.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/Masking/NCL_mask_5.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_gallery_Masking_NCL_mask_5.py>`
        to download the full example code.

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

.. _sphx_glr_gallery_Masking_NCL_mask_5.py:


NCL_mask_5.py
=============
This script illustrates the following concepts:
    - Using cartopy.feature options to display map information
    - Paneling two plots on a page with 'subplots' command
    - Drawing contours
    - Drawing contours over land only
    - Using draw order resources to mask areas in a plot
    - Adding a color bar
    - Following best practices when choosing a colormap.
      More information on colormap best practices can be found `here <https://geocat-examples.readthedocs.io/en/latest/gallery/Colors/CB_Temperature.html#sphx-glr-gallery-colors-cb-temperature-py>`_.

See following URLs to see the reproduced NCL plot & script:
    - Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/mask_5.ncl
    - Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/mask_5_lg.png

.. GENERATED FROM PYTHON SOURCE LINES 21-22

Import packages:

.. GENERATED FROM PYTHON SOURCE LINES 22-33

.. code-block:: Python


    import numpy as np
    import xarray as xr
    import cartopy.crs as ccrs
    import matplotlib.pyplot as plt
    import cartopy.feature as cfeature
    import matplotlib.patches as mpatches

    import geocat.viz as gv
    import geocat.datafiles as gdf








.. GENERATED FROM PYTHON SOURCE LINES 34-35

Read in Data

.. GENERATED FROM PYTHON SOURCE LINES 35-43

.. code-block:: Python


    # Open a netCDF data file using xarray default engine and load the data into xarrays
    ds = xr.open_dataset(gdf.get("netcdf_files/atmos.nc"), decode_times=False)
    t = ds.TS.isel(time=0)

    # Fix the artifact of not-shown-data around 0 and 360-degree longitudes
    wrap_t = gv.xr_add_cyclic_longitudes(t, "lon")








.. GENERATED FROM PYTHON SOURCE LINES 44-45

Plot:

.. GENERATED FROM PYTHON SOURCE LINES 45-128

.. code-block:: Python


    fig = plt.figure(figsize=(10, 15))

    # Plot first plot
    # Generate axes using Cartopy, draw coastlines, and add other map features
    ax = plt.subplot(2, 1, 1, projection=ccrs.PlateCarree())
    ax.coastlines(linewidths=0.5)
    ax.add_feature(cfeature.LAND, color="green")
    ax.add_feature(cfeature.LAKES, color="plum")
    ax.add_feature(cfeature.OCEAN, color="blue")

    # Cartopy does not currently have a feature that separates island land from
    # main land. There is also no feature to add ice shelf data to a projection.
    # This addition would require another data set to specify area encompassed
    # by an ice shelf in a region.

    # Create label names and define colors for the legend
    land = mpatches.Rectangle((0, 0), 1, 1, facecolor="green")
    lakes = mpatches.Rectangle((0, 0), 1, 1, facecolor="plum")
    ocean = mpatches.Rectangle((0, 0), 1, 1, facecolor="blue")

    labels = ['Land ', 'Lakes', 'Ocean']

    # Add a legend to plot
    plt.legend(
        [land, lakes, ocean], labels, loc='lower center', bbox_to_anchor=(0.5, -0.2), ncol=3
    )

    # Use geocat.viz.util convenience function to set titles and labels without
    # calling several matplotlib functions
    gv.set_titles_and_labels(
        ax, maintitle="land sea mask using 'atmos.nc'", maintitlefontsize=14
    )

    # Plot second plot
    ax1 = plt.subplot(2, 1, 2, projection=ccrs.PlateCarree())
    ax1.set_extent([-180, 180, -90, 90], ccrs.PlateCarree())
    ax1.coastlines(linewidths=0.5)

    plt.suptitle("dummy TS field (ocean-masked)", x=0.5, y=0.5, fontsize=14)

    # Contourf-plot data
    contour = wrap_t.plot.contourf(
        ax=ax1,
        transform=ccrs.PlateCarree(),
        vmin=235,
        vmax=315,
        levels=17,
        cmap='magma',
        add_colorbar=False,
    )

    # Add colorbar to bottom of plot
    cbar = plt.colorbar(
        contour,
        ax=ax1,
        orientation='horizontal',
        shrink=0.75,
        pad=0.11,
        extendrect=True,
        extendfrac='auto',
        use_gridspec=False,
        ticks=np.arange(235, 315, 5),
    )

    cbar.ax.tick_params(labelsize=10)

    # Mask ocean data by changing adding ocean feature and changing its zorder
    ax1.add_feature(cfeature.OCEAN, zorder=10, edgecolor='k')

    # Use geocat.viz.util convenience function to set titles and labels without
    # calling several matplotlib functions
    gv.set_titles_and_labels(
        ax1,
        maintitle="",
        maintitlefontsize=14,
        righttitle="degK",
        righttitlefontsize=14,
        lefttitle="temperature",
        lefttitlefontsize=14,
    )

    plt.show()



.. image-sg:: /gallery/Masking/images/sphx_glr_NCL_mask_5_001.png
   :alt: dummy TS field (ocean-masked), land sea mask using 'atmos.nc', temperature, degK
   :srcset: /gallery/Masking/images/sphx_glr_NCL_mask_5_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.366 seconds)


.. _sphx_glr_download_gallery_Masking_NCL_mask_5.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: NCL_mask_5.ipynb <NCL_mask_5.ipynb>`

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

      :download:`Download Python source code: NCL_mask_5.py <NCL_mask_5.py>`

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

      :download:`Download zipped: NCL_mask_5.zip <NCL_mask_5.zip>`


.. only:: html

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

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