copy implementation from xarray

virtualizarr/backend.py

@@ -1,13 +1,23 @@
+import os
 import warnings
 from collections.abc import Iterable, Mapping
 from enum import Enum, auto
+from functools import partial
 from pathlib import Path
 from typing import (
+    TYPE_CHECKING,
     Any,
+    Callable,
+    Literal,
     Optional,
+    Sequence,
+    cast,
 )
 
-from xarray import Dataset, Index
+from xarray import DataArray, Dataset, Index, combine_by_coords
+from xarray.backends.api import _multi_file_closer
+from xarray.backends.common import _find_absolute_paths
+from xarray.core.combine import _infer_concat_order_from_positions, _nested_combine
 
 from virtualizarr.manifests import ManifestArray
 from virtualizarr.readers import (
@@ -22,6 +32,15 @@
 from virtualizarr.readers.common import VirtualBackend
 from virtualizarr.utils import _FsspecFSFromFilepath, check_for_collisions
 
+if TYPE_CHECKING:
+    from xarray.core.types import (
+        CombineAttrsOptions,
+        CompatOptions,
+        JoinOptions,
+        NestedSequence,
+    )
+
+
 # TODO add entrypoint to allow external libraries to add to this mapping
 VIRTUAL_BACKENDS = {
     "kerchunk": KerchunkVirtualBackend,
@@ -209,3 +228,186 @@ def open_virtual_dataset(
     )
 
