Dateien nach "ILP/Vanilla" hochladen
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@@ -1,244 +1,177 @@
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#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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["CINNAMICACID", "PCOUMARICACID", "MCOUMARICACID", "BENZALDEHYD", "CAFFEICACID", "3HYDROXYBENZALDEHYD", "4HYDROXYBENZALDEHYD", "34DIHYDROXYBENZALDEHYD"]
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#H noch machen
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likelihood = []
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#Cinnamicacid
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HCINNAMICACID = {
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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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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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#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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#p-Coumaricacid
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HPCOUMARICACID = {
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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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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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#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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#m-Coumaricacid
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HMCOUMARICACID = {
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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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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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#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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#Benzaldehyd
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HBENZALDEHYD = {
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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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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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#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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#Caffeicacid
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HCAFFEICACID = {
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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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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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#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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#3-Hydroxybenzaldehyd
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H3HYDROXYBENZALDEHYD = {
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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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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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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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#4-Hydroxybenzaldehyd
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H4HYDROXYBENZALDEHYD = {
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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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#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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#3,4-Dihydroxybenzaldehyd
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H34DIHYDROXYBENZALDEHYD = {
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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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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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#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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#Experimental p-Coumaricacid
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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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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: ([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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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 Theobromine nmr
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#Experimental 4-Hydroxybenzaldehyd
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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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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: ([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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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 3,4-Dihydroxybenzaldehyd
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HNMR3 = {
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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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#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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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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@@ -294,65 +227,25 @@ def correction(spectra, corretionppm):
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return newspectra
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def main():
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spectrumref = CNMR2
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#spectra = [HXANTHINE, H1XANTHINE, H3XANTHINE, H7XANTHINE, H1XANTHINE, H17XANTHINE, H37XANTHINE, H137XANTHINE]
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spectra = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE]
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spectranames = ["XANTHINE", "1XANTHINE", "3XANTHINE", "7XANTHINE", "1XANTHINE", "17XANTHINE", "37XANTHINE", "137XANTHINE"]
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spectrumref = CNMR3
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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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#errorlist = {}
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#errorlist = []
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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 to high values.
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correctionvalues = [9.37] #for C tested np.arange(8.0, 16.1, 0.1) range(8, 12) 8.4, 8.37, 11, 9.4 (for CNMR3), 9.87 (true for all ref, 8.37 + 1.5 for the precision), 9.37 (good for first, ok for second, third because only 7 better/equal but for first much higher) np.arange(6.13, 9.86, 0.01) (only for first), for H 0.66 (not good), np.arange(0.4, 1.0, 0.01), 0.6 for first, second never first either 17 or caf higher np.arange(0.51, 0.82, 0.01) good measure
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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 = [2.63] #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
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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
|
||||
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 = []
|
||||
binwidthlist = np.arange(0.1, 3.9, 0.1) #np.arange(0.1, 3.9, 0.1)
|
||||
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)
|
||||
name = spectranames[spectra.index(spectrumtrue)]
|
||||
correctionindex = max(range(len(similaritybycorrection)), key=similaritybycorrection.__getitem__)
|
||||
print(f'{name}: {correctionindex} = {correctionvalues[correctionindex]}')
|
||||
#Maybe not the best but a mean instead?
|
||||
likelihood.append(round(sum(similaritybycorrection)/len(similaritybycorrection), 2))
|
||||
#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()
|
||||
Reference in New Issue
Block a user