Generate the Q-transform of a `TimeSeries`¶

First, we identify the GPS time of interest:

gps = 968654558

and use that to define the start and end times of our required data

duration = 32
start = int(round(gps - duration/2.))
end = start + duration

next, we import the TimeSeries and fetch some open data from LOSC:

from gwpy.timeseries import TimeSeries
data = TimeSeries.fetch_open_data('H1', start, end)

and next we generate the q_transform of these data:

qspecgram = data.q_transform()

Now, we can plot the resulting Spectrogram, focusing on a specific window around the interesting time

Note

Using crop is highly recommended at this stage because rendering the high-resolution spectrogram as it is done here is very slow (for experts this is because we’re using pcolormesh and not any sort of image interpolation, mainly to support both linear and log scaling nicely)

plot = qspecgram.crop(gps-.3, gps+.1).plot(figsize=[8, 6])
ax = plot.gca()
ax.set_epoch(gps)
ax.set_yscale('log')
ax.set_xlabel('Time [milliseconds]')
ax.set_ylim(50, 1000)
ax.grid(True, axis='y', which='both')
plot.add_colorbar(cmap='viridis', label='Normalized energy')
plot.show()

I think we just detected a gravitational wave signal! But, before you get too exited, this is an example of a ‘blind injection’, a simulated signal introduced into the interferometer(s) in order to test the detection process end-to-end. For more details, see here.

(Source code, png)

Generate the Q-transform of a TimeSeries¶

Generate the Q-transform of a `TimeSeries`¶