changeset 336:1434e851f929 draft

Uploaded

--- a/marea_2/ras_generator.py	Mon Aug 05 20:04:01 2024 +0000
+++ b/marea_2/ras_generator.py	Mon Aug 05 20:04:18 2024 +0000
@@ -1,713 +1,699 @@
-from __future__ import division
-# galaxy complains this ^^^ needs to be at the very beginning of the file, for some reason.
-import sys
-import argparse
-import collections
-import pandas as pd
-import pickle as pk
-import utils.general_utils as utils
-import utils.rule_parsing as ruleUtils
-from typing import Union, Optional, List, Dict, Tuple, TypeVar
-
-ERRORS = []
-########################## 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 genes to create a comparison's map.")
-    
-    parser.add_argument(
-        '-rs', '--rules_selector', 
-        type = utils.Model, default = utils.Model.HMRcore, choices = list(utils.Model),
-        help = 'chose which type of dataset you want use')
-    
-    parser.add_argument("-rl", "--rule_list", type = str,
-        help = "path to input file with custom rules, if provided")
-
-    parser.add_argument("-rn", "--rules_name", type = str, help = "custom rules name")
-    # ^ I need this because galaxy converts my files into .dat but I need to know what extension they were in
-    
-    parser.add_argument(
-        '-n', '--none',
-        type = utils.Bool("none"), default = True,
-        help = 'compute Nan values')
-    
-    parser.add_argument(
-        '-td', '--tool_dir',
-        type = str,
-        required = True, help = 'your tool directory')
-    
-    parser.add_argument(
-        '-ol', '--out_log',
-        type = str,
-        help = "Output log")    
-    
-    parser.add_argument(
-        '-in', '--input', #id è diventato in
-        type = str,
-        help = 'input dataset')
-    
-    parser.add_argument(
-        '-ra', '--ras_output',
-        type = str,
-        required = True, help = 'ras output')
-    
-    return parser.parse_args()
-
-############################ dataset input ####################################
-def read_dataset(data :str, name :str) -> pd.DataFrame:
-    """
-    Read a dataset from a CSV file and return it as a pandas DataFrame.
-
-    Args:
-        data (str): Path to the CSV file containing the dataset.
-        name (str): Name of the dataset, used in error messages.
-
-    Returns:
-        pandas.DataFrame: DataFrame containing the dataset.
-
-    Raises:
-        pd.errors.EmptyDataError: If the CSV file is empty.
-        sys.exit: If the CSV file has the wrong format, the execution is aborted.
-    """
-    try:
-        dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
-    except pd.errors.EmptyDataError:
-        sys.exit('Execution aborted: wrong format of ' + name + '\n')
-    if len(dataset.columns) < 2:
-        sys.exit('Execution aborted: wrong format of ' + name + '\n')
-    return dataset
-
-############################ load id e rules ##################################
-def load_id_rules(reactions :Dict[str, Dict[str, List[str]]]) -> Tuple[List[str], List[Dict[str, List[str]]]]:
-    """
-    Load IDs and rules from a dictionary of reactions.
-
-    Args:
-        reactions (dict): A dictionary where keys are IDs and values are rules.
-
-    Returns:
-        tuple: A tuple containing two lists, the first list containing IDs and the second list containing rules.
-    """
-    ids, rules = [], []
-    for key, value in reactions.items():
-            ids.append(key)
-            rules.append(value)
-    return (ids, rules)
-
-############################ check_methods ####################################
-def gene_type(l :str, name :str) -> str:
-    """
-    Determine the type of gene ID.
-
-    Args:
-        l (str): The gene identifier to check.
-        name (str): The name of the dataset, used in error messages.
-
-    Returns:
-        str: The type of gene ID ('hugo_id', 'ensembl_gene_id', 'symbol', or 'entrez_id').
-
-    Raises:
-        sys.exit: If the gene ID type is not supported, the execution is aborted.
-    """
-    if check_hgnc(l):
-        return 'hugo_id'
-    elif check_ensembl(l):
-        return 'ensembl_gene_id'
-    elif check_symbol(l):
-        return 'symbol'
-    elif check_entrez(l):
-        return 'entrez_id'
-    else:
-        sys.exit('Execution aborted:\n' +
-                 'gene ID type in ' + name + ' not supported. Supported ID'+
-                 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n')
-
-def check_hgnc(l :str) -> bool:
-    """
-    Check if a gene identifier follows the HGNC format.
-
-    Args:
-        l (str): The gene identifier to check.
-
-    Returns:
-        bool: True if the gene identifier follows the HGNC format, False otherwise.
-    """
-    if len(l) > 5:
-        if (l.upper()).startswith('HGNC:'):
-            return l[5:].isdigit()
-        else:
-            return False
-    else:
-        return False
-
-def check_ensembl(l :str) -> bool:
-    """
-    Check if a gene identifier follows the Ensembl format.
-
-    Args:
-        l (str): The gene identifier to check.
-
-    Returns:
-        bool: True if the gene identifier follows the Ensembl format, False otherwise.
-    """
-    return l.upper().startswith('ENS')
- 
-
-def check_symbol(l :str) -> bool:
-    """
-    Check if a gene identifier follows the symbol format.
-
-    Args:
-        l (str): The gene identifier to check.
-
-    Returns:
-        bool: True if the gene identifier follows the symbol format, False otherwise.
-    """
-    if len(l) > 0:
-        if l[0].isalpha() and l[1:].isalnum():
-            return True
-        else:
-            return False
-    else:
-        return False
-
-def check_entrez(l :str) -> bool:
-    """
-    Check if a gene identifier follows the Entrez ID format.
-
-    Args:
-        l (str): The gene identifier to check.
-
-    Returns:
-        bool: True if the gene identifier follows the Entrez ID format, False otherwise.
-    """ 
-    if len(l) > 0:
-        return l.isdigit()
-    else: 
-        return False
-
-############################ gene #############################################
-def data_gene(gene: pd.DataFrame, type_gene: str, name: str, gene_custom: Optional[Dict[str, str]]) -> Dict[str, str]:
-    """
-    Process gene data to ensure correct formatting and handle duplicates.
