#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
#if using model function? 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, which should 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 MAX: 4):
__numberOfExpDec=3
#expected discrete characteristic lifetimes (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 are considered)
__expectedTau_1_in_ps=108.0;
__expectedTau_2_in_ps=385.0;
__expectedTau_3_in_ps=2200.0;
__expectedTau_4_in_ps=160.0;
#fit weighting: y variance? w = 1/sqrt(y) <--- <assumption: poisson noise> otherwise the weighting is equally distributed: w = 1.0
__bUsingYVarAsWeighting=True
#background estimation:
__bkgrd_startIndex=9000;
__bkgrd_count=1000; # number of channels with respect to the 'startIndex'
#fixed background? >> if 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
#set TRUE if synthetically generated data should be used: