Dateien nach "ILP/Vanilla" hochladen

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2026-08-12 14:48:54 +02:00
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@@ -1,244 +1,177 @@
#Binning mostly for broader peaks?
#0.66 für H und 8.4 für C bei anderen TMS Werten
#Gute 13C Ergebnisse für alles +11 ppm: Im Vergeich mit Coffein haben alle disubstituierten bei wenigen Hohen Werten falsche Zuordnung, bei mono und nicht substituierten sogar keine Falsche zuornung (0.1 bis 5 mit 0.1 Schritten)
#+11 nicht universell, aber 9 bis 13 bei allen sweet spot
import math
import numpy as np
#Xanthine
HXANTHINE = {
1: ([7.96], [1]),
2: ([9.45], [1]),
3: ([7.725], [1]),
4: ([7.625], [1]),
["CINNAMICACID", "PCOUMARICACID", "MCOUMARICACID", "BENZALDEHYD", "CAFFEICACID", "3HYDROXYBENZALDEHYD", "4HYDROXYBENZALDEHYD", "34DIHYDROXYBENZALDEHYD"]
#H noch machen
likelihood = []
#Cinnamicacid
HCINNAMICACID = {
}
CXANTHINE = {
1: ([159.40], [1]),
2: ([164.01], [1]),
3: ([120.94], [1]),
4: ([161.24], [1]),
5: ([146.98], [1]),
CCINNAMICACID = {
1: ([171.56], [1]),
2: ([120.33], [1]),
3: ([151.41], [1]),
4: ([140.47], [1]),
5: ([134.20], [2]),
6: ([133.48], [2]),
7: ([135.91], [1]),
}
#1-Methylxanthine
H1XANTHINE = {
1: ([7.93], [1]),
2: ([9.45], [1]),
3: ([4.05], [3]),
4: ([7.91], [1]),
#p-Coumaricacid
HPCOUMARICACID = {
}
C1XANTHINE = {
1: ([161.50], [1]),
2: ([166.28], [1]),
3: ([120.65], [1]),
4: ([158.74], [1]),
5: ([146.25], [1]),
6: ([38.55], [1]),
CPCOUMARICACID = {
1: ([171.61], [1]),
2: ([117.18], [1]),
3: ([149.67], [1]),
4: ([132.76], [1]),
5: ([135.75], [2]),
6: ([118.65], [2]),
7: ([166.09], [1]),
}
#3-Methylxanthine
H3XANTHINE = {
1: ([4.15], [3]),
2: ([7.73], [1]),
3: ([7.99], [1]),
4: ([9.49], [1]),
#m-Coumaricacid
HMCOUMARICACID = {
}
C3XANTHINE = {
1: ([161.83], [1]),
2: ([163.37], [1]),
3: ([121.24], [1]),
4: ([163.15], [1]),
5: ([146.49], [1]),
6: ([39.71], [1]),
CMCOUMARICACID = {
1: ([172.55], [1]),
2: ([118.60], [1]),
3: ([153.69], [1]),
4: ([141.29], [1]),
5: ([121.09], [1]),
6: ([134.46], [1]),
7: ([121.09], [1]),
8: ([164.15], [1]),
9: ([122.36], [1]),
}
#7-Methylxanthine
H7XANTHINE = {
1: ([7.55], [1]),
2: ([4.47], [3]),
3: ([7.72], [1]),
4: ([7.655], [1]),
#Benzaldehyd
HBENZALDEHYD = {
}
C7XANTHINE= {
1: ([159.50], [1]),
2: ([165.47], [1]),
3: ([122.15], [1]),
4: ([162.31], [1]),
5: ([151.55], [1]),
6: ([45.06], [1]),
CBENZALDEHYD= {
1: ([195.09], [1]),
2: ([141.47], [1]),
3: ([135.86], [2]),
4: ([133.45], [2]),
5: ([139.15], [1]),
}
#Theophylline
H13XANTHINE = {
1: ([4.03], [3]),
2: ([7.98], [1]),
3: ([4.19], [3]),
4: ([9.49], [1]),
#Caffeicacid
HCAFFEICACID = {
}
C13XANTHINE = {
1: ([163.77], [1]),
2: ([165.26], [1]),
3: ([120.73], [1]),
4: ([160.99], [1]),
5: ([145.80], [1]),
6: ([40.42], [1]),
7: ([37.60], [1]),
CCAFFEICACID = {
1: ([171.66], [1]),
2: ([116.16], [1]),
3: ([149.88], [1]),
4: ([131.94], [1]),
5: ([132.99], [1]),
6: ([118.15], [1]),
7: ([155.79], [1]),
8: ([149.28], [1]),
9: ([110.95], [1]),
}
#Paraxanthine
H17XANTHINE = {
1: ([4.50], [3]),
2: ([7.70], [1]),
3: ([3.98], [3]),
4: ([7.82], [1]),
#3-Hydroxybenzaldehyd
H3HYDROXYBENZALDEHYD = {
}
C17XANTHINE = {
1: ([161.41], [1]),
2: ([167.17], [1]),
3: ([121.81], [1]),
4: ([160.18], [1]),
5: ([151.09], [1]),
6: ([45.17], [1]),
7: ([36.96], [1]),
C3HYDROXYBENZALDEHYD = {
1: ([164.39], [1]),
2: ([122.95], [1]),
