7. Generate the Q-transform of a
One of the most useful tools for filtering and visualising short-duration
features in a
TimeSeries is the
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) here.
from gwpy.timeseries import TimeSeries data = TimeSeries.fetch_open_data('H1', 1126259446, 1126259478)
Next, we generate the
q_transform of these data:
qspecgram = data.q_transform(outseg=(1126259462.2, 1126259462.5))
We can save memory by focusing on a specific window around the
interesting time. The
outseg keyword argument returns a
that is only as long as we need it to be.
Now, we can plot the resulting
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()
Here we can clearly see the trace of a compact binary coalescence, specifically a binary black hole merger! For more details on this result, please see http://www.ligo.org/science/Publication-GW150914/.