.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/example_contiguous_ragged.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_gallery_example_contiguous_ragged.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_example_contiguous_ragged.py:


Reading and using a contiguous ragged dataset
================================================

.. GENERATED FROM PYTHON SOURCE LINES 7-14

.. code-block:: Python


    import matplotlib.pyplot as plt
    from pathlib import Path
    import xarray as xr
    import trajan
    import coloredlogs








.. GENERATED FROM PYTHON SOURCE LINES 15-24

.. code-block:: Python


    # the test data: a small extract from the Sofar dataset https://sofar-spotter-archive.s3.amazonaws.com/index.html
    path_to_test_data = Path.cwd().parent / "tests" / "test_data" / "xr_spotter_bulk_test_data.nc"

    xr_data = xr.load_dataset(path_to_test_data)

    # this is a contiguous ragged dataset; the data variables are 1D arrays
    xr_data






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    </style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB
    Dimensions:                (index: 65, trajectory: 2)
    Coordinates:
        time                   (index) datetime64[ns] 520B 2022-03-15T04:07:25 .....
      * trajectory             (trajectory) &lt;U11 88B &#x27;SPOT-010102&#x27; &#x27;SPOT-010103&#x27;
    Dimensions without coordinates: index
    Data variables:
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        peakPeriod             (index) float64 520B 7.877 8.533 ... 7.314 7.314
        meanPeriod             (index) float64 520B 6.804 6.652 ... 6.629 6.497
        peakDirection          (index) float64 520B 147.9 155.5 ... 105.2 102.2
        peakDirectionalSpread  (index) float64 520B 30.37 25.48 26.04 ... 37.2 38.09
        meanDirection          (index) float64 520B 146.7 144.5 ... 127.3 120.8
        meanDirectionalSpread  (index) float64 520B 40.34 40.47 ... 50.39 51.12
        latitude               (index) float64 520B -16.41 -16.43 ... -12.64 -12.65
        longitude              (index) float64 520B 65.03 65.03 ... 70.15 70.14
        rowsize                (trajectory) int64 16B 20 45
    Attributes:
        title:          Sofar Spotter Data Archive - Bulk Wave Parameters
        institution:    Sofar Ocean
        source:         Spotter wave buoy
        creation_date:  2023-10-18 00:43:55.333537
        author:         Isabel A. Houghton
        email:          isabel.houghton@sofarocean.com
        references:     https://content.sofarocean.com/hubfs/Spotter%20product%20...
        modified:       modified to only keep a couple of buoys, to be used as a ...</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-7aef5059-e6d0-4110-b659-abc6e2a6ab08' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7aef5059-e6d0-4110-b659-abc6e2a6ab08' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>index</span>: 65</li><li><span class='xr-has-index'>trajectory</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-98f1c38c-2d6c-4978-8480-549eb212eb62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98f1c38c-2d6c-4978-8480-549eb212eb62' class='xr-section-summary' >Coordinates: <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>time</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-03-15T04:07:25 ... 2022-03-...</div><input id='attrs-309348af-45d1-4298-ad4d-c17739f48a18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-309348af-45d1-4298-ad4d-c17739f48a18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c45c84c-1f7f-4785-b0da-4dcc706026d8' class='xr-var-data-in' type='checkbox'><label for='data-9c45c84c-1f7f-4785-b0da-4dcc706026d8' title='Show/Hide data repr'><svg 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>time</dd><dt><span>long_name :</span></dt><dd>Time [UTC]</dd><dt><span>comment :</span></dt><dd>UTC time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-15T04:07:25.000000000&#x27;, &#x27;2022-03-15T05:07:25.000000000&#x27;,
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           &#x27;2022-03-15T15:04:25.000000000&#x27;, &#x27;2022-03-15T16:04:25.000000000&#x27;,
           &#x27;2022-03-15T17:04:25.000000000&#x27;, &#x27;2022-03-15T18:04:25.000000000&#x27;,
           &#x27;2022-03-15T19:04:25.000000000&#x27;, &#x27;2022-03-15T20:04:25.000000000&#x27;,
           &#x27;2022-03-15T21:04:25.000000000&#x27;, &#x27;2022-03-15T22:04:25.000000000&#x27;,
           &#x27;2022-03-15T23:04:25.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trajectory</span></div><div class='xr-var-dims'>(trajectory)</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;SPOT-010102&#x27; &#x27;SPOT-010103&#x27;</div><input id='attrs-16380361-131b-499f-823f-de4797f45566' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16380361-131b-499f-823f-de4797f45566' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a50c1b4a-829d-481d-af35-7b048703bd0d' class='xr-var-data-in' type='checkbox'><label for='data-a50c1b4a-829d-481d-af35-7b048703bd0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>trajectory_id</dd><dt><span>long_name :</span></dt><dd>Trajectory Name</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;SPOT-010102&#x27;, &#x27;SPOT-010103&#x27;], dtype=&#x27;&lt;U11&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bdaa67ec-d013-492f-a490-0c7996ca325c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bdaa67ec-d013-492f-a490-0c7996ca325c' class='xr-section-summary' >Data variables: <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>significantWaveHeight</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.736 1.817 1.851 ... 1.263 1.136</div><input id='attrs-bd1a8ad7-2195-4372-a522-b4f28e317a39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd1a8ad7-2195-4372-a522-b4f28e317a39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcf14196-87d7-4d4e-959d-cb0930c8d594' class='xr-var-data-in' type='checkbox'><label for='data-fcf14196-87d7-4d4e-959d-cb0930c8d594' title='Show/Hide data repr'><svg 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>standard_name :</span></dt><dd>significant_wave_height</dd><dt><span>long_name :</span></dt><dd>Total Significant Wave Height</dd></dl></div><div class='xr-var-data'><pre>array([1.736, 1.817, 1.851, 1.765, 1.848, 1.845, 1.972, 2.051, 2.145,
           2.182, 2.005, 1.935, 2.092, 2.001, 1.932, 1.835, 1.854, 1.952,
           1.866, 1.688, 1.116, 1.15 , 1.122, 1.15 , 1.12 , 1.122, 1.153,
           1.149, 1.148, 1.158, 1.162, 1.179, 1.169, 1.151, 1.069, 1.13 ,
           1.123, 1.178, 1.204, 1.203, 1.146, 1.127, 1.143, 1.177, 1.191,
           1.254, 1.229, 1.267, 1.284, 1.273, 1.311, 1.409, 1.314, 1.37 ,
           1.35 , 1.331, 1.273, 1.289, 1.255, 1.247, 1.27 , 1.207, 1.209,
           1.263, 1.136])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peakPeriod</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.877 8.533 8.533 ... 7.314 7.314</div><input id='attrs-1b1148a4-7b02-4e43-bb41-4f4d115206e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b1148a4-7b02-4e43-bb41-4f4d115206e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f586e368-5b68-4560-9189-3957be769e20' class='xr-var-data-in' type='checkbox'><label for='data-f586e368-5b68-4560-9189-3957be769e20' title='Show/Hide data repr'><svg 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>standard_name :</span></dt><dd>peak_period</dd><dt><span>long_name :</span></dt><dd>Peak Period</dd><dt><span>comment :</span></dt><dd>Tp</dd></dl></div><div class='xr-var-data'><pre>array([ 7.877,  8.533,  8.533,  7.877,  7.314,  7.877,  7.877, 10.24 ,
           10.24 , 10.24 ,  9.309,  9.309,  9.309,  9.309,  9.309,  9.309,
            8.533,  9.309,  8.533,  8.533, 14.628, 12.799,  7.314, 14.628,
           12.799, 12.799, 12.799, 12.799, 12.799, 12.799, 12.799, 12.799,
           12.799, 12.799, 12.799, 12.799, 12.799, 12.799, 12.799, 12.799,
            7.877, 12.799,  7.877, 12.799,  7.314,  7.877,  7.877,  7.877,
            7.877,  7.877,  7.314,  7.314,  7.314,  7.314,  7.314,  7.877,
            7.877,  7.877,  7.877,  7.314,  7.877,  7.314,  7.314,  7.314,
