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

.. only:: html

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

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

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

.. _sphx_glr_gallery_blockreduce_weights.py:


Using weights in blocked reduction
==================================

Sometimes data has outliers or less reliable points that might skew a blocked
mean or even a median. If the reduction function can take a ``weights``
argument, like ``numpy.average``, you can pass in weights to
:class:`verde.BlockReduce` to lower the influence of the offending data points.
However, :class:`verde.BlockReduce` can't produce weights for the blocked data
(for use by a gridder, for example). If you want to produced blocked weights as
well, use :class:`verde.BlockMean`.

.. GENERATED FROM PYTHON SOURCE LINES 19-81



.. image-sg:: /gallery/images/sphx_glr_blockreduce_weights_001.png
   :alt: No Weights, Weights
   :srcset: /gallery/images/sphx_glr_blockreduce_weights_001.png
   :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    Index of outliers: [1608 2347 1099 2408  674  433 1071 2452  124 1219 1126  938  875   51
     2407  892  255 1349 1112 1326]






|

.. code-block:: default

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

    import verde as vd

    # We'll test this on the California vertical GPS velocity data
    data = vd.datasets.fetch_california_gps()

    # We'll add some random extreme outliers to the data
    outliers = np.random.RandomState(2).randint(0, data.shape[0], size=20)
    data.velocity_up[outliers] += 0.08
    print("Index of outliers:", outliers)

    # Create an array of weights and set the weights for the outliers to a very low
    # value
    weights = np.ones_like(data.velocity_up)
    weights[outliers] = 1e-5

    # Now we can block average the points with and without weights to compare the
    # outputs.
    reducer = vd.BlockReduce(reduction=np.average, spacing=30 / 60, center_coordinates=True)
    coordinates, no_weights = reducer.filter(
        (data.longitude, data.latitude), data.velocity_up
    )
    __, with_weights = reducer.filter(
        (data.longitude, data.latitude), data.velocity_up, weights
    )

    # Now we can plot the data sets side by side on Mercator maps
    fig, axes = plt.subplots(
        1, 2, figsize=(9, 7), subplot_kw=dict(projection=ccrs.Mercator())
    )
    titles = ["No Weights", "Weights"]
    crs = ccrs.PlateCarree()
    maxabs = vd.maxabs(data.velocity_up)
    for ax, title, velocity in zip(axes, titles, (no_weights, with_weights)):
        ax.set_title(title)
        # Plot the locations of the outliers
        ax.plot(
            data.longitude[outliers],
            data.latitude[outliers],
            "xk",
            transform=crs,
            label="Outliers",
        )
        # Plot the block means and saturate the colorbar a bit to better show the
        # differences in the data.
        pc = ax.scatter(
            *coordinates,
            c=velocity,
            s=70,
            transform=crs,
            cmap="seismic",
            vmin=-maxabs / 3,
            vmax=maxabs / 3
        )
        cb = plt.colorbar(pc, ax=ax, orientation="horizontal", pad=0.05)
        cb.set_label("vertical velocity [m/yr]")
        vd.datasets.setup_california_gps_map(ax)
    ax.legend(loc="lower left")
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.917 seconds)


.. _sphx_glr_download_gallery_blockreduce_weights.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: blockreduce_weights.py <blockreduce_weights.py>`



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

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


.. only:: html

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

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