MatlabDataset

MatlabDataset loads and saves data to MATLAB files.

kedro_datasets.matlab.MatlabDataset

MatlabDataset(
    filepath,
    save_args=None,
    version=None,
    credentials=None,
    fs_args=None,
    metadata=None,
)

Bases: AbstractVersionedDataset[ndarray, ndarray]

MatlabDataSet loads and saves data from/to a MATLAB file using scipy.io.

Examples:

Using the YAML API:

cars:
  type: matlab.MatlabDataset
  filepath: gcs://your_bucket/cars.mat
  fs_args:
  project: my-project
  credentials: my_gcp_credentials

Using the Python API:

>>> import numpy as np
>>> from kedro_datasets.matlab import MatlabDataset
>>>
>>> data = np.array([1, 2, 3])
>>>
>>> dataset = MatlabDataset(filepath=tmp_path / "test.mat")
>>> dataset.save(data)
>>> reloaded = dataset.load()
>>> assert (data == reloaded["data"]).all()

Parameters:

• filepath (str) –

  Filepath in POSIX format to a Matlab 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. Note: http(s) doesn't support versioning.

• save_args (dict[str, Any] | None, default: None ) –

  .mat options for saving .mat files.

• version (Version | None, default: None ) –

  If specified, should be an instance of kedro.io.core.Version. If its load attribute is None, the latest version will be loaded. If its save attribute is None, save version will be autogenerated.

• credentials (dict[str, Any] | None, default: None ) –

  Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {"token": None}.

• fs_args (dict[str, Any] | None, default: None ) –

  Extra arguments to pass into underlying filesystem class constructor (e.g. {"project": "my-project"} for GCSFileSystem), as well as to pass to the filesystem's open method through nested keys open_args_load and open_args_save. Here you can find all available arguments for open: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open All defaults are preserved, except mode, which is set to wb when saving.

• metadata (dict[str, Any] | None, default: None ) –

  Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

Source code in kedro_datasets/matlab/matlab_dataset.py

def __init__(  # noqa = PLR0913
    self,
    filepath: str,
    save_args: dict[str, Any] | None = None,
    version: Version | None = None,
    credentials: dict[str, Any] | None = None,
    fs_args: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
) -> None:
    """Creates a new instance of MatlabDataSet to load and save data from/to a MATLAB file.

    Args:
        filepath: Filepath in POSIX format to a Matlab 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``.
            Note: `http(s)` doesn't support versioning.
        save_args: .mat options for saving .mat files.
        version: If specified, should be an instance of
            ``kedro.io.core.Version``. If its ``load`` attribute is
            None, the latest version will be loaded. If its ``save``
            attribute is None, save version will be autogenerated.
        credentials: Credentials required to get access to the underlying filesystem.
            E.g. for ``GCSFileSystem`` it should look like `{"token": None}`.
        fs_args: Extra arguments to pass into underlying filesystem class constructor
            (e.g. `{"project": "my-project"}` for ``GCSFileSystem``), as well as
            to pass to the filesystem's `open` method through nested keys
            `open_args_load` and `open_args_save`.
            Here you can find all available arguments for `open`:
            https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open
            All defaults are preserved, except `mode`, which is set to `wb` when saving.
        metadata: Any arbitrary metadata.
            This is ignored by Kedro, but may be consumed by users or external plugins.
    """
    _fs_args = deepcopy(fs_args) or {}
    _fs_open_args_load = _fs_args.pop("open_args_load", {})
    _fs_open_args_save = _fs_args.pop("open_args_save", {})
    _credentials = deepcopy(credentials) or {}

    protocol, path = get_protocol_and_path(filepath, version)
    self._protocol = protocol
    if protocol == "file":
        _fs_args.setdefault("auto_mkdir", True)
    self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args)
    self.metadata = metadata

    super().__init__(
        filepath=PurePosixPath(path),
        version=version,
        exists_function=self._fs.exists,
        glob_function=self._fs.glob,
    )
    # Handle default save and fs arguments
    self._save_args = {**self.DEFAULT_SAVE_ARGS, **(save_args or {})}
    self._fs_open_args_load = {
        **self.DEFAULT_FS_ARGS.get("open_args_load", {}),
        **(_fs_open_args_load or {}),
    }
    self._fs_open_args_save = {
        **self.DEFAULT_FS_ARGS.get("open_args_save", {}),
        **(_fs_open_args_save or {}),
    }

DEFAULT_FS_ARGS class-attribute instance-attribute

DEFAULT_FS_ARGS = {'open_args_save': {'mode': 'wb'}}

DEFAULT_SAVE_ARGS class-attribute instance-attribute

DEFAULT_SAVE_ARGS = {'indent': 2}

_fs instance-attribute

_fs = filesystem(_protocol, **_credentials, **_fs_args)

_fs_open_args_load instance-attribute

_fs_open_args_load = {
    None: get("open_args_load", {}),
    None: _fs_open_args_load or {},
}

_fs_open_args_save instance-attribute

_fs_open_args_save = {
    None: get("open_args_save", {}),
    None: _fs_open_args_save or {},
}

_protocol instance-attribute

_protocol = protocol

_save_args instance-attribute

_save_args = {
    None: DEFAULT_SAVE_ARGS,
    None: save_args or {},
}

metadata instance-attribute

metadata = metadata

_describe

_describe()

Source code in kedro_datasets/matlab/matlab_dataset.py

def _describe(self) -> dict[str, Any]:
    return {
        "filepath": self._filepath,
        "protocol": self._protocol,
        "save_args": self._save_args,
        "version": self._version,
    }

_exists

_exists()

Source code in kedro_datasets/matlab/matlab_dataset.py

def _exists(self) -> bool:
    try:
        load_path = get_filepath_str(self._get_load_path(), self._protocol)
    except DatasetError:
        return False

    return self._fs.exists(load_path)

_invalidate_cache

_invalidate_cache()

Invalidate underlying filesystem caches.

Source code in kedro_datasets/matlab/matlab_dataset.py

def _invalidate_cache(self) -> None:
    """Invalidate underlying filesystem caches."""
    filepath = get_filepath_str(self._filepath, self._protocol)
    self._fs.invalidate_cache(filepath)

_release

_release()

Source code in kedro_datasets/matlab/matlab_dataset.py

def _release(self) -> None:
    super()._release()
    self._invalidate_cache()

load

load()

Access the specific variable in the .mat file, e.g, data['variable_name']

Source code in kedro_datasets/matlab/matlab_dataset.py

def load(self) -> np.ndarray:
    """
    Access the specific variable in the .mat file, e.g, data['variable_name']
    """
    load_path = get_filepath_str(self._get_load_path(), self._protocol)
    with self._fs.open(load_path) as f:
        data = io.loadmat(f)
        return data

save

save(data)

Source code in kedro_datasets/matlab/matlab_dataset.py

def save(self, data: np.ndarray) -> None:
    save_path = get_filepath_str(self._get_save_path(), self._protocol)
    with self._fs.open(save_path, **self._fs_open_args_save) as f:
        io.savemat(f, {"data": data})
    self._invalidate_cache()