            7.314])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meanPeriod</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.804 6.652 6.809 ... 6.629 6.497</div><input id='attrs-9dfaf98e-695d-4603-8c17-00b4bc8c9d19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dfaf98e-695d-4603-8c17-00b4bc8c9d19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80a82af0-f59a-4e95-91d3-b4219932d27d' class='xr-var-data-in' type='checkbox'><label for='data-80a82af0-f59a-4e95-91d3-b4219932d27d' title='Show/Hide data repr'><svg 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>standard_name :</span></dt><dd>mean_period</dd><dt><span>long_name :</span></dt><dd>peak period</dd><dt><span>comment :</span></dt><dd>Tm01</dd></dl></div><div class='xr-var-data'><pre>array([6.804, 6.652, 6.809, 6.704, 6.817, 7.03 , 7.363, 7.607, 7.665,
           7.903, 7.789, 7.821, 7.624, 7.599, 7.58 , 7.3  , 7.354, 7.193,
           6.799, 6.454, 7.788, 7.82 , 7.158, 7.059, 7.232, 7.424, 7.63 ,
           7.272, 6.632, 6.237, 5.998, 6.081, 5.654, 5.854, 5.548, 5.903,
           5.754, 5.839, 5.869, 5.994, 5.957, 5.86 , 5.738, 5.696, 5.774,
           5.59 , 5.934, 5.827, 5.749, 5.645, 5.81 , 5.887, 5.854, 6.113,
           6.297, 6.207, 6.218, 6.327, 6.334, 6.323, 6.395, 6.233, 6.574,
           6.629, 6.497])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peakDirection</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>147.9 155.5 156.1 ... 105.2 102.2</div><input id='attrs-1d36bf9a-ab8b-45a8-90db-89a07d4fbf37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d36bf9a-ab8b-45a8-90db-89a07d4fbf37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38780521-5be1-4710-b9c2-416073197831' class='xr-var-data-in' type='checkbox'><label for='data-38780521-5be1-4710-b9c2-416073197831' title='Show/Hide data repr'><svg 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>degrees</dd><dt><span>standard_name :</span></dt><dd>peak_direction</dd><dt><span>long_name :</span></dt><dd>Peak Direction</dd><dt><span>comment :</span></dt><dd>Dp, measured clockwise in degrees from North and points in the direction the waves come from.</dd></dl></div><div class='xr-var-data'><pre>array([147.919, 155.52 , 156.145, 146.328, 126.87 , 134.956, 132.269,
           182.646, 179.352, 176.312, 164.351, 165.345, 160.801, 160.54 ,
           159.837, 156.94 , 150.513, 153.408, 147.949, 146.843, 210.668,
           204.762, 120.742, 208.666, 207.699, 207.046, 207.166, 207.917,
           207.744, 210.785, 208.199, 208.129, 202.83 , 207.486, 210.902,
           211.262, 208.136, 208.076, 210.719, 206.681, 122.655, 204.061,
           121.753, 205.013, 114.945, 114.011, 108.62 , 103.2  , 103.871,
           105.568, 112.036, 110.473, 103.053, 106.699, 105.194, 106.116,
            99.375, 105.319, 105.905, 104.22 ,  98.434, 116.466, 112.495,
           105.224, 102.17 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peakDirectionalSpread</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.37 25.48 26.04 ... 37.2 38.09</div><input id='attrs-5e479338-dd5d-490c-b58d-d946a1a2f2f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e479338-dd5d-490c-b58d-d946a1a2f2f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b525d523-9ae6-4e77-bede-fc41667911c8' class='xr-var-data-in' type='checkbox'><label for='data-b525d523-9ae6-4e77-bede-fc41667911c8' title='Show/Hide data repr'><svg 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>degrees</dd><dt><span>standard_name :</span></dt><dd>peak_directional_spreading</dd><dt><span>long_name :</span></dt><dd>Peak Directional Spreading</dd></dl></div><div class='xr-var-data'><pre>array([30.372, 25.476, 26.038, 28.505, 29.216, 25.002, 27.471, 21.193,
           18.081, 19.46 , 24.366, 25.591, 25.154, 20.963, 20.969, 25.505,
           23.277, 20.365, 23.035, 25.291, 24.266, 26.974, 29.841, 27.794,
           27.688, 27.393, 23.437, 29.145, 26.576, 30.171, 30.481, 27.928,
           32.518, 32.111, 37.973, 32.591, 33.491, 28.237, 29.988, 30.092,
           36.66 , 35.283, 40.178, 32.491, 31.318, 34.912, 29.951, 28.356,
