--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/COBRAxy/rps_generator.py	Wed Sep 18 10:59:10 2024 +0000
@@ -0,0 +1,255 @@
+import re
+import sys
+import csv
+import math
+import argparse
+
+import numpy  as np
+import pickle as pk
+import pandas as pd
+
+from enum   import Enum
+from typing import Optional, List, Dict, Tuple
+
+import utils.general_utils as utils
+import utils.reaction_parsing as reactionUtils
+
+########################## argparse ##########################################
+ARGS :argparse.Namespace
+def process_args() -> argparse.Namespace:
+    """
+    Processes command-line arguments.
+
+    Args:
+        args (list): List of command-line arguments.
+
+    Returns:
+        Namespace: An object containing parsed arguments.
+    """
+    parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
+                                     description = 'process some value\'s'+
+                                     ' abundances and reactions to create RPS scores.')
+    parser.add_argument('-rc', '--reaction_choice', 
+                        type = str,
+                        default = 'default',
+                        choices = ['default','custom'], 
+                        help = 'chose which type of reaction dataset you want use')
+    parser.add_argument('-cm', '--custom',
+                        type = str,
+                        help='your dataset if you want custom reactions')
+    parser.add_argument('-td', '--tool_dir',
+                        type = str,
+                        required = True,
+                        help = 'your tool directory')
+    parser.add_argument('-ol', '--out_log', 
+                        help = "Output log")    
+    parser.add_argument('-id', '--input',
+                        type = str,
+                        help = 'input dataset')
+    parser.add_argument('-rp', '--rps_output',
+                        type = str,
+                        required = True,
+                        help = 'rps output')
+    
+    args = parser.parse_args()
+    return args
+
+############################ dataset name #####################################
+def name_dataset(name_data :str, count :int) -> str:
+    """
+    Produces a unique name for a dataset based on what was provided by the user. The default name for any dataset is "Dataset", thus if the user didn't change it this function appends f"_{count}" to make it unique.
+
+    Args:
+        name_data : name associated with the dataset (from frontend input params)
+        count : counter from 1 to make these names unique (external)
+
+    Returns:
+        str : the name made unique
+    """
+    if str(name_data) == 'Dataset':
+        return str(name_data) + '_' + str(count)
+    else:
+        return str(name_data)
+    
+
+############################ get_abund_data ####################################
+def get_abund_data(dataset: pd.DataFrame, cell_line_index:int) -> Optional[pd.Series]:
+    """
+    Extracts abundance data and turns it into a series for a specific cell line from the dataset, which rows are
+    metabolites and columns are cell lines.
+
+    Args:
+        dataset (pandas.DataFrame): The DataFrame containing abundance data for all cell lines and metabolites.
+        cell_line_index (int): The index of the cell line of interest in the dataset.
+
+    Returns:
+        pd.Series or None: A series containing abundance values for the specified cell line.
+                           The name of the series is the name of the cell line.
+                           Returns None if the cell index is invalid.
+    """
+    if cell_line_index < 0 or cell_line_index >= len(dataset.index):
+        print(f"Errore: This cell line index: '{cell_line_index}' is not valid.")
+        return None
+
+    cell_line_name = dataset.columns[cell_line_index]
+    abundances_series = dataset[cell_line_name][1:]
+
+    return abundances_series
+
+
+############################ clean_metabolite_name ####################################
+def clean_metabolite_name(name :str) -> str:
+    """
+    Removes some characters from a metabolite's name, provided as input, and makes it lowercase in order to simplify
+    the search of a match in the dictionary of synonyms.
+
+    Args:
+        name : the metabolite's name, as given in the dataset.
+    
+    Returns:
+        str : a new string with the cleaned name.
+    """
+    return "".join(ch for ch in name if ch not in ",;-_'([{ }])").lower()
+
+
+############################ get_metabolite_id ####################################
+def get_metabolite_id(name :str, syn_dict :Dict[str, List[str]]) -> str:
+    """
+    Looks through a dictionary of synonyms to find a match for a given metabolite's name.
+
+    Args:
+        name : the metabolite's name, as given in the dataset.
+        syn_dict : the dictionary of synonyms, using unique identifiers as keys and lists of clean synonyms as values.
+    
+    Returns:
+        str : the internal :str unique identifier of that metabolite, used in all other parts of the model in use.
+        An empty string is returned if a match isn't found.
+    """
+    name = clean_metabolite_name(name)
+    for id, synonyms in syn_dict.items():
+        if name in synonyms: return id
+    
+    return ""
+
+############################ check_missing_metab ####################################
+def check_missing_metab(reactions: Dict[str, Dict[str, int]], dataset_by_rows: Dict[str, List[float]], cell_lines_amt :int) -> List[str]:
+    """
+    Check for missing metabolites in the abundances dictionary compared to the reactions dictionary and update abundances accordingly.
+
+    Parameters:
+        reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and stoichiometric coefficients as values.
+        dataset_by_rows (dict): A dictionary representing abundances where keys are metabolite names and values are their corresponding abundances for all cell lines.
+        cell_lines_amt : amount of cell lines, needed to add a new list of abundances for missing metabolites.
