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Dateien nach "ILP/Kaffee" hochladen

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kilian
2026-08-12 13:20:52 +02:00
parent d272aa7cb6
commit 666bdb2e65
1 changed files with 93 additions and 103 deletions
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ILP/Kaffee/nmrSimilarityCaffeine.py
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@@ -8,99 +8,99 @@ import numpy as np
#Xanthine #Xanthine
HXANTHINE = { HXANTHINE = {
1: ([7.96], [1]), 1: ([7.31], [1]),
2: ([9.45], [1]), 2: ([8.79], [1]),
3: ([7.725], [1]), 3: ([7.07], [1]),
4: ([7.625], [1]), 4: ([6.97], [1]),
} }
CXANTHINE = { CXANTHINE = {
1: ([159.40], [1]), 1: ([151.03], [1]),
2: ([164.01], [1]), 2: ([155.64], [1]),
3: ([120.94], [1]), 3: ([112.57], [1]),
4: ([161.24], [1]), 4: ([152.87], [1]),
5: ([146.98], [1]), 5: ([138.61], [1]),
} }
#1-Methylxanthine #1-Methylxanthine
H1XANTHINE = { H1XANTHINE = {
1: ([7.93], [1]), 1: ([7.28], [1]),
2: ([9.45], [1]), 2: ([8.79], [1]),
3: ([4.05], [3]), 3: ([3.39], [3]),
4: ([7.91], [1]), 4: ([7.25], [1]),
} }
C1XANTHINE = { C1XANTHINE = {
1: ([161.50], [1]), 1: ([153.13], [1]),
2: ([166.28], [1]), 2: ([157.91], [1]),
3: ([120.65], [1]), 3: ([112.28], [1]),
4: ([158.74], [1]), 4: ([150.37], [1]),
5: ([146.25], [1]), 5: ([137.88], [1]),
6: ([38.55], [1]), 6: ([30.18], [1]),
} }
#3-Methylxanthine #3-Methylxanthine
H3XANTHINE = { H3XANTHINE = {
1: ([4.15], [3]), 1: ([3.49], [3]),
2: ([7.73], [1]), 2: ([7.07], [1]),
3: ([7.99], [1]), 3: ([7.34], [1]),
4: ([9.49], [1]), 4: ([8.84], [1]),
} }
C3XANTHINE = { C3XANTHINE = {
1: ([161.83], [1]), 1: ([153.46], [1]),
2: ([163.37], [1]), 2: ([155.00], [1]),
3: ([121.24], [1]), 3: ([112.87], [1]),
4: ([163.15], [1]), 4: ([154.78], [1]),
5: ([146.49], [1]), 5: ([138.12], [1]),
6: ([39.71], [1]), 6: ([31.34], [1]),
} }
#7-Methylxanthine #7-Methylxanthine
H7XANTHINE = { H7XANTHINE = {
1: ([7.55], [1]), 1: ([6.89], [1]),
2: ([4.47], [3]), 2: ([3.81], [3]),
3: ([7.72], [1]), 3: ([7.04], [1]),
4: ([7.655], [1]), 4: ([6.98], [1]),
} }
C7XANTHINE= { C7XANTHINE= {
1: ([159.50], [1]), 1: ([151.13], [1]),
2: ([165.47], [1]), 2: ([157.10], [1]),
3: ([122.15], [1]), 3: ([113.78], [1]),
4: ([162.31], [1]), 4: ([153.94], [1]),
5: ([151.55], [1]), 5: ([143.18], [1]),
6: ([45.06], [1]), 6: ([36.69], [1]),
} }
#Theophylline #Theophylline
H13XANTHINE = { H13XANTHINE = {
1: ([4.03], [3]), 1: ([3.37], [3]),
2: ([7.98], [1]), 2: ([7.32], [1]),
3: ([4.19], [3]), 3: ([3.53], [3]),
4: ([9.49], [1]), 4: ([8.84], [1]),
} }
C13XANTHINE = { C13XANTHINE = {
1: ([163.77], [1]), 1: ([155.40], [1]),
2: ([165.26], [1]), 2: ([156.89], [1]),
3: ([120.73], [1]), 3: ([112.36], [1]),
4: ([160.99], [1]), 4: ([152.62], [1]),
5: ([145.80], [1]), 5: ([137.43], [1]),
6: ([40.42], [1]), 6: ([32.05], [1]),
7: ([37.60], [1]), 7: ([29.23], [1]),
} }
