Support this project and keep always updated about recent software releases, bug fixes and major improvements by following on github.

DLTReconvolution

Copyright (c) 2017-2019 Danny Petschke (danny.petschke@uni-wuerzburg.de). All rights reserved.

DLTReconvolution - A Python based software for the analysis of lifetime spectra using the iterative least-square reconvolution method.

This program allows the analysis of lifetime spectra using ...

either the experimentally obtained instrumental response (IRF)
or a recorded mono-exponential decay spectrum such as from 207-Bi.

Quickstart Guide

DLTReconvolution consists of 3 files ...

DReconvolutionInput.py
DReconvolutionModel.py
DReconvolutionProc.py

edit the input file DReconvolutionInput.py or just start the demonstration using the provided test data set:

# note: spectrum and IRF (or mono-exponential decay spectrum) data vectors require equal length!

# file path (and name) to the SPECTRUM data:
__filePathSpec          = 'testData/spectrum_5ps.dat'
__specDataDelimiter     = '\t'

# file path (and name) to the IRF data:
__filePathIRF           = 'testData/irf_5ps.dat'
__irfDataDelimiter      = '\t'

# if TRUE, the fitted model function according to '__modelType' will be used as IRF data
__bUsingModel           = False

# when using model function ('__bUsingModel' = True) choose type of model (defined in DReconvolutionModel.py)
# ------------------
# Gaussian       = 1
# Lorentz_Cauchy = 2
# Pseudovoigt1   = 3
# Pearson7       = 4
# ------------------
__modelType             = reconvModel.Gaussian

# define the number of rows to be skipped during the import (e.g. for ignoring the header entries)
__skipRows              = 5;

# channel/bin resolution [ps]
__channelResolutionInPs = 5.0

# binning factor:
__binningFactor         = 1;

# expected number of components (number of exponential decay functions - LIMITED to 4)
__numberOfExpDec        = 3

# expected discrete characteristic lifetimes (decay = 1/tau) -> start values in units of picoseconds [ps]
# note: the values are considered in top-down order (e.g.: for '__numberOfExpDec' = 2 --> '__expectedTau_1_in_ps' AND '__expectedTau_2_in_ps' will be considered)
__expectedTau_1_in_ps   = 110.0;
__expectedTau_2_in_ps   = 375.0;
__expectedTau_3_in_ps   = 2200.0;
__expectedTau_4_in_ps   = 160.0;

# fit weighting: y variance? w = 1/sqrt(y) for poisson noise assumption otherwise the weighting is equally distributed w = 1.0
__bUsingYVarAsWeighting = True

# background estimation
__bkgrd_startIndex      = 8900;
__bkgrd_count           = 1000; # number of channels with respect to the 'startIndex'

# fixed background? if set True, the value of the estimated background based on the calculated mean ['__bkgrd_startIndex':'__bkgrd_startIndex' + '__bkgrd_count'] will be used
__bkgrdFixed            = False;

# set TRUE if the IRF should be retrieved from a mono-exponential decay spectrum such as well annealed metals (Al, Fe, ..) or the 207-Bi isotope using the 'graphical deconvolution' technique presented by Koechlin & Raviart (1964) (in this case the IRF data will be ignored)
__bUsingMonoDecaySpecForIRF               = False

# fixed mono-decay component in units of picoseconds [ps] (1/lambda = tau):
__tau_monoDecaySpec_in_ps                 = 182.0 #[ps]

__filePathMonoDecaySpec                   = 'C:/Users/.../207_Bi.dat'
__monoDecaySpecDataDelimiter              = '\t'

# data pre-processing for indirect IRF extraction from a mono-exponential decay spectrum using the 'graphical deconvolution' technique presented by Koechlin & Raviart (1964):
# 1. stage: re-binning >> 2. stage: smoothing
# Note: re-binning is only applied in case of '__bSmoothMonoDecaySpecForIRF' = True

# 1. stage: re-binning
__bReBinMonoDecaySpecForIRF               = False
__bReBinFacMonoDecaySpecForIRF            = 4

# 2. stage: smoothing by Savitzky-Golay filtering
__bSmoothMonoDecaySpecForIRF              = False
__SmoothingWindowDecaySpecForIRF          = 11
__SmoothingPolynomialOrderDecaySpecForIRF = 3

# set TRUE if the IRF data should be artificially broadened (~FWHM) applying an additional convolution using a Gaussian kernel (e.g. for compensation of energy differences)
__bUsingAdditionalGaussianKernel          = False

__gaussianKernelFWHM                      = 90.2  #[ps]
__bVaryGaussianKernelFWHM                 = False #if TRUE, this values will be used a an additional fitting parameter

# save output as *.txt file after success?
__saveReconvolutionSpectrum             = False
__saveReconvolutionSpectrumPath         = 'C:/Users/.../Bi_207_analytical_additionalConvKernel_fitdata.txt'
__saveReconvolutionSpectrumResidualPath = 'C:/Users/.../Bi_207_analytical_additionalConvKernel_residuals.txt'

__saveIRFSpectrum                       = False
__saveIRFSpectrumPath                   = 'C:/Users/.../Bi_207_analytical_additionalConvKernel_irfdata.txt'

__saveReconvolutionResults              = False
__saveReconvolutionResultsPath          = 'C:/Users/.../Bi_207_analytical_additionalConvKernel_results.txt'

# note: IRF output is only saved if the model function is used, meaning '__bUsingModel' = True
__saveReconvolutionIRF                  = False
__saveReconvolutionIRFPath              = 'output/...*txt'
__saveReconvolutionIRFResidualPath      = 'output/...*txt'

execute DReconvolutionProc.py.
finished. You should see the results as shown above in the figures.

Related Publications using this Program

Limitations on the positron lifetime spectra decomposability applying the iterative least-square re-convolution method using the instrumental responses obtained from 207-Bi and 60-Co, Acta Physica Polonica A.

How to cite this Software?

You should at least cite the applied version of this program in your study.

You can cite all versions by using the DOI 10.5281/zenodo.1255105. This DOI represents all versions, and will always resolve to the latest one.

`v1.x`

DLTReconvolution v1.2

DLTReconvolution v1.1

DLTReconvolution v1.0

License of DLTReconvolution (BSD-3-Clause)

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
this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
Neither the name of the copyright holder "Danny Petschke" nor the names of
its contributors may be used to endorse or promote products derived from
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS
OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.