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

.. only:: html

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

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

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

.. _sphx_glr__gallery_selection_tools_linear_region_selector.py:


LinearRegionSelectors
=====================

Example showing how to use a `LinearRegionSelector` with lines. We demonstrate two use cases, a horizontal
LinearRegionSelector which selects along the x-axis and a vertical selector which moves along the y-axis.

In general, a horizontal selector on the x-axis is useful if you are displaying data where y = f(x).
Conversely, a vertical selector that selectors along the y-axis is useful for
displaying data where x = f(y). (ex: vertical histograms)

.. GENERATED FROM PYTHON SOURCE LINES 12-114



.. image-sg:: /_gallery/selection_tools/images/sphx_glr_linear_region_selector_001.webp
   :alt: linear region selector
   :srcset: /_gallery/selection_tools/images/sphx_glr_linear_region_selector_001.webp
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/runner/work/fastplotlib/fastplotlib/fastplotlib/graphics/_features/_base.py:18: UserWarning: casting float64 array to float32
      warn(f"casting {array.dtype} array to float32")







|

.. code-block:: Python


    # test_example = false

    import fastplotlib as fpl
    import numpy as np

    # names for out subplots
    names = [
        ["y = sine(x)", "x = sine(y), sine(y) > 0 = 0"],
        ["zoomed sine(x)", "zoomed sine(y)"]
    ]

    # 2 rows, 2 columns
    figure = fpl.Figure(
        (2, 2),
        size=(700, 560),
        names=names,
    )

    # preallocated size for zoomed data
    zoomed_prealloc = 1_000

    # data to plot
    xs = np.linspace(0, 10 * np.pi, 1_000)
    ys = np.sin(xs)  # y = sine(x)

    # make sine along x axis
    sine_graphic_x = figure[0, 0].add_line(np.column_stack([xs, ys]))

    # x = sine(y), sine(y) > 0 = 0
    sine_y = ys
    sine_y[sine_y > 0] = 0

    # sine along y axis
    sine_graphic_y = figure[0, 1].add_line(np.column_stack([ys, xs]))

    # offset the position of the graphic to demonstrate `get_selected_data()` later
    sine_graphic_y.position_x = 50
    sine_graphic_y.position_y = 50

    # add linear selectors
    selector_x = sine_graphic_x.add_linear_region_selector()  # default axis is "x"
    selector_y = sine_graphic_y.add_linear_region_selector(axis="y")

    # preallocate array for storing zoomed in data
    zoomed_init = np.column_stack([np.arange(zoomed_prealloc), np.zeros(zoomed_prealloc)])

    # make line graphics for displaying zoomed data
    zoomed_x = figure[1, 0].add_line(zoomed_init)
    zoomed_y = figure[1, 1].add_line(zoomed_init)


    def interpolate(subdata: np.ndarray, axis: int):
        """1D interpolation to display within the preallocated data array"""
        x = np.arange(0, zoomed_prealloc)
        xp = np.linspace(0, zoomed_prealloc, subdata.shape[0])

        # interpolate to preallocated size
        return np.interp(x, xp, fp=subdata[:, axis])  # use the y-values


    @selector_x.add_event_handler("selection")
    def set_zoom_x(ev):
        """sets zoomed x selector data"""
        # get the selected data
        selected_data = ev.get_selected_data()
        if selected_data.size == 0:
            # no data selected
            zoomed_x.data[:, 1] = 0

        # interpolate the y-values since y = f(x)
        zoomed_x.data[:, 1] = interpolate(selected_data, axis=1)
        figure[1, 0].auto_scale()


    def set_zoom_y(ev):
        """sets zoomed x selector data"""
        # get the selected data
        selected_data = ev.get_selected_data()
        if selected_data.size == 0:
            # no data selected
            zoomed_y.data[:, 1] = 0

        # interpolate the x values since this x = f(y)
        zoomed_y.data[:, 1] = -interpolate(selected_data, axis=0)
        figure[1, 1].auto_scale()


    # you can also add event handlers without a decorator
    selector_y.add_event_handler(set_zoom_y, "selection")

    # set initial selection
    selector_x.selection = selector_y.selection = (0, 4 * np.pi)


    figure.show(maintain_aspect=False)

    # NOTE: `if __name__ == "__main__"` is NOT how to use fastplotlib interactively
    # please see our docs for using fastplotlib interactively in ipython and jupyter
    if __name__ == "__main__":
        print(__doc__)
        fpl.run()


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

   **Total running time of the script:** (0 minutes 3.724 seconds)


.. _sphx_glr_download__gallery_selection_tools_linear_region_selector.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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