3: ([133.96], [1]),
4: ([123.33], [1]),
5: ([143.41], [1]),
6: ([195.16], [1]),
7: ([124.61], [1]),
}
CPARAXANTHINE = {
1: ([26.7], [1]),
2: ([32.9], [1]),
3: ([151.1], [1]),
4: ([106.5], [1]),
5: ([147.4], [1]),
6: ([155.3], [1]),
7: ([143.0], [1]),
#4-Hydroxybenzaldehyd
H4HYDROXYBENZALDEHYD = {
}
C4HYDROXYBENZALDEHYD = {
1: ([191.53], [1]),
2: ([135.08], [1]),
3: ([137.69], [2]),
4: ([118.46], [2]),
5: ([168.28], [1]),
}
#Theobromine
H37XANTHINE = {
1: ([4.49], [3]),
2: ([7.75], [1]),
3: ([4.11], [3]),
4: ([7.65], [1]),
#3,4-Dihydroxybenzaldehyd
H34DIHYDROXYBENZALDEHYD = {
}
C37XANTHINE = {
1: ([161.76], [1]),
2: ([164.86], [1]),
3: ([122.51], [1]),
4: ([164.29], [1]),
5: ([151.10], [1]),
6: ([39.33], [1]),
7: ([45.07], [1]),
C34DIHYDROXYBENZALDEHYD = {
1: ([158.08], [1]),
2: ([117.79], [1]),
3: ([133.40], [1]),
4: ([134.66], [1]),
5: ([191.41], [1]),
6: ([113.10], [1]),
7: ([149.32], [1]),
}
#Caffeine
H137XANTHINE = {
1: ([7.73], [1]),
2: ([4.15], [3]),
3: ([4.52], [3]),
4: ([4.01], [3]),
}
C137XANTHINE = {
1: ([163.66], [1]),
2: ([166.66], [1]),
3: ([122.03], [1]),
4: ([162.23], [1]),
5: ([150.50], [1]),
6: ([40.09], [1]),
7: ([45.23], [1]),
8: ([37.17], [1]),
}
CCAFFEINE = {
1: ([155.7], [1]), #166
2: ([148.8], [1]), #159
3: ([107.7], [1]), #118
4: ([152.2], [1]), #163
5: ([143.0], [1]), #154
6: ([27.2], [1]), #38
7: ([29.1], [1]), #40
8: ([32.9], [1]), #44
}
CCAFFEINEADJUSTED = {
1: ([166.7], [1]), #166
2: ([159.8], [1]), #159
3: ([118.7], [1]), #118
4: ([163.2], [1]), #163
5: ([154.0], [1]), #154
6: ([38.2], [1]), #38
7: ([40.1], [1]), #40
8: ([44.9], [1]), #44
}
CCAFFEINE2 = {
1: ([27.7], [1]),
2: ([29.3], [1]),
3: ([33.1], [1]),
4: ([151.0], [1]),
5: ([148.1], [1]),
6: ([106.6], [1]),
7: ([154.5], [1]),
8: ([142.8], [1]),
}
C137XANTHINEADJUSTED = {
1: ([155.62], [1]),
2: ([158.29], [1]),
3: ([113.66], [1]),
4: ([153.86], [1]),
5: ([142.13], [1]),
6: ([31.72], [1]),
7: ([36.86], [1]),
8: ([28.8], [1]),
}
#Experimental 7-Methylxanthine nmr
#Secundary source 11.52, 3.81
#Experimental p-Coumaricacid
HNMR1= {
1: ([10.85], [1]),
2: ([11.50], [1]),
3: ([3.82], [3]),
4: ([7.88], [1]),
1: ([12.13], [1]),
2: ([7.49], [2]),
3: ([6.79], [2]),
4: ([9.96], [1]),
5: ([6.29], [1]),
6: ([7.52], [1]),
}
CNMR1= {
1: ([155.85], [1]),
2: ([151.35], [1]),
3: ([149.30], [1]),
4: ([143.01], [1]),
5: ([106.90], [1]),
6: ([33.03], [1]),
1: ([125.36], [1]),
2: ([130.17], [2]),
3: ([115.83], [2]),
4: ([159.67], [1]),
5: ([168.05], [1]),
6: ([115.41], [1]),
7: ([144.27], [1]),
}
#Experimental Theobromine nmr
#Experimental 4-Hydroxybenzaldehyd
HNMR2 = {
1: ([11.10], [1]),
2: ([3.33], [3]),
3: ([3.84], [3]),
4: ([7.97], [1]),
1: ([7.05], [2]),
2: ([7.58], [2]),
3: ([8.44], [1]),
4: ([10.15], [1]),
}
#No Intesities mentioned
CNMR2 = {
1: ([154.9], [1]),
2: ([149.8], [1]),
3: ([107.1], [1]),
4: ([151.0], [1]),
5: ([142.8], [1]),
6: ([29.3], [1]),
7: ([33.9], [1]),
1: ([116.5], [2]),
2: ([130.3], [1]),
3: ([132.7], [2]),
4: ([163.8], [1]),
5: ([191.0], [1]),
}
#Experimental 3,4-Dihydroxybenzaldehyd
HNMR3 = {
1: ([7.44], [1]),
2: ([7.42], [1]),
3: ([7.00], [1]),
}
#Combination of methyl group and base purine rings from two papers
CNMR3 = {
1: ([154.9], [1]),
2: ([150.0], [1]),
3: ([108.1], [1]),
4: ([153.1], [1]),
5: ([142.6], [1]),
6: ([29.3], [1]),
7: ([33.9], [1]),
1: ([124.59], [1]),
2: ([115.21], [1]),
3: ([145.44], [1]),
