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447 | def open_hive(
store_root: str,
*,
aoi=None,
window: str | None = None,
anonymous: bool = False,
fabricate_cell_ids: bool | str = "auto",
decode: bool = False,
index_kind: str = "moc",
concurrency: int | None = 32,
xr_kwargs: dict[str, Any] | None = None,
**store_kwargs: Any,
):
"""Open a morton-hive store as one xarray Dataset.
Parameters
----------
store_root : str
Store root (local directory or ``s3://bucket/prefix``).
aoi : array-like, optional
Morton cover of the area of interest — packed ``uint64`` words or
decimal strings, mixed orders allowed. Shards and rows outside the
cover are excluded (rows exactly, via the ``morton`` coordinate).
window : str, optional
Window label for a time-windowed (``morton-hive/2``) store. Omitted
on such a store, the error lists the labels that exist.
anonymous : bool, optional
Unsigned S3 requests (public buckets).
fabricate_cell_ids : {"auto", True, False}, optional
NESTED ``cell_ids`` posture (englacial/zagg#262: "NESTED is
fabricated, never stored"). ``"auto"`` (default): a stored
``cell_ids`` coordinate is kept untouched; when absent (morton-only
store) an exact NESTED view is fabricated from the ``morton``
coordinate via :func:`moczarr.fabricate.fabricate_cell_ids`.
``True`` always fabricates (replacing any stored array — exact, so
a no-op on dual-written stores); ``False`` never fabricates.
Fabrication runs once post-concat on the final ``morton``
coordinate — equivalent to per-leaf (the same words, and
``mort2healpix`` is elementwise) but a single vectorized call.
The fabricated ``cell_ids`` is a Python-side convenience view: the
dataset-level ``attrs["dggs"]`` block is left untouched, so on a
morton-only store it still advertises the morton scheme while the
added coordinate is NESTED. Re-serializing such a result is not
internally consistent; the authoritative morton-only ``dggs``
discriminator is owned by the zagg#262 convention work.
decode : bool, optional
Assign the xdggs ``MortonIndex`` to the ``morton`` coordinate before
returning (``moczarr.dggs.decode``), enabling the ``ds.dggs``
accessor. Requires the ``moczarr[xdggs]`` extra; the default leaves
the result xdggs-free.
index_kind : {"moc", "pandas"}, optional
Index posture for the ``morton`` coordinate (the xdggs vocabulary).
``"moc"`` (default) is the lazy path: the on-disk
``morton``/``cell_ids`` arrays are never read — the row domain comes
from the same coverage arithmetic that selected the leaves (shard
subtrees ∩ AOI), held as a
:class:`moczarr.moc_index.MortonMocIndex` whose coordinate is
fabricated on demand. The index attaches regardless of ``decode``
(it is core, xarray-only); ``decode=True`` additionally wraps it for
the ``ds.dggs`` accessor. Requiring ``decode`` here would chain the
core lazy index to the xdggs extra, against the ratified placement.
``"pandas"`` materializes instead: the stored coordinate is read
and, with ``decode=True``, indexed through a ``PandasIndex`` — use
it when a workflow needs what the interval index cannot represent
(notably ``xr.concat`` of overlapping or out-of-order domains; the
disjoint, ascending batch-sweep concat works on the moc default).
concurrency : int or None, optional
Maximum in-flight metadata requests (the candidate leaves' stamp
GETs, and the discovery walk's per-level LISTs), default 32 — the
zarr-python knob vocabulary. ``None`` or ``1`` runs the serial path
(debugging). Leaf DATA opens stay serial either way (issue #5,
measured in the phase-3 bench; revisit with the lazy-index work).
xr_kwargs : dict, optional
Extra keyword arguments for each leaf's ``xarray.open_zarr`` (e.g.
``chunks={}`` for dask-backed laziness).
**store_kwargs
Extra keyword arguments for the object store (``region=...`` etc.).
Returns
-------
xarray.Dataset
Leaves concatenated along the cell dimension in ascending packed
morton order, with ``morton``/``cell_ids`` as coordinates and the
manifest summary under ``attrs["morton_hive"]``.
