--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/COBRAxy/metabolicModel2Tabular.py	Mon Sep 29 15:34:59 2025 +0000
@@ -0,0 +1,368 @@
+"""
+Scripts to generate a tabular file of a metabolic model (built-in or custom).
+
+This script loads a COBRA model (built-in or custom), optionally applies
+medium and gene nomenclature settings, derives reaction-related metadata
+(GPR rules, formulas, bounds, objective coefficients, medium membership,
+and compartments for ENGRO2), and writes a tabular summary.
+"""
+
+import os
+import csv
+import cobra
+import argparse
+import pandas as pd
+import utils.general_utils as utils
+from typing import Optional, Tuple, List
+import utils.model_utils as modelUtils
+import logging
+from pathlib import Path
+
+
+ARGS : argparse.Namespace
+def process_args(args: List[str] = None) -> argparse.Namespace:
+    """
+    Parse command-line arguments for metabolic_model_setting.
+    """
+
+    parser = argparse.ArgumentParser(
+        usage="%(prog)s [options]",
+        description="Generate custom data from a given model"
+    )
+
+    parser.add_argument("--out_log", type=str, required=True,
+                        help="Output log file")
+
+    parser.add_argument("--model", type=str,
+                        help="Built-in model identifier (e.g., ENGRO2, Recon, HMRcore)")
+    parser.add_argument("--input", type=str,
+                        help="Custom model file (JSON or XML)")
+    parser.add_argument("--name", type=str, required=True,
+                        help="Model name (default or custom)")
+    
+    parser.add_argument("--medium_selector", type=str, required=True,
+                        help="Medium selection option")
+
+    parser.add_argument("--gene_format", type=str, default="Default",
+                        help="Gene nomenclature format: Default (original), ENSNG, HGNC_SYMBOL, HGNC_ID, ENTREZ")
+    
+    parser.add_argument("--out_tabular", type=str,
+                        help="Output file for the merged dataset (CSV or XLSX)")
+    
+    parser.add_argument("--tool_dir", type=str, default=os.path.dirname(__file__),
+                        help="Tool directory (passed from Galaxy as $__tool_directory__)")
+
+
+    return parser.parse_args(args)
+
+################################- INPUT DATA LOADING -################################
+def load_custom_model(file_path :utils.FilePath, ext :Optional[utils.FileFormat] = None) -> cobra.Model:
+    """
+    Loads a custom model from a file, either in JSON, XML, MAT, or YML format.
+
+    Args:
+        file_path : The path to the file containing the custom model.
+        ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour.
+
+    Raises:
+        DataErr : if the file is in an invalid format or cannot be opened for whatever reason.    
+    
+    Returns:
+        cobra.Model : the model, if successfully opened.
+    """
+    ext = ext if ext else file_path.ext
+    try:
+        if ext is utils.FileFormat.XML:
+            return cobra.io.read_sbml_model(file_path.show())
+        
+        if ext is utils.FileFormat.JSON:
+            return cobra.io.load_json_model(file_path.show())
+
+        if ext is utils.FileFormat.MAT:
+            return cobra.io.load_matlab_model(file_path.show())
+
+        if ext is utils.FileFormat.YML:
+            return cobra.io.load_yaml_model(file_path.show())
+
+    except Exception as e: raise utils.DataErr(file_path, e.__str__())
+    raise utils.DataErr(
+        file_path,
+        f"Unrecognized format '{file_path.ext}'. Only JSON, XML, MAT, YML are supported."
+    )
+
+
+###############################- FILE SAVING -################################
+def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
+    """
+    Saves any dictionary-shaped data in a .csv file created at the given file_path as FilePath.
+
+    Args:
+        data : the data to be written to the file.
+        file_path : the path to the .csv file.
+        fieldNames : the names of the fields (columns) in the .csv file.
+    
+    Returns:
+        None
+    """
+    with open(file_path.show(), 'w', newline='') as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
+        writer.writeheader()
+
+        for key, value in data.items():
+            writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
+
+def save_as_csv(data :dict, file_path :str, fieldNames :Tuple[str, str]) -> None:
+    """
+    Saves any dictionary-shaped data in a .csv file created at the given file_path as string.
+
+    Args:
+        data : the data to be written to the file.
+        file_path : the path to the .csv file.
+        fieldNames : the names of the fields (columns) in the .csv file.
+    
+    Returns:
+        None
+    """
+    with open(file_path, 'w', newline='') as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
+        writer.writeheader()
+
+        for key, value in data.items():
+            writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
+
+def save_as_tabular_df(df: pd.DataFrame, path: str) -> None:
+    """
+    Save a pandas DataFrame as a tab-separated file, creating directories as needed.
+
+    Args:
+        df: The DataFrame to write.
+        path: Destination file path (will be written as TSV).
+
+    Raises:
+        DataErr: If writing the output fails for any reason.
+
+    Returns:
+        None
+    """
+    try:
+        os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
+        df.to_csv(path, sep="\t", index=False)
+    except Exception as e:
+        raise utils.DataErr(path, f"failed writing tabular output: {e}")
+    
+def is_placeholder(gid) -> bool:
+    """Return True if the gene id looks like a placeholder (e.g., 0/NA/NAN/empty)."""
+    if gid is None:
+        return True
+    s = str(gid).strip().lower()
+    return s in {"0", "", "na", "nan"}  # lowercase for simple matching
+
+def sample_valid_gene_ids(genes, limit=10):
+    """Yield up to `limit` valid gene IDs, skipping placeholders (e.g., the first 0 in RECON)."""
