--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/marea_2/utils/custom_data_generator.py	Thu Sep 19 08:34:04 2024 +0000
@@ -0,0 +1,218 @@
+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, Dict
+import utils.reaction_parsing as reactionUtils
+
+ARGS : argparse.Namespace
+def process_args() -> argparse.Namespace:
+    """
+    Interfaces the script of a module with its frontend, making the user's choices for
+    various parameters available as values in code.
+
+    Args:
+        args : Always obtained (in file) from sys.argv
+
+    Returns:
+        Namespace : An object containing the parsed arguments
+    """
+    parser = argparse.ArgumentParser(
+        usage = "%(prog)s [options]",
+        description = "generate custom data from a given model")
+    
+    parser.add_argument("-ol", "--out_log", type = str, required = True, help = "Output log")
+
+    parser.add_argument("-orules", "--out_rules", type = str, required = True, help = "Output rules")
+    parser.add_argument("-orxns", "--out_reactions", type = str, required = True, help = "Output reactions")
+    parser.add_argument("-omedium", "--out_medium", type = str, required = True, help = "Output medium")
+    parser.add_argument("-obnds", "--out_bounds", type = str, required = True, help = "Output bounds")
+
+    parser.add_argument("-id", "--input",   type = str, required = True, help = "Input model")
+    parser.add_argument("-mn", "--name",    type = str, required = True, help = "Input model name")
+    # ^ I need this because galaxy converts my files into .dat but I need to know what extension they were in
+    
+    argsNamespace = parser.parse_args()
+    argsNamespace.out_dir = "result"
+    # ^ can't get this one to work from xml, there doesn't seem to be a way to get the directory attribute from the collection
+
+    return argsNamespace
+
+################################- 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
+
+
+###############################- 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 })
+
+###############################- ENTRY POINT -################################
+def main() -> 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()
+
+    # this is the worst thing I've seen so far, congrats to the former MaREA devs for suggesting this!
+    if os.path.isdir(ARGS.out_dir) == False: os.makedirs(ARGS.out_dir)
+
+    # load custom model
+    model = load_custom_model(
+        utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
+
+    # generate data
+    rules = generate_rules(model, asParsed = False)
+    reactions = generate_reactions(model, asParsed = False)
+    bounds = generate_bounds(model)
+    medium = get_medium(model)
+
+    # save files out of collection: path coming from xml
+    save_as_csv(rules, ARGS.out_rules, ("ReactionID", "Rule"))
+    save_as_csv(reactions, ARGS.out_reactions, ("ReactionID", "Reaction"))
+    bounds.to_csv(ARGS.out_bounds, sep = '\t')
+    medium.to_csv(ARGS.out_medium, sep = '\t')
+
+if __name__ == '__main__':
+    main()
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