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kilian 2026-06-17 11:31:07 +02:00
parent c48716c589
commit 1ddeec73bd
2 changed files with 97 additions and 44 deletions
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5
ILP/caffeinesynthesis.py
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@ -47,13 +47,16 @@ VERTICES2 = {
VERTICES = ['Xanthine', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'Caffeine'] VERTICES = ['Xanthine', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'Caffeine']
NMRLIKELYHOODS = [0.0, 0.2, 0.7, 0.1, 0.1, 0.2, 0.7, 0.0] NMRLIKELYHOODS = [0.0, 0.2, 0.7, 0.1, 0.1, 0.2, 0.7, 0.0]
NMRLIKELYHOODS1 = [0.75, 0.66, 0.66, 0.89, 0.66, 0.86, 0.79, 0.65]
NMRLIKELYHOODS2 = [0.58, 0.75, 0.71, 0.91, 0.75, 0.76, 0.85, 0.82]
FIXED_FLOWS = { FIXED_FLOWS = {
1: 1, 1: 1,
14: 1, 14: 1,
} }
def build_model(name, hyperedges, vertices, nmrlikelihoods, excluded_support=None): def build_model(name, hyperedges, vertices, nmrlikelihoods, excluded_support=None):
model = Model(name) model = Model(name)
x = {e_id: model.addVar(vtype=GRB.INTEGER, lb=0, name=f"x_{e_id}") for e_id in hyperedges} x = {e_id: model.addVar(vtype=GRB.INTEGER, lb=0, name=f"x_{e_id}") for e_id in hyperedges}

136
ILP/nmrSimilarity.py
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@ -1,5 +1,7 @@
#Binning mostly for broader peaks? #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 math
import numpy as np import numpy as np
@ -102,13 +104,13 @@ C17XANTHINE = {
} }
CPARAXANTHINE = { CPARAXANTHINE = {
1: ([26.7], [1]), 1: ([26.7], [1]),
2: ([32.9], [1]), 2: ([32.9], [1]),
3: ([151.1], [1]), 3: ([151.1], [1]),
4: ([106.5], [1]), 4: ([106.5], [1]),
5: ([147.4], [1]), 5: ([147.4], [1]),
6: ([155.3], [1]), 6: ([155.3], [1]),
7: ([143.0], [1]), 7: ([143.0], [1]),
} }
@ -148,29 +150,50 @@ C137XANTHINE = {
} }
CCAFFEINE = { CCAFFEINE = {
1: ([155.7], [1]), 1: ([155.7], [1]), #166
2: ([148.8], [1]), 2: ([148.8], [1]), #159
3: ([107.7], [1]), 3: ([107.7], [1]), #118
4: ([152.2], [1]), 4: ([152.2], [1]), #163
5: ([143.0], [1]), 5: ([143.0], [1]), #154
6: ([27.2], [1]), 6: ([27.2], [1]), #38
7: ([29.1], [1]), 7: ([29.1], [1]), #40
8: ([32.9], [1]), 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 = { CCAFFEINE2 = {
1: ([27.7], [1]), 1: ([27.7], [1]),
2: ([29.3], [1]), 2: ([29.3], [1]),
3: ([33.1], [1]), 3: ([33.1], [1]),
4: ([151.0], [1]), 4: ([151.0], [1]),
5: ([148.1], [1]), 5: ([148.1], [1]),
6: ([106.6], [1]), 6: ([106.6], [1]),
7: ([154.5], [1]), 7: ([154.5], [1]),
8: ([142.8], [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 #Experimental 7-Methylxanthine nmr
#Secundary source 11.52, 3.81
HNMR1= { HNMR1= {
1: ([10.85], [1]), 1: ([10.85], [1]),
2: ([11.50], [1]), 2: ([11.50], [1]),
@ -226,7 +249,7 @@ def bin_array(spectra, highest_ppm, lowest_ppm, bin_width):
normalizedbin = np.divide(bin, np.sum(bin)) normalizedbin = np.divide(bin, np.sum(bin))
return normalizedbin return normalizedbin
def define_border_values(spectraref, spectranew): def define_border_values(spectraref, spectranew, bin_width):
shifts = [] shifts = []
for _,(shift,_) in spectraref.items(): for _,(shift,_) in spectraref.items():
shifts.append(shift[0]) shifts.append(shift[0])
@ -234,43 +257,70 @@ def define_border_values(spectraref, spectranew):
shifts.append(shift[0]) shifts.append(shift[0])
highest_ppm = math.ceil(max(shifts)) highest_ppm = math.ceil(max(shifts))
lowest_ppm = math.floor(min(shifts)) lowest_ppm = math.floor(min(shifts))
lowest_ppm = min(shifts) - bin_width/2 #Worse result. None of the previously wrong (except 0.6) become right
return (lowest_ppm, highest_ppm) return (lowest_ppm, highest_ppm)
def similarity_nmr(spectraref, spectranew, bin_width): def similarity_nmr(spectraref, spectranew, bin_width):
#Maximize likelihood or minimize Deviation #Maximize likelihood or minimize Deviation
#Values for two spectra and optimize largest for both different? #Values for two spectra and optimize largest for both different?