+
+    Returns:
+        list[str] : list of metabolite names that were missing in the original abundances dictionary and thus their aboundances were set to 1.
+
+    Side effects:
+        dataset_by_rows : mut
+    """
+    missing_list = []
+    for reaction in reactions.values():
+        for metabolite in reaction.keys():
+          if metabolite not in dataset_by_rows:
+            dataset_by_rows[metabolite] = [1] * cell_lines_amt
+            missing_list.append(metabolite)
+
+    return missing_list
+
+############################ calculate_rps ####################################
+def calculate_rps(reactions: Dict[str, Dict[str, int]], abundances: Dict[str, float], black_list: List[str], missing_list: List[str]) -> Dict[str, float]:
+    """
+    Calculate the Reaction Propensity scores (RPS) based on the availability of reaction substrates, for (ideally) each input model reaction and for each sample.
+    The score is computed as the product of the concentrations of the reacting substances, with each concentration raised to a power equal to its stoichiometric coefficient
+    for each reaction using the provided coefficient and abundance values.
+
+    Parameters:
+        reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and stoichiometric coefficients as values.
+        abundances (dict): A dictionary representing metabolite abundances where keys are metabolite names and values are their corresponding abundances.
+        black_list (list): A list containing metabolite names that should be excluded from the RPS calculation.
+        missing_list (list): A list containing metabolite names that were missing in the original abundances dictionary and thus their values were set to 1.
+
+    Returns:
+        dict: A dictionary containing Reaction Propensity Scores (RPS) where keys are reaction names and values are the corresponding RPS scores.
+    """
+    rps_scores = {}
+ 
+    for reaction_name, substrates in reactions.items():
+        total_contribution = 1
+        metab_significant = False
+        for metabolite, stoichiometry in substrates.items():
+            temp = 1 if math.isnan(abundances[metabolite]) else abundances[metabolite]
+            if metabolite not in black_list and metabolite not in missing_list:
+              metab_significant = True
+            total_contribution *= temp ** stoichiometry
+        
+        rps_scores[reaction_name] = total_contribution if metab_significant else math.nan
+    
+    return rps_scores
+
+
+############################ rps_for_cell_lines ####################################
+def rps_for_cell_lines(dataset: List[List[str]], reactions: Dict[str, Dict[str, int]], black_list: List[str], syn_dict: Dict[str, List[str]]) -> None:
+    """
+    Calculate Reaction Propensity Scores (RPS) for each cell line represented in the dataframe and creates an output file.
+
+    Parameters:
+        dataset : the dataset's data, by rows
+        reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and stoichiometric coefficients as values.
+        black_list (list): A list containing metabolite names that should be excluded from the RPS calculation.
+        syn_dict (dict): A dictionary where keys are general metabolite names and values are lists of possible synonyms.
+
+    Returns:
+        None
+    """
+    cell_lines = dataset[0][1:]
+    abundances_dict = {}
+
+    translationIsApplied = ARGS.reaction_choice == "default"
+    for row in dataset[1:]:
+        id = get_metabolite_id(row[0], syn_dict) if translationIsApplied else row[0]
+        if id: abundances_dict[id] = list(map(utils.Float(), row[1:]))
+    
+    missing_list = check_missing_metab(reactions, abundances_dict, len((cell_lines)))
+    
+    rps_scores :Dict[Dict[str, float]] = {}
+    for pos, cell_line_name in enumerate(cell_lines):
+        abundances = { metab : abundances[pos] for metab, abundances in abundances_dict.items() }
+        rps_scores[cell_line_name] = calculate_rps(reactions, abundances, black_list, missing_list)
+    
+    df = pd.DataFrame.from_dict(rps_scores)
+    df.rename(columns={'Unnamed: 0': 'Reactions'}, inplace=True)
+    df.to_csv(ARGS.rps_output, sep = '\t', na_rep = "None", index = False)
+
+############################ main ####################################
+def main() -> None:
+    """
+    Initializes everything and sets the program in motion based on the fronted input arguments.
+
+    Returns:
+        None
+    """
+    global ARGS
+    ARGS = process_args()
+
+    # TODO:use utils functions vvv
+    with open(ARGS.tool_dir + '/local/pickle files/black_list.pickle', 'rb') as bl:
+        black_list = pk.load(bl)
+
+    with open(ARGS.tool_dir + '/local/pickle files/synonyms.pickle', 'rb') as sd:
+        syn_dict = pk.load(sd)
+
+    dataset = utils.readCsv(utils.FilePath.fromStrPath(ARGS.input), '\t', skipHeader = False)
+
+    if ARGS.reaction_choice == 'default':
+        reactions = pk.load(open(ARGS.tool_dir + '/local/pickle files/reactions.pickle', 'rb'))
+    
+    elif ARGS.reaction_choice == 'custom':
+        reactions = reactionUtils.parse_custom_reactions(ARGS.custom)
+    
+    rps_for_cell_lines(dataset, reactions, black_list, syn_dict)
+    print('Execution succeded')
+
+##############################################################################
+if __name__ == "__main__":
+    main()
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