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Copyright (c) 2019-2021 Danny Petschke (danny.petschke@uni-wuerzburg.de). All rights reserved.
DMLLTDetectorPulseDiscriminator - A supervised machine learning approach for shape-sensitive detector pulse discrimination in lifetime spectroscopy applications.
A python-based frameworkproviding the functionalities for TRAINing/EVALuating and TESTing a classifier such as the naive Bayes classifier on a set of CORRECT and FALSE/REJECT detector output pulses. Moreover, it provides the generation of lifetime spectra from shape-discriminated detector pulses applying the TRAINed classifier, i.e. the classifier providing the highest prediction accuracy.
A simple exchange protocol written in native C++ providing the functionality for streaming the acquired detector output pulses on a mass storage device according to the data format required from the pyDMLLTDetectorPulseDiscriminator framework for TRAINing/EVALuating and TESTing the classifiers. This protocol is not attached to any type of digitizer enabling an universal application of this approach.
- Store a number of CORRECT (OK) and FALSE (!OK) assigned output pulses each in a separate stream for both detectors A and B. In case of you are using the DRS4 evaluation board of the Paul-Scherrer Institute (PSI, Switzerland) the software tool DDRS4PALS does this job for you.
- TRAIN/EVALuate and TEST the classifiers for A and B on this set of streamed detector pulses.
For more details see examples. - Finally, generate the lifetime spectrum using the pulse stream recorded on the studied sample material by applying the TRAINed classifiers for pulse-shape discrimination.
relPath = 'C:/dpscience/'
"""
define your TRAINing data set for detector A and B:
"""
filenameTrue_A = relPath + 'A/true/A.drs4DataStream'
filenameFalse_A = relPath + 'A/false/A.drs4DataStream'
filenameTrue_B = relPath + 'B/true/B.drs4DataStream'
filenameFalse_B = relPath + 'B/false/B.drs4DataStream'
"""
pulse stream acquired of the studied sample which is used to generate the lifetime spectrum:
"""
dataStream = 'E:/Fe/pure_iron.drs4DataStream'
"""
location and filename where the resulting lifetime spectrum is stored:
"""
spectraOutput = relPath + 'spectrum/spectrum_Fe'
"""
configure the machine params of pulse (A):
"""
mlinputA = DMachineParams()
# baseline correction
mlinputA.m_startCell = 10
mlinputA.m_cellRegion = 150
# median filter
mlinputA.m_medianFilter = True
mlinputA.m_windowSize = 5
mlinputA.m_classifier = CalibratedClassifierCV(GaussianNB(), cv=2, method='isotonic')
"""
configure the machine params of pulse (B):
"""
mlinputB = DMachineParams()
# baseline correction
mlinputB.m_startCell = 10
mlinputB.m_cellRegion = 150
# median filter
mlinputB.m_medianFilter = True
mlinputB.m_windowSize = 5
mlinputB.m_classifier = CalibratedClassifierCV(GaussianNB(), cv=2, method='isotonic')
"""
define the number of labelled detector pulses (A/B) which should be used to TRAIN the machine's classifier prior to the lifetime spectrum generation:
"""
numberOfPulsesToBeTrainedCorrect_A = 16
numberOfPulsesToBeTrainedReject_A = 14
numberOfPulsesToBeTrainedCorrect_B = 14
numberOfPulsesToBeTrainedReject_B = 18
"""
train the machine's classifier for detector (A):
"""
mlinputA = trainPulses(fileNameCorrectPulses = filenameTrue_A,
fileNameRejectPulses = filenameFalse_A,
outputMachineFileName = '',
isPositivePolarity = False,
splitAfterNPulsesCorrect = numberOfPulsesToBeTrainedCorrect_A,
splitAfterNPulsesReject = numberOfPulsesToBeTrainedReject_A,
machineInput = mlinputA,
debug = True)
"""
train the machine's classifier for detector (B):
"""
mlinputB = trainPulses(fileNameCorrectPulses = filenameTrue_B,
fileNameRejectPulses = filenameFalse_B,
outputMachineFileName = '',
isPositivePolarity = False,
splitAfterNPulsesCorrect = numberOfPulsesToBeTrainedCorrect_B,
splitAfterNPulsesReject = numberOfPulsesToBeTrainedReject_B,
machineInput = mlinputB,
debug = True)
"""
generate the lifetime spectrum:
"""
createLifetimeSpectrum(machineInputA = mlinputA,
machineInputB = mlinputB,
pulseStreamFile = dataStream,
outputName = spectraOutput,
# negative pulse polarity
isPositivePolarity = False,
binWidth_in_ps = 5,
numberOfBins = 28000, # [ps]
offset_in_ps = 17000.0, # [ps]
B_as_start_A_as_stop = True,
# constant fraction (CF) levels
cf_level_A = 25.0, # [%]
cf_level_B = 25.0, # [%]
# PHS (start)
ll_phs_start_in_mV = 259.0, # [mV]
ul_phs_start_in_mV = 319.0, # [mV]
# PHS (stop)
ll_phs_stop_in_mV = 67.0, # [mV]
ul_phs_stop_in_mV = 107.0, # [mV]
# CF determination
cubicSpline = True,
cubicSplineRenderPoints = 200,
# median filter
medianFilterA = True,
windowSizeA = 9,
medianFilterB = True,
windowSizeB = 5,
debug = True)Presentation at 15th International Workshop on Slow Positron Beam Techniques & Applications (SLOPOS-15) in Prague (Sept. 2019)
- You should at least cite the following publication.
- Additionally, you must cite the applied version of the framework in your study.
You can cite all released software versions by using the DOI 10.5281/zenodo.2616929. This DOI represents all versions, and will always resolve to the latest one.
DMLLTDetectorPulseDiscriminator v1.0
Copyright (c) 2019-2021 Danny Petschke (danny.petschke@uni-wuerzburg.de). All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
-
Redistributions of source code must retain the above copyright notice
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Copyright (c) 2019-2021 Danny Petschke (danny.petschke@uni-wuerzburg.de) All rights reserved.
This program is free software: you can redistribute it and/or modify
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