Source code for dantro.data_loaders.load_hdf5

"""Implements loading of Hdf5 files into the dantro data tree"""

import logging
import os
from typing import Dict, Union

import h5py as h5
import numpy as np

from ..base import BaseDataContainer, BaseDataGroup
from ..containers import NumpyDataContainer
from ..groups import OrderedDataGroup
from ..proxy import Hdf5DataProxy
from import decode_bytestrings, print_line
from ._tools import add_loader

# Local constants
log = logging.getLogger(__name__)

# -----------------------------------------------------------------------------

[docs]class Hdf5LoaderMixin: """Supplies functionality to load hdf5 files into the data manager. It resolves the hdf5 groups into corresponding data groups and the datasets into NumpyDataContainers. If ``enable_mapping`` is set, the class variables ``_HDF5_DSET_MAP`` and ``_HDF5_GROUP_MAP`` are used to map from a string to a container type. The class variable ``_HDF5_MAP_FROM_ATTR`` determines the default value of the attribute to read and use as input string for the mapping. Attributes: _HDF5_DSET_DEFAULT_CLS (type): the default class to use for datasets. This should be a dantro :py:class:`~dantro.base.BaseDataContainer` -derived class. Note that certain data groups can overwrite the default class for underlying members. _HDF5_GROUP_MAP (Dict[str, type]): if mapping is enabled, the equivalent dantro types for HDF5 groups are determined from this mapping. _HDF5_DSET_MAP (Dict[str, type]): if mapping is enabled, the equivalent dantro types for HDF5 datasets are determined from this mapping. _HDF5_MAP_FROM_ATTR (str): the name of the HDF5 dataset or group attribute to read in order to determine the type mapping. For example, this could be ``"content"``. This is the fallback value if no ``map_from_attr`` argument is given to :py:meth:`dantro.data_loaders.load_hdf5.Hdf5LoaderMixin._load_hdf5` _HDF5_DECODE_ATTR_BYTESTRINGS (bool): if true (default), will attempt to decode HDF5 attributes that are stored as byte arrays into regular Python strings; this can make attribute handling much easier. """ # Default values for class variables; see above for docstrings _HDF5_DSET_DEFAULT_CLS = NumpyDataContainer _HDF5_GROUP_MAP = None _HDF5_DSET_MAP = None _HDF5_MAP_FROM_ATTR = None _HDF5_DECODE_ATTR_BYTESTRINGS = True @add_loader(TargetCls=OrderedDataGroup, omit_self=False) def _load_hdf5( self, filepath: str, *, TargetCls: type, load_as_proxy: bool = False, proxy_kwargs: dict = None, lower_case_keys: bool = False, enable_mapping: bool = False, map_from_attr: str = None, direct_insertion: bool = False, progress_params: dict = None, ) -> OrderedDataGroup: """Loads the specified hdf5 file into DataGroup- and DataContainer-like objects; this completely recreates the hierarchic structure of the hdf5 file. The data can be loaded into memory completely, or be loaded as a proxy object. The h5py File and Group objects will be converted to the specified DataGroup-derived objects; the Dataset objects to the specified DataContainer-derived object. All HDF5 group or dataset attributes are carried over and are accessible under the ``attrs`` attribute of the respective dantro objects in the tree. Args: filepath (str): The path to the HDF5 file that is to be loaded TargetCls (type): The group type this is loaded into load_as_proxy (bool, optional): if True, the leaf datasets are loaded as :py:class:`dantro.proxy.hdf5.Hdf5DataProxy` objects. That way, the data is only loaded into memory when their ``.data`` property is accessed the first time, either directly or indirectly. proxy_kwargs (dict, optional): When loading as proxy, these parameters are unpacked in the ``__init__`` call. For available argument see :py:class:`~dantro.proxy.hdf5.Hdf5DataProxy`. lower_case_keys (bool, optional): whether to use only lower-case versions of the paths encountered in the HDF5 file. enable_mapping (bool, optional): If true, will use the class variables ``_HDF5_GROUP_MAP`` and ``_HDF5_DSET_MAP`` to map groups or datasets to a custom container class during loading. Which attribute to read is determined by the ``map_from_attr`` argument (see there). map_from_attr (str, optional): From which attribute to read the key that is used in the mapping. If nothing is given, the class variable ``_HDF5_MAP_FROM_ATTR`` is used. direct_insertion (bool, optional): If True, some non-crucial checks are skipped during insertion and elements are inserted (more or less) directly into the data tree, thus speeding up the data loading process. This option should only be enabled if data is loaded into a yet unpopulated part of the data tree, otherwise existing elements might be overwritten silently. This option only applies to data groups, not to containers. progress_params (dict, optional): parameters for the progress indicator. Possible keys: level (int): how verbose to print progress info; possible values are: ``0``: None, ``1``: on file level, ``2``: on dataset level. Note that this option and the ``progress_indicator`` of the DataManager are independent from each other. fstr: format string for progress report, receives the following keys: * ``progress_info`` (total progress indicator), * ``fname`` (basename of current hdf5 file), * ``fpath`` (full path of current hdf5 file), * ``name`` (current dataset name), * ``path`` (current path within the hdf5 file) Returns: OrderedDataGroup: The populated root-level group, corresponding to the base group of the file Raises: ValueError: If ``enable_mapping``, but no map attribute can be determined from the given argument or the class variable ``_HDF5_MAP_FROM_ATTR`` """ # Initialize the root group log.debug( "Loading hdf5 file %s into %s ...", filepath, TargetCls.