#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: ([7.31], [1]), 2: ([8.79], [1]), 3: ([7.07], [1]), 4: ([6.97], [1]), } CXANTHINE = { 1: ([151.03], [1]), 2: ([155.64], [1]), 3: ([112.57], [1]), 4: ([152.87], [1]), 5: ([138.61], [1]), } #1-Methylxanthine H1XANTHINE = { 1: ([7.28], [1]), 2: ([8.79], [1]), 3: ([3.39], [3]), 4: ([7.25], [1]), } C1XANTHINE = { 1: ([153.13], [1]), 2: ([157.91], [1]), 3: ([112.28], [1]), 4: ([150.37], [1]), 5: ([137.88], [1]), 6: ([30.18], [1]), } #3-Methylxanthine H3XANTHINE = { 1: ([3.49], [3]), 2: ([7.07], [1]), 3: ([7.34], [1]), 4: ([8.84], [1]), } C3XANTHINE = { 1: ([153.46], [1]), 2: ([155.00], [1]), 3: ([112.87], [1]), 4: ([154.78], [1]), 5: ([138.12], [1]), 6: ([31.34], [1]), } #7-Methylxanthine H7XANTHINE = { 1: ([6.89], [1]), 2: ([3.81], [3]), 3: ([7.04], [1]), 4: ([6.98], [1]), } C7XANTHINE= { 1: ([151.13], [1]), 2: ([157.10], [1]), 3: ([113.78], [1]), 4: ([153.94], [1]), 5: ([143.18], [1]), 6: ([36.69], [1]), } #Theophylline H13XANTHINE = { 1: ([3.37], [3]), 2: ([7.32], [1]), 3: ([3.53], [3]), 4: ([8.84], [1]), } C13XANTHINE = { 1: ([155.40], [1]), 2: ([156.89], [1]), 3: ([112.36], [1]), 4: ([152.62], [1]), 5: ([137.43], [1]), 6: ([32.05], [1]), 7: ([29.23], [1]), } #Paraxanthine H17XANTHINE = { 1: ([3.84], [3]), 2: ([7.04], [1]), 3: ([3.32], [3]), 4: ([7.16], [1]), } C17XANTHINE = { 1: ([153.04], [1]), 2: ([158.80], [1]), 3: ([113.44], [1]), 4: ([1651.81], [1]), 5: ([142.72], [1]), 6: ([36.80], [1]), 7: ([28.59], [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: ([3.83], [3]), 2: ([7.09], [1]), 3: ([3.45], [3]), 4: ([6.99], [1]), } C37XANTHINE = { 1: ([153.39], [1]), 2: ([156.49], [1]), 3: ([114.14], [1]), 4: ([155.92], [1]), 5: ([142.73], [1]), 6: ([30.96], [1]), 7: ([36.70], [1]), } #Caffeine H137XANTHINE = { 1: ([7.07], [1]), 2: ([3.49], [3]), 3: ([3.86], [3]), 4: ([3.35], [3]), } C137XANTHINE = { 1: ([155.29], [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: ([28.80], [1]), } CCAFFEINE = { 1: ([155.7], [1]), 2: ([148.8], [1]), 3: ([107.7], [1]), 4: ([152.2], [1]), 5: ([143.0], [1]), 6: ([27.2], [1]), 7: ([29.1], [1]), 8: ([32.9], [1]), } 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)) + bin_width lowest_ppm = math.floor(min(shifts)) - bin_width #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 = CNMR3 #1H-NMR Spectra ignoriert, da meiste H sauer, da an N gebunden #spectra = [HXANTHINE, H1XANTHINE, H3XANTHINE, H7XANTHINE, H1XANTHINE, H17XANTHINE, H37XANTHINE, H137XANTHINE] spectra = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE] spectranames = ["XANTHINE", "1XANTHINE", "3XANTHINE", "7XANTHINE", "1XANTHINE", "17XANTHINE", "37XANTHINE", "137XANTHINE"] likelihood = [] for spectrumtrue in spectra: similaritybycorrection = [] #Paper Chemical reviews Carbons bound to Heavy atoms (TMS) to high -> this could be reason for too high values. correctionvalues = [2.63] #np.arange(0, 1.51, 0.01) #for C tested np.arange(-0.37, 7.64, 0.1) 0, 2.63, 1 (for CNMR3), 1.5 (true for all ref, 8.37 + 1.5 for the precision), 1 (good for first, ok for second, third because only 7 better/equal but for first much higher) np.arange(-1.5, 1.49, 0.01) (only for first), for H 0 (not good), np.arange(-0.26, 0.34, 0.01), -0.06 for first, second never first either 17 or caf higher np.arange(-0.15, 0.16, 0.01) good measure 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 similaritylist = [] binwidthlist = np.arange(0.1, 3.9, 0.1) for i in binwidthlist: similaritylist.append(similarity_nmr(spectrumtrue, spectrumrefcorrected, i)) similaritymean = sum(similaritylist) / len(similaritylist) similaritybycorrection.append(similaritymean) likelihood.append(round(sum(similaritybycorrection)/len(similaritybycorrection), 2)) print(likelihood) if __name__ == "__main__": main()