coincidence_HM.find_interesting_dir#

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Summary#

Goes through trigger files for H1 and L1 in a directory and performs coincidence analysis

Signature#

def find_interesting_dir(dir_name, enumerated_epochs = None, n_epochs = None, time_shift_tol = 0.01, score_reduction_max = 5, threshold_chi2 = 60.0, max_time_slide_shift = 100, minimal_time_slide_jump = 0.1, min_veto_chi2 = 30, max_zero_lag_delay = 0.015, out_fname = None, n_cores = 1, run = 'O3a', opt_format = 'new', outfile_format = 'new', old_cand_dir_name = None, bad_times = None, veto_triggers = True, output_timeseries = True, output_coherent_score = True, score_reduction_timeseries = 10, detectors = ('H1', 'L1'), weaker_detectors = (), recompute_psd_drift = False)

Input variables#
Name	Type	Default	Description
`dir_name`			Path to a directory with json files for H1 and L1
`enumerated_epochs`		None	If desired, list of integer epochs to analyze
`n_epochs`		None	If desired, restrict to this number of H1 epochs
`time_shift_tol`		0.01	Width (s) of buckets to collect triggers into, the \`friends” of a trigger with the same calpha live within the same bucket
`score_reduction_max`		5	Absolute reduction in SNR^2 from the peak value in each bucket to retain (we also have a hardcoded relative reduction)
`threshold_chi2`		60.0	Threshold in sum(SNR^2) above which we consider triggers for the background list (or signal)
`max_time_slide_shift`		100	Max delay allowed for background triggers
`minimal_time_slide_jump`		0.1	Jumps in timeslides
`min_veto_chi2`		30	Apply vetos to candidates above this SNR^2 in a single detector
`max_zero_lag_delay`		0.015	Maximum delay between detectors within the same timeslide
`out_fname`		None	Path to npy file to save the candidates to
`n_cores`		1	Number of cores to use for splitting the veto computations
`run`		‘O3a’	String identifying the run
`opt_format`		‘new’	How we choose the finer calpha grid, changed between O1 and O2 analyses Exposed here to replicate old runs if needed
`outfile_format`		‘new’	FLag whether to save the old style (separate npy files for different arrays), or in the new style with a consolidated file per job
`old_cand_dir_name`		None	Directory with old vetoed candidate files, if we want to save on veto computations when redoing
`bad_times`		None	List of lists of times to avoid in H1 and L1, if known
`veto_triggers`		True	Flag to turn the veto on/off
`output_timeseries`		True	Flag to output timeseries for the candidates
`output_coherent_score`		True	Flag to compute the coherent score integral for the candidates
`score_reduction_timeseries`		10	Restrict triggers in timeseries to the ones with single_detector_SNR^2 > (base trigger SNR^2) - this parameter
`detectors`		(‘H1’, ‘L1’)	Tuple with names of the two detectors we will be running coincidence with (‘H1’, ‘L1’, ‘V1’ supported)
`weaker_detectors`		()	If needed, tuple with names of weaker detectors that we will compute timeseries for as well (‘H1’, ‘L1’, ‘V1’ supported) Note: Only works with outfile_format == “new”
`recompute_psd_drift`		False	Flag to recompute PSD drift correction. We needed it in O2 since the trigger files didn’t use safemean. Redundant in O3a and forwards

Output variables#

Return annotation	Docstring type	Description
`None`

Docstring#

Goes through trigger files for H1 and L1 in a directory and performs
coincidence analysis
:param dir_name: Path to a directory with json files for H1 and L1
:param enumerated_epochs: If desired, list of integer epochs to analyze
:param n_epochs: If desired, restrict to this number of H1 epochs
:param time_shift_tol:
    Width (s) of buckets to collect triggers into, the `friends" of a
    trigger with the same calpha live within the same bucket
:param score_reduction_max:
    Absolute reduction in SNR^2 from the peak value in each bucket to
    retain (we also have a hardcoded relative reduction)
:param threshold_chi2:
    Threshold in sum(SNR^2) above which we consider triggers for the
    background list (or signal)
:param max_time_slide_shift: Max delay allowed for background triggers
:param minimal_time_slide_jump: Jumps in timeslides
:param min_veto_chi2:
    Apply vetos to candidates above this SNR^2 in a single detector
:param max_zero_lag_delay:
    Maximum delay between detectors within the same timeslide
:param out_fname: Path to npy file to save the candidates to
:param n_cores: Number of cores to use for splitting the veto computations
:param run: String identifying the run
:param opt_format:
    How we choose the finer calpha grid, changed between O1 and O2 analyses
    Exposed here to replicate old runs if needed
:param outfile_format:
    FLag whether to save the old style (separate npy files for different
    arrays), or in the new style with a consolidated file per job
:param old_cand_dir_name:
    Directory with old vetoed candidate files, if we want to save on veto
    computations when redoing
:param bad_times: List of lists of times to avoid in H1 and L1, if known
:param veto_triggers: Flag to turn the veto on/off
:param output_timeseries: Flag to output timeseries for the candidates
:param output_coherent_score:
    Flag to compute the coherent score integral for the candidates
:param score_reduction_timeseries:
    Restrict triggers in timeseries to the ones with
    single_detector_SNR^2 > (base trigger SNR^2) - this parameter
:param detectors:
    Tuple with names of the two detectors we will be running coincidence
    with ('H1', 'L1', 'V1' supported)
:param weaker_detectors:
    If needed, tuple with names of weaker detectors that we will compute
    timeseries for as well ('H1', 'L1', 'V1' supported)
    Note: Only works with outfile_format == "new"
:param recompute_psd_drift:
    Flag to recompute PSD drift correction. We needed it in O2 since the
    trigger files didn't use safemean. Redundant in O3a and forwards
:return: