Source code for immuneML.pairwise_repertoire_comparison.ComparisonDataBatch

import logging
import pickle
from dataclasses import dataclass
from pathlib import Path
from typing import Dict

import numpy as np

from immuneML.util.PathBuilder import PathBuilder

[docs]@dataclass class ComparisonDataBatch: """ Arguments: matrix: array with dimension items x repertoires, where items are defined by comparison attributes specified in ComparisonData class and can include, for instance, receptor sequences or combinations of receptor sequences and V and J gene items: the item names extracted from the repertoires in the dataset on which the repertoires are evaluated (e.g. sequences or combinations of sequences and genes repertoire_index_mapping: a mapping between the repertoire identifier (a string) and a column number for faster access of columns (repertoire vectors w.r.t. given items) in the comparison data matrix where columns correspond to repertoires path (Path): path to directory where comp data is stored identifier (int): identifier of the batch """ items: list repertoire_index_mapping: Dict[str, int] path: Path identifier: int matrix: np.ndarray = None
[docs] def store(self): PathBuilder.build_from_objects(self.path) / f"{self.identifier}.npy", self.matrix) / f"{self.identifier}_items.npy", self.items) batch_vars = vars(self) del batch_vars["matrix"] del batch_vars["items"] pkl_path = self.path / f"{self.identifier}.pkl" with"wb") as file: pickle.dump(batch_vars, file)
[docs] def load(self): file_path = self.path / f'{self.identifier}.pkl' if file_path.is_file(): with'rb') as file: batch_vars = pickle.load(file) for v in batch_vars: if hasattr(self, v): setattr(self, v, batch_vars[v]) else: logging.warning(f"ComparisonDataBatch: path {file_path} does not exist, returning the same object...") return self
[docs] def get_items(self): if self.matrix is None: return np.load(self.path / f"{self.identifier}_items.npy", allow_pickle=True) else: return self.items
[docs] def get_matrix(self): if self.matrix is None: return np.load(self.path / f"{self.identifier}.npy", allow_pickle=True) else: return self.matrix