 119.72, 184.63, 123.2 , 213.47, 154.79, 235.96,\n",
       "       135.53, 126.95, 159.85, 207.4 ,  66.16, 212.89, 127.26, 196.62,\n",
       "       125.15, 113.1 , 150.91, 219.6 , 130.77, 113.49, 104.92, 227.87,\n",
       "        67.05, 206.42, 146.73], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ast_dist_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 74.34 72.9 73.52</div><input id='attrs-c5ccd16d-7917-4045-bfe9-648599fb81b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5ccd16d-7917-4045-bfe9-648599fb81b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32f2261a-263e-4d8e-bf29-88b15bf65af1' class='xr-var-data-in' type='checkbox'><label for='data-32f2261a-263e-4d8e-bf29-88b15bf65af1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Altimeter Range Acoustic Surface Tracking</dd><dt><span>standard_name :</span></dt><dd>altimeter_range</dd></dl></div><div class='xr-var-data'><pre>array([ 0.      ,  0.      ,  0.      ,  0.      , 60.475662,  0.      ,\n",
       "       41.92075 , 61.05163 , 74.65894 , 75.74271 , 76.07448 , 75.692055,\n",
       "       75.19689 , 75.82613 , 75.08403 , 75.32452 , 75.729965, 76.012314,\n",
       "       75.22676 , 75.71552 , 75.81318 , 74.594826, 75.21046 , 74.7399  ,\n",
       "       75.11347 , 74.77125 , 74.823235, 74.63242 , 74.20987 , 73.8131  ,\n",
       "       73.52494 , 73.96451 , 74.34137 , 72.897064, 73.523224],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ast_quality_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 53.5 65.63 61.81</div><input id='attrs-320d9532-1283-41d4-bfbd-9aeea6e15b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-320d9532-1283-41d4-bfbd-9aeea6e15b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d080e1ce-3043-4496-9485-e8ad4c9daa71' class='xr-var-data-in' type='checkbox'><label for='data-d080e1ce-3043-4496-9485-e8ad4c9daa71' title='Show/Hide data repr'><svg 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>Altimeter Quality Indicator Acoustic Surface Tracking</dd></dl></div><div class='xr-var-data'><pre>array([ 0.  ,  0.  ,  0.  ,  0.  , 52.53,  0.  , 23.02, 68.83, 57.15,\n",
       "       67.61, 54.94, 74.65, 53.76, 73.48, 61.66, 73.67, 56.12, 64.72,\n",
       "       65.88, 67.59, 49.72, 65.22, 54.34, 65.64, 50.62, 63.58, 60.39,\n",
       "       68.26, 58.79, 66.06, 56.41, 66.77, 53.5 , 65.63, 61.81],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ast_offset_time_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-1740b662-0412-42cd-8700-88ece894a085' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1740b662-0412-42cd-8700-88ece894a085' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8af17424-9dda-4656-b924-0d4d6261e6cf' class='xr-var-data-in' type='checkbox'><label for='data-8af17424-9dda-4656-b924-0d4d6261e6cf' title='Show/Hide data repr'><svg 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>s</dd><dt><span>long_name :</span></dt><dd>Acoustic Surface Tracking Time Offset to Velocity Ping</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsamp_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>2583 2583 2583 ... 2583 2583 2583</div><input id='attrs-fabbe260-8d67-486a-8b9f-eedd4fea9dda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fabbe260-8d67-486a-8b9f-eedd4fea9dda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dae552dd-ccea-4235-be40-b9e56d3f179f' class='xr-var-data-in' type='checkbox'><label for='data-dae552dd-ccea-4235-be40-b9e56d3f179f' title='Show/Hide data repr'><svg 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>Number of Altimeter Samples</dd></dl></div><div class='xr-var-data'><pre>array([2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583,\n",
       "       2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583,\n",
       "       2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583, 2583,\n",
       "       2583, 2583], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dsamp_altraw</span></div><div class='xr-var-dims'>(time_altraw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.024 0.024 0.024 ... 0.024 0.024</div><input id='attrs-89d8af41-5816-4940-8ea4-2093ea4f5dc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89d8af41-5816-4940-8ea4-2093ea4f5dc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d54c38ff-e368-4c5b-9440-6aadf51f3fcc' class='xr-var-data-in' type='checkbox'><label for='data-d54c38ff-e368-4c5b-9440-6aadf51f3fcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Altimeter Distance between Samples</dd></dl></div><div class='xr-var-data'><pre>array([0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024,\n",
       "       0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024,\n",
       "       0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024,\n",
       "       0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>samp_altraw</span></div><div class='xr-var-dims'>(n_altraw, time_altraw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>13.98 9.539 14.62 ... 11.14 15.56</div><input id='attrs-82fe3d43-12c0-4daa-a6c3-e0e3ef07efbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82fe3d43-12c0-4daa-a6c3-e0e3ef07efbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34037a8b-b0c9-478d-a405-74e675d60a6b' class='xr-var-data-in' type='checkbox'><label for='data-34037a8b-b0c9-478d-a405-74e675d60a6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Altimeter Samples</dd></dl></div><div class='xr-var-data'><pre>array([[1.39804688e+01, 9.53906250e+00, 1.46250000e+01, ...,\n",
