ranking_HM.Rank.rank_scores_calc_MZ =================================== Back to :doc:`Class page <../classes/ranking_HM.Rank>` Summary ------- Calculating ranking scores for all the bg, fg, lsc, inj triggers. The rank functions are constructed in a sub-function make_score_funcs_MZ(). We first group the templates based on their "glitchiness" and then make separate rank functions for different groups Signature --------- .. code-block:: python def rank_scores_calc_MZ(self, i_subbank, safety_factor = 4, matching_point = None, downsampling_correction = True, min_trigs_per_grp = 500, output_rank_func_temp_grps = False, group_coarse_calphas = False, n_calpha_dim = 2, snr2_ref = None, n_snr2_ref = 0.001, vetoed = True, rhosqs_arrays = None, calphas_arrays = None, masks_vetoed = None, **score_func_kwargs) .. list-table:: Input variables :header-rows: 1 * - Name - Type - Default - Description * - ``i_subbank`` - - - - - index of the subbank of the bank * - ``safety_factor`` - - - 4 - Add this to threshold_chi2 before estimating the rank functions to account for incompleteness related to optimization * - ``matching_point`` - - - None - Set SNR^2 at which the rank functions are matched, the default is threshold network SNR^2/2 (the rank fns are made such that all template groups within the same subbank match at self.snr2min and the least glitchy template group in all subbanks match at matching_point) * - ``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 Rank class object * - ``min_trigs_per_grp`` - - - 500 - To avoid pathologies with making the rank functions, we require that the templates in each group have more than a particular number of background triggers associated to them * - ``output_rank_func_temp_grps`` - - - False - Flag if you want to output the rank funcs for separate template groups for reference * - ``group_coarse_calphas`` - - - False - Flag whether we group the coarse or fine calphas * - ``n_calpha_dim`` - - - 2 - Number of dimensions to use for grouping calphas * - ``snr2_ref`` - - - None - Quantify templates' glitchiness by the fraction of triggers with SNR>snr2_ref * - ``n_snr2_ref`` - - - 0.001 - Set snr2_ref by demanding that in the Gaussian noise case, the expected number of triggers > snr2_ref is n_snr2_ref over all calphas. Used only if snr2_ref is None * - ``vetoed`` - - - True - Flag to compute scores only for vetoed triggers * - ``rhosqs_arrays`` - - - None - If known, 4 x n_event x 2 array of SNR^2 values for background, foreground, LVC, injections (pass to avoid looping over hdf5) * - ``calphas_arrays`` - - - None - If known, 4 x n_event x n_calpha_dim array of calphas * - ``masks_vetoed`` - - - None - If known, array of length 4 with masks for vetoed triggers This makes us set vetoed to True * - ``\*\*score_func_kwargs`` - - - - - - Output variables ---------------- .. list-table:: :header-rows: 1 * - Return annotation - Docstring type - Description * - ``None`` - - - - Docstring --------- .. code-block:: text Calculating ranking scores for all the bg, fg, lsc, inj triggers. The rank functions are constructed in a sub-function make_score_funcs_MZ(). We first group the templates based on their "glitchiness" and then make separate rank functions for different groups :param i_subbank: index of the subbank of the bank :param safety_factor: Add this to threshold_chi2 before estimating the rank functions to account for incompleteness related to optimization :param matching_point: Set SNR^2 at which the rank functions are matched, the default is threshold network SNR^2/2 (the rank fns are made such that all template groups within the same subbank match at self.snr2min and the least glitchy template group in all subbanks match at matching_point) :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 Rank class object :param min_trigs_per_grp: To avoid pathologies with making the rank functions, we require that the templates in each group have more than a particular number of background triggers associated to them :param output_rank_func_temp_grps: Flag if you want to output the rank funcs for separate template groups for reference :param group_coarse_calphas: Flag whether we group the coarse or fine calphas :param n_calpha_dim: Number of dimensions to use for grouping calphas :param snr2_ref: Quantify templates' glitchiness by the fraction of triggers with SNR>snr2_ref :param n_snr2_ref: Set snr2_ref by demanding that in the Gaussian noise case, the expected number of triggers > snr2_ref is n_snr2_ref over all calphas. Used only if snr2_ref is None :param vetoed: Flag to compute scores only for vetoed triggers :param rhosqs_arrays: If known, 4 x n_event x 2 array of SNR^2 values for background, foreground, LVC, injections (pass to avoid looping over hdf5) :param calphas_arrays: If known, 4 x n_event x n_calpha_dim array of calphas :param masks_vetoed: If known, array of length 4 with masks for vetoed triggers This makes us set vetoed to True