Source code for immuneML.simulation.implants.Signal

# quality: gold
from pathlib import Path
from typing import List

from immuneML.data_model.receptor.Receptor import Receptor
from immuneML.data_model.repertoire.Repertoire import Repertoire
from immuneML.simulation.implants.Motif import Motif
from immuneML.simulation.signal_implanting_strategy.SignalImplantingStrategy import SignalImplantingStrategy
from immuneML.util.ReflectionHandler import ReflectionHandler
from scripts.specification_util import update_docs_per_mapping

[docs] class Signal: """ This class represents the signal that will be implanted during a Simulation. A signal is represented by a list of motifs, and an implanting strategy. A signal is associated with a metadata label, which is assigned to a receptor or repertoire. For example antigen-specific/disease-associated (receptor) or diseased (repertoire). Arguments: motifs (list): A list of the motifs associated with this signal. implanting (:py:obj:`~immuneML.simulation.signal_implanting_strategy.SignalImplantingStrategy.SignalImplantingStrategy`): The strategy that is used to decide in which sequences the motifs should be implanted, and how. Valid values for this argument are class names of different signal implanting strategies. YAML specification: .. indent with spaces .. code-block:: yaml signals: my_signal: motifs: - my_simple_motif - my_gapped_motif implanting: HealthySequence sequence_position_weights: 109: 0.5 110: 0.5 """ def __init__(self, identifier: str, motifs: List[Motif], implanting_strategy: SignalImplantingStrategy): = str(identifier) self.motifs = motifs self.implanting_strategy = implanting_strategy
[docs] def implant_to_repertoire(self, repertoire: Repertoire, repertoire_implanting_rate: float, path: Path) \ -> Repertoire: processed_repertoire = self.implanting_strategy \ .implant_in_repertoire(repertoire=repertoire, repertoire_implanting_rate=repertoire_implanting_rate, signal=self, path=path) return processed_repertoire
[docs] def implant_in_receptor(self, receptor: Receptor, is_noise: bool) -> Receptor: processed_receptor = self.implanting_strategy.implant_in_receptor(receptor, self, is_noise) return processed_receptor
def __str__(self): return "Signal id: " + + "; motifs: " + ", ".join([str(motif) for motif in self.motifs])
[docs] @staticmethod def get_documentation(): initial_doc = str(Signal.__doc__) valid_implanting_values = str( ReflectionHandler.all_nonabstract_subclass_basic_names(SignalImplantingStrategy, 'Implanting', 'signal_implanting_strategy/'))[ 1:-1].replace("'", "`") docs_mapping = { "Valid values for this argument are class names of different signal implanting strategies.": f"Valid values are: {valid_implanting_values}" } doc = update_docs_per_mapping(initial_doc, docs_mapping) return doc