       "        1.00000000e+01, 1.39804688e+01, 2.00468750e+01],\n",
       "       [0.00000000e+00, 1.81328125e+01, 2.25078125e+01, ...,\n",
       "        1.71601562e+01, 1.78515625e+01, 1.39804688e+01],\n",
       "       [1.00000000e+01, 1.91406250e+01, 3.01562500e+00, ...,\n",
       "        6.02343750e+00, 1.69921875e+01, 1.92968750e+01],\n",
       "       ...,\n",
       "       [3.01562500e+00, 3.90625000e-03, 1.41484375e+01, ...,\n",
       "        6.99218750e+00, 1.30078125e+01, 3.90625000e-03],\n",
       "       [3.01562500e+00, 1.30078125e+01, 1.23046875e+01, ...,\n",
       "        1.81328125e+01, 1.61250000e+01, 1.39804688e+01],\n",
       "       [1.50546875e+01, 1.61250000e+01, 1.78515625e+01, ...,\n",
       "        6.99218750e+00, 1.11406250e+01, 1.55625000e+01]],\n",
       "      shape=(2583, 35), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>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-3919dee3-d87a-4ca6-ac0f-a592bbda69e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3919dee3-d87a-4ca6-ac0f-a592bbda69e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d9afa24-678a-4668-9697-58c589aa8d0d' class='xr-var-data-in' type='checkbox'><label for='data-0d9afa24-678a-4668-9697-58c589aa8d0d' title='Show/Hide data 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], shape=(18073,), 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-fff6c468-6e00-40b8-9f63-c08c9fe77e1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fff6c468-6e00-40b8-9f63-c08c9fe77e1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-020021e3-caa6-4fdb-b5ff-05ba4ea9979b' class='xr-var-data-in' type='checkbox'><label for='data-020021e3-caa6-4fdb-b5ff-05ba4ea9979b' title='Show/Hide data 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], shape=(18073,), 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-885c6053-df60-4385-9cc3-d0b5be4210d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-885c6053-df60-4385-9cc3-d0b5be4210d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-815e0a47-8662-4be1-aef6-8539fe5f3a3f' class='xr-var-data-in' type='checkbox'><label for='data-815e0a47-8662-4be1-aef6-8539fe5f3a3f' title='Show/Hide data 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], shape=(18073,), 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 ... 0 0 0 0 0 0 0 0</div><input id='attrs-518e25b3-fdc3-4b49-9bd1-332726999a40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-518e25b3-fdc3-4b49-9bd1-332726999a40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49af1221-8523-4727-bf8b-59aea1662fd0' class='xr-var-data-in' type='checkbox'><label for='data-49af1221-8523-4727-bf8b-59aea1662fd0' title='Show/Hide data 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], shape=(18073,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.462 0.0 -1.462 ... 0.0 0.532</div><input id='attrs-b789b1f8-1586-4281-b942-0c78810b42f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b789b1f8-1586-4281-b942-0c78810b42f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7af9e871-451b-4ff4-b7d0-fd8898458136' class='xr-var-data-in' type='checkbox'><label for='data-7af9e871-451b-4ff4-b7d0-fd8898458136' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.4619,  0.    , -1.4619,  0.    ],\n",
       "       [ 0.    , -1.4619,  0.    ,  1.4619],\n",
       "       [ 0.532 ,  0.    ,  0.532 ,  0.    ],\n",
       "       [ 0.    ,  0.532 ,  0.    ,  0.532 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.5221 -0.5163 ... 0.9992 0.9992</div><input id='attrs-0202ff75-5017-461a-b90a-9bb2aa81b995' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0202ff75-5017-461a-b90a-9bb2aa81b995' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a27930b6-a2d3-4657-a5d1-294b1f5b8883' class='xr-var-data-in' type='checkbox'><label for='data-a27930b6-a2d3-4657-a5d1-294b1f5b8883' title='Show/Hide data repr'><svg 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([[[-0.52207047, -0.51634324, -0.5209149 , ..., -0.54248476,\n",
       "         -0.54013205, -0.5434986 ],\n",
       "        [-0.71620005, -0.71592724, -0.69886136, ..., -0.8391961 ,\n",
       "         -0.84069633, -0.83851576],\n",
       "        [-0.46314135, -0.4699338 , -0.49014327, ...,  0.03821341,\n",
       "          0.03856222,  0.03873662]],\n",
       "\n",
       "       [[-0.02522682, -0.00602216,  0.03262919, ...,  0.83965343,\n",
       "          0.8411746 ,  0.83897805],\n",
       "        [-0.5298138 , -0.54569435, -0.59008396, ..., -0.5430822 ,\n",
       "         -0.54072726, -0.5441205 ],\n",
       "        [ 0.8477387 ,  0.8379626 ,  0.8066822 , ..., -0.00662736,\n",
       "         -0.00627847, -0.00697602]],\n",
       "\n",
       "       [[-0.8525292 , -0.85636055, -0.8529848 , ...,  0.02631467,\n",
       "          0.02612993,  0.02692689],\n",
       "        [ 0.45426297,  0.4355064 ,  0.4042198 , ...,  0.02849078,\n",
       "          0.02904635,  0.02870771],\n",
       "        [ 0.2585327 ,  0.27745417,  0.3301868 , ...,  0.9992476 ,\n",
