annotate COBRAxy/custom_data_generator.py @ 307:d905439271fa draft

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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:
    """
    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
    parser.add_argument('-idop', '--output_path', type = str, default='result', help = 'output path for maps')
    argsNamespace = parser.parse_args(args)
    # ^ 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(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)

    # this is the worst thing I've seen so far, congrats to the former MaREA devs for suggesting this!
    if os.path.isdir(ARGS.output_path) == False: os.makedirs(ARGS.output_path)

    # 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()