-
-    Args:
-        gene (DataFrame): DataFrame containing gene data.
-        type_gene (str): Type of gene data (e.g., 'hugo_id', 'ensembl_gene_id', 'symbol', 'entrez_id').
-        name (str): Name of the dataset.
-        gene_custom (dict or None): Custom gene data dictionary if provided.
-
-    Returns:
-        dict: A dictionary containing gene data with gene IDs as keys and corresponding values.
-    """
-    args = process_args()    
-    for i in range(len(gene)):
-        tmp = gene.iloc[i, 0]
-        gene.iloc[i, 0] = tmp.strip().split('.')[0]
-
-    gene_dup = [item for item, count in 
-               collections.Counter(gene[gene.columns[0]]).items() if count > 1]
-    pat_dup = [item for item, count in 
-               collections.Counter(list(gene.columns)).items() if count > 1]
-
-    if gene_dup:
-        if gene_custom == None:
-            if args.rules_selector == 'HMRcore':
-                gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/HMRcore_genes.p', 'rb'))
-            
-            elif args.rules_selector == 'Recon':
-                gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/Recon_genes.p', 'rb'))
-            
-            elif args.rules_selector == 'ENGRO2':
-                gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/ENGRO2_genes.p', 'rb'))
-            
-            gene_in_rule = gene_in_rule.get(type_gene)
-        
-        else:
-            gene_in_rule = gene_custom
-        tmp = []
-        for i in gene_dup:
-            if gene_in_rule.get(i) == 'ok':
-                tmp.append(i)
-        if tmp:
-            sys.exit('Execution aborted because gene ID '
-                     +str(tmp)+' in '+name+' is duplicated\n')
-    
-    if pat_dup: utils.logWarning(f"Warning: duplicated label\n{pat_dup} in {name}", ARGS.out_log)
-    return (gene.set_index(gene.columns[0])).to_dict()
-
-############################ resolve ##########################################
-def replace_gene_value(l :str, d :str) -> Tuple[Union[int, float], list]:
-    """
-    Replace gene identifiers with corresponding values from a dictionary.
-
-    Args:
-        l (str): String of gene identifier.
-        d (str): String corresponding to its value.
-
-    Returns:
-        tuple: A tuple containing two lists: the first list contains replaced values, and the second list contains any errors encountered during replacement.
-    """
-    tmp = []
-    err = []
-    while l:
-        if isinstance(l[0], list):
-            tmp_rules, tmp_err = replace_gene_value(l[0], d)
-            tmp.append(tmp_rules)
-            err.extend(tmp_err)
-        else:
-            value = replace_gene(l[0], d)
-            tmp.append(value)
-            if value == None:
-                err.append(l[0])
-        l = l[1:]
-    return (tmp, err)
-
-def replace_gene(l :str, d :str) -> Union[int, float]:
-    """
-    Replace a single gene identifier with its corresponding value from a dictionary.
-
-    Args:
-        l (str): Gene identifier to replace.
-        d (str): String corresponding to its value.
-
-    Returns:
-        float/int: Corresponding value from the dictionary if found, None otherwise.
-
-    Raises:
-        sys.exit: If the value associated with the gene identifier is not valid.
-    """
-    if l =='and' or l == 'or':
-        return l
-    else:
-        value = d.get(l, None)
-        if not(value == None or isinstance(value, (int, float))):
-            sys.exit('Execution aborted: ' + value + ' value not valid\n')
-        return value
-
-T = TypeVar("T", bound = Optional[Union[int, float]])
-def computes(val1 :T, op :str, val2 :T, cn :bool) -> T:
-    """
-    Compute the RAS value between two value and an operator ('and' or 'or').
-
-    Args:
-        val1(Optional(Union[float, int])): First value.
-        op (str): Operator ('and' or 'or').
-        val2(Optional(Union[float, int])): Second value.
-        cn (bool): Control boolean value.
-
-    Returns:
-        Optional(Union[float, int]): Result of the computation.
-    """
-    if val1 != None and val2 != None:
-        if op == 'and':
-            return min(val1, val2)
-        else:
-            return val1 + val2
-    elif op == 'and':
-        if cn is True:
-            if val1 != None:
-                return val1
-            elif val2 != None:
-                return val2
-            else:
-                return None
-        else:
-            return None
-    else:
-        if val1 != None:
-            return val1
-        elif val2 != None:
-            return val2
-        else:
-            return None
-
-# ris should be Literal[None] but Literal is not supported in Python 3.7
-def control(ris, l :List[Union[int, float, list]], cn :bool) -> Union[bool, int, float]: #Union[Literal[False], int, float]:
-    """
-    Control the format of the expression.
-
-    Args:
-        ris: Intermediate result.
-        l (list): Expression to control.
-        cn (bool): Control boolean value.
-
-    Returns:
-        Union[Literal[False], int, float]: Result of the control.
-    """
-    if len(l) == 1:
-        if isinstance(l[0], (float, int)) or l[0] == None:
-            return l[0]
-        elif isinstance(l[0], list):
-            return control(None, l[0], cn)
-        else:
-            return False
-    elif len(l) > 2:
-        return control_list(ris, l, cn)
-    else:
-        return False
-
-def control_list(ris, l :List[Optional[Union[float, int, list]]], cn :bool) -> Optional[bool]: #Optional[Literal[False]]:
-    """
-    Control the format of a list of expressions.
-
-    Args:
-        ris: Intermediate result.
-        l (list): List of expressions to control.
-        cn (bool): Control boolean value.
-
-    Returns:
-        Optional[Literal[False]]: Result of the control.
