326 lines
8.7 KiB
Python
326 lines
8.7 KiB
Python
#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]),
|
||
}
|
||
CXANTHINE = {
|
||
1: ([159.40], [1]),
|
||
2: ([164.01], [1]),
|
||
3: ([120.94], [1]),
|
||
4: ([161.24], [1]),
|
||
5: ([146.98], [1]),
|
||
}
|
||
|
||
#1-Methylxanthine
|
||
H1XANTHINE = {
|
||
1: ([7.93], [1]),
|
||
2: ([9.45], [1]),
|
||
3: ([4.05], [3]),
|
||
4: ([7.91], [1]),
|
||
}
|
||
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]),
|
||
}
|
||
|
||
#3-Methylxanthine
|
||
H3XANTHINE = {
|
||
1: ([4.15], [3]),
|
||
2: ([7.73], [1]),
|
||
3: ([7.99], [1]),
|
||
4: ([9.49], [1]),
|
||
}
|
||
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]),
|
||
}
|
||
|
||
#7-Methylxanthine
|
||
H7XANTHINE = {
|
||
1: ([7.55], [1]),
|
||
2: ([4.47], [3]),
|
||
3: ([7.72], [1]),
|
||
4: ([7.655], [1]),
|
||
}
|
||
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]),
|
||
}
|
||
|
||
#Theophylline
|
||
H13XANTHINE = {
|
||
1: ([4.03], [3]),
|
||
2: ([7.98], [1]),
|
||
3: ([4.19], [3]),
|
||
4: ([9.49], [1]),
|
||
}
|
||
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]),
|
||
}
|
||
|
||
#Paraxanthine
|
||
H17XANTHINE = {
|
||
1: ([4.50], [3]),
|
||
2: ([7.70], [1]),
|
||
3: ([3.98], [3]),
|
||
4: ([7.82], [1]),
|
||
}
|
||
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]),
|
||
}
|
||
|
||
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]),
|
||
}
|
||
|
||
|
||
#Theobromine
|
||
H37XANTHINE = {
|
||
1: ([4.49], [3]),
|
||
2: ([7.75], [1]),
|
||
3: ([4.11], [3]),
|
||
4: ([7.65], [1]),
|
||
}
|
||
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]),
|
||
}
|
||
|
||
#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
|
||
HNMR1= {
|
||
1: ([10.85], [1]),
|
||
2: ([11.50], [1]),
|
||
3: ([3.82], [3]),
|
||
4: ([7.88], [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]),
|
||
}
|
||
|
||
|
||
|
||
#Experimental Theobromine nmr
|
||
HNMR2= {
|
||
1: ([11.10], [1]),
|
||
2: ([3.33], [3]),
|
||
3: ([3.84], [3]),
|
||
4: ([7.97], [1]),
|
||
}
|
||
|
||
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]),
|
||
}
|
||
|
||
def overlap(listref, listnew):
|
||
twoleft = np.sum(np.multiply(np.concatenate((listref, [0, 0])), np.concatenate(([0, 0], listnew))))
|
||
oneleft = np.sum(np.multiply(np.concatenate((listref, [0])), np.concatenate(([0], listnew))))
|
||
neutral = np.sum(np.multiply(listref,listnew))
|
||
oneright = np.sum(np.multiply(np.concatenate(([0], listref)), np.concatenate((listnew, [0]))))
|
||
tworight = np.sum(np.multiply(np.concatenate(([0, 0], listref)), np.concatenate((listnew, [0, 0]))))
|
||
overlap = (oneleft + oneright)* 0.5 + neutral
|
||
return overlap
|
||
|
||
def bin_array(spectra, highest_ppm, lowest_ppm, bin_width):
|
||
binnumber = math.ceil((highest_ppm - lowest_ppm)/bin_width)
|
||
bin = [0] * binnumber
|
||
for peak in spectra:
|
||
(shift, height) = spectra[peak]
|
||
binindex = math.floor((shift[0] - lowest_ppm) / bin_width)
|
||
bin[binindex] += height[0]
|
||
normalizedbin = np.divide(bin, np.sum(bin))
|
||
return normalizedbin
|
||
|
||
def define_border_values(spectraref, spectranew, bin_width):
|
||
shifts = []
|
||
for _,(shift,_) in spectraref.items():
|
||
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))
|
||
lowest_ppm = min(shifts) - bin_width/2 #Worse result. None of the previously wrong (except 0.6) become right
|
||
return (lowest_ppm, highest_ppm)
|
||
|
||
def similarity_nmr(spectraref, spectranew, bin_width):
|
||
#Maximize likelihood or minimize Deviation
|
||
#Values for two spectra and optimize largest for both different?
|
||
#Spectra in Nodes to allow maximize overlapp with both spectra or one spectra.
|
||
#5.4.2 Eliminating X–H signals from 1H NMR spectra
|
||
lowest_ppm, highest_ppm = define_border_values(spectraref, spectranew, bin_width)
|
||
binref = bin_array(spectraref, highest_ppm, lowest_ppm, bin_width)
|
||
binnew = bin_array(spectranew, highest_ppm, lowest_ppm, bin_width)
|
||
crosscorr = overlap(binref, binnew)
|
||
refselfcorr = overlap(binref, binref)
|
||
newselfcorr = overlap(binnew, binnew)
|
||
simidx = crosscorr / math.sqrt(refselfcorr * newselfcorr)
|
||
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():
|
||
spectrumref = CNMR1
|
||
spectrafalse = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE]
|
||
likelihood = []
|
||
for spectrumtrue in spectrafalse:
|
||
#errorlist = {}
|
||
errorlist = []
|
||
for correctionvalue in range(8, 16):
|
||
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
|
||
error = 0
|
||
total = 0
|
||
for spectrumfalse in spectrafalse:
|
||
positive = 0
|
||
negative = 0
|
||
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(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() |