     return vds
+
+
+def open_virtual_mfdataset(
+    paths: str | Sequence[str | os.PathLike] | NestedSequence[str | os.PathLike],
+    concat_dim: (
+        str
+        | DataArray
+        | Index
+        | Sequence[str]
+        | Sequence[DataArray]
+        | Sequence[Index]
+        | None
+    ) = None,
+    compat: CompatOptions = "no_conflicts",
+    preprocess: Callable[[Dataset], Dataset] | None = None,
+    data_vars: Literal["all", "minimal", "different"] | list[str] = "all",
+    coords="different",
+    combine: Literal["by_coords", "nested"] = "by_coords",
+    parallel: Literal["lithops", "dask", False] = False,
+    join: JoinOptions = "outer",
+    attrs_file: str | os.PathLike | None = None,
+    combine_attrs: CombineAttrsOptions = "override",
+    **kwargs,
+) -> Dataset:
+    """Open multiple files as a single virtual dataset
+
+    If combine='by_coords' then the function ``combine_by_coords`` is used to combine
+    the datasets into one before returning the result, and if combine='nested' then
+    ``combine_nested`` is used. The filepaths must be structured according to which
+    combining function is used, the details of which are given in the documentation for
+    ``combine_by_coords`` and ``combine_nested``. By default ``combine='by_coords'``
+    will be used. Global attributes from the ``attrs_file`` are used
+    for the combined dataset.
+
+    Parameters
+    ----------
+    paths
+        Same as in xarray.open_mfdataset
+    concat_dim
+        Same as in xarray.open_mfdataset
+    compat
+        Same as in xarray.open_mfdataset
+    preprocess
+        Same as in xarray.open_mfdataset
+    data_vars
+        Same as in xarray.open_mfdataset
+    coords
+        Same as in xarray.open_mfdataset
+    combine
+        Same as in xarray.open_mfdataset
+    parallel : 'dask', 'lithops', or False
+        Specify whether the open and preprocess steps of this function will be
+        performed in parallel using ``dask.delayed``, in parallel using ``lithops.map``, or in serial.
+        Default is False.
+    join
+        Same as in xarray.open_mfdataset
+    attrs_file
+        Same as in xarray.open_mfdataset
+    combine_attrs
+        Same as in xarray.open_mfdataset
+    **kwargs : optional
+        Additional arguments passed on to :py:func:`virtualizarr.open_virtual_dataset`. For an
+        overview of some of the possible options, see the documentation of
+        :py:func:`virtualizarr.open_virtual_dataset`.
+
+    Returns
+    -------
+    xarray.Dataset
+
+    Notes
+    -----
+    The results of opening each virtual dataset in parallel are sent back to the client process, so must not be too large.
+    """
+
+    # TODO this is practically all just copied from xarray.open_mfdataset - an argument for writing a virtualizarr engine for xarray?
+
+    # TODO add options passed to open_virtual_dataset explicitly?
+
+    paths = _find_absolute_paths(paths)
+
+    if not paths:
+        raise OSError("no files to open")
+
+    paths1d: list[str]
+    if combine == "nested":
+        if isinstance(concat_dim, str | DataArray) or concat_dim is None:
+            concat_dim = [concat_dim]  # type: ignore[assignment]
+
+        # This creates a flat list which is easier to iterate over, whilst
+        # encoding the originally-supplied structure as "ids".
+        # The "ids" are not used at all if combine='by_coords`.
+        combined_ids_paths = _infer_concat_order_from_positions(paths)
+        ids, paths1d = (
+            list(combined_ids_paths.keys()),
+            list(combined_ids_paths.values()),
+        )
+    elif concat_dim is not None:
+        raise ValueError(
+            "When combine='by_coords', passing a value for `concat_dim` has no "
+            "effect. To manually combine along a specific dimension you should "
+            "instead specify combine='nested' along with a value for `concat_dim`.",
+        )
+    else:
+        paths1d = paths  # type: ignore[assignment]
+
+    if parallel == "dask":
+        import dask
+
+        # wrap the open_dataset, getattr, and preprocess with delayed
+        open_ = dask.delayed(open_virtual_dataset)
+        getattr_ = dask.delayed(getattr)
+        if preprocess is not None:
+            preprocess = dask.delayed(preprocess)
+    elif parallel == "lithops":
+        raise NotImplementedError()
+    elif parallel is not False:
+        raise ValueError(
+            f"{parallel} is an invalid option for the keyword argument ``parallel``"
+        )
+    else:
+        open_ = open_virtual_dataset
+        getattr_ = getattr
+
+    datasets = [open_(p, **kwargs) for p in paths1d]
+    closers = [getattr_(ds, "_close") for ds in datasets]
+    if preprocess is not None:
+        datasets = [preprocess(ds) for ds in datasets]
+
+    if parallel == "dask":
+        # calling compute here will return the datasets/file_objs lists,
+        # the underlying datasets will still be stored as dask arrays
+        datasets, closers = dask.compute(datasets, closers)
+    elif parallel == "lithops":
+        raise NotImplementedError()
+
+    # Combine all datasets, closing them in case of a ValueError
+    try:
+        if combine == "nested":
+            # Combined nested list by successive concat and merge operations
+            # along each dimension, using structure given by "ids"
+            combined = _nested_combine(
+                datasets,
+                concat_dims=concat_dim,
+                compat=compat,
+                data_vars=data_vars,
+                coords=coords,
+                ids=ids,
+                join=join,
+                combine_attrs=combine_attrs,
+            )
+        elif combine == "by_coords":
+            # Redo ordering from coordinates, ignoring how they were ordered
+            # previously
+            combined = combine_by_coords(
+                datasets,
+                compat=compat,
+                data_vars=data_vars,
+                coords=coords,
+                join=join,
+                combine_attrs=combine_attrs,
+            )
+        else:
+            raise ValueError(
+                f"{combine} is an invalid option for the keyword argument"
+                " ``combine``"
+            )
+    except ValueError:
+        for ds in datasets:
+            ds.close()
+        raise
+
+    combined.set_close(partial(_multi_file_closer, closers))
+
+    # read global attributes from the attrs_file or from the first dataset
+    if attrs_file is not None:
+        if isinstance(attrs_file, os.PathLike):
+            attrs_file = cast(str, os.fspath(attrs_file))
+        combined.attrs = datasets[paths1d.index(attrs_file)].attrs
+
+    # TODO should we just immediately close everything?
+    # TODO We should have already read everything we're ever going to read into memory at this point
+
+    return combined