           34.416, 34.771, 32.255, 29.308, 30.647, 30.612, 28.042, 30.778,
           31.787, 31.662, 39.419, 35.154, 33.107, 39.786, 36.725, 37.204,
           38.093])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meanDirection</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>146.7 144.5 144.0 ... 127.3 120.8</div><input id='attrs-a627f3bf-11df-4186-87d7-b11e4a50cb94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a627f3bf-11df-4186-87d7-b11e4a50cb94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b4d6361-1abf-4a2b-9efc-6a2209dbff66' class='xr-var-data-in' type='checkbox'><label for='data-4b4d6361-1abf-4a2b-9efc-6a2209dbff66' title='Show/Hide data repr'><svg 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>degrees</dd><dt><span>standard_name :</span></dt><dd>mean_direction</dd><dt><span>long_name :</span></dt><dd>Mean Direction</dd><dt><span>comment :</span></dt><dd>Dm, measured clockwise in degrees from North and points in the direction the waves come from.</dd></dl></div><div class='xr-var-data'><pre>array([146.718, 144.456, 143.972, 144.118, 139.933, 142.53 , 145.054,
           151.808, 152.217, 148.722, 145.461, 147.824, 142.181, 143.361,
           141.567, 143.352, 142.299, 140.563, 137.494, 133.083, 159.958,
           163.125, 156.292, 160.437, 160.777, 156.402, 161.072, 157.84 ,
           155.832, 152.543, 150.642, 151.469, 136.864, 141.906, 132.371,
           137.512, 134.479, 135.224, 129.88 , 128.635, 126.714, 125.317,
           123.422, 123.533, 117.644, 116.402, 115.866, 113.343, 115.941,
           111.505, 115.668, 114.129, 112.743, 112.283, 112.283, 114.504,
           114.371, 119.73 , 118.786, 114.571, 116.679, 119.103, 126.43 ,
           127.283, 120.801])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meanDirectionalSpread</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.34 40.47 38.86 ... 50.39 51.12</div><input id='attrs-91295d82-764d-4a6b-abf6-cf1b8470e07c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91295d82-764d-4a6b-abf6-cf1b8470e07c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c4e74d7-3650-4485-b887-61b7b8a471b1' class='xr-var-data-in' type='checkbox'><label for='data-4c4e74d7-3650-4485-b887-61b7b8a471b1' title='Show/Hide data repr'><svg 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>degrees</dd><dt><span>standard_name :</span></dt><dd>mean_directional_spreading</dd><dt><span>long_name :</span></dt><dd>Mean Directional Spreading</dd></dl></div><div class='xr-var-data'><pre>array([40.336, 40.471, 38.862, 40.356, 40.057, 40.61 , 41.533, 40.799,
           40.109, 39.521, 40.436, 41.606, 38.908, 38.739, 40.639, 41.4  ,
           39.203, 40.8  , 42.242, 42.477, 51.683, 50.613, 51.431, 49.294,
           51.063, 50.583, 51.558, 51.888, 51.998, 55.103, 54.15 , 55.069,
           54.545, 56.265, 56.719, 56.527, 55.664, 54.159, 54.165, 53.091,
           51.54 , 52.754, 52.297, 53.963, 52.288, 54.309, 52.263, 49.746,
           50.044, 47.488, 47.556, 44.634, 46.61 , 45.617, 44.975, 45.35 ,
           48.759, 47.554, 47.888, 48.848, 47.958, 49.524, 49.624, 50.386,
           51.12 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-16.41 -16.43 ... -12.64 -12.65</div><input id='attrs-eac0768f-0200-44f9-935d-1369a593fb4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eac0768f-0200-44f9-935d-1369a593fb4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c684a64-59c0-4273-897a-cf59a2782a62' class='xr-var-data-in' type='checkbox'><label for='data-3c684a64-59c0-4273-897a-cf59a2782a62' title='Show/Hide data repr'><svg 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>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-16.41417, -16.42592, -16.43607, -16.44648, -16.45737, -16.46613,
           -16.47533, -16.48528, -16.49507, -16.49905, -16.50022, -16.50192,
           -16.50268, -16.50493, -16.50728, -16.51035, -16.51498, -16.5217 ,
           -16.52765, -16.53502, -12.26875, -12.277  , -12.28927, -12.29187,
           -12.29287, -12.29653, -12.3021 , -12.30555, -12.31507, -12.3263 ,
           -12.33953, -12.35352, -12.36388, -12.37135, -12.37677, -12.38008,
