ranking_HM.veto_subthreshold_top_cands#

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Summary#

Top candidates in the cands_preveto_max list which are subthreshold (i.e. have SNR below min_veto_chi2) are passed through all the veto tests again

Signature#

def veto_subthreshold_top_cands(rank_obj, ncores = 1, num_redo_bkg = 5000, triggers_dir = None, rerun_coh_score = True, fname_to_save = None, score_final_object = True, **ranking_kwargs)

Input variables#
Name	Type	Default	Description
`rank_obj`			Individual Rank object (not a list)
`ncores`		1
`num_redo_bkg`		5000	How many background triggers (ranked by IFAR starting from the highest) to redo vetos for
`triggers_dir`		None	Directory where the triggers are stored e.g., ‘/data/jayw/IAS/GW/Data/HM_O3b_search/OutputDir/’
`rerun_coh_score`		True	Flag whether to rerun the coherent score for all the top candidates (not just the subthreshold ones). The reason to do this is that coherent score is now calculated at a higher resolution compared the version in coincidence_HM.py (the coh score is now averaged over 10 iterations and calculated with slightly larger log2n_qmc and nphi)
`fname_to_save`		None	If known, filename to save the Rank object with the new vetoes to. If None, we apply the vetoes in place, but don’t save the object
`score_final_object`		True	Flag whether to score the final object
`\\ranking_kwargs`			Any extra arguments to pass to the ranking function. Used only if score_final_object is True Below is an example script for running this code on a cluster: salloc –nodes=1 –cpus-per-task=24 –time=12:00:00 –mem-per-cpu=4GB python import sys sys.path.insert(0,”…/gw_detection_ias”) import ranking_HM as rank # Need to load in the mode r+ as we will modify veto metadatas of triggers rank_obj = rank.Rank.from_hdf5(’–.hdf5’, mode=”r+”) rank.veto_subthreshold_top_cands(rank_obj, ncores=24, num_redo_bkg=5000, triggers_dir=’–’, fname_to_save=’—.hdf5’) # Safely close the file rank_obj.fobj.close()

Output variables#

Return annotation	Docstring type	Description
`None`

Docstring#

Top candidates in the cands_preveto_max list which are subthreshold
(i.e. have SNR below min_veto_chi2)
are passed through all the veto tests again

:param rank_obj: Individual Rank object (not a list)
:param ncores:
:param num_redo_bkg:
    How many background triggers (ranked by IFAR starting from the highest)
    to redo vetos for
:param triggers_dir: Directory where the triggers are stored
    e.g., '/data/jayw/IAS/GW/Data/HM_O3b_search/OutputDir/'
:param rerun_coh_score:
    Flag whether to rerun the coherent score for all the top candidates
    (not just the subthreshold ones).
    The reason to do this is that coherent score is now calculated at a
    higher resolution compared the version in coincidence_HM.py
    (the coh score is now averaged over 10 iterations and calculated with
    slightly larger log2n_qmc and nphi)
:param fname_to_save:
    If known, filename to save the Rank object with the new vetoes to.
    If None, we apply the vetoes in place, but don't save the object
:param score_final_object: Flag whether to score the final object
:param ranking_kwargs:
    Any extra arguments to pass to the ranking function. Used only if
    score_final_object is True

Below is an example script for running this code on a cluster:
    salloc --nodes=1 --cpus-per-task=24 --time=12:00:00 --mem-per-cpu=4GB
    python
    import sys
    sys.path.insert(0,".../gw_detection_ias")
    import ranking_HM as rank
    # Need to load in the mode r+ as we will modify veto metadatas of triggers
    rank_obj = rank.Rank.from_hdf5('--.hdf5', mode="r+")
    rank.veto_subthreshold_top_cands(rank_obj, ncores=24, num_redo_bkg=5000,
        triggers_dir='--', fname_to_save='---.hdf5')
    # Safely close the file
    rank_obj.fobj.close()