Inject a signal into a FrequencySeries

It can often be useful to add some known signal to inherently random or noisy data. For example, one might want to investigate 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 loud, steady sinuosoid. We will do this in the frequency domain because it is much easier to model a sinusoid there.

Generate random data

First, we prepare one second of Gaussian noise using numpy.random.Generator.normal:

from numpy.random import default_rng

from gwpy.timeseries import TimeSeries

rng = default_rng()
noise = TimeSeries(rng.normal(scale=.1, size=1024), sample_rate=1024)

To inject a signal in the frequency domain, we need to take an FFT:

noisefd = noise.fft()

Inject noise

We can now easily inject a loud sinusoid of unit amplitude at, say, 30 Hz. To do this, we use inject():

import numpy

from gwpy.frequencyseries import FrequencySeries

signal = FrequencySeries(numpy.array([1.]), f0=30, df=noisefd.df)
injfd = noisefd.inject(signal)

Visualisation

We can then visualize the data before and after injection in the frequency domain:

from gwpy.plot import Plot

plot = Plot(
    numpy.abs(noisefd),
    numpy.abs(injfd),
    xscale="log",
    yscale="log",
)
plot.show()

Finally, for completeness we can visualize the effect before and after injection back in the time domain:

inj = injfd.ifft()
plot = Plot(
    noise,
    inj,
    separate=True,
    sharex=True,
    sharey=True,
    figsize=(12, 6),
)
plot.show()