# 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'