7. 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. 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.

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

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))

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 Spectrogram:

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()

(png)

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/.