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:.

EventTable.event_rate(stride, start=None, end=None, timecolumn=None)[source]

Calculate the rate TimeSeries for this Table.

Parameters

stride : float

size (seconds) of each time bin

start : float, LIGOTimeGPS, optional

GPS start epoch of rate TimeSeries

end : float, LIGOTimeGPS, optional

GPS end time of rate TimeSeries. This value will be rounded up to the nearest sample if needed.

timecolumn : str, optional

name of time-column to use when binning events, attempts are made to guess this

Returns

rate : TimeSeries

a TimeSeries of events per second (Hz)

Raises

ValueError

if the timecolumn cannot be guessed from the table contents

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()

(png)

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:

EventTable.binned_event_rates(stride, column, bins, operator='>=', start=None, end=None, timecolumn=None)[source]

Calculate an event rate TimeSeriesDict over a number of bins.

Parameters

stride : float

size (seconds) of each time bin

column : str

name of column by which to bin.

bins : list

a list of tuples marking containing bins, or a list of floats defining bin edges against which an math operation is performed for each event.

operator : str, callable

one of:

'<', '<=', '>', '>=', '==', '!=', for a standard mathematical operation,

'in' to use the list of bins as containing bin edges, or

a callable function that takes compares an event value against the bin value and returns a boolean.

Note

If bins is given as a list of tuples, this argument is ignored.

start : float, LIGOTimeGPS, optional

GPS start epoch of rate TimeSeries.

end : float, LIGOTimeGPS, optional

GPS end time of rate TimeSeries. This value will be rounded up to the nearest sample if needed.

timecolumn : str, optional, default: time

name of time-column to use when binning events

Returns

rates : ~gwpy.timeseries.TimeSeriesDict`

a dict of (bin, TimeSeries) pairs describing a rate of events per second (Hz) for each of the bins.

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()

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