initial commit

initial commit

Author: dpscience

DReconvolutionInput.py

@@ -33,21 +33,30 @@
 
 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
 
+#channel/bin resolution [ps]
+__channelResolutionInPs = 5.0
+
+#expected lifetimes (tau) -> start values (levenberg marquardt fit)
+__expectedTau_1_in_ps = 160.0;
+__expectedTau_2_in_ps = 455.0;
+__expectedTau_3_in_ps = 160.0;
+__expectedTau_4_in_ps = 160.0;
+
+#background calculation (right side of spectrum data):
+__bkgrd_startIndex = 8000;
+__bkgrd_count = 1500;
 
 #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"
+__filePathSpec = 'testData/spectrum_5ps.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"
+__filePathIRF = 'testData/irf_5ps.dat'
 __irfDataDelimiter = '\t'
 
 
@@ -64,4 +73,4 @@
 #Pseudovoigt1   = 3
 #Pearson7       = 4
 #------------------
-__modelType = reconvModel.Pearson7
+__modelType = reconvModel.Gaussian

DReconvolutionModel.py

@@ -68,82 +68,3 @@ def Pearson7(x, ampl, alpha, m, y0, x0, args=()):
     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
-
-
-
-
-