import math import numpy as np ["CINNAMICACID", "PCOUMARICACID", "MCOUMARICACID", "BENZALDEHYD", "CAFFEICACID", "3HYDROXYBENZALDEHYD", "4HYDROXYBENZALDEHYD", "34DIHYDROXYBENZALDEHYD"] #H noch machen: Problem, Multiplett. Cinnamic acid voll Katastrophe, kombiniertmit einem Mangel an Informationen zu peak ppm in der Literatur likelihood = [] #Cinnamicacid HCINNAMICACID = { 1: ([5.78], [1]), #s 2: ([6.55, 6.34], [1]), #d 3: ([7.79, 7.57], [1]), #d 4: ([7.63, 7.55], [2]), #d 5: ([7.50, 7.47, 7.38, 7.21], [2]), #dd 6: ([7.32 , 7.32 ], [1]), #t } CCINNAMICACID = { 1: ([171.56], [1]), 2: ([120.33], [1]), 3: ([151.41], [1]), 4: ([140.47], [1]), 5: ([134.20], [2]), 6: ([133.48], [2]), 7: ([135.91], [1]), } #p-Coumaricacid HPCOUMARICACID = { } CPCOUMARICACID = { 1: ([171.61], [1]), 2: ([117.18], [1]), 3: ([149.67], [1]), 4: ([132.76], [1]), 5: ([135.75], [2]), 6: ([118.65], [2]), 7: ([166.09], [1]), } #m-Coumaricacid HMCOUMARICACID = { } CMCOUMARICACID = { 1: ([172.55], [1]), 2: ([118.60], [1]), 3: ([153.69], [1]), 4: ([141.29], [1]), 5: ([121.09], [1]), 6: ([134.46], [1]), 7: ([121.09], [1]), 8: ([164.15], [1]), 9: ([122.36], [1]), } #Benzaldehyd HBENZALDEHYD = { } CBENZALDEHYD= { 1: ([195.09], [1]), 2: ([141.47], [1]), 3: ([135.86], [2]), 4: ([133.45], [2]), 5: ([139.15], [1]), } #Caffeicacid HCAFFEICACID = { } CCAFFEICACID = { 1: ([171.66], [1]), 2: ([116.16], [1]), 3: ([149.88], [1]), 4: ([131.94], [1]), 5: ([132.99], [1]), 6: ([118.15], [1]), 7: ([155.79], [1]), 8: ([149.28], [1]), 9: ([110.95], [1]), } #3-Hydroxybenzaldehyd H3HYDROXYBENZALDEHYD = { } C3HYDROXYBENZALDEHYD = { 1: ([164.39], [1]), 2: ([122.95], [1]), 3: ([133.96], [1]), 4: ([123.33], [1]), 5: ([143.41], [1]), 6: ([195.16], [1]), 7: ([124.61], [1]), } #4-Hydroxybenzaldehyd H4HYDROXYBENZALDEHYD = { 1: ([9.96], [1]), #s 2: ([7.85, 7.77], [2]), #d 3: ([6.72, 6.66], [2]), #d 4: ([4.75], [1]), #s } C4HYDROXYBENZALDEHYD = { 1: ([191.53], [1]), 2: ([135.08], [1]), 3: ([137.69], [2]), 4: ([118.46], [2]), 5: ([168.28], [1]), } #3,4-Dihydroxybenzaldehyd H34DIHYDROXYBENZALDEHYD = { } C34DIHYDROXYBENZALDEHYD = { 1: ([158.08], [1]), 2: ([117.79], [1]), 3: ([133.40], [1]), 4: ([134.66], [1]), 5: ([191.41], [1]), 6: ([113.10], [1]), 7: ([149.32], [1]), } #Experimental p-Coumaricacid HNMR1= { 1: ([12.13], [1]), 2: ([7.49], [2]), 3: ([6.79], [2]), 4: ([9.96], [1]), 5: ([6.29], [1]), 6: ([7.52], [1]), } CNMR1= { 1: ([125.36], [1]), 2: ([130.17], [2]), 3: ([115.83], [2]), 4: ([159.67], [1]), 5: ([168.05], [1]), 6: ([115.41], [1]), 7: ([144.27], [1]), } #Experimental 4-Hydroxybenzaldehyd HNMR2 = { 1: ([7.05], [2]), 2: ([7.58], [2]), 3: ([8.44], [1]), 4: ([10.15], [1]), } #No Intesities mentioned CNMR2 = { 1: ([116.5], [2]), 2: ([130.3], [1]), 3: ([132.7], [2]), 4: ([163.8], [1]), 5: ([191.0], [1]), } #Experimental Caffeicacid HNMR3 = { 1: ([6.99], [1]), #d 2: ([6.84], [1]), #dd 3: ([6.73], [1]), #d 4: ([7.27], [1]), #d 5: ([6.28], [1]), #d } CNMR3 = { 1: ([147.9], [1]), 2: ([146.5], [1]), 3: ([129.3], [1]), 4: ([123.1], [1]), 5: ([121.7], [1]), 6: ([116.4], [1]), 7: ([141.5], [1]), 8: ([114.6], [1]), 9: ([176.2], [1]), } #Experimental 3,4-Dihydroxybenzaldehyd HNMR4 = { 1: ([7.44], [1]), 2: ([7.42], [1]), 3: ([7.00], [1]), } CNMR4 = { 1: ([124.59], [1]), 2: ([115.21], [1]), 3: ([145.44], [1]), 4: ([151.27], [1]), 5: ([130.13], [1]), 6: ([114.22], [1]), 7: ([190.26], [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 = CNMR2 #1H-NMR Spectra ignoriert, da meiste H sauer, da an N gebunden #spectra = [HCINNAMICACID, HPCOUMARICACID, HMCOUMARICACID, HBENZALDEHYD, HCAFFEICACID, H3HYDROXYBENZALDEHYD, H4HYDROXYBENZALDEHYD, H34DIHYDROXYBENZALDEHYD] spectra = [CCINNAMICACID, CPCOUMARICACID, CMCOUMARICACID, CBENZALDEHYD, CCAFFEICACID, C3HYDROXYBENZALDEHYD, C4HYDROXYBENZALDEHYD, C34DIHYDROXYBENZALDEHYD] spectranames = ["CINNAMICACID", "PCOUMARICACID", "MCOUMARICACID", "BENZALDEHYD", "CAFFEICACID", "3HYDROXYBENZALDEHYD", "4HYDROXYBENZALDEHYD", "34DIHYDROXYBENZALDEHYD"] 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 = [2.5] #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()