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     1 import os
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     2 import csv
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     3 import cobra
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     4 import pickle
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     5 import argparse
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     6 import pandas as pd
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     7 import utils.general_utils as utils
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     8 import utils.rule_parsing  as rulesUtils
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147
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     9 from typing import Optional, Tuple, Union, List, Dict
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93
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    10 import utils.reaction_parsing as reactionUtils
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    11 
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    12 ARGS : argparse.Namespace
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147
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    13 def process_args(args:List[str] = None) -> argparse.Namespace:
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93
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    14     """
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    15     Interfaces the script of a module with its frontend, making the user's choices for
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    16     various parameters available as values in code.
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    17 
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    18     Args:
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    19         args : Always obtained (in file) from sys.argv
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    20 
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    21     Returns:
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    22         Namespace : An object containing the parsed arguments
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    23     """
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    24     parser = argparse.ArgumentParser(
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    25         usage = "%(prog)s [options]",
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    26         description = "generate custom data from a given model")
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    27     
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    28     parser.add_argument("-ol", "--out_log", type = str, required = True, help = "Output log")
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    29 
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    30     parser.add_argument("-orules", "--out_rules", type = str, required = True, help = "Output rules")
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    31     parser.add_argument("-orxns", "--out_reactions", type = str, required = True, help = "Output reactions")
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    32     parser.add_argument("-omedium", "--out_medium", type = str, required = True, help = "Output medium")
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    33     parser.add_argument("-obnds", "--out_bounds", type = str, required = True, help = "Output bounds")
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    34 
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    35     parser.add_argument("-id", "--input",   type = str, required = True, help = "Input model")
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    36     parser.add_argument("-mn", "--name",    type = str, required = True, help = "Input model name")
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    37     # ^ I need this because galaxy converts my files into .dat but I need to know what extension they were in
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147
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    38     parser.add_argument('-idop', '--output_path', type = str, default='result', help = 'output path for maps')
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    39     argsNamespace = parser.parse_args(args)
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93
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    40     # ^ 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
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    41 
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    42     return argsNamespace
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    43 
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    44 ################################- INPUT DATA LOADING -################################
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    45 def load_custom_model(file_path :utils.FilePath, ext :Optional[utils.FileFormat] = None) -> cobra.Model:
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    46     """
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    47     Loads a custom model from a file, either in JSON or XML format.
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    48 
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    49     Args:
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    50         file_path : The path to the file containing the custom model.
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    51         ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour.
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    52 
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    53     Raises:
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    54         DataErr : if the file is in an invalid format or cannot be opened for whatever reason.    
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    55     
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    56     Returns:
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    57         cobra.Model : the model, if successfully opened.
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    58     """
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    59     ext = ext if ext else file_path.ext
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    60     try:
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    61         if ext is utils.FileFormat.XML:
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    62             return cobra.io.read_sbml_model(file_path.show())
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    63         
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    64         if ext is utils.FileFormat.JSON:
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    65             return cobra.io.load_json_model(file_path.show())
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    66 
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    67     except Exception as e: raise utils.DataErr(file_path, e.__str__())
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    68     raise utils.DataErr(file_path,
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    69         f"Formato \"{file_path.ext}\" non riconosciuto, sono supportati solo file JSON e XML")
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    70 
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    71 ################################- DATA GENERATION -################################
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    72 ReactionId = str
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    73 def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
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    74     """
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    75     Generates a dictionary mapping reaction ids to rules from the model.
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    76 
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    77     Args:
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    78         model : the model to derive data from.
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    79         asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.
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    80 
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    81     Returns:
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    82         Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
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    83         Dict[ReactionId, str] : the generated dictionary of raw rules.
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    84     """
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    85     # Is the below approach convoluted? yes
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    86     # Ok but is it inefficient? probably
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    87     # Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
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    88     _ruleGetter   =  lambda reaction : reaction.gene_reaction_rule
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    89     ruleExtractor = (lambda reaction :
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    90         rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
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    91 
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    92     return {
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    93         reaction.id : ruleExtractor(reaction)
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    94         for reaction in model.reactions
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    95         if reaction.gene_reaction_rule }
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    96 
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    97 def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
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    98     """
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    99     Generates a dictionary mapping reaction ids to reaction formulas from the model.
