initial commit

initial commit

Author: dpscience

DReconvolutionInput.py

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+#*************************************************************************************************
+#**
+#** Copyright (c) 2017, 2018 Danny Petschke. All rights reserved.
+#** 
+#** Redistribution and use in source and binary forms, with or without modification, 
+#** are permitted provided that the following conditions are met:
+#**
+#** 1. Redistributions of source code must retain the above copyright notice, 
+#**    this list of conditions and the following disclaimer.
+#**
+#** 2. 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.
+#**
+#** 3. 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.
+#**
+#** Contact: danny.petschke@uni-wuerzburg.de
+#**
+#*************************************************************************************************
+
+from DReconvolutionModel import ReconvolutionModel as reconvModel
+
+#channel resolution [ps] -> x:
+__channelResInPicoseconds = 5.0
+
+#expected number of components (number of exponential decay functions - LIMITED to MAX: 4):
+__numberOfExpDec = 2
+
+
+#NOTE: Spectrum and IRF data vectors require equal length!!!
+
+#file path which contains the SPECTRUM data:
+__filePathSpec = r"C:\Users\danny\Desktop\Elsevier - Data in Brief\reconvolution\test3\AB_1_5Mio.dat"
+__specDataDelimiter = '\t'
+
+#file path which contains the IRF data:
+__filePathIRF = r"C:\Users\danny\Desktop\Elsevier - Data in Brief\reconvolution\test3\irf_AB.dat"
+__irfDataDelimiter = '\t'
+
+
+#using model function for IRF?
+__bUsingModel = True
+
+#fit weighting: y variance? w = 1/sqrt(y)
+__bUsingYVarAsWeighting = True
+
+#if using model function? choose type of model (also defined in DReconvolutionModel.py):
+#------------------
+#Gaussian       = 1
+#Lorentz_Cauchy = 2
+#Pseudovoigt1   = 3
+#Pearson7       = 4
+#------------------
+__modelType = reconvModel.Pearson7