4: ([151.27], [1]),
5: ([130.13], [1]),
6: ([114.22], [1]),
7: ([190.26], [1]),
}
def overlap(listref, listnew):
@@ -294,65 +227,25 @@ def correction(spectra, corretionppm):
return newspectra
def main():
spectrumref = CNMR2
#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"]
spectrumref = CNMR3
#1H-NMR Spectra ignoriert, da meiste H sauer, da an N gebunden
#spectra = [HCINNAMICACID, HPCOUMARICACID, HMCOUMARICACID, HBENZALDEHYD, HCAFFEICACID, H3HYDROXYBENZALDEHYD, H4HYDROXYBENZALDEHYD, H34DIHYDROXYBENZALDEHYD]
spectra = [CCINNAMICACID, CPCOUMARICACID, CMCOUMARICACID, CBENZALDEHYD, CCAFFEICACID, C3HYDROXYBENZALDEHYD, C4HYDROXYBENZALDEHYD, C34DIHYDROXYBENZALDEHYD]
spectranames = ["CINNAMICACID", "PCOUMARICACID", "MCOUMARICACID", "BENZALDEHYD", "CAFFEICACID", "3HYDROXYBENZALDEHYD", "4HYDROXYBENZALDEHYD", "34DIHYDROXYBENZALDEHYD"]
likelihood = []
for spectrumtrue in spectra:
#errorlist = {}
#errorlist = []
similaritybycorrection = []
#Paper Chemical reviews Carbons bound to Heavy atoms (TMS) to high -> this could be reason for to high values.
correctionvalues = [9.37] #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), 9.37 (good for first, ok for second, third because only 7 better/equal but for first much higher) 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
#Paper Chemical reviews Carbons bound to Heavy atoms (TMS) to high -> this could be reason for too high values.
correctionvalues = [2.63] #np.arange(0, 1.51, 0.01) #for C tested np.arange(-0.37, 7.64, 0.1) 0, 2.63, 1 (for CNMR3), 1.5 (true for all ref, 8.37 + 1.5 for the precision), 1 (good for first, ok for second, third because only 7 better/equal but for first much higher) np.arange(-1.5, 1.49, 0.01) (only for first), for H 0 (not good), np.arange(-0.26, 0.34, 0.01), -0.06 for first, second never first either 17 or caf higher np.arange(-0.15, 0.16, 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.
'''error = 0
total = 0
for spectrumfalse in spectra:
positive = 0
negative = 0
bad_binwidth = []
for i in np.arange(0.1, 2.6, 0.1): #successfull at max 3.9, but max 1.7 is lowest where nmr3 correctly classified, 1.6 increases likelihood of 3,7 over 1,7 even with 8.4 correction
truesimilarity = similarity_nmr(spectrumtrue, spectrumrefcorrected, i)
falsesimilarity = similarity_nmr(spectrumfalse, spectrumrefcorrected, i)
#print(truesimilarity)
#print(falsesimilarity)
if(truesimilarity - falsesimilarity < 0 or truesimilarity == 0):
negative += 1
bad_binwidth.append(i)
else:
positive += 1
total += 1
#print(f'Wrong similarity result: {negative} and Right similarity result: {positive}')
#print(bad_binwidth)
error += negative
#errorlist[correctionvalue] = error
errorlist.append(error)
print(min(range(len(errorlist)), key=errorlist.__getitem__))
likelihood.append(round((total - min(errorlist))/total, 2))'''
#Likelihood by mean similarity
#This method demonstrates the same problems as the other likelihood method
similaritylist = []
binwidthlist = np.arange(0.1, 3.9, 0.1) #np.arange(0.1, 3.9, 0.1)
binwidthlist = 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]}')
#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}')
print(similarity_nmr(HNMR1, HNMR2, i))
print(similarity_nmr(H1XANTHINE, HNMR1, i))
print(similarity_nmr(H3XANTHINE, HNMR1, i))
print(similarity_nmr(H7XANTHINE, HNMR1, i))
'''
if __name__ == "__main__":
main()