spotter_pickle2wavespec.ipynb

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    "import os\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import wavespectra \n",
    "import xarray as xr\n",
    "\n",
    "DATA_DIRECTORY = '/vortexfs1/home/csherwood/proj/NOPP/buoy_data/'\n",
    "DATA_FILENAME = 'hurricane_ian_spotter_data_v1.pickle'\n",
    "\n",
    "with open(os.path.join(DATA_DIRECTORY, DATA_FILENAME), 'rb') as handle:\n",
    "    spotter = pickle.load(handle)\n",
    "\n",
    "# `spotter` is a python dictionary of Pandas DataFrames, keyed by\n",
    "# each drifter ID. The drifter ids can then be accessed as follows:\n",
    "spotter_ids = list(spotter.keys())\n",
    "\n",
    "# Dictionary to store wavespectra datasets in; can be any container.\n",
    "wavespectra_datasets = {}\n",
    "\n",
    "for spotter_id in spotter.keys():\n",
    "\n",
    "    # Extract the observation times that contain spectral data.\n",
    "    only_waves = spotter[spotter_id]['energy_density'].notnull()\n",
    "    drifter = spotter[spotter_id][only_waves]\n",
    "\n",
    "    # Exract the coordinate arrays; note that the frequency array is\n",
    "    # uniform across the Spotter observations, so we can just \n",
    "    # use the array in the first index of the DataFrame.\n",
    "    time = drifter.index.to_numpy()\n",
    "    freq = drifter['frequency'][0] \n",
    "\n",
    "    # Extract the variable arrays.\n",
    "    efth = np.stack(drifter['energy_density'])\n",
    "    lat = drifter['latitude']\n",
    "    lon = drifter['longitude']\n",
    "\n",
    "    # Construct the dataset. This must match the conventions used by the\n",
    "    # wavespectra package:\n",
    "    # (https://wavespectra.readthedocs.io/en/latest/conventions.html#)\n",
    "    # Note that the directional spectrum needs to be computed using the\n",
    "    # directional moments and an estimator (e.g. MEM). As shown here, \n",
    "    # the dataset will be constructed, but it will be scalar, e.g. \n",
    "    # efth(time, freq) and not efth(time, freq, dir).\n",
    "    ds = xr.Dataset(\n",
    "        data_vars=dict(\n",
    "            # efth=([\"freq\", \"dir\", \"time\"], efth), #TODO: need to compute this\n",
    "            efth=([\"time\", \"freq\"], efth), # Delete this line if using above (Note: efth may need to be transposed)\n",
    "            lat=([\"time\"], lat),\n",
    "            lon=([\"time\"], lon),\n",
    "            site=\"\",\n",
    "        ),\n",
    "        coords=dict(\n",
    "            time=time,\n",
    "            freq=freq,\n",
    "            dir=[], #TODO: need to compute this from directional moments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            # Attributes here; wavespectra would put significant wave\n",
    "            # height, etc., here but these are a function of time so it\n",
    "            # might be reasonable to set them as data_vars instead (as \n",
    "            # a function of the time coordinate)\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Format and attach SpecArray accessor to the existing xarray \n",
    "    # dataset using the wavespectra.read_dataset() method. Store it in \n",
    "    # the dictionary keyed by spotter_id.\n",
    "    wavespectra_datasets[spotter_id] = wavespectra.read_dataset(ds)\n",
    "    "
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       "       -0.081134, -0.162268, -0.12219 , -0.158358, -0.088954, -0.087977,\n",
       "       -0.043988, -0.071359,  0.01173 , -0.046921, -0.182796, -0.12219 ,\n",
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       "       -0.58651 , -0.538612, -0.369501])"
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    "spec2d = estimate( drifter.a1[0], "
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       "Dimensions:  (time: 72)\n",
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       "  * time     (time) object 2022-09-27T00:35:55+00:00 ... 2022-09-29T23:35:55+...\n",
       "Data variables:\n",
       "    hs       (time) float64 0.5956 0.6214 0.5817 0.6008 ... 2.275 2.269 2.269\n",
       "    tp       (time) float32 dask.array&lt;chunksize=(72,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    standard_name:  sea_surface_wave_significant_height\n",
       "    units:          m</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-6d923215-27ed-428e-a221-35cc09511b59' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6d923215-27ed-428e-a221-35cc09511b59' 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>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-11ccb48f-8fe5-4a06-836e-9a608380ac43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11ccb48f-8fe5-4a06-836e-9a608380ac43' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2022-09-27T00:35:55+00:00 ... 20...