Calculating event trigger rate

Simple event rate

For discrete data in the form of event triggers, it is often very illuminating to analyse the rate at which these events occur in time - usually a high event rate is indicative of a higher noise level. One can calculate the event rate of an EventTable via the event_rate() method.

For example, using the same data as before we can calculate and plot the event rate on a 1-second stride:

>>> from gwpy.table import EventTable
>>> events = EventTable.read('H1-LDAS_STRAIN-968654552-10.xml.gz', tablename='sngl_burst', columns=['peak', 'central_freq', 'snr'])
>>> rate = events.event_rate(1, start=968654552, end=968654562, timecolumn='peak')
>>> plot = rate.step()
>>> ax = plot.gca()
>>> ax.set_ylabel('Event rate [Hz]')
>>> ax.set_title('LIGO Hanford Observatory event rate for HW100916')
>>> plot.show()

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This code is a snippet of the example Calculating (and plotting) rate versus time for an EventTable.

Binned event rate

Following from a simple rate versus time calculation, it is often useful to calculate the event rate for multiple conditions on the same table. For this, we can use the binned_event_rates() method.

For example, in the following example, we calculate the rate of events with signal-to-noise ratio (SNR) above some threshold, for the same table as above.

>>> rates = events.binned_event_rates(1, 'snr', [2, 3, 5, 8], operator='>=', start=968654552, end=968654562, timecolumn='peak')
>>> plot = rates.step()
>>> ax = plot.gca()
>>> ax.set_ylabel('Event rate [Hz]')
>>> ax.set_title('LIGO Hanford Observatory event rate for HW100916')
>>> plot.show()

(png)

(png)

This code is a snippet of the example on Plotting EventTable rate versus time for specific column bins.