+    out = []
+    for g in genes:
+        gid = getattr(g, "id", getattr(g, "gene_id", g))
+        if not is_placeholder(gid):
+            out.append(str(gid))
+            if len(out) >= limit:
+                break
+    return out
+
+
+###############################- ENTRY POINT -################################
+def main(args:List[str] = None) -> None:
+    """
+    Initialize and generate custom data based on the frontend input arguments.
+    
+    Returns:
+        None
+    """
+    # Parse args from frontend (Galaxy XML)
+    global ARGS
+    ARGS = process_args(args)
+
+
+    if ARGS.input:
+        # Load a custom model from file
+        model = load_custom_model(
+            utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
+    else:
+        # Load a built-in model
+
+        try:
+            model_enum = utils.Model[ARGS.model]  # e.g., Model['ENGRO2']
+        except KeyError:
+            raise utils.ArgsErr("model", "one of Recon/ENGRO2/HMRcore/Custom_model", ARGS.model)
+
+        # Load built-in model (Model.getCOBRAmodel uses tool_dir to locate local models)
+        try:
+            model = model_enum.getCOBRAmodel(toolDir=ARGS.tool_dir)
+        except Exception as e:
+            # Wrap/normalize load errors as DataErr for consistency
+            raise utils.DataErr(ARGS.model, f"failed loading built-in model: {e}")
+
+    # Determine final model name: explicit --name overrides, otherwise use the model id
+    
+    model_name = ARGS.name if ARGS.name else ARGS.model
+    
+    if ARGS.name == "ENGRO2" and ARGS.medium_selector != "Default":
+        df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
+        ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
+        medium = df_mediums[[ARGS.medium_selector]]
+        medium = medium[ARGS.medium_selector].to_dict()
+
+        # Reset all medium reactions lower bound to zero
+        for rxn_id, _ in model.medium.items():
+            model.reactions.get_by_id(rxn_id).lower_bound = float(0.0)
+        
+        # Apply selected medium uptake bounds (negative for uptake)
+        for reaction, value in medium.items():
+            if value is not None:
+                model.reactions.get_by_id(reaction).lower_bound = -float(value)
+
+    # Initialize translation_issues dictionary
+    translation_issues = {}
+    
+    if (ARGS.name == "Recon" or ARGS.name == "ENGRO2") and ARGS.gene_format != "Default":
+        logging.basicConfig(level=logging.INFO)
+        logger = logging.getLogger(__name__)
+
+        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='HGNC_symbol',
+            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:
+            raise utils.DataErr(
+                "Custom_model",
+                "No valid gene IDs found (many may be placeholders like 0)."
+            )
+
+        # Detect source nomenclature on the sample
+        types = []
+        for gid in ids_sample:
+            try:
+                t = modelUtils.gene_type(gid, "Custom_model")
+            except Exception as e:
+                # Keep it simple: skip problematic IDs
+                logger.debug(f"gene_type failed for {gid}: {e}")
+                t = None
+            if t:
+                types.append(t)
+
+        if not types:
+            raise utils.DataErr(
+                "Custom_model",
+                "Could not detect a known gene nomenclature from the sample."
+            )
+
+        unique_types = set(types)
+        if len(unique_types) > 1:
+            raise utils.DataErr(
+                "Custom_model",
+                "Mixed or inconsistent gene nomenclatures detected. "
+                "Please unify them before converting."
+            )
+
+        source_nomenclature = types[0]
+
+        # Convert only if needed
+        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
+            )
+
+    # generate data
+    rules = modelUtils.generate_rules(model, asParsed = False)
+    reactions = modelUtils.generate_reactions(model, asParsed = False)
+    bounds = modelUtils.generate_bounds(model)
+    medium = modelUtils.get_medium(model)
+    objective_function = modelUtils.extract_objective_coefficients(model)
+    
+    if ARGS.name == "ENGRO2":
+        compartments = modelUtils.generate_compartments(model)
+
+    df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "GPR"])
+    df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Formula"])
+
+    # Create DataFrame for translation issues
+    df_translation_issues = pd.DataFrame([
+        {"ReactionID": rxn_id, "TranslationIssues": issues}
+        for rxn_id, issues in translation_issues.items()
+    ])
+    
+    df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
+    df_medium = medium.rename(columns = {"reaction": "ReactionID"})
+    df_medium["InMedium"] = True
+
+    merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
+    merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
+    merged = merged.merge(objective_function, on = "ReactionID", how = "outer")
+    if ARGS.name == "ENGRO2": 
+        merged = merged.merge(compartments, on = "ReactionID", how = "outer")
+    merged = merged.merge(df_medium, on = "ReactionID", how = "left")
+    
+    # Add translation issues column
+    if not df_translation_issues.empty:
+        merged = merged.merge(df_translation_issues, on = "ReactionID", how = "left")
+        merged["TranslationIssues"] = merged["TranslationIssues"].fillna("")
+    else:
+        # Add empty TranslationIssues column if no issues found
+        #merged["TranslationIssues"] = ""
+        pass
+
+    merged["InMedium"] = merged["InMedium"].fillna(False)
+
+    merged = merged.sort_values(by = "InMedium", ascending = False)
+
+    if not ARGS.out_tabular:
+        raise utils.ArgsErr("out_tabular", "output path (--out_tabular) is required when output_format == tabular", ARGS.out_tabular)
+    save_as_tabular_df(merged, ARGS.out_tabular)
+    expected = ARGS.out_tabular
+
+    # verify output exists and non-empty
+    if not expected or not os.path.exists(expected) or os.path.getsize(expected) == 0:
+        raise utils.DataErr(expected, "Output not created or empty")
+
+    print("Metabolic_model_setting: completed successfully")
+
+if __name__ == '__main__':
+
+    main()