-    """
-    while l:
-        if len(l) == 1:
-            return False
-        elif (isinstance(l[0], (float, int)) or
-              l[0] == None) and l[1] in ['and', 'or']:
-            if isinstance(l[2], (float, int)) or l[2] == None:
-                ris = computes(l[0], l[1], l[2], cn)            
-            elif isinstance(l[2], list):
-                tmp = control(None, l[2], cn)
-                if tmp is False:
-                    return False
-                else:
-                    ris = computes(l[0], l[1], tmp, cn)
-            else:
-                return False
-            l = l[3:]
-        elif l[0] in ['and', 'or']:
-            if isinstance(l[1], (float, int)) or l[1] == None:
-                ris = computes(ris, l[0], l[1], cn)
-            elif isinstance(l[1], list):
-                tmp = control(None,l[1], cn)
-                if tmp is False:
-                    return False
-                else:
-                    ris = computes(ris, l[0], tmp, cn)
-            else:
-                return False
-            l = l[2:]
-        elif isinstance(l[0], list) and l[1] in ['and', 'or']:
-            if isinstance(l[2], (float, int)) or l[2] == None:
-                tmp = control(None, l[0], cn)
-                if tmp is False:
-                    return False
-                else:
-                    ris = computes(tmp, l[1], l[2], cn)
-            elif isinstance(l[2], list):
-                tmp = control(None, l[0], cn)
-                tmp2 = control(None, l[2], cn)
-                if tmp is False or tmp2 is False:
-                    return False
-                else:
-                    ris = computes(tmp, l[1], tmp2, cn)
-            else:
-                return False
-            l = l[3:]
-        else:
-            return False
-    return ris
-
-ResolvedRules = Dict[str, List[Optional[Union[float, int]]]]
-def resolve(genes: Dict[str, str], rules: List[str], ids: List[str], resolve_none: bool, name: str) -> Tuple[Optional[ResolvedRules], Optional[list]]:
-    """
-    Resolve rules using gene data to compute scores for each rule.
-
-    Args:
-        genes (dict): Dictionary containing gene data with gene IDs as keys and corresponding values.
-        rules (list): List of rules to resolve.
-        ids (list): List of IDs corresponding to the rules.
-        resolve_none (bool): Flag indicating whether to resolve None values in the rules.
-        name (str): Name of the dataset.
-
-    Returns:
-        tuple: A tuple containing resolved rules as a dictionary and a list of gene IDs not found in the data.
-    """
-    resolve_rules = {}
-    not_found = []
-    flag = False
-    for key, value in genes.items():
-        tmp_resolve = []
-        for i in range(len(rules)):
-            tmp = rules[i]
-            if tmp:
-                tmp, err = replace_gene_value(tmp, value)
-                if err:
-                    not_found.extend(err)
-                ris = control(None, tmp, resolve_none)
-                if ris is False or ris == None:
-                    tmp_resolve.append(None)
-                else:
-                    tmp_resolve.append(ris)
-                    flag = True
-            else:
-                tmp_resolve.append(None)    
-        resolve_rules[key] = tmp_resolve
-    
-    if flag is False:
-        utils.logWarning(
-            f"Warning: no computable score (due to missing gene values) for class {name}, the class has been disregarded",
-            ARGS.out_log)
-        
-        return (None, None)
-    
-    return (resolve_rules, list(set(not_found)))
-############################ create_ras #######################################
-def create_ras(resolve_rules: Optional[ResolvedRules], dataset_name: str, rules: List[str], ids: List[str], file: str) -> None:
-    """
-    Create a RAS (Reaction Activity Score) file from resolved rules.
-
-    Args:
-        resolve_rules (dict): Dictionary containing resolved rules.
-        dataset_name (str): Name of the dataset.
-        rules (list): List of rules.
-        file (str): Path to the output RAS file.
-
-    Returns:
-        None
-    """
-    if resolve_rules is None:
-        utils.logWarning(f"Couldn't generate RAS for current dataset: {dataset_name}", ARGS.out_log)
-
-    for geni in resolve_rules.values():
-        for i, valori in enumerate(geni):
-            if valori == None:
-                geni[i] = 'None'
-                
-    output_ras = pd.DataFrame.from_dict(resolve_rules)
-    
-    output_ras.insert(0, 'Reactions', ids)
-    output_to_csv = pd.DataFrame.to_csv(output_ras, sep = '\t', index = False)
-    
-    text_file = open(file, "w")
-    
-    text_file.write(output_to_csv)
-    text_file.close()
-
-################################- NEW RAS COMPUTATION -################################
-Expr = Optional[Union[int, float]]
-Ras  = Expr
-def ras_for_cell_lines(dataset: pd.DataFrame, rules: Dict[str, ruleUtils.OpList]) -> Dict[str, Dict[str, Ras]]:
-    """
-    Generates the RAS scores for each cell line found in the dataset.
-
-    Args:
-        dataset (pd.DataFrame): Dataset containing gene values.
-        rules (dict): The dict containing reaction ids as keys and rules as values.
-
-    Side effects:
-        dataset : mut
-    
-    Returns:
-        dict: A dictionary where each key corresponds to a cell line name and each value is a dictionary
-        where each key corresponds to a reaction ID and each value is its computed RAS score.
-    """
-    ras_values_by_cell_line = {}
-    dataset.set_index(dataset.columns[0], inplace=True)
-    # Considera tutte le colonne tranne la prima in cui ci sono gli hugo quindi va scartata
-    for cell_line_name in dataset.columns[1:]:
-        cell_line = dataset[cell_line_name].to_dict()
-        ras_values_by_cell_line[cell_line_name]= get_ras_values(rules, cell_line)
-    return ras_values_by_cell_line
-
-def get_ras_values(value_rules: Dict[str, ruleUtils.OpList], dataset: Dict[str, Expr]) -> Dict[str, Ras]:
-    """
-    Computes the RAS (Reaction Activity Score) values for each rule in the given dict.
-
-    Args:
-        value_rules (dict): A dictionary where keys are reaction ids and values are OpLists.
-        dataset : gene expression data of one cell line.
-
-    Returns:
-        dict: A dictionary where keys are reaction ids and values are the computed RAS values for each rule.
-    """
-    return {key: ras_op_list(op_list, dataset) for key, op_list in value_rules.items()}
-
-def get_gene_expr(dataset :Dict[str, Expr], name :str) -> Expr:
-    """
-    Extracts the gene expression of the given gene from a cell line dataset.