</div><input id='attrs-6a0f63e1-142f-4d18-a032-e289138d554c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a0f63e1-142f-4d18-a032-e289138d554c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c19a76bb-e317-4c32-9b0e-475e6a32b73d' class='xr-var-data-in' type='checkbox'><label for='data-c19a76bb-e317-4c32-9b0e-475e6a32b73d' title='Show/Hide data 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([Timestamp(&#x27;2022-09-27 00:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 01:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 02:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 03:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 04:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 05:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 06:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 07:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 08:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 09:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 10:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 11:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 12:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 13:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 14:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 15:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 16:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 17:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 18:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 19:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 20:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 21:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 22:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-27 23:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 00:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 01:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 02:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 03:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 04:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 05:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 06:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 07:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 08:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 09:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 10:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 11:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 12:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 13:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 14:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 15:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 16:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 17:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 18:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 19:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 20:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 21:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 22:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-28 23:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 00:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 01:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 02:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 03:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 04:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 05:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 06:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 07:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 08:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 09:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 10:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 11:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 12:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 13:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 14:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 15:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 16:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 17:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 18:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 19:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 20:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 21:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 22:35:55+0000&#x27;, tz=&#x27;UTC&#x27;),\n",
       "       Timestamp(&#x27;2022-09-29 23:35:55+0000&#x27;, tz=&#x27;UTC&#x27;)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-208adb2f-7a5c-42b0-8f63-f164935e9e51' class='xr-section-summary-in' type='checkbox'  checked><label for='section-208adb2f-7a5c-42b0-8f63-f164935e9e51' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hs</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5956 0.6214 ... 2.269 2.269</div><input id='attrs-c5c8f5fe-94be-40a1-8f0e-5d5725770ee7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5c8f5fe-94be-40a1-8f0e-5d5725770ee7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a35dd61-eb5b-45d1-8ded-1a059506e904' class='xr-var-data-in' type='checkbox'><label for='data-6a35dd61-eb5b-45d1-8ded-1a059506e904' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_surface_wave_significant_height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([ 0.59563445,  0.62142691,  0.58174597,  0.60084217,  0.61689307,\n",
       "        0.74097028,  0.95794121,  1.12167702,  1.2402679 ,  1.24416511,\n",
       "        1.27265947,  1.1961892 ,  1.20510892,  1.27084832,  1.45834342,\n",
       "        1.61437674,  1.72477205,  1.87417601,  2.14022208,  2.35916898,\n",
       "        2.4086171 ,  2.37730376,  2.73150626,  2.81849837,  2.97971089,\n",
       "        3.34928274,  3.7457324 ,  4.4604944 ,  5.06902149,  6.73363142,\n",
       "        8.87442273,  9.99610316, 11.80361034, 10.53594332, 10.51989654,\n",
       "        9.6160559 ,  9.01697036,  8.22656343,  8.04682843,  7.81815402,\n",
       "        7.29061537,  6.81746444,  6.62556546,  6.15708463,  6.03102523,\n",
