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

.. only:: html

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

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

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

.. _sphx_glr_gallery_lines_and_polygons_ocean_bathymetry.py:


Ocean bathymetry
----------------

Produces a map of ocean seafloor depth, demonstrating the
:class:`cartopy.io.shapereader.Reader` interface. The data is a series
of 10m resolution nested polygons obtained from Natural Earth, derived
from the NASA SRTM Plus product. Since the dataset contains a zipfile with
multiple shapefiles representing different depths, the example demonstrates
manually downloading and reading them with the general shapereader interface,
instead of the specialized `cartopy.feature.NaturalEarthFeature` interface.

.. GENERATED FROM PYTHON SOURCE LINES 14-98



.. image-sg:: /gallery/lines_and_polygons/images/sphx_glr_ocean_bathymetry_001.png
   :alt: ocean bathymetry
   :srcset: /gallery/lines_and_polygons/images/sphx_glr_ocean_bathymetry_001.png
   :class: sphx-glr-single-img





.. code-block:: Python

    from glob import glob

    import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np

    import cartopy.crs as ccrs
    import cartopy.feature as cfeature
    import cartopy.io.shapereader as shpreader


    def load_bathymetry(zip_file_url):
        """Read zip file from Natural Earth containing bathymetry shapefiles"""
        # Download and extract shapefiles
        import io
        import zipfile

        import requests
        r = requests.get(zip_file_url)
        z = zipfile.ZipFile(io.BytesIO(r.content))
        z.extractall("ne_10m_bathymetry_all/")

        # Read shapefiles, sorted by depth
        shp_dict = {}
        files = glob('ne_10m_bathymetry_all/*.shp')
        assert len(files) > 0
        files.sort()
        depths = []
        for f in files:
            depth = '-' + f.split('_')[-1].split('.')[0]  # depth from file name
            depths.append(depth)
            bbox = (90, -15, 160, 60)  # (x0, y0, x1, y1)
            nei = shpreader.Reader(f, bbox=bbox)
            shp_dict[depth] = nei
        depths = np.array(depths)[::-1]  # sort from surface to bottom
        return depths, shp_dict


    if __name__ == "__main__":
        # Load data (14.8 MB file)
        depths_str, shp_dict = load_bathymetry(
            'https://naturalearth.s3.amazonaws.com/' +
            '10m_physical/ne_10m_bathymetry_all.zip')

        # Construct a discrete colormap with colors corresponding to each depth
        depths = depths_str.astype(int)
        N = len(depths)
        nudge = 0.01  # shift bin edge slightly to include data
        boundaries = [min(depths)] + sorted(depths+nudge)  # low to high
        norm = matplotlib.colors.BoundaryNorm(boundaries, N)
        blues_cm = matplotlib.colormaps['Blues_r'].resampled(N)
        colors_depths = blues_cm(norm(depths))

        # Set up plot
        subplot_kw = {'projection': ccrs.LambertCylindrical()}
        fig, ax = plt.subplots(subplot_kw=subplot_kw, figsize=(9, 7))
        ax.set_extent([90, 160, -15, 60], crs=ccrs.PlateCarree())  # x0, x1, y0, y1

        # Iterate and plot feature for each depth level
        for i, depth_str in enumerate(depths_str):
            ax.add_geometries(shp_dict[depth_str].geometries(),
                              crs=ccrs.PlateCarree(),
                              color=colors_depths[i])

        # Add standard features
        ax.add_feature(cfeature.LAND, color='grey')
        ax.coastlines(lw=1, resolution='110m')
        ax.gridlines(draw_labels=False)
        ax.set_position([0.03, 0.05, 0.8, 0.9])

        # Add custom colorbar
        axi = fig.add_axes([0.85, 0.1, 0.025, 0.8])
        ax.add_feature(cfeature.BORDERS, linestyle=':')
        sm = plt.cm.ScalarMappable(cmap=blues_cm, norm=norm)
        fig.colorbar(mappable=sm,
                     cax=axi,
                     spacing='proportional',
                     extend='min',
                     ticks=depths,
                     label='Depth (m)')

        # Convert vector bathymetries to raster (saves a lot of disk space)
        # while leaving labels as vectors
        ax.set_rasterized(True)


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

   **Total running time of the script:** (0 minutes 19.897 seconds)


.. _sphx_glr_download_gallery_lines_and_polygons_ocean_bathymetry.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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