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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_gallery_caribbean-bathymetry.py>`
        to download the full example code

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

.. _sphx_glr_gallery_caribbean-bathymetry.py:


Bathymetry single-beam surveys of the Caribbean
-----------------------------------------------

This dataset is a compilation of several public domain single-beam bathymetry
surveys of the ocean in the Caribbean. The data display a wide range of
tectonic activity, uneven distribution, and even clear systematic errors in
some of the survey lines.

**Original source:** `NOAA NCEI
<https://ngdc.noaa.gov/mgg/geodas/trackline.html>`__

.. GENERATED FROM PYTHON SOURCE LINES 20-25

.. code-block:: default

    import pandas as pd
    import pygmt

    import ensaio








.. GENERATED FROM PYTHON SOURCE LINES 26-27

Download and cache the data and return the path to it on disk

.. GENERATED FROM PYTHON SOURCE LINES 27-30

.. code-block:: default

    fname = ensaio.fetch_caribbean_bathymetry(version=1)
    print(fname)





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

 Out:

 .. code-block:: none

    /home/runner/work/_temp/cache/ensaio/v1/caribbean-bathymetry.csv.xz




.. GENERATED FROM PYTHON SOURCE LINES 31-32

Load the CSV formatted data with pandas

.. GENERATED FROM PYTHON SOURCE LINES 32-35

.. code-block:: default

    data = pd.read_csv(fname)
    data






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>survey_id</th>
          <th>latitude</th>
          <th>longitude</th>
          <th>depth_m</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>FM0501</td>
          <td>23.13070</td>
          <td>-87.99680</td>
          <td>75</td>
        </tr>
        <tr>
          <th>1</th>
          <td>FM0501</td>
          <td>23.11940</td>
          <td>-87.98640</td>
          <td>75</td>
        </tr>
        <tr>
          <th>2</th>
          <td>FM0501</td>
          <td>23.10810</td>
          <td>-87.97610</td>
          <td>73</td>
        </tr>
        <tr>
          <th>3</th>
          <td>FM0501</td>
          <td>23.09670</td>
          <td>-87.96580</td>
          <td>73</td>
        </tr>
        <tr>
          <th>4</th>
          <td>FM0501</td>
          <td>23.08540</td>
          <td>-87.95540</td>
          <td>73</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>1938090</th>
          <td>EW0003</td>
          <td>9.92284</td>
          <td>-84.72557</td>
          <td>20</td>
        </tr>
        <tr>
          <th>1938091</th>
          <td>EW0003</td>
          <td>9.92282</td>
          <td>-84.72565</td>
          <td>20</td>
        </tr>
        <tr>
          <th>1938092</th>
          <td>EW0003</td>
          <td>9.92283</td>
          <td>-84.72570</td>
          <td>20</td>
        </tr>
        <tr>
          <th>1938093</th>
          <td>EW0003</td>
          <td>9.92284</td>
          <td>-84.72574</td>
          <td>21</td>
        </tr>
        <tr>
          <th>1938094</th>
          <td>EW0003</td>
          <td>9.92286</td>
          <td>-84.72576</td>
          <td>20</td>
        </tr>
      </tbody>
    </table>
    <p>1938095 rows × 4 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 36-37

Make a PyGMT map with the data points colored by the depth.

.. GENERATED FROM PYTHON SOURCE LINES 37-55

.. code-block:: default

    fig = pygmt.Figure()
    fig.basemap(
        region=[
            data.longitude.min(),
            data.longitude.max(),
            data.latitude.min(),
            data.latitude.max(),
        ],
        projection="M15c",
        frame=True,
    )
    pygmt.makecpt(cmap="viridis", series=[data.depth_m.min(), data.depth_m.max()])
    fig.plot(
        x=data.longitude, y=data.latitude, color=data.depth_m, cmap=True, style="c0.02c"
    )
    fig.colorbar(frame='af+l"bathymetric depth [m]"')
    fig.coast(land="#666666")
    fig.show()



.. image-sg:: /gallery/images/sphx_glr_caribbean-bathymetry_001.png
   :alt: caribbean bathymetry
   :srcset: /gallery/images/sphx_glr_caribbean-bathymetry_001.png
   :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    <IPython.core.display.Image object>





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

   **Total running time of the script:** ( 0 minutes  55.271 seconds)


.. _sphx_glr_download_gallery_caribbean-bathymetry.py:


.. only :: html

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



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

     :download:`Download Python source code: caribbean-bathymetry.py <caribbean-bathymetry.py>`



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

     :download:`Download Jupyter notebook: caribbean-bathymetry.ipynb <caribbean-bathymetry.ipynb>`


.. only:: html

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

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