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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_plot_regridded_data.py:


==========================================================
Regrid observational data and compare with cGENIE output
==========================================================

This example shows how to regrid observational data to cGENIE grids and compare with cGENIE output.

This example uses many features from the cgeniepy package, including:

#. GirdOperation (eastern to normal longitude conversion)

#. Read cGENIE output

#. Bin observational data to cGENIE grid (a coarse implementation)

#. Visualisation based on cgeniepy's customised plotting functions

#. Compare model and observational data

The GLODAPV2 data is from https://glodap.info/index.php/mapped-data-product/.

.. GENERATED FROM PYTHON SOURCE LINES 22-68



.. image-sg:: /auto_examples/images/sphx_glr_plot_regridded_data_001.png
   :alt: plot regridded data
   :srcset: /auto_examples/images/sphx_glr_plot_regridded_data_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Downloading GLODAPv2.2016b.temperature.nc to /home/docs/.cgeniepy/GLODAPv2.2016b.temperature.nc...
    Download complete.
    /home/docs/checkouts/readthedocs.org/user_builds/cgeniepy/envs/latest/lib/python3.12/site-packages/cgeniepy/model.py:58: UserWarning: No gemflag is provided, use default gemflags: [biogem]
      warnings.warn("No gemflag is provided, use default gemflags: [biogem]")
    M-score of sea surface temperature 0.8293330629170114






|

.. code-block:: Python

    import xarray as xr
    import numpy as np
    import matplotlib.pyplot as plt
    import cartopy.crs as ccrs

    import cgeniepy
    from cgeniepy.table import ScatterData
    from cgeniepy.grid import GridOperation
    from cgeniepy.array import GriddedData
    from cgeniepy.model import GenieModel
    from cgeniepy.skill import ArrComparison
    from cgeniepy.utils import download_zenodo_file


    record_id = "13786013"
    filename = "GLODAPv2.2016b.temperature.nc"
    local_file_path = download_zenodo_file(record_id, filename)

    ## load GLODAP temperature data
    glodap_temp = xr.load_dataset(local_file_path)['temperature']
    ## convert to normal longitude from eastern longitude
    glodap_temp = GridOperation().xr_e2n(glodap_temp)
    ## convert to ScatterData
    glodap_temp = ScatterData(glodap_temp.isel(depth_surface=0).to_dataframe())
    ## bin into genie coordinate and convert back to xarray
    glodap_temp = glodap_temp.to_geniebin(var='temperature').to_xarray()['temperature']

    ## This is cGENIE output
    model = cgeniepy.sample_model()
    genie_sst = model.get_var('ocn_sur_temp').isel(time=-1).normalise_longitude(method='g2n')

    ## The binned GLODAP data does not consider how land-sea mask is in cGENIE
    ## here just lightly mask the glodap data for better looking
    worlg4_mask = np.isnan(model.get_var('grid_mask').normalise_longitude(method='g2n').data)
    masked_glodap_temp = glodap_temp.where(~worlg4_mask)
    masked_glodap_temp = GriddedData(masked_glodap_temp, attrs=glodap_temp.attrs)
    masked_glodap_temp.attrs['long_name'] = 'GLODAPv2.2016b temperature'
    masked_glodap_temp.attrs['units'] = 'deg C'

    ## plot both data
    fig, axs = plt.subplots(1,2,subplot_kw={"projection": ccrs.Mollweide()})
    masked_glodap_temp.plot(ax=axs[0], outline=True, colorbar=True)
    genie_sst.plot(ax=axs[1], outline=True, colorbar=True)

    ## calculate the skill score
    print("M-score of sea surface temperature",ArrComparison(glodap_temp.values, genie_sst.data.values).mscore())


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

   **Total running time of the script:** (0 minutes 8.114 seconds)


.. _sphx_glr_download_auto_examples_plot_regridded_data.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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