a few fixes

Gammaspektroskopie/tools/gamma_tools.py

@@ -34,8 +34,6 @@ def gaussian (x, mu, sigma = 50, baseline = 5, s = 0.9, N = 1000, xmin = 0, xmax
 
 def compton_edge (x, mu, sigma = 10, A = 1, B = 0.5, baseline = 5, s = 0.9, N = 1000, xmin = 0, xmax = 100):
     return s * special.erfc((x-mu)/(np.sqrt(2) * sigma)) /(2*(mu-xmin))  * (1 + (A * (x/(mu * B) - 1)**2)) + (1-s)*baseline/(xmax-xmin)
-#    return s * special.erfc((x-mu)/(np.sqrt(2) * sigma)) /(2*(mu-xmin)) + (1-s)*baseline/(xmax-xmin)
-#    return (s * special.erfc((x-mu)/(np.sqrt(2) * sigma)) * (1 + B * (x/mu - 0.5)**2))/(xmax-xmin) + (1-s)*baseline/(xmax-xmin)
 
 
 
@@ -43,7 +41,7 @@ def compton_edge (x, mu, sigma = 10, A = 1, B = 0.5, baseline = 5, s = 0.9, N =
 # Fit a photopeak using a Gaussian model function #
 # ##################################################
 
-def fit_peak (datax, datay, x1 = None, x2 = None, showResult = True, label = None, **initparams):
+def fit_peak (datax, datay, x1 = None, x2 = None, label = None, xlabel = 'Channel', ylabel = 'Count', showResult = True, **initparams):
     # Check if a lower x-limit for the fit is given, otherwise use the minimum of the provided dataset
     if x1 == None:
         x1 = min(datax)
@@ -54,7 +52,7 @@ def fit_peak (datax, datay, x1 = None, x2 = None, showResult = True, label = Non
     datax_clipped = np.delete(datax, np.where((x1 > datax) | (datax > x2)))
     datay_clipped = np.delete(datay, np.where((x1 > datax) | (datax > x2)))
     # Set axis labels
-    axislabels = ['Channel', 'Count']
+    axislabels = [xlabel, ylabel]
     # Perform a Gaussian fit on the remaining data, choosing the position of the maximum value in the data as an initial value for the position of the peak
     fit = hist_fit_data(datax_clipped, datay_clipped, model = gaussian, limitedparams = [['mu', {'lower':0}], ['sigma', {'lower':0}], ['s', {'lower':0, 'upper':1}]], fixedparams = [["N", np.sum(datay_clipped)], ["xmin", x1], ["xmax", x2]], mu = datax_clipped[np.argmax(datay_clipped)], label = label, showResult = showResult, axislabels = axislabels, **initparams)
     # Return fit object
@@ -66,7 +64,7 @@ def fit_peak (datax, datay, x1 = None, x2 = None, showResult = True, label = Non
 # Fit a compton edge using a complementary error function #
 # ##########################################################
 
-def fit_compton (datax, datay, x1 = None, x2 = None, showResult = True, label = None, **initparams):
+def fit_compton (datax, datay, x1 = None, x2 = None, label = None, xlabel = 'Channel', ylabel = 'Count', showResult = True, **initparams):
     # Check if a lower x-limit for the fit is given, otherwise use the minimum of the provided dataset
     if x1 == None:
         x1 = min(datax)
@@ -83,7 +81,7 @@ def fit_compton (datax, datay, x1 = None, x2 = None, showResult = True, label =
         diffs.append(np.abs(np.sum(dif[i:i+25])))
     mu0 = datax_clipped[np.argmax(diffs)]
     # Set axis labels
-    axislabels = ['Channel', 'Count']
+    axislabels = [xlabel, ylabel]
     # Perform the fit on the remaining data using the estimated position as an initial value
     fit = hist_fit_data(datax_clipped, datay_clipped, model = compton_edge, limitedparams = [['mu', {'lower':0}], ['sigma', {'lower':0}], ['A', {'lower':0}], ['B', {'lower':0.3, 'upper':0.9}], ['s', {'lower':0, 'upper':1}]], fixedparams = [["N", np.sum(datay_clipped)], ["xmin", x1], ["xmax", x2]], mu = mu0, B = mu0, label = label, axislabels = axislabels, showResult = showResult, **initparams)
     # Return fit object