280 lines
8.1 KiB
Python
280 lines
8.1 KiB
Python
import math
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import numpy as np
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["CINNAMICACID", "PCOUMARICACID", "MCOUMARICACID", "BENZALDEHYD", "CAFFEICACID", "3HYDROXYBENZALDEHYD", "4HYDROXYBENZALDEHYD", "34DIHYDROXYBENZALDEHYD"]
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#H noch machen: Problem, Multiplett. Cinnamic acid voll Katastrophe, kombiniertmit einem Mangel an Informationen zu peak ppm in der Literatur
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likelihood = []
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#Cinnamicacid
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HCINNAMICACID = {
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1: ([5.78], [1]), #s
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2: ([6.55, 6.34], [1]), #d
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3: ([7.79, 7.57], [1]), #d
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4: ([7.63, 7.55], [2]), #d
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5: ([7.50, 7.47, 7.38, 7.21], [2]), #dd
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6: ([7.32 , 7.32 ], [1]), #t
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}
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CCINNAMICACID = {
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1: ([171.56], [1]),
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2: ([120.33], [1]),
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3: ([151.41], [1]),
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4: ([140.47], [1]),
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5: ([134.20], [2]),
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6: ([133.48], [2]),
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7: ([135.91], [1]),
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}
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#p-Coumaricacid
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HPCOUMARICACID = {
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}
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CPCOUMARICACID = {
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1: ([171.61], [1]),
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2: ([117.18], [1]),
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3: ([149.67], [1]),
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4: ([132.76], [1]),
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5: ([135.75], [2]),
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6: ([118.65], [2]),
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7: ([166.09], [1]),
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}
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#m-Coumaricacid
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HMCOUMARICACID = {
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}
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CMCOUMARICACID = {
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1: ([172.55], [1]),
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2: ([118.60], [1]),
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3: ([153.69], [1]),
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4: ([141.29], [1]),
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5: ([121.09], [1]),
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6: ([134.46], [1]),
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7: ([121.09], [1]),
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8: ([164.15], [1]),
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9: ([122.36], [1]),
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}
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#Benzaldehyd
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HBENZALDEHYD = {
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}
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CBENZALDEHYD= {
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1: ([195.09], [1]),
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2: ([141.47], [1]),
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3: ([135.86], [2]),
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4: ([133.45], [2]),
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5: ([139.15], [1]),
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}
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#Caffeicacid
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HCAFFEICACID = {
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}
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CCAFFEICACID = {
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1: ([171.66], [1]),
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2: ([116.16], [1]),
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3: ([149.88], [1]),
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4: ([131.94], [1]),
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5: ([132.99], [1]),
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6: ([118.15], [1]),
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7: ([155.79], [1]),
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8: ([149.28], [1]),
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9: ([110.95], [1]),
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}
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#3-Hydroxybenzaldehyd
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H3HYDROXYBENZALDEHYD = {
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}
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C3HYDROXYBENZALDEHYD = {
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1: ([164.39], [1]),
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2: ([122.95], [1]),
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3: ([133.96], [1]),
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4: ([123.33], [1]),
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5: ([143.41], [1]),
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6: ([195.16], [1]),
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7: ([124.61], [1]),
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}
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#4-Hydroxybenzaldehyd
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H4HYDROXYBENZALDEHYD = {
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1: ([9.96], [1]), #s
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2: ([7.85, 7.77], [2]), #d
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3: ([6.72, 6.66], [2]), #d
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4: ([4.75], [1]), #s
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}
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C4HYDROXYBENZALDEHYD = {
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1: ([191.53], [1]),
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2: ([135.08], [1]),
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3: ([137.69], [2]),
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4: ([118.46], [2]),
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5: ([168.28], [1]),
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}
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#3,4-Dihydroxybenzaldehyd
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H34DIHYDROXYBENZALDEHYD = {
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}
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C34DIHYDROXYBENZALDEHYD = {
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1: ([158.08], [1]),
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2: ([117.79], [1]),
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3: ([133.40], [1]),
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4: ([134.66], [1]),
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5: ([191.41], [1]),
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6: ([113.10], [1]),
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7: ([149.32], [1]),
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}
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#Experimental p-Coumaricacid
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HNMR1= {
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1: ([12.13], [1]),
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2: ([7.49], [2]),
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3: ([6.79], [2]),
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4: ([9.96], [1]),
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5: ([6.29], [1]),
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6: ([7.52], [1]),
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}
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CNMR1= {
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1: ([125.36], [1]),
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2: ([130.17], [2]),
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3: ([115.83], [2]),
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4: ([159.67], [1]),
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5: ([168.05], [1]),
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6: ([115.41], [1]),
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7: ([144.27], [1]),
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}
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#Experimental 4-Hydroxybenzaldehyd
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HNMR2 = {
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1: ([7.05], [2]),
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2: ([7.58], [2]),
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3: ([8.44], [1]),
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4: ([10.15], [1]),
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}
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#No Intesities mentioned
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CNMR2 = {
