-
Notifications
You must be signed in to change notification settings - Fork 18
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Internal refactor to separate reading and writing concerns (#231)
* split xarray.py into backend.py and accessor.py * move the kerchunk serialization code out into a new writers submodule * separate out the zarr reading code as a separate reader * actually include new accessor.py file * actually include new kerchunk writers file * actually include new zarr writer file * update test to import from the new location of zarr code * refactor to create a kerchunk 'reader' * split test_xarray.py into two files * split up the kerchunk tests into tests of writing and reading kerchunk * absolute imports in top-level init * kerchunk.py -> types.kerchunk.py * fix some mypy issues * release notes * update module paths in API docs * separate zarr writer tests out * forgot file i moved the zarr tests to * move left behind test * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Loading branch information
1 parent
d7f0c57
commit 515d157
Showing
24 changed files
with
1,247 additions
and
1,173 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,166 @@ | ||
| from pathlib import Path | ||
| from typing import ( | ||
| Callable, | ||
| Literal, | ||
| overload, | ||
| ) | ||
|
|
||
| import ujson # type: ignore | ||
| from xarray import Dataset, register_dataset_accessor | ||
|
|
||
| from virtualizarr.manifests import ManifestArray | ||
| from virtualizarr.types.kerchunk import KerchunkStoreRefs | ||
| from virtualizarr.writers.kerchunk import dataset_to_kerchunk_refs | ||
| from virtualizarr.writers.zarr import dataset_to_zarr | ||
|
|
||
|
|
||
| @register_dataset_accessor("virtualize") | ||
| class VirtualiZarrDatasetAccessor: | ||
| """ | ||
| Xarray accessor for writing out virtual datasets to disk. | ||
| Methods on this object are called via `ds.virtualize.{method}`. | ||
| """ | ||
|
|
||
| def __init__(self, ds: Dataset): | ||
| self.ds: Dataset = ds | ||
|
|
||
| def to_zarr(self, storepath: str) -> None: | ||
| """ | ||
| Serialize all virtualized arrays in this xarray dataset as a Zarr store. | ||
| Currently requires all variables to be backed by ManifestArray objects. | ||
| Not very useful until some implementation of a Zarr reader can actually read these manifest.json files. | ||
| See https://github.com/zarr-developers/zarr-specs/issues/287 | ||
| Parameters | ||
| ---------- | ||
| storepath : str | ||
| """ | ||
| dataset_to_zarr(self.ds, storepath) | ||
|
|
||
| @overload | ||
| def to_kerchunk( | ||
| self, filepath: None, format: Literal["dict"] | ||
| ) -> KerchunkStoreRefs: ... | ||
|
|
||
| @overload | ||
| def to_kerchunk(self, filepath: str | Path, format: Literal["json"]) -> None: ... | ||
|
|
||
| @overload | ||
| def to_kerchunk( | ||
| self, | ||
| filepath: str | Path, | ||
| format: Literal["parquet"], | ||
| record_size: int = 100_000, | ||
| categorical_threshold: int = 10, | ||
| ) -> None: ... | ||
|
|
||
| def to_kerchunk( | ||
| self, | ||
| filepath: str | Path | None = None, | ||
| format: Literal["dict", "json", "parquet"] = "dict", | ||
| record_size: int = 100_000, | ||
| categorical_threshold: int = 10, | ||
| ) -> KerchunkStoreRefs | None: | ||
| """ | ||
| Serialize all virtualized arrays in this xarray dataset into the kerchunk references format. | ||
| Parameters | ||
| ---------- | ||
| filepath : str, default: None | ||
| File path to write kerchunk references into. Not required if format is 'dict'. | ||
| format : 'dict', 'json', or 'parquet' | ||
| Format to serialize the kerchunk references as. | ||
| If 'json' or 'parquet' then the 'filepath' argument is required. | ||
| record_size (parquet only): int | ||
| Number of references to store in each reference file (default 100,000). Bigger values | ||
| mean fewer read requests but larger memory footprint. | ||
| categorical_threshold (parquet only) : int | ||
| Encode urls as pandas.Categorical to reduce memory footprint if the ratio | ||
| of the number of unique urls to total number of refs for each variable | ||
| is greater than or equal to this number. (default 10) | ||
| References | ||
| ---------- | ||
| https://fsspec.github.io/kerchunk/spec.html | ||
| """ | ||
| refs = dataset_to_kerchunk_refs(self.ds) | ||
|
|
||
| if format == "dict": | ||
| return refs | ||
| elif format == "json": | ||
| if filepath is None: | ||
| raise ValueError("Filepath must be provided when format is 'json'") | ||
|
|
||
| with open(filepath, "w") as json_file: | ||
| ujson.dump(refs, json_file) | ||
|
|
||
| return None | ||
| elif format == "parquet": | ||
| from kerchunk.df import refs_to_dataframe | ||
|
|
||
| if isinstance(filepath, Path): | ||
| url = str(filepath) | ||
| elif isinstance(filepath, str): | ||
| url = filepath | ||
|
|
||
| # refs_to_dataframe is responsible for writing to parquet. | ||
| # at no point does it create a full in-memory dataframe. | ||
| refs_to_dataframe( | ||
| refs, | ||
| url=url, | ||
| record_size=record_size, | ||
| categorical_threshold=categorical_threshold, | ||
| ) | ||
| return None | ||
| else: | ||
| raise ValueError(f"Unrecognized output format: {format}") | ||
|
|
||
| def rename_paths( | ||
| self, | ||
| new: str | Callable[[str], str], | ||
| ) -> Dataset: | ||
| """ | ||
| Rename paths to chunks in every ManifestArray in this dataset. | ||
| Accepts either a string, in which case this new path will be used for all chunks, or | ||
| a function which accepts the old path and returns the new path. | ||
| Parameters | ||
| ---------- | ||
| new | ||
| New path to use for all chunks, either as a string, or as a function which accepts and returns strings. | ||
| Returns | ||
| ------- | ||
| Dataset | ||
| Examples | ||
| -------- | ||
| Rename paths to reflect moving the referenced files from local storage to an S3 bucket. | ||
| >>> def local_to_s3_url(old_local_path: str) -> str: | ||
| ... from pathlib import Path | ||
| ... | ||
| ... new_s3_bucket_url = "http://s3.amazonaws.com/my_bucket/" | ||
| ... | ||
| ... filename = Path(old_local_path).name | ||
| ... return str(new_s3_bucket_url / filename) | ||
| >>> ds.virtualize.rename_paths(local_to_s3_url) | ||
| See Also | ||
| -------- | ||
| ManifestArray.rename_paths | ||
| ChunkManifest.rename_paths | ||
| """ | ||
|
|
||
| new_ds = self.ds.copy() | ||
| for var_name in new_ds.variables: | ||
| data = new_ds[var_name].data | ||
| if isinstance(data, ManifestArray): | ||
| new_ds[var_name].data = data.rename_paths(new=new) | ||
|
|
||
| return new_ds |
Oops, something went wrong.