.. _gwpy-example-miscellaneous-range-timeseries:

Generating an inspiral range timeseries
#######################################

The standard metric of the sensitivity of a gravitational-wave detector
is the distance to which a canonical binary neutron star (BNS) inspiral
(with two 1.4 solar mass components) would be detected with a
signal-to-noise ratio (SNR) of 8.

We can estimate using :func:`gwpy.astro.inspiral_range` after calculating
the power-spectral density (PSD) of the strain readout for a detector, and
can plot the variation over time by looping over a power spectral density
:class:`~gwpy.spectrogram.Spectrogram`.

First, we need to load some data, for this we can use the
`LOSC <https://losc.ligo.org>`_ public data around the GW150914 event:


.. plot::
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   :context: reset

   from gwpy.timeseries import TimeSeries
   h1 = TimeSeries.fetch_open_data('H1', 1126257414, 1126261510)
   l1 = TimeSeries.fetch_open_data('L1', 1126257414, 1126261510)

and then calculating the PSD spectrogram:


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   h1spec = h1.spectrogram(30, fftlength=4)
   l1spec = l1.spectrogram(30, fftlength=4)

To calculate the inspiral range variation, we need to create a
:class:`~gwpy.timeseries.TimeSeries` in which to store the values, then
loop over each PSD bin in the spectrogram, calculating the
:func:`gwpy.astro.inspiral_range` for each one:


.. plot::
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   import numpy
   from gwpy.astro import inspiral_range
   h1range = TimeSeries(numpy.zeros(len(h1spec)),
                        dt=h1spec.dt, t0=h1spec.t0, unit='Mpc')
   l1range = h1range.copy()
   for i in range(h1range.size):
       h1range[i] = inspiral_range(h1spec[i], fmin=10)
       l1range[i] = inspiral_range(l1spec[i], fmin=10)

We can now easily plot the timeseries to see the variation in LIGO
sensitivity over the hour or so including GW150914:

.. plot::
   :include-source:
   :context:

   from gwpy.plotter.colors import GW_OBSERVATORY_COLORS as GWO_COLORS
   plot = h1range.plot(label='LIGO-Hanford', color=GWO_COLORS['H1'])
   ax = plot.gca()
   ax.plot(l1range, label='LIGO-Livingston', color=GWO_COLORS['L1'])
   ax.set_ylabel('Angle-averaged sensitive distance [Mpc]')
   ax.set_title('LIGO sensitivity to BNS around GW150914')
   ax.set_epoch(1126259462)  # <- set 0 on plot to GW150914
   ax.legend()
   plot.show()