__name__ ) root = TargetCls() # Get the classes to use for groups and/or containers DsetCls = self._HDF5_DSET_DEFAULT_CLS # Determine from which attribute to read the mapping if not map_from_attr: # No custom value was given; use the class variable, if available if self._HDF5_MAP_FROM_ATTR: map_from_attr = self._HDF5_MAP_FROM_ATTR elif enable_mapping: # Mapping was enabled but it is unclear from which attribute # the map should be read. Need to raise an exception raise ValueError( "Could not determine from which attribute to read the " "mapping. Either set the loader argument `map_from_attr`, " "the class variable _HDF5_MAP_FROM_ATTR, or disable " "mapping altogether via the `enable_mapping` argument." ) # Prepare parameters GroupMap = self._HDF5_GROUP_MAP if enable_mapping else {} DsetMap = self._HDF5_DSET_MAP if enable_mapping else {} # Prepare progress information progress_params = progress_params if progress_params else {} plvl = progress_params.get("level", 0) pfstr = progress_params.get( "fstr", " {progress_info:} {fname:} : {path:} ... " ) # Now recursively go through the hdf5 file and add them to the roo with h5.File(filepath, "r") as h5file: if plvl >= 1: # Print information on the level of this file _info = pfstr.format( progress_info=self._progress_info_str, fpath=filepath, fname=os.path.basename(filepath), key="", path="", ) print_line(_info) # Load the file level attributes, manually re-creating the dict root.attrs = { k: self._decode_attr_val(v) for k, v in h5file.attrs.items() } # Now recursively load the data into the root group self._recursively_load_hdf5( h5file, target=root, load_as_proxy=load_as_proxy, proxy_kwargs=proxy_kwargs, lower_case_keys=lower_case_keys, DsetCls=DsetCls, GroupMap=GroupMap, DsetMap=DsetMap, map_attr=map_from_attr, direct_insertion=direct_insertion, plvl=plvl, pfstr=pfstr, ) return root @add_loader(TargetCls=OrderedDataGroup, omit_self=False) def _load_hdf5_proxy(self, *args, **kwargs) -> OrderedDataGroup: """This is a shorthand for :py:meth:`~dantro.data_loaders.load_hdf5.Hdf5LoaderMixin._load_hdf5` with the ``load_as_proxy`` flag set. """ return self._load_hdf5(*args, load_as_proxy=True, **kwargs) @add_loader(TargetCls=OrderedDataGroup, omit_self=False) def _load_hdf5_as_dask(self, *args, **kwargs) -> OrderedDataGroup: """This is a shorthand for :py:meth:`~dantro.data_loaders.load_hdf5.Hdf5LoaderMixin._load_hdf5` with the ``load_as_proxy`` flag set and ``resolve_as_dask`` passed as additional arguments to the proxy via ``proxy_kwargs``. """ return self._load_hdf5( *args, load_as_proxy=True, proxy_kwargs=dict(resolve_as_dask=True), **kwargs, ) # .........................................................................
[docs] def _recursively_load_hdf5( self, src: Union[h5.Group, h5.File], *, target: BaseDataGroup, lower_case_keys: bool, direct_insertion: bool, **kwargs, ): """Recursively loads the data from a source object (an h5.File or a h5.Group) into the target dantro group. Args: src (Union[h5.Group, h5.File]): The HDF5 source object from which to load the data. This object it iterated over. target (BaseDataGroup): The target group to populate with the data from ``src``. lower_case_keys (bool): Whether to make keys lower-case direct_insertion (bool): Whether to use direct insertion mode on the target group (and all groups below) **kwargs: Passed on to the group and container loader methods, :py:meth:`~dantro.data_loaders.load_hdf5.Hdf5LoaderMixin._container_from_h5dataset` and :py:meth:`~dantro.data_loaders.load_hdf5.Hdf5LoaderMixin._group_from_h5group`. Raises: NotImplementedError: When encountering objects other than groups or datasets in the HDF5 file """ # Go through the elements of the source object for key, obj in src.items(): if lower_case_keys and isinstance(key, str): key = key.lower() with target._direct_insertion_mode(enabled=direct_insertion): if isinstance(obj, h5.Group): # Create the new group grp = self._group_from_h5group( obj, target=target, name=key, **kwargs ) # Continue recursion self._recursively_load_hdf5( obj, target=grp, lower_case_keys=lower_case_keys, direct_insertion=direct_insertion, **kwargs, ) elif isinstance(obj, h5.Dataset): # Reached a leaf -> Import the data and attributes into a # BaseDataContainer-derived object. This assumes that the # given DsetCls supports np.ndarray-like data self._container_from_h5dataset( obj, target=target, name=key, **kwargs ) else: raise NotImplementedError( f"Object {key} is neither a dataset nor a group, but " f"of type {type(obj)}. Cannot load this!" )
