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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_generated_examples_general_plot_padding.py>`
        to download the full example code.

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

.. _sphx_glr_generated_examples_general_plot_padding.py:


Padding data
------------

Several baseline algorithms, notably :doc:`smoothing <../../../algorithms/smooth>` and
:doc:`morphological <../../../algorithms/morphological>` algorithms, pad the input data in order
to reduce edge effects from calculations. Padding is performed by :func:`~.pad_edges`, which is
a thin wrapper around :func:`numpy.pad` that also allows linear extrapolation.

.. GENERATED FROM PYTHON SOURCE LINES 12-38

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from scipy.ndimage import uniform_filter1d

    from pybaselines.utils import pad_edges


    half_window = 80  # for 161 point moving average window
    num_points = 1000
    x = np.linspace(0, 1000, num_points)
    # use an exponentially decaying line to get a large edge effect one
    # one side and a small edge effect on the other
    line = 10 * np.exp(-x / 150)
    noise = np.random.default_rng(0).normal(0, 0.5, num_points)
    y = line + noise
    pad_len = 2 * half_window + 1

    plt.plot(pad_edges(y, pad_len, mode='constant', constant_values=np.nan), label='original')
    for i, pad_mode in enumerate(('reflect', 'edge', 'extrapolate'), 1):
        padded_y = pad_edges(y, pad_len, pad_mode, extrapolate_window=int(0.1 * num_points))
        # offset each plot to show differences
        plt.plot(padded_y + 5 * i, label=pad_mode)

    plt.legend()




.. image-sg:: /generated/examples/general/images/sphx_glr_plot_padding_001.png
   :alt: plot padding
   :srcset: /generated/examples/general/images/sphx_glr_plot_padding_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 39-41

To show the effects that padding can have, the data is smoothed with a moving
average. Poor padding produces more severe edge effects from the smoothing.

.. GENERATED FROM PYTHON SOURCE LINES 41-52

.. code-block:: Python

    plt.figure()
    plt.plot(y, label='original')
    for i, pad_mode in enumerate(('reflect', 'edge', 'extrapolate'), 1):
        padded_y = pad_edges(y, pad_len, pad_mode, extrapolate_window=int(0.1 * num_points))
        plt.plot(
            uniform_filter1d(padded_y, 2 * half_window + 1)[pad_len:num_points + pad_len],
            label=pad_mode
        )

    plt.legend()
    plt.show()



.. image-sg:: /generated/examples/general/images/sphx_glr_plot_padding_002.png
   :alt: plot padding
   :srcset: /generated/examples/general/images/sphx_glr_plot_padding_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.135 seconds)


.. _sphx_glr_download_generated_examples_general_plot_padding.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_padding.zip <plot_padding.zip>`


.. only:: html

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

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