       "          0.99923646,  0.9992251 ]]], shape=(3, 3, 18073), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e88c7a9-62f2-483c-8b78-5aea96a214dd' class='xr-section-summary-in' type='checkbox'  ><label for='section-1e88c7a9-62f2-483c-8b78-5aea96a214dd' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2024-10-22 19:29:32\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature250\\&quot;,SN=104922&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2215, &quot;FPGA&quot;: 186, &quot;DIGITAL&quot;: &quot;\\&quot;I-5\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-1\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;C-1(High-Power 4 Channel)\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;I-0\\&quot;&quot;, &quot;BOOT&quot;: 25, &quot;FWMINOR&quot;: 15}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=250,TTR=2.4444,TTRB5=2.0000,TTRB5AUX=0.0000,AUXRS=0&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 471.15, &quot;BURST&quot;: 384.15, &quot;AVG&quot;: 90.34, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 471.15}, &quot;MEM&quot;: {&quot;PLAN&quot;: 1.141, &quot;BURST&quot;: 0.649, &quot;AVG&quot;: 0.492, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1.141}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: 1.15, &quot;BURSTHORZ&quot;: 6.98, &quot;BEAM5&quot;: -9.99, &quot;AVGBEAM&quot;: 0.56, &quot;BURSTBEAM&quot;: 3.37}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 1, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 35.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1800, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S104922A004_PWS_SITE_02.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 250, &quot;NSTT&quot;: 0}, &quot;AVG&quot;: {&quot;NC&quot;: 39, &quot;CS&quot;: 2.0, &quot;BD&quot;: 0.5, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: -15.0, &quot;AI&quot;: 120, &quot;VP&quot;: 0.0, &quot;VR&quot;: 5.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 120, &quot;NB&quot;: 4, &quot;CH&quot;: 0, &quot;MUX&quot;: 0, &quot;BW&quot;: &quot;\\&quot;BROAD\\&quot;&quot;, &quot;ALTI&quot;: 0, &quot;BT&quot;: 0, &quot;ICE&quot;: 0, &quot;ALTISTART&quot;: 0.5, &quot;ALTIEND&quot;: 150.0, &quot;RAWALTI&quot;: 1}, &quot;XFAVG&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.4619, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.4619, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.4619, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.4619, &quot;M31&quot;: 0.532, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.532, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.532, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.532}, &quot;BURST&quot;: {&quot;NC&quot;: 13, &quot;NB&quot;: 4, &quot;CS&quot;: 3.5, &quot;BD&quot;: 35.2, &quot;CY&quot;: &quot;\\&quot;BEAM\\&quot;&quot;, &quot;PL&quot;: -15.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1024, &quot;VR&quot;: 5.0, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 1, &quot;CH&quot;: 0, &quot;ALTI&quot;: 1, &quot;VR5&quot;: 5.0, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 1, &quot;ALTISTART&quot;: 42.0, &quot;ALTIEND&quot;: 104.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.4619, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.4619, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.4619, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.4619, &quot;M31&quot;: 0.532, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.532, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.532, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.532}, &quot;READALTIBURST&quot;: &quot;EN=1,START=42.00,END=104.00,SAMPSIZE=0.024,NB=1,NSAMP=2583,RAW=1&quot;, &quot;USER&quot;: {&quot;POFF&quot;: 9.5, &quot;DECL&quot;: 0.0, &quot;HX&quot;: 0, &quot;HY&quot;: 0, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;ON24H\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AUTOZUPDOWN\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 72, &quot;DY&quot;: 163, &quot;DZ&quot;: -42, &quot;M11&quot;: 32767, &quot;M12&quot;: 477, &quot;M13&quot;: -250, &quot;M21&quot;: -118, &quot;M22&quot;: 31551, &quot;M23&quot;: -399, &quot;M31&quot;: 838, &quot;M32&quot;: 376, &quot;M33&quot;: 32316}, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=61991026688,TC=62260248576,VS=62260248576,FF=4092,TF=4095&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=20.00,PHI=0.00,FREQ=250,BW=25,BRD=1,HWBEAM=1,ZNOM=100.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=20.00,PHI=-90.00,FREQ=250,BW=25,BRD=1,HWBEAM=2,ZNOM=100.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=20.00,PHI=180.00,FREQ=250,BW=25,BRD=1,HWBEAM=3,ZNOM=100.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=20.00,PHI=90.00,FREQ=250,BW=25,BRD=1,HWBEAM=4,ZNOM=100.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=500,BW=25,BRD=1,HWBEAM=5,ZNOM=100.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.95754e+01,P1=-6.89010e-02,P2=3.07926e-03,P3=8.38340e-05,P4=6.24624e-06,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.84575e+01,P1=-4.85768e-02,P2=3.40215e-03,P3=4.97871e-05,P4=5.28907e-06,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=2.03578e+01,P1=2.87348e-02,P2=1.24284e-03,P3=9.11659e-06,P4=9.46557e-06,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.98482e+01,P1=9.94687e-03,P2=3.95698e-03,P3=1.23846e-05,P4=5.44124e-06,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=5.14952e+00,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;639HSK16KKMKB\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;WULC7672JKMKB\\&quot;,DESC=\\&quot;Wave Mode\\&quot;,TYPE=6&quot;, &quot;KEY=\\&quot;8MTRYSYPJKMKB\\&quot;,DESC=\\&quot;64GB Recorder\\&quot;,TYPE=10&quot;], &quot;CALCOMPGET&quot;: &quot;DX=72,DY=163,DZ=-42,M11=32767,M12=477,M13=-250,M21=-118,M22=31551,M23=-399,M31=838,M32=376,M33=32316&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99836&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=-4.726771E-03,B1X=-2.648151E-04,B2X=-2.372674E-06,B3X=4.496909E-08,A1X=-1.020020E-04,A2X=1.769976E-06,A3X=-1.568007E-09&quot;, &quot;AY=1.000000E+00,B0Y=1.559532E-02,B1Y=-1.851641E-04,B2Y=-1.864571E-06,B3Y=-1.016134E-07,A1Y=-1.244390E-04,A2Y=2.423802E-06,A3Y=1.712951E-07&quot;, &quot;AZ=1.000000E+00,B0Z=-9.057428E-03,B1Z=1.972058E-04,B2Z=5.130616E-06,B3Z=-3.403830E-08,A1Z=1.938282E-04,A2Z=1.197075E-06,A3Z=-4.228993E-07&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5291115000e+02,RB0=3.7937528130e-01,RB1=-2.1489579690e-01,RB2=4.4181650000e-04,RB3=6.5130350000e-09,T0=-3.1473080000e+03,T1=2.4586100000e+00,T2=-6.5718281250e-04,T3=6.1275925000e-08,ID=\\&quot;P059353\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=1.4675400000e-01,A1=3.9377600000e-04,A2=-1.2027480470e-07,A3=1.1867438750e-11,B0=-6.2100006250e-01,B1=6.3748125000e-04,B2=-1.8018650000e-07,B3=1.6830001560e-11&quot;, &quot;C0=7.9542968750e-04,C1=-6.7488718750e-07,C2=1.9400940630e-10,C3=-1.8681940630e-14,D0=-3.0979950000e-06,D1=2.6566440630e-09,D2=-7.6620243750e-13,D3=7.3948556250e-17&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.98619,M12=0.01139,M13=0.01828,M21=-0.01080,M22=1.00513,M23=-0.00519,M31=-0.01061,M32=-0.00616,M33=0.96537&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.66,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature250</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: true, &quot;altraw&quot;: false, &quot;ast&quot;: true, &quot;echo&quot;: false, &quot;ahrs&quot;: false, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>13</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>10.31</dd><dt><span>SerialNum :</span></dt><dd>104922</dd><dt><span>nominal_corr :</span></dt><dd>89</dd><dt><span>cell_size :</span></dt><dd>3.5</dd><dt><span>blank_dist :</span></dt><dd>35.2</dd><dt><span>power_level_dB :</span></dt><dd>-15.0</dd><dt><span>tilt_over_5deg :</span></dt><dd>0</dd><dt><span>tilt_over_10deg :</span></dt><dd>0</dd><dt><span>multibeam_alt :</span></dt><dd>0</dd><dt><span>n_beams_alt :</span></dt><dd>1</dd><dt><span>power_level_alt :</span></dt><dd>high</dd><dt><span>burst_config_alt :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: false, &quot;amp&quot;: false, &quot;corr&quot;: false, &quot;le&quot;: true, &quot;altraw&quot;: true, &quot;ast&quot;: true, &quot;echo&quot;: false, &quot;ahrs&quot;: false, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_alt :</span></dt><dd>0</dd><dt><span>coord_sys_axes_alt :</span></dt><dd>beam</dd><dt><span>ambig_vel_alt :</span></dt><dd>0.0</dd><dt><span>SerialNum_alt :</span></dt><dd>104922</dd><dt><span>nominal_corr_alt :</span></dt><dd>0</dd><dt><span>cell_size_alt :</span></dt><dd>0.0</dd><dt><span>blank_dist_alt :</span></dt><dd>42.0</dd><dt><span>power_level_dB_alt :</span></dt><dd>-26.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>up</dd><dt><span>orient_status :</span></dt><dd>auto_UD</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;mag&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>beam</dd><dt><span>fs :</span></dt><dd>1</dd><dt><span>bandwidth :</span></dt><dd>&quot;BROAD&quot;</dd><dt><span>has_imu :</span></dt><dd>0</dd><dt><span>beam_angle :</span></dt><dd>20</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 11MB\n",
       "Dimensions:                 (time: 18073, time_altraw: 35, range: 13, beam: 4,\n",
       "                             dir: 4, dirIMU: 3, n_altraw: 2583, x1: 4, x2: 4,\n",
       "                             earth: 3, inst: 3)\n",
       "Coordinates:\n",
       "  * time                    (time) datetime64[ns] 145kB 2024-10-22T19:29:36.3...\n",
       "  * time_altraw             (time_altraw) datetime64[ns] 280B 2024-10-22T19:2...\n",
       "  * range                   (range) float64 104B 38.7 42.2 45.7 ... 77.2 80.7\n",
       "  * beam                    (beam) int32 16B 1 2 3 4\n",
       "  * dir                     (dir) int32 16B 1 2 3 4\n",
       "  * dirIMU                  (dirIMU) <U1 12B 'X' 'Y' 'Z'\n",
       "  * x1                      (x1) int32 16B 1 2 3 4\n",
       "  * x2                      (x2) int32 16B 1 2 3 4\n",
       "  * earth                   (earth) <U1 12B 'E' 'N' 'U'\n",
       "  * inst                    (inst) <U1 12B 'X' 'Y' 'Z'\n",
       "Dimensions without coordinates: n_altraw\n",
       "Data variables: (12/39)\n",
       "    c_sound                 (time) float32 72kB 1.503e+03 ... 1.485e+03\n",
       "    temp                    (time) float32 72kB 13.93 13.92 13.92 ... 8.23 8.23\n",
       "    pressure                (time) float32 72kB 0.768 0.765 ... 74.71 74.71\n",
       "    heading                 (time) float32 72kB 216.1 215.8 ... 212.7 212.9\n",
       "    pitch                   (time) float32 72kB -27.59 -28.03 ... 2.21 2.22\n",
       "    roll                    (time) float32 72kB 73.04 71.68 67.74 ... -0.36 -0.4\n",
       "    ...                      ...\n",
       "    low_volt_skip           (time) uint8 18kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0\n",
       "    active_config           (time) uint8 18kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0\n",
       "    telemetry_data          (time) uint8 18kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0\n",
       "    boost_running           (time) uint8 18kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0\n",
       "    beam2inst_orientmat     (x1, x2) float32 64B 1.462 0.0 -1.462 ... 0.0 0.532\n",
       "    orientmat               (earth, inst, time) float32 651kB -0.5221 ... 0.9992\n",
       "Attributes: (12/39)\n",
