comparison COBRAxy/ras_generator.py @ 509:5956dcf94277 draft default tip

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508:ca98c149ec61

    # for loop on rules
    ind = 0       
    for rule, reaction_ids in dict_rule_reactions.items():
        if len(rule) != 0:
            # there is one gene at least in the formula
            rule_split = rule.split()
            rule_split_elements = list(set(rule_split))                                # genes in formula
            
            # which genes are in the count matrix?                
            genes_in_count_matrix = [el for el in rule_split_elements if el in genes]

                                else:
                                    ras_df[ind] = or_function(matrix, axis=0)
                            else:
                                # complex expression (e.g. A or (B and C))
                                data = count_df_filtered.loc[genes_in_count_matrix]  # dataframe of genes in the GPRs

                                rule = rule.replace("-", "_")
                                tree = ast.parse(rule, mode="eval").body
                                values_by_cell = [dict(zip(data.index, data[col].values)) for col in data.columns]
                                for j, values in enumerate(values_by_cell):
                                    ras_df[ind, j] = _evaluate_ast(tree, values, or_function, and_function)
    

509:5956dcf94277

    # for loop on rules
    ind = 0       
    for rule, reaction_ids in dict_rule_reactions.items():
        if len(rule) != 0:
            # there is one gene at least in the formula
            rule = rule.replace("-","_")
            rule_split = rule.split()
            rule_split_elements = list(set(rule_split))                                # genes in formula
            
            # which genes are in the count matrix?                
            genes_in_count_matrix = [el for el in rule_split_elements if el in genes]

                                else:
                                    ras_df[ind] = or_function(matrix, axis=0)
                            else:
                                # complex expression (e.g. A or (B and C))
                                data = count_df_filtered.loc[genes_in_count_matrix]  # dataframe of genes in the GPRs
                                tree = ast.parse(rule, mode="eval").body
                                values_by_cell = [dict(zip(data.index, data[col].values)) for col in data.columns]
                                for j, values in enumerate(values_by_cell):
                                    ras_df[ind, j] = _evaluate_ast(tree, values, or_function, and_function)