[docs] def _group_from_h5group( self, h5grp: h5.Group, target: BaseDataGroup, *, name: str, map_attr: str, GroupMap: dict, **_, ) -> BaseDataGroup: """Adds a new group from a h5.Group The group types may be mapped to different dantro types; this is controlled by the extracted HDF5 attribute with the name specified in the ``_HDF5_MAP_FROM_ATTR`` class attribute. Args: h5grp (h5.Group): The HDF5 group to create a dantro group for in the ``target`` group. target (BaseDataGroup): The group in which to create a new group that represents ``h5grp`` name (str): the name of the new group GroupMap (dict): Map of names to BaseDataGroup-derived types; always needed, but may be empty map_attr (str): The HDF5 attribute to inspect in order to determine the name of the mapping **_: ignored """ # Extract attributes manually attrs = {k: self._decode_attr_val(v) for k, v in h5grp.attrs.items()} # Determine the mapping type, falling back to the group default if no # mapping was specified _GroupCls = self._evaluate_type_mapping( map_attr, attrs=attrs, tmap=GroupMap, fallback=None ) # Create and add the group, passing the attributes return target.new_group(path=name, Cls=_GroupCls, attrs=attrs)
[docs] def _container_from_h5dataset( self, h5dset: h5.Dataset, target: BaseDataGroup, *, name: str, load_as_proxy: bool, proxy_kwargs: dict, DsetCls: type, map_attr: str, DsetMap: dict, plvl: int, pfstr: str, **_, ) -> BaseDataContainer: """Adds a new data container from a h5.Dataset The group types may be mapped to different dantro types; this is controlled by the extracted HDF5 attribute with the name specified in the ``_HDF5_MAP_FROM_ATTR`` class attribute. Args: h5dset (h5.Dataset): The source dataset to load into ``target`` as a dantro data container. target (BaseDataGroup): The target group where the ``h5dset`` will be represented in as a new dantro data container. name (str): the name of the new container load_as_proxy (bool): Whether to load as :py:class:`~dantro.proxy.hdf5.Hdf5DataProxy` proxy_kwargs (dict): Upon proxy initialization, unpacked into :py:meth:`dantro.proxy.hdf5.Hdf5DataProxy.__init__` DsetCls (BaseDataContainer): The type that is used to create the dataset-equivalents in ``target``. If mapping is enabled, this serves as the fallback type. map_attr (str): The HDF5 attribute to inspect in order to determine the name of the mapping DsetMap (dict): Map of names to BaseDataContainer-derived types; always needed, but may be empty plvl (int): the verbosity of the progress indicator pfstr (str): a format string for the progress indicator """ # Progress information on loading this dataset if plvl >= 2: _info = pfstr.format( progress_info=self._progress_info_str, fpath=h5dset.file.filename, fname=os.path.basename(h5dset.file.filename), name=name,, ) print_line(_info) # Extract attributes manually attrs = {k: self._decode_attr_val(v) for k, v in h5dset.attrs.items()} # Determine the class to use for this dataset. # If the target group supplies a default container type, specify None # as the fallback, delegating the choice to the `new_container` method. # Otherwise use the default specified here. _DsetCls = self._evaluate_type_mapping( map_attr, attrs=attrs, tmap=DsetMap, fallback=DsetCls if target._NEW_CONTAINER_CLS is None else None, ) # Get the data, potentially as proxy. if load_as_proxy: data = Hdf5DataProxy( h5dset, **(proxy_kwargs if proxy_kwargs else {}) ) else: data = np.array(h5dset) # Now create and add the dataset return target.new_container( path=name, Cls=_DsetCls, data=data, attrs=attrs )
# ......................................................................... # Smaller helper methods
[docs] def _decode_attr_val(self, attr_val) -> str: """Wrapper around decode_bytestrings""" # If feature not activated, return without doing anything if not self._HDF5_DECODE_ATTR_BYTESTRINGS: return attr_val return decode_bytestrings(attr_val)
[docs] def _evaluate_type_mapping( self, key: str, *, attrs: dict, tmap: Dict[str, type], fallback: type ) -> type: """Given an attributes dict or group attributes, evaluates which type a target container should use. """ def parse_map_attr(v) -> str: """Make sure the map attribute isn't a 1-sized array!""" if isinstance(v, np.ndarray): v = v.item() return str(v) # Easy cases first: no map or mapping attribute given if not tmap or not attrs.get(key): return fallback try: return tmap[parse_map_attr(attrs[key])] except KeyError: # Fall back to default log.warning( "Could not find a mapping from map attribute %s='%s' " "(originally %s) to a dantro container or group class. " "Available keys: %s. Using fallback type instead: %s .", key, parse_map_attr(attrs[key]), attrs[key], ", ".join([k for k in tmap]), fallback.__name__ if fallback is not None else "(none)", ) return fallback