       "    filehead_config:       {\"CLOCKSTR\": {\"TIME\": \"\\\"2024-10-22 19:29:32\\\"\"}, ...\n",
       "    inst_model:            Signature250\n",
       "    inst_make:             Nortek\n",
       "    inst_type:             ADCP\n",
       "    burst_config:          {\"press_valid\": true, \"temp_valid\": true, \"compass...\n",
       "    n_cells:               13\n",
       "    ...                    ...\n",
       "    rotate_vars:           ['vel', 'accel', 'mag']\n",
       "    coord_sys:             beam\n",
       "    fs:                    1\n",
       "    bandwidth:             \"BROAD\"\n",
       "    has_imu:               0\n",
       "    beam_angle:            20"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_waves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05097477",
   "metadata": {},
   "source": [
    "### 2. Review and QC Altimeter Measurements\n",
    "\n",
    "Now that we've read in our data, our next step is to check the quality of the water surface measurements. We can do this by plotting the 'pressure_alt', 'ast_dist_alt' and 'le_dist_alt' variables.\n",
    "\n",
    "Since this dataset is a clip of the very start of a deployment, we can see that the instrument began recording before it was on the seafloor. Our next move will be to trim those measurements off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91ae222f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d8048e60d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ds_waves[\"ast_dist_alt\"].plot(label=\"Altimeter AST\")\n",
    "ds_waves[\"le_dist_alt\"].plot(label=\"Altimeter LE\")\n",
    "ds_waves[\"pressure_alt\"].plot(label=\"Pressure Sensor\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5beaac54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Range [m]')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Trim time to after Oct 22, 2024 21:30\n",
    "time_slc = slice(np.datetime64(\"2024-10-22T21:30\"), None)\n",
    "# Trim time for every time variable\n",
    "ds_waves = ds_waves.sel(time=time_slc, time_altraw=time_slc)\n",
    "\n",
    "ds_waves[\"ast_dist_alt\"].plot(label=\"Altimeter AST\")\n",
    "ds_waves[\"le_dist_alt\"].plot(label=\"Altimeter LE\")\n",
    "ds_waves[\"pressure_alt\"].plot(label=\"Pressure\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Range [m]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b16888a6",
   "metadata": {},
   "source": [
    "In the plot above we can see a couple things. One, our altimeter is operating in pulses of approximately 20 minutes, and two, our pressure sensor appears like it was not zeroed perfectly before deployment. This can be due to multiple reasons, and it is always a good thing to verify in post. We can calculate water depth from the pressure reading next and verify it against the altimeter measurements.\n",
    "\n",
    "To do so, we can check that the pressure sensor was zeroed properly by looking at the pressure data immediately before deployment, where it is reading ~0.7 dBar. Adjusting the pressure measurement by -0.7 dBar appears to then correct the depth measurement when comparing it to the altimeter readings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e65471e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Range [m]')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_waves[\"pressure\"] -= 0.7  # Adjust pressure sensor by 0.7 dBar\n",
    "\n",
    "# Calculate water depth\n",
    "dolfyn.adp.clean.water_depth_from_pressure(ds_waves, salinity=35)\n",
    "\n",
    "ds_waves[\"ast_dist_alt\"].plot(label=\"Altimeter AST\")\n",
    "ds_waves[\"le_dist_alt\"].plot(label=\"Altimeter LE\")\n",
    "ds_waves[\"depth\"].plot(label=\"Depth\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Range [m]\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb4ebc5",
   "metadata": {},
   "source": [
    "The ADCP records a quality indicator that corresponds with each altimeter measurement algorithm, ranging from 0 to 100. These indicators are saved in the dataset as variables called \"ast_quality_alt\" and \"le_quality_alt\". We can use these variables to remove poor altimeter measurements.\n",
    "\n",
    "If values are removed, it is worthwhile to replace these values using the method of your choice. The FFT algorithm is unable to handle NaNs, and any windows that contain one with result in a spectrum filled with NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "db398852",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove datapoints where quality is below 40\n",
    "ds_waves[\"ast_dist_alt\"] = ds_waves[\"ast_dist_alt\"].where(ds_waves[\"ast_quality_alt\"] > 40)\n",
    "ds_waves[\"le_dist_alt\"] = ds_waves[\"le_dist_alt\"].where(ds_waves[\"le_quality_alt\"] > 40)\n",
    "\n",
    "# Interpolate gaps smaller than 5 seconds\n",
    "ds_waves[\"ast_dist_alt\"] = ds_waves[\"ast_dist_alt\"].interpolate_na(dim=\"time\", max_gap=np.timedelta64(5, \"s\"), method=\"linear\")\n",
    "ds_waves[\"le_dist_alt\"] = ds_waves[\"le_dist_alt\"].interpolate_na(dim=\"time\", max_gap=np.timedelta64(5, \"s\"), method=\"linear\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3beeb2",
   "metadata": {},
   "source": [
    "### 3. Create Energy Spectra\n",
    "\n",
    "We'll start wave processing by first spinning up DOLfYN's binning tools to compute wave height and period. A comprehensive overview of wave measurement and analysis can be found [here](https://essopenarchive.org/doi/full/10.22541/essoar.175130482.28547248).\n",