DReconvolutionModel.py

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+#*************************************************************************************************
+#**
+#** Copyright (c) 2017, 2018 Danny Petschke. All rights reserved.
+#** 
+#** Redistribution and use in source and binary forms, with or without modification, 
+#** are permitted provided that the following conditions are met:
+#**
+#** 1. Redistributions of source code must retain the above copyright notice, 
+#**    this list of conditions and the following disclaimer.
+#**
+#** 2. 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.
+#**
+#** 3. 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.
+#**
+#** Contact: danny.petschke@uni-wuerzburg.de
+#**
+#*************************************************************************************************
+
+import numpy as np
+from scipy.special import beta
+from enum import Enum
+
+#list of available models:
+class ReconvolutionModel(Enum):
+     Gaussian       = 1
+     Lorentz_Cauchy = 2
+     Pseudovoigt1   = 3
+     Pearson7       = 4
+
+#definition of available models:
+def Gaussian(x, ampl, sigma, y0, x0, args=()):
+    h=np.zeros(x.size)
+    N=np.zeros(x.size)
+    N=1.0/(sigma*np.sqrt(2*np.pi))
+    h=N*np.exp(-0.5*((x-x0)/sigma)**2);
+    return ampl*h+y0
+
+def Lorentz_Cauchy(x, ampl, a, wing, y0, x0, args=()):
+    h=np.zeros(x.size)
+    h=wing/(np.pi*((x-x0)*(x-x0) + wing*wing))
+    return ampl*h+y0
+
+def Pseudovoigt1(x, ampl, a, sigma, wing, y0, x0, args=()):
+    G=np.zeros(x.size)
+    L=np.zeros(x.size)
+    G=(1.0/(sigma*np.sqrt(2*np.pi)))*np.exp(-0.5*((x-x0)/sigma)*((x-x0)/sigma))
+    L=wing/(np.pi*((x-x0)*(x-x0) + wing*wing))
+    return ampl*(a*G+(1-a)*L)+y0
+
+def Pearson7(x, ampl, alpha, m, y0, x0, args=()):
+    h=np.zeros(x.size)
+    h=(1/(alpha*beta(m-0.5,0.5)))*(1+((x-x0)/alpha)**2)**(-m)
+    return ampl*h+y0
+
+
+def convolveData(a, b): 
+    return np.real(np.fft.ifft(np.fft.fft(a)*np.fft.fft(b)))
+
+#1 component expontential distribution:
+def ExpDecay_1(x, ampl1, tau1, y0, x0, args=()):  
+    h = np.zeros(x.size) 
+    lengthVec = len(args)
+    
+    shift_1 = np.remainder(np.remainder(x-np.floor(x0)-1, lengthVec) + lengthVec, lengthVec)
+    shift_Incr1 = (1 - x0 + np.floor(x0))*args[shift_1.astype(int)]
+    
+    shift_2 = np.remainder(np.remainder(x-np.ceil(x0)-1, lengthVec) + lengthVec, lengthVec)
+    shift_Incr2 = (x0 - np.floor(x0))*args[shift_2.astype(int)]
+    
+    irf_shifted = (shift_Incr1 + shift_Incr2)
+    irf_norm = irf_shifted/sum(irf_shifted)
+    
+    h = ampl1*np.exp(-(x)/tau1)
+    hConvIrf_norm = convolveData(h, irf_norm) + y0
+    return hConvIrf_norm
+
+#2 component expontential distribution:
+def ExpDecay_2(x, ampl1, tau1, ampl2, tau2, y0, x0, args=()):  
+    h = np.zeros(x.size) 
+    lengthVec = len(x)
+
+    shift_1 = np.remainder(np.remainder(x-np.floor(x0)-1, lengthVec) + lengthVec, lengthVec)
+    shift_Incr1 = (1 - x0 + np.floor(x0))*args[shift_1.astype(int)]
+    
+    shift_2 = np.remainder(np.remainder(x-np.ceil(x0)-1, lengthVec) + lengthVec, lengthVec)
+    shift_Incr2 = (x0 - np.floor(x0))*args[shift_2.astype(int)]
+    
+    irf_shifted = (shift_Incr1 + shift_Incr2)
+    irf_norm = irf_shifted/sum(irf_shifted)
+    
+    h = ampl1*np.exp(-(x)/tau1) + ampl2*np.exp(-(x)/tau2)
+    hConvIrf_norm = convolveData(h, irf_norm) + y0
+    return hConvIrf_norm
+
+#3 component expontential distribution:
+def ExpDecay_3(x, ampl1, tau1, ampl2, tau2, ampl3, tau3, y0, x0, args=()):  
+    h = np.zeros(x.size) 
+    lengthVec = len(args)
+    
+    shift_1 = np.remainder(np.remainder(x-np.floor(x0)-1, lengthVec) + lengthVec, lengthVec)
+    shift_Incr1 = (1 - x0 + np.floor(x0))*args[shift_1.astype(int)]
+    
+    shift_2 = np.remainder(np.remainder(x-np.ceil(x0)-1, lengthVec) + lengthVec, lengthVec)
+    shift_Incr2 = (x0 - np.floor(x0))*args[shift_2.astype(int)]
+    
+    irf_shifted = (shift_Incr1 + shift_Incr2)
+    irf_norm = irf_shifted/sum(irf_shifted)
+    
+    h = ampl1*np.exp(-(x)/tau1) + ampl2*np.exp(-(x)/tau2) + ampl3*np.exp(-(x)/tau3)
+    hConvIrf_norm = convolveData(h, irf_norm) + y0
+    return hConvIrf_norm
+
+#4 component expontential distribution:
+def ExpDecay_4(x, ampl1, tau1, ampl2, tau2, ampl3, tau3, ampl4, tau4, y0, x0, args=()):  
+    h = np.zeros(x.size) 
+    lengthVec = len(args)
+    
+    shift_1 = np.remainder(np.remainder(x-np.floor(x0)-1, lengthVec) + lengthVec, lengthVec)
+    shift_Incr1 = (1 - x0 + np.floor(x0))*args[shift_1.astype(int)]
+    
+    shift_2 = np.remainder(np.remainder(x-np.ceil(x0)-1, lengthVec) + lengthVec, lengthVec)
+    shift_Incr2 = (x0 - np.floor(x0))*args[shift_2.astype(int)]
+    
+    irf_shifted = (shift_Incr1 + shift_Incr2)
+    irf_norm = irf_shifted/sum(irf_shifted)
+    
+    h = ampl1*np.exp(-(x)/tau1) + ampl2*np.exp(-(x)/tau2) + ampl3*np.exp(-(x)/tau3) + ampl4*np.exp(-(x)/tau4)
+    hConvIrf_norm = convolveData(h, irf_norm) + y0
+    return hConvIrf_norm
+
+
+
+
+