Dateien nach "ILP/Kaffee" hochladen

ILP/Kaffee/nmrSimilarityCaffeine.py

@@ -283,13 +283,24 @@ def correction(spectra, corretionppm):
         newspectra[id] = ([adjustedshift], height)
     return newspectra
 
+def addspectra(spectrum1, spectrum2):
+    spectrum = spectrum1.copy()
+    for _, ([ppm2], [height2]) in spectrum2.items():
+        for peak, ([ppm1], [height1]) in spectrum1.items():
+            if ppm1 == ppm2:
+                spectrum[peak] == ([ppm1], [height1 + height2])
+                continue
+        spectrum[len(spectrum) + 1] = ([ppm2], [height2])
+    return(spectrum)
+
 def main():
-    spectrumref = CNMR2
+    normalize = False
-    #1H-NMR Spectra ignoriert, da meiste H sauer, da an N gebunden
+    spectrumrefs = [CNMR1, CNMR3]
+    for spectrumref in spectrumrefs:
         #spectra = [HXANTHINE, H1XANTHINE, H3XANTHINE, H7XANTHINE, H1XANTHINE, H17XANTHINE, H37XANTHINE, H137XANTHINE]
         spectra = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE]
         spectranames = ["XANTHINE", "1XANTHINE", "3XANTHINE", "7XANTHINE", "1XANTHINE", "17XANTHINE", "37XANTHINE", "137XANTHINE"]
-    likelihood = []
+        likelihoods = []
         for spectrumtrue in spectra:
             similaritybycorrection = []
             #Paper Chemical reviews Carbons bound to Heavy atoms (TMS) to high -> this could be reason for too high values.
@@ -302,8 +313,14 @@ def main():
                     similaritylist.append(similarity_nmr(spectrumtrue, spectrumrefcorrected, i))
                 similaritymean = sum(similaritylist) / len(similaritylist)
                 similaritybycorrection.append(similaritymean)
-        likelihood.append(round(sum(similaritybycorrection)/len(similaritybycorrection), 2))
+            likelihoods.append(sum(similaritybycorrection)/len(similaritybycorrection))
-    print(likelihood)
+    if normalize:
+        normalizedlikelihood = [round(likelihood/np.sum(likelihoods), 2) for likelihood in likelihoods]
+        print(normalizedlikelihood)
+    if not normalize:
+        notnormalizedlikelihood = [round(likelihood, 2) for likelihood in likelihoods]
+        print(notnormalizedlikelihood)
+
 
 if __name__ == "__main__":
     main()