Source code for gemseo.caches.hdf5_cache

# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                         documentation
#        :author: Francois Gallard, Matthias De Lozzo
"""Caching module to store all the entries in a HDF file."""
from __future__ import annotations

import logging
from multiprocessing import RLock
from pathlib import Path
from typing import Any
from typing import Generator

import h5py

from gemseo.caches.hdf5_file_singleton import HDF5FileSingleton
from gemseo.core.cache import AbstractFullCache
from gemseo.core.cache import CacheEntry
from gemseo.core.cache import Data
from gemseo.core.cache import JacobianData
from gemseo.utils.data_conversion import nest_flat_bilevel_dict
from gemseo.utils.locks import synchronized
from gemseo.utils.string_tools import MultiLineString

LOGGER = logging.getLogger(__name__)

[docs]class HDF5Cache(AbstractFullCache): """Cache using disk HDF5 file to store the data.""" def __init__( self, hdf_file_path: str | Path = "cache.hdf5", hdf_node_path: str = "node", tolerance: float = 0.0, name: str | None = None, ) -> None: """ Args: hdf_file_path: The path of the HDF file. Initialize a singleton to access the HDF file. This singleton is used for multithreaded/multiprocessing access with a lock. hdf_node_path: The node of the HDF file. name: A name for the cache. If ``None``, use ``hdf_note_path``. Warnings: This class relies on some multiprocessing features, it is therefore necessary to protect its execution with an ``if __name__ == '__main__':`` statement when working on Windows. """ self.__hdf_node_path = hdf_node_path self.__hdf_file = HDF5FileSingleton(str(hdf_file_path)) if not name: name = hdf_node_path super().__init__(tolerance, name) self._read_hashes() @property def hdf_file(self) -> HDF5FileSingleton: """The hdf file handler.""" return self.__hdf_file def __str__(self) -> str: msg = MultiLineString() msg.add(super().__str__()) msg.indent() msg.add("HDF file path: {}", self.__hdf_file.hdf_file_path) msg.add("HDF node path: {}", self.__hdf_node_path) return str(msg) def __getstate__(self): # Pickle __init__ arguments so to call it when unpickling. return dict( tolerance=self.tolerance, hdf_file_path=self.__hdf_file.hdf_file_path, hdf_node_path=self.__hdf_node_path,, ) def __setstate__(self, state): self.__init__(**state) def _copy_empty_cache(self) -> HDF5Cache: file_path = Path(self.__hdf_file.hdf_file_path) return self.__class__( hdf_file_path=file_path.parent / ("new_" +, hdf_node_path=self.__hdf_node_path, tolerance=self.tolerance,, ) def _set_lock(self) -> RLock: return self.__hdf_file.lock @synchronized def _read_hashes(self) -> None: """Read the hashes dict in the HDF file.""" max_index = self.__hdf_file.read_hashes( self._hashes_to_indices, self.__hdf_node_path ) self._last_accessed_index.value = max_index self._max_index.value = max_index cache_size = len(self._hashes_to_indices) if cache_size > 0: msg = "Found %s entries in the cache file : %s node : %s" msg, cache_size, self.__hdf_file.hdf_file_path, self.__hdf_node_path ) def _has_group( self, index: int, group: str, ) -> bool: return self.__hdf_file.has_group(index, group, self.__hdf_node_path)
[docs] @synchronized def clear(self) -> None: super().clear() self.__hdf_file.clear(self.__hdf_node_path)
def _read_data( self, index: int, group: str, h5_open_file: h5py.File | None = None, **options: Any, ) -> tuple[Data, JacobianData]: """ Args: h5_open_file: The opened HDF file. This improves performance but is incompatible with multiprocess/treading. If ``None``, open it. """ data = self.__hdf_file.read_data( index, group, self.__hdf_node_path, h5_open_file=h5_open_file )[0] if group == self._JACOBIAN_GROUP and data is not None: data = nest_flat_bilevel_dict(data, separator=self._JACOBIAN_SEPARATOR) return data def _write_data( self, data: Data, group: str, index: int, ) -> None: self.__hdf_file.write_data( data, group, index, self.__hdf_node_path, ) @synchronized def __iter__( self, ) -> Generator[CacheEntry]: with h5py.File(self.__hdf_file.hdf_file_path, "a") as h5_open_file: yield from self._all_data(h5_open_file=h5_open_file)
[docs] @staticmethod def update_file_format( hdf_file_path: str | Path, ) -> None: """Update the format of a HDF5 file. .. seealso:: :meth:`.HDF5FileSingleton.update_file_format`. Args: hdf_file_path: A HDF5 file path. """ HDF5FileSingleton.update_file_format(hdf_file_path)