diff --git a/ILP/nmrSimilarity.py b/ILP/nmrSimilarity.py
index 8a52f5e..496ee02 100644
--- a/ILP/nmrSimilarity.py
+++ b/ILP/nmrSimilarity.py
@@ -266,8 +266,8 @@ def define_border_values(spectraref, spectranew, bin_width):
         shifts.append(shift[0])
     for _,(shift,_) in spectranew.items():
         shifts.append(shift[0])
-    highest_ppm = math.ceil(max(shifts))
-    lowest_ppm = math.floor(min(shifts))
+    highest_ppm = math.ceil(max(shifts)) + bin_width
+    lowest_ppm = math.floor(min(shifts)) - bin_width
     #lowest_ppm = min(shifts) - bin_width/2 #Worse result. None of the previously wrong (except 0.6) become right
     return (lowest_ppm, highest_ppm)
 
@@ -294,15 +294,16 @@ def correction(spectra, corretionppm):
     return newspectra
 
 def main():
-    spectrumref = CCAFFEINE2
+    spectrumref = CNMR3
+    #spectra = [HXANTHINE, H1XANTHINE, H3XANTHINE, H7XANTHINE, H1XANTHINE, H17XANTHINE, H37XANTHINE, H137XANTHINE]
     spectra = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE]
-    spectranames = ["CXANTHINE", "C1XANTHINE", "C3XANTHINE", "C7XANTHINE", "C1XANTHINE", "C17XANTHINE", "C37XANTHINE", "C137XANTHINE"]
+    spectranames = ["XANTHINE", "1XANTHINE", "3XANTHINE", "7XANTHINE", "1XANTHINE", "17XANTHINE", "37XANTHINE", "137XANTHINE"]
     likelihood = []
     for spectrumtrue in spectra:
         #errorlist = {}
         #errorlist = []
         similaritybycorrection = []
-        correctionvalues = range(8, 16) #also tested 8.4, 8.37, 11, 9.4 (for CNMR3)
+        correctionvalues =  [9.87] #for C tested np.arange(8.0, 16.1, 0.1) range(8, 12) 8.4, 8.37, 11, 9.4 (for CNMR3), 9.87 (true for all ref, 8.37 + 1.5 for the precision), np.arange(6.13, 9.86, 0.01) (only for first), for H 0.66 (not good), np.arange(0.4, 1.0, 0.01), 0.6 for first, second never first either 17 or caf higher np.arange(0.51, 0.82, 0.01) good measure
         for correctionvalue in correctionvalues: 
             spectrumrefcorrected = correction(spectrumref, correctionvalue) #CCAFFEINE 11 (klappt hier sehr gut) CCAFFEINE2 12 CPARAXANTHINE 10 CNMR1 9, 10 o 11 (sehr gut) CNMR2 10 o 11
             #Likelihood by number of higher similarity than all others.
@@ -333,14 +334,17 @@ def main():
         #Likelihood by mean similarity
         #This method demonstrates the same problems as the other likelihood method
             similaritylist = []
-            for i in np.arange(0.1, 3.9, 0.1):
+            binwidthlist = np.arange(0.1, 3.9, 0.1) #np.arange(0.1, 3.9, 0.1)
+            for i in binwidthlist:
                 similaritylist.append(similarity_nmr(spectrumtrue, spectrumrefcorrected, i))
             similaritymean = sum(similaritylist) / len(similaritylist)
             similaritybycorrection.append(similaritymean)
         name = spectranames[spectra.index(spectrumtrue)]
         correctionindex = max(range(len(similaritybycorrection)), key=similaritybycorrection.__getitem__)
         print(f'{name}: {correctionindex} = {correctionvalues[correctionindex]}')
-        likelihood.append(round(max(similaritybycorrection), 2))
+        #Maybe not the best but a mean instead?
+        likelihood.append(round(sum(similaritybycorrection)/len(similaritybycorrection), 2))
+        #likelihood.append(round(max(similaritybycorrection), 2))
     print(likelihood)
     '''for i in np.arange(0.01, 0.07, 0.01):
         print(f'Increment i: {i}')