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@@ -76,10 +76,14 @@ def print_solution(title, flow_solution, binary_solution, hyperedges):
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def main():
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VERTICES = ['Xanthine', '1-Methylxanthine', '3-Methylxanthine', '7-Methylxanthine', 'Theophylline', 'Paraxanthine', 'Theobromine', 'Caffeine']
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#Results for comparison with 7-Methylxanthine bei shift von 2.5
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#Results for comparison with 7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 3.9
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NMR1 = [0.46, 0.49, 0.43, 0.6, 0.49, 0.6, 0.45, 0.51]
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#Results for comparison with 3,7-Methylxanthine bei shift von 2.5
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#Results for comparison with 7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 1.1
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NMR1 = [0.12, 0.18, 0.12, 0.25, 0.18, 0.3, 0.1, 0.3]
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#Results for comparison with 3,7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 3.9
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NMR2 = [0.45, 0.52, 0.52, 0.63, 0.52, 0.57, 0.59, 0.59]
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#Results for comparison with 3,7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 1.1
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NMR2 = [0.12, 0.18, 0.12, 0.25, 0.18, 0.3, 0.1, 0.3]
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#Chosable parameters
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modes = ["Product", "Average"]
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@@ -308,7 +308,7 @@ def main():
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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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binwidthlist = np.arange(0.1, 1.1, 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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