SlidingWindowMethod

class gdt.core.trigger.SlidingWindowMethod(algorithms: dict, background_window: int, background_offset: int, resolution: int, channel_edges: list, det_names: list, n: int = 1, verbose: bool = True)

Bases: object

Class for applying a sliding window trigger which identifies transients by looking for windows where n detectors to exceed a given significance threshold above background.

Parameters:

• algorithms (dict) – Dictionary defining the set of trigger algorithms applied in the trigger method

• background_window (int) – Length of time in integer milliseconds used to compute background counts

• background_offset (int) – Time offset in integer milliseconds between the end of the trigger and background windows

• resolution (int) – The time resolution in integer milliseconds of binned PHAII data used for the trigger method. All other timescales (windows, offsets) should be multiples of this value

• channel_edges (list) – List of channel edges used to define energy bins for PHAII data

• det_names (list) – Names of required detector numbers for the trigger method

• n (int) – Minimum number of detectors needed for trigger

• verbose (float) – Show progress bars when True

Attributes Summary

algorithms

Dictionary defining the set of trigger algorithms

Methods Summary

apply_holdoff(triggers, holdoff)	Apply a holdoff which ignores triggers occurring within the holdoff timescale of a prior trigger.
apply_trigger([holdoff, debug])	Applies the set of trigger algorithms to prepared data
lightcurve_plot(trigger[, time_range, ...])	Plot detector light curves with trigger overlay.
prepare_data(ttes[, time_range])	Method to prepare TTE data into a PHAII format for use with trigger algorithms.
waterfall_plot(triggers, **kwargs)	Create a waterfall plot showing all trigger windows as a function of algorithm number versus time.

Attributes Documentation

algorithms

Dictionary defining the set of trigger algorithms

Type:

(dict)

Methods Documentation

apply_holdoff(triggers: list, holdoff: float)

Apply a holdoff which ignores triggers occurring within the holdoff timescale of a prior trigger.

Parameters:

• triggers (list) – List of Trigger objects

• holdoff (float) – Trigger holdoff time in seconds

Returns:

(list)

apply_trigger(holdoff: float = None, debug: bool = False)

Applies the set of trigger algorithms to prepared data

Parameters:

• holdoff (float, optional) – Trigger holdoff time in seconds. Set to 300 sec to replicate GCN trigger notices.

• debug (bool, optional) – Show debugging information when True

Returns:

(list of Trigger)

lightcurve_plot(trigger: Trigger, time_range: tuple = None, detectors: list = None, figsize: tuple = None, resolution: int = None, legend: bool = True)

Plot detector light curves with trigger overlay.

Parameters:

• trigger (Trigger) – The trigger to overlay

• time_range (tuple) – Start and end of time range to plot in seconds

• detectors (list) – List of detector numbers to plot

• figsize (tuple) – Dimensions of the figure in inches

• resolution (int) – Lightcurve resolution in integer milliseconds

• legend (bool) – Show legend when True

Returns:

(tuple)

prepare_data(ttes: list, time_range: list = None)

Method to prepare TTE data into a PHAII format for use with trigger algorithms.

Parameters:

• ttes (list) – List of Tte objects

• time_range (list) – List with [tstart, tstop]

waterfall_plot(triggers: list, **kwargs)

Create a waterfall plot showing all trigger windows as a function of algorithm number versus time.

Parameters:

• triggers (list) – List of triggers

• kwargs (optional) – Optional arguments passed to rectangular patches drawn for each trigger

Returns:

(list)