comparison COBRAxy/metabolicModel2Tabular.py @ 494:5397559097dc draft

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493:a92d21f92956

            logger=logger
        )

    if ARGS.name == "Custom_model" and ARGS.gene_format != "Default":
        logging.basicConfig(level=logging.INFO)
        logger = logging.getLogger(__name__)

        tmp_check = []
        for g in model.genes[1:5]:  # check first 3 genes only
            tmp_check.append(modelUtils.gene_type(g.id, "Custom_model"))
        
        if len(set(tmp_check)) > 1:
            raise utils.DataErr("Custom_model", "The custom model contains genes with mixed or unrecognized nomenclature. Please ensure all genes use the same recognized nomenclature before applying gene_format conversion.")
        else:
            source_nomenclature = tmp_check[0]

        if source_nomenclature != ARGS.gene_format:
            model, translation_issues = modelUtils.translate_model_genes(
                model=model,
                mapping_df= pd.read_csv(ARGS.tool_dir + "/local/mappings/genes_human.csv", dtype={'entrez_id': str}),
                target_nomenclature=ARGS.gene_format,
                source_nomenclature=source_nomenclature,
                logger=logger
            )




    if ARGS.name == "Custom_model" and ARGS.gene_format != "Default":
        logger = logging.getLogger(__name__)

        # Take a small, clean sample of gene IDs (skipping placeholders like 0)
        ids_sample = sample_valid_gene_ids(model.genes, limit=10)
        if not ids_sample:

494:5397559097dc

            logger=logger
        )

    if ARGS.name == "Custom_model" and ARGS.gene_format != "Default":
        logging.basicConfig(level=logging.INFO)
        logger = logging.getLogger(__name__)

        # Take a small, clean sample of gene IDs (skipping placeholders like 0)
        ids_sample = sample_valid_gene_ids(model.genes, limit=10)
        if not ids_sample: