import basf2
import modularAnalysis as ma
from typing import List
from variables import variables as vm
import pandas as pd
from vibe.core.utils.misc import fancy_validation_mode_header
from vibe.core.validation_mode import ValidationModeBaseClass
from vibe.core.helper.skims.skim_attribute_tools import SkimAttributes, AnalysisParameters
from vibe.core.helper.histogram_tools import HistVariable, Histogram, HistComponent
from vibe.core.helper.root_helper import makeROOTCompatible
__all__ = [
"fei_compare",
]
[docs]
@fancy_validation_mode_header
class fei_compare(ValidationModeBaseClass):
name = "fei_compare"
[docs]
def get_skim_attributes(self):
this_skim = SkimAttributes(
# [REQUIRED] List of reconstruction types to use on a given lpn
skim_name = ['feiSL', 'feiHadronic'],
# [OPTIONAL] Globaltag
globaltag = 'analysis_tools_light-2305-korat',
# [OPTIONAL] When True set the number of events to be reconstructed to 100 (Decrease runtime for testing)
grid_test_mode = False,
# [OPTIONAL] If not None, full pipeline will be engaged and the AnalysisParameters is the space for the user to configure the analysis. What this allows the users
# to do is add globaltags and kwargs to the dataset_dict inside of skim_prodcution_mode.json that is automatically produced and updated during runtime
skim_to_analysis_pipeline = AnalysisParameters(
kwargs= {
"feiSL" : {"treename" : "B0:feiSL"},
"feiHadronic" : {"treename" : "B0:feiHadronic"}}
)
)
return this_skim
# Returning an empty path as b2luigi will be submitting a custom steering script
[docs]
def create_basf2_path(self, treename : str):
main_path = basf2.Path()
ma.matchMCTruth(
treename,
path=main_path,
)
# select signal + build ROE
ma.buildRestOfEvent(treename, path=main_path)
# define ROE mask cuts
tracksCut = "abs(d0) < 10.0 and abs(z0) < 20.0"
clustersCut1 = "[clusterE1E9 > 0.4 or E > 0.075] and [[E > 0.062 and abs(clusterTiming)<18 and clusterReg==1] or "
clustersCut2 = (
"[E > 0.060 and abs(clusterTiming)<20 and clusterReg==2]"
" or [E > 0.056 and abs(clusterTiming)<44 and clusterReg==3]]"
)
clustersCut = clustersCut1 + clustersCut2
# define and append ROE masks
m1 = ("m1", tracksCut, clustersCut)
ma.appendROEMasks(treename, [m1], path=main_path)
ma.rankByHighest(
treename,
"extraInfo(sigProb)",
#allowMultiRank=True,
outputVariable="sigProbRank",
path=main_path,
)
ma.buildEventShape(inputListNames=[treename], path=main_path)
ma.buildEventKinematics(inputListNames=[treename], path=main_path)
vm.addAlias('sigProbRank', 'extraInfo(sigProbRank)')
vm.addAlias('cosThetaBY', 'cosThetaBetweenParticleAndNominalB')
vm.addAlias(f"dmID", "extraInfo(decayModeID)")
self.variables_to_validation_ntuple(
decay_str=treename,
variables=list(
{
"cosThetaBY",
"isSignalAcceptMissingNeutrino",
"sigProbRank",
f"dmID",
"Mbc",
"foxWolframR2"
}
),
path=main_path,
)
return main_path
@property
def analysis_validation_histograms(self) -> List[Histogram]:
return [
Histogram(
name='cosThetaBY_allVSrank1',
title=r"cos$\theta_{BY}$: rank 1 vs rest",
hist_variable=HistVariable(
df_label=makeROOTCompatible(variable="cosThetaBY"),
label=r"cos$\theta_{BY}$",
unit=r"",
bins=50,
scope=(-5, 5),
),
hist_components = [
HistComponent(
label='all ranks'
),
HistComponent(
label="rank1 B's",
additional_cut_str= "sigProbRank==1",
),
]
),
Histogram(
name='Mbc',
title=r"$B^0$ Mbc",
hist_variable=HistVariable(
df_label=makeROOTCompatible(variable="Mbc"),
label=r"Mbc",
unit=r"GeV/c$^2$",
bins=50,
scope=(5, 5.3),
),
hist_components= [HistComponent(
label="Mbc"
)
]
),
Histogram(
name='dmID',
title=r"$B^0$ dmID",
hist_variable=HistVariable(
df_label=makeROOTCompatible(variable="dmID"),
label=r"dmID",
unit=r"",
bins=30,
scope=(0,30),
),
hist_components= [HistComponent(
label="dmID"
)
]
),
Histogram(
name='foxWolframR2',
title=r"foxWolframR2",
hist_variable=HistVariable(
df_label=makeROOTCompatible(variable="foxWolframR2"),
label=r"foxWolframR2",
unit=r"",
bins=10,
scope=(0,1),
),
hist_components=[HistComponent(
label="foxWolframR2"
)
]
)
]
[docs]
def get_number_of_signal_for_efficiency(self, df: pd.DataFrame) -> float:
return df["isSignalAcceptMissingNeutrino"].sum()