An ``aoi`` (or ``window``) that intersects no coverage returns a
schema-correct EMPTY dataset — every data variable and coordinate
present with its stored name/dtype/attrs (schema read from one
covered leaf's metadata) and zero rows along the cell dimension —
and emits a ``UserWarning`` naming the store (issue #4). The empty
result composes with ``decode``, ``index_kind="moc"`` (an empty
interval domain), and ``fabricate_cell_ids``. ``xr.concat`` of the
empty result with a non-empty one preserves dtypes on either
``index_kind`` — both sides carry every variable, so xarray fills
nothing and no int→float NaN promotion occurs; on the moc default
the empty side contributes no intervals, so the concat returns the
non-empty domain unchanged (issue #4's empty-composes-through-concat
contract, pinned in ``tests/test_open.py``).
Raises ``ValueError`` when the root is not a hive store (no
manifest), and :class:`moczarr.NoCoverageError` — a ``ValueError``
subclass — when the store has no stamped coverage anywhere: with
zero committed leaves there is no schema source at all, whatever
the query.
"""
import xarray as xr
from zarr.storage import ObjectStore
if fabricate_cell_ids not in ("auto", True, False):
raise ValueError(
f"fabricate_cell_ids={fabricate_cell_ids!r}: expected 'auto', True, or False"
)
if fabricate_cell_ids != "auto":
fabricate_cell_ids = bool(fabricate_cell_ids)
if index_kind not in ("pandas", "moc"):
raise ValueError(f"index_kind={index_kind!r}: expected 'pandas' or 'moc'")
if anonymous:
store_kwargs.setdefault("anonymous", True)
# ONE store construction pair for the whole open (issue #5): the obstore
# handle serves every JSON/sidecar read; the zarr wrapper serves every
# leaf open via deep paths through the parentless digit tree.
obstore_store = open_object_store(store_root, **store_kwargs)
zarr_store = ObjectStore(obstore_store, read_only=True)
manifest = read_manifest(store_root, store=obstore_store)
if manifest is None:
raise ValueError(f"no morton_hive.json at {store_root} — not a hive store root")
aoi_words = _aoi_words(aoi) if aoi is not None else None
group = str(manifest["cell_order"])
opened = []
candidates = _candidate_leaves(
store_root, manifest, aoi_words, window, store=obstore_store, concurrency=concurrency
)
stamps = read_commits(store_root, candidates, store=obstore_store, concurrency=concurrency)
domain = None # index_kind="moc": the accumulated interval-set row domain
for rel, stamp in zip(candidates, stamps):
if stamp is None:
continue # debris or a MOC-listed shard whose leaf is gone (D4)
if aoi_words is not None:
coverage = parse_leaf_coverage(stamp)
if coverage is not None and coverage.get("box"):
if box_and(coverage, aoi_words).size == 0:
continue # conservative reject: false positives only
if index_kind == "moc":
# The leaf's row domain is arithmetic: zagg leaves are dense
# within a shard, so rows = subtree ∩ AOI — the exact set the
# pandas path's aoi_mask keeps, computed without reading the
# stored coordinate (interval space, moczarr.ranges).
from moczarr.ranges import MortonRanges
shard, _label = split_leaf_name(rel.rsplit("/", 1)[-1])
leaf_domain = MortonRanges.from_shards([morton_word(shard)], int(group))
if aoi_words is not None:
leaf_domain = leaf_domain.intersect(aoi_words)
if leaf_domain.size == 0:
continue # same skip the pandas path's empty aoi_mask takes
ds = xr.open_zarr(
zarr_store,
group=f"{rel}/{group}",
consolidated=False,
zarr_format=3,
**(xr_kwargs or {}),
)
coords = [name for name in ("morton", "cell_ids") if name in ds]
ds = ds.set_coords(coords)
if index_kind == "moc":
# Drop the on-disk cell arrays before concat — lazily built, so
# no chunk was read; the coordinate is the index's to fabricate.
moc_dim = ds["morton"].dims[0] if "morton" in ds.coords else "cells"
ds = ds.drop_vars(coords)
if aoi_words is not None:
full = MortonRanges.from_shards([morton_word(shard)], int(group))
ds = ds.isel({moc_dim: full.rank(leaf_domain.fabricate())})
domain = leaf_domain if domain is None else domain.union(leaf_domain)
elif aoi_words is not None and "morton" in ds.coords:
keep = aoi_mask(np.asarray(ds["morton"].values, dtype=np.uint64), aoi_words)
if not keep.any():
continue
ds = ds.isel({ds["morton"].dims[0]: keep})
opened.append(ds)
if not opened:
