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

.. only:: html

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

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

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

.. _sphx_glr_gallery_trend.py:


Polynomial trend
================

Verde offers the :class:`verde.Trend` class to fit a 2D polynomial trend to
your data. This can be useful for isolating a regional component of your data,
for example, which is a common operation for gravity and magnetic data. Let's
look at how we can use Verde to remove the clear trend from our Texas
temperature dataset (:func:`verde.datasets.fetch_texas_wind`).

.. GENERATED FROM PYTHON SOURCE LINES 17-89



.. image-sg:: /gallery/images/sphx_glr_trend_001.png
   :alt: Original data, Regional trend, Residual, Distribution of data
   :srcset: /gallery/images/sphx_glr_trend_001.png
   :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    Original data:
      station_id  longitude  ...  wind_speed_east_knots  wind_speed_north_knots
    0        0F2   -97.7756  ...               1.032920               -2.357185
    1        11R   -96.3742  ...               1.692155                2.982564
    2        2F5  -101.9018  ...              -1.110056               -0.311412
    3        3T5   -96.9500  ...               1.695097                3.018448
    4        5C1   -98.6946  ...               1.271400                1.090743

    [5 rows x 6 columns]

    Trend estimator: Trend(degree=1)

    Updated DataFrame:
      station_id  longitude  latitude  ...  wind_speed_north_knots      trend  residual
    0        0F2   -97.7756   33.6017  ...               -2.357185   9.067526  0.168585
    1        11R   -96.3742   30.2189  ...                2.982564  14.727121 -0.512816
    2        2F5  -101.9018   32.7479  ...               -0.311412   8.508071 -1.438626
    3        3T5   -96.9500   29.9100  ...                3.018448  14.931638 -0.434877
    4        5C1   -98.6946   29.7239  ...                1.090743  14.434636 -1.476303

    [5 rows x 8 columns]






|

.. code-block:: default

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

    import verde as vd

    # Load the Texas wind and temperature data as a pandas.DataFrame
    data = vd.datasets.fetch_texas_wind()
    print("Original data:")
    print(data.head())

    # Fit a 1st degree 2D polynomial to the data
    coordinates = (data.longitude, data.latitude)
    trend = vd.Trend(degree=1).fit(coordinates, data.air_temperature_c)
    print("\nTrend estimator:", trend)

    # Add the estimated trend and the residual data to the DataFrame
    data["trend"] = trend.predict(coordinates)
    data["residual"] = data.air_temperature_c - data.trend
    print("\nUpdated DataFrame:")
    print(data.head())


    # Make a function to plot the data using the same colorbar
    def plot_data(column, i, title):
        "Plot the column from the DataFrame in the ith subplot"
        crs = ccrs.PlateCarree()
        ax = plt.subplot(2, 2, i, projection=ccrs.Mercator())
        ax.set_title(title)
        # Set vmin and vmax to the extremes of the original data
        maxabs = vd.maxabs(data.air_temperature_c)
        mappable = ax.scatter(
            data.longitude,
            data.latitude,
            c=data[column],
            s=50,
            cmap="seismic",
            vmin=-maxabs,
            vmax=maxabs,
            transform=crs,
        )
        # Set the proper ticks for a Cartopy map
        vd.datasets.setup_texas_wind_map(ax)
        return mappable


    plt.figure(figsize=(10, 9.5))

    # Plot the data fields and capture the mappable returned by scatter to use for
    # the colorbar
    mappable = plot_data("air_temperature_c", 1, "Original data")
    plot_data("trend", 2, "Regional trend")
    plot_data("residual", 3, "Residual")

    # Make histograms of the data and the residuals to show that the trend was
    # removed
    ax = plt.subplot(2, 2, 4)
    ax.set_title("Distribution of data")
    ax.hist(data.air_temperature_c, bins="auto", alpha=0.7, label="Original data")
    ax.hist(data.residual, bins="auto", alpha=0.7, label="Residuals")
    ax.legend()
    ax.set_xlabel("Air temperature (C)")

    # Add a single colorbar on top of the histogram plot where there is some space
    cax = plt.axes((0.35, 0.44, 0.10, 0.01))
    cb = plt.colorbar(
        mappable,
        cax=cax,
        orientation="horizontal",
    )
    cb.set_label("C")

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.498 seconds)


.. _sphx_glr_download_gallery_trend.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: trend.py <trend.py>`



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

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


.. only:: html

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

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