#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]), } #Combination of methyl group and base purine rings from two papers CNMR3 = { 1: ([154.9], [1]), 2: ([150.0], [1]), 3: ([108.1], [1]), 4: ([153.1], [1]), 5: ([142.6], [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 = CCAFFEINE2 spectra = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE] spectranames = ["CXANTHINE", "C1XANTHINE", "C3XANTHINE", "C7XANTHINE", "C1XANTHINE", "C17XANTHINE", "C37XANTHINE", "C137XANTHINE"] likelihood = [] for spectrumtrue in spectra: #errorlist = {} #errorlist = [] similaritybycorrection = [] correctionvalues = range(8, 16) #also tested 8.4, 8.37, 11, 9.4 (for CNMR3) 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 #Likelihood by number of higher similarity than all others. '''error = 0 total = 0 for spectrumfalse in spectra: positive = 0 negative = 0 bad_binwidth = [] for i in np.arange(0.1, 2.6, 0.1): #successfull at max 3.9, but max 1.7 is lowest where nmr3 correctly classified, 1.6 increases likelihood of 3,7 over 1,7 even with 8.4 correction 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) print(min(range(len(errorlist)), key=errorlist.__getitem__)) likelihood.append(round((total - min(errorlist))/total, 2))''' #Likelihood by mean similarity #This method demonstrates the same problems as the other likelihood method similaritylist = [] for i in np.arange(0.1, 3.9, 0.1): similaritylist.append(similarity_nmr(spectrumtrue, spectrumrefcorrected, i)) similaritymean = sum(similaritylist) / len(similaritylist) similaritybycorrection.append(similaritymean) name = spectranames[spectra.index(spectrumtrue)] correctionindex = max(range(len(similaritybycorrection)), key=similaritybycorrection.__getitem__) print(f'{name}: {correctionindex} = {correctionvalues[correctionindex]}') likelihood.append(round(max(similaritybycorrection), 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()