from pathlib import Path
from immuneML.IO.dataset_export.DataExporter import DataExporter
from immuneML.dsl.symbol_table.SymbolTable import SymbolTable
from immuneML.dsl.symbol_table.SymbolType import SymbolType
from immuneML.util.ParameterValidator import ParameterValidator
from immuneML.util.ReflectionHandler import ReflectionHandler
from immuneML.workflows.instructions.SimulationInstruction import SimulationInstruction
[docs]class SimulationParser:
"""
YAML specification:
.. highlight:: yaml
.. code-block:: yaml
definitions:
dataset:
my_dataset:
...
motifs:
m1:
seed: AAC # "/" character denotes the gap in the seed if present (e.g. AA/C)
instantiation:
GappedKmer:
# probability that when hamming distance is allowed a letter in the seed will be replaced by
# other alphabet letters - alphabet_weights
alphabet_weights:
A: 0.2
C: 0.2
D: 0.4
E: 0.2
# Relative probabilities of choosing each position in the seed for hamming distance modification.
# The probabilities will be scaled to sum to one - position_weights
position_weights:
0: 1
1: 0
2: 0
hamming_distance_probabilities:
0: 0.5 # Hamming distance of 0 (no change) with probability 0.5
1: 0.5 # Hamming distance of 1 (one letter change) with probability 0.5
min_gap: 0
max_gap: 1
signals:
s1:
motifs: # list of all motifs for signal which will be uniformly sampled to get a motif instance for implanting
- m1
sequence_position_weights: # likelihood of implanting at IMGT position of receptor sequence
107: 0.5
implanting: HealthySequence # choose only sequences with no other signals for to implant one of the motifs
simulations:
sim1: # one Simulation object consists of a dict of Implanting objects
i1:
dataset_implanting_rate: 0.5 # percentage of repertoire where the signals will be implanted
repertoire_implanting_rate: 0.01 # percentage of sequences within repertoire where the signals will be implanted
signals:
- s1
instructions:
my_simulation_instruction:
type: Simulation
dataset: my_dataset
simulation: sim1
export_formats: [AIRR, ImmuneML]
"""
[docs] def parse(self, key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) -> SimulationInstruction:
ParameterValidator.assert_keys(instruction.keys(), ["dataset", "simulation", "type", "export_formats"], "SimulationParser", key)
signals = [signal.item for signal in symbol_table.get_by_type(SymbolType.SIGNAL)]
simulation = symbol_table.get(instruction["simulation"])
dataset = symbol_table.get(instruction["dataset"])
exporters = self.parse_exporters(instruction)
process = SimulationInstruction(signals=signals, simulation=simulation, dataset=dataset, name=key, exporters=exporters)
return process
[docs] def parse_exporters(self, instruction):
if instruction["export_formats"] is not None:
class_path = "dataset_export/"
ParameterValidator.assert_all_in_valid_list(instruction["export_formats"],
ReflectionHandler.all_nonabstract_subclass_basic_names(DataExporter, 'Exporter', class_path),
location="SimulationParser", parameter_name="export_formats")
exporters = [ReflectionHandler.get_class_by_name(f"{item}Exporter", class_path) for item in instruction["export_formats"]]
else:
exporters = None
return exporters