.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorial-quick/eg_03_quick_spectra.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_tutorial-quick_eg_03_quick_spectra.py>`
        to download the full example code

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

.. _sphx_glr_tutorial-quick_eg_03_quick_spectra.py:


Getting spectra data
^^^^^^^^^^^^^^^^^^^^

It can often be useful to have a look at the spectral content of time data. The
quick functions make it easy to get the spectra data of a single time series
recording.

Note that spectra data are calculated after decimation and spectra data objects
include data for multiple decimation levels.

The dataset in this example has been provided for use by the SAMTEX consortium.
For more information, please refer to [Jones2009]_. Additional details about the
dataset can be found at https://www.mtnet.info/data/kap03/kap03.html.

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.. code-block:: default

    from pathlib import Path
    import seedir as sd
    import plotly
    import resistics.letsgo as letsgo








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Define the data path. This is dependent on where the data is stored.

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.. code-block:: default

    time_data_path = Path("..", "..", "data", "time", "quick", "kap123")
    sd.seedir(str(time_data_path), style="emoji")





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

 Out:

 .. code-block:: none

    📁 kap123/
    ├─📄 data.npy
    └─📄 metadata.json




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Get the spectra data.

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.. code-block:: default

    spec_data = letsgo.quick_spectra(time_data_path)








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Once the spectra data has been calculated, it can be plotted in a variety of
ways. The default plotting function plots the spectral data for multiple
decimation levels.

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.. code-block:: default

    fig = spec_data.plot()
    fig.update_layout(height=900)
    plotly.io.show(fig)




.. raw:: html
    :file: images/sphx_glr_eg_03_quick_spectra_001.html





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It is also possible to plot spectra data for a particular decimation level.
In the below example, an optional grouping is being used to stack spectra data
for the decimation level into certain time groups

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.. code-block:: default

    fig = spec_data.plot_level_stack(level=0, grouping="3D")
    fig.update_layout(height=900)
    plotly.io.show(fig)





.. raw:: html
    :file: images/sphx_glr_eg_03_quick_spectra_002.html





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It is also possible to plot spectra heatmaps for a decimation level. Here, the
sphinx_gallery_defer_figures

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.. code-block:: default

    fig = spec_data.plot_level_section(level=0, grouping="6H")
    fig.update_layout(height=900)
    plotly.io.show(fig)



.. raw:: html
    :file: images/sphx_glr_eg_03_quick_spectra_003.html






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

   **Total running time of the script:** ( 0 minutes  3.709 seconds)


.. _sphx_glr_download_tutorial-quick_eg_03_quick_spectra.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: eg_03_quick_spectra.py <eg_03_quick_spectra.py>`



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

     :download:`Download Jupyter notebook: eg_03_quick_spectra.ipynb <eg_03_quick_spectra.ipynb>`


.. only:: html

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