# Issue #4 contract: emptiness against a covered store is a data
# answer — a schema-correct 0-row dataset plus a UserWarning. The
# schema comes from ONE stamped leaf's metadata (an AOI-rejected
# candidate when one exists, else _schema_leaf's store-wide search);
# only a store with no stamped leaf anywhere has no schema to serve
# and raises NoCoverageError.
schema_rel = next((r for r, s in zip(candidates, stamps) if s is not None), None)
schema_from_walk = False
if schema_rel is None:
schema_rel = _schema_leaf(
store_root,
window,
store=obstore_store,
concurrency=concurrency,
path_grouping=manifest_path_grouping(manifest),
)
schema_from_walk = True
if schema_rel is None:
raise NoCoverageError(
f"nothing to open at {store_root}: the store has no stamped coverage "
f"anywhere (no committed leaf exists to define a schema)"
)
scope = [
part
for part, active in (
("the given AOI", aoi_words is not None),
(f"window {window!r}", window is not None),
)
if active
]
if not scope and schema_from_walk:
# Unscoped whole-store open (aoi=None, window=None) where the root
# MOC lists no openable leaf, yet _schema_leaf's walk found a
# committed leaf on disk. That is a STALE root MOC, not an empty
# store: silently returning 0 cells here (issue #4's empty
# contract is scoped to an AOI/window over no coverage) would
# hide committed data from a whole-store open. Raise instead —
# never auto-walk the read path; regenerate the coverage
# explicitly. Opting this case into the empty return is a
# one-line change if that lean is preferred later.
raise ValueError(
f"stale root MOC at {store_root}: the root coverage lists no "
f"openable leaf, but a committed leaf exists on disk at "
f"{schema_rel!r}. Regenerate the root coverage before opening "
f"(the store's writer / zagg's refresh_root_coverage / the "
f"coverage sweep)."
)
# scope is non-empty here: the only unscoped way into this path is a
# stale root MOC, handled by the raise above.
warnings.warn(
f"{' in '.join(scope)} intersects no coverage at {store_root}"
"; returning a schema-correct empty dataset (0 cells)",
UserWarning,
stacklevel=2,
)
ds = xr.open_zarr(
zarr_store,
group=f"{schema_rel}/{group}",
consolidated=False,
zarr_format=3,
**(xr_kwargs or {}),
)
coords = [name for name in ("morton", "cell_ids") if name in ds]
ds = ds.set_coords(coords)
empty_dim = ds["morton"].dims[0] if "morton" in ds.coords else "cells"
if index_kind == "moc":
from moczarr.ranges import MortonRanges
moc_dim = empty_dim
ds = ds.drop_vars(coords)
domain = MortonRanges(np.empty((0, 2), dtype=np.uint64), int(group))
opened.append(ds.isel({empty_dim: slice(0)}))
dim = opened[0]["morton"].dims[0] if "morton" in opened[0].coords else "cells"
if index_kind == "moc":
dim = moc_dim
result = xr.concat(opened, dim=dim) if len(opened) > 1 else opened[0]
if index_kind == "moc":
from moczarr.moc_index import MortonMocIndex
assert domain is not None # a leaf set it, or the empty path did
index = MortonMocIndex(domain, dim=dim, name="morton")
result = result.assign_coords(xr.Coordinates.from_xindex(index))
if "morton" in result.coords and (
fabricate_cell_ids is True
or (fabricate_cell_ids == "auto" and "cell_ids" not in result.coords)
):
ids = _fabricate_cell_ids(
np.asarray(result["morton"].values, dtype=np.uint64),
level=int(manifest["cell_order"]),
# +1 frame vs a direct call so the >24 warning lands on the
# user's open_hive(...) line, not this internal call site.
_stacklevel=4,
)
result = result.assign_coords(cell_ids=(result["morton"].dims, ids))
# Data-variable order is otherwise the completion order of zarr-python's
# async member listing — non-deterministic run to run (a fresh open
# reshuffles ds.data_vars). Sort lexically so a given store always opens
# with the same variable order: reproducible reprs and stable notebook
# outputs. Coordinates and the morton index ride along unchanged.
result = result[sorted(result.data_vars)]
result.attrs["morton_hive"] = {
k: manifest[k] for k in ("spec", "cell_order", "shard_order", "dataset")
}
if decode:
from moczarr import dggs # lazy: raises the pointed extra hint when absent
result = dggs.decode(result, index_kind=index_kind)
return result
|