changeset 6:eddc256691a0 draft

Deleted selected files

--- a/rps_generator.py	Wed May 22 13:10:31 2024 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,255 +0,0 @@
-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)
-    
-    pd.DataFrame.from_dict(rps_scores) \
-                .rename(columns={'Unnamed: 0': 'Reactions'}, inplace=True) \
-                .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()
\ No newline at end of file