#Paraxanthine #Paraxanthine
H17XANTHINE = { H17XANTHINE = {
1: ([4.50], [3]), 1: ([3.84], [3]),
2: ([7.70], [1]), 2: ([7.04], [1]),
3: ([3.98], [3]), 3: ([3.32], [3]),
4: ([7.82], [1]), 4: ([7.16], [1]),
} }
C17XANTHINE = { C17XANTHINE = {
1: ([161.41], [1]), 1: ([153.04], [1]),
2: ([167.17], [1]), 2: ([158.80], [1]),
3: ([121.81], [1]), 3: ([113.44], [1]),
4: ([160.18], [1]), 4: ([1651.81], [1]),
5: ([151.09], [1]), 5: ([142.72], [1]),
6: ([45.17], [1]), 6: ([36.80], [1]),
7: ([36.96], [1]), 7: ([28.59], [1]),
} }
CPARAXANTHINE = { CPARAXANTHINE = {
@@ -116,60 +116,50 @@ CPARAXANTHINE = {
#Theobromine #Theobromine
H37XANTHINE = { H37XANTHINE = {
1: ([4.49], [3]), 1: ([3.83], [3]),
2: ([7.75], [1]), 2: ([7.09], [1]),
3: ([4.11], [3]), 3: ([3.45], [3]),
4: ([7.65], [1]), 4: ([6.99], [1]),
} }
C37XANTHINE = { C37XANTHINE = {
1: ([161.76], [1]), 1: ([153.39], [1]),
2: ([164.86], [1]), 2: ([156.49], [1]),
3: ([122.51], [1]), 3: ([114.14], [1]),
4: ([164.29], [1]), 4: ([155.92], [1]),
5: ([151.10], [1]), 5: ([142.73], [1]),
6: ([39.33], [1]), 6: ([30.96], [1]),
7: ([45.07], [1]), 7: ([36.70], [1]),
} }
#Caffeine #Caffeine
H137XANTHINE = { H137XANTHINE = {
1: ([7.73], [1]), 1: ([7.07], [1]),
2: ([4.15], [3]), 2: ([3.49], [3]),
3: ([4.52], [3]), 3: ([3.86], [3]),
4: ([4.01], [3]), 4: ([3.35], [3]),
} }
C137XANTHINE = { C137XANTHINE = {
1: ([163.66], [1]), 1: ([155.29], [1]),
2: ([166.66], [1]), 2: ([158.29], [1]),
3: ([122.03], [1]), 3: ([113.66], [1]),
4: ([162.23], [1]), 4: ([153.86], [1]),
5: ([150.50], [1]), 5: ([142.13], [1]),
6: ([40.09], [1]), 6: ([31.72], [1]),
7: ([45.23], [1]), 7: ([36.86], [1]),
8: ([37.17], [1]), 8: ([28.80], [1]),
} }
CCAFFEINE = { CCAFFEINE = {
1: ([155.7], [1]), #166 1: ([155.7], [1]),
2: ([148.8], [1]), #159 2: ([148.8], [1]),
3: ([107.7], [1]), #118 3: ([107.7], [1]),
4: ([152.2], [1]), #163 4: ([152.2], [1]),
5: ([143.0], [1]), #154 5: ([143.0], [1]),
6: ([27.2], [1]), #38 6: ([27.2], [1]),
7: ([29.1], [1]), #40 7: ([29.1], [1]),
8: ([32.9], [1]), #44 8: ([32.9], [1]),
} }
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 = { CCAFFEINE2 = {
1: ([27.7], [1]), 1: ([27.7], [1]),
2: ([29.3], [1]), 2: ([29.3], [1]),
@@ -303,7 +293,7 @@ def main():
for spectrumtrue in spectra: for spectrumtrue in spectra:
similaritybycorrection = [] similaritybycorrection = []
#Paper Chemical reviews Carbons bound to Heavy atoms (TMS) to high -> this could be reason for too high values. #Paper Chemical reviews Carbons bound to Heavy atoms (TMS) to high -> this could be reason for too high values.
correctionvalues = [11] #np.arange(8.37, 9.88, 0.01) #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 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: 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 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
similaritylist = [] similaritylist = []