kilian
20824b6400
Now ILP solver picks optimal combination of two different similarities and similarity can also be calcuated using the mean of similarity instead of the comparitive value
353 lines
10 KiB
Python
353 lines
10 KiB
Python
#Binning mostly for broader peaks?
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#0.66 für H und 8.4 für C bei anderen TMS Werten
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#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)
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#+11 nicht universell, aber 9 bis 13 bei allen sweet spot
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import math
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import numpy as np
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#Xanthine
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HXANTHINE = {
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1: ([7.96], [1]),
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2: ([9.45], [1]),
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3: ([7.725], [1]),
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4: ([7.625], [1]),
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}
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CXANTHINE = {
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1: ([159.40], [1]),
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2: ([164.01], [1]),
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3: ([120.94], [1]),
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4: ([161.24], [1]),
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5: ([146.98], [1]),
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}
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#1-Methylxanthine
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H1XANTHINE = {
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1: ([7.93], [1]),
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2: ([9.45], [1]),
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3: ([4.05], [3]),
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4: ([7.91], [1]),
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}
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C1XANTHINE = {
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1: ([161.50], [1]),
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2: ([166.28], [1]),
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3: ([120.65], [1]),
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4: ([158.74], [1]),
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5: ([146.25], [1]),
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6: ([38.55], [1]),
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}
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#3-Methylxanthine
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H3XANTHINE = {
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1: ([4.15], [3]),
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2: ([7.73], [1]),
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3: ([7.99], [1]),
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4: ([9.49], [1]),
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}
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C3XANTHINE = {
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1: ([161.83], [1]),
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2: ([163.37], [1]),
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3: ([121.24], [1]),
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4: ([163.15], [1]),
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5: ([146.49], [1]),
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6: ([39.71], [1]),
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}
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#7-Methylxanthine
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H7XANTHINE = {
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1: ([7.55], [1]),
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2: ([4.47], [3]),
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3: ([7.72], [1]),
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4: ([7.655], [1]),
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}
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C7XANTHINE= {
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1: ([159.50], [1]),
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2: ([165.47], [1]),
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3: ([122.15], [1]),
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4: ([162.31], [1]),
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5: ([151.55], [1]),
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6: ([45.06], [1]),
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}
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#Theophylline
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H13XANTHINE = {
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1: ([4.03], [3]),
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2: ([7.98], [1]),
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3: ([4.19], [3]),
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4: ([9.49], [1]),
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}
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C13XANTHINE = {
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1: ([163.77], [1]),
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2: ([165.26], [1]),
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3: ([120.73], [1]),
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4: ([160.99], [1]),
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5: ([145.80], [1]),
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6: ([40.42], [1]),
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7: ([37.60], [1]),
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}
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#Paraxanthine
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H17XANTHINE = {
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1: ([4.50], [3]),
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2: ([7.70], [1]),
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3: ([3.98], [3]),
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4: ([7.82], [1]),
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}
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C17XANTHINE = {
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1: ([161.41], [1]),
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2: ([167.17], [1]),
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3: ([121.81], [1]),
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4: ([160.18], [1]),
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5: ([151.09], [1]),
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6: ([45.17], [1]),
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7: ([36.96], [1]),
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}
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CPARAXANTHINE = {
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1: ([26.7], [1]),
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2: ([32.9], [1]),
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3: ([151.1], [1]),
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4: ([106.5], [1]),
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5: ([147.4], [1]),
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6: ([155.3], [1]),
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7: ([143.0], [1]),
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}
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#Theobromine
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H37XANTHINE = {
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1: ([4.49], [3]),
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2: ([7.75], [1]),
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3: ([4.11], [3]),
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4: ([7.65], [1]),
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}
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C37XANTHINE = {
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1: ([161.76], [1]),
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2: ([164.86], [1]),
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3: ([122.51], [1]),
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4: ([164.29], [1]),
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5: ([151.10], [1]),
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6: ([39.33], [1]),
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7: ([45.07], [1]),
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}
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#Caffeine
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H137XANTHINE = {
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1: ([7.73], [1]),
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2: ([4.15], [3]),
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3: ([4.52], [3]),
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4: ([4.01], [3]),
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}
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C137XANTHINE = {
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1: ([163.66], [1]),
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2: ([166.66], [1]),
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3: ([122.03], [1]),
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4: ([162.23], [1]),
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5: ([150.50], [1]),
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6: ([40.09], [1]),
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7: ([45.23], [1]),
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8: ([37.17], [1]),
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}
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CCAFFEINE = {
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1: ([155.7], [1]), #166
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2: ([148.8], [1]), #159
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3: ([107.7], [1]), #118
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4: ([152.2], [1]), #163
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5: ([143.0], [1]), #154
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6: ([27.2], [1]), #38
