"""Serialization helpers for grouping chains.
The existing :func:`cfdmod.io.write_processing_metadata` takes a generic
``config: dict`` which it serializes to YAML inside the HDF5 file. To
record a grouping chain, callers pass ``{"groupings": [spec.model_dump()
for spec in chain]}`` (alongside ``"filters"``, etc.). The helpers in
this module wrap that idiom so call sites stay legible:
write_processing_metadata(h5, "/", {"groupings": dump_groupings(chain)})
md = read_processing_metadata(h5, "/")
chain = load_groupings(md["config"]["groupings"])
Round-trip: ``load_groupings(dump_groupings(chain)) == chain`` for any
valid chain.
"""
from __future__ import annotations
from typing import Any
from pydantic import TypeAdapter
from cfdmod.geometry.grouping.specs import GroupingSpec
_GROUPING_LIST_ADAPTER = TypeAdapter(list[GroupingSpec])
[docs]
def dump_groupings(groupings: list[GroupingSpec]) -> list[dict[str, Any]]:
"""Serialize a chain of grouping specs to plain dicts (YAML/JSON-safe).
Each entry retains its ``kind`` discriminator so :func:`load_groupings`
can route it back to the correct spec class.
Args:
groupings: Chain of validated spec instances.
Returns:
``list[dict]`` suitable for ``write_processing_metadata`` or any
YAML/JSON serializer.
"""
return [spec.model_dump(mode="python") for spec in groupings]
[docs]
def load_groupings(serialized: list[dict[str, Any]]) -> list[GroupingSpec]:
"""Re-hydrate a chain of grouping specs from their dict form.
Uses the ``GroupingSpec`` discriminated union, so each entry must
carry a ``kind`` key matching one of the registered spec classes.
Args:
serialized: Output of :func:`dump_groupings` (or any list of
dicts with a valid ``kind`` discriminator).
Returns:
Validated spec instances ready to feed to
:func:`cfdmod.geometry.grouping.apply_groupings`.
Raises:
pydantic.ValidationError: If any entry is not a valid spec.
"""
return _GROUPING_LIST_ADAPTER.validate_python(serialized)
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