cluster_position_algo.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
 
 
 
# This steering file to create an localdb containing PXDClusterPosition payloads
# in a localdb
#
# Execute as: basf2 cluster_position_algo.py
 
from ROOT import Belle2
from ROOT.Belle2 import IntervalOfValidity
 
# Only initialize RootInput, as we do not loop over events,
# only load persistent objects stored during data collection
clusteralgo = Belle2.PXDClusterPositionCalibrationAlgorithm()
 
# Set the prefix manually if you want to use the hotpixelkiller for a specific collector
clusteralgo.setPrefix("PXDClusterPositionCollector")
 
# Can use a Python list of input files/wildcards. It will resolve the existing files
clusteralgo.setInputFileNames(["PXDClusterPositionCollectorOutput_kind_0.root", "PXDClusterPositionCollectorOutput_kind_1.root",
                               "PXDClusterPositionCollectorOutput_kind_2.root", "PXDClusterPositionCollectorOutput_kind_3.root"])
 
# Here, we can play around with algo parameters
# Minimum number of collected clusters for estimating shape likelyhood
clusteralgo.minClusterForShapeLikelyhood = 250
# Minimum number of collected clusters for estimating cluster position offsets
clusteralgo.minClusterForPositionOffset = 1000
# Maximum number of eta bins for estimating cluster position offsets
clusteralgo.maxEtaBins = 10
# Select the clusterkinds to calibrate. This should match with the supplied source files.
clusteralgo.clusterKinds.push_back(0)
clusteralgo.clusterKinds.push_back(1)
clusteralgo.clusterKinds.push_back(2)
clusteralgo.clusterKinds.push_back(3)
 
# Could also define an IoV for your calibrations at the start of execution
iov = IntervalOfValidity.always()
print("Result of calibration =", clusteralgo.execute([], 0, iov))
# IoV is applied to payloads during execution and gets used during commit
clusteralgo.commit()