kedro_datasets_experimental.netcdf.NetCDFDataset

class kedro_datasets_experimental.netcdf.NetCDFDataset(*, filepath, temppath=None, load_args=None, save_args=None, fs_args=None, credentials=None, metadata=None)

NetCDFDataset loads/saves data from/to a NetCDF file using an underlying filesystem (e.g.: local, S3, GCS). It uses xarray to handle the NetCDF file.

Example usage for the YAML API:

single-file:
  type: netcdf.NetCDFDataset
  filepath: s3://bucket_name/path/to/folder/data.nc
  save_args:
    mode: a
  load_args:
    decode_times: False

multi-file:
  type: netcdf.NetCDFDataset
  filepath: s3://bucket_name/path/to/folder/data*.nc
  load_args:
    concat_dim: time
    combine: nested
    parallel: True

Example usage for the Python API:

 from kedro_datasets.netcdf import NetCDFDataset
 import xarray as xr
 ds = xr.DataArray(
...     [0, 1, 2], dims=["x"], coords={"x": [0, 1, 2]}, name="data"
... ).to_dataset()
 dataset = NetCDFDataset(
...     filepath=tmp_path / "data.nc",
...     save_args={"mode": "w"},
... )
 dataset.save(ds)
 reloaded = dataset.load()
 assert ds.equals(reloaded)

Attributes

DEFAULT_LOAD_ARGS
DEFAULT_SAVE_ARGS

Methods

exists()	Checks whether a dataset's output already exists by calling the provided _exists() method.
from_config(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
load()	Loads data by delegation to the provided load method.
release()	Release any cached data.
save(data)	Saves data by delegation to the provided save method.
to_config()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_LOAD_ARGS: dict[str, Any] = {}

DEFAULT_SAVE_ARGS: dict[str, Any] = {}

__init__(*, filepath, temppath=None, load_args=None, save_args=None, fs_args=None, credentials=None, metadata=None)

Creates a new instance of NetCDFDataset pointing to a concrete NetCDF file on a specific filesystem

Parameters:

• filepath (str) – Filepath in POSIX format to a NetCDF file prefixed with a protocol like s3://. If prefix is not provided, file protocol (local filesystem) will be used. The prefix should be any protocol supported by fsspec. It can also be a path to a glob. If a glob is provided then it can be used for reading multiple NetCDF files.

• temppath (Optional[str]) – Local temporary directory, used when reading from remote storage, since NetCDF files cannot be directly read from remote storage.

• load_args (Optional[dict[str, Any]]) – Additional options for loading NetCDF file(s). Here you can find all available arguments when reading single file: https://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html Here you can find all available arguments when reading multiple files: https://xarray.pydata.org/en/stable/generated/xarray.open_mfdataset.html All defaults are preserved.

• save_args (Optional[dict[str, Any]]) – Additional saving options for saving NetCDF file(s). Here you can find all available arguments: https://xarray.pydata.org/en/stable/generated/xarray.Dataset.to_netcdf.html All defaults are preserved.

• fs_args (Optional[dict[str, Any]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“cache_regions”: “us-east-1”} for s3fs.S3FileSystem).

• credentials (Optional[dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {“token”: None}.

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

exists()

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)

Create a dataset instance using the configuration provided.

Parameters:

• name (str) – Data set name.

• config (dict[str, Any]) – Data set config dictionary.

• load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

• save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()

Loads data by delegation to the provided load method.

Return type:

Dataset

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

save(data)

Saves data by delegation to the provided save method.

Parameters:

data (Dataset) – the value to be saved by provided save method.

Raises:

• DatasetError – when underlying save method raises error.

• FileNotFoundError – when save method got file instead of dir, on Windows.

• NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None

to_config()

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:

dict[str, Any]

Returns:

A dictionary containing the dataset’s type and initialization arguments.