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7: ([29.1], [1]), #40
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8: ([32.9], [1]), #44
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}
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CCAFFEINEADJUSTED = {
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1: ([166.7], [1]), #166
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2: ([159.8], [1]), #159
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3: ([118.7], [1]), #118
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4: ([163.2], [1]), #163
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5: ([154.0], [1]), #154
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6: ([38.2], [1]), #38
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7: ([40.1], [1]), #40
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8: ([44.9], [1]), #44
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}
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CCAFFEINE2 = {
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1: ([27.7], [1]),
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2: ([29.3], [1]),
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3: ([33.1], [1]),
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4: ([151.0], [1]),
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5: ([148.1], [1]),
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6: ([106.6], [1]),
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7: ([154.5], [1]),
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8: ([142.8], [1]),
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}
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C137XANTHINEADJUSTED = {
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1: ([155.62], [1]),
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2: ([158.29], [1]),
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3: ([113.66], [1]),
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4: ([153.86], [1]),
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5: ([142.13], [1]),
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6: ([31.72], [1]),
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7: ([36.86], [1]),
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8: ([28.8], [1]),
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}
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#Experimental 7-Methylxanthine nmr
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#Secundary source 11.52, 3.81
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HNMR1= {
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1: ([10.85], [1]),
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2: ([11.50], [1]),
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3: ([3.82], [3]),
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4: ([7.88], [1]),
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}
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CNMR1= {
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1: ([155.85], [1]),
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2: ([151.35], [1]),
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3: ([149.30], [1]),
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4: ([143.01], [1]),
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5: ([106.90], [1]),
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6: ([33.03], [1]),
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}
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#Experimental Theobromine nmr
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HNMR2 = {
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1: ([11.10], [1]),
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2: ([3.33], [3]),
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3: ([3.84], [3]),
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4: ([7.97], [1]),
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}
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CNMR2 = {
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1: ([154.9], [1]),
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2: ([149.8], [1]),
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3: ([107.1], [1]),
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4: ([151.0], [1]),
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5: ([142.8], [1]),
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6: ([29.3], [1]),
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7: ([33.9], [1]),
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}
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#Combination of methyl group and base purine rings from two papers
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CNMR3 = {
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1: ([154.9], [1]),
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2: ([150.0], [1]),
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3: ([108.1], [1]),
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4: ([153.1], [1]),
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5: ([142.6], [1]),
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6: ([29.3], [1]),
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7: ([33.9], [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))
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lowest_ppm = math.floor(min(shifts))
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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 = CCAFFEINE2
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spectra = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE]
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spectranames = ["CXANTHINE", "C1XANTHINE", "C3XANTHINE", "C7XANTHINE", "C1XANTHINE", "C17XANTHINE", "C37XANTHINE", "C137XANTHINE"]
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likelihood = []
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for spectrumtrue in spectra:
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#errorlist = {}
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#errorlist = []
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similaritybycorrection = []
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correctionvalues = range(8, 16) #also tested 8.4, 8.37, 11, 9.4 (for CNMR3)
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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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#Likelihood by number of higher similarity than all others.
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'''error = 0
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total = 0
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for spectrumfalse in spectra:
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positive = 0
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negative = 0
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bad_binwidth = []
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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
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truesimilarity = similarity_nmr(spectrumtrue, spectrumrefcorrected, i)
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falsesimilarity = similarity_nmr(spectrumfalse, spectrumrefcorrected, i)
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#print(truesimilarity)
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#print(falsesimilarity)
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if(truesimilarity - falsesimilarity < 0 or truesimilarity == 0):
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negative += 1
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bad_binwidth.append(i)
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else:
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positive += 1
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total += 1
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#print(f'Wrong similarity result: {negative} and Right similarity result: {positive}')
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#print(bad_binwidth)
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error += negative
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#errorlist[correctionvalue] = error
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errorlist.append(error)
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print(min(range(len(errorlist)), key=errorlist.__getitem__))
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likelihood.append(round((total - min(errorlist))/total, 2))'''
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#Likelihood by mean similarity
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#This method demonstrates the same problems as the other likelihood method
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similaritylist = []
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for i in np.arange(0.1, 3.9, 0.1):
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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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name = spectranames[spectra.index(spectrumtrue)]
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correctionindex = max(range(len(similaritybycorrection)), key=similaritybycorrection.__getitem__)
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print(f'{name}: {correctionindex} = {correctionvalues[correctionindex]}')
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likelihood.append(round(max(similaritybycorrection), 2))
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print(likelihood)
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'''for i in np.arange(0.01, 0.07, 0.01):
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print(f'Increment i: {i}')
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print(similarity_nmr(HNMR1, HNMR2, i))
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print(similarity_nmr(H1XANTHINE, HNMR1, i))
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print(similarity_nmr(H3XANTHINE, HNMR1, i))
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print(similarity_nmr(H7XANTHINE, HNMR1, i))
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'''
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if __name__ == "__main__":
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main() |