Mercurial > repos > bimib > cobraxy

comparison COBRAxy/flux_to_map.py @ 241:049aa0f4844f draft

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Uploaded
author francesco_lapi
date Mon, 13 Jan 2025 15:16:18 +0000
parents 63f5078627a9
children c6d78b0d324d
comparison
equal deleted inserted replaced
240:63f5078627a9 241:049aa0f4844f
840 missing_reactions = [reaction for reaction in required_reactions if reaction not in dataset.index] 840 missing_reactions = [reaction for reaction in required_reactions if reaction not in dataset.index]
841 841
842 if missing_reactions: 842 if missing_reactions:
843 sys.exit(f'Execution aborted: Missing required reactions {missing_reactions} in {datasetName}\n') 843 sys.exit(f'Execution aborted: Missing required reactions {missing_reactions} in {datasetName}\n')
844 844
845
845 # Calculate new rows using safe division 846 # Calculate new rows using safe division
846 lact_glc = np.divide( 847 lact_glc = np.divide(
847 dataset.loc['EX_lac__L_e'], -dataset.loc['EX_glc__D_e'], 848 np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
848 out=np.zeros_like(dataset.loc['EX_lac__L_e']), where=dataset.loc['EX_glc__D_e'] != 0 849 np.clip(dataset.loc['EX_glc__D_e'].to_numpy(), a_min=None, a_max=0),
850 out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan), # Prepara un array con NaN come output di default
851 where=dataset.loc['EX_glc__D_e'].to_numpy() != 0 # Condizione per evitare la divisione per zero
849 ) 852 )
850 lact_gln = np.divide( 853 lact_gln = np.divide(
851 dataset.loc['EX_lac__L_e'], -dataset.loc['EX_gln__L_e'], 854 np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
852 out=np.zeros_like(dataset.loc['EX_lac__L_e']), where=dataset.loc['EX_gln__L_e'] != 0 855 np.clip(dataset.loc['EX_gln__L_e'].to_numpy(), a_min=None, a_max=0),
856 out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan),
857 where=dataset.loc['EX_gln__L_e'].to_numpy() != 0
858 )
859 lact_o2 = np.divide(
860 np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
861 np.clip(dataset.loc['EX_o2_e'].to_numpy(), a_min=None, a_max=0),
862 out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan),
863 where=dataset.loc['EX_o2_e'].to_numpy() != 0
853 ) 864 )
854 glu_gln = np.divide( 865 glu_gln = np.divide(
855 dataset.loc['EX_glu__L_e'], -dataset.loc['EX_gln__L_e'], 866 dataset.loc['EX_glu__L_e'].to_numpy(),
856 out=np.zeros_like(dataset.loc['EX_glu__L_e']), where=dataset.loc['EX_gln__L_e'] != 0 867 np.clip(dataset.loc['EX_gln__L_e'].to_numpy(), a_min=None, a_max=0),
868 out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan),
869 where=dataset.loc['EX_gln__L_e'].to_numpy() != 0
857 ) 870 )
858 871
859 # Create a DataFrame for the new rows 872 # Create a DataFrame for the new rows
860 new_rows = pd.DataFrame({ 873 new_rows = pd.DataFrame({
861 dataset.index.name: ['LactGlc', 'LactGln', 'GluGln'], 874 dataset.index.name: ['LactGlc', 'LactGln','LactO2', 'GluGln'],
862 **{col: [lact_glc[i], lact_gln[i], glu_gln[i]] for i, col in enumerate(dataset.columns)} 875 **{col: [lact_glc[i], lact_gln[i],lact_o2[i], glu_gln[i]] for i, col in enumerate(dataset.columns)}
863 }) 876 })
864 877
865 # Reset the index of the original dataset and append new rows 878 # Reset the index of the original dataset and append new rows
866 dataset.reset_index(inplace=True) 879 dataset.reset_index(inplace=True)
867 dataset = pd.concat([dataset, new_rows], ignore_index=True) 880 dataset = pd.concat([dataset, new_rows], ignore_index=True)