-
-    Args:
-        dataset : gene expression data of one cell line.
-        name : gene name.
-    
-    Returns:
-        Expr : the gene's expression value.
-    """
-    expr = dataset.get(name, None)
-    if expr is None: ERRORS.append(name)
-  
-    return expr
-
-def ras_op_list(op_list: ruleUtils.OpList, dataset: Dict[str, Expr]) -> Ras:
-    """
-    Computes recursively the RAS (Reaction Activity Score) value for the given OpList, considering the specified flag to control None behavior.
-
-    Args:
-        op_list (OpList): The OpList representing a rule with gene values.
-        dataset : gene expression data of one cell line.
-
-    Returns:
-        Ras: The computed RAS value for the given OpList.
-    """
-    op = op_list.op
-    ras_value :Ras = None
-    if not op: return get_gene_expr(dataset, op_list[0])
-    if op is ruleUtils.RuleOp.AND and not ARGS.none and None in op_list: return None
-
-    for i in range(len(op_list)):
-        item = op_list[i]
-        if isinstance(item, ruleUtils.OpList):
-            item = ras_op_list(item, dataset)
-
-        else:
-          item = get_gene_expr(dataset, item)
-
-        if item is None:
-          if op is ruleUtils.RuleOp.AND and not ARGS.none: return None
-          continue
-
-        if ras_value is None:
-          ras_value = item
-        else:
-          ras_value = ras_value + item if op is ruleUtils.RuleOp.OR else min(ras_value, item)
-
-    return ras_value
-
-def save_as_tsv(rasScores: Dict[str, Dict[str, Ras]], reactions :List[str]) -> None:
-    """
-    Save computed ras scores to the given path, as a tsv file.
-
-    Args:
-        rasScores : the computed ras scores.
-        path : the output tsv file's path.
-    
-    Returns:
-        None
-    """
-    for scores in rasScores.values(): # this is actually a lot faster than using the ootb dataframe metod, sadly
-        for reactId, score in scores.items():
-            if score is None: scores[reactId] = "None"
-
-    output_ras = pd.DataFrame.from_dict(rasScores)
-    output_ras.insert(0, 'Reactions', reactions)
-    output_ras.to_csv(ARGS.ras_output, sep = '\t', index = False)
-
-############################ MAIN #############################################
-#TODO: not used but keep, it will be when the new translator dicts will be used.
-def translateGene(geneName :str, encoding :str, geneTranslator :Dict[str, Dict[str, str]]) -> str:
-    """
-    Translate gene from any supported encoding to HugoID.
-
-    Args:
-        geneName (str): the name of the gene in its current encoding.
-        encoding (str): the encoding.
-        geneTranslator (Dict[str, Dict[str, str]]): the dict containing all supported gene names
-        and encodings in the current model, mapping each to the corresponding HugoID encoding.
-
-    Raises:
-        ValueError: When the gene isn't supported in the model.
-
-    Returns:
-        str: the gene in HugoID encoding.
-    """
-    supportedGenesInEncoding = geneTranslator[encoding]
-    if geneName in supportedGenesInEncoding: return supportedGenesInEncoding[geneName]
-    raise ValueError(f"Gene \"{geneName}\" non trovato, verifica di star utilizzando il modello corretto!")
-
-def load_custom_rules2() -> Dict[str, ruleUtils.OpList]:
-    """
-    Opens custom rules file and extracts the rules. If the file is in .csv format an additional parsing step will be
-    performed, significantly impacting the runtime.
-
-    Returns:
-        Dict[str, ruleUtils.OpList] : dict mapping reaction IDs to rules.
-    """
-    datFilePath = utils.FilePath.fromStrPath(ARGS.rule_list) # actual file, stored in galaxy as a .dat
-    
-    try: filenamePath = utils.FilePath.fromStrPath(ARGS.rules_name) # file's name in input, to determine its original ext
-    except utils.PathErr as err:
-        raise utils.PathErr(filenamePath, f"Please make sure your file's name is a valid file path, {err.msg}")
-    
-     
-    if filenamePath.ext is utils.FileFormat.PICKLE: return utils.readPickle(datFilePath)
-
-    # csv rules need to be parsed, those in a pickle format are taken to be pre-parsed.
-    return { line[0] : ruleUtils.parseRuleToNestedList(line[1]) for line in utils.readCsv(datFilePath) }
-
-def load_custom_rules() -> Dict[str, ruleUtils.OpList]:
-    """
-    Opens custom rules file and extracts the rules. If the file is in .csv format an additional parsing step will be
-    performed, significantly impacting the runtime.
-
-    Returns:
-        Dict[str, ruleUtils.OpList] : dict mapping reaction IDs to rules.
-    """
-    dataset = pd.read_csv(ARGS.rule_list, sep = '\t', header = 0, engine='python')
-    
-    # csv rules need to be parsed, those in a pickle format are taken to be pre-parsed.
-    return { line[0] : ruleUtils.parseRuleToNestedList(line[1]) for line in dataset }
-
-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()
-    
-    # read dataset
-    dataset = read_dataset(ARGS.input, "dataset")
-    dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
-
-    # remove versioning from gene names
-    dataset.iloc[:, 0] = dataset.iloc[:, 0].str.split('.').str[0]
-
-    # handle custom models
-    model :utils.Model = ARGS.rules_selector
-    if model is utils.Model.Custom:
-        rules = load_custom_rules()
-        reactions = list(rules.keys())
-
-        save_as_tsv(ras_for_cell_lines(dataset, rules), reactions)
-        if ERRORS: utils.logWarning(
-            f"The following genes are mentioned in the rules but don't appear in the dataset: {ERRORS}",
-            ARGS.out_log)
-        
-        return
-    
-    # This is the standard flow of the ras_generator program, for non-custom models.
