ranking_HM.maximize_over_banks#

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Summary#

Note that Seth vetoed the triggers before the bank assignment

Signature#

def maximize_over_banks(list_of_rank_objs, maxopts_filepath = None, incoherent_score_func = utils.incoherent_score, coherent_score_func = utils.coherent_score, mask_veto_criteria = None, apply_veto_before_scoring = False, global_maximization_format = 'new', matching_point = None, downsampling_correction = True, include_vetoed_triggers = False, p_veto_real_event = (DEFAULT_P_VETO, DEFAULT_P_VETO), **ranking_kwargs)

Input variables#
Name	Type	Default	Description
`list_of_rank_objs`			List of Rank objects
`maxopts_filepath`		None	Path to a hdf5 file to save the maximization options to. If it doesn’t exist, it’s created and populated with the given options. If it exists, it’s overwritten.
`incoherent_score_func`		utils.incoherent_score	Function that accepts two processedclists for a trigger and returns an incoherent score
`coherent_score_func`		utils.coherent_score	Function that accepts the coherent terms for a trigger and returns the coherent score (just the sum by default)
`mask_veto_criteria`		None	If needed, pass in a mask on rank_obj.veto_metadata_keys to identify glitch tests that we use, to override the default (everything)
`apply_veto_before_scoring`		False	Whether to apply the vetoes before scoring (we always apply them after scoring anyway) Can be a boolean variable, or a mask on rank_obj.veto_metadata_keys To reproduce O3a, i.e., 2201.02252, pass a mask with ones everywhere except at the entry corresponding to ‘Secondary_peak_timeseries’ False is the recommended input for all future catalogs
`global_maximization_format`		‘new’	‘new’ for the new format, ‘old’ for the old format If ‘new’, we ensure that the maximization over banks/subbanks does not depend on the ordering ‘old’ was the default for all published catalogs before 08-29-2024 ‘new’ is the recommended input for all future catalogs
`matching_point`		None	Where we match the rank functions
`downsampling_correction`		True	If the triggers were downsampled compared to a chi-sq distribution because of an additional cut (e.g., based on whether the mode ratios A33/A22 or A44/A22 are physical). This flag corrects the rank function so that it follows the chi-sq behavior again. This flag needs a file downsamp_corr_path.npy to be input when creating the Rank instance
`include_vetoed_triggers`		False	Flag whether to include the triggers which failed the vetos in our final list
`p_veto_real_event`		(DEFAULT_P_VETO, DEFAULT_P_VETO)	Tuple with functions for the probability that a real event fails the vetoes in each detector, which in the most general case can be a function of all properties of the trigger. The functions should accept a list of entries of scores_(non)vetoed_max and yield an array of probabilities. Used only if include_vetoed_triggers is True
`\\ranking_kwargs`

Output variables#

Return annotation	Docstring type	Description
`None`		Considers all the banks in list_of_rank_objs together, assigns each trigger to a single (bank, subbank) pair and populates cands_preveto_max in all the banks

Docstring#

Note that Seth vetoed the triggers before the bank assignment
:param list_of_rank_objs: List of Rank objects
:param maxopts_filepath:
    Path to a hdf5 file to save the maximization options to.
    If it doesn't exist, it's created and populated with the given options.
    If it exists, it's overwritten.
:param incoherent_score_func:
    Function that accepts two processedclists for a trigger and returns an
    incoherent score
:param coherent_score_func:
    Function that accepts the coherent terms for a trigger and returns
    the coherent score (just the sum by default)
:param mask_veto_criteria:
    If needed, pass in a mask on rank_obj.veto_metadata_keys to
    identify glitch tests that we use, to override the default (everything)
:param apply_veto_before_scoring:
    Whether to apply the vetoes before scoring (we always apply them after
    scoring anyway)
    Can be a boolean variable, or a mask on rank_obj.veto_metadata_keys
    To reproduce O3a, i.e., 2201.02252, pass a mask with ones everywhere
    except at the entry corresponding to 'Secondary_peak_timeseries'
    False is the recommended input for all future catalogs
:param global_maximization_format:
    'new' for the new format, 'old' for the old format
    If 'new', we ensure that the maximization over banks/subbanks does not
    depend on the ordering
    'old' was the default for all published catalogs before 08-29-2024
    'new' is the recommended input for all future catalogs
:param downsampling_correction:
    If the triggers were downsampled compared to a chi-sq distribution
    because of an additional cut (e.g., based on whether the mode ratios
    A33/A22 or A44/A22 are physical). This flag corrects the rank function
    so that it follows the chi-sq behavior again. This flag needs a file
    downsamp_corr_path.npy to be input when creating the Rank instance
:param matching_point: Where we match the rank functions
:param include_vetoed_triggers: Flag whether to include the triggers which
    failed the vetos in our final list
:param p_veto_real_event:
    Tuple with functions for the probability that a real event fails the
    vetoes in each detector, which in the most general case can be a
    function of all properties of the trigger. The functions should accept a
    list of entries of scores_(non)vetoed_max and yield an array of
    probabilities. Used only if include_vetoed_triggers is True
:return:
    Considers all the banks in list_of_rank_objs together, assigns each
    trigger to a single (bank, subbank) pair and populates
    cands_preveto_max in all the banks