Plotting EventTable rate versus time for specific column bins

I would like to study the rate at which event triggers are generated by the ExcessPower gravitational-wave burst detection algorithm, over a small stretch of data, binned by various thresholds on signal-to-noise ratio (SNR).

The data from which these events were generated contain a simulated gravitational-wave signal, or hardware injection, used to validate the performance of the LIGO detectors and downstream data analysis procedures.

First, we import the EventTable object and read in a set of events from a LIGO_LW-format XML file containing a sngl_burst table

from gwpy.table import EventTable
events = EventTable.read(
    "H1-LDAS_STRAIN-968654552-10.xml.gz",
    tablename="sngl_burst",
    columns=["peak", "snr"],
)

Note

Here we manually specify the columns to read in order to optimise the read() operation to parse only the data we actually need.

Now we can use the binned_event_rates() method to calculate the event rate in a number of bins of SNR.

rates = events.binned_event_rates(
    1,
    "snr",
    [2, 3, 5, 8],
    operator=">=",
    start=968654552,
    end=968654562,
)

Note

The list [2, 3, 5, 8] and operator >= specifies SNR tresholds of 2, 3, 5, and 8.

Finally, we can make a plot:

plot = rates.step()
ax = plot.gca()
ax.set_xlim(968654552, 968654562)
ax.set_ylabel("Event rate [Hz]")
ax.set_title("LIGO Hanford Observatory event rate for HW100916")
ax.legend(title="SNR $>=$")
plot.show()