-    name = "RAS Dataset"
-    type_gene = gene_type(dataset.iloc[0, 0], name)
-
-    rules      = model.getRules(ARGS.tool_dir)
-    genes      = data_gene(dataset, type_gene, name, None)
-    ids, rules = load_id_rules(rules.get(type_gene))
-    
-    resolve_rules, err = resolve(genes, rules, ids, ARGS.none, name)
-    create_ras(resolve_rules, name, rules, ids, ARGS.ras_output)
-    
-    if err: utils.logWarning(
-        f"Warning: gene(s) {err} not found in class \"{name}\", " +
-        "the expression level for this gene will be considered NaN",
-        ARGS.out_log)
-    
-    print("Execution succeded")
-
-###############################################################################
-if __name__ == "__main__":
+from __future__ import division
+# galaxy complains this ^^^ needs to be at the very beginning of the file, for some reason.
+import sys
+import argparse
+import collections
+import pandas as pd
+import pickle as pk
+import utils.general_utils as utils
+import utils.rule_parsing as ruleUtils
+from typing import Union, Optional, List, Dict, Tuple, TypeVar
+
+ERRORS = []
+########################## 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 genes to create a comparison's map.")
+    
+    parser.add_argument(
+        '-rs', '--rules_selector', 
+        type = utils.Model, default = utils.Model.HMRcore, choices = list(utils.Model),
+        help = 'chose which type of dataset you want use')
+    
+    parser.add_argument("-rl", "--rule_list", type = str,
+        help = "path to input file with custom rules, if provided")
+
+    parser.add_argument("-rn", "--rules_name", type = str, help = "custom rules name")
+    # ^ I need this because galaxy converts my files into .dat but I need to know what extension they were in
+    
+    parser.add_argument(
+        '-n', '--none',
+        type = utils.Bool("none"), default = True,
+        help = 'compute Nan values')
+    
+    parser.add_argument(
+        '-td', '--tool_dir',
+        type = str,
+        required = True, help = 'your tool directory')
+    
+    parser.add_argument(
+        '-ol', '--out_log',
+        type = str,
+        help = "Output log")    
+    
+    parser.add_argument(
+        '-in', '--input', #id è diventato in
+        type = str,
+        help = 'input dataset')
+    
+    parser.add_argument(
+        '-ra', '--ras_output',
+        type = str,
+        required = True, help = 'ras output')
+    
+    return parser.parse_args()
+
+############################ dataset input ####################################
+def read_dataset(data :str, name :str) -> pd.DataFrame:
+    """
+    Read a dataset from a CSV file and return it as a pandas DataFrame.
+
+    Args:
+        data (str): Path to the CSV file containing the dataset.
+        name (str): Name of the dataset, used in error messages.
+
+    Returns:
+        pandas.DataFrame: DataFrame containing the dataset.
+
+    Raises:
+        pd.errors.EmptyDataError: If the CSV file is empty.
+        sys.exit: If the CSV file has the wrong format, the execution is aborted.
+    """
+    try:
+        dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
+    except pd.errors.EmptyDataError:
+        sys.exit('Execution aborted: wrong format of ' + name + '\n')
+    if len(dataset.columns) < 2:
+        sys.exit('Execution aborted: wrong format of ' + name + '\n')
+    return dataset
+
+############################ load id e rules ##################################
+def load_id_rules(reactions :Dict[str, Dict[str, List[str]]]) -> Tuple[List[str], List[Dict[str, List[str]]]]:
+    """
+    Load IDs and rules from a dictionary of reactions.
+
+    Args:
+        reactions (dict): A dictionary where keys are IDs and values are rules.
+
+    Returns:
+        tuple: A tuple containing two lists, the first list containing IDs and the second list containing rules.
+    """
+    ids, rules = [], []
+    for key, value in reactions.items():
+            ids.append(key)
+            rules.append(value)
+    return (ids, rules)
+
+############################ check_methods ####################################
+def gene_type(l :str, name :str) -> str:
+    """
+    Determine the type of gene ID.
+
+    Args:
+        l (str): The gene identifier to check.
+        name (str): The name of the dataset, used in error messages.
+
+    Returns:
+        str: The type of gene ID ('hugo_id', 'ensembl_gene_id', 'symbol', or 'entrez_id').
+
+    Raises:
+        sys.exit: If the gene ID type is not supported, the execution is aborted.
+    """
+    if check_hgnc(l):
+        return 'hugo_id'
+    elif check_ensembl(l):
+        return 'ensembl_gene_id'
+    elif check_symbol(l):
+        return 'symbol'
+    elif check_entrez(l):
+        return 'entrez_id'
+    else:
+        sys.exit('Execution aborted:\n' +
+                 'gene ID type in ' + name + ' not supported. Supported ID'+
+                 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n')
+
+def check_hgnc(l :str) -> bool:
+    """
+    Check if a gene identifier follows the HGNC format.
+
+    Args:
+        l (str): The gene identifier to check.
+
+    Returns:
+        bool: True if the gene identifier follows the HGNC format, False otherwise.
+    """
+    if len(l) > 5:
+        if (l.upper()).startswith('HGNC:'):
+            return l[5:].isdigit()
+        else:
+            return False
+    else:
+        return False
+
+def check_ensembl(l :str) -> bool:
+    """
+    Check if a gene identifier follows the Ensembl format.
+
+    Args:
+        l (str): The gene identifier to check.
+
+    Returns:
+        bool: True if the gene identifier follows the Ensembl format, False otherwise.
+    """
+    return l.upper().startswith('ENS')
+ 
+
+def check_symbol(l :str) -> bool:
+    """
+    Check if a gene identifier follows the symbol format.
+
+    Args:
+        l (str): The gene identifier to check.
+
+    Returns:
+        bool: True if the gene identifier follows the symbol format, False otherwise.
+    """
+    if len(l) > 0:
+        if l[0].isalpha() and l[1:].isalnum():
+            return True
+        else:
+            return False
+    else:
+        return False
+
+def check_entrez(l :str) -> bool:
+    """
+    Check if a gene identifier follows the Entrez ID format.
+
+    Args:
+        l (str): The gene identifier to check.
+
+    Returns:
+        bool: True if the gene identifier follows the Entrez ID format, False otherwise.
+    """ 
+    if len(l) > 0:
+        return l.isdigit()
+    else: 
+        return False
+
+############################ gene #############################################
+def data_gene(gene: pd.DataFrame, type_gene: str, name: str, gene_custom: Optional[Dict[str, str]]) -> Dict[str, str]:
+    """
+    Process gene data to ensure correct formatting and handle duplicates.
+
+    Args:
+        gene (DataFrame): DataFrame containing gene data.
+        type_gene (str): Type of gene data (e.g., 'hugo_id', 'ensembl_gene_id', 'symbol', 'entrez_id').
+        name (str): Name of the dataset.
+        gene_custom (dict or None): Custom gene data dictionary if provided.
+
+    Returns:
+        dict: A dictionary containing gene data with gene IDs as keys and corresponding values.
+    """
+    args = process_args()    
+    for i in range(len(gene)):
+        tmp = gene.iloc[i, 0]
+        gene.iloc[i, 0] = tmp.strip().split('.')[0]
+
+    gene_dup = [item for item, count in 
+               collections.Counter(gene[gene.columns[0]]).items() if count > 1]
+    pat_dup = [item for item, count in 
+               collections.Counter(list(gene.columns)).items() if count > 1]
+
+    if gene_dup:
+        if gene_custom == None:
+            if args.rules_selector == 'HMRcore':
+                gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/HMRcore_genes.p', 'rb'))
+            
+            elif args.rules_selector == 'Recon':
+                gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/Recon_genes.p', 'rb'))
+            
+            elif args.rules_selector == 'ENGRO2':
+                gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/ENGRO2_genes.p', 'rb'))
+            
+            gene_in_rule = gene_in_rule.get(type_gene)
+        
+        else:
+            gene_in_rule = gene_custom
+        tmp = []
+        for i in gene_dup:
+            if gene_in_rule.get(i) == 'ok':
+                tmp.append(i)
+        if tmp:
+            sys.exit('Execution aborted because gene ID '
+                     +str(tmp)+' in '+name+' is duplicated\n')
+    
+    if pat_dup: utils.logWarning(f"Warning: duplicated label\n{pat_dup} in {name}", ARGS.out_log)
+    return (gene.set_index(gene.columns[0])).to_dict()
+
+############################ resolve ##########################################
+def replace_gene_value(l :str, d :str) -> Tuple[Union[int, float], list]:
+    """
+    Replace gene identifiers with corresponding values from a dictionary.
+
+    Args:
+        l (str): String of gene identifier.
+        d (str): String corresponding to its value.
+
+    Returns:
+        tuple: A tuple containing two lists: the first list contains replaced values, and the second list contains any errors encountered during replacement.
+    """
+    tmp = []
+    err = []
+    while l:
+        if isinstance(l[0], list):
+            tmp_rules, tmp_err = replace_gene_value(l[0], d)
+            tmp.append(tmp_rules)
+            err.extend(tmp_err)
+        else:
+            value = replace_gene(l[0], d)
+            tmp.append(value)
+            if value == None:
+                err.append(l[0])
+        l = l[1:]
+    return (tmp, err)
+
+def replace_gene(l :str, d :str) -> Union[int, float]:
+    """
+    Replace a single gene identifier with its corresponding value from a dictionary.
+
+    Args:
+        l (str): Gene identifier to replace.
+        d (str): String corresponding to its value.
+
+    Returns:
+        float/int: Corresponding value from the dictionary if found, None otherwise.
+
+    Raises:
+        sys.exit: If the value associated with the gene identifier is not valid.
+    """
+    if l =='and' or l == 'or':
+        return l
+    else:
+        value = d.get(l, None)
+        if not(value == None or isinstance(value, (int, float))):
+            sys.exit('Execution aborted: ' + value + ' value not valid\n')
+        return value
+
+T = TypeVar("T", bound = Optional[Union[int, float]])
+def computes(val1 :T, op :str, val2 :T, cn :bool) -> T:
+    """
+    Compute the RAS value between two value and an operator ('and' or 'or').
+
+    Args:
+        val1(Optional(Union[float, int])): First value.
+        op (str): Operator ('and' or 'or').
+        val2(Optional(Union[float, int])): Second value.
+        cn (bool): Control boolean value.
+
+    Returns:
+        Optional(Union[float, int]): Result of the computation.
+    """
+    if val1 != None and val2 != None:
+        if op == 'and':
+            return min(val1, val2)
+        else:
+            return val1 + val2
+    elif op == 'and':
+        if cn is True:
+            if val1 != None:
+                return val1
+            elif val2 != None:
+                return val2
+            else:
+                return None
+        else:
+            return None
+    else:
+        if val1 != None:
+            return val1
+        elif val2 != None:
+            return val2
+        else:
+            return None
+
+# ris should be Literal[None] but Literal is not supported in Python 3.7
+def control(ris, l :List[Union[int, float, list]], cn :bool) -> Union[bool, int, float]: #Union[Literal[False], int, float]:
+    """
+    Control the format of the expression.
+
+    Args:
+        ris: Intermediate result.
+        l (list): Expression to control.
+        cn (bool): Control boolean value.
+
+    Returns:
+        Union[Literal[False], int, float]: Result of the control.
+    """
+    if len(l) == 1:
+        if isinstance(l[0], (float, int)) or l[0] == None:
+            return l[0]
+        elif isinstance(l[0], list):
+            return control(None, l[0], cn)
+        else:
+            return False
+    elif len(l) > 2:
+        return control_list(ris, l, cn)
+    else:
+        return False
+
+def control_list(ris, l :List[Optional[Union[float, int, list]]], cn :bool) -> Optional[bool]: #Optional[Literal[False]]:
+    """
+    Control the format of a list of expressions.
+
+    Args:
+        ris: Intermediate result.
+        l (list): List of expressions to control.
+        cn (bool): Control boolean value.
+
+    Returns:
+        Optional[Literal[False]]: Result of the control.
