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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_Markers_from_signal.py:


Creating Markers from a signal
==============================

This example shows how to create markers from a signal. This is useful for creating lazy
markers from some operation such as peak finding on a signal. Here we show how to create
markers from a simple map function which finds the maximum value and plots a marker at
that position.

.. GENERATED FROM PYTHON SOURCE LINES 10-66



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/Markers/images/sphx_glr_from_signal_001.png
         :alt: from signal
         :srcset: /auto_examples/Markers/images/sphx_glr_from_signal_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/Markers/images/sphx_glr_from_signal_002.png
         :alt:  Signal
         :srcset: /auto_examples/Markers/images/sphx_glr_from_signal_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none


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|

.. code-block:: Python

    import numpy as np
    import hyperspy.api as hs


    # Making some artificial data
    def find_maxima(data, scale, offset):
        ind = np.array(np.unravel_index(np.argmax(data, axis=None), data.shape)).astype(int)
        d = data[ind]
        ind = ind * scale + offset  # convert to physical units
        print(ind)
        print(d)
        return np.array(
            [
                [ind[0], d[0]],
            ]
        )


    def find_maxima_lines(data, scale, offset):
        ind = np.array(np.unravel_index(np.argmax(data, axis=None), data.shape)).astype(int)
        ind = ind * scale + offset  # convert to physical units
        return ind


    def gaussian(x, mu, sig):
        return (
            1.0 / (np.sqrt(2.0 * np.pi) * sig) * np.exp(-np.power((x - mu) / sig, 2.0) / 2)
        )


    data = np.empty((4, 120))
    for i in range(4):
        x_values = np.linspace(-3 + i * 0.1, 3 + i * 0.1, 120)
        data[i] = gaussian(x_values, mu=0, sig=10)

    s = hs.signals.Signal1D(data)
    s.axes_manager.signal_axes[0].scale = 6 / 120
    s.axes_manager.signal_axes[0].offset = -3


    scale = s.axes_manager.signal_axes[0].scale
    offset = s.axes_manager.signal_axes[0].offset
    max_values = s.map(find_maxima, scale=scale, offset=offset, inplace=False, ragged=True)
    max_values_lines = s.map(
        find_maxima_lines, scale=scale, offset=offset, inplace=False, ragged=True
    )

    point_markers = hs.plot.markers.Points.from_signal(max_values, signal_axes=None)
    line_markers = hs.plot.markers.VerticalLines.from_signal(
        max_values_lines, signal_axes=None
    )


    s.plot()
    s.add_marker(point_markers)
    s.add_marker(line_markers)


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

   **Total running time of the script:** (0 minutes 0.767 seconds)


.. _sphx_glr_download_auto_examples_Markers_from_signal.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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