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

.. only:: html

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

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

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

.. _sphx_glr_tutorials_trends.py:


Trend Estimation
================

Trend estimation and removal is a common operation, particularly when dealing
with geophysical data. Moreover, some of the interpolation methods, like
:class:`verde.Spline`, can struggle with long-wavelength trends in the data.
The :class:`verde.Trend` class fits a 2D polynomial trend of arbitrary degree
to the data and can be used to remove it.

.. GENERATED FROM PYTHON SOURCE LINES 17-23

.. 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 24-26

Our sample air temperature data from Texas has a clear trend from land to the
ocean:

.. GENERATED FROM PYTHON SOURCE LINES 26-44

.. code-block:: default


    data = vd.datasets.fetch_texas_wind()
    coordinates = (data.longitude, data.latitude)

    plt.figure(figsize=(8, 6))
    ax = plt.axes(projection=ccrs.Mercator())
    plt.scatter(
        data.longitude,
        data.latitude,
        c=data.air_temperature_c,
        s=100,
        cmap="plasma",
        transform=ccrs.PlateCarree(),
    )
    plt.colorbar().set_label("Air temperature (C)")
    vd.datasets.setup_texas_wind_map(ax)
    plt.show()




.. image-sg:: /tutorials/images/sphx_glr_trends_001.png
   :alt: trends
   :srcset: /tutorials/images/sphx_glr_trends_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 45-46

We can estimate the polynomial coefficients for this trend:

.. GENERATED FROM PYTHON SOURCE LINES 46-50

.. code-block:: default


    trend = vd.Trend(degree=1).fit(coordinates, data.air_temperature_c)
    print(trend.coef_)





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

 Out:

 .. code-block:: none

    [102.4946959    0.44373823  -1.48922224]




.. GENERATED FROM PYTHON SOURCE LINES 51-53

More importantly, we can predict the trend values and remove them from our
data:

.. GENERATED FROM PYTHON SOURCE LINES 53-91

.. code-block:: default


    trend_values = trend.predict(coordinates)
    residuals = data.air_temperature_c - trend_values

    fig, axes = plt.subplots(
        1, 2, figsize=(10, 6), subplot_kw=dict(projection=ccrs.Mercator())
    )

    ax = axes[0]
    ax.set_title("Trend")
    tmp = ax.scatter(
        data.longitude,
        data.latitude,
        c=trend_values,
        s=60,
        cmap="plasma",
        transform=ccrs.PlateCarree(),
    )
    plt.colorbar(tmp, ax=ax, orientation="horizontal", pad=0.06)
    vd.datasets.setup_texas_wind_map(ax)

    ax = axes[1]
    ax.set_title("Residuals")
    maxabs = vd.maxabs(residuals)
    tmp = ax.scatter(
        data.longitude,
        data.latitude,
        c=residuals,
        s=60,
        cmap="bwr",
        vmin=-maxabs,
        vmax=maxabs,
        transform=ccrs.PlateCarree(),
    )
    plt.colorbar(tmp, ax=ax, orientation="horizontal", pad=0.08)
    vd.datasets.setup_texas_wind_map(ax)
    plt.show()




.. image-sg:: /tutorials/images/sphx_glr_trends_002.png
   :alt: Trend, Residuals
   :srcset: /tutorials/images/sphx_glr_trends_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 92-94

The fitting, prediction, and residual calculation can all be done in a single
step using the :meth:`~verde.Trend.filter` method:

.. GENERATED FROM PYTHON SOURCE LINES 94-101

.. code-block:: default


    # ``filter`` always outputs coordinates and weights as well, which we don't
    # need and will ignore here.
    __, res_filter, __ = vd.Trend(degree=1).filter(coordinates, data.air_temperature_c)

    print(np.allclose(res_filter, residuals))





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

 Out:

 .. code-block:: none

    True




.. GENERATED FROM PYTHON SOURCE LINES 102-105

Additionally, :class:`verde.Trend` implements the :ref:`gridder interface
<overview>` and has the :meth:`~verde.Trend.grid` and
:meth:`~verde.Trend.profile` methods.


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

   **Total running time of the script:** ( 0 minutes  0.457 seconds)


.. _sphx_glr_download_tutorials_trends.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: trends.py <trends.py>`



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

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


.. only:: html

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

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