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1: ([116.5], [2]),
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2: ([130.3], [1]),
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3: ([132.7], [2]),
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4: ([163.8], [1]),
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5: ([191.0], [1]),
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}
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#Experimental Caffeicacid
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HNMR3 = {
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1: ([6.99], [1]), #d
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2: ([6.84], [1]), #dd
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3: ([6.73], [1]), #d
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4: ([7.27], [1]), #d
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5: ([6.28], [1]), #d
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}
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CNMR3 = {
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1: ([147.9], [1]),
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2: ([146.5], [1]),
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3: ([129.3], [1]),
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4: ([123.1], [1]),
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5: ([121.7], [1]),
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6: ([116.4], [1]),
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7: ([141.5], [1]),
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8: ([114.6], [1]),
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9: ([176.2], [1]),
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}
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#Experimental 3,4-Dihydroxybenzaldehyd
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HNMR4 = {
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1: ([7.44], [1]),
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2: ([7.42], [1]),
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3: ([7.00], [1]),
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}
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CNMR4 = {
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1: ([124.59], [1]),
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2: ([115.21], [1]),
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3: ([145.44], [1]),
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4: ([151.27], [1]),
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5: ([130.13], [1]),
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6: ([114.22], [1]),
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7: ([190.26], [1]),
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}
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def overlap(listref, listnew):
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twoleft = np.sum(np.multiply(np.concatenate((listref, [0, 0])), np.concatenate(([0, 0], listnew))))
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oneleft = np.sum(np.multiply(np.concatenate((listref, [0])), np.concatenate(([0], listnew))))
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neutral = np.sum(np.multiply(listref,listnew))
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oneright = np.sum(np.multiply(np.concatenate(([0], listref)), np.concatenate((listnew, [0]))))
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tworight = np.sum(np.multiply(np.concatenate(([0, 0], listref)), np.concatenate((listnew, [0, 0]))))
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overlap = (oneleft + oneright)* 0.5 + neutral
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return overlap
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def bin_array(spectra, highest_ppm, lowest_ppm, bin_width):
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binnumber = math.ceil((highest_ppm - lowest_ppm)/bin_width)
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bin = [0] * binnumber
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for peak in spectra:
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(shift, height) = spectra[peak]
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binindex = math.floor((shift[0] - lowest_ppm) / bin_width)
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bin[binindex] += height[0]
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normalizedbin = np.divide(bin, np.sum(bin))
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return normalizedbin
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def define_border_values(spectraref, spectranew, bin_width):
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shifts = []
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for _,(shift,_) in spectraref.items():
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shifts.append(shift[0])
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for _,(shift,_) in spectranew.items():
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shifts.append(shift[0])
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highest_ppm = math.ceil(max(shifts)) + bin_width
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lowest_ppm = math.floor(min(shifts)) - bin_width
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#lowest_ppm = min(shifts) - bin_width/2 #Worse result. None of the previously wrong (except 0.6) become right
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return (lowest_ppm, highest_ppm)
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def similarity_nmr(spectraref, spectranew, bin_width):
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#Maximize likelihood or minimize Deviation
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#Values for two spectra and optimize largest for both different?
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#Spectra in Nodes to allow maximize overlapp with both spectra or one spectra.
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#5.4.2 Eliminating X–H signals from 1H NMR spectra
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lowest_ppm, highest_ppm = define_border_values(spectraref, spectranew, bin_width)
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binref = bin_array(spectraref, highest_ppm, lowest_ppm, bin_width)
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binnew = bin_array(spectranew, highest_ppm, lowest_ppm, bin_width)
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crosscorr = overlap(binref, binnew)
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refselfcorr = overlap(binref, binref)
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newselfcorr = overlap(binnew, binnew)
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simidx = crosscorr / math.sqrt(refselfcorr * newselfcorr)
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return(simidx)
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def correction(spectra, corretionppm):
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newspectra = {}
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for id, (shift, height) in spectra.items():
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shiftvalue = shift[0]
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adjustedshift = shiftvalue + corretionppm
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newspectra[id] = ([adjustedshift], height)
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return newspectra
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def main():
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spectrumref = CNMR2
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#1H-NMR Spectra ignoriert, da meiste H sauer, da an N gebunden
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#spectra = [HCINNAMICACID, HPCOUMARICACID, HMCOUMARICACID, HBENZALDEHYD, HCAFFEICACID, H3HYDROXYBENZALDEHYD, H4HYDROXYBENZALDEHYD, H34DIHYDROXYBENZALDEHYD]
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spectra = [CCINNAMICACID, CPCOUMARICACID, CMCOUMARICACID, CBENZALDEHYD, CCAFFEICACID, C3HYDROXYBENZALDEHYD, C4HYDROXYBENZALDEHYD, C34DIHYDROXYBENZALDEHYD]
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spectranames = ["CINNAMICACID", "PCOUMARICACID", "MCOUMARICACID", "BENZALDEHYD", "CAFFEICACID", "3HYDROXYBENZALDEHYD", "4HYDROXYBENZALDEHYD", "34DIHYDROXYBENZALDEHYD"]
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likelihood = []
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for spectrumtrue in spectra:
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similaritybycorrection = []
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#Paper Chemical reviews Carbons bound to Heavy atoms (TMS) to high -> this could be reason for too high values.
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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
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for correctionvalue in correctionvalues:
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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
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similaritylist = []
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binwidthlist = np.arange(0.1, 3.9, 0.1)
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for i in binwidthlist:
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similaritylist.append(similarity_nmr(spectrumtrue, spectrumrefcorrected, i))
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similaritymean = sum(similaritylist) / len(similaritylist)
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similaritybycorrection.append(similaritymean)
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likelihood.append(round(sum(similaritybycorrection)/len(similaritybycorrection), 2))
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print(likelihood)
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if __name__ == "__main__":
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main() |