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

.. only:: html

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

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

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

.. _sphx_glr_tutorials_decimation.py:


Data Decimation
===============

Often times, raw spatial data can be highly oversampled in a direction. In
these cases, we need to decimate the data before interpolation to avoid
aliasing effects.

.. GENERATED FROM PYTHON SOURCE LINES 15-21

.. code-block:: default

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

    import verde as vd








.. GENERATED FROM PYTHON SOURCE LINES 22-24

For example, our sample shipborne bathymetry data has a higher sampling
frequency along the tracks than between tracks:

.. GENERATED FROM PYTHON SOURCE LINES 24-38

.. code-block:: default


    # Load the data as a pandas.DataFrame
    data = vd.datasets.fetch_baja_bathymetry()

    # Plot it using matplotlib and Cartopy
    crs = ccrs.PlateCarree()
    plt.figure(figsize=(7, 7))
    ax = plt.axes(projection=ccrs.Mercator())
    ax.set_title("Locations of bathymetry measurements from Baja California")
    # Plot the bathymetry data locations as black dots
    plt.plot(data.longitude, data.latitude, ".k", markersize=1, transform=crs)
    vd.datasets.setup_baja_bathymetry_map(ax)
    plt.show()




.. image-sg:: /tutorials/images/sphx_glr_decimation_001.png
   :alt: Locations of bathymetry measurements from Baja California
   :srcset: /tutorials/images/sphx_glr_decimation_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 39-53

Class :class:`verde.BlockReduce` can be used to apply a reduction/aggregation
operation (mean, median, standard deviation, etc) to the data in regular
blocks. All data inside each block will be replaced by their aggregated
value. :class:`~verde.BlockReduce` takes an aggregation function as input. It
can be any function that receives a numpy array as input and returns a single
scalar value. The :func:`numpy.mean` or :func:`numpy.median` functions are
usually what we want.

Blocked means and medians are good ways to decimate data for interpolation.
Let's use a blocked median on our data to decimate it to our desired grid
interval of 5 arc-minutes. The reason for using a median over a mean is
because bathymetry data can vary abruptly and a mean would smooth the data
too much. For data varies more smoothly (like gravity and magnetic data), a
mean would be a better option.

.. GENERATED FROM PYTHON SOURCE LINES 53-56

.. code-block:: default

    reducer = vd.BlockReduce(reduction=np.median, spacing=5 / 60)
    print(reducer)





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

 Out:

 .. code-block:: none

    BlockReduce(reduction=<function median at 0x7f626a5a8550>,
                spacing=0.08333333333333333)




.. GENERATED FROM PYTHON SOURCE LINES 57-58

Use the :meth:`~verde.BlockReduce.filter` method to apply the reduction:

.. GENERATED FROM PYTHON SOURCE LINES 58-71

.. code-block:: default

    coordinates, bathymetry = reducer.filter(
        coordinates=(data.longitude, data.latitude), data=data.bathymetry_m
    )

    plt.figure(figsize=(7, 7))
    ax = plt.axes(projection=ccrs.Mercator())
    ax.set_title("Locations of decimated data")
    # Plot the bathymetry data locations as black dots
    plt.plot(*coordinates, ".k", markersize=1, transform=crs)
    vd.datasets.setup_baja_bathymetry_map(ax)
    plt.show()





.. image-sg:: /tutorials/images/sphx_glr_decimation_002.png
   :alt: Locations of decimated data
   :srcset: /tutorials/images/sphx_glr_decimation_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 72-76

By default, the coordinates of the decimated data are obtained by applying
the same reduction operation to the coordinates of the original data.
Alternatively, we can tell :class:`~verde.BlockReduce` to return the
coordinates of the center of each block:

.. GENERATED FROM PYTHON SOURCE LINES 76-91

.. code-block:: default

    reducer_center = vd.BlockReduce(
        reduction=np.median, spacing=5 / 60, center_coordinates=True
    )
    coordinates_center, bathymetry = reducer_center.filter(
        coordinates=(data.longitude, data.latitude), data=data.bathymetry_m
    )

    plt.figure(figsize=(7, 7))
    ax = plt.axes(projection=ccrs.Mercator())
    ax.set_title("Locations of decimated data using block centers")
    # Plot the bathymetry data locations as black dots
    plt.plot(*coordinates_center, ".k", markersize=1, transform=crs)
    vd.datasets.setup_baja_bathymetry_map(ax)
    plt.show()




.. image-sg:: /tutorials/images/sphx_glr_decimation_003.png
   :alt: Locations of decimated data using block centers
   :srcset: /tutorials/images/sphx_glr_decimation_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 92-93

Now the data are ready for interpolation.


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

   **Total running time of the script:** ( 0 minutes  3.833 seconds)


.. _sphx_glr_download_tutorials_decimation.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: decimation.py <decimation.py>`



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

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


.. only:: html

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

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