From cc702732e7d147abf63e7898005d830aa95cff46 Mon Sep 17 00:00:00 2001 From: kilian Date: Fri, 18 Sep 2026 12:41:42 +0200 Subject: [PATCH] Dateien nach "ILP/Kaffee" hochladen --- ILP/Kaffee/caffeinesynthesis.py | 8 ++++++-- ILP/Kaffee/nmrSimilarityCaffeine.py | 14 +++++++------- 2 files changed, 13 insertions(+), 9 deletions(-) diff --git a/ILP/Kaffee/caffeinesynthesis.py b/ILP/Kaffee/caffeinesynthesis.py index 4f99bd7..961fdf2 100644 --- a/ILP/Kaffee/caffeinesynthesis.py +++ b/ILP/Kaffee/caffeinesynthesis.py @@ -76,10 +76,14 @@ def print_solution(title, flow_solution, binary_solution, hyperedges): def main(): VERTICES = ['Xanthine', '1-Methylxanthine', '3-Methylxanthine', '7-Methylxanthine', 'Theophylline', 'Paraxanthine', 'Theobromine', 'Caffeine'] - #Results for comparison with 7-Methylxanthine bei shift von 2.5 + #Results for comparison with 7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 3.9 NMR1 = [0.46, 0.49, 0.43, 0.6, 0.49, 0.6, 0.45, 0.51] - #Results for comparison with 3,7-Methylxanthine bei shift von 2.5 + #Results for comparison with 7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 1.1 + NMR1 = [0.12, 0.18, 0.12, 0.25, 0.18, 0.3, 0.1, 0.3] + #Results for comparison with 3,7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 3.9 NMR2 = [0.45, 0.52, 0.52, 0.63, 0.52, 0.57, 0.59, 0.59] + #Results for comparison with 3,7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 1.1 + NMR2 = [0.12, 0.18, 0.12, 0.25, 0.18, 0.3, 0.1, 0.3] #Chosable parameters modes = ["Product", "Average"] diff --git a/ILP/Kaffee/nmrSimilarityCaffeine.py b/ILP/Kaffee/nmrSimilarityCaffeine.py index b49109d..c8e5fff 100644 --- a/ILP/Kaffee/nmrSimilarityCaffeine.py +++ b/ILP/Kaffee/nmrSimilarityCaffeine.py @@ -308,18 +308,18 @@ def main(): for correctionvalue in correctionvalues: 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 similaritylist = [] - binwidthlist = np.arange(0.1, 3.9, 0.1) + binwidthlist = np.arange(0.1, 1.1, 0.1) for i in binwidthlist: similaritylist.append(similarity_nmr(spectrumtrue, spectrumrefcorrected, i)) similaritymean = sum(similaritylist) / len(similaritylist) similaritybycorrection.append(similaritymean) likelihoods.append(sum(similaritybycorrection)/len(similaritybycorrection)) - if normalize: - normalizedlikelihood = [round(likelihood/np.sum(likelihoods), 2) for likelihood in likelihoods] - print(normalizedlikelihood) - if not normalize: - notnormalizedlikelihood = [round(likelihood, 2) for likelihood in likelihoods] - print(notnormalizedlikelihood) + if normalize: + normalizedlikelihood = [round(likelihood/np.sum(likelihoods), 2) for likelihood in likelihoods] + print(normalizedlikelihood) + if not normalize: + notnormalizedlikelihood = [round(likelihood, 2) for likelihood in likelihoods] + print(notnormalizedlikelihood) if __name__ == "__main__":