204 lines
4.6 KiB
Python
204 lines
4.6 KiB
Python
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]),
|
||
}
|
||
|
||
#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
|
||
H37XANTHINE= {
|
||
1: ([7.73], [1]),
|
||
2: ([4.15], [3]),
|
||
3: ([4.52], [3]),
|
||
4: ([4.01], [3]),
|
||
}
|
||
C37XANTHINE= {
|
||
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]),
|
||
}
|
||
|
||
#Neue Quelle für 13CNMR für beide
|
||
#Experimental 7-Methylxanthine nmr
|
||
HNMR1= {
|
||
1: ([10.85], [1]),
|
||
2: ([11.50], [1]),
|
||
3: ([3.82], [3]),
|
||
4: ([7.88], [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 similarity_nmr(spectraref, spectranew, bin_width, highest_ppm, lowest_ppm):
|
||
|
||
#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
|
||
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 main():
|
||
print(similarity_nmr(H1XANTHINE, HNMR1, 0.4, 13, 3))
|
||
print(similarity_nmr(H3XANTHINE, HNMR1, 0.4, 13, 3))
|
||
print(similarity_nmr(H7XANTHINE, HNMR1, 0.4, 13, 3))
|
||
|
||
if __name__ == "__main__":
|
||
main() |