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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_gallery_scalar_data_contour_transforms.py>`
        to download the full example code

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

.. _sphx_glr_gallery_scalar_data_contour_transforms.py:


Contour transform options
=========================

This example demonstrates the difference between transforming
the points before/after generating the contours. It uses the
**transform_first** keyword argument to indicate that Cartopy should
transform the points before calling the contouring algorithm,
which can have a significant impact on speed (it is much faster
to transform points than it is to transform patches). This does
have a negative impact on the wrapped coordinates as one can see in the
second axes that the data does not extend to the full global extent.

.. GENERATED FROM PYTHON SOURCE LINES 14-69



.. image-sg:: /gallery/scalar_data/images/sphx_glr_contour_transforms_001.png
   :alt: transform_first=False, transform_first=True
   :srcset: /gallery/scalar_data/images/sphx_glr_contour_transforms_001.png
   :class: sphx-glr-single-img





.. code-block:: default

    import cartopy.crs as ccrs
    import matplotlib.pyplot as plt


    def sample_data(shape=(73, 145)):
        """Return ``lons``, ``lats`` and ``data`` of some fake data."""
        import numpy as np

        nlats, nlons = shape
        lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)
        lons = np.linspace(0, 2 * np.pi, nlons)
        lons, lats = np.meshgrid(lons, lats)
        wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)
        mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)

        lats = np.rad2deg(lats)
        lons = np.rad2deg(lons)
        data = wave + mean

        return lons, lats, data


    def main():

        # Use the same sample data as the waves example, but make it
        # more dependent on y for more interesting contours.
        x, y, z = sample_data((20, 40))
        z = z * -1.5 * y

        # Setup a global EckertIII map with faint coastlines.
        fig = plt.figure()
        ax1 = fig.add_subplot(2, 1, 1, projection=ccrs.EckertIII())
        ax1.set_title("transform_first=False")
        ax2 = fig.add_subplot(2, 1, 2, projection=ccrs.EckertIII())
        ax2.set_title("transform_first=True")

        for ax, transform_first in zip([ax1, ax2], [False, True]):
            ax.set_global()
            ax.coastlines('110m', alpha=0.1)

            # Add colourful filled contours.
            filled_c = ax.contourf(x, y, z, transform=ccrs.PlateCarree(),
                                   transform_first=transform_first)

            # And black line contours.
            ax.contour(x, y, z, levels=filled_c.levels,
                       colors=['black'],
                       transform=ccrs.PlateCarree(),
                       transform_first=transform_first)

        plt.show()


    if __name__ == '__main__':
        main()


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

   **Total running time of the script:** ( 0 minutes  0.535 seconds)


.. _sphx_glr_download_gallery_scalar_data_contour_transforms.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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