.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/frequencyseries/hoff.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_hoff.py>`
        to download the full example code.

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

.. _sphx_glr_examples_frequencyseries_hoff.py:


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

Calculate and plot a `FrequencySeries`
######################################

One of the principal means of estimating the sensitivity of a
gravitational-wave detector is to esimate it's amplitude spectral density
(ASD).
The ASD is a measurement of how a signal's amplitude varies across
different frequencies.

The ASD can be estimated directly from a `TimeSeries` using the
:meth:`~gwpy.timeseries.TimeSeries.asd` method.

.. GENERATED FROM PYTHON SOURCE LINES 37-42

Data access
-----------
For this example we choose to estimate the ASD around |GW200115|_,
one of the first observations of a neutron star-black hole binary.
We can use the |gwosc-mod| Python package to query for the relevant GPS time:

.. GENERATED FROM PYTHON SOURCE LINES 42-46

.. code-block:: Python


    from gwosc.datasets import event_gps
    gps = event_gps("GW200115")








.. GENERATED FROM PYTHON SOURCE LINES 47-51

In order to generate a `FrequencySeries` we need to import the
`~gwpy.timeseries.TimeSeries` and use
:meth:`~gwpy.timeseries.TimeSeries.fetch_open_data` to download the strain
records:

.. GENERATED FROM PYTHON SOURCE LINES 51-56

.. code-block:: Python


    from gwpy.timeseries import TimeSeries
    lho = TimeSeries.fetch_open_data("H1", gps - 16, gps + 16)
    llo = TimeSeries.fetch_open_data("L1", gps - 16, gps + 16)








.. GENERATED FROM PYTHON SOURCE LINES 57-62

Calculate the ASDs
------------------
We can then call the :meth:`~gwpy.timeseries.TimeSeries.asd` method to
calculated the amplitude spectral density for each
`~gwpy.timeseries.TimeSeries`:

.. GENERATED FROM PYTHON SOURCE LINES 62-66

.. code-block:: Python


    lhoasd = lho.asd(4, 2)
    lloasd = llo.asd(4, 2)








.. GENERATED FROM PYTHON SOURCE LINES 67-71

Visualisation
-------------
We can then :meth:`~FrequencySeries.plot` the spectra using the 'standard'
colour scheme:

.. GENERATED FROM PYTHON SOURCE LINES 71-80

.. code-block:: Python


    plot = lhoasd.plot(label="LIGO-Hanford", color="gwpy:ligo-hanford")
    ax = plot.gca()
    ax.plot(lloasd, label="LIGO-Livingston", color="gwpy:ligo-livingston")
    ax.set_xlim(16, 1600)
    ax.set_ylim(1e-24, 1e-21)
    ax.set_ylabel(r"Strain ASD [1/$\sqrt{\mathrm{Hz}}]$")
    ax.legend(frameon=False, bbox_to_anchor=(1., 1.), loc="lower right", ncol=2)
    plot.show()



.. image-sg:: /examples/frequencyseries/images/sphx_glr_hoff_001.png
   :alt: hoff
   :srcset: /examples/frequencyseries/images/sphx_glr_hoff_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.609 seconds)


.. _sphx_glr_download_examples_frequencyseries_hoff.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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