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Dateien nach "ILP" hochladen

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kilian 2026-06-08 10:44:45 +02:00
parent b07cf2fd09
commit c461c50f42
1 changed files with 52 additions and 10 deletions
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ILP/nmrSimilarity.py
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@ -1,7 +1,5 @@
#Also ILP? Minimize distance becuse two vectors might be shifted to oneanother (high weight on height distance to keep same height) import math
#Zu hohe Komplexität bei Skalierung import numpy as np
import gurobipy as gp
from gurobipy import GRB, Model, quicksum
#Xanthine #Xanthine
HXANTHINE= { HXANTHINE= {
@ -51,13 +49,13 @@ C3XANTHINE= {
} }
#7-Methylxanthine #7-Methylxanthine
H3XANTHINE= { H7XANTHINE= {
1: ([7.55], [1]), 1: ([7.55], [1]),
2: ([4.47], [3]), 2: ([4.47], [3]),
3: ([7.72], [1]), 3: ([7.72], [1]),
4: ([7.655], [1]), 4: ([7.655], [1]),
} }
C3XANTHINE= { C7XANTHINE= {
1: ([159.50], [1]), 1: ([159.50], [1]),
2: ([165.47], [1]), 2: ([165.47], [1]),
3: ([122.15], [1]), 3: ([122.15], [1]),
@ -147,16 +145,60 @@ HNMR1= {
#Experimental Theobromine nmr #Experimental Theobromine nmr
HNMR1= { HNMR2= {
1: ([11.10], [1]), 1: ([11.10], [1]),
2: ([3.33], [3]), 2: ([3.33], [3]),
3: ([3.84], [3]), 3: ([3.84], [3]),
4: ([7.97], [1]), 4: ([7.97], [1]),
} }
def build_model(name, nmrnode, nmrmeasured, excluded_support=None): CNMR2= {
model = Model(name) 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 #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.
#Vergleiche PCA, Binning (Größere Schwankungen zwischen den Methoden) #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()