9. Inject a known signal into a
It can often be useful to add some known signal to an inherently random or noisy timeseries. For example, one might want to examine what would happen if a binary black hole merger signal occured at or near the time of a glitch. In LIGO data analysis, this procedure is referred to as an _injection_.
In the example below, we will create a stream of random, white Gaussian noise, then inject a simulation of GW150914 into it at a known time.
First, we prepare one second of Gaussian noise:
from numpy import random from gwpy.timeseries import TimeSeries noise = TimeSeries(random.normal(scale=.1, size=16384), sample_rate=16384)
Then we can download a simulation of the GW150914 signal from GWOSC:
from astropy.utils.data import get_readable_fileobj url = ("https://gwosc.org/s/events/GW150914/P150914/" "fig2-unfiltered-waveform-H.txt") with get_readable_fileobj(url) as f: signal = TimeSeries.read(f, format='txt') signal.t0 = .5 # make sure this intersects with noise time samples
Note, since this simulation cuts off before a certain time, it is
important to taper its ends to zero to avoid ringing artifacts.
We can accomplish this using the
signal = signal.taper()
Since the time samples overlap, we can inject this into our noise data
data = noise.inject(signal)
Finally, we can visualize the full process in the time domain:
from gwpy.plot import Plot plot = Plot(noise, signal, data, separate=True, sharex=True, sharey=True) plot.gca().set_epoch(0) plot.show()
We can clearly see that the loud GW150914-like signal has been layered on top of Gaussian noise with the correct amplitude and phase evolution.