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

.. only:: html

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

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

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

.. _sphx_glr_examples_frequencyseries_variance.py:


.. sectionauthor:: Duncan Macleod <duncan.macleod@ligo.org>
.. currentmodule:: gwpy.timeseries

Generating a `SpectralVariance` histogram
#########################################

The most common visualisation of the spectral content of a data series is
via the power or amplitude spectral density calculations (PSD or ASD).
However, these typically involve averaging of the data over a period, which
can wash out transient noise (as is often desired).

The `SpectralVariance` histogram provide by `gwpy.frequencyseries` allows
us to look at the spectral sensitivity in a different manner, displaying
which frequencies sit at which amplitude *most* of the time, but also
highlighting excursions from normal behaviour.

.. GENERATED FROM PYTHON SOURCE LINES 38-40

To demonstate this, we can load some data from the LIGO Livingston
interferometer around the time of the GW151226 gravitational wave detection:

.. GENERATED FROM PYTHON SOURCE LINES 40-44

.. code-block:: Python


    from gwpy.timeseries import TimeSeries
    llo = TimeSeries.fetch_open_data("L1", 1135136228, 1135140324)








.. GENERATED FROM PYTHON SOURCE LINES 45-49

We can then call the :meth:`~gwpy.timeseries.TimeSeries.spectral_variance`
method of the ``llo`` `~gwpy.timeseries.TimeSeries` by calculating an ASD
every 5 seconds and counting the amount of time each frequency bin spends
at each ASD value:

.. GENERATED FROM PYTHON SOURCE LINES 49-60

.. code-block:: Python


    variance = llo.spectral_variance(
        5,
        fftlength=2,
        overlap=1,
        log=True,
        low=1e-24,
        high=1e-19,
        nbins=100,
    )








.. GENERATED FROM PYTHON SOURCE LINES 61-62

We can then :meth:`~SpectralVariance.plot` the `SpectralVariance`

.. GENERATED FROM PYTHON SOURCE LINES 62-73

.. code-block:: Python


    plot = variance.plot(yscale="log", norm="log", vmin=.5, cmap="plasma")
    ax = plot.gca()
    ax.grid()
    ax.set_xlim(20, 1500)
    ax.set_ylim(1e-24, 1e-20)
    ax.set_xlabel("Frequency [Hz]")
    ax.set_ylabel(r"[strain/$\sqrt{\mathrm{Hz}}$]")
    ax.set_title("LIGO-Livingston sensitivity variance")
    plot.show()




.. image-sg:: /examples/frequencyseries/images/sphx_glr_variance_001.png
   :alt: LIGO-Livingston sensitivity variance
   :srcset: /examples/frequencyseries/images/sphx_glr_variance_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 74-77

From this we see that in general the sensitivity varies a few parts in
10:sup:`-23`, while many of the lines (narrow-band peaks in the spectrum)
are much more stationary.


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

   **Total running time of the script:** (0 minutes 2.735 seconds)


.. _sphx_glr_download_examples_frequencyseries_variance.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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