276 lines
6.3 KiB
Python
276 lines
6.3 KiB
Python
#Binning mostly for broader peaks?
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#
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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]),
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2: ([148.8], [1]),
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3: ([107.7], [1]),
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4: ([152.2], [1]),
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5: ([143.0], [1]),
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6: ([27.2], [1]),
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7: ([29.1], [1]),
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8: ([32.9], [1]),
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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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#Experimental 7-Methylxanthine nmr
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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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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):
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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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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)
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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 main():
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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 [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 25.0, 30.0, 35.0]:
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print(similarity_nmr(CNMR1, CNMR2, i), i)
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if(similarity_nmr(CCAFFEINE2, CCAFFEINE, i) - similarity_nmr(CPARAXANTHINE, CCAFFEINE, i) < 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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print(f'Wrong similarity result: {negative} and Right similarity result: {positive}')
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print(bad_binwidth)'''
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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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if __name__ == "__main__":
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main() |