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Use pax refactored S1 AFT probability calculation #193

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Jan 9, 2018
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Use pax refactored S1 AFT probability calculation #193

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34 changes: 8 additions & 26 deletions hax/treemakers/posrec.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
from pax.configuration import load_configuration
from pax import utils
from pax import exceptions
from pax.plugins.interaction_processing.S1AreaFractionTopProbability import binom_test, binom_pmf
from pax.plugins.interaction_processing.S1AreaFractionTopProbability import s1_area_fraction_top_probability
from keras.models import model_from_json
from hax.corrections_handler import CorrectionsHandler

Expand Down Expand Up @@ -214,34 +214,16 @@ def extract_data(self, event):
event_data['s1_area_lower_injection_fraction'] = area_lower_injection / s1.area

# Want S1 AreaFractionTop Probability
if s1.area < self.low_pe_threshold:
s1_frac = s1.area / self.low_pe_threshold
hits_top = s1.n_hits * s1.hits_fraction_top
s1_top = s1.area * s1.area_fraction_top
size_top = hits_top * (1. - s1_frac) + s1_top * s1_frac
size_tot = s1.n_hits * (1. - s1_frac) + s1.area * s1_frac
else:
size_top = s1.area * s1.area_fraction_top
size_tot = s1.area

aft = self.corrections_handler.get_correction_from_map(
"s1_aft_map", self.run_number, [self.x[event_index],
self.y[event_index], self.z[event_index]])
aft_prob = self.corrections_handler.get_correction_from_map(
"s1_aft_map", self.run_number, [self.x[event_index], self.y[event_index], self.z[event_index]])

event_data['s1_area_fraction_top_binomial'] = binom_pmf(size_top, size_tot, aft)
event_data['s1_area_fraction_top_probability_hax'] = binom_test(size_top, size_tot, aft)
aft_args = aft_prob, s1.area, s1.area_fraction_top, s1.n_hits, s1.hits_fraction_top

# Use area below S1=10 instead of hits
if s1.area < self.low_pe_threshold:
size_top = s1.area * s1.area_fraction_top
size_tot = s1.area
event_data['s1_area_fraction_top_probability_hax'] = s1_area_fraction_top_probability(*aft_args)
event_data['s1_area_fraction_top_binomial'] = s1_area_fraction_top_probability(*(aft_args + (10, 'pmf')))

event_data['s1_area_fraction_top_probability_nothresh'] = binom_test(size_top, size_tot, aft)
event_data['s1_area_fraction_top_binomial_nothresh'] = binom_pmf(size_top, size_tot, aft)

else:
event_data['s1_area_fraction_top_probability_nothresh'] = event_data['s1_area_fraction_top_probability_hax']
event_data['s1_area_fraction_top_binomial_nothresh'] = event_data['s1_area_fraction_top_binomial']
event_data['s1_area_fraction_top_probability_nothresh'] = s1_area_fraction_top_probability(*(aft_args + (0,)))
event_data['s1_area_fraction_top_binomial_nothresh'] = s1_area_fraction_top_probability(*(aft_args + (0, 'pmf')))

# Now do s1_pattern_fit
apc = np.array(list(s1.area_per_channel))
Expand Down