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Add open_virtual_mfdataset #349
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Original file line number | Diff line number | Diff line change |
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@@ -59,6 +59,7 @@ test = [ | |
"ruff", | ||
"s3fs", | ||
"scipy", | ||
"lithops", | ||
"virtualizarr[hdf_reader]" | ||
] | ||
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Original file line number | Diff line number | Diff line change | ||
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@@ -1,13 +1,21 @@ | ||||
import os | ||||
import warnings | ||||
from collections.abc import Iterable, Mapping | ||||
from enum import Enum, auto | ||||
from pathlib import Path | ||||
from typing import ( | ||||
TYPE_CHECKING, | ||||
Any, | ||||
Callable, | ||||
Literal, | ||||
Optional, | ||||
Sequence, | ||||
cast, | ||||
) | ||||
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from xarray import Dataset, Index | ||||
from xarray import DataArray, Dataset, Index, combine_by_coords | ||||
from xarray.backends.common import _find_absolute_paths | ||||
from xarray.core.combine import _infer_concat_order_from_positions, _nested_combine | ||||
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from virtualizarr.manifests import ManifestArray | ||||
from virtualizarr.readers import ( | ||||
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@@ -22,6 +30,15 @@ | |||
from virtualizarr.readers.common import VirtualBackend | ||||
from virtualizarr.utils import _FsspecFSFromFilepath, check_for_collisions | ||||
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if TYPE_CHECKING: | ||||
from xarray.core.types import ( | ||||
CombineAttrsOptions, | ||||
CompatOptions, | ||||
JoinOptions, | ||||
NestedSequence, | ||||
) | ||||
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# TODO add entrypoint to allow external libraries to add to this mapping | ||||
VIRTUAL_BACKENDS = { | ||||
"kerchunk": KerchunkVirtualBackend, | ||||
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@@ -209,3 +226,215 @@ def open_virtual_dataset( | |||
) | ||||
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return vds | ||||
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def open_virtual_mfdataset( | ||||
paths: str | ||||
| os.PathLike | ||||
| 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. | ||||
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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. | ||||
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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`. | ||||
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Returns | ||||
------- | ||||
xarray.Dataset | ||||
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Notes | ||||
----- | ||||
The results of opening each virtual dataset in parallel are sent back to the client process, so must not be too large. | ||||
""" | ||||
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# TODO this is practically all just copied from xarray.open_mfdataset - an argument for writing a virtualizarr engine for xarray? | ||||
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# TODO list kwargs passed to open_virtual_dataset explicitly? | ||||
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paths = _find_absolute_paths(paths) | ||||
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if not paths: | ||||
raise OSError("no files to open") | ||||
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paths1d: list[str] | ||||
if combine == "nested": | ||||
if isinstance(concat_dim, str | DataArray) or concat_dim is None: | ||||
concat_dim = [concat_dim] # type: ignore[assignment] | ||||
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# 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] | ||||
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if parallel == "dask": | ||||
import dask | ||||
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# 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": | ||||
import lithops | ||||
Comment on lines
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I believe all of this could also be useful upstream in |
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# TODO use RetryingFunctionExecutor instead? | ||||
# TODO what's the easiest way to pass the lithops config in? | ||||
fn_exec = lithops.FunctionExecutor() | ||||
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# lithops doesn't have a delayed primitive | ||||
open_ = open_virtual_dataset | ||||
Comment on lines
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I think the code would be more straightforward if the parallel primitive we used for lithops was the same as the one we used for dask. |
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# TODO I don't know how best to chain this with the getattr, or if that closing stuff is even necessary for virtual datasets | ||||
# getattr_ = getattr | ||||
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 | ||||
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if parallel == "dask": | ||||
virtual_datasets = [open_(p, **kwargs) for p in paths1d] | ||||
closers = [getattr_(ds, "_close") for ds in virtual_datasets] | ||||
if preprocess is not None: | ||||
virtual_datasets = [preprocess(ds) for ds in virtual_datasets] | ||||
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# calling compute here will return the datasets/file_objs lists, | ||||
# the underlying datasets will still be stored as dask arrays | ||||
virtual_datasets, closers = dask.compute(virtual_datasets, closers) | ||||
elif parallel == "lithops": | ||||
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def generate_refs(path): | ||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This is the equivalent of @thodson-usgs 's
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# allows passing the open_virtual_dataset function to lithops without evaluating it | ||||
vds = open_(path, **kwargs) | ||||
# TODO perhaps we should just load the loadable_vars here and close before returning? | ||||
return vds | ||||
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futures = fn_exec.map(generate_refs, paths1d) | ||||
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# wait for all the serverless workers to finish, and send their resulting virtual datasets back to the client | ||||
# TODO do we need download_results? | ||||
completed_futures, _ = fn_exec.wait(futures, download_results=True) | ||||
virtual_datasets = completed_futures.get_result() | ||||
elif parallel is False: | ||||
virtual_datasets = [open_(p, **kwargs) for p in paths1d] | ||||
closers = [getattr_(ds, "_close") for ds in virtual_datasets] | ||||
if preprocess is not None: | ||||
virtual_datasets = [preprocess(ds) for ds in virtual_datasets] | ||||
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# 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_vds = _nested_combine( | ||||
virtual_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_vds = combine_by_coords( | ||||
virtual_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 vds in virtual_datasets: | ||||
vds.close() | ||||
raise | ||||
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# combined_vds.set_close(partial(_multi_file_closer, closers)) | ||||
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# 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_vds.attrs = virtual_datasets[paths1d.index(attrs_file)].attrs | ||||
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# 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 | ||||
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return combined_vds |
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Note to self: the docs and especially the readme should be rewritten to put this function front and center.