Source code for immuneML.api.galaxy.GalaxySimulationTool

import logging
import shutil
from pathlib import Path

import yaml

from immuneML.api.galaxy.GalaxyTool import GalaxyTool
from immuneML.api.galaxy.Util import Util
from immuneML.workflows.instructions.SimulationInstruction import SimulationInstruction

[docs]class GalaxySimulationTool(GalaxyTool): """ GalaxySimulationTool is an alternative to running immuneML with the simulation instruction directly. It accepts a YAML specification file and a path to the output directory. It implants the signals in the dataset that was provided either as an existing dataset with a set of files or in the random dataset as described in the specification file. This tool is meant to be used as an endpoint for Galaxy tool that will create a Galaxy collection out of a dataset in immuneML format that can be readily used by other immuneML-based Galaxy tools. The specification supplied for this tool is identical to immuneML specification, except that it can include only one instruction which has to be of type 'Simulation': .. code-block: yaml definitions: datasets: my_synthetic_dataset: format: RandomRepertoireDataset params: repertoire_count: 100 labels: {} motifs: my_simple_motif: # a simple motif without gaps or hamming distance seed: AAA instantiation: GappedKmer my_complex_motif: # complex motif containing a gap + hamming distance seed: AA/A # ‘/’ denotes gap position if present, if not, there’s no gap instantiation: GappedKmer: min_gap: 1 max_gap: 2 hamming_distance_probabilities: # probabilities for each number of 0: 0.7 # modification to the seed 1: 0.3 position_weights: # probabilities for modification per position 0: 1 1: 0 # note that index 2, the position of the gap, 3: 0 # is excluded from position_weights alphabet_weights: # probabilities for using each amino acid in A: 0.2 # a hamming distance modification C: 0.2 D: 0.4 E: 0.2 signals: my_signal: motifs: - my_simple_motif - my_complex_motif implanting: HealthySequence sequence_position_weights: 109: 1 110: 2 111: 5 112: 1 simulations: my_simulation: my_implanting: signals: - my_signal dataset_implanting_rate: 0.5 repertoire_implanting_rate: 0.25 instructions: 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 number_of_processes: 4 # how many parallel processes to use during execution export_formats: [AIRR] # in which formats to export the dataset, ImmuneML format will be added automatically output: # the output format format: HTML """ def __init__(self, specification_path: Path, result_path: Path, **kwargs): Util.check_parameters(specification_path, result_path, kwargs, GalaxySimulationTool.__name__) super().__init__(specification_path, result_path, **kwargs) def _run(self): self.prepare_specs() Util.run_tool(self.yaml_path, self.result_path) dataset_location = list(self.result_path.glob("*/exported_dataset/*/"))[0] shutil.copytree(dataset_location, self.result_path / 'result/')"{GalaxySimulationTool.__name__}: immuneML has finished and the signals were implanted in the dataset.")
[docs] def prepare_specs(self): with"r") as file: specs = yaml.safe_load(file) instruction_name = Util.check_instruction_type(specs, GalaxySimulationTool.__name__, SimulationInstruction.__name__[:-11]) Util.check_export_format(specs, GalaxySimulationTool.__name__, instruction_name) Util.update_dataset_key(specs, GalaxySimulationTool.__name__) Util.check_paths(specs, "GalaxySimulationTool") Util.update_result_paths(specs, self.result_path, self.yaml_path)