mirror of
https://github.com/p2p-ld/nwb-linkml.git
synced 2024-11-13 02:04:29 +00:00
89 lines
No EOL
3.2 KiB
Python
89 lines
No EOL
3.2 KiB
Python
"""
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Adapter for NWB groups to linkml Classes
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"""
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import pdb
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from typing import List
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from linkml_runtime.linkml_model import ClassDefinition, SlotDefinition
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from nwb_schema_language import Dataset, Group, ReferenceDtype, CompoundDtype, DTypeType
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from nwb_linkml.adapters.classes import ClassAdapter
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from nwb_linkml.adapters.dataset import DatasetAdapter
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from nwb_linkml.adapters.adapter import BuildResult
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from nwb_linkml.maps import QUANTITY_MAP
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class GroupAdapter(ClassAdapter):
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cls: Group
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def build(self) -> BuildResult:
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nested_res = self.build_subclasses()
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# we don't propagate slots up to the next level since they are meant for this
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# level (ie. a way to refer to our children)
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res = self.build_base(extra_attrs=nested_res.slots)
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# we do propagate classes tho
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res.classes.extend(nested_res.classes)
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return res
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def handle_children(self, children: List[Group]) -> BuildResult:
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"""
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Make a special LinkML `children` slot that can
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have any number of the objects that are of `neurodata_type_inc` class
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Args:
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children (List[:class:`.Group`]): Child groups
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"""
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child_slot = SlotDefinition(
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name='children',
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multivalued=True,
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any_of=[{'range': cls.neurodata_type_inc} for cls in children]
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)
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return BuildResult(slots=[child_slot])
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def build_subclasses(self) -> BuildResult:
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"""
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Build nested groups and datasets
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Create ClassDefinitions for each, but then also create SlotDefinitions that
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will be used as attributes linking the main class to the subclasses
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"""
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# Datasets are simple, they are terminal classes, and all logic
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# for creating slots vs. classes is handled by the adapter class
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dataset_res = BuildResult()
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for dset in self.cls.datasets:
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# if dset.name == 'timestamps':
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# pdb.set_trace()
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dset_adapter = DatasetAdapter(cls=dset, parent=self)
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dataset_res += dset_adapter.build()
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# Actually i'm not sure we have to special case this, we could handle it in
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# i/o instead
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# Groups are a bit more complicated because they can also behave like
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# range declarations:
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# eg. a group can have multiple groups with `neurodata_type_inc`, no name, and quantity of *,
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# the group can then contain any number of groups of those included types as direct children
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# group_res = BuildResult()
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# children = []
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# for group in self.cls.groups:
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# if not group.name and \
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# group.quantity == '*' and \
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# group.neurodata_type_inc:
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# children.append(group)
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# else:
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# group_adapter = GroupAdapter(cls=group, parent=self)
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# group_res += group_adapter.build()
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#
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# group_res += self.handle_children(children)
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group_res = BuildResult()
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for group in self.cls.groups:
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group_adapter = GroupAdapter(cls=group, parent=self)
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group_res += group_adapter.build()
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res = dataset_res + group_res
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return res |