           -12.38533, -12.38983, -12.39568, -12.40178, -12.40957, -12.41497,
           -12.41952, -12.4286 , -12.43642, -12.44145, -12.44872, -12.45775,
           -12.46702, -12.48093, -12.49607, -12.51042, -12.52175, -12.53167,
           -12.54167, -12.55257, -12.56385, -12.57605, -12.58838, -12.59995,
           -12.61272, -12.62548, -12.6334 , -12.64405, -12.64983])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>65.03 65.03 65.03 ... 70.15 70.14</div><input id='attrs-20fcc2e2-6b83-43d0-9cfe-6566873d4a2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20fcc2e2-6b83-43d0-9cfe-6566873d4a2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-882359e8-4f06-4620-b621-604ca1b61a0d' class='xr-var-data-in' type='checkbox'><label for='data-882359e8-4f06-4620-b621-604ca1b61a0d' title='Show/Hide data repr'><svg 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>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([65.02732, 65.02607, 65.02658, 65.02682, 65.0277 , 65.02987,
           65.03018, 65.02932, 65.03262, 65.03535, 65.0357 , 65.0345 ,
           65.03393, 65.03235, 65.02633, 65.01658, 65.005  , 64.99142,
           64.97752, 64.96347, 70.86967, 70.84977, 70.8337 , 70.81832,
           70.80372, 70.7884 , 70.77198, 70.75307, 70.72943, 70.70698,
           70.68638, 70.66672, 70.65038, 70.63328, 70.61583, 70.59903,
           70.58043, 70.56597, 70.55075, 70.5363 , 70.5181 , 70.4998 ,
           70.48538, 70.47188, 70.45518, 70.43695, 70.41753, 70.3975 ,
           70.3763 , 70.3547 , 70.33423, 70.31645, 70.29987, 70.28273,
           70.26532, 70.24922, 70.23332, 70.21892, 70.2066 , 70.19465,
           70.18573, 70.17352, 70.161  , 70.15377, 70.14275])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rowsize</span></div><div class='xr-var-dims'>(trajectory)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>20 45</div><input id='attrs-9f0855dd-d1ac-401e-8729-92bb21a2063e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f0855dd-d1ac-401e-8729-92bb21a2063e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9d3fd4b-c6ee-4136-93f0-8cc26f8eb3ea' class='xr-var-data-in' type='checkbox'><label for='data-e9d3fd4b-c6ee-4136-93f0-8cc26f8eb3ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Number of observations for each trajectory</dd><dt><span>comments :</span></dt><dd>Use to select trajectory along index dimension</dd><dt><span>sample_dimension :</span></dt><dd>trajectory</dd></dl></div><div class='xr-var-data'><pre>array([20, 45])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b047f04a-637a-4bab-9bbc-49373c9c8296' class='xr-section-summary-in' type='checkbox'  ><label for='section-b047f04a-637a-4bab-9bbc-49373c9c8296' 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>trajectory</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-fcd276b1-97ad-4379-a19f-5187e514d74e' class='xr-index-data-in' type='checkbox'/><label for='index-fcd276b1-97ad-4379-a19f-5187e514d74e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;SPOT-010102&#x27;, &#x27;SPOT-010103&#x27;], dtype=&#x27;str&#x27;, name=&#x27;trajectory&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61d6cc12-7059-4f3d-b819-a4735d6ffc6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61d6cc12-7059-4f3d-b819-a4735d6ffc6f' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Sofar Spotter Data Archive - Bulk Wave Parameters</dd><dt><span>institution :</span></dt><dd>Sofar Ocean</dd><dt><span>source :</span></dt><dd>Spotter wave buoy</dd><dt><span>creation_date :</span></dt><dd>2023-10-18 00:43:55.333537</dd><dt><span>author :</span></dt><dd>Isabel A. Houghton</dd><dt><span>email :</span></dt><dd>isabel.houghton@sofarocean.com</dd><dt><span>references :</span></dt><dd>https://content.sofarocean.com/hubfs/Spotter%20product%20documentation%20page/Sofar%20-%20Technical_Reference_Manual.pdf</dd><dt><span>modified :</span></dt><dd>modified to only keep a couple of buoys, to be used as a test data file for the trajan package</dd></dl></div></li></ul></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 25-30