+    """
+    while l:
+        if len(l) == 1:
+            return False
+        elif (isinstance(l[0], (float, int)) or
+              l[0] == None) and l[1] in ['and', 'or']:
+            if isinstance(l[2], (float, int)) or l[2] == None:
+                ris = computes(l[0], l[1], l[2], cn)            
+            elif isinstance(l[2], list):
+                tmp = control(None, l[2], cn)
+                if tmp is False:
+                    return False
+                else:
+                    ris = computes(l[0], l[1], tmp, cn)
+            else:
+                return False
+            l = l[3:]
+        elif l[0] in ['and', 'or']:
+            if isinstance(l[1], (float, int)) or l[1] == None:
+                ris = computes(ris, l[0], l[1], cn)
+            elif isinstance(l[1], list):
+                tmp = control(None,l[1], cn)
+                if tmp is False:
+                    return False
+                else:
+                    ris = computes(ris, l[0], tmp, cn)
+            else:
+                return False
+            l = l[2:]
+        elif isinstance(l[0], list) and l[1] in ['and', 'or']:
+            if isinstance(l[2], (float, int)) or l[2] == None:
+                tmp = control(None, l[0], cn)
+                if tmp is False:
+                    return False
+                else:
+                    ris = computes(tmp, l[1], l[2], cn)
+            elif isinstance(l[2], list):
+                tmp = control(None, l[0], cn)
+                tmp2 = control(None, l[2], cn)
+                if tmp is False or tmp2 is False:
+                    return False
+                else:
+                    ris = computes(tmp, l[1], tmp2, cn)
+            else:
+                return False
+            l = l[3:]
+        else:
+            return False
+    return ris
+
+ResolvedRules = Dict[str, List[Optional[Union[float, int]]]]
+def resolve(genes: Dict[str, str], rules: List[str], ids: List[str], resolve_none: bool, name: str) -> Tuple[Optional[ResolvedRules], Optional[list]]:
+    """
+    Resolve rules using gene data to compute scores for each rule.
+
+    Args:
+        genes (dict): Dictionary containing gene data with gene IDs as keys and corresponding values.
+        rules (list): List of rules to resolve.
+        ids (list): List of IDs corresponding to the rules.
+        resolve_none (bool): Flag indicating whether to resolve None values in the rules.
+        name (str): Name of the dataset.
+
+    Returns:
+        tuple: A tuple containing resolved rules as a dictionary and a list of gene IDs not found in the data.
+    """
+    resolve_rules = {}
+    not_found = []
+    flag = False
+    for key, value in genes.items():
+        tmp_resolve = []
+        for i in range(len(rules)):
+            tmp = rules[i]
+            if tmp:
+                tmp, err = replace_gene_value(tmp, value)
+                if err:
+                    not_found.extend(err)
+                ris = control(None, tmp, resolve_none)
+                if ris is False or ris == None:
+                    tmp_resolve.append(None)
+                else:
+                    tmp_resolve.append(ris)
+                    flag = True
+            else:
+                tmp_resolve.append(None)    
+        resolve_rules[key] = tmp_resolve
+    
+    if flag is False:
+        utils.logWarning(
+            f"Warning: no computable score (due to missing gene values) for class {name}, the class has been disregarded",
+            ARGS.out_log)
+        
+        return (None, None)
+    
+    return (resolve_rules, list(set(not_found)))
+############################ create_ras #######################################
+def create_ras(resolve_rules: Optional[ResolvedRules], dataset_name: str, rules: List[str], ids: List[str], file: str) -> None:
+    """
+    Create a RAS (Reaction Activity Score) file from resolved rules.
+
+    Args:
+        resolve_rules (dict): Dictionary containing resolved rules.
+        dataset_name (str): Name of the dataset.
+        rules (list): List of rules.
+        file (str): Path to the output RAS file.
+
+    Returns:
+        None
+    """
+    if resolve_rules is None:
+        utils.logWarning(f"Couldn't generate RAS for current dataset: {dataset_name}", ARGS.out_log)
+
+    for geni in resolve_rules.values():
+        for i, valori in enumerate(geni):
+            if valori == None:
+                geni[i] = 'None'
+                
+    output_ras = pd.DataFrame.from_dict(resolve_rules)
+    
+    output_ras.insert(0, 'Reactions', ids)
+    output_to_csv = pd.DataFrame.to_csv(output_ras, sep = '\t', index = False)
+    
+    text_file = open(file, "w")
+    
+    text_file.write(output_to_csv)
+    text_file.close()
+
+################################- NEW RAS COMPUTATION -################################
+Expr = Optional[Union[int, float]]
+Ras  = Expr
+def ras_for_cell_lines(dataset: pd.DataFrame, rules: Dict[str, ruleUtils.OpList]) -> Dict[str, Dict[str, Ras]]:
+    """
+    Generates the RAS scores for each cell line found in the dataset.
+
+    Args:
+        dataset (pd.DataFrame): Dataset containing gene values.
+        rules (dict): The dict containing reaction ids as keys and rules as values.
+
+    Side effects:
+        dataset : mut
+    
+    Returns:
+        dict: A dictionary where each key corresponds to a cell line name and each value is a dictionary
+        where each key corresponds to a reaction ID and each value is its computed RAS score.
+    """
+    ras_values_by_cell_line = {}
+    dataset.set_index(dataset.columns[0], inplace=True)
+    # Considera tutte le colonne tranne la prima in cui ci sono gli hugo quindi va scartata
+    for cell_line_name in dataset.columns[1:]:
+        cell_line = dataset[cell_line_name].to_dict()
+        ras_values_by_cell_line[cell_line_name]= get_ras_values(rules, cell_line)
+    return ras_values_by_cell_line
+
+def get_ras_values(value_rules: Dict[str, ruleUtils.OpList], dataset: Dict[str, Expr]) -> Dict[str, Ras]:
+    """
+    Computes the RAS (Reaction Activity Score) values for each rule in the given dict.
+
+    Args:
+        value_rules (dict): A dictionary where keys are reaction ids and values are OpLists.
+        dataset : gene expression data of one cell line.
+
+    Returns:
+        dict: A dictionary where keys are reaction ids and values are the computed RAS values for each rule.