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   100 
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   101     Args:
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   102         model : the model to derive data from.
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   103         asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.
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   104 
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   105     Returns:
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   106         Dict[ReactionId, str] : the generated dictionary.
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   107     """
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   108 
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   109     unparsedReactions = {
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   110         reaction.id : reaction.reaction
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   111         for reaction in model.reactions
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   112         if reaction.reaction 
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   113     }
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   114 
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   115     if not asParsed: return unparsedReactions
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   116     
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   117     return reactionUtils.create_reaction_dict(unparsedReactions)
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   118 
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   119 def get_medium(model:cobra.Model) -> pd.DataFrame:
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   120     trueMedium=[]
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   121     for r in model.reactions:
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   122         positiveCoeff=0
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   123         for m in r.metabolites:
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   124             if r.get_coefficient(m.id)>0:
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   125                 positiveCoeff=1;
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   126         if (positiveCoeff==0 and r.lower_bound<0):
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   127             trueMedium.append(r.id)
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   128 
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   129     df_medium = pd.DataFrame()
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   130     df_medium["reaction"] = trueMedium
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   131     return df_medium
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   132 
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   133 def generate_bounds(model:cobra.Model) -> pd.DataFrame:
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   134 
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   135     rxns = []
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   136     for reaction in model.reactions:
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   137         rxns.append(reaction.id)
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   138 
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   139     bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
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   140 
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   141     for reaction in model.reactions:
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   142         bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
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   143     return bounds
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   144 
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   145 
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   146 ###############################- FILE SAVING -################################
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   147 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
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   148     """
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   149     Saves any dictionary-shaped data in a .csv file created at the given file_path as FilePath.
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   150 
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   151     Args:
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   152         data : the data to be written to the file.
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   153         file_path : the path to the .csv file.
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   154         fieldNames : the names of the fields (columns) in the .csv file.
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   155     
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   156     Returns:
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   157         None
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   158     """
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   159     with open(file_path.show(), 'w', newline='') as csvfile:
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   160         writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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   161         writer.writeheader()
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   162 
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   163         for key, value in data.items():
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   164             writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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   165 
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   166 def save_as_csv(data :dict, file_path :str, fieldNames :Tuple[str, str]) -> None:
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   167     """
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   168     Saves any dictionary-shaped data in a .csv file created at the given file_path as string.
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   169 
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   170     Args:
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   171         data : the data to be written to the file.
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   172         file_path : the path to the .csv file.
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   173         fieldNames : the names of the fields (columns) in the .csv file.
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   174     
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   175     Returns:
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   176         None
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   177     """
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   178     with open(file_path, 'w', newline='') as csvfile:
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   179         writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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   180         writer.writeheader()
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   181 
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   182         for key, value in data.items():
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   183             writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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   184 
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   185 ###############################- ENTRY POINT -################################
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147
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   186 def main(args:List[str] = None) -> None:
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   187     """
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   188     Initializes everything and sets the program in motion based on the fronted input arguments.
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   189     
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   190     Returns:
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   191         None
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   192     """
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   193     # get args from frontend (related xml)
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   194     global ARGS
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   195     ARGS = process_args(args)
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   196 
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   197     # this is the worst thing I've seen so far, congrats to the former MaREA devs for suggesting this!
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   198     if os.path.isdir(ARGS.output_path) == False: os.makedirs(ARGS.output_path)
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   199 
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   200     # load custom model
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   201     model = load_custom_model(
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   202         utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
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   203 
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   204     # generate data
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   205     rules = generate_rules(model, asParsed = False)
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   206     reactions = generate_reactions(model, asParsed = False)
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   207     bounds = generate_bounds(model)
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   208     medium = get_medium(model)
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   209 
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   210     # save files out of collection: path coming from xml
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   211     save_as_csv(rules, ARGS.out_rules, ("ReactionID", "Rule"))
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   212     save_as_csv(reactions, ARGS.out_reactions, ("ReactionID", "Reaction"))
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   213     bounds.to_csv(ARGS.out_bounds, sep = '\t')
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   214     medium.to_csv(ARGS.out_medium, sep = '\t')
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   215 
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   216 if __name__ == '__main__':
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   217     main() |