    "\n",
    "Wave stats are typically computing windows of 30 minutes to an hour. Shorter windows can be used but they have lower statistical certainty. \n",
    "\n",
    "In this case, the instrument was running on a duty cycle, with 600 samples measured at 1 Hz (10 minutes). Therefore, we'll use 600 datapoints in each binning window. Additionally, because we're on a duty cycle, we will create one PSD from each set of samples; so, to avoid jumping duty cycle bins, we'll set the FFT overlap to 0%. If we were sampling continuously, we'd use an FFT overlap of 50%.\n",
    "\n",
    "Now, we can calculate the energy spectrum for the surface using `power_spectral_density`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "140ac43a",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_duty_cycle = 600\n",
    "pct_overlap = 0\n",
    "fft_tool = dolfyn.VelBinner(\n",
    "        n_bin=n_duty_cycle, fs=ds_waves.fs, n_fft=n_duty_cycle\n",
    "    )\n",
    "\n",
    "Szz_ast = fft_tool.power_spectral_density(ds_waves[\"ast_dist_alt\"], freq_units=\"Hz\", pct_overlap=pct_overlap)\n",
    "Szz_le = fft_tool.power_spectral_density(ds_waves[\"le_dist_alt\"], freq_units=\"Hz\", pct_overlap=pct_overlap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d82c21",
   "metadata": {},
   "source": [
    "We will also go ahead and set the bounds for the wave frequencies that we want to analyze with the instrument. Langrangian drifters like buoys are unabled to measure wave periods less than 22 seconds, but ADCPs do not have this same limitation. ADCPs are limited in their waves analysis by their ability to measure orbital velocities. Because of this, we'll set the frequency bounds for waves between 5 seconds at the high end and 30 seconds at the low end, typically the largest gravity swells. \n",
    "\n",
    "Further discussion of acoustic limitations are discussed in Section 3.2.3 [here](https://essopenarchive.org/doi/full/10.22541/essoar.175130482.28547248)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da0f401b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trim frequency vector to 0.2 > f > 0.05 Hz (wave periods between 5 and 30 s)\n",
    "slc_freq = slice(0.05, 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73dcb9f0",
   "metadata": {},
   "source": [
    "If we plot the energy spectra we just calculated, we can see the differences between the two methods. The methods begin to diverge at high frequencies (wave periods < 5 s), where each spectrum becomes noiser. This could be due to wind waves, which populate this region of the spectrum, starting to break. Linear wave theory cannot account for breaking waves, so these spectra will not be accurate at quantifying their estimation.\n",
    "\n",
    "The spectra also begin to drift at low frequencies, which may only be due to the short length of time we are analyzing here.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc8f103b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d804f6e0d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(11, 5))\n",
    "ax[0].loglog(Szz_ast[\"freq\"], Szz_ast.median(\"time_psd\"), label=\"Altimeter AST\")\n",
    "ax[0].loglog(Szz_le[\"freq\"], Szz_le.median(\"time_psd\"), label=\"Altimeter LE\")\n",
    "ax[0].set(xlabel=\"Frequency [Hz]\", ylabel=\"Energy Density [m$^2$/Hz]\", title =\"Full frequency range\", xlim=(5e-3, 0.5), ylim=(5e-3, 5))\n",
    "ax[1].loglog(Szz_ast[\"freq\"].sel(freq=slc_freq), Szz_ast.sel(freq=slc_freq).median(\"time_psd\"), label=\"Altimeter AST\")\n",
    "ax[1].loglog(Szz_le[\"freq\"].sel(freq=slc_freq), Szz_le.sel(freq=slc_freq).median(\"time_psd\"), label=\"Altimeter LE\")\n",
    "ax[1].set(xlabel=\"Frequency [Hz]\", title=\"Frequency limits of 1/22 to 1/3 Hz\", xlim=(5e-3, 0.5), ylim=(5e-3, 5))\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b26a34c6",
   "metadata": {},
   "source": [
    "### 4. Compute Basic Wave Statistics\n",
    "\n",
    "To calculate some basic wave statistics, like significant wave height and peak wave period, we will use functions from the MHKiT Wave module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d3d0e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Select frequency range for wave calculations\n",
    "Szz_ast = Szz_ast.sel(freq=slc_freq)\n",
    "Szz_le = Szz_le.sel(freq=slc_freq)\n",
    "\n",
    "# Convert to pandas DataFrame for MHKiT wave resource functions\n",
    "Szz_ast = Szz_ast.T.to_pandas()\n",
    "# Calculate wave parameters\n",
    "Hs_ast = wave.resource.significant_wave_height(Szz_ast)\n",
    "Tm_ast = wave.resource.average_wave_period(Szz_ast)\n",
    "Tp_ast = wave.resource.peak_period(Szz_ast)\n",
    "\n",
    "Szz_le = Szz_le.T.to_pandas()\n",
    "Hs_le = wave.resource.significant_wave_height(Szz_le)\n",
    "Tm_le = wave.resource.average_wave_period(Szz_le)\n",
    "Tp_le = wave.resource.peak_period(Szz_le)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9c4a6f",
   "metadata": {},
   "source": [
    "Next we'll create create a bin-averaged dataset using DOLfYN's tools to save these stats in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c304d50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create averaged dataset\n",
    "ds_waves_avg = fft_tool.bin_average(ds_waves)\n",
    "# Convert wave parameters to xarray and add to averaged dataset\n",
    "ds_waves_avg[\"wave_height_ast\"] = Hs_ast.to_xarray()\n",
    "ds_waves_avg[\"mean_period_ast\"] = Tm_ast.to_xarray()\n",
