.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_d13C_comp.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_d13C_comp.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_d13C_comp.py:


=======================================================
Search the nearest grid point for a given location
=======================================================

This example is a combination of ScatterData and GriddedData to search the nearest grid point for a given location. I use CESM model output(You can download them from https://zenodo.org/records/13786013) and LGM d13C data from Peterson et al. 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013PA002552) as an example.

.. GENERATED FROM PYTHON SOURCE LINES 8-48



.. image-sg:: /auto_examples/images/sphx_glr_plot_d13C_comp_001.png
   :alt: Model vs Observation
   :srcset: /auto_examples/images/sphx_glr_plot_d13C_comp_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Downloading CESM_LGM_var_regrid.nc to /home/docs/.cgeniepy/CESM_LGM_var_regrid.nc...
    Download complete.
    File LGM_d13c_CLEAN.txt already exists at /home/docs/.cgeniepy/LGM_d13c_CLEAN.txt. Skipping download.






|

.. code-block:: Python


    from cgeniepy.table import ScatterData
    from cgeniepy.array import GriddedData
    import xarray as xr
    from cgeniepy.utils import download_zenodo_file

    record_id = "13786013"
    filename = "CESM_LGM_var_regrid.nc"
    local_file_path = download_zenodo_file(record_id, filename)

    ## read in the data and construct GriddedData object
    cesm_lgm = xr.load_dataset(local_file_path)
    cesm_13C = GriddedData(cesm_lgm['CISO_DIC_d13C'], attrs=cesm_lgm['CISO_DIC_d13C'].attrs)
    cesm_13C_last = cesm_13C.isel(time=-1)

    ## read in the proxy data and construct ScatterData object
    filename = "LGM_d13c_CLEAN.txt"
    local_file_path = download_zenodo_file(record_id, filename)

    proxy_d13C = ScatterData(local_file_path, delimiter="\t", header=None)
    proxy_d13C.data.columns = ["Longitude", "Latitude", "Depth", "observational d13C","Event"]
    proxy_d13C.set_index(["Latitude", "Longitude", "Depth"])

    model_data = []

    for i in proxy_d13C.data.index:
        lat, lon, depth = i
        depth = depth * 100 ## not necessary for cGENIE whose depth is in m
        pos = (depth, lat, lon)
    
        data = cesm_13C_last.search_point(pos, ignore_na=True)

        model_data.append(data)

    ## add the model data to the dataframe
    proxy_d13C.data["CESM_d13C"] = model_data

    ## plot the comparison
    ## by default, model data is in the col, and observational col is in the second
    proxy_d13C.compare("CESM_d13C","observational d13C").plot()


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

   **Total running time of the script:** (1 minutes 34.093 seconds)


.. _sphx_glr_download_auto_examples_plot_d13C_comp.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_d13C_comp.ipynb <plot_d13C_comp.ipynb>`

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

      :download:`Download Python source code: plot_d13C_comp.py <plot_d13C_comp.py>`

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

      :download:`Download zipped: plot_d13C_comp.zip <plot_d13C_comp.zip>`


.. only:: html

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

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