Source code for immuneML.workflows.instructions.SimulationInstruction

from pathlib import Path
from typing import List

from immuneML.IO.dataset_export.DataExporter import DataExporter
from immuneML.data_model.dataset.RepertoireDataset import RepertoireDataset
from immuneML.simulation.Simulation import Simulation
from immuneML.simulation.SimulationState import SimulationState
from immuneML.util.ReflectionHandler import ReflectionHandler
from immuneML.workflows.instructions.Instruction import Instruction
from immuneML.workflows.steps.SignalImplanter import SignalImplanter
from scripts.specification_util import update_docs_per_mapping

[docs] class SimulationInstruction(Instruction): """ A simulation is an instruction that implants synthetic signals into the given dataset according to given parameters. This results in a new dataset containing modified sequences, and is annotated with metadata labels according to the implanted signals. Arguments: dataset (RepertoireDataset): original dataset which will be used as a basis for implanting signals from the simulation simulation (Simulation): definition of how to perform the simulation. export_formats: in which formats to export the dataset after simulation. Valid formats are class names of any non-abstract class inheriting :py:obj:`~immuneML.IO.dataset_export.DataExporter.DataExporter`. Important note: Binary files in ImmuneML might not be compatible between different immuneML versions. YAML specification: .. indent with spaces .. code-block:: yaml my_simulation_instruction: # user-defined name of the instruction type: Simulation # which instruction to execute dataset: my_dataset # which dataset to use for implanting the signals simulation: my_simulation # how to implanting the signals - definition of the simulation export_formats: [AIRR] # in which formats to export the dataset """ def __init__(self, signals: list, simulation: Simulation, dataset: RepertoireDataset, name: str = None, exporters: List[DataExporter] = None): self.exporters = exporters self.state = SimulationState(signals, simulation, dataset, name=name)
[docs] def run(self, result_path: Path): self.state.result_path = result_path / self.state.resulting_dataset = self.export_dataset() return self.state
[docs] def export_dataset(self): dataset_name = if is not None else self.state.resulting_dataset.identifier paths = {dataset_name: {}} formats = [] if self.exporters is not None and len(self.exporters) > 0: for exporter in self.exporters: export_format = exporter.__name__[:-8] path = self.state.result_path / f"exported_dataset/{exporter.__name__.replace('Exporter', '').lower()}/" exporter.export(self.state.resulting_dataset, path) paths[dataset_name][export_format] = path formats.append(export_format) self.state.paths = paths self.state.formats = formats
[docs] @staticmethod def get_documentation(): doc = str(SimulationInstruction.__doc__) valid_strategy_values = ReflectionHandler.all_nonabstract_subclass_basic_names(DataExporter, "Exporter", "dataset_export/") valid_strategy_values = str(valid_strategy_values)[1:-1].replace("'", "`") mapping = { "Valid formats are class names of any non-abstract class inheriting " ":py:obj:`~immuneML.IO.dataset_export.DataExporter.DataExporter`.": f"Valid values are: {valid_strategy_values}.", "exporter": "export_format", "simulation (Simulation)": "simulation", "dataset (RepertoireDataset)": "dataset" } doc = update_docs_per_mapping(doc, mapping) return doc