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

.. only:: html

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

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

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

.. _sphx_glr_gallery_scipygridder.py:


Gridding with Scipy
===================

Scipy offers a range of interpolation methods in :mod:`scipy.interpolate` and 3
specifically for 2D data (linear, nearest neighbors, and bicubic). Verde offers
an interface for these 3 scipy interpolators in :class:`verde.ScipyGridder`.

All of these interpolations work on Cartesian data, so if we want to grid
geographic data (like our Baja California bathymetry) we need to project them
into a Cartesian system. We'll use `pyproj
<https://github.com/jswhit/pyproj>`__ to calculate a Mercator projection for
the data.

For convenience, Verde still allows us to make geographic grids by passing the
``projection`` argument to :meth:`verde.ScipyGridder.grid` and the like. When
doing so, the grid will be generated using geographic coordinates which will be
projected prior to interpolation.

.. GENERATED FROM PYTHON SOURCE LINES 26-94



.. image-sg:: /gallery/images/sphx_glr_scipygridder_001.png
   :alt: scipygridder
   :srcset: /gallery/images/sphx_glr_scipygridder_001.png
   :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    Gridder used: ScipyGridder()
    Data region: (245.0, 254.705, 20.0, 29.99131)
    /usr/share/miniconda3/envs/test/lib/python3.9/site-packages/verde/base/base_classes.py:463: FutureWarning: The 'spacing', 'shape' and 'region' arguments will be removed in Verde v2.0.0. Please use the 'verde.grid_coordinates' function to define grid coordinates and pass them as the 'coordinates' argument.
      warnings.warn(
    Generated geographic grid:
    <xarray.Dataset>
    Dimensions:       (latitude: 121, longitude: 117)
    Coordinates:
      * longitude     (longitude) float64 245.0 245.1 245.2 ... 254.5 254.6 254.7
      * latitude      (latitude) float64 20.0 20.08 20.17 ... 29.82 29.91 29.99
    Data variables:
        bathymetry_m  (latitude, longitude) float64 nan nan nan nan ... nan nan nan
    Attributes:
        metadata:  Generated by ScipyGridder()






|

.. code-block:: default

    import cartopy.crs as ccrs
    import matplotlib.pyplot as plt
    import numpy as np
    import pyproj

    import verde as vd

    # We'll test this on the Baja California shipborne bathymetry data
    data = vd.datasets.fetch_baja_bathymetry()

    # Before gridding, we need to decimate the data to avoid aliasing because of
    # the oversampling along the ship tracks. We'll use a blocked median with 5
    # arc-minute blocks.
    spacing = 5 / 60
    reducer = vd.BlockReduce(reduction=np.median, spacing=spacing)
    coordinates, bathymetry = reducer.filter(
        (data.longitude, data.latitude), data.bathymetry_m
    )

    # Project the data using pyproj so that we can use it as input for the gridder.
    # We'll set the latitude of true scale to the mean latitude of the data.
    projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())
    proj_coordinates = projection(*coordinates)

    # Now we can set up a gridder for the decimated data
    grd = vd.ScipyGridder(method="cubic").fit(proj_coordinates, bathymetry)
    print("Gridder used:", grd)

    # Get the grid region in geographic coordinates
    region = vd.get_region((data.longitude, data.latitude))
    print("Data region:", region)

    # The 'grid' method can still make a geographic grid if we pass in a projection
    # function that converts lon, lat into the easting, northing coordinates that
    # we used in 'fit'. This can be any function that takes lon, lat and returns x,
    # y. In our case, it'll be the 'projection' variable that we created above.
    # We'll also set the names of the grid dimensions and the name the data
    # variable in our grid (the default would be 'scalars', which isn't very
    # informative).
    grid = grd.grid(
        region=region,
        spacing=spacing,
        projection=projection,
        dims=["latitude", "longitude"],
        data_names="bathymetry_m",
    )
    print("Generated geographic grid:")
    print(grid)

    # Cartopy requires setting the coordinate reference system (CRS) of the
    # original data through the transform argument. Their docs say to use
    # PlateCarree to represent geographic data.
    crs = ccrs.PlateCarree()

    plt.figure(figsize=(7, 6))
    # Make a Mercator map of our gridded bathymetry
    ax = plt.axes(projection=ccrs.Mercator())
    ax.set_title("Gridded Bathymetry Using Scipy")
    # Plot the gridded bathymetry
    pc = grid.bathymetry_m.plot.pcolormesh(
        ax=ax, transform=crs, vmax=0, zorder=-1, add_colorbar=False
    )
    plt.colorbar(pc).set_label("meters")
    # Plot the locations of the decimated data
    ax.plot(*coordinates, ".k", markersize=0.5, transform=crs)
    # Use an utility function to setup the tick labels and the land feature
    vd.datasets.setup_baja_bathymetry_map(ax)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.622 seconds)


.. _sphx_glr_download_gallery_scipygridder.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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