.. code-block:: Python


    # trajan is able to plot etc
    xr_data.traj.plot()
    plt.show()




.. image-sg:: /gallery/images/sphx_glr_example_contiguous_ragged_001.png
   :alt: example contiguous ragged
   :srcset: /gallery/images/sphx_glr_example_contiguous_ragged_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    2026-07-01 10:58:44 runnervmmklqx trajan.accessor[2898] DEBUG Detecting trajectory dimension
    2026-07-01 10:58:44 runnervmmklqx trajan.accessor[2898] DEBUG 1D storage dataset; detected: obs_dim = 'index', timecoord = 'time', trajectory_dim = 'trajectory', rowsize
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG Setting up new plot object.
    2026-07-01 10:58:44 runnervmmklqx trajan.plot[2898] DEBUG Plotting lines
    2026-07-01 10:58:44 runnervmmklqx trajan.accessor[2898] DEBUG Detecting trajectory dimension
    2026-07-01 10:58:44 runnervmmklqx trajan.accessor[2898] DEBUG Detecting time-variable for "index"..
    2026-07-01 10:58:44 runnervmmklqx trajan.accessor[2898] DEBUG Detected obs-dim: index, detected time-variable: time.
    2026-07-01 10:58:44 runnervmmklqx trajan.accessor[2898] DEBUG Detected Ragged trajectory dataset
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.plot[2898] DEBUG Creating new figure and axes..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2026-07-01 10:58:44 runnervmmklqx trajan.traj[2898] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..




