comparison COBRAxy/ras_generator.py @ 381:0a3ca20848f3 draft

Uploaded

380:03a7ba63813f

        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]:

    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(args:List[str] = None) -> None:
    """
    Initializes everything and sets the program in motion based on the fronted input arguments.
    

    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}\", " +

381:0a3ca20848f3

        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)
    
    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]:

    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)

    dict_rule = {}
    for line in utils.readCsv(datFilePath, delimiter = "\t"):
        if line[2] == "":
            dict_rule[line[0]] = ruleUtils.OpList([""])
        else:
            dict_rule[line[0]] = ruleUtils.parseRuleToNestedList(line[2])

    # csv rules need to be parsed, those in a pickle format are taken to be pre-parsed.
    return dict_rule

def main(args:List[str] = None) -> None:
    """
    Initializes everything and sets the program in motion based on the fronted input arguments.
    

    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}\", " +