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Commit 9dafaba5 authored by Klaus Rabbertz's avatar Klaus Rabbertz
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Add combine settings for new aggressive merging

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[Paths]
raw_dir = .
out_dir = Combined
[Observables]
ALL
#> rebin an observable
# observable > observable_rebin : [x0, x1, x2, ... xN]
[Parts]
LO
V
R
RRa
RRb
VV
RV
[Final]
LO = LO
R = R
V = V
RRa = RRa
RRb = RRb
RV = RV
VV = VV
NLO = LO + R + V
NNLO = LO + R + V + RRa + RRb + RV + VV
NLO_only = R + V
NNLO_only = RRa + RRb + RV + VV
[Options]
#> search the directory structure recursive for data files
# recursive = True
#> output weight tables: for APPLfast
weights = True
#> restrict merge to only certain columns: e.g. drop channel breakdown for performance
# columns = ['tot_scale01', 'tot_scale02', 'tot_scale03', 'tot_scale04', 'tot_scale05', 'tot_scale06', 'tot_scale07']
# The outlier rejection is controlled by two numbers:
# - The threshold (something like a deviation from the mean in units of sigma;
# but computed in terms of the inter-quantile distances).
# Lowering this value will flag more points as outliers (default = 4).
# - The highest fraction of points that are allowed to be removed.
# If step 1 flags too many points in a bin, adjust it dynamically to always stay below this fraction.
# This is a safety feature to avoid rejecting too many points (default = 0.05 = 5%).
#
# instead of ~4 sigma, start flagging at ~3 sigma
#trim = (4, 0.05)
#trim = (4, 0.08)
#trim = (3, 0.05)
#trim = (3, 0.08)
trim = (3.5, 0.03)
# By default do not use the option below to allow a better suppression of outliers.
# However, if order independence is important like when cross checking two identical
# NNLOJET inputs against each other, e.g. from a fixed- vs. flexible-scale grid comparison,
# then do use the following option.
#k-scan = (None, None, None)
k-scan = (None, 3, 0.75)
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