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

.. only:: html

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

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

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

.. _sphx_glr_gallery_scalar_data_wrapping_global.py:


Adding a cyclic point to help with wrapping of global data
==========================================================

Cartopy represents data in Cartesian projected coordinates, meaning that 350
degrees longitude, is not just 10 degrees away from 0 degrees as it is when
represented in spherical coordinates. This means that the plotting methods will
not plot data between the last and the first longitude.

To help with this, the data and longitude/latitude coordinate arrays can be
expanded with a cyclic point to close this gap. The routine
`~cartopy.util.add_cyclic` repeats the last data column. It can also add the
first longitude plus the cyclic keyword (defaults to 360) to the end of the
longitude array so that the data values at the ending longitudes will be closed
to the wrap point.

.. GENERATED FROM PYTHON SOURCE LINES 18-94



.. image-sg:: /gallery/scalar_data/images/sphx_glr_wrapping_global_001.png
   :alt: 1d longitudes, central longitude=180, 1d longitudes, central longitude=0, Cyclic 1d longitudes, central longitude=180, Cyclic 2d longitudes, central longitude=0
   :srcset: /gallery/scalar_data/images/sphx_glr_wrapping_global_001.png
   :class: sphx-glr-single-img





.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np

    import cartopy.crs as ccrs
    import cartopy.util as cutil


    def main():

        # data with longitude centers from 0 to 360
        nlon = 24
        nlat = 12
        # 7.5, 22.5, ..., 337.5, 352.5
        dlon = 360//nlon
        lon = np.linspace(dlon/2., 360.-dlon/2., nlon)
    # -82.5, -67.5, ..., 67.5, 82.5
        dlat = 180//nlat
        lat = np.linspace(-90.+dlat/2., 90.-dlat/2., nlat)
        # 0, 1, ..., 10, 11, 11, 10, ..., 1, 0
        data = np.concatenate((np.arange(nlon // 2),
                               np.arange(nlon // 2)[::-1]))
        data = np.tile(data, nlat).reshape((nlat, nlon))

        fig = plt.figure()

        # plot with central longitude 180
        ax1 = fig.add_subplot(2, 2, 1,
                              projection=ccrs.Robinson(central_longitude=180))
        ax1.set_title("1d longitudes, central longitude=180",
                      fontsize='small')
        ax1.set_global()
        ax1.contourf(lon, lat, data,
                     transform=ccrs.PlateCarree(), cmap='RdBu')
        ax1.coastlines()

        # plot with central longitude 0
        ax2 = fig.add_subplot(2, 2, 2,
                              projection=ccrs.Robinson(central_longitude=0))
        ax2.set_title("1d longitudes, central longitude=0",
                      fontsize='small')
        ax2.set_global()
        ax2.contourf(lon, lat, data,
                     transform=ccrs.PlateCarree(), cmap='RdBu')
        ax2.coastlines()

        # add cyclic points to data and longitudes
        # latitudes are unchanged in 1-dimension
        cdata, clon, clat = cutil.add_cyclic(data, lon, lat)
        ax3 = fig.add_subplot(2, 2, 3,
                              projection=ccrs.Robinson(central_longitude=180))
        ax3.set_title("Cyclic 1d longitudes, central longitude=180",
                      fontsize='small')
        ax3.set_global()
        ax3.contourf(clon, clat, cdata,
                     transform=ccrs.PlateCarree(), cmap='RdBu')
        ax3.coastlines()

        # add_cyclic also works with 2-dimensional data
        # Cyclic points are added to data, longitudes, and latitudes to
        # ensure the dimensions of the returned arrays are all the same shape.
        lon2d, lat2d = np.meshgrid(lon, lat)
        cdata, clon2d, clat2d = cutil.add_cyclic(data, lon2d, lat2d)
        ax4 = fig.add_subplot(2, 2, 4,
                              projection=ccrs.Robinson(central_longitude=0))
        ax4.set_title("Cyclic 2d longitudes, central longitude=0",
                      fontsize='small')
        ax4.set_global()
        ax4.contourf(clon2d, clat2d, cdata,
                     transform=ccrs.PlateCarree(), cmap='RdBu')
        ax4.coastlines()

        plt.show()


    if __name__ == '__main__':
        main()


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

   **Total running time of the script:** (0 minutes 0.620 seconds)


.. _sphx_glr_download_gallery_scalar_data_wrapping_global.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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