Inject a known signal into a TimeSeries

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:

url = "https://gwosc.org/s/events/GW150914/P150914/fig2-unfiltered-waveform-H.txt"
signal = TimeSeries.read(url, 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 taper() method.

signal = signal.taper()

Since the time samples overlap, we can inject this into our noise data using inject():

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.