--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/COBRAxy/custom_data_generator_beta.py	Mon Sep 08 14:44:15 2025 +0000
@@ -0,0 +1,343 @@
+import os
+import csv
+import cobra
+import pickle
+import argparse
+import pandas as pd
+import utils.general_utils as utils
+import utils.rule_parsing  as rulesUtils
+from typing import Optional, Tuple, Union, List, Dict
+import utils.reaction_parsing as reactionUtils
+
+ARGS : argparse.Namespace
+def process_args(args: List[str] = None) -> argparse.Namespace:
+    """
+    Parse command-line arguments for CustomDataGenerator.
+    """
+
+    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 or XML 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())
+
+    except Exception as e: raise utils.DataErr(file_path, e.__str__())
+    raise utils.DataErr(file_path,
+        f"Formato \"{file_path.ext}\" non riconosciuto, sono supportati solo file JSON e XML")
+
+################################- DATA GENERATION -################################
+ReactionId = str
+def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
+    """
+    Generates a dictionary mapping reaction ids to rules from the model.
+
+    Args:
+        model : the model to derive data from.
+        asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.
+
+    Returns:
+        Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
+        Dict[ReactionId, str] : the generated dictionary of raw rules.
+    """
+    # Is the below approach convoluted? yes
+    # Ok but is it inefficient? probably
+    # Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
+    _ruleGetter   =  lambda reaction : reaction.gene_reaction_rule
+    ruleExtractor = (lambda reaction :
+        rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
+
+    return {
+        reaction.id : ruleExtractor(reaction)
+        for reaction in model.reactions
+        if reaction.gene_reaction_rule }
+
+def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
+    """
+    Generates a dictionary mapping reaction ids to reaction formulas from the model.
+
+    Args:
+        model : the model to derive data from.
+        asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.
+
+    Returns:
+        Dict[ReactionId, str] : the generated dictionary.
+    """
+
+    unparsedReactions = {
+        reaction.id : reaction.reaction
+        for reaction in model.reactions
+        if reaction.reaction 
+    }
+
+    if not asParsed: return unparsedReactions
+    
+    return reactionUtils.create_reaction_dict(unparsedReactions)
+
+def get_medium(model:cobra.Model) -> pd.DataFrame:
+    trueMedium=[]
+    for r in model.reactions:
+        positiveCoeff=0
+        for m in r.metabolites:
+            if r.get_coefficient(m.id)>0:
+                positiveCoeff=1;
+        if (positiveCoeff==0 and r.lower_bound<0):
+            trueMedium.append(r.id)
+
+    df_medium = pd.DataFrame()
+    df_medium["reaction"] = trueMedium
+    return df_medium
+
+def generate_bounds(model:cobra.Model) -> pd.DataFrame:
+
+    rxns = []
+    for reaction in model.reactions:
+        rxns.append(reaction.id)
+
+    bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
+
+    for reaction in model.reactions:
+        bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
+    return bounds
+
+
+
+def generate_compartments(model: cobra.Model) -> pd.DataFrame:
+    """
+    Generates a DataFrame containing compartment information for each reaction.
+    Creates columns for each compartment position (Compartment_1, Compartment_2, etc.)
+    
+    Args:
+        model: the COBRA model to extract compartment data from.
+        
+    Returns:
+        pd.DataFrame: DataFrame with ReactionID and compartment columns
+    """
+    pathway_data = []
+
+    # First pass: determine the maximum number of pathways any reaction has
+    max_pathways = 0
+    reaction_pathways = {}
+
+    for reaction in model.reactions:
+        # Get unique pathways from all metabolites in the reaction
+        if type(reaction.annotation['pathways']) == list:
+            reaction_pathways[reaction.id] = reaction.annotation['pathways']
+            max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
+        else:
+            reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
+
+    # Create column names for pathways
+    pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]
+
+    # Second pass: create the data
+    for reaction_id, pathways in reaction_pathways.items():
+        row = {"ReactionID": reaction_id}
+        
+        # Fill pathway columns
+        for i in range(max_pathways):
+            col_name = pathway_columns[i]
+            if i < len(pathways):
+                row[col_name] = pathways[i]
+            else:
+                row[col_name] = None  # or "" if you prefer empty strings
+
+        pathway_data.append(row)
+
+    return pd.DataFrame(pathway_data)
+
+
+###############################- 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:
+    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}")
+
+
+###############################- ENTRY POINT -################################
+def main(args:List[str] = None) -> None:
+    """
+    Initializes everything and sets the program in motion based on the fronted input arguments.
+    
+    Returns:
+        None
+    """
+    # get args from frontend (related xml)
+    global ARGS
+    ARGS = process_args(args)
+
+
+    if ARGS.input:
+        # load custom model
+        model = load_custom_model(
+            utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
+    else:
+        # load 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()
+
+        # Set all reactions to zero in the medium
+        for rxn_id, _ in model.medium.items():
+            model.reactions.get_by_id(rxn_id).lower_bound = float(0.0)
+        
+        # Set medium conditions
+        for reaction, value in medium.items():
+            if value is not None:
+                model.reactions.get_by_id(reaction).lower_bound = -float(value)
+
+    if ARGS.name == "ENGRO2" and ARGS.gene_format != "Default":
+
+        model = utils.convert_genes(model, ARGS.gene_format.replace("HGNC_", "HGNC "))
+
+    # generate data
+    rules = generate_rules(model, asParsed = False)
+    reactions = generate_reactions(model, asParsed = False)
+    bounds = generate_bounds(model)
+    medium = get_medium(model)
+    if ARGS.name == "ENGRO2":
+        compartments = generate_compartments(model)
+
+    df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
+    df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])
+
+    df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
+    df_medium = medium.rename(columns = {"reaction": "ReactionID"})
+    df_medium["InMedium"] = True # flag per indicare la presenza nel medium
+
+    merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
+    merged = merged.merge(df_bounds, 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")
+
+    merged["InMedium"] = merged["InMedium"].fillna(False)
+
+    merged = merged.sort_values(by = "InMedium", ascending = False)
+
+    #out_file = os.path.join(ARGS.output_path, f"{os.path.basename(ARGS.name).split('.')[0]}_custom_data")
+
+    #merged.to_csv(out_file, sep = '\t', index = 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 non creato o vuoto")
+
+    print("CustomDataGenerator: completed successfully")
+
+if __name__ == '__main__':
+    main()
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