.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/examples/general/plot_padding_extrapolate.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_extrapolate.py>`
        to download the full example code.

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

.. _sphx_glr_generated_examples_general_plot_padding_extrapolate.py:


Padding with 'extrapolate' mode
-------------------------------

Since version 0.7.0 of pybaselines, a list/tuple of two separate
values can be specified for `extrapolate_window`, giving more control
over the padding.

.. GENERATED FROM PYTHON SOURCE LINES 11-53



.. image-sg:: /generated/examples/general/images/sphx_glr_plot_padding_extrapolate_001.png
   :alt: plot padding extrapolate
   :srcset: /generated/examples/general/images/sphx_glr_plot_padding_extrapolate_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from pybaselines.utils import _check_scalar, gaussian, pad_edges


    num_points = 1000
    pad_len = 100

    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 = 5 * np.exp(-x / 200) + gaussian(x, 5, 900, 20) + gaussian(x, 5, 200, 20)
    noise = np.random.default_rng(0).normal(0, 0.1, num_points)
    y = line + noise

    _, ax = plt.subplots()

    patch = ax.plot(pad_edges(y, pad_len, mode='constant', constant_values=np.nan))[0]
    extrapolate_windows = (1, 100, [100, 40])
    legend = [[patch], ['original']]
    for i, extrapolate_window in enumerate(extrapolate_windows, 1):
        padded_y = pad_edges(y, pad_len, extrapolate_window=extrapolate_window)
        # offset each plot to show differences
        padded_y += 5 * i
        patch = ax.plot(padded_y)[0]
        legend[0].append(patch)
        legend[1].append(f'extrapolate_window={extrapolate_window}')

        # show only the section used for extrapolation
        padded_y[:pad_len] = np.nan
        padded_y[-pad_len:] = np.nan
        windows = _check_scalar(extrapolate_window, 2, True)[0]
        padded_y[pad_len + windows[0]:-pad_len - windows[1]] = np.nan
        section_patch = ax.plot(padded_y, 'c.')[0]

    ax.set_ylim([None, 31])
    legend[0].insert(1, section_patch)
    legend[1].insert(1, 'fit points')
    ax.legend(*legend)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.076 seconds)


.. _sphx_glr_download_generated_examples_general_plot_padding_extrapolate.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_extrapolate.ipynb <plot_padding_extrapolate.ipynb>`

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

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

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

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


.. only:: html

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

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