    "ds_waves_avg[\"peak_period_ast\"] = Tp_ast.to_xarray()\n",
    "\n",
    "ds_waves_avg[\"wave_height_le\"] = Hs_le.to_xarray()\n",
    "ds_waves_avg[\"mean_period_le\"] = Tm_le.to_xarray()\n",
    "ds_waves_avg[\"peak_period_le\"] = Tp_le.to_xarray()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f86c69d7",
   "metadata": {},
   "source": [
    "And plot. We can see a significant wave height between 1-2 m, mean wave period around 3 s, and a peak period around 9 s. There are some small differences between wave height and period below due to small differences between the AST and LE spectra. Both methods have the same spectral peak, so the peak period is equivalent. Again, the LE method determines the surface as the rising edge of the return acoustic signal, while AST determines the surface as the maximum point of the return acoustic signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da81435e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 15.0), Text(0, 0.5, 'Peak Period [s]'), Text(0.5, 0, 'Time')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(10, 7), constrained_layout=True)\n",
    "ax[0].plot(\n",
    "    ds_waves_avg[\"time_psd\"],\n",
    "    ds_waves_avg[\"wave_height_ast\"],\n",
    "    \".-\",\n",
    "    label=\"Altimeter AST\",\n",
    ")\n",
    "ax[0].plot(\n",
    "    ds_waves_avg[\"time_psd\"],\n",
    "    ds_waves_avg[\"wave_height_le\"],\n",
    "    \".-\",\n",
    "    label=\"Altimeter LE\",\n",
    ")\n",
    "ax[0].legend()\n",
    "ax[0].set(ylim=(0,5), ylabel=\"Significant Wave Height [m]\")\n",
    "\n",
    "ax[1].plot(\n",
    "    ds_waves_avg[\"time_psd\"],\n",
    "    ds_waves_avg[\"mean_period_ast\"],\n",
    "    \".-\",\n",
    "    label=\"Altimeter AST\",\n",
    ")\n",
    "ax[1].plot(\n",
    "    ds_waves_avg[\"time_psd\"],\n",
    "    ds_waves_avg[\"mean_period_le\"],\n",
    "    \".-\",\n",
    "    label=\"Altimeter LE\",\n",
    ")\n",
    "ax[1].legend()\n",
    "ax[1].set(ylim=(0, 15), ylabel=\"Mean Period [s]\")\n",
    "ax[2].plot(\n",
    "    ds_waves_avg[\"time_psd\"],\n",
    "    ds_waves_avg[\"peak_period_ast\"],\n",
    "    \".-\",\n",
    "    label=\"Altimeter AST\",\n",
    ")\n",
    "ax[2].plot(\n",
    "    ds_waves_avg[\"time_psd\"],\n",
    "    ds_waves_avg[\"peak_period_le\"],\n",
    "    \".-\",\n",
    "    label=\"Altimeter LE\",\n",
    ")\n",
    "ax[2].legend()\n",
    "ax[2].set(ylim=(0, 15), ylabel=\"Peak Period [s]\", xlabel=\"Time\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f250b8f",
   "metadata": {},
   "source": [
    "### 5. Review and QC Surface Velocity Measurements\n",
    "\n",
    "ADCPs can be used to measure directional wave statistics, albeit to a lower degree of accuracy than one can get using a surface buoy. This can be done using the so-called the \"SUV\" method (short for \"Surface\" - \"U velocity\" - \"V velocity\"), which uses surface height and the horizontal velocity vector to estimate wave orbital speed and direction. \n",
    "\n",
    "First, it is useful to see what velocity data the ADCP is measuring while in wave mode. We'll start by doing some basic processing and plot the velocity data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c477be85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is already in the beam coordinate system\n",
      "Data is already in the beam coordinate system\n"
     ]
    }
   ],
   "source": [
    "# The ADCP transducers were measured to be 0.5 m from the feet of the lander\n",
    "dolfyn.adp.clean.set_range_offset(ds_waves, 0.5)\n",
    "# Remove surface sidelobe interference and low correlation values\n",
    "ds_waves = dolfyn.adp.clean.remove_surface_interference(ds_waves)\n",
    "ds_waves = dolfyn.adp.clean.correlation_filter(ds_waves, thresh=30)\n",
    "# Correct magnetic declination\n",
    "dolfyn.set_declination(ds_waves, 14.8, inplace=True)  # 14.8 deg Eastz\n",
    "dolfyn.rotate2(ds_waves, \"earth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59afeff9",
   "metadata": {},
   "source": [
    "In the plot below, one can see that the ADCP is only measuring the top half of the water column. In order to measure wave direction, the ADCP needs to be able to capture the wave orbital velocity created by the waves. Because we still need to remove depth bins at the surface that are corrupted by sidelobe interference, the ADCP will generally only be able to measure the direction of long-period gravity waves (i.e., the \"peak\" or \"dominant\" wave direction)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9cfbf3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mcve343\\AppData\\Local\\Temp\\ipykernel_13508\\212097317.py:10: MatplotlibDeprecationWarning: Getting the array from a PolyQuadMesh will return the full array in the future (uncompressed). To get this behavior now set the PolyQuadMesh with a 2D array .set_array(data2d).\n",
      "  fig.colorbar(h1, ax=ax[0], label=label, fraction=0.05, pad=0.02)\n",
      "C:\\Users\\mcve343\\AppData\\Local\\Temp\\ipykernel_13508\\212097317.py:21: MatplotlibDeprecationWarning: Getting the array from a PolyQuadMesh will return the full array in the future (uncompressed). To get this behavior now set the PolyQuadMesh with a 2D array .set_array(data2d).\n",
      "  fig.colorbar(h2, ax=ax[1], label=label, fraction=0.05, pad=0.02)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Time'), Text(0, 0.5, 'Depth [m]'), (0.0, 78.0)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, figsize=(10, 6), constrained_layout=True)\n",
