Support this project and keep always updated about recent software releases, bug fixes and major improvements by following on github.
Copyright (c) 2020-2023 Dr. Danny Petschke (danny.petschke@uni-wuerzburg.de). All rights reserved.
pyDCONTINPALS - A program in Python for running the historical FORTRAN code CONTIN-PALS initially provided by Provencher (1982) and Gregory et al. (1990/1991). CONTIN-PALS program solves Fredholm integral equations with convoluted exponential decays as kernels of the type that occur in the analysis of Positron Annihilation Lifetime Spectra (PALS).
pyDCONTINPALS
consists of 3 files ...
pyDCONTINPALS.py
pyDCONTINPALSInput.py
pyDCONTINPALSSpecSimulator.py
pyDCONTINPALSInput.py
:__demoMode = True # disable if running from real data
# NOTE: SPECTRUM and IRF (or mono-exponential decay spectrum) data vectors require equal length!
__roi_start = 0
__roi_end = 7400 # Note: number of channels is internally limited by CONTIN to <= 4000, so adjust the '__binFactor' in order to fit the given number of channels into this range
# file path (and name) to the SPECTRUM data:
__usingRefSpectrum = True # if set to FALSE the '__irfXXX' related parameters are considered
__filePathSpec = 'testData/spectrum_10ps.dat'
__specDataDelimiter = '\t'
# file path (and name) to the IRF data:
__filePathRefOrIRFSpec = 'testData/ref_10ps.dat'
__refDataDelimiter = '\t'
# define the number of rows to be skipped during the import of the data (e.g. for ignoring the header entries):
__skipRows = 5;
# fixed mono-decay component in units of picoseconds [ps] (1/lambda = tau):
# Note: set to values below 1E-6 if you are providing numerical IRF data as input otherwise the decay rate in [ps]:
__tau_monoDecaySpec_in_ps = 182. # [ps]
# used to model the IRF data in case '__usingRefSpectrum' = False:
__t_zero = 2000 # channel number
__irf_fwhm = [270.04,498.63] # [ps]
__irf_intensity = [0.9382,0.0618] # [ps]
__irf_t0 = [0.,6.6] # [ps]
# grid of characteristic lifetimes with equally distributed grid points defining the resulting intensity spectrum to be expected as output:
__gridTau_start = 10.0 # [ps]
__gridTau_stop = 3000.0 # [ps]
__gridPoints = 100 # 10 ... 100 Note: this value is internally limited by CONTIN
# channel/bin resolution [ps]:
__channelResolutionInPs = 10. # >= 10 ... Note: this value is internally limited by CONTIN. If lower, increase '__binFactor' to fit into this range
__binFactor = 2 # Note: number of channels is internally limited by CONTIN to <= 4000, so adjust the '__binFactor' in order to fit the given number of channels into this range
# background estimation/calculation region:
__bkgrd_startIndex = 6500;
__bkgrd_count = 900; # number of channels with respect to the 'startIndex''
execute pyDCONTINPALS.py
finished. You should see the results as shown above in the figures when running in the demo mode (__demoMode = True).
You can cite all released software versions by using the DOI 10.5281/zenodo.3665474. This DOI represents all versions, and will always resolve to the latest one.
v1.x
pyDCONTINPALS v1.04
pyDCONTINPALS v1.03
pyDCONTINPALS v1.02
pyDCONTINPALS v1.01
pyDCONTINPALS v1.0
Copyright (c) 2020-2023 Dr. Danny Petschke (danny.petschke@uni-wuerzburg.de) All rights reserved.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
For more details see GNU General Public License v3