       "        5.51677356,  5.4560017 ,  5.01602079,  4.75371913,  4.51754137,\n",
       "        4.53699015,  4.30902682,  4.02355944,  3.74055459,  3.61747628,\n",
       "        3.58900677,  3.52627746,  3.45835047,  3.37384171,  3.28147349,\n",
       "        3.11565824,  3.02087616,  2.91098637,  2.74900506,  2.77097671,\n",
       "        2.7439431 ,  2.47237436,  2.41341219,  2.350304  ,  2.27515828,\n",
       "        2.26924227,  2.2692523 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(72,), meta=np.ndarray&gt;</div><input id='attrs-372aac32-c67d-42b0-b4e0-0799096cb1d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-372aac32-c67d-42b0-b4e0-0799096cb1d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7c27162-657a-4852-a1fd-b99d2e24f233' class='xr-var-data-in' type='checkbox'><label for='data-e7c27162-657a-4852-a1fd-b99d2e24f233' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_surface_wave_period_at_variance_spectral_density_maximum</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 288 B </td>\n",
       "                        <td> 288 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (72,) </td>\n",
       "                        <td> (72,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 30 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "\n",
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       "  <text x=\"60.000000\" y=\"47.054036\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >72</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5250de4-6246-4371-88a0-3b4ee5ceeffc' class='xr-section-summary-in' type='checkbox'  ><label for='section-f5250de4-6246-4371-88a0-3b4ee5ceeffc' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-faf22998-b509-4143-86ab-27ef05988857' class='xr-index-data-in' type='checkbox'/><label for='index-faf22998-b509-4143-86ab-27ef05988857' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2022-09-27 00:35:55+00:00, 2022-09-27 01:35:55+00:00,\n",
       "       2022-09-27 02:35:55+00:00, 2022-09-27 03:35:55+00:00,\n",
       "       2022-09-27 04:35:55+00:00, 2022-09-27 05:35:55+00:00,\n",
       "       2022-09-27 06:35:55+00:00, 2022-09-27 07:35:55+00:00,\n",
       "       2022-09-27 08:35:55+00:00, 2022-09-27 09:35:55+00:00,\n",
       "       2022-09-27 10:35:55+00:00, 2022-09-27 11:35:55+00:00,\n",
       "       2022-09-27 12:35:55+00:00, 2022-09-27 13:35:55+00:00,\n",
       "       2022-09-27 14:35:55+00:00, 2022-09-27 15:35:55+00:00,\n",
       "       2022-09-27 16:35:55+00:00, 2022-09-27 17:35:55+00:00,\n",
       "       2022-09-27 18:35:55+00:00, 2022-09-27 19:35:55+00:00,\n",
       "       2022-09-27 20:35:55+00:00, 2022-09-27 21:35:55+00:00,\n",
       "       2022-09-27 22:35:55+00:00, 2022-09-27 23:35:55+00:00,\n",
       "       2022-09-28 00:35:55+00:00, 2022-09-28 01:35:55+00:00,\n",
       "       2022-09-28 02:35:55+00:00, 2022-09-28 03:35:55+00:00,\n",
       "       2022-09-28 04:35:55+00:00, 2022-09-28 05:35:55+00:00,\n",
       "       2022-09-28 06:35:55+00:00, 2022-09-28 07:35:55+00:00,\n",
       "       2022-09-28 08:35:55+00:00, 2022-09-28 09:35:55+00:00,\n",
       "       2022-09-28 10:35:55+00:00, 2022-09-28 11:35:55+00:00,\n",
       "       2022-09-28 12:35:55+00:00, 2022-09-28 13:35:55+00:00,\n",
       "       2022-09-28 14:35:55+00:00, 2022-09-28 15:35:55+00:00,\n",
       "       2022-09-28 16:35:55+00:00, 2022-09-28 17:35:55+00:00,\n",
       "       2022-09-28 18:35:55+00:00, 2022-09-28 19:35:55+00:00,\n",
       "       2022-09-28 20:35:55+00:00, 2022-09-28 21:35:55+00:00,\n",
       "       2022-09-28 22:35:55+00:00, 2022-09-28 23:35:55+00:00,\n",
       "       2022-09-29 00:35:55+00:00, 2022-09-29 01:35:55+00:00,\n",
       "       2022-09-29 02:35:55+00:00, 2022-09-29 03:35:55+00:00,\n",
       "       2022-09-29 04:35:55+00:00, 2022-09-29 05:35:55+00:00,\n",
       "       2022-09-29 06:35:55+00:00, 2022-09-29 07:35:55+00:00,\n",
       "       2022-09-29 08:35:55+00:00, 2022-09-29 09:35:55+00:00,\n",
       "       2022-09-29 10:35:55+00:00, 2022-09-29 11:35:55+00:00,\n",
       "       2022-09-29 12:35:55+00:00, 2022-09-29 13:35:55+00:00,\n",
       "       2022-09-29 14:35:55+00:00, 2022-09-29 15:35:55+00:00,\n",
       "       2022-09-29 16:35:55+00:00, 2022-09-29 17:35:55+00:00,\n",
       "       2022-09-29 18:35:55+00:00, 2022-09-29 19:35:55+00:00,\n",
       "       2022-09-29 20:35:55+00:00, 2022-09-29 21:35:55+00:00,\n",
       "       2022-09-29 22:35:55+00:00, 2022-09-29 23:35:55+00:00],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c2eaea7a-84ae-492a-b54d-a80c490b0bed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2eaea7a-84ae-492a-b54d-a80c490b0bed' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_surface_wave_significant_height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 72)\n",
       "Coordinates:\n",
       "  * time     (time) object 2022-09-27T00:35:55+00:00 ... 2022-09-29T23:35:55+...\n",
       "Data variables:\n",
       "    hs       (time) float64 0.5956 0.6214 0.5817 0.6008 ... 2.275 2.269 2.269\n",
       "    tp       (time) float32 dask.array<chunksize=(72,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    standard_name:  sea_surface_wave_significant_height\n",
       "    units:          m"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wavespectra_datasets[spotter_ids[0]].spec.stats(['hs','tp'])"
   ]
  }
 ],
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