    "h1 = ax[0].pcolor(\n",
    "    ds_waves[\"time\"].values,\n",
    "    ds_waves[\"range\"].values,\n",
    "    ds_waves[\"vel\"][0],\n",
    "    cmap=\"coolwarm_r\",\n",
    ")\n",
    "ax[0].plot(ds_waves[\"time\"], ds_waves[\"depth\"], \"k-\")\n",
    "label = \"Velocity East [m/s]\"\n",
    "fig.colorbar(h1, ax=ax[0], label=label, fraction=0.05, pad=0.02)\n",
    "ax[0].set(xlabel=\"Time\", ylabel=\"Depth [m]\", ylim=(0, 78))\n",
    "\n",
    "h2 = ax[1].pcolor(\n",
    "    ds_waves[\"time\"].values,\n",
    "    ds_waves[\"range\"].values,\n",
    "    ds_waves[\"vel\"][1],\n",
    "    cmap=\"coolwarm_r\",\n",
    ")\n",
    "ax[1].plot(ds_waves[\"time\"], ds_waves[\"depth\"], \"k-\")\n",
    "label = \"Velocity North [m/s]\"\n",
    "fig.colorbar(h2, ax=ax[1], label=label, fraction=0.05, pad=0.02)\n",
    "ax[1].set(xlabel=\"Time\", ylabel=\"Depth [m]\", ylim=(0, 78))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c4437e",
   "metadata": {},
   "source": [
    "### 6. Compute Directional Wave Statistics\n",
    "\n",
    "The following codeblock runs through the computational steps to estimate peak wave direction and spread. We start by creating a 3x matrix with the highest depth bin for the east and north velocity vectors, plus the surface vector. We next calculate the auto-spectra of each of those vectors, along with their cross-spectra. The auto- and cross-spectra are used to then compute the Fourier coefficients (a1, b1, a2, b2), which define the peak wave direction and spread.\n",
    "\n",
    "To follow this process using DOLfYN's tools, we'll need to use the `cross_spectral_density` function, which is located in the ADV's API.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "317a1faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "## \"SUV\" method - AST, u velocity, v velocity\n",
    "fft_tool = dolfyn.adv.api.ADVBinner(\n",
    "    n_bin=n_duty_cycle * ds_waves.fs, fs=ds_waves.fs, n_fft=n_duty_cycle\n",
    ")\n",
    "# Create 2D tensor for spectral analysis\n",
    "disp = xr.DataArray(\n",
    "    data=np.array(\n",
    "        [\n",
    "            ds_waves[\"vel\"][0, 7].values,  # first bin continuously below water surface\n",
    "            ds_waves[\"vel\"][1, 7].values,\n",
    "            ds_waves[\"ast_dist_alt\"].values,\n",
    "        ]\n",
    "    ),\n",
    "    coords={\"dir\": [\"u\", \"v\", \"z\"], \"time\": ds_waves[\"time\"]},\n",
    ")\n",
    "\n",
    "# fill NaN values with mean to not affect statistics\n",
    "disp = dolfyn.adv.clean.fill_nan_ensemble_mean(\n",
    "    disp, disp.isnull(), int(ds_waves.fs), window=n_duty_cycle\n",
    ")\n",
    "# Auto-spectra\n",
    "psd = fft_tool.power_spectral_density(disp, freq_units=\"Hz\", pct_overlap=pct_overlap)\n",
    "psd = psd.sel(freq=slc_freq)\n",
    "Sxx = psd.sel(S=\"Sxx\")\n",
    "Syy = psd.sel(S=\"Syy\")\n",
    "Szz = psd.sel(S=\"Szz\")\n",
    "\n",
    "# Cross-spectra\n",
    "csd = fft_tool.cross_spectral_density(disp, freq_units=\"Hz\", pct_overlap=pct_overlap)\n",
    "csd = csd.sel(coh_freq=slc_freq)\n",
    "Cxz = csd.sel(C=\"Cxz\").real\n",
    "Cxy = csd.sel(C=\"Cxy\").real\n",
    "Cyz = csd.sel(C=\"Cyz\").real\n",
    "\n",
    "# Calculate peak wave direction and spread\n",
    "a1 = Cxz.values / np.sqrt((Sxx + Syy) * Szz)\n",
    "b1 = Cyz.values / np.sqrt((Sxx + Syy) * Szz)\n",
    "a2 = (Sxx - Syy) / (Sxx + Syy)\n",
    "b2 = 2 * Cxy.values / (Sxx + Syy)\n",
    "theta = np.rad2deg(np.arctan2(b1, a1))  # degrees CCW from East, \"to\" convention\n",
    "phi = np.rad2deg(np.sqrt(2 * (1 - np.sqrt(a1**2 + b1**2))))\n",
    "\n",
    "# Get peak frequency - fill nan slices with 0\n",
    "peak_idx = Szz.fillna(0).argmax(\"freq\")\n",
    "# degrees CW from North (\"from\" convention)\n",
    "direction = (270 - theta[:, peak_idx]) % 360\n",
    "# Set direction from -180 to 180\n",
    "direction[direction > 180] -= 360\n",
    "spread = phi[:, peak_idx]\n",
    "\n",
    "ds_waves_avg[\"wave_a1_value\"] = a1\n",
    "ds_waves_avg[\"wave_b1_value\"] = b1\n",
    "ds_waves_avg[\"wave_a2_value\"] = a2\n",
    "ds_waves_avg[\"wave_b2_value\"] = b2\n",
    "ds_waves_avg[\"peak_wave_direction\"] = direction\n",
    "ds_waves_avg[\"peak_wave_spread\"] = spread"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9ee95d",
   "metadata": {},
   "source": [
    "And plot. Some datapoints may be missing for velocity data that was quality controlled out, but we can see that the highest energy waves are coming from -80 degees (10 degrees north of due West), with a spread of approximately 10-20 degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa7c5d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 90.0), Text(0, 0.5, 'Peak Wave Spread [deg]')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(10, 5), constrained_layout=True)\n",
    "ax[0].plot(\n",
    "    ds_waves_avg[\"time_psd\"],\n",
    "    ds_waves_avg[\"peak_wave_direction\"],\n",
    "    \".-\",\n",
    ")\n",
    "ax[0].set(ylim=(-180, 180), ylabel=\"Peak Wave Direction\\n[deg CW from N]\")\n",
    "\n",
    "ax[1].plot(\n",
    "    ds_waves_avg[\"time_psd\"],\n",
    "    ds_waves_avg[\"peak_wave_spread\"],\n",
    "    \".-\",\n",
    ")\n",
    "ax[1].set(ylim=(0, 90), ylabel=\"Peak Wave Spread [deg]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d05b8b5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "work",
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}