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Update rates-based statistics to be modular #4608
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Update rates-based statistics to be modular #4608
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for feature in opts.statistic_features: | ||
if feature not in _allowed_statistic_features: | ||
err_msg = f"--statistic-feature {feature} not recognised" | ||
raise NotImplementedError(err_msg) |
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This shouldn't actually happen due to the argparse choices, but safety is best
**extra_kwargs) | ||
trigger_times = sds['end_time'] | ||
stat_t = rank_method.rank_stat_single((ifo, sds)) | ||
trigger_times = trigs['end_time'][:][trigger_keep_ids] |
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Some singles objects don't have the end time included
@@ -33,9 +33,18 @@ | |||
from .eventmgr_cython import logsignalrateinternals_computepsignalbins | |||
from .eventmgr_cython import logsignalrateinternals_compute2detrate | |||
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_allowed_statistic_features = [ |
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I'm not sure where is best to describe each feature here to be honest
# Assume best case scenario and use maximum signal rate | ||
s1 -= 2. * self.hist_max | ||
s1[s1 < 0] = 0 | ||
return s1 ** 0.5 | ||
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class ExpFitStatistic(QuadratureSumStatistic): | ||
class ExpFitStatistic(PhaseTDStatistic): |
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Subclassing PhaseTDStatistic here in order to get the phasetd stuff in init
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The numbers in the comparison table have been updated I am sure it is not a coincidence that the error in the coincs is double that of the singles, but I think a ~4e-6 difference is not too important given the dynamic range of the statistic. |
I am adding a description of the statistics to the docs - I am writing it in the |
Note that I found and fixed a bug in the way that |
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I thought it best to check the memory usage, and for pycbc_coinc_findtrigs with 1/140 of the bank and the
OLD:
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Noting here that it was suggested on today's call that we should wait to merge this until after creating the new PyCBC Live branch intended for the rest of O4 |
…ha_below_thresh keyword
…eclimate to be quiet" This reverts commit 4f082ea.
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Overview of changes
The exponential fit statistics are all similar, with simple factors added or subtracted, as discussed in #4594
I have refactored the
ExpFitStatistic
to be able to use the different features using a--statistic-features
option. These then all useexp_fit
as the--ranking-statistic
optionThe available features are:
phasetd
kde
dq
chirp_mass
sensitive_volume
normalize_fit_rate
exp_fit
statistics, but is implemented here explicitly so that theexp_fit_csnr
statistic can reuse thelognoiserate
function from theExpFitStatistic
I have also removed the different treatment of triggers with sngl_ranking below threshold; this is now required explicitly as
--statistic-keywords alpha_below_threshold:6
. Again this was so that theexp_fit_csnr
statistic can reuse thelognoiserate
function from theExpFitStatistic
In addition, there are some minor changes as well to fix some of the statistics which didn't work at all on previous master. There is a (just for reference) PR at #4607 to show these.
Testing
I have tested all existing (and working) statistics to check that given the appropriate features, the output remains identical.
The testing is done against the codes in #4607, so that we can test statistics against what they should be, rather than what they are.
Initial tests with a very small fraction of the bank have shown that the SNR-like statistics output files have identical hashes. The exp_fit statistics outputs are all the same to within a numpy.isclose test, i.e. ~1e-6 difference for values O(1). But I will add the results of more stringent testing here.
phasetd sensitive_volume normalize_fit_rate
alpha_below_thresh:6
phasetd sensitive_volume normalize_fit_rate chirp_mass
alpha_below_thresh:6
phasetd sensitive_volume normalize_fit_rate kde
alpha_below_thresh:6
phasetd sensitive_volume normalize_fit_rate dq
alpha_below_thresh:6
phasetd sensitive_volume normalize_fit_rate dq kde
alpha_below_thresh:6