#Spectra in Nodes to allow maximize overlapp with both spectra or one spectra. #Spectra in Nodes to allow maximize overlapp with both spectra or one spectra.
#5.4.2 Eliminating X–H signals from 1H NMR spectra #5.4.2 Eliminating X–H signals from 1H NMR spectra
lowest_ppm, highest_ppm = define_border_values(spectraref, spectranew) lowest_ppm, highest_ppm = define_border_values(spectraref, spectranew, bin_width)
binref = bin_array(spectraref, highest_ppm, lowest_ppm, bin_width) binref = bin_array(spectraref, highest_ppm, lowest_ppm, bin_width)
binnew = bin_array(spectranew, highest_ppm, lowest_ppm, bin_width) binnew = bin_array(spectranew, highest_ppm, lowest_ppm, bin_width)
crosscorr = overlap(binref, binnew) crosscorr = overlap(binref, binnew)
refselfcorr = overlap(binref, binref) refselfcorr = overlap(binref, binref)
newselfcorr = overlap(binnew, binnew) newselfcorr = overlap(binnew, binnew)
simidx = crosscorr / math.sqrt(refselfcorr * newselfcorr) simidx = crosscorr / math.sqrt(refselfcorr * newselfcorr)
return(simidx) return(simidx)
def correction(spectra, corretionppm):
newspectra = {}
for id, (shift, height) in spectra.items():
shiftvalue = shift[0]
adjustedshift = shiftvalue + corretionppm
newspectra[id] = ([adjustedshift], height)
return newspectra
def main(): def main():
positive = 0 spectrumref = CNMR1
negative = 0 spectrafalse = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE]
bad_binwidth = [] likelihood = []
for spectrumtrue in spectrafalse:
'''for i in [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 25.0, 30.0, 35.0]: #errorlist = {}
print(similarity_nmr(CNMR1, CNMR2, i), i) errorlist = []
if(similarity_nmr(CCAFFEINE2, CCAFFEINE, i) - similarity_nmr(CPARAXANTHINE, CCAFFEINE, i) < 0): for correctionvalue in range(8, 16):
negative += 1 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
bad_binwidth.append(i) error = 0
else: total = 0
positive += 1 for spectrumfalse in spectrafalse:
print(f'Wrong similarity result: {negative} and Right similarity result: {positive}') positive = 0
print(bad_binwidth)''' negative = 0
for i in np.arange(0.01, 0.07, 0.01): bad_binwidth = []
for i in np.arange(0.1, 3.9, 0.1): #Irgendwie hat 0.99999999999 einen Fehler den 1.0 nicht hat
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)
likelihood.append(round((total - min(errorlist))/total, 2))
print(likelihood)
'''for i in np.arange(0.01, 0.07, 0.01):
print(f'Increment i: {i}') print(f'Increment i: {i}')
print(similarity_nmr(HNMR1, HNMR2, i)) print(similarity_nmr(HNMR1, HNMR2, i))
print(similarity_nmr(H1XANTHINE, HNMR1, i)) print(similarity_nmr(H1XANTHINE, HNMR1, i))
print(similarity_nmr(H3XANTHINE, HNMR1, i)) print(similarity_nmr(H3XANTHINE, HNMR1, i))
print(similarity_nmr(H7XANTHINE, HNMR1, i)) print(similarity_nmr(H7XANTHINE, HNMR1, i))
'''
if __name__ == "__main__": if __name__ == "__main__":
main() main()