+    """
+    return {key: ras_op_list(op_list, dataset) for key, op_list in value_rules.items()}
+
+def get_gene_expr(dataset :Dict[str, Expr], name :str) -> Expr:
+    """
+    Extracts the gene expression of the given gene from a cell line dataset.
+
+    Args:
+        dataset : gene expression data of one cell line.
+        name : gene name.
+    
+    Returns:
+        Expr : the gene's expression value.
+    """
+    expr = dataset.get(name, None)
+    if expr is None: ERRORS.append(name)
+  
+    return expr
+
+def ras_op_list(op_list: ruleUtils.OpList, dataset: Dict[str, Expr]) -> Ras:
+    """
+    Computes recursively the RAS (Reaction Activity Score) value for the given OpList, considering the specified flag to control None behavior.
+
+    Args:
+        op_list (OpList): The OpList representing a rule with gene values.
+        dataset : gene expression data of one cell line.
+
+    Returns:
+        Ras: The computed RAS value for the given OpList.
+    """
+    op = op_list.op
+    ras_value :Ras = None
+    if not op: return get_gene_expr(dataset, op_list[0])
+    if op is ruleUtils.RuleOp.AND and not ARGS.none and None in op_list: return None
+
+    for i in range(len(op_list)):
+        item = op_list[i]
+        if isinstance(item, ruleUtils.OpList):
+            item = ras_op_list(item, dataset)
+
+        else:
+          item = get_gene_expr(dataset, item)
+
+        if item is None:
+          if op is ruleUtils.RuleOp.AND and not ARGS.none: return None
+          continue
+
+        if ras_value is None:
+          ras_value = item
+        else:
+          ras_value = ras_value + item if op is ruleUtils.RuleOp.OR else min(ras_value, item)
+
+    return ras_value
+
+def save_as_tsv(rasScores: Dict[str, Dict[str, Ras]], reactions :List[str]) -> None:
+    """
+    Save computed ras scores to the given path, as a tsv file.
+
+    Args:
+        rasScores : the computed ras scores.
+        path : the output tsv file's path.
+    
+    Returns:
+        None
+    """
+    for scores in rasScores.values(): # this is actually a lot faster than using the ootb dataframe metod, sadly
+        for reactId, score in scores.items():
+            if score is None: scores[reactId] = "None"
+
+    output_ras = pd.DataFrame.from_dict(rasScores)
+    output_ras.insert(0, 'Reactions', reactions)
+    output_ras.to_csv(ARGS.ras_output, sep = '\t', index = False)
+
+############################ MAIN #############################################
+#TODO: not used but keep, it will be when the new translator dicts will be used.
+def translateGene(geneName :str, encoding :str, geneTranslator :Dict[str, Dict[str, str]]) -> str:
+    """
+    Translate gene from any supported encoding to HugoID.
+
+    Args:
+        geneName (str): the name of the gene in its current encoding.
+        encoding (str): the encoding.
+        geneTranslator (Dict[str, Dict[str, str]]): the dict containing all supported gene names
+        and encodings in the current model, mapping each to the corresponding HugoID encoding.
+
+    Raises:
+        ValueError: When the gene isn't supported in the model.
+
+    Returns:
+        str: the gene in HugoID encoding.
+    """
+    supportedGenesInEncoding = geneTranslator[encoding]
+    if geneName in supportedGenesInEncoding: return supportedGenesInEncoding[geneName]
+    raise ValueError(f"Gene \"{geneName}\" non trovato, verifica di star utilizzando il modello corretto!")
+
+def load_custom_rules() -> Dict[str, ruleUtils.OpList]:
+    """
+    Opens custom rules file and extracts the rules. If the file is in .csv format an additional parsing step will be
+    performed, significantly impacting the runtime.
+
+    Returns:
+        Dict[str, ruleUtils.OpList] : dict mapping reaction IDs to rules.
+    """
+    datFilePath = utils.FilePath.fromStrPath(ARGS.rule_list) # actual file, stored in galaxy as a .dat
+    
+    try: filenamePath = utils.FilePath.fromStrPath(ARGS.rules_name) # file's name in input, to determine its original ext
+    except utils.PathErr as err:
+        raise utils.PathErr(filenamePath, f"Please make sure your file's name is a valid file path, {err.msg}")
+     
+    if filenamePath.ext is utils.FileFormat.PICKLE: return utils.readPickle(datFilePath)
+
+    # csv rules need to be parsed, those in a pickle format are taken to be pre-parsed.
+    return { line[0] : ruleUtils.parseRuleToNestedList(line[1]) for line in utils.readCsv(datFilePath) }
+
+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()
+    
+    # read dataset
+    dataset = read_dataset(ARGS.input, "dataset")
+    dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
+
+    # remove versioning from gene names
+    dataset.iloc[:, 0] = dataset.iloc[:, 0].str.split('.').str[0]
+
+    # handle custom models
+    model :utils.Model = ARGS.rules_selector
+    if model is utils.Model.Custom:
+        rules = load_custom_rules()
+        reactions = list(rules.keys())
+
+        save_as_tsv(ras_for_cell_lines(dataset, rules), reactions)
+        if ERRORS: utils.logWarning(
+            f"The following genes are mentioned in the rules but don't appear in the dataset: {ERRORS}",
+            ARGS.out_log)
+        
+        return
+    
+    # This is the standard flow of the ras_generator program, for non-custom models.
+    name = "RAS Dataset"
+    type_gene = gene_type(dataset.iloc[0, 0], name)
+
+    rules      = model.getRules(ARGS.tool_dir)
+    genes      = data_gene(dataset, type_gene, name, None)
+    ids, rules = load_id_rules(rules.get(type_gene))
+    
+    resolve_rules, err = resolve(genes, rules, ids, ARGS.none, name)
+    create_ras(resolve_rules, name, rules, ids, ARGS.ras_output)
+    
+    if err: utils.logWarning(
+        f"Warning: gene(s) {err} not found in class \"{name}\", " +
+        "the expression level for this gene will be considered NaN",
+        ARGS.out_log)
+    
+    print("Execution succeded")
+
+###############################################################################
+if __name__ == "__main__":
     main()
\ No newline at end of file