.. GENERATED FROM PYTHON SOURCE LINES 31-44

.. code-block:: Python


    # Convert ContiguousRaggedArray to RaggedArray (non-contiguous) dataset:

    # compare:
    print(f"{xr_data = }")

    # with:
    xr_data_as_ragged = xr_data.traj.to_ragged()
    print(f"{xr_data_as_ragged = }")

    # Dumping the Ragged dataset to disk:
    xr_data_as_ragged.to_netcdf("./xr_spotter_bulk_test_data_as_ragged.nc")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    xr_data = <xarray.Dataset> Size: 5kB
    Dimensions:                (index: 65, trajectory: 2)
    Coordinates:
        time                   (index) datetime64[ns] 520B 2022-03-15T04:07:25 .....
      * trajectory             (trajectory) <U11 88B 'SPOT-010102' 'SPOT-010103'
    Dimensions without coordinates: index
    Data variables:
        significantWaveHeight  (index) float64 520B 1.736 1.817 ... 1.263 1.136
        peakPeriod             (index) float64 520B 7.877 8.533 ... 7.314 7.314
        meanPeriod             (index) float64 520B 6.804 6.652 ... 6.629 6.497
        peakDirection          (index) float64 520B 147.9 155.5 ... 105.2 102.2
        peakDirectionalSpread  (index) float64 520B 30.37 25.48 26.04 ... 37.2 38.09
        meanDirection          (index) float64 520B 146.7 144.5 ... 127.3 120.8
        meanDirectionalSpread  (index) float64 520B 40.34 40.47 ... 50.39 51.12
        latitude               (index) float64 520B -16.41 -16.43 ... -12.64 -12.65
        longitude              (index) float64 520B 65.03 65.03 ... 70.15 70.14
        rowsize                (trajectory) int64 16B 20 45
    Attributes:
        title:          Sofar Spotter Data Archive - Bulk Wave Parameters
        institution:    Sofar Ocean
        source:         Spotter wave buoy
        creation_date:  2023-10-18 00:43:55.333537
        author:         Isabel A. Houghton
        email:          isabel.houghton@sofarocean.com
        references:     https://content.sofarocean.com/hubfs/Spotter%20product%20...
        modified:       modified to only keep a couple of buoys, to be used as a ...
    xr_data_as_ragged = <xarray.Dataset> Size: 7kB
    Dimensions:                (trajectory: 2, obs: 45)
    Coordinates:
      * trajectory             (trajectory) <U11 88B 'SPOT-010102' 'SPOT-010103'
    Dimensions without coordinates: obs
    Data variables:
        time                   (trajectory, obs) datetime64[ns] 720B 2022-03-15T0...
        significantWaveHeight  (trajectory, obs) float64 720B 1.736 1.817 ... 1.136
        peakPeriod             (trajectory, obs) float64 720B 7.877 8.533 ... 7.314
        meanPeriod             (trajectory, obs) float64 720B 6.804 6.652 ... 6.497
        peakDirection          (trajectory, obs) float64 720B 147.9 155.5 ... 102.2
        peakDirectionalSpread  (trajectory, obs) float64 720B 30.37 25.48 ... 38.09
        meanDirection          (trajectory, obs) float64 720B 146.7 144.5 ... 120.8
        meanDirectionalSpread  (trajectory, obs) float64 720B 40.34 40.47 ... 51.12
        lat                    (trajectory, obs) float64 720B -16.41 ... -12.65
        lon                    (trajectory, obs) float64 720B 65.03 65.03 ... 70.14
    Attributes:
        title:            Sofar Spotter Data Archive - Bulk Wave Parameters
        institution:      Sofar Ocean
        source:           Spotter wave buoy
        creation_date:    2023-10-18 00:43:55.333537
        author:           Isabel A. Houghton
        email:            isabel.houghton@sofarocean.com
        references:       https://content.sofarocean.com/hubfs/Spotter%20product%...
        modified:         modified to only keep a couple of buoys, to be used as ...
        trajan_modified:  this was initially a contiguous ragged Dataset, which w...





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.976 seconds)


.. _sphx_glr_download_gallery_example_contiguous_ragged.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: example_contiguous_ragged.ipynb <example_contiguous_ragged.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: example_contiguous_ragged.py <example_contiguous_ragged.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: example_contiguous_ragged.zip <example_contiguous_ragged.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_