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

.. only:: html

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

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

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

.. _sphx_glr_examples_timeseries_qscan.py:


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

Generate the Q-transform of a `TimeSeries`
##########################################

One of the most useful tools for filtering and visualising short-duration
features in a `TimeSeries` is the
`Q-transform <https://en.wikipedia.org/wiki/Constant-Q_transform>`_.
This is regularly used by the Detector Characterization working groups of
the LIGO Scientific Collaboration and the Virgo Collaboration to produce
high-resolution time-frequency maps of transient noise (glitches) and
potential gravitational-wave signals.

This algorithm was used to visualise the first ever gravitational-wave
detection GW150914, so we can reproduce `that result (bottom panel of figure 1)
<https://doi.org/10.1103/PhysRevLett.116.061102>`_ here.

.. GENERATED FROM PYTHON SOURCE LINES 40-42

First, we need to download the `TimeSeries` record for the H1 strain
measurement from |GWOSC|_:

.. GENERATED FROM PYTHON SOURCE LINES 42-46

.. code-block:: Python


    from gwpy.timeseries import TimeSeries
    data = TimeSeries.fetch_open_data("H1", 1126259446, 1126259478)








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Next, we generate the `~TimeSeries.q_transform` of these data:

.. GENERATED FROM PYTHON SOURCE LINES 48-50

.. code-block:: Python

    qspecgram = data.q_transform(outseg=(1126259462.2, 1126259462.5))








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.. note::
   We can save memory by focusing on a specific window around the
   interesting time. The ``outseg`` keyword argument returns a `Spectrogram`
   that is only as long as we need it to be.

Now, we can plot the resulting `~gwpy.spectrogram.Spectrogram`:

.. GENERATED FROM PYTHON SOURCE LINES 57-68

.. code-block:: Python


    plot = qspecgram.plot(figsize=[8, 4])
    ax = plot.gca()
    ax.set_xscale("seconds")
    ax.set_yscale("log")
    ax.set_ylim(20, 500)
    ax.set_ylabel("Frequency [Hz]")
    ax.grid(True, axis="y", which="both")
    ax.colorbar(cmap="viridis", label="Normalized energy")
    plot.show()




.. image-sg:: /examples/timeseries/images/sphx_glr_qscan_001.png
   :alt: qscan
   :srcset: /examples/timeseries/images/sphx_glr_qscan_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 69-72

Here we can clearly see the trace of a compact binary coalescence,
specifically a binary black hole merger!
For more details on this historic result, please see |GW150914|_.


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

   **Total running time of the script:** (0 minutes 2.076 seconds)


.. _sphx_glr_download_examples_timeseries_qscan.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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