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

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240:63f5078627a9

    missing_reactions = [reaction for reaction in required_reactions if reaction not in dataset.index]

    if missing_reactions:
        sys.exit(f'Execution aborted: Missing required reactions {missing_reactions} in {datasetName}\n')

    # Calculate new rows using safe division
    lact_glc = np.divide(
        dataset.loc['EX_lac__L_e'], -dataset.loc['EX_glc__D_e'], 
        out=np.zeros_like(dataset.loc['EX_lac__L_e']), where=dataset.loc['EX_glc__D_e'] != 0
    )
    lact_gln = np.divide(
        dataset.loc['EX_lac__L_e'], -dataset.loc['EX_gln__L_e'], 
        out=np.zeros_like(dataset.loc['EX_lac__L_e']), where=dataset.loc['EX_gln__L_e'] != 0
    )
    glu_gln = np.divide(
        dataset.loc['EX_glu__L_e'], -dataset.loc['EX_gln__L_e'], 
        out=np.zeros_like(dataset.loc['EX_glu__L_e']), where=dataset.loc['EX_gln__L_e'] != 0
    )

    # Create a DataFrame for the new rows
    new_rows = pd.DataFrame({
        dataset.index.name: ['LactGlc', 'LactGln', 'GluGln'],
        **{col: [lact_glc[i], lact_gln[i], glu_gln[i]] for i, col in enumerate(dataset.columns)}
    })

    # Reset the index of the original dataset and append new rows
    dataset.reset_index(inplace=True)
    dataset = pd.concat([dataset, new_rows], ignore_index=True)

241:049aa0f4844f

    missing_reactions = [reaction for reaction in required_reactions if reaction not in dataset.index]

    if missing_reactions:
        sys.exit(f'Execution aborted: Missing required reactions {missing_reactions} in {datasetName}\n')


    # Calculate new rows using safe division
    lact_glc = np.divide(
        np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
        np.clip(dataset.loc['EX_glc__D_e'].to_numpy(), a_min=None, a_max=0),
        out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan),  # Prepara un array con NaN come output di default
        where=dataset.loc['EX_glc__D_e'].to_numpy() != 0  # Condizione per evitare la divisione per zero
    )
    lact_gln = np.divide(
        np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
        np.clip(dataset.loc['EX_gln__L_e'].to_numpy(), a_min=None, a_max=0),
        out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan), 
        where=dataset.loc['EX_gln__L_e'].to_numpy() != 0
    )
    lact_o2 = np.divide(
        np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
        np.clip(dataset.loc['EX_o2_e'].to_numpy(), a_min=None, a_max=0),
        out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan), 
        where=dataset.loc['EX_o2_e'].to_numpy() != 0
    )
    glu_gln = np.divide(
        dataset.loc['EX_glu__L_e'].to_numpy(),
        np.clip(dataset.loc['EX_gln__L_e'].to_numpy(), a_min=None, a_max=0), 
        out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan),
        where=dataset.loc['EX_gln__L_e'].to_numpy() != 0
    )

    # Create a DataFrame for the new rows
    new_rows = pd.DataFrame({
        dataset.index.name: ['LactGlc', 'LactGln','LactO2', 'GluGln'],
        **{col: [lact_glc[i], lact_gln[i],lact_o2[i], glu_gln[i]] for i, col in enumerate(dataset.columns)}
    })

    # Reset the index of the original dataset and append new rows
    dataset.reset_index(inplace=True)
    dataset = pd.concat([dataset, new_rows], ignore_index=True)